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Dietary and nutritional approaches for prevention and management of type 2 diabetes

Food for thought, click here to read other articles in this collection.

  • Related content
  • Peer review
  • Nita G Forouhi , professor 1 ,
  • Anoop Misra , professor 2 ,
  • Viswanathan Mohan , professor 3 ,
  • Roy Taylor , professor 4 ,
  • William Yancy , director 5 6 7
  • 1 MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
  • 2 Fortis-C-DOC Centre of Excellence for Diabetes, Metabolic Diseases and Endocrinology, and National Diabetes, Obesity and Cholesterol Foundation, New Delhi, India
  • 3 Dr Mohan’s Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
  • 4 Magnetic Resonance Centre, Institute of Cellular Medicine, Newcastle University, Newcastle, UK
  • 5 Duke University Diet and Fitness Center, Durham, North Carolina, USA
  • 6 Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
  • 7 Center for Health Services Research in Primary Care, Department of Veterans Affairs, Durham, North Carolina, USA
  • Correspondence to: N G Forouhi nita.forouhi{at}mrc-epid.cam.ac.uk

Common ground on dietary approaches for the prevention, management, and potential remission of type 2 diabetes can be found, argue Nita G Forouhi and colleagues

Dietary factors are of paramount importance in the management and prevention of type 2 diabetes. Despite progress in formulating evidence based dietary guidance, controversy and confusion remain. In this article, we examine the evidence for areas of consensus as well as ongoing uncertainty or controversy about dietary guidelines for type 2 diabetes. What is the best dietary approach? Is it possible to achieve remission of type 2 diabetes with lifestyle behaviour changes or is it inevitably a condition causing progressive health decline? We also examine the influence of nutrition transition and population specific factors in the global context and discuss future directions for effective dietary and nutritional approaches to manage type 2 diabetes and their implementation.

Why dietary management matters but is difficult to implement

Diabetes is one of the biggest global public health problems: the prevalence is estimated to increase from 425 million people in 2017 to 629 million by 2045, with linked health, social, and economic costs. 1 Urgent solutions for slowing, or even reversing, this trend are needed, especially from investment in modifiable factors including diet, physical activity, and weight. Diet is a leading contributor to morbidity and mortality worldwide according to the Global Burden of Disease Study carried out in 188 countries. 2 The importance of nutrition in the management and prevention of type 2 diabetes through its effect on weight and metabolic control is clear. However, nutrition is also one of the most controversial and difficult aspects of the management of type 2 diabetes.

The idea of being on a “diet” for a chronic lifelong condition like diabetes is enough to put many people off as knowing what to eat and maintaining an optimal eating pattern are challenging. Medical nutrition therapy was introduced to guide a systematic and evidence based approach to the management of diabetes through diet, and its effectiveness has been demonstrated, 3 but difficulties remain. Although most diabetes guidelines recommend starting pharmacotherapy only after first making nutritional and physical activity lifestyle changes, this is not always followed in practice globally. Most physicians are not trained in nutrition interventions and this is a barrier to counselling patients. 4 5 Moreover, talking to patients about nutrition is time consuming. In many settings, outside of specialised diabetes centres where trained nutritionists/educators are available, advice on nutrition for diabetes is, at best, a printed menu given to the patient. In resource poor settings, when type 2 diabetes is diagnosed, often the patient leaves the clinic with a list of new medications and little else. There is wide variation in the use of dietary modification alone to manage type 2 diabetes: for instance, estimates of fewer than 5-10% of patients with type 2 diabetes in India 6 and 31% in the UK are reported, although patients treated by lifestyle measures may be less closely managed than patients on medication for type 2 diabetes. 7 Although systems are usually in place to record and monitor process measures for diabetes care in medical records, dietary information is often neglected, even though at least modest attention to diet is needed to achieve adequate glycaemic control. Family doctors and hospital clinics should collect this information routinely but how to do this is a challenge. 5 8

Progress has been made in understanding the best dietary advice for diabetes but broader problems exist. For instance, increasing vegetable and fruit intake is recommended by most dietary guidelines but their cost is prohibitively high in many settings: the cost of two servings of fruits and three servings of vegetables a day per individual (to fulfil the “5-a-day” guidance) accounted for 52%, 18%, 16%, and 2% of household income in low, low to middle, upper to middle, and high income countries, respectively. 9 An expensive market of foods labelled for use by people with diabetes also exists, with products often being no healthier, and sometimes less healthy, than regular foods. After new European Union legislation, food regulations in some countries, including the UK, were updated as recently as July 2016 to ban such misleading labels. This is not the case elsewhere, however, and what will happen to such regulation after the UK leaves the European Union is unclear, which highlights the importance of the political environment.

Evidence for current dietary guidelines

In some, mostly developed, countries, dietary guidelines for the management of diabetes have evolved from a focus on a low fat diet to the recognition that more important considerations are macronutrient quality (that is, the type versus the quantity of macronutrient), avoidance of processed foods (particularly processed starches and sugars), and overall dietary patterns. Many systematic reviews and national dietary guidelines have evaluated the evidence for optimal dietary advice, and we will not repeat the evidence review. 10 11 12 13 14 15 16 17 18 We focus instead in the following sections on some important principles where broad consensus exists in the scientific and clinical community and highlight areas of uncertainty, but we begin by outlining three underpinning features.

Firstly, an understanding of healthy eating for the prevention and management of type 2 diabetes has largely been derived from long term prospective studies and limited evidence from randomised controlled trials in general populations, supplemented by evidence from people with type 2 diabetes. Many published guidelines and reviews have applied grading criteria and this evidence is often of moderate quality in the hierarchy of evidence that places randomised controlled trials at the top. Elsewhere, it is argued that different forms of evidence evaluating consistency across multiple study designs including large population based prospective studies of clinical endpoints, controlled trials of intermediate pathways, and where feasible randomised trials of clinical endpoints should be used collectively for evidence based nutritional guidance. 19

Secondly, it is now recognised that dietary advice for both the prevention and management of type 2 diabetes should converge, and they should not be treated as different entities ( fig 1 ). However, in those with type 2 diabetes, the degree of glycaemic control and type and dose of diabetes medication should be coordinated with dietary intake. 12 With some dietary interventions, such as very low calorie or low carbohydrate diets, people with diabetes would usually stop or reduce their diabetes medication and be monitored closely, as reviewed in a later section.

Dietary advice for different populations for the prevention and management of type 2 diabetes

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Thirdly, while recognising the importance of diet for weight management, there is now greater understanding 10 of the multiple pathways through which dietary factors exert health effects through both obesity dependent and obesity independent mechanisms. The influence of diet on weight, glycaemia, and glucose-insulin homeostasis is directly relevant to glycaemic control in diabetes, while other outcomes such as cardiovascular complications are further influenced by the effect of diet on blood lipids, apolipoproteins, blood pressure, endothelial function, thrombosis, coagulation, systemic inflammation, and vascular adhesion. The effect of food and nutrients on the gut microbiome may also be relevant to the pathogenesis of diabetes but further research is needed. Therefore, diet quality and quantity over the longer term are relevant to the prevention and management of diabetes and its complications through a wide range of metabolic and physiological processes.

Areas of consensus in guidelines

Weight management.

Type 2 diabetes is most commonly associated with overweight or obesity and insulin resistance. Therefore, reducing weight and maintaining a healthy weight is a core part of clinical management. Weight loss is also linked to improvements in glycaemia, blood pressure, and lipids and hence can delay or prevent complications, particularly cardiovascular events.

Energy balance

Most guidelines recommend promoting weight loss among overweight or obese individuals by reducing energy intake. Portion control is one strategy to limit energy intake together with a healthy eating pattern that focuses on a diet composed of whole or unprocessed foods combined with physical activity and ongoing support.

Dietary patterns

The evidence points to promoting patterns of food intake that are high in vegetables, fruit, whole grains, legumes, nuts, and dairy products such as yoghurt but with some cautions. Firstly, some dietary approaches (eg, low carbohydrate diets) recommend restricting the intake of fruits, whole grains, and legumes because of their sugar or starch content. For fruit intake, particularly among those with diabetes, opinion is divided among scientists and clinicians (see appendix on bmj.com). Many guidelines continue to recommend fruit, however, on the basis that fructose intake from fruits is preferable to isocaloric intake of sucrose or starch because of the additional micronutrient, phytochemical, and fibre content of fruit. Secondly, despite evidence from randomised controlled trials and prospective studies 10 that nuts may help prevent type 2 diabetes, some (potentially misplaced) concern exists about their high energy content. Further research in people with type 2 diabetes should help to clarify this.

There is also consensus on the benefits of certain named dietary patterns such as the Mediterranean diet for prevention and management of type 2 diabetes. Expert guidelines also support other healthy eating patterns that take account of local sociocultural factors and personal preferences.

Foods to avoid

Consensus exists on reducing or avoiding the intake of processed red meats, refined grains and sugars (especially sugar sweetened drinks) both for prevention and management of type 2 diabetes, again with some cautions. Firstly, for unprocessed red meat, the evidence of possible harm because of the development of type 2 diabetes is less consistent and of a smaller magnitude. More research is needed on specific benefits or harms in people with type 2 diabetes. Secondly, evidence is increasing on the relevance of carbohydrate quality: that is that whole grains and fibre are better choices than refined grains and that fibre intake should be at least as high in people with type 2 diabetes as recommended for the general population, that diets that have a higher glycaemic index and load are associated with an increased risk of type 2 diabetes, and that there is a modest glycaemic benefit in replacing foods with higher glycaemic load with foods with low glycaemic load. However, debate continues about the independence of these effects from the intake of dietary fibre. Some evidence exists that consumption of potato and white rice may increase the risk of type 2 diabetes but this is limited and further research is needed.

Moreover, many guidelines also highlight the importance of reducing the intake of in foods high in sodium and trans fat because of the relevance of these specifically for cardiovascular health.

Areas of uncertainty in guidelines

Optimal macronutrient composition.

One of the most contentious issues about the management of type 2 diabetes has been on the best macronutrient composition of the diet. Some guidelines continue to advise macronutrient quantity goals, such as the European or Canadian recommendation of 45–60% of total energy as carbohydrate, 10–20% as protein, and less than 35% as fat, 13 20 or the Indian guidelines that recommend 50-60% energy from carbohydrates, 10-15% from protein, and less than 30% from fat. 21 In contrast, the most recent nutritional guideline from the American Diabetes Association concluded that there is no ideal mix of macronutrients for all people with diabetes and recommended individually tailored goals. 12 Alternatively, a low carbohydrate diet for weight and glycaemic control has gained popularity among some experts, clinicians, and the public (reviewed in a later section). Others conclude that a low carbohydrate diet combined with low saturated fat intake is best. 22

For weight loss, three points are noteworthy when comparing dietary macronutrient composition. Firstly, evidence from trials points to potentially greater benefits from a low carbohydrate than a low fat diet but the difference in weight loss between diets is modest. 23 Secondly, a comparison of named diet programmes with different macronutrient composition highlighted that the critical factor in effectiveness for weight loss was the level of adherence to the diet over time. 24 Thirdly, the quality of the diet in low carbohydrate or low fat diets is important. 25 26

Research to date on weight or metabolic outcomes in diabetes is complicated by the use of different definitions for the different macronutrient approaches. For instance, the definition of a low carbohydrate diet has ranged from 4% of daily energy intake from carbohydrates (promoting nutritional ketosis) to 40%. 15 Similarly, low fat diets have been defined as fat intake less than 30% of daily energy intake or substantially lower. Given these limitations, the best current approach may be an emphasis on the use of individual assessment for dietary advice and a focus on the pattern of eating that most readily allows the individual to limit calorie intake and improve macronutrient quality (such as avoiding refined carbohydrates).

Regular fish intake of at least two servings a week, including one serving of oily fish (eg, salmon, mackerel, and trout) is recommended for cardiovascular risk prevention but fish intake has different associations with the risk of developing type 2 diabetes across the world—an inverse association, no association, and a positive association. 27 It is thought that the type of fish consumed, preparation or cooking practices, and possible contaminants (eg, methyl mercury and polychlorinated biphenyls) vary by geographical location and contributed to this heterogeneity. More research is needed to resolve whether fish intake should be recommended for the prevention of diabetes. However, the current evidence supports an increase in consumption of oily fish for individuals with diabetes because of its beneficial effects on lipoproteins and prevention of coronary heart disease. Most guidelines agree that omega 3 polyunsaturated fatty acid (fish oil) supplementation for cardiovascular prevention in people with diabetes should not be recommended but more research is needed and the results of the ASCEND (A Study of Cardiovascular Events in Diabetes) trial should help to clarify this. 28

Dairy foods are encouraged for the prevention of type 2 diabetes, with more consistent evidence of the benefits of fermented dairy products, such as yoghurt. Similar to population level recommendations about limiting the intake of foods high in saturated fats and replacing them with foods rich in polyunsaturated fat, the current advice for diabetes also favours low fat dairy products but this is debated. More research is needed to resolve this question.

Uncertainty continues about certain plant oils and tropical oils such as coconut or palm oil as evidence from prospective studies or randomised controlled trials on clinical events is sparse or non-existent. However, olive oil, particularly extra virgin olive oil, has been studied in greater detail with evidence of potential benefits for the prevention and management of type 2 diabetes 29 and the prevention of cardiovascular disease within the context of a Mediterranean diet 30 (see article in this series on dietary fats). 31

Difficulties in setting guidelines

Where dietary guidelines exist (in many settings there are none, or they are adapted from those in developed countries and therefore may not be applicable to the local situation), they vary substantially in whether they are evidence based or opinion pieces, and updated in line with scientific progress or outdated. Their accessibility—both physical availability (eg, through a website or clinic) and comprehensibility— for patients and healthcare professionals varies. They vary also in scope, content, detail, and emphasis on the importance of individualised dietary advice, areas of controversy, and further research needs. The quality of research that informs dietary guidelines also needs greater investment from the scientific community and funders. Moreover, lack of transparency in the development of guidelines and bias in the primary nutritional studies can undermine the development of reliable dietary guidelines; recommendations for their improvement must be heeded. 32

Reversing type 2 diabetes through diet

Type 2 diabetes was once thought to be irreversible and progressive after diagnosis, but much interest has arisen about the potential for remission. Consensus on the definition of remission is a sign of progress: glucose levels lower than the diagnostic level for diabetes in the absence of medications for hyperglycaemia for a period of time (often proposed to be at least one year). 33 34 However, the predominant role of energy deficit versus macronutrient composition of the diet in achieving remission is still controversial.

Remission through a low calorie energy deficit diet

Although the clinical observation of the lifelong, steadily progressive nature of type 2 diabetes was confirmed by the UK Prospective Diabetes Study, 35 rapid normalisation of fasting plasma glucose after bariatric surgery suggested that deterioration was not inevitable. 36 As the main change was one of sudden calorie restriction, a low calorie diet was used as a tool to study the mechanisms involved. In one study of patients with type 2 diabetes, fasting plasma glucose normalised within seven days of following a low calorie diet. 37 This normalisation through diet occurred despite simultaneous withdrawal of metformin therapy. Gradually over eight weeks, glucose stimulated insulin secretion returned to normal. 37 Was this a consequence of calorie restriction or composition of the diet? To achieve the degree of weight loss obtained (15 kg), about 610 kcal a day was provided—510 kcal as a liquid formula diet and about 100 kcal as non-starchy vegetables. The formula diet consisted of 59 g of carbohydrate (30 g as sugars), 11.4 g of fat, and 41 g of protein, including required vitamins and minerals. This high “sugar” approach to controlling blood glucose may be surprising but the critical aspect is not what is eaten but the gap between energy required and taken in. Because of this deficit, the body must use previously stored energy. Intrahepatic fat is used first, and the 30% decrease in hepatic fat in the first seven days appears sufficient to normalise the insulin sensitivity of the liver. 37 In addition, pancreatic fat content fell over eight weeks and beta cell function improved. This is because insulin secretory function was regained by re-differentiation after fat removal. 38

The permanence of these changes was tested by a nutritional and behavioural approach to achieve long term isocaloric eating after the acute weight loss phase. 39 It was successful in keeping weight steady over the next six months of the study. Calorie restriction was associated with both hepatic and pancreatic fat content remaining at the low levels achieved. The initial remission of type 2 diabetes was closely associated with duration of diabetes, and the individuals with type 2 diabetes of shorter duration who achieved normal levels of blood glucose maintained normal physiology during the six month follow-up period. Recently, 46% of a UK primary care cohort remained free of diabetes at one year during a structured low calorie weight loss programme (the DiRECT trial). 40 These results are convincing, and four years of follow-up are planned.

A common criticism of the energy deficit research has been that very low calorie diets may not be achievable or sustainable. Indeed, adherence to most diets in the longer term is an important challenge. 24 However, Look-AHEAD, the largest randomised study of lifestyle interventions in type 2 diabetes (n=5145), randomised individuals to intensive lifestyle management, including the goal to reduce total calorie intake to 1200-1800 kcal/d through a low fat diet assisted by liquid meal replacements, and this approach achieved greater weight loss and non-diabetic blood glucose levels at year 1 and year 4 in the intervention than the control group. 41

Considerable interest has arisen about whether low calorie diets associated with diabetes remission can also help to prevent diabetic complications. Evidence is sparse because of the lack of long term follow-up studies but the existing research is promising. A return to the non-diabetic state brings an improvement in cardiovascular risk (Q risk decreasing from 19.8% to 5.4%) 39 ; case reports of individuals facing foot amputation record a return to a low risk state over 2-4 years with resolution of painful neuropathy 42 43 ; and retinal complications are unlikely to occur or progress. 44 However, other evidence highlights that worsening of treatable maculopathy or proliferative retinopathy may occur following a sudden fall in plasma glucose levels, 45 46 so retinal imaging in 4-6 months is recommended for individuals with more than minimal retinopathy if following a low calorie remission diet. Annual review is recommended for all those in the post-diabetic state, and a “diabetes in remission” code (C10P) is now available in the UK. 34

Management or remission through a low carbohydrate diet

Before insulin was developed as a therapy, reducing carbohydrate intake was the main treatment for diabetes. 47 48 Carbohydrate restriction for the treatment of type 2 diabetes has been an area of intense interest because, of all the macronutrients, carbohydrates have the greatest effect on blood glucose and insulin levels. 49

In a review by the American Diabetes Association, interventions of low carbohydrate (less than 40% of calories) diets published from 2001 to 2010 were identified. 15 Of 11 trials, eight were randomised and about half reported greater improvement in HbA1c on the low carbohydrate diet than the comparison diet (usually a low fat diet), and a greater reduction in the use of medicines to lower glucose. Notably, calorie reduction coincided with carbohydrate restriction in many of the studies, even though it was not often specified in the dietary counselling. One of the more highly controlled studies was an inpatient feeding study, 50 which reported a decline in mean HbA1c from 7.3% to 6.8% (P=0.006) over just 14 days on a low carbohydrate diet.

For glycaemia, other reviews of evidence from randomised trials on people with type 2 diabetes have varying conclusions. 51 52 53 54 55 56 Some concluded that low carbohydrate diets were superior to other diets for glycaemic control, or that a dose response relationship existed, with stricter low carbohydrate restriction resulting in greater reductions in glycaemia. Others cautioned about short term beneficial effects not being sustained in the longer term, or found no overall advantage over the comparison diet. Narrative reviews have generally been more emphatic on the benefits of low carbohydrate diets, including increased satiety, and highlight the advantages for weight loss and metabolic parameters. 57 58 More recently, a one year clinic based study of the low carbohydrate diet designed to induce nutritional ketosis (usually with carbohydrate intake less than 30 g/d) was effective for weight loss, and for glycaemic control and medication reduction. 59 However, the study was not randomised, treatment intensity differed substantially in the intervention versus usual care groups, and participants were able to select their group.

Concerns about potential detrimental effects on cardiovascular health have been raised as low carbohydrate diets are usually high in dietary fat, including saturated fat. For lipid markers as predictors of future cardiovascular events, several studies found greater improvements in high density lipoprotein cholesterol and triglycerides with no relative worsening of low density lipoprotein cholesterol in patients with type 2 diabetes following carbohydrate restriction, 15 with similar conclusions in non-diabetic populations. 57 60 61 62 Low density lipoprotein cholesterol tends to decline more, however, in a low fat comparison diet 61 63 and although low density lipoprotein cholesterol may not worsen with a low carbohydrate diet 63 in the short term, the longer term effects are unclear. Evidence shows that low carbohydrate intake can lower the more atherogenic small, dense low density lipoprotein particles. 57 64 Because some individuals may experience an increase in serum low density lipoprotein cholesterol when following a low carbohydrate diet high in saturated fat, monitoring is important.

Another concern is the effect of the potentially higher protein content of low carbohydrate diets on renal function. Evidence from patients with type 2 diabetes with normal baseline renal function and from individuals without diabetes and with normal or mildly impaired renal function has not shown worsening renal function at one or up to two years of follow-up, respectively. 22 65 66 67 Research in patients with more severely impaired renal function, with or without diabetes, has not been reported to our knowledge. Other potential side effects of a very low carbohydrate diet include headache, fatigue, and muscle cramping but these side effects can be avoided by adequate fluid and sodium intake, particularly in the first week or two after starting the diet when diuresis is greatest. Concern about urinary calcium loss and a possible contribution to increased future risk of kidney stones or osteoporosis 68 have not been verified 69 but evidence is sparse and warrants further investigation. The long term effects on cardiovascular disease and chronic kidney disease in patients with diabetes need further evaluation.

Given the hypoglycaemic effect of carbohydrate restriction, patients with diabetes who adopt low carbohydrate diets and their clinicians must understand how to avoid hypoglycaemia by appropriately reducing glucose lowering medications. Finally, low carbohydrate diets can restrict whole grain intake and although some low carbohydrate foods can provide the fibre and micronutrients contained in grains, it may require greater effort to incorporate such foods. This has led some experts to emphasise restricting refined starches and sugars but retaining whole grains.

Nutrition transition and population specific factors

Several countries in sub-Saharan Africa, South America, and Asia (eg, India and China) have undergone rapid nutrition transition in the past two decades. These changes have paralleled economic growth, foreign investment in the fast food industry, urbanisation, direct-to-consumer marketing of foods high in calories, sale of ultraprocessed foods, and as a result, lower consumption of traditional diets. The effect of these factors on nutrition have led to obesity and type 2 diabetes on the one hand, and co-existing undernutrition and micronutrient deficiencies on the other.

Dietary shifts in low and middle income countries have been stark: in India, these include a substantial increase in fat intake in the setting of an already high carbohydrate intake, with a slight increase in total energy and protein, 70 and a decreasing intake of coarse cereals, pulses, fruits, and vegetables 71 ; in China, animal protein and fat as a percentage of energy has also increased, while cereal intake has decreased. 72 An almost universal increase in the intake of caloric beverages has also occurred, with sugar sweetened soda drinks being the main beverage contributing to energy intake, for example among adults and children in Mexico, 73 or the substantial rise in China in sales of sugar sweetened drinks from 10.2 L per capita in 1998 to 55.0 L per capita in 2012. 74 The movement of populations from rural to urban areas within a country may also be linked with shifts in diets to more unhealthy patterns, 75 while acculturation of immigrant populations into their host countries also results in dietary shifts. 76

In some populations, such as South Asians, rice and wheat flour bread are staple foods, with a related high carbohydrate intake (60-70% of calories). 77 Although time trends show that intake of carbohydrate has decreased among South Asian Indians, the quality of carbohydrates has shifted towards use of refined carbohydrates. 71 The use of oils and traditional cooking practices also have specific patterns in different populations. For instance, in India, the import and consumption of palm oil, often incorporated in the popular oil vanaspati (partially hydrogenated vegetable oil, high in trans fats), is high. 78 Moreover, the traditional Indian cooking practice of frying at high temperatures and re-heating increases trans fatty acids in oils. 79 Such oils are low cost, readily available, and have a long shelf life, and thus are more attractive to people from the middle and low socioeconomic strata but their long term effects on type 2 diabetes are unknown.

Despite the nutrition transition being linked to an increasing prevalence of type 2 diabetes, obesity and other non-communicable diseases, strong measures to limit harmful foods are not in place in many countries. Regulatory frameworks including fiscal policies such as taxation for sugar sweetened beverages need to be strengthened to be effective and other preventive interventions need to be properly implemented. Efforts to control trans fatty acids in foods have gained momentum but are largely confined to developed countries. To reduce consumption in low and middle income countries will require both stringent regulations and the availability and development of alternative choices of healthy and low cost oils, ready made food products, and consumer education. 80 The need for nutritional labelling is important but understanding nutrition labels is a problem in populations with low literacy or nutrition awareness, which highlights the need for educational activities and simpler forms of labelling. The role of dietary/nutritional factors in the predisposition of some ethnic groups to developing type 2 diabetes at substantially lower levels of obesity than European populations 81 is poorly researched and needs investigation.

Despite the challenges of nutritional research, considerable progress has been made in formulating evidence based dietary guidance and some common principles can be agreed that should be helpful to clinicians, patients, and the public. Several areas of uncertainty and controversy remain and further research is needed to resolve these. While adherence to dietary advice is an important challenge, weight management is still a cornerstone in diabetes management, supplemented with new developments, including the potential for the remission of type 2 diabetes through diet.

Future directions

Nutritional research is difficult. Although much progress has been made to improve evidence based dietary guidelines, more investment is needed in good quality research with a greater focus on overcoming the limitations of existing research. Experts should also strive to build consensus using research evidence based on a combination of different study designs, including randomised experiments and prospective observational studies

High quality research is needed that compares calorie restriction and carbohydrate restriction to assess effectiveness and feasibility in the long term. Consensus is needed on definitions of low carbohydrate nutrition. Use of the findings must take account of individual preferences, whole diets, and eating patterns

Further research is needed to resolve areas of uncertainty about dietary advice in diabetes, including the role of nuts, fruits, legumes, fish, plant oils, low fat versus high fat dairy, and diet quantity and quality

Given recent widespread recommendations (such as from the World Health Organization 82 and the UK Scientific Advisory Committee on Nutrition 83 ) to reduce free sugars to under 10% or even 5% of total energy intake in the general population and to avoid sugar sweetened drinks, we need targeted research on the effect of non-nutritive sweeteners on health outcomes in people with diabetes and in the whole population

Most dietary guidelines are derived from evidence from Western countries. Research is needed to better understand the specific aetiological factors that link diet/nutrition and diabetes and its complications in different regions and different ethnic groups. This requires investment in developing prospective cohorts and building capacity to undertake research in low and middle income settings and in immigrant ethnic groups. Up-to-date, evidence based dietary guidelines are needed that are locally relevant and readily accessible to healthcare professionals, patients, and the public in different regions of the world. Greater understanding is also needed about the dietary determinants of type 2 diabetes and its complications at younger ages and in those with lower body mass index in some ethnic groups

We need investment in medical education to train medical students and physicians in lifestyle interventions, including incorporating nutrition education in medical curricula

Individual, collective, and upstream factors are important. Issuing dietary guidance does not ensure its adoption or implementation. Research is needed to understand the individual and societal drivers of and barriers to healthy eating. Educating and empowering individuals to make better dietary choices is an important strategy; in particular, the social aspects of eating need attention as most people eat in family or social groups and counselling needs to take this into account. Equally important is tackling the wider determinants of individual behaviour—the “foodscape”, sociocultural and political factors, globalisation, and nutrition transition

Key messages

Considerable evidence supports a common set of dietary approaches for the prevention and management of type 2 diabetes, but uncertainties remain

Weight management is a cornerstone of metabolic health but diet quality is also important

Low carbohydrate diets as the preferred choice in type 2 diabetes is controversial. Some guidelines maintain that no single ideal percentage distribution of calories from different macronutrients (carbohydrates, fat, or protein) exists, but there are calls to review this in light of emerging evidence on the potential benefits of low carbohydrate diets for weight management and glycaemic control

The quality of carbohydrates such as refined versus whole grain sources is important and should not get lost in the debate on quantity

Recognition is increasing that the focus of dietary advice should be on foods and healthy eating patterns rather than on nutrients. Evidence supports avoiding processed foods, refined grains, processed red meats, and sugar sweetened drinks and promoting the intake of fibre, vegetables, and yoghurt. Dietary advice should be individually tailored and take into account personal, cultural, and social factors

An exciting recent development is the understanding that type 2 diabetes does not have to be a progressive condition but instead there is potential for remission with dietary intervention

Acknowledgments

We thank Sue Brown as a patient representative of Diabetes UK for her helpful comments and insight into this article.

Contributors and sources: The authors have experience and research interests in the prevention and management of type 2 diabetes (NGF, AM, VM, RT, WY), in guideline development (NGF, AM, VM, WY), and in nutritional epidemiology (NGF, VM). Sources of information for this article included published dietary guidelines or medical nutrition therapy guidelines for diabetes, and systematic reviews and primary research articles based on randomised clinical trials or prospective observational studies. All authors contributed to drafting this manuscript, with NGF taking a lead role and she is also the guarantor of the manuscript. All authors gave intellectual input to improve the manuscript and have read and approved the final version.

Competing interests: We have read and understood BMJ policy on declaration of interests and declare the following: NGF receives funding from the Medical Research Council Epidemiology Unit (MC_UU_12015/5). NGF is a member (unpaid) of the Joint SACN/NHS-England/Diabetes-UK Working Group to review the evidence on lower carbohydrate diets compared with current government advice for adults with type 2 diabetes and is a member (unpaid) of ILSI-Europe Qualitative Fat Intake Task Force Expert Group on update on health effects of different saturated fats. AM received honorarium and research funding from Herbalife and Almond Board of California. VM has received funding from Abbott Health Care for meal replacement studies, the Cashew Export Promotion Council of India, and the Almond Board of California for studies on nuts. RT has received funding from Diabetes UK for the Diabetes Remission Clinical Trial and he is a member (unpaid) of the Joint SACN/NHS-England/Diabetes-UK Working Group to review the evidence on lower carbohydrate diets compared to current government advice for adults with type 2 diabetes. WY has received funding from the Veterans Affairs for research projects examining a low carbohydrate diet in patients with diabetes.

Provenance and peer review: Commissioned, externally peer reviewed

This article is one of a series commissioned by The BMJ . Open access fees for the series were funded by Swiss Re, which had no input in to the commissioning or peer review of the articles. The BMJ thanks the series advisers, Nita Forouhi and Dariush Mozaffarian, for valuable advice and guiding selection of topics in the series.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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research article on diabetes management

  • Frontiers in Clinical Diabetes and Healthcare
  • Diabetes Nutrition and Dietetics
  • Research Topics

An Integrated Approach: Nutrition Strategies for People with Diabetes

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​The increasing prevalence of diabetes and its impact on global health necessitate effective strategies to manage the condition. Nutrition plays a pivotal role in diabetes management, and an integrated approach is crucial to optimize the health outcomes of individuals with diabetes. This Research Topic aims ...

Keywords : Diabetes Management, Integrated Approach, Nutrition Strategies, Nutritional Education

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  • Methodology
  • Open access
  • Published: 18 January 2021

Diabetes management intervention studies: lessons learned from two studies

  • Bettina Petersen 1 ,
  • Iris Vesper 1 ,
  • Bernhild Pachwald 1 ,
  • Nicole Dagenbach 1 ,
  • Sina Buck   ORCID: orcid.org/0000-0001-8428-1038 2 ,
  • Delia Waldenmaier 2 &
  • Lutz Heinemann 3  

Trials volume  22 , Article number:  61 ( 2021 ) Cite this article

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Introduction

Several clinical studies investigated improvements of patient outcomes due to diabetes management interventions. However, chronic disease management is intricate with complex multifactorial behavior patterns. Such studies thus have to be well designed in order to allocate all observed effects to the defined intervention and to exclude effects of other confounders as well as possible.

This article aims to provide challenges in interpreting diabetes management intervention studies and suggests approaches for optimizing study implementation and for avoiding pitfalls based on current experiences.

Lessons from the STeP and ProValue studies demonstrated the difficulty in medical device studies that rely on behavioral changes in intervention group patients. To successfully engage patients, priority should be given to health care professionals being engaged, operational support in technical issues being available, and adherence being assessed in detail.

Another difficulty is to avoid contamination of the control group with the intervention; therefore, strict allocation concealment should be maintained. However, randomization and blinding are not always possible. A limited effect size due to improvements regarding clinical endpoints in the control group is often caused by the Hawthorne effect. Improvements in the control group can also be caused with increased attention paid to the subjects. In order to reduce improvements in the control group, it is essential to identify the specific reasons and adjust study procedures accordingly. A pilot phase is indispensable for this. Another option is to include a third study arm to control for enhanced standard of care and study effects. Furthermore, retrospective data collection could be a feasible option. Adaptive study designs might reduce the necessity of a separate pilot study and combine the exploratory and confirmatory stages of an investigation in one single study.

There are several aspects to consider in medical device studies when using interventions that rely on changes in behavior to achieve an effective implementation and significant study results. Improvements in the control group may reduce effect sizes and limit statistical significance; therefore, alternatives to the traditional randomized controlled trials may be considered.

Peer Review reports

Patients with diabetes require a life-long treatment that is not limited to a standardized intake of drugs, but requires a more complex disease management. In particular, type 1 diabetes management involves frequent treatment decisions like adjustment of insulin doses depending on the current glucose status, meal intake, and physical activity level. This requires the use of medical devices, adequate handling by the patient, and translation of the measurement results into appropriate therapeutic decisions. Health care professionals (HCPs) support patients with regular monitoring of markers of glucose control and adjustment of the treatment plan. These factors have a complicated interaction with one another to influence the achievement of a therapeutic goal. If individual components of diabetes management are investigated, e.g., in a clinical study, this interaction has to be taken into account. For example, frequent use of a CGM system and adequate interpretation of glucose values will more likely lead to improvements in diabetes management [ 1 ].

Therapeutic improvements that are observed as a result of device usage are not driven by the device itself, but by the behavioral changes the device enables. In clinical studies with medical devices for diabetes management, behavioral changes of study participants, not only those planned for the intervention group, but also unintended changes in control group participants, as well as those of the HCPs, should be taken into account and adequately considered in study design, implementation, and analysis.

Over the last several years, a number of studies have been published that investigated improvements in patient outcomes driven by interventions in their diabetes management with medical devices [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].

The Structured Testing Program (STeP) study and the two ProValue studies are examples of studies in which diabetes management interventions on clinical outcomes were investigated [ 17 , 18 ]. All of these studies were multicenter cluster randomized controlled trials (RCTs) with the primary outcome being improvements in glucose control (HbA1c reduction) in patients with type 2 diabetes.

In the STeP study, subjects in the intervention group performed structured self-monitoring of blood glucose (SMBG) and received enhanced care, while subjects in the control group received standard of care. In the ProValue studies, two levels of ambulatory care, namely diabetes specialized practices (first ProValue study) and general medical practices (second ProValue study), were examined. The ProValue studies also had an identical study design, where subjects in the intervention group used an integrated Personalized Diabetes Management (iPDM) system to support their diabetes therapy while subjects in the control group were to receive standard of care. The primary outcome was an improvement in glycemic control of participants using the iPDM tools compared to the subjects in the control group.

The STeP and the ProValue studies reported significant differences in the reductions in HbA1c between the intervention and control groups; however, considerable improvements in glucose control were observed in the control groups as well [ 3 , 19 , 20 ]. Improvements in the control group reduced the size of the effect of the intervention in these studies. Similar outcomes were observed in other studies, which report conservative or weakened between-group differences due to improvements in the control group [ 6 , 11 , 14 , 21 ]. Therefore, solely the recruitment to a clinical trial, independent of the interventions, resulted in improvements in HbA1c [ 22 ]. When designing an interventional study, such effects have to be taken into account; otherwise, the observed effects cannot be attributed to the defined intervention due to the profound effects of confounding factors. This is especially important when including behavioral interventions, as comprehensive support of HCPs is essential to utilize the full potential of the intervention.

This article aims to provide challenges in interpreting diabetes management intervention studies gained from the STeP and ProValue studies and suggests approaches for optimizing study implementation and for avoiding pitfalls for further studies with similar medical devices.

Study implementation

Engaging participants in the intervention group.

A major difficulty in intervention studies that rely on behavioral changes is to ensure that the intervention triggers the desired effect (Table  1 ).

While in drug studies an intervention is defined by a given medication scheme that the participants simply have to follow, behavioral changes cannot be triggered that straightforward [ 23 , 24 ].

There is no single intervention strategy to guarantee intervention adherence of all participants. Furthermore, a distinction between unintentional and intentional non-adherence should be taken into account. Whereas unintentional non-adherence depends on modifiable factors like poor understanding of the treatment or low health literacy or numeracy, in intentional non-adherence, participants rationally decide to not adhere by weighing the benefits and risk of following an intervention [ 25 ].

A set of key factors influencing attempts to improve participants’ unintentional non-adherence include a clear and effective communication between HCP and participant as well as realistic assessments of participants’ health literacy and knowledge. Health literacy is essential to the ability to adhere to study intervention as well as the ability to remember the details of the recommendations made to participants during visits influences the adherence. Thus, the explanation of the specific steps of the study regimen, review of the most important details, and written instructions may increase adherence. Furthermore, the risk of non-adherence is reduced with a more familiar relationship between HCP and participant. This enables HCPs to understand participants’ beliefs, attitudes, subjective norms, cultural context or social supports, and other relevant elements that are crucial for patient’s adherence [ 26 , 27 ].

Many different behavior change techniques are already included in the standard of care in diabetes therapy, such as providing feedback on HbA1c levels and setting personal goals [ 25 , 28 ].

In the ProValue studies, for example, the diabetes management intervention comprised a blood glucose meter and the corresponding software. Subjects in the intervention group had to perform structured SMBG with an individualized testing regimen selected by the treating physicians. This selection, as well as treatment adjustments, was supported by the diabetes management software. To ensure optimal engagement of the involved HCPs with a leading role in the implementation of the intervention, numerous local investigator meetings were organized. During these investigator meetings, HCPs of intervention group sites were trained by the coordinating investigator and the sponsor’s medical advisor in the use and concept of the diabetes management program. These meetings also enabled an exchange of experiences and individual patient cases could be discussed. HCPs could thus find a way to implement the iPDM into their daily practice and individual treatment patterns, and they were motivated to appropriately use the program. It was expected that this confidence and commitment of the HCPs would increase subjects’ compliance as well [ 29 ]. Inherently, the investigator meetings and the behavior changes of HCPs varied widely, partly because the initial knowledge and skills in the use of technology already differed between the study sites. Additional attempts for a standardization of handling iPDM would have increased comparability, but would have impaired the real-life aspects and competed with the need for actions individually tailored to each patient. As the desired behavioral changes are not expected to occur in one stroke but rather gradually in the course of the study and with the active use of the iPDM, repeated training sessions of study sites were supportive.

One instrument used in the STeP study was the “peer-to-peer review” that scheduled a review of intervention group patient data and subsequent therapy and advised by an uninvolved practitioner. The independent physician applied the same principles of the iPDM used in the regular study procedures and gave direct feedback to the treating physician on how to intensify or improve therapy adjustments. Even though the iPDM used in ProValue was constructed as an ongoing circle, most improvements were observed rapidly after study start [ 20 ]. This should be considered when the optimal timing of training activities has to be determined.

To be able to evaluate whether a behavioral intervention is successful, it is necessary to verify that recommendations have actually been followed. For the ProValue studies, this takes into account whether the HCP makes use of proposals made by the software and whether participants adhere to their HCP’s advices and instructions. While adherence to some instructions like the number of daily SMBG measurements can be assessed easily when device data are downloaded, other instructions like the consideration of SMBG results for insulin dosing decisions or dietary recommendations are difficult to be traced. Information on the intentional use of the intervention might be helpful in subgroup analysis to evaluate the effect of the intervention when completely implemented. In the ProValue studies, this was captured by questionnaires about the perception of the tools among HCPs and participants and by capturing detailed information about therapy adaptations and HCP recommendations. Data to identify and trace potentials and hurdles were available during each stage of the iPDM circle. In this regard, including an analysis of the “per protocol population” that fully adhered to the study intervention procedures is highly recommended in addition to results for the intention to treat population. Bartolo et al., for instance, could not show an advantage of a diabetes management intervention compared to standard care in their study [ 14 ]. However, they reported compliance to SMBG of less than approximately 50% in both groups and they reported larger improvements among patients that were more compliant. An inadequate use that did not lead to the intended behavior changes in the study might have been a reason for the limited effects.

However, the introduction of a new medical device is time and energy consuming for HCPs. For example, software like the one used in the ProValue studies is, at least in Germany, not as common as one would expect at the present time, especially among general practitioners. Support in all technical issues should be provided on an individual level by study sponsors, adjusted to the particular knowledge and requirements, on demand and on site. Moreover, to be able to support the study sites in an appropriate and targeted manner, technical trainers have to be familiar with study aims and procedures too. In addition to technical training for the HCPs, messages from such a digital technology-based intervention have to be conveyed to the patients in a way they can understand. One lesson learned from STeP and ProValue is that visualization is an important factor. In STeP, patients graphically documented their blood glucose levels in a paper tool with color grades, while in ProValue, downloaded data were reported in a traffic light scheme (Figs.  1 and 2 ). Such visual feedback provides a link between technology, HCPs, and patients and facilitates the implementation of the intervention.

figure 1

Paper tool with color grades for BG level documentation used in the STeP study

figure 2

Traffic light scheme used in the ProValue studies

Management of the control group

Another major difficulty in intervention studies that rely on behavioral change is to keep participants in the control group distant from the intervention, i.e., that behavioral changes desired in the intervention group do not occur in the control group as well (Table 1 ). While in drug studies finding an adequate control (e.g., a placebo) is mostly straightforward, control group design for behavioral studies is complex and the achievement of a truly “inactive” control group that strictly stays with standard care and does not change behavior is almost impossible. Usually, randomization and blinding are the preferred tools, but the implementation is not always feasible.

Adoption of behavioral changes requires an active involvement of both patients and HCPs; blinding is therefore not an option. If HCPs are also part of the intervention, like in STeP and ProValue studies, study personnel which gained knowledge from treating the intervention group subjects could transfer this to those in the control group, at least to a given extent. Thus, cluster-randomization, i.e., randomization of the study sites rather than the individual subjects, is necessary to avoid “contamination” of subjects in the control group [ 23 ]. This means cluster-randomization is suitable for interventions that are unlikely to be available for HCPs and patients in the trial [ 23 ].

Cluster-randomization, however, limits the possibility to control for differences between the sites, such as their implementation of standard of care. As the control sites are aware of the intervention done at the intervention sites, there is the risk that they tend to try to improve therapy in control group patients too and are more attentive to patient care than usual.

Statistical power of cluster-randomized studies is limited; they thus require larger sample sizes. Based on the variation between the clusters and the expected effect size, the optimal number of clusters, i.e., study sites, and subjects per cluster can be calculated [ 30 ]. However, scheduling the required numbers is often complicated by feasibility, ethical justifiability, and affordability, which may enforce the acceptance of compromises.

A limited effect size due to improvements also in the control group is an often-observed incident, caused by the so-called Hawthorne effect. This effect is attributable to subjects’ knowledge of being part of a study, i.e., being observed and having data collected. This study effect can improve the health status of a subject without any further intervention [ 31 , 32 ]. Asking questions, for instance, induces rethinking about the current behavior and might induce respective changes [ 33 ]. Another reason for improvements in the control group is increased attention paid to the subjects by their HCPs. In principle, this increase in attention should be kept to a minimum; however, in reality, it is difficult to avoid. In addition, an intensive data gathering approach as used in the ProValue studies induces a high engagement of the participating HCPs (and also of the patients in both study groups) leading to improvements in the control group as well [ 20 ].

Also, the monitoring effort of clinical research associates (CRAs) regarding study implementation by HCPs, which is an absolutely necessary study procedure, has an impact on study implementation.

Limiting information about the intervention might be a possibility to reduce control group effect. This is not in strict compliance with the guideline for Good Clinical Practice. However, as long as the safety of study participants is paramount, a degree of concealment is accepted by research ethics committees for behavioral intervention studies [ 34 ]. According to this, all subjects and HCPs regardless of the study group have to be fully informed about the background and procedures of the study prior to the start. As such, in the ProValue studies, participants in the control group and HCPs were fully aware of the hypothesis that an iPDM and structured SMBG were expected to improve glycemic control. Participants randomized to the control group might therefore, whether or not intentionally, have sought for a comparable treatment or intensified their therapy on their own [ 31 , 35 ]. Because of the detailed assessment of therapy adjustments and recommendations of HCPs in the ProValue studies, the main triggers for behavior changes that were identified for the subjects in the intervention group could also be detected among those in the control group [ 20 ]. Control group patients typically receive “standard of care” or “treatment as usual,” but these conditions are often less defined and monitored than the interventional treatment [ 29 ].

Standard care differs across countries, hospitals, and over time, depending on the respective health care provisions and updated guidelines and technologies that are introduced at variable rates. Especially in multicenter studies, the actual implementation might vary considerably between study sites and this cannot be controlled in cluster-randomized studies [ 36 ]. A clear definition of what is regarded as “standard” is essential for the validity of a study and should receive as much attention as the definition of the intervention. Mostly, patient care within a study is rather an enhanced standard of care for all the reasons discussed above. A meta-analysis of randomized control trials (RCTs) that investigated standard care conditions in control groups of behavior change studies in patients with diabetes showed that those control group patients that received a higher quality of standard care also showed larger improvements in study outcomes, thus reducing the effect size of the intervention [ 36 ].

Nevertheless, chronic disease management like diabetes therapy is complex and, like the intervention, standard of care cannot be fully standardized but has to be adapted to the individual patient and their compliance.

Alternative study designs

Due to recruitment and “contamination” problems in interventional trials requiring behavioral changes, the realization of standard RCTs may be difficult [ 23 ]. Alternatives to the standard RCT when designing a medical device studies that rely on behavioral changes may be considered. While the above-described aspects concern the detailed implementation of a study, some variations in the general design might be considered with regard to the effect size, which was often observed to be lower than expected.

If control group effects are expected, it is essential to identify the particular reasons or triggers for behavior changes that may occur. Once identified, study procedures can be adjusted to avoid them or to even include them into the intervention. A pilot phase or study is indispensable to identify such factors and should therefore be included, especially if a large trial is planned. Therefore, more and more studies consider the additional effort of a pilot trial [ 7 , 8 , 11 , 37 , 38 ].

As the Hawthorne effect is described to be temporary and of relatively short duration [ 39 ], one approach towards a reduction of influencing the behavior of the subjects in the control group is to add one or several pretest periods to the study design [ 40 , 41 ]. This means additional data collection before and after the pretest period, without an interventional treatment in any of the groups. Randomization and initiation of the intervention starts after this period using data obtained after the pretest as baseline data. Because it is expected that the majority of improvements induced by study effects occur between the first and the second data collection, the data used for the assessment of study outcomes will not be impaired, or at least less. However, the inclusion of a pretest period is cost and time expensive and might require a pilot study to determine an adequate duration. For STeP and the ProValue studies, a 3-month pretest period would have been sufficient, as the results indicate the strongest control group effects within the first 3 months of the study. Nevertheless, because these studies were accompanied by a lot of preparations for intervention group sites, such as training sessions, a postponement of randomization procedures would have interrupted the whole study flow.

One possibility for control conditions in RCTs is using a waitlist control [ 42 ]. Subjects of the control group that are on a waiting list, i.e., they expect to receive the active intervention at a later time point, have been shown to improve less than patients that receive only standard of care throughout the whole study [ 29 ]. A waiting control group could therefore be a more efficient way to influence the effect size than an inactive control group.

Other options include a third study arm to control for enhanced standard of care and study effects. Schwartz et al. proposed a design in which one arm receives the intervention, while the control condition has two arms, each with a crossover between a waiting list with standard of care and receiving the intervention [ 43 ]. Several data collection points are required for such a design. The crossover design reduces the heterogeneity within a group due to individually tailored implementation of the intervention, increasing statistical efficiency. Nevertheless, feasibility of a crossover depends on the kind of intervention and the expected long-term effects. Additionally, inclusion of further study arms reduces statistical power, and accordingly, it requires the inclusion of more subjects which also increases financial cost and study duration [ 41 ].

In this regard, retrospective data collection could be a feasible option, but only if required data are limited to standard assessments during usual patient visits, as expected when standard of care is claimed for control group subjects.

Use of historical controls, i.e., data assessed in other independent studies that already were conducted, is another promising option if study effects shall be reduced [ 44 , 45 ]. In addition, with the use of historical controls, more resources become available for the intervention arm (which could be used for a larger sample size and therefore an increased power). Identification of a suitable control data set for the respective objective, however, is challenging, as well as the correct use of these data. In addition, the progress in treatment standards, assessment technologies, and other factors over time have to be considered.

A better separation of intervention and control group might be reached by using two separate protocols for the two groups. Consequently, all other participants such as CRAs should be exclusively assigned to one of the groups. The ProValue studies already worked with two protocols, but those were divided by the type of practice of the study sites rather than by study groups. A separate control group protocol would on the one hand enable a clear definition of “standard of care” and on the other hand allow a reduction of procedures in the control group to an absolute minimum. This applies not only to contacts between subjects and HCPs, but also between HCPs and further study staff. In addition, suitably designed informed consent forms should avoid inclusion of interventional aspects.

To prevent patients from consenting to therapy forms they may not get, a two-stage randomization could be another option. Accordingly, all patients give consent for follow-up first. An additional consent for study intervention is only provided to a randomly selected sample. Thus, patients randomized to the control group do not feel disadvantaged not receiving the intervention [ 46 ]. However, ethical concerns remain because there is only a personal consent to patient’s treatment and no full consent to the project from all patients [ 23 , 46 ].

Adaptive study designs are becoming more and more common, however, not yet in medical device studies, but rather in drug studies, as adaptive designs are in particular effective in investigating dose-response relationships. Nevertheless, some of the several different approaches might also be used for medical device studies. Adaptive design means that procedures or conditions of a study are modified during the ongoing study based on results from interim analyses. However, these changes have to be planned and defined in advance [ 47 , 48 ]. Adaptations include, e.g., randomization based on baseline data or sample size re-estimation to ensure the desired power. Implementation of adequate adaptations in studies including behavioral change has yet to be investigated. Nonetheless, such an approach could reduce the necessity of a separate pilot study and combine the exploratory and confirmatory stages of an investigation in one single study [ 38 , 49 ]. Performance of an underpowered trial may furthermore be prevented [ 47 ]. In addition, it might be a better reflection of clinical practice if those patients that prove to be compliant and susceptible for an intervention are selected. Considerations about whether or not introducing new therapeutic options (might they be behavioral changes and/or diagnostic/treatment options) are usually made by HCPs based on their experiences with the respective patients.

Based on experiences from the STeP and ProValue studies, several crucial aspects have to be considered in medical device studies when using interventions that rely on changes in behavior of study participants and their HCPs to achieve an effective implementation and significant results.

The article summarizes experiences gained from the three studies and provides suggestions for the implementation of other studies with similar medical devices.

In particular, definition of control group conditions and an integrative support of the intervention group have to be included. Improvements in the control group may reduce effect sizes and limit statistical significance; therefore, alternatives to the traditional RCT, like pretest periods or separate study protocols, are worth to be considered. As there is no ideal design for such studies, integration of experiences from other studies is essential to achieve the best possible study outcome.

Availability of data and materials

Not applicable

Abbreviations

Continuous glucose monitoring

Clinical research associates

Health care professionals

Integrated Personalized Diabetes Management

  • Randomized controlled trials

Self-monitoring of blood glucose

Structured Testing Program

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Acknowledgements

The authors would like to thank Guido Freckmann for the support in writing the manuscript.

Scientific writing was funded by Roche Diabetes Care. The reported STeP study was funded by Roche Diabetes Care. Roche Diabetes Care was involved in the concept and design of the reported STeP and ProValue studies. Roche Diabetes Care contributed to subsequent revisions of the manuscript and approved the final version.

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BP, IV, BP, and ND were involved in the concept and design of the STeP and ProValue studies. DW performed a literature search and wrote the first draft of the manuscript, and all authors contributed to subsequent revisions of the manuscript and approved the final version.

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DW and SB are employees of the IfDT (Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany), which carries out clinical studies on the evaluation of BG meters and medical devices for diabetes therapy on its own initiative and on behalf of various companies. IfDT has received speakers’ honoraria or consulting fees from Abbott, Ascensia, Dexcom, LifeScan, Menarini Diagnostics, Metronom Health, Novo Nordisk, Roche, Sanofi, Sensile, and Ypsomed.

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The burden and risks of emerging complications of diabetes mellitus

  • Dunya Tomic   ORCID: orcid.org/0000-0003-2471-2523 1 , 2 ,
  • Jonathan E. Shaw   ORCID: orcid.org/0000-0002-6187-2203 1 , 2   na1 &
  • Dianna J. Magliano   ORCID: orcid.org/0000-0002-9507-6096 1 , 2   na1  

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  • Diabetes complications
  • Type 1 diabetes
  • Type 2 diabetes

The traditional complications of diabetes mellitus are well known and continue to pose a considerable burden on millions of people living with diabetes mellitus. However, advances in the management of diabetes mellitus and, consequently, longer life expectancies, have resulted in the emergence of evidence of the existence of a different set of lesser-acknowledged diabetes mellitus complications. With declining mortality from vascular disease, which once accounted for more than 50% of deaths amongst people with diabetes mellitus, cancer and dementia now comprise the leading causes of death in people with diabetes mellitus in some countries or regions. Additionally, studies have demonstrated notable links between diabetes mellitus and a broad range of comorbidities, including cognitive decline, functional disability, affective disorders, obstructive sleep apnoea and liver disease, and have refined our understanding of the association between diabetes mellitus and infection. However, no published review currently synthesizes this evidence to provide an in-depth discussion of the burden and risks of these emerging complications. This Review summarizes information from systematic reviews and major cohort studies regarding emerging complications of type 1 and type 2 diabetes mellitus to identify and quantify associations, highlight gaps and discrepancies in the evidence, and consider implications for the future management of diabetes mellitus.

With advances in the management of diabetes mellitus, evidence is emerging of an increased risk and burden of a different set of lesser-known complications of diabetes mellitus.

As mortality from vascular diseases has declined, cancer and dementia have become leading causes of death amongst people with diabetes mellitus.

Diabetes mellitus is associated with an increased risk of various cancers, especially gastrointestinal cancers and female-specific cancers.

Hospitalization and mortality from various infections, including COVID-19, pneumonia, foot and kidney infections, are increased in people with diabetes mellitus.

Cognitive and functional disability, nonalcoholic fatty liver disease, obstructive sleep apnoea and depression are also common in people with diabetes mellitus.

As new complications of diabetes mellitus continue to emerge, the management of this disorder should be viewed holistically, and screening guidelines should consider conditions such as cancer, liver disease and depression.

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Introduction

Diabetes mellitus is a common, albeit potentially devastating, medical condition that has increased in prevalence over the past few decades to constitute a major public health challenge of the twenty-first century 1 . Complications that have traditionally been associated with diabetes mellitus include macrovascular conditions, such as coronary heart disease, stroke and peripheral arterial disease, and microvascular conditions, including diabetic kidney disease, retinopathy and peripheral neuropathy 2 (Fig.  1 ). Heart failure is also a common initial manifestation of cardiovascular disease in patients with type 2 diabetes mellitus (T2DM) 3 and confers a high risk of mortality in those with T1DM or T2DM 4 . Although a great burden of disease associated with these traditional complications of diabetes mellitus still exists, rates of these conditions are declining with improvements in the management of diabetes mellitus 5 . Instead, as people with diabetes mellitus are living longer, they are becoming susceptible to a different set of complications 6 . Population-based studies 7 , 8 , 9 show that vascular disease no longer accounts for most deaths among people with diabetes mellitus, as was previously the case 10 . Cancer is now the leading cause of death in people with diabetes mellitus in some countries or regions (hereafter ‘countries/regions’) 9 , and the proportion of deaths due to dementia has risen since the turn of the century 11 . In England, traditional complications accounted for more than 50% of hospitalizations in people with diabetes mellitus in 2003, but for only 30% in 2018, highlighting the shift in the nature of complications of this disorder over this corresponding period 12 .

figure 1

The traditional complications of diabetes mellitus include stroke, coronary heart disease and heart failure, peripheral neuropathy, retinopathy, diabetic kidney disease and peripheral vascular disease, as represented on the left-hand side of the diagram. With advances in the management of diabetes mellitus, associations between diabetes mellitus and cancer, infections, functional and cognitive disability, liver disease and affective disorders are instead emerging, as depicted in the right-hand side of the diagram. This is not an exhaustive list of complications associated with diabetes mellitus.

Cohort studies have reported associations of diabetes mellitus with various cancers, functional and cognitive disability, liver disease, affective disorders and sleep disturbance, and have provided new insights into infection-related complications of diabetes mellitus 13 , 14 , 15 , 16 , 17 . Although emerging complications have been briefly acknowledged in reviews of diabetes mellitus morbidity and mortality 11 , 17 , no comprehensive review currently specifically provides an analysis of the evidence for the association of these complications with diabetes mellitus. In this Review, we synthesize information published since the year 2000 on the risks and burden of emerging complications associated with T1DM and T2DM.

Diabetes mellitus and cancer

The burden of cancer mortality.

With the rates of cardiovascular mortality declining amongst people with diabetes mellitus, cancer deaths now constitute a larger proportion of deaths among this population in some countries/regions 8 , 9 . Although the proportion of deaths due to cancer appears to be stable, at around 16–20%, in the population with diabetes mellitus in the USA 7 , in England it increased from 22% to 28% between 2001 and 2018 (ref. 9 ), with a similar increase reported in Australia 8 . Notably, in England, cancer has overtaken vascular disease as the leading cause of death in people with diabetes mellitus and it is the leading contributor to excess mortality in those with diabetes mellitus compared with those without 9 . These findings are likely to be due to a substantial decline in the proportion of deaths from vascular diseases, from 44% to 24% between 2001 and 2018, which is thought to reflect the targeting of prevention measures in people with diabetes mellitus 18 . Over the same time period, cancer mortality rates fell by much less in the population with diabetes mellitus than in that without diabetes 9 , suggesting that clinical approaches for diabetes mellitus might focus too narrowly on vascular complications and might require revision 19 . In addition, several studies have reported that female patients with diabetes mellitus receive less-aggressive treatment for breast cancer compared with patients without diabetes mellitus, particularly with regard to chemotherapy 20 , 21 , 22 , suggesting that this treatment approach might result in increased cancer mortality rates in women with diabetes mellitus compared with those without diabetes mellitus. Although substantial investigation of cancer mortality in people with diabetes mellitus has been undertaken in high-income countries/regions, there is a paucity of evidence from low-income and middle-income countries/regions. It is important to understand the potential effect of diabetes mellitus on cancer mortality in these countries/regions owing to the reduced capacity of health-care systems in these countries/regions to cope with the combination of a rising prevalence of diabetes mellitus and rising cancer mortality rates in those with diabetes mellitus. One study in Mauritius showed a significantly increased risk of all-cause cancer mortality in patients with T2DM 23 , but this study has yet to be replicated in other low-income and middle-income countries/regions.

Gastrointestinal cancers

Of the reported associations between diabetes mellitus and cancer (Table  1 ), some of the strongest have been demonstrated for gastrointestinal cancers.

Hepatocellular carcinoma

In the case of hepatocellular carcinoma, the most rigorous systematic review on the topic — comprising 18 cohort studies with a combined total of more than 3.5 million individuals — reported a summary relative risk (SRR) of 2.01 (95% confidence interval (CI) 1.61–2.51) for an association with diabetes mellitus 24 . This increased risk of hepatocellular carcinoma with diabetes mellitus is supported by the results of another systematic review that included case–control studies 25 . Another review also found that diabetes mellitus independently increased the risk of hepatocellular carcinoma in the setting of hepatitis C virus infection 26 .

Pancreatic cancer

The risk of pancreatic cancer appears to be approximately doubled in patients with T2DM compared with patients without T2DM. A meta-analysis of 36 studies found an adjusted odds ratio (OR) of 1.82 (95% CI 1.66–1.89) for pancreatic cancer among people with T2DM compared with patients without T2DM 27 (Table  1 ). However, it is possible that these findings are influenced by reverse causality — in this scenario, diabetes mellitus is triggered by undiagnosed pancreatic cancer 28 , with pancreatic cancer subsequently being clinically diagnosed only after the diagnosis of diabetes mellitus. Nevertheless, although the greatest risk (OR 2.05, 95% CI 1.87–2.25) of pancreatic cancer was seen in people diagnosed with T2DM 1–4 years previously compared with people without T2DM, those with a diagnosis of T2DM of more than 10 years remained at increased risk of pancreatic cancer (OR 1.51, 95% CI 1.16–1.96) 27 , suggesting that reverse causality can explain only part of the association between T2DM and pancreatic cancer. Although T2DM accounts for ~90% of all cases of diabetes mellitus 29 , a study incorporating data from five nationwide diabetes registries also reported an increased risk of pancreatic cancer amongst both male patients (HR 1.53, 95% CI 1.30–1.79) and female patients (HR 1.25, 95% CI 1.02–1.53) with T1DM 30 .

Colorectal cancer

For colorectal cancer, three systematic reviews have shown a consistent 20–30% increased risk associated with diabetes mellitus 31 , 32 , 33 . One systematic review, which included more than eight million people across 30 cohort studies, reported an incidence SRR of 1.27 (95% CI 1.21–1.34) of colorectal cancer 31 , independent of sex and family history (Table  1 ). Similar increases in colorectal cancer incidence in patients with diabetes mellitus were reported in a meta-analysis of randomized controlled trials (RCTs) and cohort studies 32 and in a systematic review that included cross-sectional studies 33 .

Female-specific cancers

Endometrial, breast and ovarian cancers all occur more frequently in women with diabetes mellitus than in women without diabetes mellitus.

Endometrial cancer

For endometrial cancer, one systematic review of 29 cohort studies and a combined total of 5,302,259 women reported a SRR of 1.89 (95% CI 1.46–2.45) and summary incidence rate ratio (IRR) of 1.61 (95% CI 1.51–1.71) 34 (Table  1 ). Similar increased risks were found in two systematic reviews incorporating cross-sectional studies 35 , 36 , one of which found a particularly strong association of T1DM (relative risk (RR) 3.15, 95% CI 1.07–9.29) with endometrial cancer.

Breast cancer

The best evidence for a link between diabetes mellitus and breast cancer comes from a systematic review of six prospective cohort studies and more than 150,000 women, in which the hazard ratio (HR) for the incidence of breast cancer in women with diabetes mellitus compared with women without diabetes mellitus was 1.23 (95% CI 1.12–1.34) 32 (Table  1 ). Two further systematic reviews have also shown this increased association 37 , 38 .

The association of diabetes mellitus with breast cancer appears to vary according to menopausal status. In a meta-analysis of studies of premenopausal women with diabetes mellitus, no significant association with breast cancer was found 39 , whereas in 11 studies that included only postmenopausal women, the SRR was 1.15 (95% CI 1.07–1.24). The difference in breast cancer risk between premenopausal and postmenopausal women with diabetes mellitus was statistically significant. The increased risk of breast cancer after menopause in women with diabetes mellitus compared with women without diabetes mellitus might result from the elevated concentrations and increased bioavailability of oestrogen that are associated with adiposity 40 , which is a common comorbidity in those with T2DM; oestrogen synthesis occurs in adipose tissue in postmenopausal women, while it is primarily gonadal in premenopausal women 41 . Notably, however, there is evidence that hormone-receptor-negative breast cancers, which typically carry a poor prognosis, occur more frequently in women with breast cancer and diabetes mellitus than in women with breast cancer and no diabetes mellitus 42 , indicating that non-hormonal mechanisms also occur.

Ovarian cancer

Diabetes mellitus also appears to increase the risk of ovarian cancer, with consistent results from across four systematic reviews. A pooled RR of 1.32 (95% CI 1.14–1.52) was reported across 15 cohort studies and a total of more than 2.3 million women 43 (Table  1 ). A SRR of 1.19 (95% CI 1.06–1.34) was found across 14 cohort studies and 3,708,313 women 44 . Similar risks were reported in meta-analyses that included cross-sectional studies 45 , 46 .

Male-specific cancers: prostate cancer

An inverse association between diabetes mellitus and prostate cancer has been observed in a systematic review (RR 0.91, 95% CI 0.86–0.96) 47 , and is probably due to reduced testosterone levels that occur secondary to the low levels of sex hormone-binding globulin that are commonly seen in men with T2DM and obesity 48 . Notably, however, the systematic review that showed the inverse association involved mostly white men (Table  1 ), whereas a systematic review of more than 1.7 million men from Taiwan, Japan, South Korea and India found that diabetes mellitus increased prostate cancer risk 49 , suggesting that ethnicity might be an effect modifier of the diabetes mellitus–prostate cancer relationship. The mechanisms behind this increased risk in men in regions of Asia such as Taiwan and Japan, where most study participants came from, remain unclear. Perhaps, as Asian men develop diabetes mellitus at lower levels of total adiposity than do white men 50 , the adiposity associated with diabetes mellitus in Asian men might have a lesser impact on sex hormone-binding globulin and testosterone than it does in white men. Despite the reported inverse association between diabetes mellitus and prostate cancer in white men, however, evidence suggests that prostate cancers that do develop in men with T2DM are typically more aggressive, conferring higher rates of disease-specific mortality than prostate cancers in men without diabetes mellitus 51 .

An assessment of cancer associations

As outlined above, a wealth of data has shown that diabetes mellitus is associated with an increased risk of various cancers. It has been argued, however, that some of these associations could be due to detection bias resulting from increased surveillance of people with diabetes mellitus in the immediate period after diagnosis 52 , or reverse causality, particularly in the case of pancreatic cancer 53 . However, neither phenomenon can account for the excess risks seen in the longer term. An Australian study exploring detection bias and reverse causality found that standardized mortality ratios (SMRs) for several cancer types in people with diabetes mellitus compared with the general population fell over time, but remained elevated beyond 2 years for pancreatic and liver cancers 54 , suggesting that diabetes mellitus is a genuine risk factor for these cancer types.

A limitation of the evidence that surrounds diabetes mellitus and cancer risk is high clinical and methodological heterogeneity across several of the large systematic reviews, which makes it difficult to be certain of the effect size in different demographic groups. Additionally, many of the studies exploring a potential association between diabetes mellitus and cancer were unable to adjust for BMI, which is a major confounder. However, a modelling study that accounted for BMI found that although 2.1% of cancers worldwide in 2012 were attributable to diabetes mellitus as an independent risk factor, twice as many cancers were attributable to high BMI 55 , so it is likely that effect sizes for cancer risk associated with diabetes mellitus would be attenuated after adjustment for BMI. Notably, however, low-income and middle-income countries/regions had the largest increase in the numbers of cases of cancer attributable to diabetes mellitus both alone and in combination with BMI 55 , highlighting the need for public health intervention, given that these countries/regions are less equipped than high-income countries/regions to manage a growing burden of cancer.

As well as the cancer types outlined above, diabetes mellitus has also been linked to various other types of cancer, including kidney cancer 56 , bladder cancer 57 and haematological malignancies; however, the evidence for these associations is not as strong as for the cancers discussed above 58 . Diabetes mellitus might also be associated with other cancer types such as small intestine cancer, but the rarity of some of these types makes it difficult to obtain sufficient statistical power in analyses of any potential association.

Potential aetiological mechanisms

Several aetiological mechanisms that might be involved in linking diabetes mellitus to cancer have been proposed, including hyperinsulinaemia, hyperglycaemia, inflammation and cellular signalling mechanisms.

Hyperinsulinaemia

Most cancer cells express insulin receptors, through which hyperinsulinaemia is thought to stimulate cancer cell proliferation and metastasis 59 . Hyperinsulinaemia might also promote carcinogenesis through increased local levels of insulin-like growth factor 1 (IGF1), which has potent mitogenic and anti-apoptotic activities 60 , owing to decreased levels of insulin-like growth factor binding proteins. As outlined above, people with diabetes mellitus show a strong risk of pancreatic and liver cancers; this increased risk might occur because insulin is produced by pancreatic β-cells and transported to the liver via the portal vein 61 , thereby exposing the liver and pancreas to high levels of endogenous insulin 59 .

Hyperglycaemia and inflammation

Hyperglycaemia can induce DNA damage 62 , increase the generation of reactive oxygen species 63 and downregulate antioxidant expression 64 , all of which are associated with cancer development. Inflammatory markers, including cytokines such as IL-6, appear to have an important role in the association between diabetes and cancer 65 .

Cellular signalling mechanisms

Several cellular signalling components are common to the pathogenesis of T2DM and cancer. These include the mechanistic target of rapamycin (mTOR), a central controller of cell growth and proliferation; AMP-activated protein kinase, a cellular energy sensor and signal transducer 66 ; and the phosphatidylinositol 3-kinase (PI3K)–AKT pathway, which transduces growth factor signals during organismal growth, glucose homeostasis and cell proliferation 67 . Dysregulation of any of these cellular signalling components or pathways could contribute to the development of cancer and metabolic disorders, including T2DM, and glucose-lowering drugs such as metformin have been associated with a reduction in cancer cell proliferation through effective inhibition of some of these components 68 .

Diabetes mellitus and infections

Infection-related complications.

Although infection has long been recognized as a complication of diabetes mellitus, an association between diabetes mellitus and infection has not been well documented in epidemiological studies 69 . Only in the past decade have major studies quantified the burden of infection-related complications in people with diabetes mellitus and explored the specific infections accounting for this burden. In a US cohort of 12,379 participants, diabetes mellitus conferred a significant risk of infection-related hospitalization, with an adjusted HR of 1.67 (95% CI 1.52–1.83) compared with people without diabetes mellitus 70 (Table  2 ). The association was most pronounced for foot infections (HR 5.99, 95% CI 4.38–8.19), with significant associations also observed for respiratory infection, urinary tract infection, sepsis and post-operative infection, but not for gastrointestinal infection, a category that included appendicitis and gastrointestinal abscesses but not viral or bacterial gastroenteritis. Interestingly, a report from Taiwan demonstrated an association between the use of metformin and a lower risk of appendicitis 71 .

In an analysis of the entire Hong Kong population over the period 2001–2016, rates of hospitalization for all types of infection remained consistently higher in people with diabetes mellitus than in those without diabetes mellitus 72 . The strongest association was seen for hospitalization due to kidney infections, for which the adjusted RR was 4.9 (95% CI 3.9–6.2) in men and 3.2 (95% CI 2.8–3.7) in women with diabetes mellitus compared with those without diabetes mellitus in 2016 (Table  2 ). Diabetes mellitus roughly doubled the risk of hospitalization from tuberculosis or sepsis. The most common cause of infection-related hospitalization was pneumonia, which accounted for 39% of infections across the study period, while no other single cause accounted for more than 25% of infections across the same period. Pneumonia-related hospitalization rates increased substantially from 2001 to 2005, probably as a result of the 2003 severe acute respiratory syndrome (SARS) epidemic and the decreased threshold for pneumonia hospitalization in the immediate post-epidemic period. Rates for hospitalization for influenza increased from 2002 to 2016, possibly because of changes in the virus and increased testing for influenza. Declining rates of hospitalization for tuberculosis, urinary tract infections, foot infections and sepsis could be due to improvements in the management of diabetes mellitus.

Infection-related mortality rates were found to be significantly elevated among 1,108,982 Australians with diabetes mellitus studied over the period 2000–2010 compared with rates in people without diabetes mellitus 73 . For overall infection-related mortality, SMRs were 4.42 (95% CI 3.68–5.34) for T1DM and 1.47 (95% CI 1.42–1.53) for people with T2DM compared with those without diabetes mellitus (Table  2 ). Substantially higher infection-related mortality rates were seen in people with T1DM compared with those with T2DM for all infection types, even after accounting for age. Hyperglycaemia is thought to be a driver of infection amongst people with diabetes mellitus (see below) 73 , which might explain the higher SMRs amongst people with T1DM, in whom hyperglycaemia is typically more severe, than in those with T2DM. The highest SMRs were seen for osteomyelitis, and SMRs for septicaemia and pneumonia were also greater than 1.0 for both types of diabetes mellitus compared with those without diabetes mellitus.

Post-operative infection

Post-operative infection is also an important complication of diabetes mellitus. In a meta-analysis, diabetes mellitus was found to be associated with an OR of 1.77 (95% CI 1.13–2.78) for surgical site infection across studies that adjusted for confounding factors 74 (Table  2 ). The effect size appears to be greatest after cardiac procedures, and one US study of patients undergoing coronary artery bypass grafting found diabetes mellitus to be an independent predictor of surgical site infection, with an OR of 4.71 (95% CI 2.39–9.28) compared with those without diabetes mellitus 75 . Risks of infection of more than threefold were reported in some studies of gynaecological 76 and spinal surgery 77 in people with diabetes mellitus compared with those without diabetes mellitus. Increased risks of infection among people with diabetes mellitus were also observed in studies of colorectal and breast surgery and arthroplasty, suggesting that the association between diabetes mellitus and post-operative infection is present across a wide range of types of surgery 74 .

Respiratory infections

The incidence of hospitalizations due to respiratory infections among people with diabetes mellitus was increasing substantially even before the onset of the coronavirus disease 2019 (COVID-19) pandemic, probably owing to increased life expectancy in these patients as well as an increased likelihood of them being hospitalized for conditions such as respiratory infections, which occur mostly in older age 12 . This rising burden of respiratory infection, in combination with the rising prevalence of diabetes mellitus, highlights the importance of addressing the emerging complications of diabetes mellitus to minimize impacts on health-care systems in current and future global epidemics.

Although diabetes mellitus does not appear to increase the risk of becoming infected with COVID-19 (ref. 78 ), various population-based studies have reported increased risks of COVID-19 complications among people with diabetes mellitus. In a study of the total Scottish population, people with diabetes mellitus were found to have an increased risk of fatal or critical care unit-treated COVID-19, with an adjusted OR of 1.40 (95% CI 1.30–1.50) compared with those without diabetes mellitus 79 (Table  2 ). The risk was particularly high for those with T1DM (OR 2.40, 95% CI 1.82–3.16) 79 . Both T1DM and T2DM have been linked to a more than twofold increased risk of hospitalization with COVID-19 in a large Swedish cohort study 80 . In South Korean studies, T2DM was linked to intensive care unit admission among patients with COVID-19 infection 81 , and diabetes mellitus (either T1DM or T2DM) was linked to a requirement for ventilation and oxygen therapy 82 in patients with COVID-19. Diabetes mellitus appears to be the primary predisposing factor for opportunistic infection with mucormycosis in individuals with COVID-19 (ref. 83 ). The evidence for diabetes mellitus as a risk factor for post-COVID-19 syndrome is inconclusive 84 , 85 . Interestingly, an increase in the incidence of T1DM during the COVID-19 pandemic has been reported in several countries/regions 86 , and some data suggest an increased risk of T1DM after COVID-19 infection 87 , but the evidence regarding a causal effect is inconclusive.

Pneumonia, MERS, SARS and H1N1 influenza

The data regarding diabetes mellitus and COVID-19 are consistent with the published literature regarding other respiratory infections, such as pneumonia, for which diabetes mellitus has been shown to increase the risk of hospitalization 88 and mortality 88 , with similar effect sizes to those seen for COVID-19, compared with no diabetes mellitus. Diabetes mellitus has also been also linked to adverse outcomes in people with Middle East respiratory syndrome (MERS), SARS and H1N1 influenza 89 , 90 , 91 , 92 , suggesting that mechanisms specific to COVID-19 are unlikely to be responsible for the relationship between diabetes mellitus and COVID-19. Unlike the case for COVID-19, there is evidence that people with diabetes mellitus are at increased risk of developing certain other respiratory infections, namely pneumonia 93 and possibly also MERS 94 .

The mechanisms that might link diabetes mellitus and infection include a reduced T cell response, reduced neutrophil function and disorders of humoral immunity.

Mononuclear cells and monocytes of individuals with diabetes mellitus secrete less IL-1 and IL-6 than the same cells from people without diabetes mellitus 95 . The release of IL-1 and IL-6 by T cells and other cell types in response to infection has been implicated in the response to several viral infections 96 . Thus, the reduced secretion of these cytokines in patients with diabetes mellitus might be associated with the poorer responses to infection observed among these patients compared with people without diabetes mellitus.

In the context of neutrophil function, hyperglycaemic states might give rise to reductions in the mobilization of polymorphonuclear leukocytes, phagocytic activity and chemotaxis 97 , resulting in a decreased immune response to infection. Additionally, increased levels of glucose in monocytes isolated from patients with obesity and/or diabetes mellitus have been found to promote viral replication in these cells, as well as to enhance the expression of several cytokines, including pro-inflammatory cytokines that are associated with the COVID-19 ‘cytokine storm’; furthermore, glycolysis was found to sustain the SARS coronavirus 2 (SARS-CoV-2)-induced monocyte response and viral replication 98 .

Elevated glucose levels in people with diabetes mellitus are also associated with an increase in glycation, which, by promoting a change in the structure and/or function of several proteins and lipids, is responsible for many of the complications of diabetes mellitus 99 . In people with diabetes mellitus, antibodies can become glycated, a process that is thought to impair their biological function 100 . Although the clinical relevance of this impairment is not clear, it could potentially explain the results of an Israeli study that reported reduced COVID-19 vaccine effectiveness among people with T2DM compared with those without T2DM 101 .

Diabetes mellitus and liver disease

Nonalcoholic fatty liver disease.

The consequences of nonalcoholic fatty liver disease (NAFLD) make it important to recognize the burden of this disease among people with diabetes mellitus. NAFLD and nonalcoholic steatohepatitis (NASH; an advanced form of NAFLD) are major causes of liver transplantation in the general population. In the USA, NASH accounted for 19% of liver transplantations in 2016 — second only to alcoholic liver disease, which was the cause of 24% of transplantations 102 . In Australia and New Zealand, NAFLD was the primary diagnosis in 9% of liver transplant recipients in 2019, only slightly below the figure for alcoholic cirrhosis of 13% 103 . In Europe, NASH increased as the reason for transplantations from 1% in 2002 to more than 8% in 2016, in parallel with the rising prevalence of diabetes mellitus 104 .

NAFLD is highly prevalent among people with T2DM. In a systematic review of 80 studies across 20 countries/regions, the prevalence of NAFLD among 49,419 people with T2DM was 56% 105 , while the global prevalence of NAFLD in the general population is estimated to be 25% 106 . In a Chinese cohort study of 512,891 adults, diabetes mellitus was associated with an adjusted HR of 1.76 (95% CI 1.47–2.16) for NAFLD compared with no diabetes mellitus 107 (Table  3 ). Another smaller longitudinal Chinese study also reported an increased risk of developing NAFLD among those with T2DM compared with those without T2DM 108 . However, most evidence regarding the association between NAFLD and diabetes mellitus is from cross-sectional studies 109 , 110 , 111 .

NASH and fibrosis

Diabetes mellitus appears to enhance the risk of NAFLD complications, including NASH and fibrosis. An analysis of 892 people with NAFLD and T2DM across 10 studies showed that the prevalence of NASH was 37% (ref. 105 ); figures for the prevalence of NASH in the general population with NAFLD vary greatly across different study populations, ranging from 16% to 68% 112 . Amongst 439 people with T2DM and NAFLD in seven studies, 17% had advanced fibrosis 105 . An analysis of 1,069 people with NAFLD in a US study found that diabetes mellitus was an independent predictor for NASH (OR 1.93, 95% CI 1.37–2.73) and fibrosis (3.31, 95% CI 2.26–4.85) 113 .

Bidirectional relationship between diabetes mellitus and liver disease

The relationship between diabetes mellitus and NAFLD is bidirectional, as NAFLD is associated with an increased risk of developing T2DM 114 . There is also a notable bidirectional relationship between diabetes mellitus and liver cirrhosis. The prevalence of diabetes mellitus in people with liver cirrhosis has been reported as 20–63%, depending on the severity of liver damage, aetiology and diagnostic criteria 115 . In an Italian study of 401 participants with cirrhosis, 63% of those with decompensated liver disease had diabetes mellitus compared with 10% of those with well-compensated liver disease 116 , suggesting that diabetes mellitus is more common in severe cases of liver damage. The association between diabetes mellitus and cirrhosis also varies according to the cause of liver disease. In a US study of 204 people with cirrhosis, the prevalence of diabetes mellitus was 25% among those with cirrhosis caused by hepatitis C virus, 19% among those with cirrhosis from alcoholic liver disease and only 1% among those with cirrhosis due to cholestatic liver disease 117 . Among the causes of cirrhosis, haemochromatosis has the strongest association with diabetes mellitus, with diabetes mellitus mainly resulting from the iron deposition that is characteristic of haemochromatosis 118 .

Several factors have been implicated in the aetiology of liver disease in people with diabetes mellitus, with insulin resistance being the most notable 119 .

Insulin resistance

Insulin resistance causes lipolysis, thereby increasing the circulating levels of free fatty acids, which are then taken up by the liver as an energy source 120 . These fatty acids overload the mitochondrial β-oxidation system in the liver, resulting in the accumulation of fatty acids and, consequently, NAFLD 121 . Of those individuals with NAFLD, 2–3% develop hepatic inflammation, necrosis and fibrosis, which are the hallmarks of NASH 122 . The exact mechanisms leading to steatohepatitis are unclear, although dysregulated peripheral lipid metabolism appears to be important 14 .

Ectopic adipose deposition

Excessive or ectopic deposition of adipose tissue around the viscera and in the liver might be an important mechanism underlying both T2DM and liver disease, particularly NAFLD 123 . Dysfunction of long-term adipose storage in white adipose tissue is known to lead to ectopic adipose deposition in the liver. In this state, increased levels of fatty acyl-coenzyme As, the activated form of fatty acids, might lead to organ dysfunction, including NAFLD 124 . Ectopic adipose deposition leading to organ-specific insulin resistance has emerged as a major hypothesis for the pathophysiological basis of T2DM, and ectopic adipose in the pancreas could contribute to β-cell dysfunction and, thus, the development of T2DM 125 .

Diabetes mellitus and affective disorders

The prevalence of depression appears to be high among people with diabetes mellitus. The strongest evidence for an association comes from a systematic review of 147 studies among people with T2DM, which revealed a mean prevalence of depression of 28% 126 , while the global prevalence of depression in the general population is estimated at around 13% 127 . For T1DM, a systematic review reported a pooled prevalence of depression of 12% compared with only 3% in those without T1DM 128 . The risk of depression among people with diabetes mellitus appears to be roughly 25% greater than the risk in the general population, with consistent findings across several meta-analyses (Table  4 ). A 2013 study found an adjusted RR of 1.25 (95% CI 1.10–1.44) for incident depression among people with diabetes mellitus compared with those without diabetes mellitus 129 . Another systematic review of people with T2DM reported a near identical effect size 130 .

Anxiety and eating disorders

Evidence exists for an association of diabetes mellitus with anxiety, and of T1DM with eating disorders. In a systematic review involving 2,584 individuals with diabetes mellitus, a prevalence of 14% was found for generalized anxiety disorder and 40% for anxiety symptoms, whereas the prevalence of generalized anxiety disorder in the general population is estimated as only 3–4% 131 . People with diabetes mellitus had an increased risk of anxiety disorders (OR 1.20, 95% CI 1.10–1.31) and anxiety symptoms (OR 1.48, 95% CI 1.02–1.93) compared with those without diabetes mellitus in a meta-analysis 132 (Table  4 ), although these findings were based on cross-sectional data. Across 13 studies, 7% of adolescents with T1DM were found to have eating disorders, compared with 3% of peers without diabetes mellitus 133 .

Broader psychological impacts

There is a substantial literature on a broad range of psychological impacts of diabetes mellitus. Social stigma 134 can have profound impacts on the quality of life of not only people with diabetes mellitus, but their families and carers, too 135 . In a systematic review, diabetes mellitus distress was found to affect around one-third of adolescents with T1DM, which was consistent with the results of studies of adults with diabetes mellitus 136 . Diabetes mellitus burnout appears to be a distinct concept, and is characterized by exhaustion and detachment, accompanied by the experience of a loss of control over diabetes mellitus 137 .

Diabetes mellitus and depression appear to have common biological origins. Activation of the innate immune system and acute-phase inflammation contribute to the pathogenesis of T2DM — increased levels of inflammatory cytokines predict the onset of T2DM 138 — and there is growing evidence implicating cytokine-mediated inflammation in people with depression in the absence of diabetes mellitus 139 . Dysregulation of the hypothalamic–pituitary–adrenal axis is another potential biological mechanism linking depression and diabetes mellitus 140 . There have been numerous reports of hippocampal atrophy, which might contribute to chronic activation of the hypothalamic–pituitary–adrenal axis, in individuals with T2DM as well as those with depression 141 , 142 . A meta-analysis found that, although hypertension modified global cerebral atrophy in those with T2DM, it had no effect on hippocampal atrophy 143 . This suggests that, although global cerebral atrophy in individuals with T2DM might be driven by atherosclerotic disease, hippocampal atrophy is an independent effect that provides a common neuropathological aetiology for the comorbidity of T2DM with depression. There is a lack of relevant information regarding the potential aetiological mechanisms that link diabetes to other affective disorders.

Diabetes mellitus and sleep disturbance

Obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is highly prevalent among people with diabetes mellitus. In a systematic review of 41 studies of adults with diabetes mellitus, the prevalence of OSA was found to be 60% 144 , whereas reports for OSA prevalence in the general population range from 9% to 38% 145 . In a UK study of 1,656,739 participants, T2DM was associated with an IRR for OSA of 1.48 (95% CI 1.42–1.55) compared with no T2DM 146 . A population-based US study reported a HR of 1.53 (95% CI 1.32–1.77) for OSA in people with T2DM compared with those without diabetes mellitus 147 . However, the association in this latter report was attenuated after adjustment for BMI and waist circumference (1.08, 95% CI 1.00–1.16), suggesting that the excess risk of OSA among people with diabetes mellitus might be mainly explained by the comorbidity of obesity. Although most studies on OSA have focused on T2DM, a meta-analysis of people with T1DM revealed a similar prevalence of 52% 148 ; however, this meta-analysis was limited to small studies. The association between T2DM and OSA is bidirectional: the severity of OSA was shown to be positively associated with the incidence of T2DM, independent of adiposity, in a large US cohort study 149 .

The mechanism by which T2DM might increase the risk of developing OSA is thought to involve dysregulation of the autonomic nervous system leading to sleep-disordered breathing 150 . Conversely, the specific mechanism behind OSA as a causative factor for T2DM remains poorly understood. It has been suggested that OSA is able to induce insulin resistance 151 , 152 and is a risk factor for the development of glucose intolerance 152 . However, once T2DM has developed, there is no clear evidence that OSA worsens glycaemic control, as an RCT of people with T2DM found that treating OSA had no effect on glycaemic control 153 .

Diabetes mellitus and cognitive disability

Dementia and cognitive impairment.

Dementia is emerging as a major cause of mortality in both individuals with diabetes mellitus and the general population, and is now the leading cause of death in some countries/regions 9 . However, compared with the general population, diabetes mellitus increases the risk of dementia, particularly vascular dementia. The association is supported by several systematic reviews, including one of eight population-based studies with more than 23,000 people, which found SRRs of 2.38 (95% CI 1.79–3.18) for vascular dementia and 1.39 (95% CI 1.16–1.66) for Alzheimer disease comparing people with diabetes mellitus with those without diabetes mellitus 154 (Table  4 ). Similar results, as well as a RR of 1.21 (95% CI 1.02–1.45) for mild cognitive impairment (MCI), were reported across 19 population-based studies of 44,714 people, 6,184 of whom had diabetes mellitus 155 . Two meta-analyses of prospective cohort studies have shown increased risks of all-cause dementia in people with diabetes mellitus compared with those without diabetes mellitus 156 , 157 , and T2DM has been shown to increase progression to dementia in people with MCI 158 .

The boundaries between Alzheimer disease and vascular dementia remain controversial, and these conditions are often difficult to differentiate clinically 159 . Consequently, vascular dementia might have been misdiagnosed as Alzheimer disease in some studies investigating diabetes mellitus and dementia, resulting in an overestimation of the effect size of the association between diabetes mellitus and Alzheimer disease. Although a cohort study found a significant association between diabetes mellitus and Alzheimer disease using imaging 160 , autopsy studies have failed to uncover an association between diabetes mellitus and Alzheimer disease pathology 161 , 162 , suggesting that vascular mechanisms are the key driver of cognitive decline in people with diabetes mellitus.

Another important finding is a 45% prevalence of MCI among people with T2DM in a meta-analysis, compared with a prevalence of 3–22% reported for the general population 163 . Notably, however, the prevalence of MCI in individuals with T2DM was similar in people younger than 60 years (46%) and those older than 60 years (44%), which is at odds with previous research suggesting that MCI is most common in older people, particularly those aged more than 65 years 164 However, another meta-analysis found cognitive decline in people with T2DM who are younger than 65 years 165 , suggesting that a burden of cognitive disease exists among younger people with diabetes mellitus.

Although there is solid evidence that links diabetes mellitus to cognitive disability, our understanding of the underlying mechanisms is incomplete. Mouse models suggest a strong association between hyperglycaemia, the advanced glycation end products glyoxal and methylglyoxal, enhanced blood–brain barrier (BBB) permeability and cognitive dysfunction in both T1DM and T2DM 166 . The BBB reduces the access of neurotoxic compounds and pathogens to the brain and sustains brain homeostasis, so disruption to the BBB can result in cognitive dysfunction through dysregulation of transport of molecules between the peripheral circulation and the brain 167 . There appears to be a continuous relationship between glycaemia and cognition, with associations found between even high-normal blood levels of glucose and cognitive decline 168 . Another hypothetical mechanism involves a key role for impaired insulin signalling in the pathogenesis of Alzheimer disease. Brain tissue obtained post mortem from individuals with Alzheimer disease showed extensive abnormalities in insulin and insulin-like growth factor signalling mechanisms compared with control brain tissue 169 . Although the synthesis of insulin-like growth factors occurred normally in people with Alzheimer disease, their expression levels were markedly reduced, which led to the subsequent proposal of the term ‘type 3 diabetes’ to characterize Alzheimer disease.

Diabetes mellitus and disability

Functional disability.

Disability (defined as a difficulty in functioning in one or more life domains as experienced by an individual with a health condition in interaction with contextual factors) 170 is highly prevalent in people with diabetes mellitus. In a systematic review, lower-body functional limitation was found to be the most prevalent disability (47–84%) among people with diabetes mellitus 171 The prevalence of difficulties with activities of daily living among people with diabetes mellitus ranged from 12% to 55%, although most studies were conducted exclusively in individuals aged 60 years and above, so the results are not generalizable to younger age groups. A systematic review showed a significant association between diabetes mellitus and falls in adults aged 60 years and above 172 . A 2013 meta-analysis 173 showed an increased risk of mobility disability, activities of daily living disability and independent activities of daily living disability among people with diabetes mellitus compared with those without diabetes mellitus (Table  4 ). Although this analysis included cross-sectional data, results were consistent across longitudinal and cross-sectional studies, suggesting little effect of reverse causality. However, people with functional disabilities that limit mobility (for example, people with osteoarthritis or who have had a stroke) might be more prone to developing diabetes mellitus owing to physical inactivity 174 .

Workplace productivity

Decreased productivity while at work, increased time off work and early dropout from the workforce 175 are all associated with diabetes mellitus, probably partly due to functional disability, and possibly also to comorbidities such as obesity and physical inactivity 176 . Given that young-onset diabetes is becoming more common, and most people with diabetes mellitus in middle-income countries/regions are less than 65 years old 177 , a pandemic of diabetes mellitus-related work disability among a middle-aged population does not bode well for the economies of these regions.

The mechanisms by which diabetes mellitus leads to functional disability remain unclear. One suggestion is that hyperglycaemia leads to systemic inflammation, which is one component of a multifactorial process that results in disability 154 . The rapid loss of skeletal muscle strength and quality seen among people with diabetes mellitus might be another cause of functional disability 178 (Box  1 ). In addition, complications of diabetes mellitus, including stroke, peripheral neuropathy and cardiac dysfunction, can obviously directly cause disability 179 .

Box 1 Diabetes mellitus and skeletal muscle atrophy

Individuals with diabetes mellitus exhibit skeletal muscle atrophy that is typically mild in middle age and becomes more substantial with increasing age.

This muscle loss leads to reduced strength and functional capacity and, ultimately, increased mortality.

Skeletal muscle atrophy results from a negative balance between the rate of synthesis and degradation of contractile proteins, which occurs in response to disuse, ageing and chronic diseases such as diabetes mellitus.

Degradation of muscle proteins is more rapid in diabetes mellitus, and muscle protein synthesis has also been reported to be decreased.

Proposed mechanisms underlying skeletal muscle atrophy include systemic inflammation (affecting both protein synthesis and degradation), dysregulation of muscle protein anabolism and lipotoxicity.

Mouse models have also revealed a key role for the WWP1/KLF15 pathway, mediated by hyperglycaemia, in the pathogenesis of muscle atrophy.

See refs 195 , 196 , 197 , 198 .

Diabetes management and control

Although a detailed discussion of the impacts of anti-diabetes mellitus medications and glucose control on emerging complications is beyond the scope of this Review, their potential effect on these complications must be acknowledged.

Medications

Anti-diabetes mellitus medications and cancer.

In the case of cancer as an emerging complication, the use of medications for diabetes mellitus was not controlled for in most studies of diabetes mellitus and cancer and might therefore be a confounding factor. People taking metformin have a lower cancer risk than those not taking metformin 180 . However, this association is mainly accounted for by other factors. For example, metformin is less likely to be administered to people with diabetes mellitus who have kidney disease 181 , who typically have longer duration diabetes mellitus, which increases cancer risk. A review of observational studies into the association between metformin and cancer found that many studies reporting significant reductions in cancer incidence or mortality associated with metformin were affected by immortal time bias and other time-related biases, casting doubt on the ability of metformin to reduce cancer mortality 182 . Notably, the use of insulin was associated with an increased risk of several cancers in a meta-analysis 183 . However, in an RCT of more than 12,000 people with dysglycaemia, randomization to insulin glargine (compared with standard care) did not increase cancer incidence 184 . Furthermore, cancer rates in people with T1DM and T2DM do not appear to vary greatly, despite substantial differences in insulin use between people with these types of diabetes mellitus.

Anti-diabetes mellitus medications and other emerging complications

Anti-diabetes medications appear to affect the onset and development of some other emerging complications of diabetes mellitus. Results from RCTs suggest that metformin might confer therapeutic effects against depression 185 , and its use was associated with reduced dementia incidence in a systematic review 186 . In an RCT investigating a potential association between metformin and NAFLD, no improvement in NAFLD histology was found among people using metformin compared with those given placebo 187 . An RCT reported benefits of treatment with the glucagon-like peptide 1 receptor agonist dulaglutide on cognitive function in a post hoc analysis 188 , suggesting that trials designed specifically to test the effects of dulaglutide on cognitive function should be undertaken.

Glucose control

Another important consideration is glycaemic control, which appears to have variable effects on emerging complications. A meta-analysis found no association of glycaemic control with cancer risk among those with diabetes mellitus 189 , and an RCT found no effect of intensive glucose lowering on cognitive function in people with T2DM 190 . However, glycaemic control has been associated with improved physical function 191 , decreased COVID-19 mortality 192 and a decreased risk of NAFLD 193 in observational studies of patients with diabetes mellitus; notably, no RCTs have yet confirmed these associations.

Conclusions

With advances in the management of diabetes mellitus and associated increased life expectancy, the face of diabetes mellitus complications is changing. As the management of glycaemia and traditional complications of diabetes mellitus is optimized, we are beginning instead to see deleterious effects of diabetes mellitus on the liver, brain and other organs. Given the substantial burden and risk of these emerging complications, future clinical and public health strategies should be updated accordingly. There is a need to increase the awareness of emerging complications among primary care physicians at the frontline of diabetes mellitus care, and a place for screening for conditions such as depression, liver disease and cancers in diabetes mellitus guidelines should be considered. Clinical care for older people with diabetes mellitus should target physical activity, particularly strength-based activity, to reduce the risk of functional disability in ageing populations. Ongoing high-quality surveillance of diabetes mellitus outcomes is imperative to ensure we know where the main burdens lie. Given the growing burden of these emerging complications, the traditional management of diabetes mellitus might need to broaden its horizons.

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Acknowledgements

D.T. is supported by an Australian Government Research Training Program (RTP) Scholarship and Monash Graduate Excellence Scholarship. J.E.S. is supported by a National Health and Medical Research Council Investigator Grant. D.J.M. is supported by a National Health and Medical Research Council Senior Research Fellowship.

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These authors jointly supervised this work: Jonathan E. Shaw and Dianna J. Magliano.

Authors and Affiliations

Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

Dunya Tomic, Jonathan E. Shaw & Dianna J. Magliano

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

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D.T. researched data for the article and wrote the article. J.E.S and D.J.M. contributed substantially to discussion of the content. D.T., J.E.S. and D.J.M reviewed and/or edited the manuscript before submission.

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Correspondence to Dianna J. Magliano .

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Tomic, D., Shaw, J.E. & Magliano, D.J. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol 18 , 525–539 (2022). https://doi.org/10.1038/s41574-022-00690-7

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Article Contents

Importance of patient-provider communication, psychosocial conditions in diabetes, diabetes self-management education and support, navigating therapeutic options, overcoming therapeutic inertia, reducing risk of cardiovascular disease and microvascular complications, prevention and management of hypoglycemia, use of telehealth visits, integrating technology into diabetes management, conclusions, acknowledgments, disclosures, abbreviations, prioritizing patient experiences in the management of diabetes and its complications: an endocrine society position statement.

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Rita R Kalyani, Myriam Z Allende-Vigo, Kellie J Antinori-Lent, Kelly L Close, Sandeep R Das, Phyllisa Deroze, Steven V Edelman, Nuha A El Sayed, David Kerr, Joshua J Neumiller, Anna Norton, Prioritizing Patient Experiences in the Management of Diabetes and Its Complications: An Endocrine Society Position Statement, The Journal of Clinical Endocrinology & Metabolism , 2024;, dgad745, https://doi.org/10.1210/clinem/dgad745

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Diabetes can be an arduous journey both for people with diabetes (PWD) and their caregivers. While the journey of every person with diabetes is unique, common themes emerge in managing this disease. To date, the experiences of PWD have not been fully considered to successfully implement the recommended standards of diabetes care in practice. It is critical for health-care providers (HCPs) to recognize perspectives of PWD to achieve optimal health outcomes. Further, existing tools are available to facilitate patient-centered care but are often underused. This statement summarizes findings from multistakeholder expert roundtable discussions hosted by the Endocrine Society that aimed to identify existing gaps in the management of diabetes and its complications and to identify tools needed to empower HCPs and PWD to address their many challenges. The roundtables included delegates from professional societies, governmental organizations, patient advocacy organizations, and social enterprises committed to making life better for PWD. Each section begins with a clinical scenario that serves as a framework to achieve desired health outcomes and includes a discussion of resources for HCPs to deliver patient-centered care in clinical practice. As diabetes management evolves, achieving this goal will also require the development of new tools to help guide HCPs in supporting PWD, as well as concrete strategies for the efficient uptake of these tools in clinical practice to minimize provider burden. Importantly, coordination among various stakeholders including PWD, HCPs, caregivers, policymakers, and payers is critical at all stages of the patient journey.

The public health effect of diabetes is staggering. Globally, more than 500 million people live with diabetes ( 1 ). According to the US Centers for Disease Control and Prevention (CDC), not only do an estimated 37.3 million people have diabetes in the United States, of whom almost one-quarter have undiagnosed diabetes, an additional 96 million American adults have prediabetes ( 2 ). New cases of diabetes are being diagnosed in large numbers. In 2019, 1.4 million US adults were diagnosed with diabetes, most between the ages of 45 and 64 years.

After diagnosis, diabetes can be a challenging journey both for the person with diabetes (PWD) and their caregivers, particularly for those who require complex therapeutic regimens or have diabetes complications. This often involves navigating frequent visits with health-care providers (HCPs), weighing the pros and cons of therapeutic options, and making lifestyle and medication adjustments to avoid both hyperglycemia and hypoglycemia and other potential complications. Engaging with specialists when needed is a key aspect of the patient journey, and coordination of care is essential. When advanced technologies are used appropriately, they have enabled smoother and stronger connections between PWD and providers, with the potential to improve health outcomes.

Although many evidence-based treatment algorithms can now help guide HCPs in managing diabetes and its microvascular and macrovascular complications ( 3-7 ), few guidelines have fully considered patient experiences to inform how to best implement the recommended standards of diabetes care. The relative paucity of voices of PWD in important clinical decisions and research remains a substantial barrier to achieving optimal health outcomes ( 8-10 ). Further, many existing tools are available to facilitate patient-centered care. However, these tools are often underused in practice. Thus, diabetes care needs to be redefined by looking beyond the important clinical goals of glucose lowering, weight management, and prevention of diabetic complications (ie, cardiovascular disease [CVD] and kidney disease), and fully understanding and considering a patient's journey from diagnosis through a lifetime of diabetes management. Achieving this goal will also require new tools to help guide HCPs in supporting PWD at each stage of their journey as well as coordination among various stakeholders including HCPs, PWD, caregivers, policymakers, and payers.

While the individual experiences of PWD are unique, common themes emerge in diabetes management. To explore these in patient care, in 2022 the Endocrine Society (ES) hosted multistakeholder diabetes consensus roundtables that included delegates from professional societies (American College of Cardiology [ACC], American College of Physicians [ACP], American Diabetes Association [ADA], Association of Diabetes Care & Education Specialists [ADCES], Diabetes Technology Society), governmental organizations (CDC), patient advocacy organizations (DiabetesSisters, Close Concerns, Taking Control of Your Diabetes), and social enterprises (dQ&A). The goals of these roundtables were to recognize existing gaps in the management of diabetes and its complications; build consensus around a comprehensive approach to diabetes management that focuses on addressing these gaps; identify tools needed to empower HCPs and PWD to address the challenges they face; and evaluate the role of technology in helping PWD, caregivers, and HCPs improve disease management. A summary of the findings from these roundtables is presented in this position statement. Each section begins with a common clinical scenario that illustrates key gaps and offers a framework for leveraging patient experiences to optimize health outcomes and equipping HCPs with tools to deliver patient-centered care in clinical practice.

Juan is a 42-year-old male construction worker with type 2 diabetes (T2D) on basal insulin at bedtime. He was told by his provider during a clinic visit 2 weeks ago that “everything is fine.” He wears a continuous glucose monitor (CGM) and is now noticing that his glucose readings are running higher, with more frequent high alerts during the day. He also recently developed a cough and nasal congestion. Juan called his provider's office on several occasions to seek advice about his concerns. During one of the calls, a staff member told him the provider would call back and to continue the same treatment regimen for now. He called his provider's office again and scheduled a virtual visit. During the visit, the provider reviewed his glucose numbers from the past few days, which were in the range of 300 to 400 mg/dL throughout the day, and recommended taking a much higher insulin dose. Juan's father had a severe hypoglycemic reaction many years ago while taking insulin for his diabetes. Fearful of low glucose values himself, Juan did not increase the insulin as high as recommended and did not inform the provider of this decision. Shortly thereafter, he was admitted to the hospital with a fever, profound dehydration, and acute renal failure.

Diabetes is a chronic condition that requires lifelong lifestyle changes. For PWD, compassionate communication during interactions with an HCP may be nearly as critical as taking medications consistently ( 11 , 12 ). Mounting evidence shows a significant relationship between effective patient-provider communication at diagnosis and throughout follow-up on medication-taking behavior and psychological stress levels ( 13 ). As an example, in one study, PWD reported feeling dissatisfied with their level of communication with their HCP, and they also lacked understanding about their treatment and were not keeping up with it ( 11 ).

For HCPs, communicating regimen changes to PWD can be challenging, particularly given the limited time often allotted for a clinical visit. It can be just as challenging for PWD to implement those changes, especially if they do not understand them or are not able to follow through for a variety of reasons (cost of medications, socioeconomic factors, etc). If communication is impeded, attaining clinical goals will not happen. Many evidence-based strategies have been proven to enhance patient care ( 14 ), and effective communication is among them. Bidirectional communication between PWD and their HCPs is needed to achieve mutual understanding of the goals and plan of care. Taking the time to discuss care options along with barriers (time, cost, ability) strengthens the relationship between the HCP and PWD while subsequently increasing the likelihood that recommended changes will take place. Effective information sharing is predicated on patients’ trust and confidence in their HCP ( 15 ), and it is critically important that the PWD remain at the center of the dialogue. In the aforementioned clinical scenario, the patient did not fully understand the reason for the medication changes. In addition, information regarding how the patient's father's experience fueled the patient's fear of hypoglycemia was not discussed. If these issues had been addressed during the clinic visit, or followed up on afterward, the hospital admission may have been prevented ( 16 ).

To be effective, HCPs must be aware of and responsive to PWD's specific concerns while being attuned to their preferred language, cultural considerations, and level of health literacy—and health ( 17 ). Cultural competency training can help HCPs improve communication with their patients ( 18 ). Patients’ memory and recall of medical information relayed by HCPs can vary ( 19 ). Simple and specific instructions are better understood than general statements. Other evidence-based tools HCPs can use to ensure patients understand what they have been taught or asked to do include the teach-back technique ( 20 ). The framework simply asks the patient to repeat the information back in their own words to ensure accurate understanding. It also permits them time to ask questions or raise concerns without being cumbersome ( 20 ). Motivational interviewing ( 21-24 ) is another methodology that can be used. It is a guided style of communication that includes engaging in a working relationship; focusing on a problem to change; evoking the person's desire to change, and planning the change ( 25 , 26 ). These communication strategies improve understanding of the after-visit responsibilities of self-care ( 27 ), and ultimately they reduce glucose levels and complications among patients with chronic diseases like diabetes ( 28-30 ). It is important that HCPs be familiar with these patient-provider communication techniques due to their positive effect on patient care and quality of life (QoL).

Many communication tools and resources are available for PWD, their caregivers, and HCPs ( 31-36 ). These stress that HCPs use language that is neutral and nonjudgmental; free of stigma; strengths-based and inclusive; fosters collaboration between HCPs and PWD, and is person-centered. These tools and resources include educational videos, articles, downloadable materials, and structured educational and training materials ( 21-24 ). Capacity-building for diabetes care teams should recognize the importance of communication by including training for open-ended questions, shared decision-making, cultural competency, and patient-centeredness. Resources on patient-centered language ( 32 , 33 ) ( Table 1 ) and culturally sensitive language are readily available. Patient-centered language places emphasis on the person, rather than their disease. A Spanish resource on patient-centered language in diabetes has also been developed by DiabetesSisters ( 37 ).

Nonpatient-centered vs patient-centered language

Additional resources include health literacy videos, teach-back methods, and motivational interviewing scripts, and practical toolkits to enhance communication such as those developed by the American Medical Association Foundation ( 38 ), the National Institute of Diabetes, Digestive, and Kidney Diseases ( 35 ), the UCLA Center for Human Nutrition ( 39 ), and the Rudd Center for Food Policy and Obesity ( 36 ). The Agency for Healthcare Research and Quality (AHRQ) has developed materials to educate patients on how to get the most out of their healthcare visits by increasing their health literacy and helping them ask more effective and relevant questions during their visits (AHRQ) ( 40 ).

The field of patient-provider communication is broad and involves many topic areas such as delivery and understanding of medical information and how information is translated into action. Although it has been shown that good communication can improve health outcomes among PWD, there are persistent gaps in research. These include how cultural and language differences affect communication, approaches to promote relationship building between PWD and their HCPs, the effect of stress on comprehension and decision-making, approaches to behavioral interviewing, and effects of shared decision-making on health-care outcomes. Patients differ in the ways they receive and interpret information from their HCP. Cultural differences may also affect these dynamics because the same word may have different meanings for various populations. Characteristics of the HCP who delivers information can also affect how it is received because trust levels can vary depending on whether information is delivered by a physician or other HCPs as well as other factors such as whether the HCP is the same race/ethnicity or speaks the same language as the PWD. Thus, having access to providers who understand an individual patient's needs is critical. Finally, recognizing and integrating peer support from others with diabetes and social support from relatives or friends into care delivery and planning can improve the ability of HCPs to communication effectively with PWD ( 41-43 ).

There is also a need to develop new communication tools to support HCPs in communicating with PWD that additionally consider social determinants of health, especially when treating diverse populations. Tailored scripts for patient interviews and motivational interviewing should be prepared based on common hurdles that PWD face adhering to dietary restrictions, weight management, and medication adherence. These scripts would facilitate effective and standard communication on key barriers to effective care. Adapting patient-centered language sheets similar to those in Table 1 that HCPs can reference in many different languages would facilitate effective communication strategies for PWD globally. Because some PWD face language barriers or have low health literacy, graphical representations of key instructions and self-management elements could be developed to help HCPs ensure their messaging is effective. Finally, processes that provide HCPs with feedback about their communication skills should be implemented by health-care organizations and HCP performance metrics should include this aspect of care delivery, such as patient satisfaction surveys specifically on the effectiveness of patient-provider communication during the visit and on providing areas for the HCP to address.

Dr Anderson reviewed Tallulah's blood test results and remarked “Everything looks great, keep doing what you’re doing.” Rather than getting a pleasant response back, Tallulah lowered her head and exhaled. “What's wrong?” Dr Anderson asked. She replied, “Everything was great up until a few weeks ago, but recently I’ve been feeling down, and it's been more difficult to take care of my diabetes. I’m overwhelmed by family issues. I’ve decided to go on a vacation with friends who can offer the emotional support I need right now. I know that it will be difficult to prioritize checking my blood sugar or staying within range during my vacation, but I need a break. I already know that my numbers won’t be great the next time I come in to see you.”

Diabetes management requires considerable behavioral and lifestyle changes that can be burdensome for PWD and their families ( 44 ). Addressing the emotional and psychosocial needs of PWD is an often-overlooked aspect of diabetes management. A recent study highlighted the effect of familial support on the management and well-being of PWD ( 45 ). Diabetes management involves an array of daily tasks and behaviors including blood glucose monitoring, dietary and behavioral regulation, taking medications, and self-management—all of which occur during times when they are not with their HCP. Newly diagnosed PWD sometimes struggle to accept the chronic nature of the disease and that they will take medication throughout their lifetime ( 46 ).

While psychosocial conditions like depression, anxiety disorders, eating disorders, and other serious mental illnesses may not occur in all individuals, they are generally much more common over time among PWD ( Table 2 ), and people with depression may also be at a higher risk of developing diabetes, highlighting the potential bidirectional nature of this relationship ( 47 ). The higher prevalence of obesity in people with depression and severe mental illness (which may be related to use of medications like second-generation antipsychotics) or unhealthy lifestyle behaviors, further elevates the risk of diabetes in these populations ( 48 ).

Common psychosocial conditions in diabetes

In PWD, disease fears contributing to symptoms of anxiety might include worrying about complications, hypoglycemia, or invasive self-care behaviors like injections, self-monitoring blood glucose, insertion of insulin pumps, and use of CGMs ( 49 ). Disordered eating behaviors have been reported at a higher frequency among PWD, particularly among adolescents and women ( 50 ). Environmental risk factors like poverty and stress, particularly among PWD with low socioeconomic status and those who identify with racial/ethnic minority groups, may increase the risk of developing mental illnesses. By contrast, interventions that address psychosocial conditions in PWD can improve glycemic control and clinical outcomes, and contribute to QoL ( 51 ).

The clinical scenario presented earlier—a PWD who is stressed, needs social support, and is anxious about the future—is only one of many that reflects the challenges of living with diabetes and the factors that can affect patients’ physical and mental health and their ability to self-manage over the long term. PWD may face their lifelong disease with limited support or encouragement between visits with their HCP. When these visits do occur, they can be less than satisfactory both for PWD and HCPs due to several system-wide and individual reasons:

HCP time constraints during appointments and insufficient or lack of follow-up of the care team with PWD

PWD's anxiety due to low health literacy, lack of self-efficacy, lack of culturally appropriate communication from their HCP, language barriers, potential mental health challenges, and the knowledge that prescribed treatments are not affordable.

Lack of access to or no knowledge about diabetes education through diabetes self-management and education support (DSMES), certified diabetes care and education specialists (CDCES), and other, nonphysician HCPs

For many PWD, the emphasis on numbers is familiar—glycated hemoglobin A 1c (HbA 1c ), blood pressure (BP), cholesterol, weight—yet sometimes the definition and relevance of these numbers are not adequately explained, which can exacerbate underlying anxiety. To improve diabetes outcomes, HCPs should incorporate strategies that facilitate patient education and development of treatment plans that focus on the individual rather than their disease. HCPs can begin these efforts by simply asking PWD and their caregivers, if appropriate, to summarize their daily routine and sharing their CGM or blood glucose meter readings. By understanding these routines and assessing the PWD's social support system (if they have any), HCPs can better focus on individualized needs such as diet, exercise, capacity for technology adoption, medications, or other treatments. Concerning PWD from historically underserved communities, HCPs should strive to learn about patients’ culture, language, and foods as well as social determinants of health such as neighborhood characteristics that may contribute to psychosocial health that either facilitate or hinder diabetes management plans ( 52 ). Questions that HCPs can ask PWD to prompt discussion in clinical practice might include, “What are you most concerned about with your diabetes?” ( 53 ) Taken together, these efforts can reduce the stress that PWD often encounter in the health-care setting, ultimately empowering them to optimize interactions with their HCP, as well as with tools and other resources that can help them improve self-management.

The well-established effect of depression, anxiety, and diabetes-related distress on diabetes self-management and outcomes provides a rationale for the importance of screening for these conditions in the primary care setting. There are multiple opportunities for HCPs to screen PWD for psychosocial issues: at diagnosis, during regular clinic visits, during hospitalizations, with new-onset complications, and during transitions of care such as from pediatric to adult care. Several screening tools are readily available for these settings (see Table 2 ) ( 54 ). There are also simple questions that providers can ask PWD to determine the need for additional psychosocial screening ( Fig. 1 ). In PWD for whom mental illness is suspected, referral to a mental health specialist should not be delayed. The ADA has a professional directory listing to help HCPs find mental health professionals who have expertise in diabetes care; nonetheless, adequate access and availability of providers for referrals remains a substantial barrier in many areas ( 55 ).

Questions that health-care providers can ask people with diabetes to help determine the need for additional psychosocial screening and resources.

Questions that health-care providers can ask people with diabetes to help determine the need for additional psychosocial screening and resources.

Technological advances have generated additional resources to help address psychosocial concerns among PWD. Online and in-person peer support communities are feasible and acceptable ( 56 ), when they are available, and can benefit PWD within reasonable expectations by helping them learn more about living with the disease and by sharing experiences with others who have similar challenges. These communities can also facilitate sharing of success stories in attaining a proper diagnosis, accessing medications, and troubleshooting issues with diabetes technology.

Despite evidence that psychosocial conditions occur more frequently in PWD, screening for these issues does not occur as often as it should, and referrals to mental health professions are insufficient ( 57 ). To address these lost opportunities, HCPs and other office-based staff should educate PWD about availability of mental health professionals and their integral roles as members of the care team, make referrals for interested and motivated individuals, and guide PWD to reputable resources as needed such as peer support communities. System-wide tools that can facilitate quick screenings for psychosocial issues by HCPs in the clinic setting are needed, such as integration of checkbox screening tools into the electronic health record (EHR). By assessing and acting on information concerning whether PWD require additional support to manage their psychosocial health, HCPs can take an active role in mitigating the effect of mental health challenges on diabetes self-management and related outcomes.

Cameron is a 23-year-old woman who was diagnosed with type 1 diabetes (T1D) 2 years ago. She was initially seen by an HCP with limited knowledge of T1D and was put on an insulin pump and a stand-alone CGM that did not communicate with the pump. Since her diagnosis, Cameron's HbA 1c has been above 10% despite being seen every 3 months and doing everything she was told by her HCP. She was never shown how to adjust alarms for high or low glucose readings in her CGM. In addition, Cameron was not taught how to respond to the trend arrows on her CGM. She did not have a low cartridge alert set on her insulin pump. These shortfalls had significant consequences. She lets the pump calculate her prandial dose of insulin based on her current blood glucose and the number of carbohydrates that she estimates without considering anticipated exercise or trend arrows. She does not know how to adjust her insulin and carbohydrate intake before, during, or after exercise, nor did she receive instruction on how to respond to the erratic glucose values that occurred before and during her menstrual periods. She has never been referred to a DSME program and asks her HCP if it could help.

DSMES is essential to help PWD meet their health-care goals. Despite the availability of many novel treatment options and scientific advances targeting diabetes management, achieving glycemic targets remains a challenge for many PWD ( 58 ). Although there are many reasons why achieving these targets is elusive, a key factor is the difficulty in comprehending complex therapies and recognizing and implementing needed lifestyle and behavioral modifications ( 13 ). One study examining the effect of written information on people with T2D found that interventions as simple as providing educational leaflets to PWD during clinical visits significantly improved medication-taking behavior ( 59 ). DSMES services are provided by an array of HCPs, including nurses, dieticians, pharmacists, and physicians. Collectively, HCPs who provide DSMES are referred to as CDCES . The goal of DSMES is to help people learn to live successfully with their diabetes. These services include basic diabetes education, skill training (eg, blood glucose testing, insulin administration, use of technology such as pumps, sensors, smart pens, apps), problem solving, routine follow-up care between HCP visits, and education regarding preventive care measures to reduce the risk of diabetes complications. Community health workers, defined broadly as complementary health-care workers who interact with PWD (eg, peer educators or patient navigators), can also work collaboratively with CDCES to extend the reach of DSMES care teams and reduce health disparities given their knowledge and experience within the cultural and socioeconomic contexts of the communities in which they work ( 60-62 ).

DSMES improves health outcomes and QoL ( 63-65 ). It can lower HbA 1c and help PWDs develop and sustain healthy behaviors that facilitate self-management like improved eating patterns, increased coping skills, and decreased diabetes-related distress ( 64 , 66 ). As noted in the aforementioned scenario, HCPs generally make few referrals to this effective intervention, resulting in underuse of these core services. Ease of referral to CDCES and coordination of care may also be more challenging if these services are not located in the same facility. Less than 5% of Medicare beneficiaries with diabetes and 6.8% of privately insured people with diabetes in the United States receive DSMES services within the first year of diagnosis ( 67 , 68 ). Even when PWD are referred, they may face challenges to optimizing DSMES because of the extra trips to another HCP. However, integration of DSMES into primary care settings improves diabetes outcomes and is relevant to understanding how fundamental changes in diabetes care delivery and workflows may benefit PWD in the future, though it may be more difficult to implement in resource-poor settings ( 69 ). This research underscores the importance of implementing innovative and effective models of care that incorporate CDCES into routine care.

It is important to stress that populations in which referral to DSMES is low includes not only people with diagnosed diabetes, but also women with gestational diabetes who may not receive follow-up DSMES after they give birth despite the well-established risk of developing T2D later in life ( 70 ). Other challenges to effective referral to DSMES involves geography. PWD who live in rural or other underserved areas may not have convenient access to CDCES professionals. The delivery of DSMES by telehealth modalities can help address these disparities and are feasible ( 71 ).

PWD also face an array of barriers that hinder access to DSMES when they are referred. Lack of financial resources and accessibility are among the most common factors that create barriers between PWD and DSMES. These factors are linked to difficulties securing transportation to clinics and intervention programs, lack of health insurance coverage, and rurality ( 72-76 ). Given that PWD who have lower educational attainment, and those who are older and have lower incomes, are less likely to participate in self-management of diabetes, there is little question that social determinants of health (SDoH) exacerbate health inequities in affected groups, highlighting the need to optimize DSMES and other resources to close these gaps ( 72 , 76-78 ).

Cultural factors can add to the challenge of effective self-management. One study of a Hispanic/Latino community showed that PWD had difficulty implementing self-management practices due to language barriers, family commitments, and challenges in incorporating culturally relevant foods into their healthier diets ( 76 ). These findings highlight that understanding cultural context is vital for implementation of diabetes self-management and that it must be considered when identifying the best DSMES approach.

There are 4 critical times when referrals to DSMES should be considered by the HCP, and these are shown in Fig. 2 . CDC's DSMES Toolkit supports HCPs and PWD by providing evidence-based tools that can improve the health of individuals and populations. The DSMES toolkit website ( 79 ) contains many resources ranging from basic DSMES information for PWD to resources that facilitate HCP referrals to DSMES programs. Importantly, the DSMES toolkit includes materials that address individual, pragmatic, and provider-level barriers to DSMES and recommendations on how multidisciplinary teams and bidirectional e-referral systems can efficiently connect HCPs, health-care systems, and community-based DSMES service providers to optimize these services ( 80 ).

The 4 critical times for diabetes self-management and education support (DSMES) referrals. While DSMES can be offered to people with diabetes at any time, there are 4 critical times to consider referral and delivery of DSMES: at diagnosis, during annual assessment or when treatment targets are not being met, when a person with diabetes has new complicating factors, and on transitions in life or care. © 2022. Reproduced with permission of the Association of Diabetes Care & Education Specialists. All rights reserved. May not be reproduced or distributed without the written approval of ADCES.

The 4 critical times for diabetes self-management and education support (DSMES) referrals. While DSMES can be offered to people with diabetes at any time, there are 4 critical times to consider referral and delivery of DSMES: at diagnosis, during annual assessment or when treatment targets are not being met, when a person with diabetes has new complicating factors, and on transitions in life or care. © 2022. Reproduced with permission of the Association of Diabetes Care & Education Specialists. All rights reserved. May not be reproduced or distributed without the written approval of ADCES.

In addition to CDC resources, professional societies and other partner groups have also created and disseminated tools to promote DSMES referrals. The ADA and the ADCES have tools that facilitate the ability of PWD and HCPs to find local DSMES programs ( 81 ) addressing the longstanding challenge of connecting DSMES services and PWD.

HCPs should routinely support their patients by referring them to DSMES services, and they should also assess and help address barriers that hinder PWD from acting on these referrals. Strategies to promote use of DSMES include e-referrals that are supported by most EHRs and DSMES service providers. Future tools and resources that can facilitate referral and accessibility to DSMES programs in a timely manner, particularly in regions where they are currently underused or unavailable, are critical so that all PWD can realize the benefits of having a comprehensive diabetes care team.

James is a 56-year-old man with T2D of 9 years’ duration. He presents to the primary care clinic to receive diabetes care after moving to the area. He has not seen an HCP in many months. James is currently taking metformin 1000 mg twice daily, lisinopril 10 mg once daily, and a multivitamin. He weighs 225 pounds (∼102 kg), has a body mass index of 32, and his BP today is 152/90 mm Hg. Key initial fasting laboratory values obtained include HbA 1c 8.2%, estimated glomerular filtration rate 43 mL/min/1.73 m 2 , urine albumin-to-creatinine ratio (UACR) 320 mg/g, total cholesterol 270 mg/dL, and low-density lipoprotein (LDL) cholesterol 180 mg/dL. During the visit, James's HCP explains that based on his laboratory values and physical findings, James could benefit from lifestyle changes and the addition of medications to both improve his glucose management and protect his heart and kidneys. James replies that he “feels fine” and “doesn’t like to take a bunch of medications.”

Optimal diabetes management combines behavioral interventions like healthy eating and physical activity with guideline-directed pharmacotherapies that are collectively aimed at achieving and maintaining individual glycemic targets and minimizing risk of CVD and kidney disease ( 3 , 82 ). Often, PWD take multiple medications that may have different administration schedules (ie, taken at different times during the day) or even on a weekly basis. Many PWD do not meet all metabolic goals to prevent the risk of complications ( 83-85 ), yet initiating treatments to lower blood glucose, BP, and cholesterol reduces the risk of vascular and renal complications and improves long-term health outcomes. The Diabetes Control and Complications Trial ( 86 ) and the United Kingdom Prospective Diabetes Study (UKPDS) ( 87 , 88 ) demonstrated the long-term benefits of meeting HbA 1c targets among people both with T1D and T2D. Yet, fears, misconceptions, therapeutic complexity, and access to necessary resources (such as time with the HCP and better integration of CDCES in clinical settings) challenge both the providers’ and patients’ ability to meet HbA 1c goals.

Engaging PWD in patient-centered discussions about therapeutic options is recommended because it helps them become informed and actively engaged in their care if they are not already ( 3 ). Improved understanding of the pathophysiology of T2D has resulted in a rapid expansion of glucose-lowering options for T2D treatment ( 3 ). In addition, evolving evidence from glucagon-like peptide-1 receptor agonist (GLP-1RA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i) trials continues to drive changes in recommendations for T2D management ( 3 , 5 , 82 , 89 ). Leading guidelines now recommend glucose-lowering agents with evidence of atherosclerotic cardiovascular disease (ASCVD), heart failure, and/or chronic kidney disease (CKD) benefit in at-risk PWD regardless of their HbA 1c level or goal ( 3-5 , 89 ). Recommendations for use of GLP-1RAs and SGLT2is for CVD and kidney risk reduction without regard to HbA 1c reflects a paradigm shift in care for PWD. To facilitate uptake of these therapies in clinical practice, widespread education of HCPs on agents indicated to lower glucose, improve cardiorenal outcomes, or both, is critical ( Fig. 3 ). Achievement and maintenance of weight-management goals is also important to consider on an individual basis ( 90 ).

Therapeutic options that lower glucose, improve cardiorenal outcomes, or both. Selection of therapies for people with type 2 diabetes should involve a collaborative discussion of available therapies between patients and providers. A key component of the discussion should include consideration of person-centered glycemic goals and comorbidities that may affect agent selection. A discussion of options that lower glucose, improve outcomes, or both is a critical component of the discussion. Weight management goals should also be considered. *Glucose lowering therapies to attain individualized A1C targets can prevent and/or delay complications from diabetes in the long term. †Inclusive of glucose-lowering agents that have demonstrated benefit on cardiorenal outcomes in dedicated outcome trials. GLP-1RAs, glucagon-like peptide-1 receptor agonists; RAS, renin angiotensin system; SGLT2is, sodium-glucose cotransporter-2 inhibitors.

Therapeutic options that lower glucose, improve cardiorenal outcomes, or both. Selection of therapies for people with type 2 diabetes should involve a collaborative discussion of available therapies between patients and providers. A key component of the discussion should include consideration of person-centered glycemic goals and comorbidities that may affect agent selection. A discussion of options that lower glucose, improve outcomes, or both is a critical component of the discussion. Weight management goals should also be considered. *Glucose lowering therapies to attain individualized A1C targets can prevent and/or delay complications from diabetes in the long term. †Inclusive of glucose-lowering agents that have demonstrated benefit on cardiorenal outcomes in dedicated outcome trials. GLP-1RAs, glucagon-like peptide-1 receptor agonists; RAS, renin angiotensin system; SGLT2is, sodium-glucose cotransporter-2 inhibitors.

Despite the large number of available therapeutic options for PWD, only a small share of US adults with diabetes meet combined goals for HbA 1c , BP, cholesterol, and smoking cessation ( 85 ). Likewise, among those who would benefit according to current guidelines, the proportion of PWD who are taking GLP-1RAs or SGLT2is for reducing ASCVD and kidney risk is low ( 91 , 92 ). In general, the newer glucose-lowering therapies are more expensive, and cost limitations are important barriers that contribute to underuse of these recommended therapies ( 93 ). As illustrated in the aforementioned clinical scenario, PWD and caregivers can feel overwhelmed managing complex medication regimens because they often include glucose-lowering agents as well as a potentially large number of other medications to treat diabetes-related complications, CV risk, or progression of clinical disease ( 3 ). Because polypharmacy is extremely common among PWD, patient-centered diabetes education is critical to promote medication adherence and optimized outcomes ( 94 ). Finally, guidelines for management of T2D and its complications are updated frequently in response to rapidly evolving evidence, and staying current on these changes is challenging for HCPs.

Taken together, patient- and provider-level barriers contribute to suboptimal use of approved agents with demonstrated ASCVD and kidney benefits. With GLP-1RAs and SGLT2is now penetrating cardiology and nephrology practices, fragmentation of care can occur, making care coordination among specialists even more important for PWD.

Effective multidisciplinary care models that draw on the strengths of all members of the diabetes care team (including prescribers, pharmacists, nurses, dietitians, and diabetes care and DCES) are needed to optimize available therapies based on patient-specific needs and preferences. Even when access barriers are addressed, effective patient education is critical to ensure that PWD and caregivers understand why they are taking each medication, how the medication should be taken, and how self-monitoring should be conducted.

Resources are available to help PWD, their caregivers, and HCPs understand and optimize use of currently available diabetes therapies. The ES maintains a patient engagement web page that provides PWD information about their condition and available resources ( 95 ), and the ADA and ES have a diabetes educator resource page that provides a range of HCP- and patient-focused resources ( 96 ). Getinsulin.org and Insulinhelp.org ( 97 ) are two curated resources to help PWD and HCPs troubleshoot insulin cost and access challenges. The diaTribe website ( 98 ) is another resource offering education on an array of nutrition, medication, and technology topics targeting both HCPs and PWD. The MedlinePlus website ( 99 ) provides basic, reliable medication information for patients as well as links to other medication information resources. Resources that feature individuals who are Black, Indigenous, or other people of color to help PWD overcome cultural barriers are also available by dQ&A ( 100 ). These resources can supplement individual medication counseling and diabetes education from the HCP.

Despite the availability of many tools to assist PWD choose and manage their therapies in collaboration with their HCP, more work is needed in this area. Tools in languages other than English that provide patient education on therapeutic options and key medication counseling points, along with incorporation of video components, can help address persistent gaps rooted in low literacy and health literacy. To maximize relevance and patient-centeredness, development of tools and resources to help PWD navigate therapeutic options should continue to incorporate patient input and patient advocacy groups whenever possible. Best practices for leveraging multidisciplinary teams to provide patient education, optimal use of recommended therapies, and coordinated care are needed. The cost of therapies, particularly for brand name and newer drugs, unfortunately remains prohibitive for many populations, especially those in underserved areas. Development and continued curation of a medication assistance portal to help PWD, caregivers, and HCPs troubleshoot financial barriers would also help improve medication access, adherence, and outcomes.

Lien is a 58-year-old woman with central obesity and T2D diagnosed 15 years ago. She is currently on maximum doses of metformin and a low-dose sulfonylurea. Her HbA 1c has been above 8% for 3 years. She tests her glucose at home once or twice a week. Lien also has high triglycerides, low high-density lipoprotein, and stage 3 CKD with a UACR greater than 300 mg/g. Additionally, she has hypertension that is not well controlled, with typical measures above 140/90 mm Hg when taken at the HCP office. She takes no other prescription medications for her other medical conditions, but she does use over-the-counter dietary supplements. Lien sees her HCP 2 or 3 times each year, with visits lasting about 15 minutes each. At each visit, she is counseled to work on losing weight and exercising more. Her HCP is not as familiar with prescribing the newer glucose-lowering therapies nor recent guidelines recommending preferred use of SGLT2is in patients like Lien to reduce risk of CKD progression and CVD. Lien expresses no specific concerns during the visit, and her HCP makes no changes to her diabetes medication regimen.

Therapeutic and clinical inertia are two important concepts in diabetes management and a common issue affecting patients ( 101 ). Therapeutic inertia refers to the failure to advance, intensify, or deescalate therapy despite not achieving a therapeutic goal. Clinical inertia is the lack of HCP adherence to guideline recommendations in appropriate clinical situations ( 102 , 103 ). Because diabetes is a progressive disease requiring treatment intensification over time to maintain glycemic goals, treatment guidelines recommend the monitoring of HbA1c every 3 months ( 101 ). Despite this fact, treatment adjustments indicated by quality of glycemic management are often delayed for 6 months to 1 year, and attainment of metabolic goals in PWD remains suboptimal ( 103 , 104 ). Therapeutic and clinical inertia contribute to suboptimal health outcomes and underuse of guideline-directed therapies.

Therapeutic and clinical inertia can result from multiple HCP-related factors including heavy clinical load, time constraints, rapidly changing approval of new therapeutic agents, development of new technologies, frequently updated practice guidelines, basic gaps in knowledge, lack of guidelines for key clinical issues, mistrust of or disagreement with guidelines, inadequate communication skills, implicit biases, failure to set therapeutic goals, fear of side effects from aggressive therapy, and underestimation of patients’ needs and ability to manage their own disease or use complex therapies ( 105-107 ). In the aforementioned clinical scenario, the HCP lacked knowledge of the benefits of SGLT2is in patients with CKD ( 3 ), and their inability to keep up with recent guidelines contributed to both therapeutic and clinical inertia. In addition, the problem is compounded by a patient who is not fully educated or seemingly motivated to manage her diabetes.

For therapeutic inertia, numerous patient-level factors can be in play. These include low health literacy, poor understanding of the disease, denial, cost, access, competing priorities, mental health challenges like depression, loss of trust in the health-care system, side effects of medications, SDoH, and lack of participation in diabetes self-management education ( 102 , 105-107 ).

System-level factors also contribute to clinical and therapeutic inertia ( Fig. 4 ). These include fragmentation of care, formulary restrictions, lack of transparency or accuracy in a patient's insurance formulary, lack of decision support tools, lack of clarity in local guidelines and protocols, failure to identify high-risk patients and to provide access to engaging in diabetes education, and lack of team-based approaches to care ( 102 , 105 , 107 ).

Factors contributing to therapeutic inertia. This figure lists the 3 most important pillars responsible for therapeutic inertia: patient inertia, health-care provider inertia, and system-level factors. There are numerous variables that contribute to each of these areas, and many are listed and detailed in this section.

Factors contributing to therapeutic inertia. This figure lists the 3 most important pillars responsible for therapeutic inertia: patient inertia, health-care provider inertia, and system-level factors. There are numerous variables that contribute to each of these areas, and many are listed and detailed in this section.

Although clinical and therapeutic inertia have been defined and studied in the context of health services research, they need to be better addressed both by HCPs and PWD in the clinical setting. For example, issues such as access to care, cost, polypharmacy, and medication side effects may be anticipated by providers, but other issues such as competing sources of medical advice or caregiver burnout may dramatically affect care, yet these may not be considered during routine office visits. SDoH such as housing or food insecurity, which may affect financial resources available for PWD to cover medication costs, can also contribute to therapeutic inertia from patients. Burnout of HCPs due to greater demands placed on them and limited time during clinical visits with PWD also need to be acknowledged and addressed.

Multidisciplinary care models with the use of educational decision aids are potential strategies to combat therapeutic inertia, thereby optimizing use of organ-protective therapies ( 108-110 ). HCPs have an opportunity to address inertia by understanding the challenges that PWD face and engaging them to be active participants in their care rather than passive recipients of HCP instructions. This requires improved communication between PWD and HCPs, as noted elsewhere in this position statement, using open-ended questions such as, “What is the hardest thing for you dealing with your diabetes?” Existing tools include the ADA's Overcoming Therapeutic Inertia Initiative website ( 111 ), which provides information about therapeutic inertia as well as tools and resources to facilitate patient engagement and practice improvement. To help HCPs stay current on treatment recommendations and to overcome clinical inertia, the ADA also offers a wide array of guidelines and other resources, including a readily accessible Standards of Care app and an Abridged Standards of Care for Primary Care Providers, and the ES also distributes abridged guidelines for HCPs ( 112 , 113 ).

New tools to address therapeutic and clinical inertia are needed in the diabetes setting. For example, continuing medical education programs that help HCPs remain up to date on the latest guidelines for management of diabetes can facilitate evidence-based care and reduce clinical inertia. Although many online programs are currently available, these need to be adapted into a format that is readily accessible for the busy HCP (ie, podcasts that could be heard during the daily commute). Other issues that can affect patient-HCP interactions include implicit bias—a form of bias that occurs automatically and unintentionally and affects judgments, decisions, and behaviors—and this can be addressed with HCP training and improved bidirectional communication. Resource limitations in some practice settings—especially when guidelines tend to be written by physicians from well-resourced, large academic centers—also pose fundamental challenges for many HCPs. Guidelines could be adapted in ways that reflect the special challenges of underresourced practice settings. Additionally, from the system perspective, consideration must be given to develop approaches to care delivery that combat inertia. These could include enhanced emphasis on multidisciplinary care models, systematic screening for complications, aligning quality metrics with patient-reported outcomes and preferences, and possibly shared medical appointments that involve a group of patients meeting with an HCP to optimize scarce resources.

Telehealth may help reduce therapeutic inertia for some PWD, especially those who may have transportation difficulties that hinder office-based visits, or PWD who are fearful of attending in-person visits during weather or other emergencies like the COVID-19 pandemic. Access to diabetes technologies such as CGMs may also reduce therapeutic inertia by allowing the HCP to pinpoint patterns in glycemic excursions, but these do not guarantee success for PWD, just as having a scale or diet plan does ensure weight loss. However, educational interventions that enhance self-efficacy can reduce patient-related factors contributing to therapeutic inertia and improve key outcomes among PWD. To achieve these goals, HCPs may need to evolve away from the traditional model of care that is based on “routine clinic” appointments toward a model that is flexible in access to care when needed, supported by technology, and problem-oriented from the PWD's perspective, not burdensome, empathetic, and affordable to all stakeholders in terms of time burden and cost. To be sustainable, these models must be appropriately reimbursed in the future.

Vijay is a 56-year-old man who presents to his HCP for posthospital follow-up after a recent myocardial infarction (MI) and percutaneous coronary intervention. Prior to his MI, he “never had any health problems.” During his hospital admission, Vijay had a creatinine of 1.5 mg/dL, estimated glomerular filtration rate 51 mL/min/1.73 m 2 , UACR 76 mg/g, LDL of 155 mg/dL, and an HbA 1c of 7.8%. His echocardiogram was normal. He was discharged on prasugrel 10 mg, aspirin 81 mg, and atorvastatin 80 mg daily, plus metoprolol 25 mg and metformin 500 mg twice a day. Today in the office, Vijay's body mass index is 31 kg/m 2 and his BP is 152/96 mm Hg. He's anxious about his new diagnoses and asks his HCP what steps he can take to reduce his risk of having another heart attack or developing other complications from diabetes in the future.

Comprehensive diabetes care focuses on prevention strategies to reduce the risk of both acute and long-term complications. ASCVD is a key driver of morbidity and mortality in PWD—the majority of people with T2D die of some form of heart disease or stroke, and heart disease-related mortality rates are more than 2-fold higher in adults with diabetes ( 5 , 114 ). Among people with T1D and T2D, the Diabetes Control and Complications Trial and UKPDS demonstrated the benefits of intensive (ie, HbA 1c <7%) as compared to conventional glucose-lowering strategies on reducing the risk of microvascular complications but not as definitively for MI ( 86-88 ). Diabetic kidney disease is also a significant risk factor for CVD ( 115 ), and it is the leading cause of CKD and end-stage renal disease worldwide ( 116 , 117 ). Up to half of people with T2D develop diabetic kidney disease ( 115 ).

Most guidelines on CVD prevention ( 118 ) for PWD ( 119 ) recommend evidence-based approaches to manage CVD risk using a combination of lifestyle changes, risk factor modification (ie, drugs to lower levels of blood glucose, BP, or cholesterol), and medications for primary and secondary CVD prevention (eg, statins or aspirin) ( 3 , 5 ). Recommended lifestyle changes include smoking cessation, exercise, and a healthy diet because these can lead to weight loss and improve CVD risk factors such as high BP and dyslipidemia. These changes have a favorable effect on CVD outcomes, prevent incidence and progression of CKD, and can reduce progression of other microvascular complications like retinopathy.

Considerable evidence demonstrates that specific SGLT2i and GLP-1RA agents improve ASCVD outcomes in PWD ( 5 , 120 ). This triggered a paradigm shift for diabetes treatment from a historical focus on glucose and CVD risk factor management to a broader strategy of comprehensive ASCVD risk reduction with use of newer glucose-lowering agents that have demonstrated CVD benefits. Because these medications directly affect health outcomes, there has been renewed emphasis on the need for HCPs to routinely risk-stratify PWD for presence of CVD and CKD to guide patient care. In the clinical scenario presented in this section, the patient had a recent MI and would benefit from the addition of a glucose-lowering agent with demonstrated CVD benefit to his medication regimen.

Although an expanding evidence base supports the benefit of CV risk factor modification on CV and renal outcomes in PWD, this evidence has not rapidly translated into practice ( 121 ). Some HCPs may lack understanding of data supporting current guidelines for BP targets or use of statins and aspirin for primary CVD prevention. Others may be unfamiliar with current approaches to staging ASCVD and CKD that help match PWD with appropriate medications. Even when medications are prescribed, system-wide issues including access to these therapies and the out-of-pocket cost of drugs also contribute to their uptake. Although considerable attention has focused on CV and renal outcomes, the importance of prevention and screening to reduce other complications like neuropathy, retinopathy, and peripheral artery disease remains. Understanding the role of diabetic complications and how they relate to recommended medications may introduce new challenges. The explosion of therapeutic options for various aspects of diabetes risk management can exacerbate the effect of clinical inertia by fragmenting care among specialists. To optimally integrate cardiologists, nephrologists, ophthalmologists, and neurologists in the care of PWD, it will be necessary to improve communication and strategic alignment among all members of the care team.

Fortunately, several tools are available to help PWD and HCPs understand and navigate this complex and challenging landscape. The ACC and the American Heart Association (AHA) offer a risk calculator that quantifies a patient's risk of ASCVD ( 122 ). The AHA has also collaborated with the ADA in the “Know Diabetes by Heart” project ( 123 ), which provides easy-to-use information both for PWD and HCPs, and the American Society of Nephrology offers educational material focused on kidney disease as part of its diabetic kidney disease collaborative ( 124 ). DiabetesSisters additionally offers resources for PWD in Spanish on both kidney and heart health ( 125 , 126 ).

Although current web-based and other educational materials are extensive and well designed, there are still opportunities to improve and expand these tools. Existing educational materials may be biased by what providers think PWD are or should be most concerned about. A potentially important improvement could be to explicitly capture the perspective of PWD and target the specific concerns they care most about. These concerns may include avoiding dialysis rather than slowing CKD progression, for instance, the latter being a more HCP-focused goal. Similarly, incorporating vignettes from PWD with diabetes complications into educational information can be effective. Taking this one step further, although some professional societies have started to incorporate patients’ perspectives into the development of clinical guidelines, inclusion of these perspectives should become routine. To engage PWD and caregivers in developing a collaborative management plan, development of “roadmap” tools that graphically describe where PWD are in their disease process, goals of treatment including both provider- and patient-identified priorities for diabetes management (“where they want to be”), and proposed steps in their “journey” that are necessary to reach the long-term goal of reducing the risk of complications would be helpful ( Fig. 5 ). In the aforementioned clinical scenario, such a roadmap could help the PWD feel less anxious by developing a step-by-step plan with the HCP on what needs to be done now to reduce the risk of developing additional complications over the long term.

Management roadmap to improved outcomes in diabetes. Optimal care requires a multifactorial approach as detailed here. Lifestyle changes; CVD risk factor modification; routine screening for complications; use of primary and secondary preventative therapies; and incorporation of glucose-lowering agents with demonstrated benefit in reducing ASCVD events, HF hospitalization, and/or CKD progression (ie, specific SGLT2is or GLP-1 RAs) are all important signposts in the roadmap that act synergistically to improve cardiovascular and kidney outcomes. CKD, chronic kidney disease; CVD, cardiovascular disease; HF, heart failure.

Management roadmap to improved outcomes in diabetes. Optimal care requires a multifactorial approach as detailed here. Lifestyle changes; CVD risk factor modification; routine screening for complications; use of primary and secondary preventative therapies; and incorporation of glucose-lowering agents with demonstrated benefit in reducing ASCVD events, HF hospitalization, and/or CKD progression (ie, specific SGLT2is or GLP-1 RAs) are all important signposts in the roadmap that act synergistically to improve cardiovascular and kidney outcomes. CKD, chronic kidney disease; CVD, cardiovascular disease; HF, heart failure.

An HCP reviews the blood glucose levels of their patient, Noah, and notes several episodes of hypoglycemia over the last several weeks. These include readings of 63, 54, 58, and 65 mg/dL. Further analysis reveals notable rebound hyperglycemia approaching 300 mg/dL. Noah is taking multiple daily doses of insulin for his diabetes and expresses frustration with his blood glucose “roller coaster.” He asks if any changes can be made to “even things out” and why the “ups and downs” are happening. Noah also shares feeling “wiped-out and exhausted” after the lows and explains how they can “ruin” the rest of the day. He recalls learning about hypoglycemia when first diagnosed with T1D a few years ago but reports being uncertain if he is treating the lows correctly or not.

Managing diabetes is challenging, and PWD put varying degrees of effort into maintaining and monitoring blood glucose goals 24 hours a day, 7 days a week. A PWD's engagement with their own diabetes management may depend on many factors. For instance, individuals with T1D or those with T2D on multiple daily injections of insulin often need to more closely monitor blood glucose levels, compared to those with T2D on oral treatments. The importance of these efforts in preventing both microvascular and macrovascular complications is well established. However, aggressive management of blood glucose is not without risk. Hypoglycemia is the most common, often treatment-limiting, serious adverse drug effect (ADE) of diabetes therapy, particularly for those on agents that have a high-risk of hypoglycemia ( 127 ). Hypoglycemia was identified as 1 of the top 3 preventable ADEs by the US Department of Health and Human Services and is a common cause of ADE-associated emergency department visits and hospitalizations ( 128 ). Despite the prevalence, higher health-care use and costs, diminished work productivity, impaired QoL, and substantial morbidity and mortality associated with hypoglycemia, it has been described as a “neglected complication” that is underrecognized by PWD and clinicians alike, especially in older adults ( 129-135 ).

Unlike vascular complications, hypoglycemia can present abruptly, progress rapidly, and require urgent intervention. Episodes can range from mild to severe, and without prompt recognition and treatment, they can lead to confusion, loss of consciousness, seizures, and even death ( 136 , 137 ). Hypoglycemia is therefore considered a limiting factor to diabetes treatment ( 138 ) if insulin is needed because of the morbidity and mortality associated with recurrent episodes ( 139 ). It should be emphasized that diabetes medications are associated with varying risks of hypoglycemia. PWD who take insulin, a sulfonylurea, or a meglitinide are at highest risk for developing hypoglycemia. In the clinical scenario in this section, the patient is at high risk for hypoglycemia given that he is on insulin. Patient education on how to prevent and treat hypoglycemia is therefore critical at the time that these glucose-lowering medications are initially prescribed. Assessing for the frequency, severity, and patterns of hypoglycemia is also critically important during every follow-up clinical visit ( 140 ). This conversation should also include a discussion about treatments for hypoglycemia and the use of prescribed glucagon for appropriate PWD at risk for severe hypoglycemia. When an additional need for education on hypoglycemia is identified, it should be provided realizing that diabetes education can improve hypoglycemia acknowledgement and reduce future events ( 141 ).

A discussion of hypoglycemia is not complete without considering how it affects the PWD. A multicenter, prospective observational study in people with T1D or T2D who used CGM found that PWD spent an average of 8% of their total day (1.92 hours) in a hypoglycemic state defined as glucose less than 70 mg/dL ( 142 ). A systematic review detailed the emotional effect that the fear of hypoglycemia has on PWD, emphasizing its negative influence on QoL ( 143 ). A survey of more than 7000 PWD and caregivers found that hypoglycemia was self-reported by people with T1D and T2D as 1 of the top 5 most frequently mentioned barriers to optimal diabetes management ( 144 ). Several studies have shown that people with T2D underreport hypoglycemia events to their HCP, suggesting a need for continued assessment and patient education regarding hypoglycemia symptoms, prevention, and treatment. Without this information, HCPs are limited in their ability to intervene, thereby hindering their ability to prevent future and potentially more severe episodes ( 145-147 ). In addition to the personal and psychological toll on PWD, hypoglycemia research demonstrates the considerable economic burden of hypoglycemic events on workplace productivity and health-care system use ( 148 , 149 ).

Self-monitoring of blood glucose, from home fingerstick blood glucose testing to CGM, plays an important role in identifying hypoglycemia and guiding medication adjustments. Review of logbooks, average glucose, daily graphs, trend charts, and time in range can be used to improve glycemic management, including hypoglycemia prevention. CGM, however, is unique in that it detects glycemic variability that would otherwise be missed with intermittent fingerstick measurements. These technologies also offer alarms that when set appropriately can alert PWD of pending or real-time hypoglycemia. It should be emphasized that alarms are especially important during sleep when fingerstick checks are not performed ( 142 ). Although diabetes technologies support hypoglycemia-mitigation efforts, factors like comfort with technology continue to affect technology uptake. Ultimately this affects the positive effect these tools can have on attaining goals of care. CGM is not perfect, however. Human factors such as individual beliefs around hypoglycemia also affect hypoglycemia outcomes among PWD who have adopted these technologies ( 150 , 151 ). This is especially true for those who have experienced severe hypoglycemia events and those diagnosed with anxiety disorder. A qualitative study identified several challenges that can affect a patient's perspective on how CGM can be used to prevent hypoglycemia. These beliefs include (1) accuracy of glucose hypoglycemia values, (2) accuracy of predictive glucose technology, (3) lack of knowledge on how to use CGM features and data, and (4) nuisance of alarms. Targeted diabetes self-management education and support, with a focus on behavioral risk reduction, can address these concerns and improve outcomes ( 152-154 ).

There are many tools to aid in the management of hypoglycemia. Use of a hypoglycemia patient questionnaire during a clinical visit, for example, can give insights to help troubleshoot and guide discussions between HCPs and PWD for identification, prevention, and treatment of hypoglycemia ( Fig. 6 , cited from Seaquist et al ( 136 )). Another tool is a visual graphic that depicts the neurogenic and neuroglycopenic signs and symptoms of hypoglycemia. It can provide a more tangible understanding of the critical nature of treating hypoglycemia in a timely manner ( Fig. 7 ). Other available resources and strategies to prevent hypoglycemia include the following:

Best practices for assessment of hypoglycemia in everyday practice (ie, clinical decision support tools) ( 136 )

Use of agents that do not cause hypoglycemia in those at high risk

Obtain CGM for all individuals on any insulin regimen and/or those who have episodes of hypoglycemia

Risk stratification of those PWD at greatest risk for hypoglycemia (ie, EHR templates) ( 155 )

Deintensification of diabetes medication regimens—why, when, and how—for safety to reduce risk of hypoglycemia, particularly in older adults ( 3 )

Clinician guidance for managing hypoglycemia unawareness ( 136 )

Effective treatments for hypoglycemia including newer formulations, oral treatments, and fast-acting carbohydrates ( 156 )

Hypoglycemia Risk Factors educational tool ( 127 )

Training programs to increase awareness of and decrease hypoglycemia events ( 157 , 158 )

Association of Diabetes Care & Education Specialists’ Low Blood Sugar Road Map ( 159 )

Hypoglycemia patient education resources in Spanish ( 160 ) and other local languages

Education regarding available automated insulin delivery systems for those taking multiple daily insulin injections ( 161 )

Hypoglycemia questionnaire for people with diabetes. Use of a screening questionnaire during a clinical visit can give insights to help troubleshoot and guide discussions between health-care providers and people with diabetes for identification, prevention, and treatment of hypoglycemia. Reprinted from Seaquist ER et al. J Clin Endocrinol Metab, 2013; 98 (5). © Endocrine Society.

Hypoglycemia questionnaire for people with diabetes. Use of a screening questionnaire during a clinical visit can give insights to help troubleshoot and guide discussions between health-care providers and people with diabetes for identification, prevention, and treatment of hypoglycemia. Reprinted from Seaquist ER et al. J Clin Endocrinol Metab, 2013; 98 (5). © Endocrine Society.

Warning signs and symptoms of hypoglycemia. Symptoms and thresholds of hypoglycemia are shown, including a visual presentation of the scope of hypoglycemia. This includes the initial warning signs associated with neurogenic symptoms (top row), beginning of neuroglycopenic symptoms (middle row) with progression to cognitive impairment, confusion, delirium, coma and seizure (bottom row), which warrants immediate action to prevent harm. The gauge to the left provides a direct correlation between symptoms and glucose values. Patients who do not experience symptoms have hypoglycemia unawareness. This warrants review of glucose goals, medications, and stringent glucose monitoring to prevent harm.

Warning signs and symptoms of hypoglycemia. Symptoms and thresholds of hypoglycemia are shown, including a visual presentation of the scope of hypoglycemia. This includes the initial warning signs associated with neurogenic symptoms (top row), beginning of neuroglycopenic symptoms (middle row) with progression to cognitive impairment, confusion, delirium, coma and seizure (bottom row), which warrants immediate action to prevent harm. The gauge to the left provides a direct correlation between symptoms and glucose values. Patients who do not experience symptoms have hypoglycemia unawareness. This warrants review of glucose goals, medications, and stringent glucose monitoring to prevent harm.

Although a wide array of tools are available to help PWD and their HCPs manage the hypoglycemia risk, a comprehensive resource that provides an updated list of hypoglycemia treatment options, along with their costs, may be beneficial both for HCPs and PWD as they work together to identify ideal risk mitigation and treatment options in clinical practice. For PWD, these tools and educational resources should include explicit information that tells PWD when to consult with their HCP if they are experiencing or are concerned about hypoglycemia. Many forms of emergency medical identification cards and jewelry are available; however, use of these are inconsistent and provides an opportunity for further patient education. Caregiver education regarding treatment of hypoglycemia in emergency settings is also critical, and tools or resources designed to address this gap are needed.

Rose enjoyed her virtual visit with Dr Brown during the COVID-19 pandemic because she saved money on transportation, and doctor visits did not take time away from with her family. Today, Rose is sitting near a window in the family room of the home she shares with her husband and daughter. Rose's daughter connects her to her doctor's online platform, and she meets with Dr Brown, who notices that Rose is not alone in the room, and he hesitates to share the results of an abnormal laboratory test. Before ending the appointment, Rose says, “Dr Brown, before we finish, I forgot to tell you that I hurt my toes a few weeks ago, and the color has changed from red to dark purple, and now there's swelling. What should I do?” Dr Brown asks Rose to show her foot, but Rose is unable to do so because, she says, “If I move from this spot the internet connection will be lost.”

As noted elsewhere in this position statement, engaging effectively in diabetes self-management can be time-consuming for PWD, and the time they spend interacting directly with HCP is limited ( 162 ). Additional pressures are likely to be placed on PWD in trying to access their HCP because increasing numbers of people developing diabetes will drive more demands on a shrinking supply of HCPs, especially endocrinologists. However, an unexpected consequence of the COVID-19 pandemic was the dramatic increase in the use of telehealth to deliver remote diabetes care, and research has shown that people with T1D and T2D who saw their HCP by telehealth modalities were able to effectively lower their HbA 1c levels ( 163 , 164 ). Telehealth is also likely cost-effective in complex diabetes management ( 165 ). Thus, integration of telehealth into diabetes care practices can have substantial health benefits in the appropriate clinical setting.

Telehealth involves the use of telecommunications technologies for synchronous patient-clinician visits conducted over video or telephone. The opportunities for data sharing from wearable and other devices offer additional opportunities to improve efficiencies, redesign care delivery, and optimize accessibility of telehealth more consistently. For PWD using medical devices such as CGMs, manufacturers offer software portals that allow remote access to patient data. The ability to leverage telehealth is timely because of a growing body of evidence showing that these technologies may facilitate desired health outcomes both in T1D and T2D ( 166 , 167 ), including among PWD who live in rural locations and those with disabilities or other challenges that create transportation barriers ( 168 ).

Although telehealth undoubtedly offers substantial value for PWD and their HCP, important limitations must be addressed. For some PWD, adopting telehealth involved a simple switch from a face-to-face consultation to one that takes place through video or telephonic systems, whereas for others, the use of technology to connect with the HCP was more difficult to navigate. This heterogeneity raises concerns that some PWD who lack digital access or literacy may be left behind as remote care delivery evolves and telehealth moves into the mainstream. Moreover, among people who have minimum levels of digital access, the success of telemedicine for PWD is predicated on having devices and systems that address persistent challenges in trust, access, and self-efficacy, as well as broader infrastructure challenges associated with maintaining reliable internet connectivity ( 169 ) and privacy concerns linked to where PWD may choose to connect for their HCP visit, as reflected in the aforementioned clinical scenario.

Digital literacy and internet connectivity are SDoH that affect access to, and uptake of, telemedicine and other diabetes technologies. Yet, equitable access to a private and secure internet connection is essential to realize the full benefits of telehealth. Although telehealth can play an important role in providing high-quality “anytime, anywhere” health-care services, not all PWD are able to benefit from telehealth. Accordingly, HCPs need to routinely assess patient-level factors that may affect telehealth access and appropriateness in various clinical scenarios. Existing tools are available with questions that can help prompt the HCP when considering use of telehealth for PWD in outpatient care ( Fig. 8 , adapted from Klonoff et al 170 ). The ES has also published a policy perspective to guide endocrinologists on appropriate use of telehealth visits and strategies to support delivery of high-quality endocrine care by telehealth ( 171 ).

Questions HCPs can use to consider telemedicine readiness in diabetes care. These are examples of questions that the HCP might consider before a telemedicine encounter.

Questions HCPs can use to consider telemedicine readiness in diabetes care. These are examples of questions that the HCP might consider before a telemedicine encounter.

Rapid adoption of telemedicine during the COVID-19 pandemic was welcomed by many stakeholders, yet several areas of diabetes-related telemedicine research warrant urgent attention. These include a lack of standard metrics of telemedicine success; the need to develop telehealth standards of care; the absence of data on underserved communities’ experiences with telehealth and their barriers to access; the effect of telehealth on patient outcomes; and research addressing practical challenges such as PWD who need to be examined in person to avoid or delay diabetes complications, as illustrated by the clinical scenario at the beginning of this section. During the first North American COVID-19 pandemic surge, there was a decline in lower-extremity vascular procedures yet in-hospital mortality increased for those with claudication and acute limb ischemia, likely due to a reluctance of people to go into their HCP's office for evaluation and decreases in foot exams ( 172 ). Further limiting widespread uptake of telehealth are persistent gaps in data on the cost-effectiveness of these technologies for PWD, an issue that will undoubtedly affect future coverage and requirements for telehealth reimbursement. Although coverage for telehealth was expanded during the COVID-19 pandemic, coverage varies widely post pandemic and is often geographically limited. New tools to help HCPs navigate coverage of telehealth by different payers in the postpandemic era, and developing easy-to-navigate telehealth resources for PWD who may not be as familiar with use of these technologies, is important particularly in underserved areas where remote visits may offer the opportunity for greater accessibility to health care.

Shortly after her 48th birthday, Camila was diagnosed with T2D. Her HCP told her that everyone struggles with making lifestyle changes, so Camila started metformin immediately. Three months later her HbA 1c and blood glucose levels were still above target, so her HCP added a sulfonylurea. She was advised to self-monitor her blood glucose levels at home, which she did daily in the morning. Because she wanted to lose weight, Camila started searching the internet for information on different diets for T2D. Based on her research, Camila began reducing her carbohydrate intake. Almost immediately, she started experiencing poor sleep, as well as occasional sweating and light-headedness when driving long distances. Her morning blood glucose levels were usually within the goal range. Camila mentioned this to her HCP, who suggested she wear a blinded CGM for 1 week, and he used the appropriate billing codes for this procedure. The device showed clear evidence of frequent nocturnal hypoglycemia. The sulfonylurea was stopped, and the unpleasant symptoms resolved immediately. She ultimately decided to wear a real-time CGM, which was covered by insurance due to her overnight lows. A few months later, Camila's HbA 1c is 6.5% with virtually no hypoglycemia.

Given that the time available for direct contact between HCPs and PWD is limited, these interactions must ensure that the PWD remains firmly at the center of care ( 162 ). To support this effort, diabetes technologies are increasingly offered by HCPs ( 173 ) to supplement medical information collected in clinic visits and to facilitate achievement of individual care goals. With the steadily increasing diabetes incidence over the past few decades, there have been significant changes in diabetes technologies ( 174 ). Diabetes technologies encompass a range of hardware, devices, and software that PWD can use to help manage their diabetes. These include technologies to deliver insulin, monitor glucose levels, or both. Examples of devices include real-time, intermittently scanned, and blinded or professional CGMs; blood glucose meters capable of sharing data remotely; smart insulin pens; smartphone applications; and hybrid closed-loop artificial pancreas systems.

Diabetes technology is not only changing how PWD are cared for but also how data are viewed and used ( 175 ). CGMs provide maximal information about changing blood glucose levels throughout the day and facilitate optimal treatment decisions. In principle, technology can allow CGM readings to control insulin delivery, alarm the PWD to rapid changes, and pause insulin delivery with sudden changes ( 176 ). The modern age of continuous subcutaneous insulin infusion use began in the 1980s with multiple manufacturers and numerous features to improve accuracy, safety, dosing decisions, convenience, and overall usability ( 177 ). Although diabetes technology has considerable potential to offer better outcomes for PWD, clinicians are not only faced with the need to adopt these new technologies, but also to effectively and rapidly interpret the massive amount of data generated by these devices ( 178 ).

A large body of evidence supports the positive effects on physical and mental health as well as cost-effectiveness of diabetes-related technologies for adults and children with T1D, and also for adults with insulin-treated T2D ( 179-182 ). Clinical trials demonstrate the benefits of real-time CGM on reducing HbA 1c and/or hypoglycemia in people with T1D ( 182-184 ), and observational studies in T1D report reductions in HbA 1c and severe hypoglycemia with use of intermittently scanned CGMs ( 185 ). There are fewer clinical trials in people with T2D, but these have shown benefits of real-time CGMs in reducing HbA 1c among PWD taking multiple daily injections or basal insulin ( 186 , 187 ), and observational studies in T2D have reported HbA 1c reductions with use of intermittently scanned CGMs ( 188 ). There is growing interest in using CGMs for people with noninsulin-treated T2D and prediabetes ( 189 ). For CGMs, there are benefits to using a standard approach to analysis, like the ambulatory glucose profile (AGP) ( 190 ) ( Fig. 9 ), with other CGM-based metrics also proposed ( 191 , 192 ). Newer CGM models do not routinely require calibration or confirmation with fingerstick blood glucose readings, an improvement that has dramatically changed the QoL for many PWD using CGMs. For all these reasons, the HCP should discuss and offer these diabetes technologies to PWD in appropriate clinical circumstances.

Ambulatory glucose profile (AGP) report from a continuous glucose monitor. The AGP shows a summary of glucose values from a specified time period (usually a few weeks or months), with medians and percentiles for the overall glucose values during an average day. Daily glucose profiles can also be displayed. A target time in range for nonpregnant adults is usually considered greater than 70%. Other metrics often included in an AGP include average glucose (goal <154 mg/dL), glucose variability (goal ≤36%), and a glucose management indicator (goal <7%). Reprinted from Holt et al. Diabetes Care, 2021; 44(11): 2589-2625. © by the American Diabetes Association.

Ambulatory glucose profile (AGP) report from a continuous glucose monitor. The AGP shows a summary of glucose values from a specified time period (usually a few weeks or months), with medians and percentiles for the overall glucose values during an average day. Daily glucose profiles can also be displayed. A target time in range for nonpregnant adults is usually considered greater than 70%. Other metrics often included in an AGP include average glucose (goal <154 mg/dL), glucose variability (goal ≤36%), and a glucose management indicator (goal <7%). Reprinted from Holt et al. Diabetes Care, 2021; 44(11): 2589-2625. © by the American Diabetes Association.

Despite the rapid evolution and improving quality and utility of these technologies, uptake by PWD is not uniform. Persistent patient-level barriers related to access, time requirements, and out-of-pocket costs associated with use of these technologies ( 193 ) hinder many PWD from realizing their full benefit. For communities already facing health disparities, digital connectivity and digital literacy also remain considerable challenges ( 194 ) to the use of diabetes technologies. In addition, not all HCPs may be familiar with features of the newest diabetes technologies, or they may not have access to DCES who can educate PWD on their use, two issues that challenge integration of these technologies in clinical practice. New billing codes have been introduced for CGM interpretation, and these have strict guidelines regarding documentation efforts to justify the codes. Regardless, apart from clinicians who are involved in fellowship training, the literature available to practicing clinicians to guide them on how to adopt rapidly evolving technology within the limitations of current models of care and time constraints is not as readily accessible ( 178 ).

Adoption of diabetes technology in real-world settings can be improved through campaigns that heighten awareness of existing resources both for HCPs and PWD. In the aforementioned clinical scenario, in which the HCP is aware of specific codes (ie, Current Procedural Terminology codes 95250 and 95251 ) that can be used to offer blinded CGMs, this technology was able to provide important insights into undetected hypoglycemia at night and the patient ultimately decided to wear a real-time CGM. A variety of tools are available to support HCPs and PWD and help them integrate diabetes technologies in practice and decide among the many different devices available on the market. These include online resources such as Diabetes Wise: Helping You Find the Right Diabetes Devices For Your Life ( 195 ), diaTribe Learn: Diabetes Devices ( 196 ), and Diabetes Sisters: Five Popular Technology Choices , to name a few ( 197 ). Danatech, powered by ADCES, is a resource to support HCPs with up-to-date product information, device training, and professional education related to diabetes technology ( 198 ). Additionally, the ADA has a variety of resources on choosing a glucose meter, including CGMs ( 199 ), and the ES maintains a technology portal for PWD ( 200 ).

Beyond ensuring equitable access to available diabetes technologies, new tools are needed to help guide clinicians on when and to whom to offer CGM devices in the appropriate clinical setting. Ongoing development of tools to link CGM data to EHRs can allow this information to be readily available for HCPs to interpret and discuss with PWD in the future. Further, development of resources targeting diabetes technology education for primary care professionals with little or no training in diabetes care can also facilitate use of these technologies in practice. From the perspective of PWD, tools that can help guide them in understanding the different CGM metrics and how to upload their data to online platforms for HCPs to access will be tremendously useful in maximizing the benefit of these technologies.

Of note, there is considerable interest in technologies that can monitor physiological variables (eg, physical activity, sleep, and stress) in addition to glucose ( 201 ). As efforts to create a “digital phenotype” that compiles data from multiple wearable devices are underway, new tools will be needed to help HCPs understand how to interpret and incorporate these data into practice. Multimodular monitoring could allow technology-derived data to provide clinically relevant information that supplements genetic, physiologic, and environmental information as part of larger efforts to promote precision medicine in care of PWD. These efforts will require novel collaborations among HCPs, PWD, experts in machine learning and/or artificial intelligence, advocacy organizations, and other partners to ensure that the demands of these technological advances are not a barrier to, and do not outweigh, improved care.

Recent years have seen a surge in new insulin formulations, pharmacotherapy options, combination therapies, and innovations in delivery mechanisms, with new and emerging options that offer a wider array of treatment regimens. Yet, there is a persistent need to address the challenges faced by PWD. It is important for HCPs to be aware of the common needs that PWD encounter throughout their diabetes journey and address them during interactions, while at the same time providing tools to support HCPs within the time constraints they also face during the clinical visit. Priority targets include the following:

Striving for effective communication during clinical interactions both at diagnosis and throughout the disease course

Addressing the emotional and psychosocial needs of PWD and their caregivers

Ensuring referrals to DSMES programs are timely and accessible to all PWD

Effectively navigating available therapeutic options together and explaining complex regimens to PWD to support medication-taking behavior

Minimizing therapeutic and clinical inertia

Discussing strategies for hypoglycemia assessment and risk reduction

Improving CV and renal outcomes using newer therapeutic options as clinically indicated

Using telehealth in the appropriate clinical setting

Integrating technologies such as insulin pumps and CGM systems into routine diabetes management

Fortunately, many existing tools are available to help the busy HCP address these areas in clinical practice. These include interview scripts and guided communication styles to facilitate effective communication between HCPs and PWD; guidance on preferred and nonpreferred language in diabetes; clinical screening tools to assess psychosocial conditions and directories to assist in referrals to mental health providers that are experienced in diabetes care; toolkits to facilitate referrals to DSMES programs; patient education resources from several professional and patient advocacy organizations to help navigate many aspects of living with diabetes, including available pharmacotherapies; abridged guideline documents and continuing medical educational programs to help HCPs stay up to date on the latest clinical practice recommendations and to overcome therapeutic inertia; online risk calculators to stratify CV and renal risk and identify the need for further preventive therapies; quick assessment tools to screen for hypoglycemia and graphical tools to educate PWD about hypoglycemia symptoms, prevention, and treatment; checklists to determine the appropriateness of telehealth for individual patients; and online technology portals and resources to guide the selection of devices that are most appropriate for PWD.

However, in the ever-changing landscape of diabetes and its management, HCPs and PWD will continue to need new and evolving tools that respond to changing care delivery models, treatments, and technologies. New tools might include culturally or language-specific interview scripts that can be used to enhance communication between HCPs with PWD from diverse backgrounds during clinic visits; facilitating smoother integration of patient data from diabetes devices into the EHR; tools to support care coordination among specialists and enable timely referrals to DSMES programs, mental health providers, and other members of the care team when appropriate and directly from EHR systems; development of medication assistance portals to help HCPs and PWD troubleshoot financial barriers at the time of prescribing; or more explicitly capturing patients’ perspectives and health-care priorities in the development of clinical practice guidelines. It should be noted that concrete strategies to efficiently facilitate integration of these patient-centered tools in clinical practice, while also minimizing provider burden, are urgently needed in the future.

In this position statement, we have summarized key findings from multistakeholder consensus roundtables that aimed to use the lens of patient journeys to redefine the management of diabetes and its complications, and to identify tools needed to empower HCPs, PWD, and caregivers to address the challenges they face. To facilitate optimal implementation of currently recommended standards of diabetes care in real-world settings, it is critical that HCPs acknowledge the breadth of PWD's experiences and use them to inform the management of diabetes and its complications. Continued adoption, dissemination, and development of practical tools that can facilitate delivery of patient-centered care are critically important to attain desired health outcomes for all PWD.

The writing committee acknowledges the contribution of Bryce Smith, PhD, MSSW, from the Centers for Disease Control and Prevention and is grateful for his advice, expertise, and support. The committee members thank Kristine Metter, MS, CAE, president of Crystal Lake Partners, for staff support.

The Endocrine Society hosted a virtual consensus roundtable, titled “Redefining Diabetes and Its Complications: A Patient Journey Roundtable” that included delegates from professional societies, patient advocacy organizations, and government agencies. The program consisted of 2 half-day roundtable meetings held in February 2022 and May 2022 that were supported by educational grants to the Endocrine Society from Abbott, Medtronic, Novo Nordisk, and Vertex. This position statement is a summary of findings from those consensus roundtables. The sponsors had no input into the development of or content of the statement.

Rita R. Kalyani , MD, MHS, declares no conflicts of interest and has received no compensation for service on American Diabetes Association, Maryland Region Board of Directors. Myriam Allende-Vigo , MD, MBA, CDCES, has received compensation from Abbvie as an advisor. Kellie Antinori-Lent , MSN, RN, CDCES, served as immediate past-president of the American Association of Diabetes Educators in 2021. Kelly L. Close has received grant support (diaTribe) from Abbott, Ascencia, Bigfoot Biomedical, Dexcom, Embecta Insulet, LifeScan, Lilly, Mannkind, Medtronic, Novo Nordisk, One Drop, Roche, Sanofi Senseonics, Tandem, Xeris, Ypsomed, and Zealand; and news service subscription revenue (Close Concerns) from Abbott, Agamatrix, Air Liquide, Ascencia, BD, Beta Bionics, Bigfoot Biomedical, Biolinq, Cecilia Health, Cequr, DarioHealth, Dexcom, DreamED Diabetes, Glooko, Insulet, LifeScan, Lilly, MannKind, Medtronic, Novo Nordisk, One Drop, Roche, Sanofi, Senseonics, Tandem, Tidepool, Unomedical, ViCentra, Virta Health, VTv Therapeutics, Welldoc, WW, Xeris, Ypsomed, Zealand, Zence, and Zucara. More details at diaTribe.org/our-supporters , closeconcerns.com/disclosure/php . Sandeep R. Das , MD, MPH, received no compensation for service on the American Heart Association, Dallas Region Board of Directors and received compensation as associate editor of Circulation . Phyllisa Deroze , PhD, declares no conflicts of interest. Steven Edelman , MD, has served as a consultant/speaker for Sanofi, Lilly, NovoNordisk, ProventionBio, Xeris, Embecta, and Mannkind. He has also served on the Senseonics board of directors. Nuha A. El Sayed , MD, MMSc, is a part-time physician for the Joslin Diabetes Center. David Kerr , MBChB, DM, received research support from Abbott Diabetes Care and NovoNordisk and consultancy fees from Sanofi, Abbott Rapid Diagnostics and Proteomics and share options from Glooko, Hi.Health, and Snaq. Joshua J. Neumiller , PharmD, CDCES, is on the Dexcom Speaker's Bureau, Sanofi Advisory Board, Boehringer Ingelheim Advisory Board, and Eli Lilly Advisory Board. He also serves as a consultant for Bayer. Anna Norton , MS, declares no conflicts of interest.

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Acosta   JN , Falcone   GJ , Rajpurkar   P , Topol   EJ . Multimodal biomedical AI . Nat Med . 2022 ; 28 ( 9 ): 1773 ‐ 1784 .

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Recent Advances

ADA-funded researchers use the money from their awards to conduct critical diabetes research. In time, they publish their findings in order to inform fellow scientists of their results, which ensures that others will build upon their work. Ultimately, this cycle drives advances to prevent diabetes and to help people burdened by it. In 2018 alone, ADA-funded scientists published over 200 articles related to their awards!

Identification of a new player in type 1 diabetes risk

Type 1 diabetes is caused by an autoimmune attack of insulin-producing beta-cells. While genetics and the environment are known to play important roles, the underlying factors explaining why the immune system mistakenly recognize beta-cells as foreign is not known. Now, Dr. Delong has discovered a potential explanation. He found that proteins called Hybrid Insulin Peptides (HIPs) are found on beta-cells of people with type 1 diabetes and are recognized as foreign by their immune cells. Even after diabetes onset, immune cells are still present in the blood that attack these HIPs.

Next, Dr. Delong wants to determine if HIPs can serve as a biomarker or possibly even targeted to prevent or treat type 1 diabetes. Baker, R. L., Rihanek, M., Hohenstein, A. C., Nakayama, M., Michels, A., Gottlieb, P. A., Haskins, K., & Delong, T. (2019). Hybrid Insulin Peptides Are Autoantigens in Type 1 Diabetes. Diabetes , 68 (9), 1830–1840.

Understanding the biology of body-weight regulation in children

Determining the biological mechanisms regulating body-weight is important for preventing type 2 diabetes. The rise in childhood obesity has made this even more urgent. Behavioral studies have demonstrated that responses to food consumption are altered in children with obesity, but the underlying biological mechanisms are unknown. This year, Dr. Schur tested changes in brain and hormonal responses to a meal in normal-weight and obese children. Results from her study show that hormonal responses in obese children are normal following a meal, but responses within the brain are reduced. The lack of response within the brain may predispose them to overconsumption of food or difficulty with weight-loss.

With this information at hand, Dr. Schur wants to investigate how this information can be used to treat obesity in children and reduce diabetes.

Roth, C. L., Melhorn, S. J., Elfers, C. T., Scholz, K., De Leon, M. R. B., Rowland, M., Kearns, S., Aylward, E., Grabowski, T. J., Saelens, B. E., & Schur, E. A. (2019). Central Nervous System and Peripheral Hormone Responses to a Meal in Children. The Journal of Clinical Endocrinology and Metabolism , 104 (5), 1471–1483.

A novel molecule to improve continuous glucose monitoring

To create a fully automated artificial pancreas, it is critical to be able to quantify blood glucose in an accurate and stable manner. Current ways of continuously monitoring glucose are dependent on the activity of an enzyme which can change over time, meaning the potential for inaccurate readings and need for frequent replacement or calibration. Dr. Wang has developed a novel molecule that uses a different, non-enzymatic approach to continuously monitor glucose levels in the blood. This new molecule is stable over long periods of time and can be easily integrated into miniaturized systems.

Now, Dr. Wang is in the process of patenting his invention and intends to continue research on this new molecule so that it can eventually benefit people living with diabetes.

Wang, B. , Chou, K.-H., Queenan, B. N., Pennathur, S., & Bazan, G. C. (2019). Molecular Design of a New Diboronic Acid for the Electrohydrodynamic Monitoring of Glucose. Angewandte Chemie (International Ed. in English) , 58 (31), 10612–10615.

Addressing the legacy effect of diabetes

Several large clinical trials have demonstrated the importance of tight glucose control for reducing diabetes complications. However, few studies to date have tested this in the real-world, outside of a controlled clinical setting. In a study published this year, Dr. Laiteerapong found that indeed in a real-world setting, people with lower hemoglobin A1C levels after diagnosis had significantly lower vascular complications later on, a phenomenon known as the ‘legacy effect’ of glucose control. Her research noted the importance of early intervention for the best outcomes, as those with the low A1C levels just one-year after diagnosis had significantly lower vascular disease risk compared to people with higher A1C levels.

With these findings in hand, physicians and policymakers will have more material to debate and determine the best course of action for improving outcomes in people newly diagnosed with diabetes.

Laiteerapong, N. , Ham, S. A., Gao, Y., Moffet, H. H., Liu, J. Y., Huang, E. S., & Karter, A. J. (2019). The Legacy Effect in Type 2 Diabetes: Impact of Early Glycemic Control on Future Complications (The Diabetes & Aging Study). Diabetes Care , 42 (3), 416–426.

A new way to prevent immune cells from attacking insulin-producing beta-cells

Replacing insulin-producing beta-cells that have been lost in people with type 1 diabetes is a promising strategy to restore control of glucose levels. However, because the autoimmune disease is a continuous process, replacing beta-cells results in another immune attack if immunosorbent drugs are not used, which carry significant side-effects. This year, Dr. Song reported on the potential of an immunotherapy he developed that prevents immune cells from attacking beta-cells and reduces inflammatory processes. This immunotherapy offers several potential benefits, including eliminating the need for immunosuppression, long-lasting effects, and the ability to customize the treatment to each patient.

The ability to suppress autoimmunity has implications for both prevention of type 1 diabetes and improving success rates of islet transplantation.

Haque, M., Lei, F., Xiong, X., Das, J. K., Ren, X., Fang, D., Salek-Ardakani, S., Yang, J.-M., & Song, J . (2019). Stem cell-derived tissue-associated regulatory T cells suppress the activity of pathogenic cells in autoimmune diabetes. JCI Insight , 4 (7).

A new target to improve insulin sensitivity

The hormone insulin normally acts like a ‘key’, traveling through the blood and opening the cellular ‘lock’ to enable the entry of glucose into muscle and fat cells. However, in people with type 2 diabetes, the lock on the cellular door has, in effect, been changed, meaning insulin isn’t as effective. This phenomenon is called insulin resistance. Scientists have long sought to understand what causes insulin resistance and develop therapies to enable insulin to work correctly again. This year, Dr. Summers determined an essential role for a molecule called ceramides as a driver of insulin resistance in mice. He also presented a new therapeutic strategy for lowering ceramides and reversing insulin resistance. His findings were published in one of the most prestigious scientific journals, Science .

Soon, Dr. Summers and his team will attempt to validate these findings in humans, with the ultimate goal of developing a new medication to help improve outcomes in people with diabetes.

Chaurasia, B., Tippetts, T. S., Mayoral Monibas, R., Liu, J., Li, Y., Wang, L., Wilkerson, J. L., Sweeney, C. R., Pereira, R. F., Sumida, D. H., Maschek, J. A., Cox, J. E., Kaddai, V., Lancaster, G. I., Siddique, M. M., Poss, A., Pearson, M., Satapati, S., Zhou, H., … Summers, S. A. (2019). Targeting a ceramide double bond improves insulin resistance and hepatic steatosis. Science (New York, N.Y.) , 365 (6451), 386–392.

Determining the role of BPA in type 2 diabetes risk

Many synthetic chemicals have infiltrated our food system during the period in which rates of diabetes has surged. Data has suggested that one particular synthetic chemical, bisphenol A (BPA), may be associated with increased risk for developing type 2 diabetes. However, no study to date has determined whether consumption of BPA alters the progression to type 2 diabetes in humans. Results reported this year by Dr. Hagobian demonstrated that indeed when BPA is administered to humans in a controlled manner, there is an immediate, direct effect on glucose and insulin levels.

Now, Dr. Hagobian wants to conduct a larger clinical trial including exposure to BPA over a longer period of time to determine precisely how BPA influences glucose and insulin. Such results are important to ensure the removal of chemicals contributing to chronic diseases, including diabetes.

Hagobian, T. A. , Bird, A., Stanelle, S., Williams, D., Schaffner, A., & Phelan, S. (2019). Pilot Study on the Effect of Orally Administered Bisphenol A on Glucose and Insulin Response in Nonobese Adults. Journal of the Endocrine Society , 3 (3), 643–654.

Investigating the loss of postmenopausal protection from cardiovascular disease in women with type 1 diabetes

On average, women have a lower risk of developing heart disease compared to men. However, research has shown that this protection is lost in women with type 1 diabetes. The process of menopause increases rates of heart disease in women, but it is not known how menopause affects women with type 1 diabetes in regard to risk for developing heart disease. In a study published this year, Dr. Snell-Bergeon found that menopause increased risk markers for heart disease in women with type 1 diabetes more than women without diabetes.

Research has led to improved treatments and significant gains in life expectancy for people with diabetes and, as a result, many more women are reaching the age of menopause. Future research is needed to address prevention and treatment options.

Keshawarz, A., Pyle, L., Alman, A., Sassano, C., Westfeldt, E., Sippl, R., & Snell-Bergeon, J. (2019). Type 1 Diabetes Accelerates Progression of Coronary Artery Calcium Over the Menopausal Transition: The CACTI Study. Diabetes Care , 42 (12), 2315–2321.

Identification of a potential therapy for diabetic neuropathy related to type 1 and type 2 diabetes

Diabetic neuropathy is a type of nerve damage that is one of the most common complications affecting people with diabetes. For some, neuropathy can be mild, but for others, it can be painful and debilitating. Additionally, neuropathy can affect the spinal cord and the brain. Effective clinical treatments for neuropathy are currently lacking. Recently, Dr. Calcutt reported results of a new potential therapy that could bring hope to the millions of people living with diabetic neuropathy. His study found that a molecule currently in clinical trials for the treatment of depression may be valuable for diabetic neuropathy, particularly the type affecting the brain.

Because the molecule is already in clinical trials, there is the potential that it can benefit patients sooner than later.

Jolivalt, C. G., Marquez, A., Quach, D., Navarro Diaz, M. C., Anaya, C., Kifle, B., Muttalib, N., Sanchez, G., Guernsey, L., Hefferan, M., Smith, D. R., Fernyhough, P., Johe, K., & Calcutt, N. A. (2019). Amelioration of Both Central and Peripheral Neuropathy in Mouse Models of Type 1 and Type 2 Diabetes by the Neurogenic Molecule NSI-189. Diabetes , 68 (11), 2143–2154.

ADA-funded researcher studying link between ageing and type 2 diabetes

One of the most important risk factors for developing type 2 diabetes is age. As a person gets older, their risk for developing type 2 diabetes increases. Scientists want to better understand the relationship between ageing and diabetes in order to determine out how to best prevent and treat type 2 diabetes. ADA-funded researcher Rafael Arrojo e Drigo, PhD, from the Salk Institute for Biological Studies, is one of those scientists working hard to solve this puzzle.

Recently, Dr. Arrojo e Drigo published results from his research in the journal Cell Metabolism . The goal of this specific study was to use high-powered microscopes and novel cellular imaging tools to determine the ‘age’ of different cells that reside in organs that control glucose levels, including the brain, liver and pancreas. He found that, in mice, the cells that make insulin in the pancreas – called beta-cells – were a mosaic of both old and young cells. Some beta-cells appeared to be as old as the animal itself, and some were determined to be much younger, indicating they recently underwent cell division.

Insufficient insulin production by beta-cells is known to be a cause of type 2 diabetes. One reason for this is thought to be fewer numbers of functional beta-cells. Dr. Arrojo e Drigo believes that people with or at risk for diabetes may have fewer ‘young’ beta-cells, which are likely to function better than old ones. Alternatively, if we can figure out how to induce the production of younger, high-functioning beta-cells in the pancreas, it could be a potential treatment for people with diabetes.

In the near future, Dr. Arrojo e Drigo’s wants to figure out how to apply this research to humans. “The next step is to look for molecular or morphological features that would allow us to distinguish a young cell from and old cell,” Dr. Arrojo e Drigo said.

The results from this research are expected to provide a unique insight into the life-cycle of beta-cells and pave the way to novel therapeutic avenues for type 2 diabetes.

Watch a video of Dr. Arrojo e Drigo explaining his research!

Arrojo E Drigo, R. , Lev-Ram, V., Tyagi, S., Ramachandra, R., Deerinck, T., Bushong, E., … Hetzer, M. W. (2019). Age Mosaicism across Multiple Scales in Adult Tissues. Cell Metabolism , 30 (2), 343-351.e3.

Researcher identifies potential underlying cause of type 1 diabetes

Type 1 diabetes occurs when the immune system mistakenly recognizes insulin-producing beta-cells as foreign and attacks them. The result is insulin deficiency due to the destruction of the beta-cells. Thankfully, this previously life-threatening condition can be managed through glucose monitoring and insulin administration. Still, therapies designed to address the underlying immunological cause of type 1 diabetes remain unavailable.

Conventional approaches have focused on suppressing the immune system, which has serious side effects and has been mostly unsuccessful. The American Diabetes Association recently awarded a grant to Dr. Kenneth Brayman, who proposed to take a different approach. What if instead of suppressing the whole immune system, we boost regulatory aspects that already exist in the system, thereby reigning in inappropriate immune cell activation and preventing beta-cell destruction? His idea focused on a molecule called immunoglobulin M (IgM), which is responsible for limiting inflammation and regulating immune cell development.

In a paper published in the journal Diabetes , Dr. Brayman and a team of researchers reported exciting findings related to this approach. They found that supplementing IgM obtained from healthy mice into mice with type 1 diabetes selectively reduced the amount of autoreactive immune cells known to target beta-cells for destruction. Amazingly, this resulted in reversal of new-onset diabetes. Importantly, the authors of the study determined this therapy is translatable to humans. IgM isolated from healthy human donors also prevented the development of type 1 diabetes in a humanized mouse model of type 1 diabetes.

The scientists tweaked the original experiment by isolating IgM from mice prone to developing type 1 diabetes, but before it actually occurred. When mice with newly onset diabetes were supplemented with this IgM, their diabetes was not reversed. This finding suggests that in type 1 diabetes, IgM loses its capacity to serve as a regulator of immune cells, which may be contribute to the underlying cause of the disease.

Future studies will determine exactly how IgM changes its regulatory properties to enable diabetes development. Identification of the most biologically optimal IgM will facilitate transition to clinical applications of IgM as a potential therapeutic for people with type 1 diabetes.    Wilson, C. S., Chhabra, P., Marshall, A. F., Morr, C. V., Stocks, B. T., Hoopes, E. M., Bonami, R.H., Poffenberger, G., Brayman, K.L. , Moore, D. J. (2018). Healthy Donor Polyclonal IgM’s Diminish B Lymphocyte Autoreactivity, Enhance Treg Generation, and Reverse T1D in NOD Mice. Diabetes .

ADA-funded researcher designs community program to help all people tackle diabetes

Diabetes self-management and support programs are important adjuncts to traditional physician directed treatment. These community-based programs aim to give people with diabetes the knowledge and skills necessary to effectively self-manage their condition. While several clinical trials have demonstrated the value of diabetes self-management programs in terms of improving glucose control and reducing health-care costs, whether this also occurs in implemented programs outside a controlled setting is unclear, particularly in socially and economically disadvantaged groups.

Lack of infrastructure and manpower are often cited as barriers to implementation of these programs in socioeconomically disadvantaged communities. ADA-funded researcher Dr. Briana Mezuk addressed this challenge in a study recently published in The Diabetes Educator . Dr. Mezuk partnered with the YMCA to evaluate the impact of the Diabetes Control Program in Richmond, Virginia. This community-academic partnership enabled both implementation and evaluation of the Diabetes Control Program in socially disadvantaged communities, who are at higher risk for developing diabetes and the complications that accompany it.

Dr. Mezuk had two primary research questions: (1) What is the geographic and demographic reach of the program? and (2) Is the program effective at improving diabetes management and health outcomes in participants? Over a 12-week study period, Dr. Mezuk found that there was broad geographic and demographic participation in the program. The program had participants from urban, suburban and rural areas, most of which came from lower-income zip codes. HbA1C, mental health and self-management behaviors all improved in people taking part in the Greater Richmond Diabetes Control Program. Results from this study demonstrate the value of diabetes self-management programs and their potential to broadly improve health outcomes in socioeconomically diverse communities. Potential exists for community-based programs to address the widespread issue of outcome disparities related to diabetes.  Mezuk, B. , Thornton, W., Sealy-Jefferson, S., Montgomery, J., Smith, J., Lexima, E., … Concha, J. B. (2018). Successfully Managing Diabetes in a Community Setting: Evidence from the YMCA of Greater Richmond Diabetes Control Program. The Diabetes Educator , 44 (4), 383–394.

Using incentives to stimulate behavior changes in youth at risk for developing diabetes

Once referred to as ‘adult-onset diabetes’, incidence of type 2 diabetes is now rapidly increasing in America’s youth. Unfortunately, children often do not have the ability to understand how everyday choices impact their health. Could there be a way to change a child’s eating behaviors? Davene Wright, PhD, of Seattle Children’s Hospital was granted an Innovative Clinical or Translational Science award to determine whether using incentives, directed by parents, can improve behaviors related to diabetes risk. A study published this year in Preventive Medicine Reports outlined what incentives were most desirable and feasible to implement. A key finding was that incentives should be tied to behavior changes and not to changes in body-weight.

With this information in hand, Dr. Wright now wants to see if incentives do indeed change a child’s eating habits and risk for developing type 2 diabetes. She is also planning to test whether an incentive program can improve behavior related to diabetes management in youth with type 1 diabetes. Jacob-Files, E., Powell, J., & Wright, D. R. (2018). Exploring parent attitudes around using incentives to promote engagement in family-based weight management programs. Preventive Medicine Reports , 10 , 278–284.

Determining the genetic risk for gestational diabetes

Research has identified more than 100 genetic variants linked to risk for developing type 2 diabetes in humans. However, the extent to which these same genetic variants might affect a woman’s probability for getting gestational diabetes has not been investigated.

Pathway to Stop Diabetes ® Accelerator awardee Marie-France Hivert, MD, of Harvard University set out to answer this critical question. Dr. Hivert found that indeed genetic determinants of type 2 diabetes outside of pregnancy are also strong risk factors for gestational diabetes. This study was published in the journal Diabetes .

The implications? Because of this finding, doctors in the clinic may soon be able to identify women at risk for getting gestational diabetes and take proactive steps to prevent it. Powe, C. E., Nodzenski, M., Talbot, O., Allard, C., Briggs, C., Leya, M. V., … Hivert, M.-F. (2018). Genetic Determinants of Glycemic Traits and the Risk of Gestational Diabetes Mellitus. Diabetes , 67 (12), 2703–2709.

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Benefits Associated With DSMES

Providing dsmes, four critical times to refer to dsmes, medical nutrition therapy as a core component of quality diabetes care, identifying and addressing barriers, reimbursement, conclusions, article information, diabetes self-management education and support in adults with type 2 diabetes: a consensus report of the american diabetes association, the association of diabetes care & education specialists, the academy of nutrition and dietetics, the american academy of family physicians, the american academy of pas, the american association of nurse practitioners, and the american pharmacists association.

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Margaret A. Powers , Joan K. Bardsley , Marjorie Cypress , Martha M. Funnell , Dixie Harms , Amy Hess-Fischl , Beulette Hooks , Diana Isaacs , Ellen D. Mandel , Melinda D. Maryniuk , Anna Norton , Joanne Rinker , Linda M. Siminerio , Sacha Uelmen; Diabetes Self-management Education and Support in Adults With Type 2 Diabetes: A Consensus Report of the American Diabetes Association, the Association of Diabetes Care & Education Specialists, the Academy of Nutrition and Dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American Pharmacists Association. Diabetes Care 1 July 2020; 43 (7): 1636–1649. https://doi.org/10.2337/dci20-0023

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Diabetes is a complex and challenging disease that requires daily self-management decisions made by the person with diabetes. Diabetes self-management education and support (DSMES) addresses the comprehensive blend of clinical, educational, psychosocial, and behavioral aspects of care needed for daily self-management and provides the foundation to help all people with diabetes navigate their daily self-care with confidence and improved outcomes ( 1 , 2 ).

The prevalence of diagnosed diabetes is projected to increase in the U.S. from 22.3 million (9.1% of the total population) in 2014, to 39.7 million (13%) in 2030, and to 60.6 million (17%) in 2060 ( 3 ). Approximately 90–95% of those with diabetes have type 2 diabetes ( 4 ). Diabetes is an expensive disease, and the medical costs of health care alone for a person with diabetes are 2.3 times more than for a person without diabetes ( 5 ). Confounding the diabetes epidemic and high costs, therapeutic targets are not being met ( 6 ). There is a lack of improvement in reaching clinical target goals since 2005 despite advancements in medication and technology treatment modalities. Indeed, between 2010 and 2016 improved outcomes stalled or reversed ( 6 ).

The goals of this Consensus Report are to improve clinical care and education services, to improve the health of individuals and populations, and to reduce diabetes-associated per capita health care costs ( 1 , 7 ). This article is specifically directed toward health care providers (physicians, nurse practitioners, physician assistants [PAs]), referred to herein as providers, as it outlines the benefits of DSMES, defines four critical times to provide and modify DSMES (see Fig. 1 ), proposes how to locate DSMES-related resources, and discusses potential solutions to access and utilization barriers. This report provides guidance to others as well: health systems and organizations can use this report to anticipate and address the needs of persons with diabetes and create access to DSMES services; persons with diabetes can increase their awareness of DSMES services as part of quality care and can advocate for self-management education and support; and payers and policy makers can work to design reimbursement processes that support participation in DSMES. The Consensus Report’s recommendations are listed in Table 1 .

Figure 1. The four critical times to provide and modify diabetes self-management education and support.

The four critical times to provide and modify diabetes self-management education and support.

DSMES Consensus Report recommendations

This Consensus Report focuses on a component of diabetes care that is often not accessed or utilized effectively—DSMES. DSMES is identified as one of the essential elements of comprehensive diabetes medical care, along with medical nutrition therapy (MNT) (see medical nutrition therapy as a core component of quality diabetes care ). DSMES improves health outcomes and quality of life and is cost effective (see benefits associated with dsmes ). Current utilization is quite low because of a variety of barriers, yet solutions are available (see providing dsmes and identifying and addressing barriers ). Solutions begin with an organizational commitment to the value of access to, and participation in, DSMES. Financial support for DSMES services is available yet requires special attention (see reimbursement ). Key stakeholders can use this Consensus Report and the current Standards of Medical Care in Diabetes from the American Diabetes Association (ADA) ( 8 ) to develop action plans for increased referral to and utilization of DSMES. These efforts are needed to increase the focus on achieving treatment targets early and maintaining them throughout a person’s lifetime.

The purpose of DSMES is to give people with diabetes the knowledge, skills, and confidence to accept responsibility for their self-management. This includes collaborating with their health care team, making informed decisions, solving problems, developing personal goals and action plans, and coping with emotions and life stresses ( 9 ). This Consensus Report focuses on the particular needs of adults with type 2 diabetes. DSMES needs are critical to those living with type 1 diabetes, prediabetes, and gestational diabetes mellitus; however, the evidence and examples referred to in this Consensus Report are for adults with type 2 diabetes.

A call to action for all health care systems and organizations is to engage needed resources and to effectively and efficiently manage and address this expensive epidemic affecting health outcomes. We must address barriers that result in therapeutic inertia created by health policy, health systems, providers, people with diabetes, and the environment, including social determinants of health ( 10 ), which encompass the conditions in which people live, work, learn, and play ( 11 ). Rather than being overwhelmed and nonattentive to this crisis, all stakeholders must be creative and responsive to the needs of all involved and make it their priority.

This Consensus Report is an update of the 2015 joint position statement on DSMES ( 12 ). The panel of experts authoring this report includes representatives from the three national organizations that jointly published the original article (ADA, American Association of Diabetes Educators [AADE], and Academy of Nutrition and Dietetics), and, in an effort to widen the reach and stakeholder input, the American Academy of Family Physicians, American Academy of PAs, American Association of Nurse Practitioners, American Pharmacists Association, and a patient advocate were invited to participate. At the beginning of the writing process all members of the expert panel participated in two surveys related to the 2015 joint position statement and its impact and the desired future use of this Consensus Report: one survey from their perspective and one completed while interviewing colleagues. The expert panel agreed on the direction for this Consensus Report, established writing teams to author the various sections of the report, and reviewed the entire updated manuscript after each step. An outside market research company was used to conduct the literature search and was paid using ADA funds. Monthly calls were held between March 2019 and December 2019, with additional e-mail and web-based collaboration. Two in-person meetings were conducted to provide organization to the process, establish the review process, reach consensus on the content and key definitions (see Table 2 ), and discuss and deliberate the recommendations. Once the draft was completed, the structured peer review process was implemented and the report was sent to two additional representatives from each of the seven participating organizations. A final draft was completed and submitted to all seven national organizations for final review and approval. The recommendations are the informed, expert consensus of the seven contributing organizations.

Key definitions

Consensus recommendation

• Providers should discuss with all persons with diabetes the benefits and value of initial and ongoing DSMES.

The benefits of DSMES are multifaceted and include clinical, psychosocial, and behavioral outcomes benefits. Key clinical benefits are improved hemoglobin A 1c (A1C) with reductions that are additive to lifestyle and drug therapy ( 13 – 16 ). Based on recent data ( 13 , 14 , 16 ), DSMES results in an average A1C reduction of 0.45–0.57% when compared with usual care for people with type 2 diabetes treated with a variety of modalities (lifestyle alone, oral and injected medication) ( 13 – 17 ), as well as reduction in the onset and/or worsening of diabetes-related complications ( 18 , 19 ) and reduction of all-cause mortality ( 20 ). DSMES improves quality of life ( 15 , 21 – 23 ) and promotes lifestyle behaviors including healthful meal planning and engagement in regular physical activity ( 24 ). In addition, participation in DSMES services shows enhancement of self-efficacy and empowerment ( 25 ), increased healthy coping ( 26 ), and decreased diabetes-related distress ( 27 ). These improvements clearly affirm the importance and benefits of utilizing DSMES and justify efforts to facilitate participation as a necessary part of quality diabetes care. Table 3 highlights the multiple and varied benefits that make DSMES services a critical component of quality diabetes care and compares its effects to metformin therapy ( 17 ).

Comparing the benefits of DSMES/MNT vs. metformin therapy ( 17 )

N/A, not applicable. *Psychosocial benefits include improvements to quality of life, self-efficacy, empowerment, healthy coping, knowledge, self-care behaviors, meal planning, healthier food choices, more activity, use of glucose monitoring, lower blood pressure and lipids and reductions in problems in managing diabetes, diabetes distress, and the risk of long-term complications (and prevention of acute complications).

Evidence supports that better health outcomes are associated with an increased amount of time spent with a diabetes care and education specialist ( 13 , 28 , 29 ). People with diabetes who completed more than 10 h of DSMES over the course of 6–12 months and those who participated on an ongoing basis were found to have significant reductions in mortality ( 20 ) and A1C (average absolute reduction of 0.57%) ( 16 ) compared with those who spent less time with a diabetes care and education specialist.

Research shows that those who participate in diabetes education are more likely to use best practices and have lower health care costs ( 28 , 30 ). Even though outpatient and pharmacy costs are higher for those who use diabetes education, t hese costs are offset by lower acute care costs ( 28 ). DSMES is cost-effective by reducing emergency department visits, hospital admissions, and hospital readmissions ( 28 , 30 – 33 ). The cost of diabetes in the U.S. in 2017 was reported to be $327 billion including direct medical costs ($176 billion) and lost productivity ($69 billion) ( 5 ). The cost of care for people with diabetes accounts for about one in four health care dollars spent in the U.S.; 61% of costs are attributed to people over age 65 and are incurred by Medicare ( 5 ). The U.S. health care system cannot sustain the costs of care associated with the increasing incidence of diabetes and diabetes-related complications. DSMES offers a pathway to decrease these costs and improve outcomes.

DSMES improves quality of life and health outcomes and is cost-effective. All members of the health care team and health systems should promote the benefits, emphasize the value, and support participation in initial and ongoing DSMES for all people with diabetes (see Table 4 ).

Summary of DSMES benefits to discuss with people with diabetes ( 15 – 28 , 30 – 33 , 40 , 89 )

• Health policy, payers, health systems, providers, and health care teams need to expand awareness, access, and utilization of innovative and nontraditional DSMES services.

A variety of DSMES approaches and settings need to be presented and discussed with people with diabetes, thus enabling self-selection of a method that best meets their specific needs ( 34 ). Historically, DSMES services were provided in a formal series of didactic classes where people with diabetes and their family members participated at a hospital-based/health care facility location. Evolving health care delivery systems, primary care needs, and the needs of people with diabetes have resulted in the incorporation of DSMES services into additional and nontraditional settings such as those located within patient-centered medical homes, community health centers, pharmacies, and accountable care organizations (ACOs), as well as faith-based organizations and home settings.

Technology-based services including web-based programs, telehealth, mobile applications, and remote monitoring enable and promote increased access and connectivity for ongoing management and support ( 35 ). Recent health care concerns are rapidly expanding the use of these services, especially telehealth. In conjunction with formal DSMES, online peer support communities are growing in popularity. Involvement in these groups can be a beneficial adjunct to learning, serving as an option for ongoing diabetes peer support ( 36 , 37 ) ( Supplementary Table 1 ).

Creative, person-centered approaches to meet individual needs that consider various learning preferences, literacy, numeracy, language, culture, physical challenges, scheduling challenges, social determinants of health, and financial challenges should be widely available. It is important to ensure access in communities at highest risk for diabetes, such as racial and ethnic minorities and underserved communities.

Office-based health care teams without in-house resources can partner with local diabetes care and education specialists within their community to explore opportunities to reach people with diabetes and overcome some barriers to participation at the point of care ( 38 ). If the office-based care team assumes responsibility for providing diabetes education and support, every effort should be made to ensure they receive up-to-date training in diabetes care and education and utilize the details in Tables 5 and 6 .

Sample questions to guide a person-centered assessment ( 56 )

Regardless of the DSMES approach or setting, personalized and comprehensive methods are necessary to promote effective self-management required for day-to-day living with diabetes. Effective delivery involves expertise in clinical, educational, psychosocial, and behavioral diabetes care ( 39 , 40 ). It is essential for the referring provider to mutually establish personal treatment plans and clinical goals with the person with diabetes and communicate these to the DSMES team. Ongoing communication and support of recommendations and progress toward goals between the person with diabetes, education team, referring provider, and other members of the health care team are critical.

A person-centered approach to DSMES beginning at diagnosis of diabetes provides the foundation for current and future decisions. Without the focus on a person’s beliefs and desires, ongoing treatment goals can rarely be met. Diabetes self-management is not a static process and requires ongoing assessment and modification, as identified by the four critical times (see Fig. 1 ). Initial and ongoing DSMES helps the person overcome barriers and cope with the enduring and changing demands throughout the continuum of diabetes treatment and life transitions.

Providers and other members of the immediate health care team have an important role in providing education and ongoing support for self-management needs. New behaviors can be difficult to maintain and require reinforcement at a minimum of every 6 months ( 41 ). In addition to the providers, the care team may include diabetes care and education specialists (DCES); registered dietitian nutritionists (RDNs); nutrition and dietetics technicians, registered (NDTRs); nurse educators; care managers; pharmacists; exercise and rehabilitation specialists; and behavioral or mental health care providers. In addition, others have a role in helping to sustain the benefits gained from DSMES, including community health workers, nurses, care managers, trained peers, home health care service workers, social workers, and mental health counselors and other support people (e.g., family members) ( 42 – 46 ). Professional associations may help identify specific services in the local area such as the Visiting Nurse Association and block nurse programs (see Supplementary Table 1 ).

Factors that indicate referral to DSMES services is needed

Family members and peers are an underutilized resource for ongoing support and often struggle with how to best provide help ( 47 , 48 ). Including family members in the DSMES process can help facilitate their involvement ( 49 – 51 ). Such support people can be especially helpful and serve as cultural navigators in health care systems and as liaisons to the community ( 52 ). Community programs such as healthy cooking classes, walking groups, peer support communities, and faith-based groups may lend support for implementing healthy behavior changes, promoting emotional health, and meeting personal health goals ( 12 ).

All health care providers and/or systems need to identify adequate resources available in their respective communities, demonstrate commitment to support these services, and offer them as part of quality diabetes care. Health care providers need to be aware of the DSMES resources in their health system and communities and make appropriate referrals.

• Providers should initiate referral to and facilitate participation in DSMES at the four critical times 1) at diagnosis, 2) annually and/or when not meeting treatment targets, 3) when complicating factors develop, and 4) when transitions in life and care occur.

There are four critical times to provide and modify DSMES: 1 ) at diagnosis, 2 ) annually and/or when not meeting treatment targets, 3 ) when complicating factors develop, and 4 ) when transitions in life and care occur. These critical times are moments when people with diabetes may need the most assistance to achieve and/or adjust their goals and care plans for successful daily self-management. Although these four critical times are listed, it is important to recognize diabetes is a chronic disease that progresses over time and requires vigilant care to meet changing physiologic needs and goals ( 53 ).

The existing treatment plan may become ineffective due to changing situations that can arise at any time. Such situations include progression of the disease, changes in personal goals, unmet targets, major life changes, or new barriers identified when assessing social determinants of health.

It is prudent to be proactive when changes are identified or emerging. Additional support from the entire care team and referral to DSMES are appropriate responses to any of these needs. Quality ongoing, routine diabetes care includes continuous assessment, ongoing education and learning, self-management planning, and ongoing support.

The AADE7 Self-Care Behaviors provide the overarching framework for identifying key components of education and support ( 54 ). The seven self-care behaviors are healthy coping, healthy eating, being active, taking medication, monitoring, reducing risks, and problem solving. Mastery of skills and behaviors related to each of these areas requires practice and experience. Often, a series of ongoing education and support visits are necessary to allow participants the time to practice new skills and behaviors, to develop problem-solving skills, and to improve their ability and self-efficacy to set and reach personal self-management goals ( 55 ). Targeted questions, such as those now used in social determinants of health surveys utilized by many organizations, systems, and credentialed DSMES programs, can identify and facilitate addressing the immediate needs of the person with diabetes ( 56 ) and/or facilitate referral to the most appropriate team member (see Table 7 ).

Checklist for providing and modifying DSMES at four critical times

Care and education plans at each of the four critical times focus on the needs and personal goals of the individual. Therefore, the plan should be based on personal experiences that are relevant to self-management and applicable to personal goals, treatment targets, and objectives and acknowledge that adults possess expertise about their own lives ( 57 ). Tables 5 and 6 serve as checklists to ensure clinical teams and health systems offer necessary diabetes services (factors that indicate DSMES needs and what DSMES provides).

Overview of MNT: an evidence-based application of the nutrition care process provided by the RDN ( 1 , 40 , 69 – 72 )

Note: The Academy of Nutrition and Dietetics recognizes the use of registered dietitian (RD) and registered dietitian nutritionist (RDN). RD and RDN can only be used by those credentialed by the Commission on Dietetic Registration.

1. At Diagnosis

For an individual and family, the diagnosis of diabetes is often overwhelming ( 58 , 59 ), with fears, anger, myths, and personal, family, and life circumstances influencing this reaction. Immediate care addresses these concerns through listening, providing emotional support, and answering questions. Providers typically first set the stage for a lifetime chronic condition that requires focus, hope, and resources to manage on a daily basis. A person-centered approach at diagnosis is essential for establishing rapport and developing a personal and feasible treatment plan.

Despite the wide range of knowledge and skills that are required to self-manage diabetes, caution should be taken to not confound the overwhelming nature of the diagnosis but to determine what the person needs from the care team at this time to safely navigate self-management during the first days and weeks. Responses to such questions as shown in Table 7 (also see Tables 5 and 6 ) guide and set direction for each person. Immediate referral to DSMES services establishes a personal education and support plan and highlights the value of initial and ongoing education. Initial DSMES at diagnosis typically includes a series of visits or contacts to build on clinical, psychosocial, and behavioral needs. See Table 6 for suggested content.

Education at diagnosis focuses on safety concerns, often referred to as survival-level skills education, and addresses “what do I need to do once I leave your office?” To begin the process of managing the diagnosis and incorporating self-management into daily life, a diabetes care and education specialist and/or other members of the health care team work closely with the person with diabetes and his or her family members and/or significant others to answer questions, address initial concerns, and provide support and referrals to needed resources.

It is recommended that all persons with diabetes be offered a referral for individualized MNT with a registered dietitian nutritionist (RD/RDN) knowledgeable and skilled in diabetes-specific MNT and a mental health assessment, as indicated, from qualified providers with expertise in diabetes management ( 60 ) (see Supplementary Table 1 ). These team members are critical at all four critical times.

Important discussions at diagnosis include the natural history of type 2 diabetes, what the journey will involve in terms of lifestyle and possibly medication, and acknowledgment that a range of emotional responses is common. Emphasizing the importance of involving family members and/or significant others in ongoing education and support is also a key part of the process ( 47 – 51 ). Diabetes is largely self-managed and care management involves trial and error. The role of the health care team is to provide information and discuss effective strategies to reach chosen treatment targets and goals. The many tasks of self-management are not easy, yet worth the effort ( 61 ) (see benefits associated with dsmes ).

2. Annually and/or When Not Meeting Treatment Targets

The health care team and others support the adoption and maintenance of daily self-management tasks ( 8 , 40 ), as many people with diabetes find sustaining these behaviors difficult. They need to identify education and other needs expeditiously in order to address the nuances of self-management and highlight the value of ongoing education. Table 6 provides details of DSMES at this critical time. Annual assessment of knowledge, skills, and behaviors is necessary for those who achieve diabetes treatment targets and personal goals as well as for those who do not.

Primary care visits for people with diabetes typically occur every 3–6 months ( 60 ). These visits are opportunities to assess all areas of self-management, including laboratory results, and a review of behavioral changes and coping strategies, problem-solving skills, strengths and challenges of living with diabetes, use of technology, questions about medication therapy and lifestyle changes, and other environmental factors that might impact self-management ( 40 ). It is challenging for primary care providers to address all assessments during a visit, which points to the need to utilize established DSMES resources and champion new ones to meet these needs, ensuring personal goals are met. See Table 5 for indications for referral.

Possible barriers to achieving treatment goals, such as financial and psychosocial issues, life stresses, diabetes-related distress, fears, side effects of medications, misinformation, cultural barriers, or misperceptions, should be assessed and addressed. People with diabetes are sometimes unwilling or embarrassed to discuss these problems unless specifically asked ( 62 , 63 ).

Frequent DSMES visits may be needed when the individual is starting a new diabetes medication such as insulin ( 64 ), is experiencing unexplained hypoglycemia or hyperglycemia, has worsening clinical indicators, or has unmet goals. Importantly, diabetes care and education specialists are charged with communicating the revised plan to the referring provider and assisting the person with diabetes in implementing the new treatment plan.

3. When Complicating Factors Develop

The identification of diabetes-related complications or other individual factors that may influence self-management should be considered a critical indicator of the need for DSMES that requires immediate attention and adequate resources. During clinical care, the provider may identify factors other than diabetes that may influence the individual’s diabetes treatment and associated self-management plan (see Tables 5 and 6 ). These factors may require a change in self-management or affect an individual’s ability to manage their diabetes and may involve additional medications, new physical limitations, and/or new emotional needs. Examples could include a new diagnosis of renal disease or visual impairment, starting steroids, planning pregnancy, and/or psychosocial factors such as depression and anxiety.

The diagnosis of other health conditions often makes management more complex and adds additional tasks onto daily management. DSMES addresses the integration of multiple medical conditions into overall care with a focus on maintaining or appropriately adjusting medication, meal plans, and physical activity levels to maximize outcomes and quality of life. In addition to the need to adjust or learn new self-management skills, effective coping, defined as a positive attitude toward diabetes and self-management, positive relationships with others, and enhanced quality of life are addressed in DSMES services ( 16 , 26 ). Focused emotional support may be needed for anxiety, stress, and diabetes-related distress and/or depression.

The progression of diabetes can increase the emotional and treatment burden of diabetes and distress ( 65 , 66 ). Diabetes-related distress, which is distinct from major depressive disorder, is particularly common, with overall prevalence rates reported to be 36% ( 67 ). It has a greater impact on behavioral and metabolic outcomes than does depression ( 66 ). Diabetes-related distress is responsive to intervention, including DSMES-focused interventions ( 68 ) and family support ( 49 ). However, additional mental health resources are generally required to address severe diabetes-related distress, clinical depression, and anxiety ( 65 ). It is important to recognize the psychological issues related to diabetes and prescribe treatment as appropriate.

4. When Transitions in Life and Care Occur

Throughout the life span many factors such as aging, living situation, schedule changes, or health insurance coverage may require a re-evaluation of diabetes treatment and self-management needs (see Tables 5 and 6 ). Critical transition periods may include transitioning into adulthood, living on one’s own, hospitalization, and moving into an assisted living or skilled nursing facility, correctional facility, or rehabilitation center. They may also include life milestones: marriage, divorce, becoming a parent, moving, death of a loved one, starting or completing college, loss of employment, starting a new job, retirement, and other life circumstances. Changing health care providers can also be a time at which additional support is needed.

DSMES affords important benefits to people with diabetes during transitions in life and care. Providing input into the development of practical and realistic self-management and treatment plans can be an effective asset for successful navigation of changing situations.

The health care provider can make a referral to a diabetes care and education specialist to add input to the transition plan, provide education and problem solving, and support successful transitions. The goal is to minimize disruptions in therapy during any transition, while addressing clinical, psychosocial, and behavioral needs.

• Providers should ensure coordination of the medical nutrition therapy plan with the overall management strategy, including the DSMES plan, medications, and physical activity on an ongoing basis.

MNT can reduce A1C by up to 2%, making it an essential component of initial and ongoing diabetes care ( 1 , 69 , 70 ). Additionally, MNT helps prevent, delay, or treat other complications commonly found with diabetes such as hypertension, cardiovascular disease, renal disease, celiac disease, and gastroparesis. MNT provided by an RD/RDN is cost-effective, and people who have received MNT show improved clinical outcomes and quality of life ( 69 ). MNT is integral to quality diabetes care and should be incorporated into the overall care plan, medication plan, and DSMES plan on an ongoing basis ( 1 , 40 , 69 – 72 ) ( Table 8) .

Referral to the RD/RDN for MNT along with DSMES is recommended as a separate and distinct service provided by an RD/RDN. Although basic nutrition content is covered as part of DSMES, people with diabetes need both initial and ongoing MNT and DSMES; referrals to both can be made through many electronic health records as well as through hard copy or faxed referral methods (see Supplementary Table 1 for specific resources).

Everyday decisions about what to eat must be driven by evidence and personal, cultural, religious, economic, and other preferences and needs ( 69 – 71 ). With an in-depth understanding of a person’s food intake, factors influencing eating behaviors, coping strategies related to stress, and nutrition goals, the RD/RDN can work closely with the health care team to attain treatment goals, optimize medication management, or minimize the need for medications to meet glycemic targets and support progress toward other goals influenced by food intake.

The entire health care team should provide consistent messages and recommendations regarding nutrition therapy and its importance as a foundation for quality diabetes care based on national recommendations ( 70 ). Ongoing collaboration and communication with RD/RDNs can facilitate this aspect of care and support self-management and everyday food decisions.

Consensus recommendations

• Providers should identify and address barriers affecting participation with DSMES services following referral.

• Health policy, payers, health systems, providers, and health care teams should identify and address barriers influencing providers’ referrals to DSMES services.

Despite the proven value and effectiveness of DSMES, a looming threat to its success is low utilization due to a variety of barriers. In order to reduce barriers, a focus on processes that streamline referral practices must be implemented and supported system wide. Once this major barrier is addressed, the diabetes care and education specialist can be invaluable in addressing other barriers that the person may have. Without this, it will be increasingly difficult to access DSMES services, particularly in rural and underserved communities. With focus and effort, the challenges can be addressed and benefits realized.

The Centers for Disease Control and Prevention reported that only 6.8% of privately insured individuals with newly diagnosed type 2 diabetes participated in DSMES within 12 months of diagnosis ( 73 ). Furthermore, the Centers for Medicare & Medicaid Services (CMS) state that only 5% of Medicare participants receive DSMES during the first year of diagnosis ( 74 ). This low initial participation in DSMES was also reported in a recent AADE practice survey, with most people engaging in a diabetes program diagnosed for more than a year ( 75 ). These low numbers are seen even in areas where cost is less of a barrier because of national health insurance. Analysis of National Health Service data in the U.K. reveals that only 8% of those referred to formal diabetes education, an annually reviewed standard of care, attended. This highlights the need to identify and utilize resources that address all barriers including those related to health systems, health care providers, participants, and the environment. In addition, efforts are being made by national organizations to correct the identified access and utilization barriers.

Health system or programmatic barriers include lack of administrative leadership support, limited numbers of diabetes care and education specialists, geographic location, limited or lack of access to services, referral to DSMES services not effectively embedded in the health system service structure, limited resources for marketing, and limited or low reimbursement rates ( 76 ). DSMES services should be designed and delivered with input from the target population and critically evaluated to ensure they are patient-centered.

Despite the value and proven benefits of these services, barriers within the benefit design of Medicare and other insurance programs limit access. Using Medicare as an example, some of these barriers include the following: hours allowed in the first year the benefit is used and subsequent years are predefined and not based on individual needs; a referral is required and must be made by the primary provider managing diabetes; there is a requirement of diabetes diagnosis using methods other than A1C; and costly copays and deductibles apply. A person cannot have Medicare DSMES and MNT visits either face to face or through telehealth on the same day, thus requiring separate days to receive both of these valuable services and possibly delaying questions, education, and support.

Referring health care providers’ barriers include lack of awareness of DSMES services, limitations of referring providers to those providing ongoing treatment of diabetes, misunderstanding of the necessity and effectiveness of DSMES, confusion regarding when and how to make referrals, and inconvenient or limited access ( 77 – 80 ). Referrals may also be limited by unconscious or implicit bias, which perpetuates health care disparities and leads to therapeutic inertia. The provider may too quickly judge an individual’s potential to benefit from DSMES ( 81 ) and may incorrectly assume the person’s willingness/ability to participate. To address these barriers, providers can meet with those currently providing DSMES services in their area to better understand the benefits, access, and referral processes and to develop collaborative partnerships.

Participant-related barriers include logistical factors such as cost, timing, transportation, and medical status ( 34 , 77 , 78 , 82 ). For those who avail themselves of DSMES services, few complete their planned education due to such factors. The 2017 AADE practice survey of over 4,696 diabetes educators reported that only 23% of participants in diabetes education services completed 75% or more of the program ( 75 ). Underutilization of services may be because of a lack of understanding or knowledge of the benefits, cultural factors, a desire to keep diabetes private due to perceived stigma and shame, lack of family support, and perceptions that the standard program did not meet their needs and is not relevant for their life, and the referring providers may not emphasize the value and benefits of initial and ongoing DSMES ( 34 , 79 , 80 , 82 ).

Health systems, clinical practices, people with diabetes, and those providing DSMES services can collaborate to identify solutions to the barriers to utilization of DSMES for the population they serve. Creative and innovative solutions include offering a variety of DSMES options that meet individual needs within a population such as telehealth formats, coaching programs, just-in-time services, online resources, discussion groups, and intense programs for select groups, while maximizing community resources related to supporting healthy behaviors.

Credentialed DSMES programs as well as individual diabetes care and education specialists perform a comprehensive assessment of needs for each participant, including factors contributing to social determinants of health such as food access, financial means, health literacy and numeracy, social support systems, and health beliefs and attitudes. This allows the diabetes care and education specialist to individualize a plan that meets the needs of the person with diabetes and provide referrals to resources that address those factors that may not be directly addressed in DSMES. It is best that all potential participants are not funneled into a set program; classes based on a person-centered curriculum designed to address social determinants of health and self-determined goal setting can meet the varied needs of each person.

Environment-related barriers include limited transportation services and inadequate offerings to meet the various cultural, language, and ethnic needs of the population. Additionally, these types of barriers include those related to social determinants of health—the economic, environmental, political, and social conditions in which one lives ( 83 ). The health system may be limited in changing some of these conditions but needs to help each person navigate their situation to maximize their choices that affect their health. It is important to recognize that some individuals are less likely to attend DSMES services, including those who are older, male, nonwhite, less educated, of lower socioeconomic status, and with clinically greater disease severity ( 84 , 85 ). Further, studies support the importance of cultural considerations in achieving successful outcomes ( 84 – 87 ). Solutions include exploring community resources to address factors that affect health behaviors, providing seamless referral and access to such programs, and offering flexible programing that is affordable and engages persons from many backgrounds and living situations. The key is creating community-clinic partnerships that provide the right interventions, at the right time, in the right place, and using the right workforces ( 88 ).

• Health policy, payers, health systems, providers, and health care teams need to facilitate reimbursement processes and other means of financial support in consideration of cost savings related to the benefits of DSMES services.

Several common payment models and newer emerging models that reimburse for DSMES services are described below. For a list of diabetes education codes that can be submitted for reimbursement, see Supplementary Table 2 (Billing codes to maximize return on investment (ROI) in diabetes care and education).

CMS has reimbursed diabetes education services billed as diabetes self-management training since 2001 ( 40 , 89 ). DSMES services must receive accreditation by one of the current national accrediting organizations (Association of Diabetes Care & Education Specialists and ADA) to be eligible for reimbursement. In order to meet the requirements, DSMES services must adhere to National Standards for Diabetes Self-Management Education and Support and meet the billing provider requirements ( 40 , 89 ).

Ten hours are available for the first year of receiving this benefit and 2 h in subsequent years. Any provider (physician, nurse practitioner, PA) who is the primary provider of diabetes treatment can make a referral; there is a copay to use these services.

CMS also reimburses for diabetes MNT, which expands access to needed education and support. Three hours are available the first year of receiving this benefit and 2 h are available in subsequent years. A physician can request additional MNT hours through an MNT referral that describes why more hours are needed, such as a change in diagnosis, medical condition, or treatment plan. There are no specific limits set for additional hours. There is no copay or need to meet a Part B deductible in order to use these services. Many other payers also provide reimbursement for diabetes MNT ( 90 ). Additional discipline-specific counseling that further enhances DSMES includes medication therapy management delivered by pharmacists and psychosocial counseling offered by mental health professionals, also reimbursed through CMS and/or third-party payers ( 40 , 77 ).

Reimbursement by private payers is highly variable. Many will match CMS guidelines, and those who recognize the immediate and longer-term cost savings associated with DSMES will expand coverage, sometimes with no copay.

With the transition to value-based health care, organizations may receive financial returns if they meet specified quality performance measures. Diabetes is typically part of a set of contracted quality measures impacting the payment model. Health systems should maximize the benefits of DSMES and factor them into the potential financial structure.

There are reimbursable billing codes available for remote monitoring of blood glucose and other health parameters that are related to diabetes. The use of devices that can monitor glucose, blood pressure, weight, and sleep allow the health care team to review the data, provide intervention, and recommend treatment changes remotely.

Sample referral forms that provide the information required by CMS and other payers for referral to DSMES and MNT are available along with reimbursement resources (see Supplementary Tables 1 and 2 ). These or similar forms can be embedded into an electronic health record for easy referral.

Health systems and clinical organizations can maximize billing potential by facilitating the reimbursement process, ensuring all applicable codes are being utilized and submitted appropriately. This usually requires support from those who frequently work with health care codes such as staff in billing and compliance departments. Shared medical appointments can be performed with DSMES and they are reimbursable medical visits.

This Consensus Report is a resource for the entire health care team and describes the four critical times to refer to DSMES services with very specific recommendations for ensuring that all adults with diabetes receive these benefits. Diabetes is a complex condition that requires the person with diabetes to make numerous daily decisions regarding their self-management. DSMES delivered by qualified personnel using best practice methods has a profound effect on the ability to effectively undertake these responsibilities and is supported by strong evidence presented in this report. DSMES has a positive effect on clinical, psychosocial, and behavioral aspects of diabetes. DSMES provides the foundation with ongoing support to promote achievement of personal goals and influence optimal outcomes. Despite proven benefits and demonstrated value of DSMES, the number of people with diabetes who are referred to and receive DSMES is significantly low ( 73 – 75 ). Barriers will not disappear without intentional, holistic interventions recognizing the roles of the entire health care team, individuals with diabetes, and systems in overcoming issues of therapeutic inertia ( 10 ). The increasing prevalence of type 2 diabetes requires accountability by all stakeholders to ensure these important services are available and utilized.

The U.S. health care system has changed with increased attention on primary care, technology, and quality measures ( 91 ). DSMES services that directly connect with primary care are effective in improving clinical, psychosocial, and behavioral outcomes ( 92 – 95 ).

This changing health care environment provides a platform to use DSMES services as an effective, cost saving, high-impact resource integral to a person’s ability to self-manage diabetes. A variety of culturally appropriate services need to be offered in a variety of settings, utilizing technology to facilitate access to DSMES services, support self-management decisions, and decrease therapeutic inertia.

This article is being published simultaneously in Diabetes Care (DOI: 10.2337/dci20-0023 ), The Diabetes Educator (DOI: 10.1177/0145721720930959 ), the Journal of the Academy of Nutrition and Dietetics (DOI: 10.1016/j.jand.2020.04.020 ), the Journal of the American Academy of Physician Assistants (DOI: 10.1097/01.JAA.0000668828.47294.2a ), the Journal of the American Association of Nurse Practitioners (DOI: 10.1097/JXX.0000000000000473 ), and the Journal of the American Pharmacists Association (DOI: 10.1016/j.japh.2020.04.018 ).

Additional resources are available at http://www.diabeteseducator.org/consensusreport .

This article contains supplementary material online at https://doi.org/10.2337/figshare.12098571 .

This article is featured in a podcast available at https://www.diabetesjournals.org/content/diabetes-core-update-podcasts .

Acknowledgments. The authors would like to acknowledge Mindy Saraco (Managing Director, Scientific and Medical Affairs) from the ADA for her help with the development of the Consensus Report and related meetings and presentations, as well as the ADA Professional Practice Committee for providing valuable review and feedback. The authors also acknowledge Leslie Kolb, Chief Science and Practice Officer, Association of Diabetes Care & Education Specialists, for her review and support of the Consensus Report. The authors acknowledge the invited peer reviewers who provided comments on an earlier draft of this report: Christine Beebe (Quantumed Consulting, San Diego, CA), Anne L. Burns (American Pharmacists Association, Alexandria, VA), Amy Butts (Wheeling Hospital at the Wellsburg Clinic, Wellsburg, PA), Susan Chiarito (Mission Primary Care Clinic, Vicksburg, MS), Maria Duarte-Gardea (The University of Texas at El Paso, El Paso, TX), Joy A. Dugan (Touro University California, Vallejo, CA), Paulina N. Duker (Health Solutions Consultant, King of Prussia, PA), Lisa Hodgson (Saratoga Hospital, Saratoga Springs, NY), Wahida Karmally (Columbia University, New York, NY), Darlene Lawrence (MedStar Health, Washington, DC), Anne Norman (American Association of Nurse Practitioners, Austin, TX), Jim Owen (American Pharmacists Association, Alexandria, VA), Diane Padden (American Association of Nurse Practitioners, Austin, TX), Teresa Pearson (Innovative Health Care Designs, LLC, Minneapolis, MN), Barb Schreiner (Capella University, Pearland, TX), Eva M. Vivian (University of Wisconsin, Madison, WI), and Gretchen Youssef (MedStar Health, Washington, DC).

Funding. This activity was funded by the ADA and the Association of Diabetes Care & Education Specialists.

Duality of Interest. M.A.P. reports research funding from Abbott Nutrition, is a senior advisor for ADA’s Nutrition Interest Group, and is a member of ADA/American Heart Association Science Advisory Group for Know Diabetes by Heart. J.K.B reports being a past chair of the Certification Board for Diabetes Care and Education, is the program chair for the Association of Diabetes Care & Education Specialists annual meeting, and has been a consultant to Joslin Diabetes Center. M.M.F. is on an advisory board of Eli Lilly. D.H. is the treasurer for the American Academy of Nurse Practitioners Certification Board of Commissioners and Vice President of the American Nurse Practitioner Foundation. A.H.-F. reports receiving an honorarium from ADA as an Education Recognition Program auditor and is a participant in a speakers bureau sponsored by Abbott Diabetes Care and Xeris. D.I. reports being a participant in a speakers bureau/consultant for Xeris Pharmaceuticals, Novo Nordisk, Dexcom, and Lifescan. M.D.M. reports being a paid consultant of Diabetes – What to Know, Arkray, and DayTwo. A.N. reports being a participant in speakers bureaus sponsored by Boehringer Ingelheim, Novo Nordisk, and Xeris. L.M.S. reports research grant funding from Becton Dickinson. S.U. has received honoraria from ADA. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. All authors were responsible for drafting the article and revising it critically for important intellectual content. All authors approved the version to be published.

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  • Research article
  • Open access
  • Published: 16 May 2018

The effect of diabetes self-management education on HbA1c and quality of life in African-Americans: a systematic review and meta-analysis

  • Amy T. Cunningham   ORCID: orcid.org/0000-0003-2953-8261 1 ,
  • Denine R. Crittendon 2 ,
  • Neva White 3 ,
  • Geoffrey D. Mills 1 ,
  • Victor Diaz 1 &
  • Marianna D. LaNoue 1  

BMC Health Services Research volume  18 , Article number:  367 ( 2018 ) Cite this article

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Type 2 diabetes presents a major morbidity and mortality burden in the United States. Diabetes self-management education (DSME) is an intervention associated with improved hemoglobin A1c(HbA1c) and quality of life(QOL), and is recommended for all individuals with type 2 diabetes. African-Americans have disproportionate type 2 diabetes morbidity and mortality, yet no prior meta-analyses have examined DSME outcomes exclusively in this population. This systematic review and meta-analysis examined the impact of DSME on HbA1c and QOL in African-Americans compared to usual care.

Randomized controlled trials, cluster-randomized trials, and quasi-experimental interventions were included. 352 citations were retrieved; 279 abstracts were reviewed, and 44 full-text articles were reviewed. Fourteen studies were eligible for systematic review and 8 for HbA1c meta-analysis; QOL measures were too heterogeneous to pool. Heterogeneity of HbA1c findings was assessed with Cochran’s Q and I 2 .

HbA1c weighted mean difference between intervention and usual care participants was not significant: − 0.08%[− 0.40–0.23]; χ 2  = 84.79 ( p  < .001), I 2  = 92%, ( n  = 1630). Four of five studies measuring QOL reported significant improvements for intervention participants.

Conclusions

Meta-analysis results showed non-significant effect of DSME on HbA1c in African-Americans. QOL did show improvement and is an important DSME outcome to measure in future trials. Further research is needed to understand effectiveness of DSME on HbA1c in this population.

Trial registration

PROSPERO registration: CRD42017057282 .

Peer Review reports

Type 2 diabetes is responsible for a staggering morbidity and mortality burden. As of 2015, 9.4% percent of the United States population has diabetes; 95% of these individuals have type 2 diabetes [ 1 ]. Type 2 diabetes is associated with microvascular complications such as retinopathy, neuropathy and nephropathy, and with higher risk of macrovascular complications, including coronary artery disease, peripheral arterial disease, and stroke. Currently, type 2 diabetes is the seventh-leading cause of death in the United States [ 1 ].

Furthermore, profound racial and ethnic disparities exist in type 2 diabetes morbidity and mortality in the United States, particularly for African-Americans. Currently, 12.7% of African-Americans have type 2 diabetes [ 1 ]. African-Americans are less likely to have controlled HbA1c than non-Hispanic whites [ 2 ], are also more likely to develop retinopathy and nephropathy [ 3 ], and more likely to be hospitalized with diabetes-related complications [ 4 ]. African-Americans with type 2 diabetes also report higher levels of diabetes-related distress than non-Hispanic whites [ 5 ]. Ultimately, African-Americans have the highest diabetes-related mortality rates of any racial or ethnic group in the United States [ 3 ].

These Type 2 diabetes disparities result from a complex mix of factors. Low birth-weight and maternal-fetal stress are more common in African-American children and increase the risk of developing type 2 diabetes [ 6 ]. Higher type 2 diabetes prevalence and poorer HbA1c control may result from ethnic differences in obesity rates, body fat distribution, and glucose metabolism [ 6 ]. Cultural food practices and customs may also pose a challenge to diabetes management, such as consumption of breaded and fried meats and simple carbohydrates [ 7 ]. Additionally, African-Americans are disproportionately affected by socioeconomic factors such as poverty, poorer quality housing, lack of neighborhood spaces for physical activity, and limited access to healthy food [ 8 ]. Health care access barriers and lower quality of care also contribute to poorer diabetes outcomes in African-Americans, as can patient-provider racial discordance, perceived racial bias in medical encounters, and resulting patient mistrust in healthcare providers and systems [ 9 ].

Self-management of type 2 diabetes requires regular blood glucose monitoring, management of diet, physical activity, medications, and ongoing medical care. A key goal of diabetes self-management is the control of hemoglobin A1c (HbA1c), which is a measure of average blood glucose over several months. Poorly-controlled HbA1c is associated with microvascular and macrovascular complications [ 1 ]. The demands of managing this complex illness also affect many dimensions of quality of life (QOL), which encompasses physical, emotional and social well-being. Individuals with diabetes report lower QOL than individuals without chronic illnesses [ 10 ]. Contributors to lower QOL include diabetes-related distress; in the recent Diabetes Attitudes, Wishes and Needs second (DAWN2) study, 44.6% of those with type 2 diabetes reported distress regarding hypoglycemic events, physical health, emotional well-being, and financial strain [ 11 ]. In turn, lower QOL affects the ability to manage HbA1c and other diabetes care activities [ 12 ].

Recognizing the many challenges of managing type 2 diabetes, the American Diabetes Association (ADA) recommends that all individuals receive diabetes self-management education (DSME) at the time of a type 2 diabetes diagnosis, as well as ongoing self-management support as needed [ 13 ]. The goal of DSME is to increase an individual’s self-efficacy to manage diet, physical activity, glucose monitoring, stress management, and other necessary skills and behaviors for successful diabetes outcomes [ 13 ]. Meta-analyses have established the impact of DSME on glycemic control and QOL. In a 2002 meta-analysis, DSME participants demonstrated reductions of 0.76% in hemoglobin A1c (HbA1c) at immediate follow-up, with reductions in HbA1c attenuating to 0.24% at follow-up points 4 or more months post-intervention. The authors found three interventions measuring QOL, two of which showed QOL improvements in DSME participants; they did not combine these studies in a meta-analysis [ 14 ]. A more recent meta-analysis of group DSME programs showed HbA1c declines of 0.44% six months post-intervention, and 0.46% at 12 months. Three studies were eligible for a QOL meta-analysis; QOL changes were not significant, but the authors stipulated that the heterogeneity of the included studies was high [ 15 ]. However, neither meta-analysis examined outcomes by racial/ethnic group.

Increasingly, attention has been paid to the differential impact of DSME in racial and ethnic minority groups—including African-Americans--and development of DSME that is culturally-adapted for the language, beliefs, values, and customs of particular groups. In their DSME position statement the (ADA), the American Association of Diabetes Educators (AADE) and Academy of Nutrition and Dietetics call for DSME that addresses a patient’s “cultural needs,” [ 13 ] and the AADE lists provision of “culturally competent supportive care across the lifespan” as a competency for diabetes educators [ 16 ]. Nam et al.’s 2012 meta-analysis of 12 culturally-tailored DSME interventions—four of which targeted African-Americans—showed an effect size of − 0.29 on HbA1c [ 17 ], indicating a small effect. A 2014 meta-analysis of the impact of DSME on HbA1c in ethnic minorities found an overall 0.31% HbA1c reduction in the 39 included studies; 33% of these studies included African-Americans [ 18 ]. However, these meta-analyses did not explore HbA1c results for African-Americans separately, nor did they examine QOL as an outcome.

Despite the higher type 2 diabetes morbidity and mortality burden in African-Americans, no systematic reviews or meta-analyses have specifically analyzed the impact of DSME on two critical measures--HbA1c and QOL-in this population. Further, none have examined whether certain DSME characteristics, such as number of contact hours or culturally-adapted interventions, might result in better outcomes for African-Americans. The purpose of this systematic review and meta-analysis is to examine the impact of DSME in African-American adults with type 2 diabetes mellitus on HbA1c and QOL. Subgroup analyses also examined the impact of several DSME characteristics, including cultural adaptations, on HbA1c.

The systematic review and meta-analysis study protocol was developed prospectively and reported using Preferred Reporting for Systematic Review and Meta Analyses (PRISMA) guidelines [ 19 ]. The systematic review and meta-analysis procedures used were developed in consultation with the Cochrane Handbook for Systematic Reviews of Interventions [ 20 ]. The protocol was registered at the international prospective register of systematic reviews (PROSPERO) (ID: CRD42017057282) [ 21 ].

Search strategy

The search strategy, including databases used and search terms, was developed in consultation with a medical librarian. An initial search was developed for OVID MEDLINE using keywords, medical subject (MeSH) terms and publication types based on the PICO framework (participants, comparison, intervention, and outcomes). Participants were African Americans (“African Americans,” “African Americans”[MeSH] with type 2 diabetes (“type 2 diabetes,” “type 2 diabetes mellitus,” “diabetes,” “T2DM”(type 2 diabetes mellitus), “Diabetes Mellitus”[MeSH], “Diabetes Mellitus, Type 2”[MeSH], “NIDDM” (Non-insulin dependent diabetes mellitus), or “Non-insulin dependent diabetes mellitus.”) The intervention was DSME (“diabetes self-management education,” “self management education,” “DSME,” “education.” “health education,” “diabetes education,” “Patient Education as Topic”[MeSH], or “Self Care”[MeSH]); the comparator was a control group in a randomized-controlled trial or quasi-experimental study with matched controls (“randomized controlled trial.” “controlled clinical trial,” “randomly,” “randomized,” “trial,” “control,” “groups,” or “quasi-experimental”). Outcomes were HbA1c (“HbA1c,” “A1c,” “glycemic control,” “Hemoglobin A, Glycosylated”[MeSH], or “hemoglobin A1c protein, human”[MeSH]) and QOL (“HRQL,” “QoL,” “health-related quality of life,” “Quality of Life”[MeSH]), “QOL tools OR questionnaires OR surveys,” “SF-36,” “WHOQOL,” “DQOL.” “well-being,” “psychological well-being,” or “emotional well-being”). A sample OVID MEDLINE search strategy may be found in Additional file  1 .

Databases searched were OVID MEDLINE, Ovid Eric, PsycINFO, Scopus, CINAHL EBSCO, and the Cochrane Central Register of Controlled Trials. To minimize the potential omission of relevant studies, the citation lists of included studies were reviewed to identify additional studies for potential inclusion. Additionally the tables of contents for selected journals ( Diabetes Care , The Diabetes Educator , Annals of Internal Medicin e, and Annals of Family Medicine ) were hand-checked. The search strategy also included grey literature sources such as non peer-reviewed government and nonprofit publications (the Agency for Healthcare Research and Quality, the ADA, and the Centers for Disease Control and Prevention).

Inclusion and exclusion criteria

All citations were reviewed against pre-determined inclusion and exclusion criteria for eligibility in the systematic review. Included study designs were randomized-controlled trials or quasi-experimental studies with a matched control group comparing DSME to usual care. The inclusion of quasi-experimental study designs was consistent with the Cochrane Consumers and Communication Review Group standards for evaluation of complex interventions [ 22 ]. “Usual care” could consist of usual primary care, assignment to a wait-list, or a minimal educational intervention. The definition of DSME was based on the ADA and AADEs’ National Standards for Diabetes Self-Management Education and Support; e.g., a program to “facilitate the development of knowledge, skills, and abilities that are required for successful self-management of diabetes” [ 13 ]. Further, the intervention needed to support at least one of the AADE7 Self-Care Behaviors: healthy eating, being active, monitoring, taking medications, problem solving, healthy coping, and reducing risks [ 23 ].

Participants were African-American adults with type 2 diabetes mellitus; to be included, interventions either needed to have exclusively African-American participants, or to report the outcomes for African-American participants separately. All potential settings (clinics, hospitals, community settings, virtual/telehealth/phone, or combinations) were included. Studies selected for the systematic review were eligible for inclusion in the HbA1c meta-analysis if they measured HbA1c mean and standard deviation both pre- and post-intervention; similarly, studies included in the systematic review were eligible for inclusion in the QOL meta-analysis if they measured QOL mean and standard deviation both pre- and post-intervention.

Studies were excluded if: 1) the study population was not exclusively African-American or results for African-Americans are not reported separately; 2) the study had participants with type 1 diabetes, unless type 1 and type 2 diabetes results are reported separately; 3) the study control groups received anything other than usual care; 4) the intervention targeted providers or systems, rather than patients; 5) the intervention was a diabetes disease management or care management intervention, rather than DSME (for example, studies focusing exclusively on medical nutrition therapy or disease management); or 6) the study did not measure either HbA1c or QOL as an outcome. A study was defined as measuring QOL if it used one or more general or diabetes-specific QOL measures, which were pre-specified through a comprehensive literature search using keywords and phrases related to quality of life and frequently-used synonyms (diabetes and “quality of life” or “health-related quality of life” or “psychosocial adjustment” or “distress”). There were no study exclusions based on participant age or sex, article language, or publication date.

Study selection

Two independent reviewers (AC and DC) conducted the selection process through each phase of the review. All citations identified through the search were imported into a shared bibliography, and duplicate records of the same report were removed. The reviewers independently extracted information from the abstracts into structured evidence tables based on the pre-determined inclusion and exclusion criteria. Based on these criteria, they independently assessed the abstracts’ eligibility for full-text review. The two reviewers compared their results and reached consensus; a third reviewer (ML) served as a tiebreaker when needed. From this process, articles were selected for full-text review. The two reviewers independently read and assessed the full-text articles using the inclusion and exclusion criteria and met to compare results and reach consensus, with the third reviewer serving as a tiebreaker. Through this full-text review, the reviewers identified the final set of articles eligible for inclusion in the systematic review.

Data extraction

For the articles included in the systemic review, the two reviewers extracted further study data for inclusion in a structured evidence table. Descriptive categories included source citation, number of participants, mean participant age, percentage of participants who were African-American, and study design. Reviewers also recorded whether the intervention was group or individual-based, intervention content, presence of cultural tailoring (according to the studies’ authors), the intervention’s definition of usual care, duration, number of contact hours, provider type, DSME topics addressed, and attrition rate. The HbA1c and QOL measures used, HbA1c/QOL measurement frequency, and results were also recorded.

Bias and quality appraisal

Risk of bias was examined as an outcome across studies using the Cochrane Collaboration’s Risk of Bias tool, which assesses the presence of biases that pose threats to internal validity [ 24 ]. Types of bias examined in the Cochrane Risk of Bias tool included selection bias (random sequence generation and allocation concealment), performance bias (blinding participants and researchers to the intervention a participant receives), detection bias (blinding of outcome assessment from knowledge of what intervention a participant received), attrition bias, reporting bias, and other bias [ 24 ]. Studies were judged to have a low, high, or unclear risk of bias for each of these criteria. Quasi-experimental studies were automatically designated to have a high risk of bias on the random sequence generation item of the tool [ 22 ]. Two reviewers (AC and DC) independently assessed study bias and then met to compare results and reach consensus. Although assessment of publication bias was included in our protocol, due to the small number of studies in our HbA1c meta-analysis, publication bias could not be assessed. When fewer than ten studies are included in a meta-analysis, tests for forest plot asymmetry are not recommended due the low power to detect a real asymmetry [ 25 ].

The overall quality of included studies was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria. In the GRADE system, evidence can be rated as high, moderate, low, or very low. Randomized controlled trials begin with a rating of high quality, and observational studies with a grade of low quality. Factors that can lower a quality rating include limitations in design and execution, heterogeneity (inconsistency of results), indirectness (research does not measure desired intervention or outcomes), imprecision (few patients or events), and publication bias. Factors that can increase a rating include a large magnitude of effect, a dose-response continuum, and plausible residual confounding in observational studies [ 26 ]. Two reviewers assessed study quality independently (AC and DC) and met to reach consensus.

Meta-analysis

All analyses were performed in Review Manager version 5.2 [ 27 ]. For studies containing both pre-and post-intervention HbA1c levels, these values were extracted as mean ± standard deviation. First, a meta-analysis was conducted to assess possible baseline HbA1c differences between intervention and control groups. Next, the mean HbA1c for both intervention and control groups at the conclusion of the intervention was transformed into a weighted mean difference (WMD), in which the contribution of each study to the mean difference is weighted by its sample size, and 95% confidence intervals (CIs) were calculated and combined in a random-effects meta-analysis. A random-effects meta-analysis is appropriate when combining studies with differences in the treatment effect [ 20 ]. A forest plot was also generated for the HbA1c WMD.

Study heterogeneity was explored using Cochran’s Q and I 2 , with p  < .05 for Cochran’s Q and I 2  ≥ 50% indicating substantial heterogeneity [ 28 ]. In addition, several subgroup analyses were conducted for HbA1c. First examined was the impact of culturally-adapted versus non culturally-adapted DSME based on the authors’ descriptions of their interventions. Additionally, subgroup analyses were conducted based on intervention contact hours (< 10 versus ≥10), given that 10 or more contact hours has been shown to lead to better DSME outcomes; DSME provider type(s) (e.g., individual (physician, nurse, dietician, pharmacist, health educator), or multiple provider types), individual, group, or combination individual/group DSME, and attrition rate. For QOL, studies with pre-and post-intervention QOL mean ± standard deviation were eligible for inclusion in a meta-analysis.

Fig.  1 shows the PRISMA diagram for the study selection process. A total of 352 citations were retrieved from OVID MEDLINE, Ovid Eric, PsycINFO, Scopus, CINAHL EBSCO, Cochrane Central Register of Controlled Trials, grey literature, and hand searches. After removing duplicates, 279 abstracts remained. After abstract review, 44 articles were selected for full-text review. Ultimately, 14 of those 44 articles were eligible for inclusion in the systematic review; all were from the peer-reviewed literature [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ].

PRISMA Flow Diagram

Table  1 displays the characteristics of interventions included in the systematic review. Publication dates ranged from 1997 to 2015. Ten were randomized-controlled trials, [ 29 , 30 , 31 , 33 , 34 , 35 , 36 , 38 , 40 , 42 ] two were cluster-randomized trials, [ 32 , 39 ] and two were quasi-experimental studies [ 37 , 41 ]. Thirteen of the studies exclusively enrolled adult African-Americans with type 2 diabetes; one study recruited both African-American and Hispanic adults with type 2 diabetes, but reported findings on the two racial/ethnic groups separately [ 38 ]. The mean participant age was 59. In all studies, more than half of the participants were female; two studies only included female participants [ 36 , 42 ].

DSME interventions were heterogeneous in terms of setting, structure, content, contact hours, and provider type. Settings included primary care offices, hospitals, community health centers, diabetes education centers, churches, and patient homes. Seven provided individual DSME, [ 32 , 33 , 35 , 36 , 38 , 40 , 42 ] four provided group DSME, [ 29 , 30 , 37 , 41 ] and three utilized both individual and group sessions [ 31 , 34 , 39 ]. The number of contact hours varied from 4 to 27; in two studies the contact hours were not specified [ 36 , 39 ]. In half of the interventions, DSME was delivered by one type of health professional, most commonly a diabetes educator or nurse educator; other studies utilized combinations of diabetes educators, nurse case managers, registered dieticians, pharmacists, peer educators, and community health workers. Attrition was addressed in all but one study [ 41 ]; the mean attrition rate was 22.1%.

Twelve of the fourteen interventions were described by their authors as culturally-adapted for African-Americans [ 29 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Seven of these authors provided further descriptions of their cultural adaptations, which consisted primarily of incorporation of African-American dietary preferences in nutrition education and/or use of race-concordant diabetes educators, peer educators, or community health workers.

All studies included in the systematic review measured change in HbA1c % as an outcome; 10 studies compared changes in HbA1c for intervention participants versus usual care [ 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Of these 10 studies, five reported HbA1c changes favoring the intervention group [ 32 , 33 , 34 , 35 , 39 ]. Eight studies reported pre and post HbA1c means and standard deviations for both the intervention and control groups and were therefore eligible for the HbA1c meta-analysis [ 29 , 30 , 31 , 32 , 34 , 36 , 38 , 39 ].

Eight studies included in the systematic review measured QOL as an outcome, with several using multiple QOL assessment tools. QOL measures included Mental Well Being and Social Well Being, [ 36 ] Psychological Distress Scale, [ 37 ] Healthy Days Measure Scale, [ 37 ] the 12-Item Short-Form Survey, (SF-12) [ 34 ] the Problem Areas in Diabetes Survey (PADS), [ 41 , 42 ] the Diabetes Care Profile (DCP), [ 30 ] the Diabetes Empowerment Scale Short-Form (DESSF), [ 30 ] Diabetes Attitude Scale, [ 30 ] Diabetes Symptom Distress Scale, [ 40 ] and Quality of Life in Diabetes [ 40 ]. Five of these studies compared changes in QOL for intervention participants compared to usual care [ 30 , 33 , 39 , 40 , 42 ]. Four reported statistically significant improvements in intervention participants’ QOL compared to usual care, including improved physical, [ 33 ] mental, [ 33 , 39 , 40 ] and social well-being [ 40 ] and improved psychosocial adjustment [ 42 ]. Only two studies included pre- and post-intervention QOL means and standard deviations, and these studies used different QOL tools. Use of the standardized mean difference for comparing different patient-reported outcomes such as QOL score in a meta-analysis is cautioned against because the responsiveness of different QOL instruments to change may vary dramatically [ 43 ]. Therefore, QOL results were ultimately not combined in a meta-analysis.

Meta-analysis results

Figure  2 shows the forest plot for HbA1c in DSME participants versus usual care. First, a random-effects meta-analysis was conducted to assess possible baseline HbA1c differences between intervention and control groups; a non-significant mean baseline HbA1c difference was observed: 0.1% [95% CI -0.25-0.5%]. A second random-effects meta-analysis estimated the WMD in HbA1c in the intervention versus usual care group post-intervention. The HbA1c WMD between intervention and usual care participants was not significant: 0.08% [− 0.40–0.23]; heterogeneity was high: χ 2  = 84.79 ( p  < .001), I 2  = 92% ( n  = 1630). Subgroup analyses of HbA1c by intervention versus usual care for culturally tailored interventions, individual versus group curriculum, intervention contact hours (< 10 versus ≥10), provider type, and attrition rate (< 20% versus ≥20%) were also non-significant (Table  2 ).

Forest Plot for HbA1c Meta-Analysis

Bias and quality assessment results

For most types of bias assessed, the risk of bias was low for the majority of studies included in the systematic review. Risk of selection bias due to random sequence generation was high for two studies and unclear for three; bias sue to allocation concealment was unclear for 10 studies. One study had unclear performance bias, all studies had low detection bias, one had unclear reporting bias, and none had other biases detected. The complete risk of bias ratings for included articles may be found in Additional file  2 .

Given that the majority of included studies were randomized-controlled trials, the overall evidence was initially assessed as high-quality per GRADE criteria. One point was deducted from the evidence quality for heterogeneity of the study findings. As noted earlier, due to the small number of studies publication bias could not be assessed. Ultimately, the quality of the evidence was graded as moderate.

This meta-analysis found no significant impact of DSME on HbA1c in African-American DSME participants. This finding contrasts with prior DSME meta-analyses that have found HbA1c reductions ranging from 0.44–0.76% in the general population [ 14 , 15 ] to 0.31% in DSME targeted at ethnic minorities [ 18 ]. The subgroup analysis of < 10 versus ≥10 contact hours also contrasts with a prior meta-analysis [ 14 ]; although Ricci-Cabello et al. found that effects did not vary by contact hours or intervention intensity [ 18 ]. The similarity of HbA1c outcomes for individual versus group DSME is consistent with prior meta-analyses [ 14 , 18 ]. Likewise, the variation in DSME settings, delivery methods, intensity and contact hours is similar to the findings of other DSME meta-analyses [ 14 , 18 ]. The high heterogeneity of HbA1c changes ( I 2  = 92%) may be a result of the substantial variations in these intervention characteristics.

The smaller number of DSME interventions measuring QOL relative to HbA1c is consistent with prior DSME meta-analyses, which have found a greater focus on surrogate outcomes such as HbA1c rather than patient-reported outcomes such as QOL [ 14 ]. It is promising that four of the five studies measuring QOL found statistically significant improvements in participants’ QOL versus controls; however, no studies explained if the statistically significant differences in QOL scores translated into clinically meaningful QOL improvements for patients.

The variety of QOL scales used likely reflects that QOL is a complex construct without a universal definition; however, this variety and the small number of DSME studies measuring QOL hampers the ability to compare findings across studies. In order to better understand the impact of DSME on QOL, more DSME studies should include QOL measures, which would allow for eventual pooling of studies using the same/similar QOL measures in meta-analyses. Future DSME research could also examine with relationship of QOL and potential moderators such as self-efficacy and social support [ 44 ].

Notably, in a subgroup analysis culturally-adapted DSME interventions did not yield better HbA1c results than non-culturally-adapted DSME. Prior DSME meta-analyses have not compared culturally to non-culturally tailored DSME. In Nam et al.’s meta-analysis of culturally-tailored DSME in ethnic minorities, the authors noted that more research was needed to determine the most effective culturally-tailored elements for various racial and ethnic groups [ 17 ]. Similarly, for a number of the studies in our meta-analysis it was difficult to ascertain the types of cultural adaptations made, and it was unclear whether certain features—such as use of race-concordant educators or recipe modifications—had a greater impact than others. More detailed guidelines are needed for the development and evaluation of culturally-adapted DSME in specific populations. Future research should also more rigorously assess approaches to cultural adaptations of DSME for African-Americans and the relative effectiveness of various culturally-tailored approaches. Furthermore, the included studies in this meta-analysis did not explicitly address social and systems-level contributors to diabetes disparities in African-Americans, such as socioeconomic status, racial discrimination or mistrust in the medical system. When developing DSME in the United States and globally, educators should be sensitive to the experiences of marginalized groups and how these experiences can impact diabetes self-management [ 45 ].

Strengths and limitations

Our study has a number of strengths: it is the first systematic review and meta-analysis to examine the impact of DSME on HbA1c and QOL in African-American participants, and to include a subgroup analysis of the impact of culturally versus non-culturally tailored DSME on HbA1c in African-Americans. The study also benefitted from a prospectively-created study protocol utilizing a comprehensive search strategy that included hand searching of selected journals and grey literatures searches. Additionally, the protocol did not apply search restrictions based on publication year or language, which helped to ensure that all relevant interventions were captured. Of the fourteen studies in the systematic review, 10 were RCTs. strengthening the internal validity. For all risks of bias assessed, the majority of studies had low risk of bias, and the overall body of evidence was rated to be moderate quality per the GRADE criteria.

Limitations included the high risk of bias in random sequence generation for the two quasi-experimental studies included in the systematic review, and the unclear risks of bias across several studies, particularly for allocation concealment. Additionally, the HbA1c results had significant heterogeneity, as reflected by the large CIs and I 2 value. Although subgroup analyses were performed, the small number of studies ( n  = 8) eligible for inclusion in the HbA1c meta-analysis limits the ability to draw conclusions about the optimal DSME intensity and delivery methods for African-Americans. The smaller number of articles measuring QOL( n  = 5) and the inability to pool studies also warrants caution for drawing conclusions for the relationship between DSME and QOL in African-Americans. Finally, the limited number of studies included in the meta-analysis precluded assessment of publication bias. However, publication bias typically results in studies with significant findings being more likely to be published. Since the HbA1c meta-analysis was non-significant, this may lessen the possibility of publication bias in the included studies.

In addition, participants in the included studies may not be fully representative of the African-American population. For instance, in all of the studies the majority of participants were female; therefore, the findings may be less applicable to African-American men. Most studies reported limited socioeconomic status information, such as education level or income data; how these characteristics were measured varied across studies, making it difficult to compare these samples to the socioeconomic status of the African-American population. This may limit the external validity of the findings.

Significant disparities remain in type 2 diabetes prevalence and outcomes among African-Americans, and DSME is recommended as part of standard type 2 diabetes care. Our study adds to the body of knowledge of the impact of DSME in African-Americans by showing a non-significant impact on HbA1c in African-American participants. The high levels of heterogeneity in the HbA1c findings--as evidenced by wide CIs and I 2 values—demonstrate a need for more rigorously-designed DSME trials for African-Americans and further research to understand what DSME intervention characteristics, if any, consistently contribute to improved HbA1c in this population. Finally, the smaller number of interventions measuring QOL indicates the need for greater prioritization of QOL and other patient-important outcomes in future DSME research among African-Americans.

Abbreviations

American Association of Diabetes Educators

American Diabetes Association

confidence interval

Diabetes Attitudes, Wishes, and Needs second study

diabetes self-management education

diabetes quality of life

Grading of Recommendations, Assessment, Development, and Evaluation criteria

hemoglobin A1c

health-related quality of life

medical subject headings

non insulin-dependent diabetes mellitus

patient, intervention, comparison, outcome

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

international prospective register of systematic reviews

quality of life

short-form 36

type 2 diabetes mellitus

World Health Organization Quality of Life

weighted mean difference

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AC conceived of the study and developed the protocol; served as a primary abstract and full-text article reviewer, and selected the final articles for inclusion; conducted the random-effects meta-analysis, and wrote the manuscript. DC developed the protocol; served as a primary abstract and full-text article reviewer and selected the final articles for inclusion; conducted the random-effects meta-analysis, interpreted findings, and reviewed/edited the manuscript. ML developed the protocol, served as a tie-breaker abstract/article reviewer, conducted the random-effects meta-analysis, interpreted findings, and reviewed/edited the manuscript. VD, GM, and NW assisted with interpretation of the findings and reviewed/edited the manuscript. All authors gave approval of the final version to be published. AC claims final responsibility for the content of the article.

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Cunningham, A.T., Crittendon, D.R., White, N. et al. The effect of diabetes self-management education on HbA1c and quality of life in African-Americans: a systematic review and meta-analysis. BMC Health Serv Res 18 , 367 (2018). https://doi.org/10.1186/s12913-018-3186-7

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  • Type 2 diabetes
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Revolutionizing Diabetes Care: Unveiling the Genetic Blueprint for Personalized Treatment

In a groundbreaking study, researchers have unveiled 1,289 genetic markers linked to type 2 diabetes, heralding a new era in personalized diabetes management. this significant discovery opens the door to customized treatment strategies, promising more effective care tailored to individual genetic profiles..

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The realm of diabetes management is on the brink of a transformation, with the latest research unveiling a comprehensive set of 1,289 genetic markers linked to Type 2 diabetes. This landmark study not only deepens our understanding of the genetic underpinnings of this widespread chronic condition but also paves the way for a future where diabetes care is highly personalized, moving beyond the one-size-fits-all approach that has dominated the field for years.

Type 2 diabetes affects millions of people worldwide, posing significant health risks and challenges in management. Traditional treatment methods have largely been standardized, with little consideration for individual differences in genetic makeup that could influence responses to medication, diet, and lifestyle interventions. The discovery of over a thousand genetic markers associated with Type 2 diabetes marks a pivotal shift toward addressing this gap, offering hope for more effective and tailored treatment plans.

The Genesis of a Genetic Revolution in Diabetes Care

The study, conducted by an international team of researchers, represents one of the largest and most comprehensive analyses in the realm of diabetes genetics to date. By examining the genetic profiles of tens of thousands of individuals, scientists were able to identify specific genetic variations that contribute to the development and progression of Type 2 diabetes. This vast array of markers provides invaluable insights into the biological pathways and processes involved in the disease, some of which were previously unrecognized.

From Genetic Insights to Personalized Treatment Plans

The implications of this research are far-reaching. For individuals living with Type 2 diabetes, the identification of these genetic markers opens the door to personalized medicine. Shortly, healthcare providers could use this genetic information to develop tailored treatment strategies that consider an individual's unique genetic risk factors. This could include specific medications that are more effective based on genetic makeup, personalized dietary recommendations, and targeted lifestyle interventions that offer the best chance for managing the disease effectively.

Navigating the Challenges Ahead

Despite the excitement surrounding these findings, there are challenges to integrating this knowledge into everyday clinical practice. One of the main hurdles is the need for accessible and affordable genetic testing that can identify these markers in patients. Furthermore, healthcare systems must adapt to incorporate genetic counseling and interpretation into diabetes care, ensuring that patients and providers can make informed decisions based on genetic risk profiles.

Additionally, there's the task of educating both healthcare providers and patients about the benefits and limitations of personalized medicine. As with any new medical innovation, a clear understanding of what genetic testing can and cannot offer is crucial for its successful implementation.

Looking Forward: The Future of Diabetes Management

As research continues to unravel the genetic complexities of Type 2 diabetes, the future of diabetes care looks increasingly personalized. This study is just the beginning, more research is needed to understand how these genetic markers interact with environmental factors, lifestyle choices, and other health conditions. The ultimate goal is a holistic approach to diabetes management that considers the whole person — their genetics, environment, lifestyle, and preferences — in designing the most effective care plan.

The discovery of 1,289 genetic markers linked to Type 2 diabetes is a monumental step forward in the fight against this chronic disease. By paving the way for personalized treatment strategies, this research offers a glimpse into a future where diabetes care is tailored to the individual, improving outcomes and enhancing the quality of life for millions of people around the world. As we stand on the cusp of this new era in healthcare, the promise of personalized medicine brings hope for a healthier, more empowered future for those living with Type 2 diabetes.

In the ongoing journey to revolutionize diabetes care, the melding of genetic insights with clinical practice heralds a new dawn of personalized medicine — a future where each patient's genetic blueprint guides the path to optimal health and wellness.

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Research Article

Enablers and barriers to effective diabetes self-management: A multi-national investigation

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft

* E-mail: [email protected] , [email protected]

Affiliation College of Medicine and Dentistry, James Cook University, Townsville, Australia

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Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliation College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia

Roles Conceptualization, Methodology, Project administration, Supervision, Validation, Writing – review & editing

  • Mary D. Adu, 
  • Usman H. Malabu, 
  • Aduli E. O. Malau-Aduli, 
  • Bunmi S. Malau-Aduli

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  • Published: June 5, 2019
  • https://doi.org/10.1371/journal.pone.0217771
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Table 1

The study aimed to identify the common gaps in skills and self-efficacy for diabetes self-management and explore other factors which serve as enablers of, and barriers to, achieving optimal diabetes self-management. The information gathered could provide health professionals with valuable insights to achieving better health outcomes with self-management education and support for diabetes patients.

International online survey and telephone interviews were conducted on adults who have type 1 or type 2 diabetes. The survey inquired about their skills and self-efficacy in diabetes self-management, while the interviews assessed other enablers of, and barriers to, diabetes self-management. Surveys were analysed using descriptive and inferential statistics. Interviews were analysed using inductive thematic analysis.

Survey participants (N = 217) had type 1 diabetes (38.2%) or type 2 diabetes (61.8%), with a mean age of 44.56 SD 11.51 and were from 4 continents (Europe, Australia, Asia, America). Identified gaps in diabetes self-management skills included the ability to: recognize and manage the impact of stress on diabetes, exercise planning to avoid hypoglycemia and interpreting blood glucose pattern levels. Self-efficacy for healthy coping with stress and adjusting medications or food intake to reach ideal blood glucose levels were minimal. Sixteen participants were interviewed. Common enablers of diabetes self-management included: (i) the will to prevent the development of diabetes complications and (ii) the use of technological devices. Issues regarding: (i) frustration due to dynamic and chronic nature of diabetes (ii) financial constraints (iii) unrealistic expectations and (iv) work and environment-related factors limited patients’ effective self-management of diabetes.

Conclusions

Educational reinforcement using technological devices such as mobile application has been highlighted as an enabler of diabetes self-management and it could be employed as an intervention to alleviate identified gaps in diabetes self-management. Furthermore, improved approaches that address financial burden, work and environment-related factors as well as diabetes distress are essential for enhancing diabetes self-management.

Citation: Adu MD, Malabu UH, Malau-Aduli AEO, Malau-Aduli BS (2019) Enablers and barriers to effective diabetes self-management: A multi-national investigation. PLoS ONE 14(6): e0217771. https://doi.org/10.1371/journal.pone.0217771

Editor: Simone Rodda, University of Auckland, NEW ZEALAND

Received: December 11, 2018; Accepted: May 19, 2019; Published: June 5, 2019

Copyright: © 2019 Adu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The first author of this study (MDA) is funded by the Australian Government International Research Training Program Scholarship. The funder had no role in the study design, data collection and analysis, decision to publish or preparation of manuscript.

Competing interests: The authors have declared that no competing interest exist.

Abbreviations: SD, Standard deviation; T1D, Type 1 Diabetes mellitus; T2D, Type 2 Diabetes mellitus

Introduction

Diabetes mellitus is a major public health problem with rapidly increasing prevalence. In 2017, the global prevalence of diabetes among people aged 20–79 years was 425 million, mainly comprising type 1 or type 2 [ 1 ]. Diabetes is one of the top 10 global causes of mortality. In 2015, it was responsible for 1.6 million deaths, indicating a 60% increase in 15 years from less than 1 million in 2000 [ 2 ]. International audits have found that regimen adherence is less than optimal in both types 1 and 2 diabetes patients [ 3 ]. As a consequence, the majority of these patients are at risk of serious health complications that endanger life [ 1 , 4 ] and impose great economic burden on affected individuals and the health care system [ 1 ].

Consistent engagement in diabetes self-management has been found to be correlated with the attainment of health outcomes in terms of good blood glucose control, fewer complications [ 1 , 5 ], improved quality of life [ 6 , 7 ] and reduction in diabetes-related death risks [ 8 ]. The term “self-management” refers to day to day activities or actions an individual must undertake to control or reduce the impact of disease on their health and wellbeing [ 9 ] in order to prevent further illness [ 10 ]. Diabetes self-management actions involve engagement in recommended behavioural activities such as healthy eating, medication adherence, being active, monitoring, reducing risks, problem-solving and healthy coping, which are all necessary for the successful management of the disease [ 11 ]. Level of adherence to diabetes self-management differs in patients, which implies that decision-making processes for self-management are influenced by various factors, which could either serve as enablers or barriers.

Enablers of self-management

Enablers of self-management are mechanisms or factors that foster the ability of patients to undertake their recommended self-management regimen. Such factors are diverse and include effective social support with assistance and encouragement from family members [ 12 , 13 ] or peers who have diabetes or close relatives familiar with its management [ 14 ]. Likewise, individual resolution to prevent or reduce the risk of developing diabetes complications helps with the determination to engage in self-management [ 12 , 15 ]. Studies have also noted positive decision making about diabetes self-management as a result of effective health care provider-patient communication [ 16 ], characterized by trust, respect and shared decision-making in planning health goals [ 17 , 18 ]. In addition, patient support with the use of health technological interventions such as mobile phone applications [ 19 ] and self-management education [ 20 , 21 ], facilitate efective diabetes management. Individual factors, particularly higher educational level [ 22 , 23 ] and gender [ 24 ], also contribute to patients’ ability to care for their diabetes.

More importantly, adequate self-management skills [ 25 ] and self-efficacy (confidence) [ 26 ] to perform these skills are major enabling factors for engagement in diabetes self-management. This is because skills and self-efficacy operate in tandem to foster full engagement with self-management. Self-management skills result from knowledge about the disease [ 25 ], and understanding the interrelationships between different self-management activities and their impact on health outcomes [ 27 ]. On the other hand, self-efficacy refers to ‘‘one’s belief in his/her own innate ability to perform specific tasks required to reach a desired goal” [ 28 ]. Unless people believe they can produce desired effects by their action, they have little incentive to act [ 29 ], regardless of other enabling factors which may be available to them. In diabetes management, patients’ level of self-efficacy is influenced by their level of skills for self-management. Hence, patients with adequate skills and efficacy have more likelihood to adhere to prescribed behavioural regimen necessary to attain optimal health [ 25 , 30 – 32 ]. Acquiring diabetes self-management skills and efficacy is an ongoing learning process [ 20 , 25 ]. While some skills and efficacy are easily acquired, others are often difficult to attain. Further research is therefore needed to adequately identify gaps in diabetes patients’ skills set and self-efficacy levels for self-management of their health issues. Information on identified gaps will guide health care providers in their development of educational support programs that foster self-management among diabetes patients.

Barriers to self-management

Non-adherence to recommended diabetes self-management regimen is influenced by barriers encountered by patients. These barriers make managing the disease more difficult. Only few studies have examined patients’ perceived barriers to general diabetes self-management from a global perspective. An international study identified diabetes related distress as a major factor responsible for poor adherence to self-management in patients [ 3 ]. Local studies reported that difficulty in making lifestyle changes [ 33 ] and inadequate health care system communication interface [ 34 ] were related to poor diabetes self-management. In addition, financial constraints resulted in patients’ inability to access diabetes clinical supplies and eat in line with appropriate dietary recommendations [ 35 – 37 ]. Other studies have examined barriers to some specific areas of diabetes self-management. Nagelkerk et al., [ 16 ] and Ghimire [ 38 ] reported that patients’ lack of knowledge of a specific diet plan and perceived belief in social unacceptability of healthy behaviours hindered healthy eating and participation in physical exercise. Furthermore, depressive symptoms and personal belief about medication were observed to be associated with lower adherence to diabetes medications [ 39 ].

The empirical and conceptual research findings mentioned above are not exhaustive because only a few have an international focus [ 3 ]. Additionally, the studies are mostly focused on barriers to self-management in patients with type 2 diabetes only [ 33 – 36 , 38 , 40 ], older populations [ 35 ], those from low income background without indicating the type of diabetes the respondents had [ 41 ], or few areas of diabetes self-management [ 38 – 40 ]. The above limitations in previous studies emphasize the need for further and detailed exploration of factors serving as barriers to self-management in both types 1 and 2 diabetes patients. This will provide strategies that adequately address such challenges and foster better adherence to self-management for better health outcomes in both patient groups.

There is diversity in the level of self-management between patients. The ability to self-manage diabetes is influenced by various factors that can either serve as enablers or barriers. However, to the best our knowledge, global perspectives on the crucial enablers of self-management in terms of skills and self-efficacy, among types 1 and 2 diabetes patients is relatively scarce. Likewise, studies on other enablers and potential barriers to general self-management as perceived by these patient groups is scanty in the published literature. There is special interest in elucidating this information from an international perspective because issues encountered in self-management by both patient groups are likely to include common experiences and challenges. Identifying these commonalities could provide health professionals with an in-depth understanding of patients’ experiences and help guide the development and enhancement of intervention strategies to improve patients’ self-management of diabetes. Therefore, this study aimed to: i) identify the common gaps in skills and self-efficacy for self-management among individuals with type 1 or type 2 diabetes; ii) examine factors associated with self-management skills and self-efficacy; iii) explore other factors which serve as enablers of, and barriers to, achieving optimum diabetes self-management.

Recruitment procedure

A maximum variation purposive sampling technique was employed in recruiting participants aged ≥ 18 years who had type 1 or type 2 diabetes. Participants were recruited globally using diverse recruitment strategies. The aim of this sampling method was to obtain a mix of participants with diverse experiences and identify common patterns that cut across the population sample with regards to the subject of interest [ 42 ]. Officially approved advertisement for the study was placed on various health organizations’ websites. These websites included Diabetes UK and Diabetes Australia. In addition, the advertisement was placed in local digital newspapers, Twitter and Facebook pages focusing on diabetes support. Data collection was conducted between November 2017 and June 2018. There was no limit to sample size in order to capture the maximum number of people with type 1 or type 2 diabetes. The study requested participants’ socio demographic characteristics of age, gender, educational level and geographic location. Details of the recruitment strategy and participants’ characteristics have been fully described in our previous publication [ 43 ].

Study design

A sequential mixed methods approach was used; comprising quantitative and qualitative data collection methods [ 44 ]. The quantitative phase of the study involved a cross sectional survey and data analysis. This was followed by qualitative telephone interviews of a subsample of the participants in order to provide a more complete and comprehensive understanding of the results which were integrated into the data interpretative phase [ 44 ]. Quantitative data were obtained through an online survey that focused on assessing participants’ self-reported skills and self-efficacy (confidence) as part of the factors that enable diabetes self-management. Qualitative data were collected through individual telephone interviews which further explored additional factors that serve as enablers and barriers to diabetes self-management.

Quantitative measures–survey.

The survey questions were divided into two parts. First, the following health characteristics which were likely to influence skills and self-efficacy for diabetes management were assessed: type of diabetes, duration of diagnosis and whether participants had recently received (within the previous 12 months) diabetes self-management education (DSME) from a member of their health care team.

Second, novel LMC Skills, Confidence and Preparedness Index (SCPI) tool was used to assess skills and self-efficacy in core behaviours central to diabetes self-management such as healthy eating, blood glucose monitoring, being active, healthy coping, medication adherence, problem solving and reducing risk [ 11 , 45 ]. The SCPI tool had been previously validated, where its construct validity for different ages, ethnicity, gender and level of education was established [ 32 ]. Additionally, the validity of the tool for use in different settings is established by the fact that, as a new tool, the questions reflect the current recommended self-management regimen for diabetes patients, and this has not been fully explored by previous tools [ 45 ]. It has excellent readability and reliability. Permission was obtained to use the tool. The SCPI tool consists of three subscales: skills, confidence and preparedness. The skills subscale was used to assess perceived ability to perform the self-management activities mentioned above. The confidence subscale was used to assess self-efficacy in being able to perform the skills. The preparedness scale was not used in this study because this subscale assesses the readiness of patients to implement behavioural changes following an educational session; which was not applicable in the present study.

The skills and confidence domains consist of nine (9) and eight (8) items respectively. Two of these items focus on skills and confidence to use insulin. These skills were adapted to accommodate participants who have type 2 diabetes but do not use insulin/other medications as part of their treatment regimen. All items were rated using a visual analogue scale, with scores between 1 and 10. Each of the items in the domains produced its own score out of 10. The total score was the mean score in each of the subscales, where higher scores denoted better skills and confidence. The scoring process is not affected by demographic factors such as age, gender, level of education or ethnicity [ 45 ], hence, its’ applicability for use in study populations with diverse social and health characteristics. The instrument was administered in English Language.

Qualitative measures–phone interviews.

Through online survey, all participants were invited to an individual telephone interview session. They were requested to indicate interest by providing their best contact number and availability. A single independent resource person (male) who is an experienced researcher in qualitative studies conducted all interviews. The interviewer was trained on the aims of the study and the interview guide by the first author of this study (MDA). The guide was then pilot tested between the interviewer and MDA before actual use. Additionally, MDA was present in the first three interviews to ensure appropriateness of data collection. While the interviews were used to reflect on the interview guide, no changes were made to the guide afterwards. There was no interaction or previous relationship between MDA and the participants. The interviewer was located in a private office at James Cook University, Townsville, Australia. Prior to the commencement of the interview, each respondent was asked if they were located in a comfortable place for an interview, and were briefly presented with the general idea of the study and key diabetes self-management activities. The interviewer did not have prior relationship with the participants. Each Interview was audio recorded and lasted between 7 and 20 minutes in duration. Data saturation was achieved through recurring explicit ideas [ 46 ] after completing the 14 th interview. However, the interview was conducted for the remaining two participants who had indicated interest in order to ensure that no main idea was unintentionally discarded. Repeat interviews were not required and due to the remoteness of the study participants, there was no post interview debriefing. The semi-structured interview guide was developed by the research team. Topics covered in the interview included open ended questions and probes to facilitate discussion (See S1 Appendix for details of the interview questions).

Ethics and consent

The study procedures (registration number: H7087) were approved by James Cook University’s Human Research Ethics Committee. The protocol contained detailed information on the ethical obligations of researchers toward participants engaging in online research activities. Essentially, these obligations included confidentiality, anonymity, scientific value, maximising benefits, minimizing harms, and informed consent [ 47 ]. All these obligations were strictly adhered to during the research process. Furthermore, as part of the application process for advertisement of the study on the website of health organisations, the ethics approval document was made available to the appropriate and designated officials of these organisations. All prospective study participants were provided with the study information along with the privacy policy prior to the survey. Therefore, participants were informed about the use of their answers for analysis under anonymity. Informed consent was implied by submission of the online survey, while all telephone interviewees provided verbal consent.

Data analyses

SPSS (Version 23) was used for quantitative data analysis. Cronbach’s alpha of the subscales of measures used in this study were acceptable (.92 and .91 for skills and confidence scales respectively). Participants’ demographics and health variables were presented using descriptive statistics. Items in the skills and self-efficacy domains were reported as means and standard deviations (SD). For the purpose of explaining and discussing the results, scores were graded as high (≥ 7), moderate (4–6) or poor (≤ 3). Mean scores were calculated for demographic and health variable subgroups. Bivariate analyses were performed using Independent sample t-test and Analysis of Variance (ANOVA) to test the relationship between participants’ subgroups and level of skills and confidence. Specifically, t-test was used for variables with two categories (i.e. type of diabetes, received DSME or not, gender) while ANOVA was used for variables with three or more categories (i.e. educational status, duration of diagnosis, geographic location, age range). Effect sizes were calculated using Eta squared values to show the magnitude of difference in mean scores between categories within each variable. Pearson correlation coefficients were used to estimate the strength of association between skills and self-efficacy scores. Additionally, multiple regression analysis was used to estimate the contributions of the different independent variables to participants’ reported skills levels. Significant variables in the bivariate analysis were included in the regression. In all statistical analysis, values were considered statistically significant at p < 0.05 (two tailed).

For qualitative data analysis, audio recordings were transcribed verbatim by an independent professional transcriber and reviewed by the first author (MDA) for accuracy. The transcripts were uploaded into a qualitative data analysis software (QSR NVivo 11). Emerging themes were identified using in-depth inductive thematic analysis [ 48 ] undertaken in six steps: (i) re-reading of data line by line to ensure familiarization (ii) identification of patterns within data and organization into codes (iii) grouping of initial codes through constant comparison to identify emerging themes (iv) grouping and review of identified themes into general themes (v) refining themes and (vi) selection of representative quotes to support themes [ 48 ]. The first coding and generation of themes was done by MDA. In order to enhance result credibility and validity, raw data transcripts, coded data and themes were independently reviewed by the last-named author (BMA). Data were cross-checked in a consensus meeting and there was 90% degree of congruence between both authors’ coding, themes and classifications. Discrepancies were resolved through discussion and mutual agreement. Both MDA and BMA have experience in qualitative research methods. The remaining two researchers (UMA and AEOMA) checked the quotes and themes to ensure consistency. Key themes were reported along with relevant quotes affixed with an assigned number code and the type of diabetes the respondent has (for instance P3, T2D). The final manuscript was subjected to COREQ checklist for consolidated criteria for reporting qualitative research (See S1 Checklist ) [ 49 ].

Socio-demographic and health characteristics

A total of 217 complete responses to the online survey was received. Respondents were located in four geographic regions; namely, Europe (35%), Australia (34.6%), Asia (29.5%) and America (0.9%). The mean age of respondents was 44.65 ± 14.0 years (range 18–76 years) and 56.7% of them were females. More than half of the respondents had type 2 diabetes (61.8%) and had received DSME in the previous 12 months prior to the study (64.1%). About half of them were diagnosed in the last 5 years (52.5%) while 20.3% were diagnosed 6–10 years ago and the remaining 27.2% over 10 years. Over half of the respondents (56.2%) reported having a minimum of bachelor’s degree, 20.3% completed high school, while 18.9% completed technical college and 4.6% attained other forms of education.

A total of 31 respondents (14.3%) expressed interest to participate in the telephone interview. However, about half of them declined at time of interview or never responded to phone calls, leaving a final respondent number of 16 individuals who were interviewed. The participants were mostly males; 56.2% (9/16), had type 1 diabetes; 62.5% (10/16) and lived in Australia; 87.5% (14/16), with age ranging from 26 to 61 years [mean age of 44.56 (SD 11.51)].

Diabetes self-management skills and self-efficacy (confidence)

research article on diabetes management

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https://doi.org/10.1371/journal.pone.0217771.t001

research article on diabetes management

There was a strong positive correlation between the scores in the two domains, r = .906, p<0 .001, where higher levels of perceived skills were associated with higher levels of perceived self-efficacy. Coefficient of determination (R 2 ) indicates that level of skills explained 82% of the variation in respondents’ scores on self-efficacy.

Relationship between participants’ characteristics and levels of skills and self-efficacy.

Table 2 shows the relationship between demographic and health characteristics and the levels of skills and self-efficacy for diabetes management in participants. All demographic characteristics except geographic location, gender and age, were significantly associated with perceived skills and self-efficacy.

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https://doi.org/10.1371/journal.pone.0217771.t002

Participants who had type 1 diabetes had higher levels of skills compared to those with type 2 diabetes, t (215) = 17.41, p < 0.001, eta squared = 0.123. Additionally, receiving DSME within the past 12 months prior to participating in the study had a moderate but significant association with level of skills, t (215) = 2.01, p = .045, eta squared = 0.018. There was a significant difference in duration of diabetes diagnosis, F (4, 215) = 5.59, p <0.001, eta squared = 0.095. Skill scores were significantly higher in the >15 years ( M = 8.28, SD = 1.22) when compared to <1 year ( M = 6.28, SD = 1.82), 1–5 years ( M = 6.98, SD = 2.08) and 6–10 years ( M = 6.97, SD = 2.14) of diabetes diagnosis. There was no significant difference for those with 10–15 years of diagnosis ( M = 7.00, SD = 1.58). In addition, level of educational qualification significantly influenced the level of skills, F (4, 215) = 7.87, p <0.001, eta squared = 0.132. Skill scores were significantly higher among postgraduate degree holders ( M = 7.76, SD = 1.12) in comparison to high school ( M = 6.13, SD = 2.21) and technical school ( M = 6.43, SD = 2.25) certificate holders. No significant difference was observed when compared to those with bachelor’s degree ( M = 7.76, SD = 1.53).

For self-efficacy (confidence), type 1 diabetes participants had higher confidence levels compared to their type 2 counterparts, t (215) = 5.46, p = 0.02, eta squared = 0.051. Furthermore, confidence score was significantly associated with duration of diagnosis, F (4, 215) = 3.23, p = 0.013, eta squared = 0.057. Confidence was significantly higher in the >15 years ( M = 7.95, SD = 1.30) when compared to <1 year ( M = 6.50, SD = 1.68) only. Furthermore, level of educational qualification significantly influenced confidence level, F (4, 215) = 6.77, p <0.001, eta squared = 0.11. Participants with postgraduate degree had significantly higher confidence ( M = 7.71, SD = 1.55) in comparison to those with high school ( M = 6.42, SD = 1.98) and technical school ( M = 6.47, SD = 2.11) certificates. No significant difference was observed for those with bachelor’s degree ( M = 7.48, SD = 1.39).

Multiple regression analysis identified the simultaneous contributions of duration of diagnosis, type of diabetes, educational qualification and receiving DSME within 12 months prior to the study on participants’ level of skills. These variables predicted 22% of the variation in level of skills F (2, 216) = 14.815, p <0.001, R 2 = .218. All variables, except receiving DSME, were statistical significant at p < .05.

Other enablers of self-management

Two major themes were identified as factors which could facilitate diabetes self-management. These were patients’ determination to prevent the development of complications and the use of health technological devices or software.

1. Determination to prevent diabetes complications.

The decision to regularly engage in self-management was fostered by participants’ resolution to prevent the development of diabetes complications. Participants ensured that they engaged in the necessary lifestyle behavioural activities due to their determination to maintain better quality of life and thereby avoid what was observed in their peers who had already developed some form of diabetes complications:

‘‘I see a lot of other people who already have diabetes talking about their diabetes on social media . Looking at others who are worse off than me and the problems they struggle with , I guess is keeping me in check saying , hell no , I’m not going down that path” . [P6, T2D]

Furthermore, the determination to prevent diabetes complication was expressed by refusal to purchase certain foods which participants believed could increase the risk of progressing type 2 diabetes management into requiring the use of insulin injection:

‘‘It’s just the fact that I don’t want to get to the stage of having injections many times a day… . I have to remind myself of that always . I’m quite happy to walk past some chocolate… . knowing fully well that whilst I might enjoy a ** (name of a chocolate brand) , … . then I get an injection at the end , which I don’t want , which mean I will leave (name of a chocolate brand) alone” . [P3, T2D]

Respondents acknowledged that having good knowledge and problem solving skills in diabetes has proven useful to aid their self-management. Awareness of how foods impact their health was reported as highly essential:

‘‘I think having knowledge of the foods and the type of foods and diet and portion sizes are very important . Also , I found understanding what hypo or hyper , and understanding how my body reacts and how I can resolve that has been very useful in managing my diabetes . ” [P15, T1D]

2. Use of health technological software and devices.

Participants mentioned the use of mobile technological devices specifically, smart phone application (apps), insulin pump and continuous glucose monitors (CGM) as supporting tools which have enhanced their self-management.

2.1 APPS: Some of the participants use smart phone apps to record their blood glucose data. They noted that having access to such previously stored data on their phones gave them insight into the best self-management strategy which had assisted in adequate glycemic control:

‘‘I have been diagnosed for a long time and back in the days I used to write it on a note book . But in these days , I record it using a smart phone app , which allows me to search . So it allows me to access the data quickly and make a sort of best guess for now based on what happened in the past . If it’s not working , as it has done recently , I can go back to strategies that I might have been using years ago , that seems to work then “ . [P14, T1D]

Reminder feature in apps were found useful to give alert for recurring tasks such as taking medications thereby improving medication taking behaviour especially during busy schedules:

‘‘I’m only kind of new to this (newly diagnosed) , so I am actually looking for ways to remind myself of the tablets (medication) am meant to be taking . When I get really busy I forgot ‥ so my app pings at me a certain time of the day…just to kind of prompt me” [P3, T2D]

Also, motivations and encouragements were received through the use of app especially in the event of unstable blood glucose control. Participants stated that whenever their blood glucose level fluctuated and differed from the prescribed limits despite all efforts to stabilize it, looking at good data previously stored in apps provided an assurance that their blood glucose levels will not always be unstable:

‘‘Sometimes it is simple as realizing it’s not all terrible . Being able to flip back on my smart phone . If you’ve had a rough four or five days , it can feel like it’s a long time since you’ve seen numbers that felt like relatively stable or in range . You can get disheartened but if you can just check back you and see , actually no , it’s fine because two weeks ago it was all right , so I’ll be able to get back to that again . So having access to that sort of information storage allows me to be a little bit more relaxed when inevitable things start to wobble and go adrift again” . [P14, T1D]

2.2 INSULIN PUMP AND CGM: Participants with type 1 diabetes reported the use of insulin pump or continuous glucose monitor (CGM) as external aids which made it easier for them to manage and effectively monitor their health. In this regard, one participant stated that:

‘‘With the insulin pump, I find it easier to manage. Also, I’ve got the CGM and I can see what my sugar is on the screen all the time….you know that changed my life”. [P1, T1D]

Participants also indicated that use of insulin pump provided additional support and relief from pains experienced while using needles:

‘‘ I’m quite a thin bloke…… and have no body fat so inserting needles really hurt . My insulin pump definitely helps . So the best way I’ve managed my diabetes is through the insulin pump” . [P12, T1D]

In spite of the factors that foster effective self-management of diabetes, the key themes that emerged from the interview indicated that people with diabetes encountered diverse challenges in performing their self-management due to the: i) dynamic and chronic nature of diabetes; ii) financial constraints iii) work and environment related factors; and (iv) unrealistic expectations

Theme 1: Dynamic and chronic nature of diabetes.

The most common complaint reported by participants was the dynamic and chronic nature of diabetes and how these attributes make diabetes self-management require multiple needs. Participants felt there were many reasons including environmental conditions, which may demand an adjustment in their self-management even within short time periods. They believed the constant requirement to modify needs of the condition denoted certain things they were not doing right in their self-management and they always had to put in great effort to meet up with their health requirements:

‘‘Because I live with type 1 diabetes I have to do a complete insulin replacement , which involves balancing for activity , ambient temperature , stress levels , insulin sensitivity of my body . It could be so much easier if you could just work out what your insulin to carb sensitivity portion is , work out how to behave around exercise , work out correction factors and that would be all . But no , my experience is that that’s it for a week and then your basal requirement would have changed . Then the weather get warmer , you may need to re-evaluate your insulin sensitivity and carb ratio . So it’s just- you are never getting it right and you’re just always constantly trying to play catch up” . [P14 T1D]

Likewise, the effects of self-management on diabetes outcome was referred to as a system which could not be automatically controlled. Participants described how similar behavioral activity such as eating the same diet over time could impact their health differently.

‘‘It is a dynamic disease . I mean what works today doesn’t work tomorrow . You can eat something today and you can be okay , eat something tomorrow and it can be completely different . So you can never just put it on a cruise control and away you go” . [P2, T1D]

The weariness about the never-ending need for self-management because diabetes is a lifetime disease was expressed:

‘‘The biggest thing that fazes me is just the fact that it’s something that you have to do 24 hours a day , seven days a week and nothing ever going to change that” . [P4, T1D]

Participants were sometimes unwilling to undertake their self-management because they felt it is not a permanent cure for the disease, diabetes is chronic, so what is the point?:

‘‘ ‥ Probably my mind frame , in just getting yourself down to the fact that it’s never going to ‥ I’m always going to have it . So you sort of question what’s the point (of management) ? It’s hard to comprehend” . [P11, T1D]

The presence of other diabetes related complications or health problems such as neuropathy and depression in some participants limited their ability to actively engage in behavioral activities especially physical exercise or healthy eating:

‘ ‘Physical exercise is difficult…Yeah , I have peripheral neuropathy of the leg , a collapse in the foot and yeah , problems with the other foot” . [P10, T2D] ‘‘Nutrition is something that is hard to keep on top of . I suffer from a major depressive disorder , so I have a lot more trouble following my optimum diet” . [P7, T1D]

Theme 2: Financial burden.

The difficulty in meeting the financial cost for some diabetes medical tests and other treatment requirements was also identified as a barrier. Participants voiced out the financial burden they experienced by citing the need to pay for some clinical tests and diabetes supplies which are not covered by their health insurance such as the glycosylated hemoglobin (HbA1c) test and continuous glucose monitor. They expressed the desire to receive more support from the government:

‘‘I manage my diabetes fairly closely and I pay for HbA1c , you know …the financial cost is quite large . In Australia , our health system’s pretty good but you still have to pay for a lot of equipment which the government doesn’t seem to agree necessarily . Continuous Glucose Monitor should be government funded for over 21s for Christ sake” . [P2, T1D]

Another participant based in the United Kingdom (UK) stated:

‘‘I don’t have unimpeded access to Continuous Glucose Monitor (CGM) . I mean ‥ the situation of health care in UK is that it’s (CGM) not often funded by National Health Service (NHS) apart from people that are in quite profound need . I don’t get that assistance … So that’s a challenge and access issue” . [P14, T1D]

Theme 3: Work and environment-related conditions.

3.1: Occupation : Job requirements especially those involving a lot of travelling serves as deterrent to maintaining a healthy diet. Participants stated that the inability to get healthy choices of foods in most restaurants or public places when unavoidably required to eat out due to travelling long distances to fulfill their job requirements:

‘‘My work requires a lot of travelling . If you are actually going to eat something that is actually not good and could put you in the circumstance where you know… Like I had a 16 hour travelling the other day and everywhere I turned , I couldn’t touch any of it . I had some but I had to acknowledge that it was not what I really needed to eat” [P3, T2D]

Work related stress was also reported as a hindrance to attaining optimal blood sugar levels:

‘‘With me personally , it’s stress . I’m an electrician , and I’m full time employed , so stress gets me . When I get stressed , my blood sugar level goes downhill” [P13, T1D]

3.2: Weather condition : Participants find it difficult to engage in physical exercise in hot weather conditions:

‘‘ ‥ Exercise is something I have trouble getting around to doing . Like during the summer , the heat hits me big time . So I’m loving the cooler weather we’re starting to have because I can start to work a bit more , but during the heat , I cannot do it” . [P2, T1D]

Theme 4: Unrealistic demands.

Unrealistic expectations and advice about self-management from family or friends especially those not diagnosed with diabetes could be a hindrance to effective care. Participants’ found such wrong advice irritating as evident in the following comment:

‘‘You know I don’t think a lot of non-diabetic actually get to know how much it can take to actually manage a high or a low (Blood sugar) potentially . You know , you get comments from people that you’re low and they know you are diabetic saying , oh , should you be eating that ? Well , I’m going to say this nicely , you want me to die now or not or to go into coma ? Because I need to eat this . They go oh , you didn’t need to say it like that . You go well , stop asking a stupid question that you don’t know anything about” . [P4, T1D]

Additionally, discrepancy between patients and their health professionals’ (HP) perception of care could be a barrier to self-management. Participants felt that some recommendations from HPs were contrary to their opinions on what their diabetes self-management should entail:

‘ ‘My doctor doesn’t feel I need to be using a glucose meter to monitor my sugar levels and the diabetes educator doesn’t think I need to be on any sort of diet , even though I’ve had increases in diabetes medications” . [P10, T2D]

To the best of our knowledge, this is the first mixed methods study that has investigated enablers and barriers to general self-management among a multinational audience of people who have type 1 or type 2 diabetes. Most importantly, our findings emphasise the consequential impact of currency of exposure to DSME (within the previous 12 months), duration of diagnosis, level of educational qualification and use of technological devices on self-management skills and self-efficacy, regardless of geographical location or ethnicity. This implies that provision of ongoing self-management education/support through the use of mobile phones may help address the various difficulties (including time/financial constraint, diabetes distress, and limited access to care providers) encountered by patients and foster adherence to recommended self-management activities, which are necessary to prevent the risk of developing diabetes complications. Furthermore, this study presents an in-depth understanding of the experiences of diabetic patients and provides useful insights to health professionals and researchers on how to improve the frequency and quality of self-management support provided to diabetic patients to achieve better health outcomes.

Skills and self-efficacy for diabetes self-management

The overall skills score was found to be high and many participants reported good level of ability for self-management. This is specifically in the area of accurate monitoring to assess the impact of diet, medication or physical activities on blood glucose levels. Similar findings were observed in a previous study [ 25 ]. Accurate monitoring of blood glucose in relation to foods consumed and physical activities are important because they predict good outcomes in diabetes management [ 50 ].

Although the participants in this study scored high in their ability to monitor blood glucose, their capacity to interpret their blood glucose patterns over time was only moderate. Self-monitoring of blood glucose is important to assess glycemic pattern, hence accurate interpretation of these patterns is highly important to ensure effective management of glycaemia related problems encountered in diabetes management [ 51 ]. More emphasis should be laid on glucose pattern management during diabetes self-management educational sessions in order to expatiate patients’ skills on effective monitoring and interpretation of blood glucose data and the resulting health implications.

Participants in this study possessed lower skills related to planning for physical exercise in order to avoid hypoglycemia and adjusting medication to reach targeted blood glucose levels. This result corroborates previous findings [ 52 ]. The ability to manage and make appropriate adjustment to multiple regimens often determines success with other core areas of diabetes self-management and glycemic control [ 51 ]. For instance, studies have reported that due to the fear of hypoglycemia, patients have resorted to unhealthy behaviours (such as reducing or eliminating medication dose, inappropriate food choices and /or avoiding physical activities) that increase glucose levels [ 53 ]. Diabetic patients have an increased risk of developing hypoglycemia particularly when treated with insulin or insulin secretagogues [ 53 ]. Hence, they should be provided with regular refresher courses and continuous training on blood glucose levels awareness and strategies to balance exercise which could promote glycemic control and adherence to self-management.

Healthy coping strategies to identify and manage the impact of stress on diabetes management may be a difficult aspect of diabetes care because the participants in this study scored lowest in this area for both the skills and self-efficacy domains. All forms of stress either physical or mental, negatively impact blood glucose levels in those with diabetes [ 54 ] and it is a potential obstacle to attaining effective self-management and optimal health outcomes [ 55 ]. Patients’ understanding of dimensions of diabetes related stress is a clinically important factor and forms of stress that are potentially modifiable should be prioritized to guide clinical and educational interventions. This can include regular educational information on the impact of stress on health of diabetes patients and suggestions to reduce it.

Contrary to the findings of a previous study [ 56 ] that reported people with type 1 diabetes as having poorer self-management; our study participants who had type 1 diabetes scored higher than those with type 2 diabetes in skills and self-efficacy to care for their diabetes. Additionally, there was a significant positive relationship between the duration of diabetes and both skills and confidence for self-management. Patients with type 1 diabetes are typically diagnosed at an early age that may correspond to longer duration of diabetes. This pattern might have afforded them prolonged and regular exposure to health education, which is a significant predictor of successful diabetes self-management [ 20 ].

Overall, the strong correlation between the level of skills and self-efficacy found in this study strengthens the body of evidence supporting this link [ 32 ]. This pattern may be related to high level of education among most of the study respondents as also observed in a previous study [ 57 ]. Patients who possess higher skills usually have higher perceived level of efficacy and are most likely to actually engage in their self-management [ 25 , 32 ]. Building patients’ skills and confidence in their ability to self-manage diabetes is therefore imperative. Regular encouragement which could either be provided verbally or through other means of contact (e.g text messages through phones or emails) could be beneficial to patients [ 58 ]. While for those with limited educational backgrounds, the use of clear and simple communication styles when providing diabetes education to them will be essential to foster their skills and confidence [ 57 ].

Based on the results of the interviews, the most commonly perceived factor that fostered regular self-management was the will to prevent the development of diabetes complications. This result corroborates previous findings [ 12 , 59 ] and indicates that the participants in this study took responsibility for their choices and respective consequences. Discipline and proactive approaches to self-management are essential to reducing or preventing the development of diabetes complications. Regular reinforcement of education and motivation of patients could provide in-depth information about the disease and foster the will to mitigate its’ clinical course.

Furthermore, our study findings confirm those of other studies that the use of mobile technologies such as smartphone applications [ 19 ], insulin pump [ 60 ] and continuous glucose monitor [ 61 ] could enhance diabetes self-management in patients. Technology interventions have positive impact on diabetes outcomes such as adherence to self-management activities, glycosylated hemoglobin and diabetes self-efficacy [ 19 ]. Therefore, health professionals could recommend the use of mobile health technologies to patients who are capable of using them as they benefit from them.

The lack of enthusiasm towards regular self-management due to the chronic and dynamic nature of diabetes was not entirely unexpected. This phenomenon could be referred to as diabetes distress which is the emotional stress resulting from living with diabetes and the ‘‘burden of relentless management” [ 62 ]. High diabetes distress results in sub-optimal diabetes management and compromised quality of life [ 3 , 63 ]. Diabetes distress is common among patients and impacts on their self-management and health outcomes. Therefore, the importance of providing appropriate regular support to all patients in this regard cannot be overemphasized. Health professionals could ask patients at every consultation about how they are coping with diabetes, encourage them to express particular diabetes related issues causing them distress and offer encouragement and suggestions on ways to deal with it on a daily basis.

For many of the respondents in this study, the need to meet up with job requirements especially frequent travelling, makes adherence to healthy eating difficult. Additionally, work related stress impacts greatly on their blood glucose levels. These findings echo the results of Chao et al. [ 39 ]. Recommendations to patients to engage in creative planning and social support are strategies to help address this barrier. Social support from families are essential. Families should be encouraged to attend educational training sessions with patients so as to offer appropriate support which can assist patients to make healthy food choices and decisions regarding their diabetes management [ 12 , 13 ].

Furthermore, financial burden associated with diabetes could be a hindrance to self-management especially those associated with out-of-pocket expenditure for medical needs. Campbell et al., (2017) observed that the predominant area of management where patients experience financial burdens are medications, diabetes supply and healthy food [ 37 ]. People with diabetes require regular self-management and clinical monitoring to prevent the development of complications and foster optimal health outcomes; hence the associated financial demand. Health care providers could inform patients about resources available to them to buffer financial constraints that limit adherence to treatment plans. Such resources may include referring patients to specific social programs or compassionate relief programs to support financial burdens and enable easier access to necessary services.

Differences in patients’ and health care professionals’ (HCP) views of what constitutes the best approach to care was also identified as a barrier to self-management. This may be due to gaps in the way treatment recommendations were communicated to patients. Often times, HCPs’ view of good care are based on adhering to stipulated biomedical care model, structured communication and central decision making [ 64 ], whereas patients perceived quality health care is how the scientific knowledge of HCPs’ aligns with their own experiential knowledge and personal preferences [ 65 ]. Therefore, patients are always seeking exhaustive information about their diagnosis and treatment [ 65 ]. There is responsibility on the part of HCP’s to advice and educate their patients on different treatment options and the reasons they are placed on a particular option and not the other. This patient centered-approach will empower patients and foster their health outcomes.

Integration of findings and recommendations for future interventions

The survey results show that many patients have limited capacity for healthy coping strategies to identify and manage the impact of diabetes related stress. This finding was confirmed in the interviews where diabetes distress was reported as a major barrier to self-management. Given that stress is a potential contributor to chronic elevated blood glucose levels, it is essential for health care professionals to assist patients with identifying approaches to reducing diabetes distress. Additionally, increased access to healthcare providers through expanded clinic hours could be a means of easing the burden of diabetes diagnosis [ 41 ].

The quantitative data also showed that higher educational level was the strongest predictor of better self-management skills in patients and this was affirmed by the highly skilled interviewees who identified the use of technological devices as an enabler to their self-management. This corroborates that higher educational level is a good predictor of eHealth usage [ 66 ]. In addition, in accordance with previous literature [ 67 ], good overall self-efficacy level observed in the survey might have influenced the positive report on the usefulness of technology in diabetes management. Therefore, given that use of health technologies provides both short and long term health improvement in diabetes patients [ 68 ], active usage should be encouraged where necessary especially among patients who are educated and have the ability to engage with them. Furthermore, it is important to device avenues to improve patients’ self-efficacy in their ability to manage the disease as this could increase their likelihood of engaging with technology for their self-management [ 69 ].

The interviews revealed that determination to prevent development of complications is one of the major enablers to diabetes-self-management. This might explain the overall high score in skills and self-efficacy observed in the survey. Therefore, we suggest that educators could focus on improving patients’ skills and self-efficacy for diabetes self-management thereby raising patients’ awareness of the negative effect of diabetes. This approach could in turn stimulate the patients’ determination to engage in diabetes self-management and thereby reduce their risk of developing complications.

A unique perspective from the qualitative results revealed that patients and HCPs have divergent views/opinions about what should constitute patient care. It is therefore, imperative that HCPs ensure that patients understand the reasons for the recommended treatments and engage them in shared decision making which is essential for patients’ satisfaction and engagement in self-management practices [ 70 ].

Lastly, it has been advocated that people with diabetes should receive self-management education and support in an ongoing and consistent manner [ 71 ], but the reality of facilitating face-to-face diabetes education between patients and HCPs on an ongoing basis is low due to limited human and organisational resources. Health behavioural treatments and therapies such as diabetes self-management education/support could be provided to patients on an on-going daily basis outside the clinical setting through the use of ecological momentary interventions such as mobile technologies [ 72 ]. Apart from the fact that apps were opined by patients to enable self-management in this study, the World Health Organisation (WHO) also confirmed that the use of mobile technologies (such as apps) can support attainment of health outcomes which could transform health service delivery globally [ 73 ]. Considering that Apps are cost effective avenues for providing ongoing delivery of care to patients outside the clinical environment [ 74 ], diabetes self-management educational (DSME) messages could be developed and integrated into apps for patients. Such DSME should be targeted at improving patients’ skills and self-efficacy capacity for effective self-management.

Strengths and limitations

The strength of this work is that it provides a multinational picture of skills and confidence for self-management in people with type 1 or type 2 diabetes. Such an elaborate and international approach to assessing the capacity and confidence levels for self-management is scanty in the literature. In addition, the data identified a number of factors serving as enablers and barriers to diabetes self-management emanated from patients’ perspectives and their lived experiences. Therefore, the results are tenable for providing immense insights into improved strategies for supporting patients in their self-management.

There are some limitations to this study. Firstly, the reliability and validity of the quantitative tool used have not been previously demonstrated at multinational/multicultural levels, therefore, this may limit the interpretation of our findings. Although, in a previous study [ 32 ], the construct validity of the scale was tested among type 1 and type 2 diabetic patients who were from different ethnic backgrounds (Asians, Caribbeans, Caucasians etc.), but living in the same regional location. The study reported that the scale was not influenced by ethnicity. Secondly, the small sample size/groups for the survey which mainly comprised of participants from three continents, may limit the generalization of our findings to other settings. Thirdly, the quantitative data were self-reported and therefore susceptible to bias, which may not reflect participants’ actual skills and confidence levels for self-management. Hence, under or over reporting could result in inaccurate identification of common gaps in skills and confidence requiring intervention. Nevertheless, self-report can be made more reliable when questions are asked in a non-judgmental manner as obtained in the SCPI tool used in this study. Lastly, the small number of interview participants is also acknowledged and the interview sessions were brief because additional compensation was not offered to interviewees. Short interview duration was utilised to foster increased participant numbers because long interviews may not be justifiable for participants’ time involvement in the study. Published literature has shown that the anonymity of telephone interview reduces interviewer bias which makes the interview setting more calming and forthcoming, thus fostering a more accurate and truthful data collection [ 75 ].

This study identified common gaps in the skills and self-efficacy of people with type 1 or type 2 diabetes mellitus as well as other perceived enablers of, and barriers to, self-management in this population. Diabetes health care stakeholders may consider strategies for regular educational reinforcement in patients in order to foster healthy coping with diabetes stress, exercise planning to avoid hypoglycemia, interpreting blood glucose patterns and adjusting medications or foods to reach the targeted blood glucose levels. Furthermore, designing of interventions that capitalize on how to improve patients’ desire to reduce the progression of diabetes and the use of relevant technological devices could enhance diabetes self-management. Improved approaches to address diabetes distress, financial burden, discrepancy between patients and their health professionals’ perception of care as well as work and environment related factors are essential to foster improved self-management in patients. Finally, attention should be paid to type of diabetes, level of education and duration of diagnosis when counselling patients on diabetes self-management. Consideration of these areas of educational reinforcement and interventions could enhance self-management in patients and consequently improve their health outcomes.

Supporting information

S1 checklist. coreq checklist..

https://doi.org/10.1371/journal.pone.0217771.s001

S1 Appendix. Interview guide.

https://doi.org/10.1371/journal.pone.0217771.s002

Acknowledgments

The authors would like to thank Mr Aaron Drovandi for his assistance with the study interview, and the health organisations who helped with the study advertisement. In addition, we sincerely appreciate the participants of this study.

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Enablers and barriers to effective diabetes self-management: A multi-national investigation

Mary d. adu.

1 College of Medicine and Dentistry, James Cook University, Townsville, Australia

Usman H. Malabu

Aduli e. o. malau-aduli.

2 College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia

Bunmi S. Malau-Aduli

Associated data.

All relevant data are within the manuscript and its Supporting Information files.

The study aimed to identify the common gaps in skills and self-efficacy for diabetes self-management and explore other factors which serve as enablers of, and barriers to, achieving optimal diabetes self-management. The information gathered could provide health professionals with valuable insights to achieving better health outcomes with self-management education and support for diabetes patients.

International online survey and telephone interviews were conducted on adults who have type 1 or type 2 diabetes. The survey inquired about their skills and self-efficacy in diabetes self-management, while the interviews assessed other enablers of, and barriers to, diabetes self-management. Surveys were analysed using descriptive and inferential statistics. Interviews were analysed using inductive thematic analysis.

Survey participants (N = 217) had type 1 diabetes (38.2%) or type 2 diabetes (61.8%), with a mean age of 44.56 SD 11.51 and were from 4 continents (Europe, Australia, Asia, America). Identified gaps in diabetes self-management skills included the ability to: recognize and manage the impact of stress on diabetes, exercise planning to avoid hypoglycemia and interpreting blood glucose pattern levels. Self-efficacy for healthy coping with stress and adjusting medications or food intake to reach ideal blood glucose levels were minimal. Sixteen participants were interviewed. Common enablers of diabetes self-management included: (i) the will to prevent the development of diabetes complications and (ii) the use of technological devices. Issues regarding: (i) frustration due to dynamic and chronic nature of diabetes (ii) financial constraints (iii) unrealistic expectations and (iv) work and environment-related factors limited patients’ effective self-management of diabetes.

Conclusions

Educational reinforcement using technological devices such as mobile application has been highlighted as an enabler of diabetes self-management and it could be employed as an intervention to alleviate identified gaps in diabetes self-management. Furthermore, improved approaches that address financial burden, work and environment-related factors as well as diabetes distress are essential for enhancing diabetes self-management.

Introduction

Diabetes mellitus is a major public health problem with rapidly increasing prevalence. In 2017, the global prevalence of diabetes among people aged 20–79 years was 425 million, mainly comprising type 1 or type 2 [ 1 ]. Diabetes is one of the top 10 global causes of mortality. In 2015, it was responsible for 1.6 million deaths, indicating a 60% increase in 15 years from less than 1 million in 2000 [ 2 ]. International audits have found that regimen adherence is less than optimal in both types 1 and 2 diabetes patients [ 3 ]. As a consequence, the majority of these patients are at risk of serious health complications that endanger life [ 1 , 4 ] and impose great economic burden on affected individuals and the health care system [ 1 ].

Consistent engagement in diabetes self-management has been found to be correlated with the attainment of health outcomes in terms of good blood glucose control, fewer complications [ 1 , 5 ], improved quality of life [ 6 , 7 ] and reduction in diabetes-related death risks [ 8 ]. The term “self-management” refers to day to day activities or actions an individual must undertake to control or reduce the impact of disease on their health and wellbeing [ 9 ] in order to prevent further illness [ 10 ]. Diabetes self-management actions involve engagement in recommended behavioural activities such as healthy eating, medication adherence, being active, monitoring, reducing risks, problem-solving and healthy coping, which are all necessary for the successful management of the disease [ 11 ]. Level of adherence to diabetes self-management differs in patients, which implies that decision-making processes for self-management are influenced by various factors, which could either serve as enablers or barriers.

Enablers of self-management

Enablers of self-management are mechanisms or factors that foster the ability of patients to undertake their recommended self-management regimen. Such factors are diverse and include effective social support with assistance and encouragement from family members [ 12 , 13 ] or peers who have diabetes or close relatives familiar with its management [ 14 ]. Likewise, individual resolution to prevent or reduce the risk of developing diabetes complications helps with the determination to engage in self-management [ 12 , 15 ]. Studies have also noted positive decision making about diabetes self-management as a result of effective health care provider-patient communication [ 16 ], characterized by trust, respect and shared decision-making in planning health goals [ 17 , 18 ]. In addition, patient support with the use of health technological interventions such as mobile phone applications [ 19 ] and self-management education [ 20 , 21 ], facilitate efective diabetes management. Individual factors, particularly higher educational level [ 22 , 23 ] and gender [ 24 ], also contribute to patients’ ability to care for their diabetes.

More importantly, adequate self-management skills [ 25 ] and self-efficacy (confidence) [ 26 ] to perform these skills are major enabling factors for engagement in diabetes self-management. This is because skills and self-efficacy operate in tandem to foster full engagement with self-management. Self-management skills result from knowledge about the disease [ 25 ], and understanding the interrelationships between different self-management activities and their impact on health outcomes [ 27 ]. On the other hand, self-efficacy refers to ‘‘one’s belief in his/her own innate ability to perform specific tasks required to reach a desired goal” [ 28 ]. Unless people believe they can produce desired effects by their action, they have little incentive to act [ 29 ], regardless of other enabling factors which may be available to them. In diabetes management, patients’ level of self-efficacy is influenced by their level of skills for self-management. Hence, patients with adequate skills and efficacy have more likelihood to adhere to prescribed behavioural regimen necessary to attain optimal health [ 25 , 30 – 32 ]. Acquiring diabetes self-management skills and efficacy is an ongoing learning process [ 20 , 25 ]. While some skills and efficacy are easily acquired, others are often difficult to attain. Further research is therefore needed to adequately identify gaps in diabetes patients’ skills set and self-efficacy levels for self-management of their health issues. Information on identified gaps will guide health care providers in their development of educational support programs that foster self-management among diabetes patients.

Barriers to self-management

Non-adherence to recommended diabetes self-management regimen is influenced by barriers encountered by patients. These barriers make managing the disease more difficult. Only few studies have examined patients’ perceived barriers to general diabetes self-management from a global perspective. An international study identified diabetes related distress as a major factor responsible for poor adherence to self-management in patients [ 3 ]. Local studies reported that difficulty in making lifestyle changes [ 33 ] and inadequate health care system communication interface [ 34 ] were related to poor diabetes self-management. In addition, financial constraints resulted in patients’ inability to access diabetes clinical supplies and eat in line with appropriate dietary recommendations [ 35 – 37 ]. Other studies have examined barriers to some specific areas of diabetes self-management. Nagelkerk et al., [ 16 ] and Ghimire [ 38 ] reported that patients’ lack of knowledge of a specific diet plan and perceived belief in social unacceptability of healthy behaviours hindered healthy eating and participation in physical exercise. Furthermore, depressive symptoms and personal belief about medication were observed to be associated with lower adherence to diabetes medications [ 39 ].

The empirical and conceptual research findings mentioned above are not exhaustive because only a few have an international focus [ 3 ]. Additionally, the studies are mostly focused on barriers to self-management in patients with type 2 diabetes only [ 33 – 36 , 38 , 40 ], older populations [ 35 ], those from low income background without indicating the type of diabetes the respondents had [ 41 ], or few areas of diabetes self-management [ 38 – 40 ]. The above limitations in previous studies emphasize the need for further and detailed exploration of factors serving as barriers to self-management in both types 1 and 2 diabetes patients. This will provide strategies that adequately address such challenges and foster better adherence to self-management for better health outcomes in both patient groups.

There is diversity in the level of self-management between patients. The ability to self-manage diabetes is influenced by various factors that can either serve as enablers or barriers. However, to the best our knowledge, global perspectives on the crucial enablers of self-management in terms of skills and self-efficacy, among types 1 and 2 diabetes patients is relatively scarce. Likewise, studies on other enablers and potential barriers to general self-management as perceived by these patient groups is scanty in the published literature. There is special interest in elucidating this information from an international perspective because issues encountered in self-management by both patient groups are likely to include common experiences and challenges. Identifying these commonalities could provide health professionals with an in-depth understanding of patients’ experiences and help guide the development and enhancement of intervention strategies to improve patients’ self-management of diabetes. Therefore, this study aimed to: i) identify the common gaps in skills and self-efficacy for self-management among individuals with type 1 or type 2 diabetes; ii) examine factors associated with self-management skills and self-efficacy; iii) explore other factors which serve as enablers of, and barriers to, achieving optimum diabetes self-management.

Recruitment procedure

A maximum variation purposive sampling technique was employed in recruiting participants aged ≥ 18 years who had type 1 or type 2 diabetes. Participants were recruited globally using diverse recruitment strategies. The aim of this sampling method was to obtain a mix of participants with diverse experiences and identify common patterns that cut across the population sample with regards to the subject of interest [ 42 ]. Officially approved advertisement for the study was placed on various health organizations’ websites. These websites included Diabetes UK and Diabetes Australia. In addition, the advertisement was placed in local digital newspapers, Twitter and Facebook pages focusing on diabetes support. Data collection was conducted between November 2017 and June 2018. There was no limit to sample size in order to capture the maximum number of people with type 1 or type 2 diabetes. The study requested participants’ socio demographic characteristics of age, gender, educational level and geographic location. Details of the recruitment strategy and participants’ characteristics have been fully described in our previous publication [ 43 ].

Study design

A sequential mixed methods approach was used; comprising quantitative and qualitative data collection methods [ 44 ]. The quantitative phase of the study involved a cross sectional survey and data analysis. This was followed by qualitative telephone interviews of a subsample of the participants in order to provide a more complete and comprehensive understanding of the results which were integrated into the data interpretative phase [ 44 ]. Quantitative data were obtained through an online survey that focused on assessing participants’ self-reported skills and self-efficacy (confidence) as part of the factors that enable diabetes self-management. Qualitative data were collected through individual telephone interviews which further explored additional factors that serve as enablers and barriers to diabetes self-management.

Quantitative measures–survey

The survey questions were divided into two parts. First, the following health characteristics which were likely to influence skills and self-efficacy for diabetes management were assessed: type of diabetes, duration of diagnosis and whether participants had recently received (within the previous 12 months) diabetes self-management education (DSME) from a member of their health care team.

Second, novel LMC Skills, Confidence and Preparedness Index (SCPI) tool was used to assess skills and self-efficacy in core behaviours central to diabetes self-management such as healthy eating, blood glucose monitoring, being active, healthy coping, medication adherence, problem solving and reducing risk [ 11 , 45 ]. The SCPI tool had been previously validated, where its construct validity for different ages, ethnicity, gender and level of education was established [ 32 ]. Additionally, the validity of the tool for use in different settings is established by the fact that, as a new tool, the questions reflect the current recommended self-management regimen for diabetes patients, and this has not been fully explored by previous tools [ 45 ]. It has excellent readability and reliability. Permission was obtained to use the tool. The SCPI tool consists of three subscales: skills, confidence and preparedness. The skills subscale was used to assess perceived ability to perform the self-management activities mentioned above. The confidence subscale was used to assess self-efficacy in being able to perform the skills. The preparedness scale was not used in this study because this subscale assesses the readiness of patients to implement behavioural changes following an educational session; which was not applicable in the present study.

The skills and confidence domains consist of nine (9) and eight (8) items respectively. Two of these items focus on skills and confidence to use insulin. These skills were adapted to accommodate participants who have type 2 diabetes but do not use insulin/other medications as part of their treatment regimen. All items were rated using a visual analogue scale, with scores between 1 and 10. Each of the items in the domains produced its own score out of 10. The total score was the mean score in each of the subscales, where higher scores denoted better skills and confidence. The scoring process is not affected by demographic factors such as age, gender, level of education or ethnicity [ 45 ], hence, its’ applicability for use in study populations with diverse social and health characteristics. The instrument was administered in English Language.

Qualitative measures–phone interviews

Through online survey, all participants were invited to an individual telephone interview session. They were requested to indicate interest by providing their best contact number and availability. A single independent resource person (male) who is an experienced researcher in qualitative studies conducted all interviews. The interviewer was trained on the aims of the study and the interview guide by the first author of this study (MDA). The guide was then pilot tested between the interviewer and MDA before actual use. Additionally, MDA was present in the first three interviews to ensure appropriateness of data collection. While the interviews were used to reflect on the interview guide, no changes were made to the guide afterwards. There was no interaction or previous relationship between MDA and the participants. The interviewer was located in a private office at James Cook University, Townsville, Australia. Prior to the commencement of the interview, each respondent was asked if they were located in a comfortable place for an interview, and were briefly presented with the general idea of the study and key diabetes self-management activities. The interviewer did not have prior relationship with the participants. Each Interview was audio recorded and lasted between 7 and 20 minutes in duration. Data saturation was achieved through recurring explicit ideas [ 46 ] after completing the 14 th interview. However, the interview was conducted for the remaining two participants who had indicated interest in order to ensure that no main idea was unintentionally discarded. Repeat interviews were not required and due to the remoteness of the study participants, there was no post interview debriefing. The semi-structured interview guide was developed by the research team. Topics covered in the interview included open ended questions and probes to facilitate discussion (See S1 Appendix for details of the interview questions).

Ethics and consent

The study procedures (registration number: H7087) were approved by James Cook University’s Human Research Ethics Committee. The protocol contained detailed information on the ethical obligations of researchers toward participants engaging in online research activities. Essentially, these obligations included confidentiality, anonymity, scientific value, maximising benefits, minimizing harms, and informed consent [ 47 ]. All these obligations were strictly adhered to during the research process. Furthermore, as part of the application process for advertisement of the study on the website of health organisations, the ethics approval document was made available to the appropriate and designated officials of these organisations. All prospective study participants were provided with the study information along with the privacy policy prior to the survey. Therefore, participants were informed about the use of their answers for analysis under anonymity. Informed consent was implied by submission of the online survey, while all telephone interviewees provided verbal consent.

Data analyses

SPSS (Version 23) was used for quantitative data analysis. Cronbach’s alpha of the subscales of measures used in this study were acceptable (.92 and .91 for skills and confidence scales respectively). Participants’ demographics and health variables were presented using descriptive statistics. Items in the skills and self-efficacy domains were reported as means and standard deviations (SD). For the purpose of explaining and discussing the results, scores were graded as high (≥ 7), moderate (4–6) or poor (≤ 3). Mean scores were calculated for demographic and health variable subgroups. Bivariate analyses were performed using Independent sample t-test and Analysis of Variance (ANOVA) to test the relationship between participants’ subgroups and level of skills and confidence. Specifically, t-test was used for variables with two categories (i.e. type of diabetes, received DSME or not, gender) while ANOVA was used for variables with three or more categories (i.e. educational status, duration of diagnosis, geographic location, age range). Effect sizes were calculated using Eta squared values to show the magnitude of difference in mean scores between categories within each variable. Pearson correlation coefficients were used to estimate the strength of association between skills and self-efficacy scores. Additionally, multiple regression analysis was used to estimate the contributions of the different independent variables to participants’ reported skills levels. Significant variables in the bivariate analysis were included in the regression. In all statistical analysis, values were considered statistically significant at p < 0.05 (two tailed).

For qualitative data analysis, audio recordings were transcribed verbatim by an independent professional transcriber and reviewed by the first author (MDA) for accuracy. The transcripts were uploaded into a qualitative data analysis software (QSR NVivo 11). Emerging themes were identified using in-depth inductive thematic analysis [ 48 ] undertaken in six steps: (i) re-reading of data line by line to ensure familiarization (ii) identification of patterns within data and organization into codes (iii) grouping of initial codes through constant comparison to identify emerging themes (iv) grouping and review of identified themes into general themes (v) refining themes and (vi) selection of representative quotes to support themes [ 48 ]. The first coding and generation of themes was done by MDA. In order to enhance result credibility and validity, raw data transcripts, coded data and themes were independently reviewed by the last-named author (BMA). Data were cross-checked in a consensus meeting and there was 90% degree of congruence between both authors’ coding, themes and classifications. Discrepancies were resolved through discussion and mutual agreement. Both MDA and BMA have experience in qualitative research methods. The remaining two researchers (UMA and AEOMA) checked the quotes and themes to ensure consistency. Key themes were reported along with relevant quotes affixed with an assigned number code and the type of diabetes the respondent has (for instance P3, T2D). The final manuscript was subjected to COREQ checklist for consolidated criteria for reporting qualitative research (See S1 Checklist ) [ 49 ].

Socio-demographic and health characteristics

A total of 217 complete responses to the online survey was received. Respondents were located in four geographic regions; namely, Europe (35%), Australia (34.6%), Asia (29.5%) and America (0.9%). The mean age of respondents was 44.65 ± 14.0 years (range 18–76 years) and 56.7% of them were females. More than half of the respondents had type 2 diabetes (61.8%) and had received DSME in the previous 12 months prior to the study (64.1%). About half of them were diagnosed in the last 5 years (52.5%) while 20.3% were diagnosed 6–10 years ago and the remaining 27.2% over 10 years. Over half of the respondents (56.2%) reported having a minimum of bachelor’s degree, 20.3% completed high school, while 18.9% completed technical college and 4.6% attained other forms of education.

A total of 31 respondents (14.3%) expressed interest to participate in the telephone interview. However, about half of them declined at time of interview or never responded to phone calls, leaving a final respondent number of 16 individuals who were interviewed. The participants were mostly males; 56.2% (9/16), had type 1 diabetes; 62.5% (10/16) and lived in Australia; 87.5% (14/16), with age ranging from 26 to 61 years [mean age of 44.56 (SD 11.51)].

Diabetes self-management skills and self-efficacy (confidence)

Table 1 shows the mean scores for each of the items across the skills and self-efficacy domains. Scores were highest in the skills for knowing the appropriate time to check blood glucose levels in order to reflect either the impact of meals consumed ( x ¯ = 7.81 ± 2.33) or medications/physical activities ( x ¯ = 7.47 ± 2.37). In addition, participants possessed a high ability to recognize the effect of missed physical activity or excess carbohydrate consumption on their health and knew the corrective steps to take ( x ¯ = 7.35 ± 2.35). The lowest scores were in the areas of skills for: identifying and managing the impact of stress on diabetes ( x ¯ = 6.88 ± 2.43), exercise planning to avoid hypoglycemia ( x ¯ = 6.88 ± 2.48), and interpreting blood glucose patterns ( x ¯ = 6.84 ± 2.58).

a T2D: Type 2 diabetes mellitus

b HbAIC: Glycosylated hemoglobin

In relation to participants’ self-efficacy levels, the highest scores were in confidence to reduce risk by preventing and monitoring diabetes complications ( x ¯ = 8.08 ± 1.85), and using blood glucose results to plan for meal intake ( x ¯ = 7.22 ± 2.06). Participants scored lowest in their confidence for healthy coping with stress ( x ¯ = 6.72 ± 2.28) and adjusting medications or food intake to reach targeted blood glucose levels ( x ¯ = 6.87 ± 2.62).

There was a strong positive correlation between the scores in the two domains, r = .906, p<0 .001, where higher levels of perceived skills were associated with higher levels of perceived self-efficacy. Coefficient of determination (R 2 ) indicates that level of skills explained 82% of the variation in respondents’ scores on self-efficacy.

Relationship between participants’ characteristics and levels of skills and self-efficacy

Table 2 shows the relationship between demographic and health characteristics and the levels of skills and self-efficacy for diabetes management in participants. All demographic characteristics except geographic location, gender and age, were significantly associated with perceived skills and self-efficacy.

a DMSE: Diabetes Self-Management Education

b Post Graduate

c Others: Professional qualifications, graduate diploma

Participants who had type 1 diabetes had higher levels of skills compared to those with type 2 diabetes, t (215) = 17.41, p < 0.001, eta squared = 0.123. Additionally, receiving DSME within the past 12 months prior to participating in the study had a moderate but significant association with level of skills, t (215) = 2.01, p = .045, eta squared = 0.018. There was a significant difference in duration of diabetes diagnosis, F (4, 215) = 5.59, p <0.001, eta squared = 0.095. Skill scores were significantly higher in the >15 years ( M = 8.28, SD = 1.22) when compared to <1 year ( M = 6.28, SD = 1.82), 1–5 years ( M = 6.98, SD = 2.08) and 6–10 years ( M = 6.97, SD = 2.14) of diabetes diagnosis. There was no significant difference for those with 10–15 years of diagnosis ( M = 7.00, SD = 1.58). In addition, level of educational qualification significantly influenced the level of skills, F (4, 215) = 7.87, p <0.001, eta squared = 0.132. Skill scores were significantly higher among postgraduate degree holders ( M = 7.76, SD = 1.12) in comparison to high school ( M = 6.13, SD = 2.21) and technical school ( M = 6.43, SD = 2.25) certificate holders. No significant difference was observed when compared to those with bachelor’s degree ( M = 7.76, SD = 1.53).

For self-efficacy (confidence), type 1 diabetes participants had higher confidence levels compared to their type 2 counterparts, t (215) = 5.46, p = 0.02, eta squared = 0.051. Furthermore, confidence score was significantly associated with duration of diagnosis, F (4, 215) = 3.23, p = 0.013, eta squared = 0.057. Confidence was significantly higher in the >15 years ( M = 7.95, SD = 1.30) when compared to <1 year ( M = 6.50, SD = 1.68) only. Furthermore, level of educational qualification significantly influenced confidence level, F (4, 215) = 6.77, p <0.001, eta squared = 0.11. Participants with postgraduate degree had significantly higher confidence ( M = 7.71, SD = 1.55) in comparison to those with high school ( M = 6.42, SD = 1.98) and technical school ( M = 6.47, SD = 2.11) certificates. No significant difference was observed for those with bachelor’s degree ( M = 7.48, SD = 1.39).

Multiple regression analysis identified the simultaneous contributions of duration of diagnosis, type of diabetes, educational qualification and receiving DSME within 12 months prior to the study on participants’ level of skills. These variables predicted 22% of the variation in level of skills F (2, 216) = 14.815, p <0.001, R 2 = .218. All variables, except receiving DSME, were statistical significant at p < .05.

Other enablers of self-management

Two major themes were identified as factors which could facilitate diabetes self-management. These were patients’ determination to prevent the development of complications and the use of health technological devices or software.

1. Determination to prevent diabetes complications

The decision to regularly engage in self-management was fostered by participants’ resolution to prevent the development of diabetes complications. Participants ensured that they engaged in the necessary lifestyle behavioural activities due to their determination to maintain better quality of life and thereby avoid what was observed in their peers who had already developed some form of diabetes complications:

‘‘I see a lot of other people who already have diabetes talking about their diabetes on social media . Looking at others who are worse off than me and the problems they struggle with , I guess is keeping me in check saying , hell no , I’m not going down that path” . [P6, T2D]

Furthermore, the determination to prevent diabetes complication was expressed by refusal to purchase certain foods which participants believed could increase the risk of progressing type 2 diabetes management into requiring the use of insulin injection:

‘‘It’s just the fact that I don’t want to get to the stage of having injections many times a day… . I have to remind myself of that always . I’m quite happy to walk past some chocolate… . knowing fully well that whilst I might enjoy a ** (name of a chocolate brand) , … . then I get an injection at the end , which I don’t want , which mean I will leave (name of a chocolate brand) alone” . [P3, T2D]

Respondents acknowledged that having good knowledge and problem solving skills in diabetes has proven useful to aid their self-management. Awareness of how foods impact their health was reported as highly essential:

‘‘I think having knowledge of the foods and the type of foods and diet and portion sizes are very important . Also , I found understanding what hypo or hyper , and understanding how my body reacts and how I can resolve that has been very useful in managing my diabetes . ” [P15, T1D]

2. Use of health technological software and devices

Participants mentioned the use of mobile technological devices specifically, smart phone application (apps), insulin pump and continuous glucose monitors (CGM) as supporting tools which have enhanced their self-management.

2.1 APPS: Some of the participants use smart phone apps to record their blood glucose data. They noted that having access to such previously stored data on their phones gave them insight into the best self-management strategy which had assisted in adequate glycemic control:

‘‘I have been diagnosed for a long time and back in the days I used to write it on a note book . But in these days , I record it using a smart phone app , which allows me to search . So it allows me to access the data quickly and make a sort of best guess for now based on what happened in the past . If it’s not working , as it has done recently , I can go back to strategies that I might have been using years ago , that seems to work then “ . [P14, T1D]

Reminder feature in apps were found useful to give alert for recurring tasks such as taking medications thereby improving medication taking behaviour especially during busy schedules:

‘‘I’m only kind of new to this (newly diagnosed) , so I am actually looking for ways to remind myself of the tablets (medication) am meant to be taking . When I get really busy I forgot ‥ so my app pings at me a certain time of the day…just to kind of prompt me” [P3, T2D]

Also, motivations and encouragements were received through the use of app especially in the event of unstable blood glucose control. Participants stated that whenever their blood glucose level fluctuated and differed from the prescribed limits despite all efforts to stabilize it, looking at good data previously stored in apps provided an assurance that their blood glucose levels will not always be unstable:

‘‘Sometimes it is simple as realizing it’s not all terrible . Being able to flip back on my smart phone . If you’ve had a rough four or five days , it can feel like it’s a long time since you’ve seen numbers that felt like relatively stable or in range . You can get disheartened but if you can just check back you and see , actually no , it’s fine because two weeks ago it was all right , so I’ll be able to get back to that again . So having access to that sort of information storage allows me to be a little bit more relaxed when inevitable things start to wobble and go adrift again” . [P14, T1D]

2.2 INSULIN PUMP AND CGM: Participants with type 1 diabetes reported the use of insulin pump or continuous glucose monitor (CGM) as external aids which made it easier for them to manage and effectively monitor their health. In this regard, one participant stated that:

‘‘With the insulin pump, I find it easier to manage. Also, I’ve got the CGM and I can see what my sugar is on the screen all the time….you know that changed my life”. [P1, T1D]

Participants also indicated that use of insulin pump provided additional support and relief from pains experienced while using needles:

‘‘ I’m quite a thin bloke…… and have no body fat so inserting needles really hurt . My insulin pump definitely helps . So the best way I’ve managed my diabetes is through the insulin pump” . [P12, T1D]

In spite of the factors that foster effective self-management of diabetes, the key themes that emerged from the interview indicated that people with diabetes encountered diverse challenges in performing their self-management due to the: i) dynamic and chronic nature of diabetes; ii) financial constraints iii) work and environment related factors; and (iv) unrealistic expectations

Theme 1: Dynamic and chronic nature of diabetes

The most common complaint reported by participants was the dynamic and chronic nature of diabetes and how these attributes make diabetes self-management require multiple needs. Participants felt there were many reasons including environmental conditions, which may demand an adjustment in their self-management even within short time periods. They believed the constant requirement to modify needs of the condition denoted certain things they were not doing right in their self-management and they always had to put in great effort to meet up with their health requirements:

‘‘Because I live with type 1 diabetes I have to do a complete insulin replacement , which involves balancing for activity , ambient temperature , stress levels , insulin sensitivity of my body . It could be so much easier if you could just work out what your insulin to carb sensitivity portion is , work out how to behave around exercise , work out correction factors and that would be all . But no , my experience is that that’s it for a week and then your basal requirement would have changed . Then the weather get warmer , you may need to re-evaluate your insulin sensitivity and carb ratio . So it’s just- you are never getting it right and you’re just always constantly trying to play catch up” . [P14 T1D]

Likewise, the effects of self-management on diabetes outcome was referred to as a system which could not be automatically controlled. Participants described how similar behavioral activity such as eating the same diet over time could impact their health differently.

‘‘It is a dynamic disease . I mean what works today doesn’t work tomorrow . You can eat something today and you can be okay , eat something tomorrow and it can be completely different . So you can never just put it on a cruise control and away you go” . [P2, T1D]

The weariness about the never-ending need for self-management because diabetes is a lifetime disease was expressed:

‘‘The biggest thing that fazes me is just the fact that it’s something that you have to do 24 hours a day , seven days a week and nothing ever going to change that” . [P4, T1D]

Participants were sometimes unwilling to undertake their self-management because they felt it is not a permanent cure for the disease, diabetes is chronic, so what is the point?:

‘‘ ‥ Probably my mind frame , in just getting yourself down to the fact that it’s never going to ‥ I’m always going to have it . So you sort of question what’s the point (of management) ? It’s hard to comprehend” . [P11, T1D]

The presence of other diabetes related complications or health problems such as neuropathy and depression in some participants limited their ability to actively engage in behavioral activities especially physical exercise or healthy eating:

‘ ‘Physical exercise is difficult…Yeah , I have peripheral neuropathy of the leg , a collapse in the foot and yeah , problems with the other foot” . [P10, T2D] ‘‘Nutrition is something that is hard to keep on top of . I suffer from a major depressive disorder , so I have a lot more trouble following my optimum diet” . [P7, T1D]

Theme 2: Financial burden

The difficulty in meeting the financial cost for some diabetes medical tests and other treatment requirements was also identified as a barrier. Participants voiced out the financial burden they experienced by citing the need to pay for some clinical tests and diabetes supplies which are not covered by their health insurance such as the glycosylated hemoglobin (HbA1c) test and continuous glucose monitor. They expressed the desire to receive more support from the government:

‘‘I manage my diabetes fairly closely and I pay for HbA1c , you know …the financial cost is quite large . In Australia , our health system’s pretty good but you still have to pay for a lot of equipment which the government doesn’t seem to agree necessarily . Continuous Glucose Monitor should be government funded for over 21s for Christ sake” . [P2, T1D]

Another participant based in the United Kingdom (UK) stated:

‘‘I don’t have unimpeded access to Continuous Glucose Monitor (CGM) . I mean ‥ the situation of health care in UK is that it’s (CGM) not often funded by National Health Service (NHS) apart from people that are in quite profound need . I don’t get that assistance … So that’s a challenge and access issue” . [P14, T1D]

Theme 3: Work and environment-related conditions

3.1: Occupation : Job requirements especially those involving a lot of travelling serves as deterrent to maintaining a healthy diet. Participants stated that the inability to get healthy choices of foods in most restaurants or public places when unavoidably required to eat out due to travelling long distances to fulfill their job requirements:

‘‘My work requires a lot of travelling . If you are actually going to eat something that is actually not good and could put you in the circumstance where you know… Like I had a 16 hour travelling the other day and everywhere I turned , I couldn’t touch any of it . I had some but I had to acknowledge that it was not what I really needed to eat” [P3, T2D]

Work related stress was also reported as a hindrance to attaining optimal blood sugar levels:

‘‘With me personally , it’s stress . I’m an electrician , and I’m full time employed , so stress gets me . When I get stressed , my blood sugar level goes downhill” [P13, T1D]

3.2: Weather condition : Participants find it difficult to engage in physical exercise in hot weather conditions:

‘‘ ‥ Exercise is something I have trouble getting around to doing . Like during the summer , the heat hits me big time . So I’m loving the cooler weather we’re starting to have because I can start to work a bit more , but during the heat , I cannot do it” . [P2, T1D]

Theme 4: Unrealistic demands

Unrealistic expectations and advice about self-management from family or friends especially those not diagnosed with diabetes could be a hindrance to effective care. Participants’ found such wrong advice irritating as evident in the following comment:

‘‘You know I don’t think a lot of non-diabetic actually get to know how much it can take to actually manage a high or a low (Blood sugar) potentially . You know , you get comments from people that you’re low and they know you are diabetic saying , oh , should you be eating that ? Well , I’m going to say this nicely , you want me to die now or not or to go into coma ? Because I need to eat this . They go oh , you didn’t need to say it like that . You go well , stop asking a stupid question that you don’t know anything about” . [P4, T1D]

Additionally, discrepancy between patients and their health professionals’ (HP) perception of care could be a barrier to self-management. Participants felt that some recommendations from HPs were contrary to their opinions on what their diabetes self-management should entail:

‘ ‘My doctor doesn’t feel I need to be using a glucose meter to monitor my sugar levels and the diabetes educator doesn’t think I need to be on any sort of diet , even though I’ve had increases in diabetes medications” . [P10, T2D]

To the best of our knowledge, this is the first mixed methods study that has investigated enablers and barriers to general self-management among a multinational audience of people who have type 1 or type 2 diabetes. Most importantly, our findings emphasise the consequential impact of currency of exposure to DSME (within the previous 12 months), duration of diagnosis, level of educational qualification and use of technological devices on self-management skills and self-efficacy, regardless of geographical location or ethnicity. This implies that provision of ongoing self-management education/support through the use of mobile phones may help address the various difficulties (including time/financial constraint, diabetes distress, and limited access to care providers) encountered by patients and foster adherence to recommended self-management activities, which are necessary to prevent the risk of developing diabetes complications. Furthermore, this study presents an in-depth understanding of the experiences of diabetic patients and provides useful insights to health professionals and researchers on how to improve the frequency and quality of self-management support provided to diabetic patients to achieve better health outcomes.

Skills and self-efficacy for diabetes self-management

The overall skills score was found to be high and many participants reported good level of ability for self-management. This is specifically in the area of accurate monitoring to assess the impact of diet, medication or physical activities on blood glucose levels. Similar findings were observed in a previous study [ 25 ]. Accurate monitoring of blood glucose in relation to foods consumed and physical activities are important because they predict good outcomes in diabetes management [ 50 ].

Although the participants in this study scored high in their ability to monitor blood glucose, their capacity to interpret their blood glucose patterns over time was only moderate. Self-monitoring of blood glucose is important to assess glycemic pattern, hence accurate interpretation of these patterns is highly important to ensure effective management of glycaemia related problems encountered in diabetes management [ 51 ]. More emphasis should be laid on glucose pattern management during diabetes self-management educational sessions in order to expatiate patients’ skills on effective monitoring and interpretation of blood glucose data and the resulting health implications.

Participants in this study possessed lower skills related to planning for physical exercise in order to avoid hypoglycemia and adjusting medication to reach targeted blood glucose levels. This result corroborates previous findings [ 52 ]. The ability to manage and make appropriate adjustment to multiple regimens often determines success with other core areas of diabetes self-management and glycemic control [ 51 ]. For instance, studies have reported that due to the fear of hypoglycemia, patients have resorted to unhealthy behaviours (such as reducing or eliminating medication dose, inappropriate food choices and /or avoiding physical activities) that increase glucose levels [ 53 ]. Diabetic patients have an increased risk of developing hypoglycemia particularly when treated with insulin or insulin secretagogues [ 53 ]. Hence, they should be provided with regular refresher courses and continuous training on blood glucose levels awareness and strategies to balance exercise which could promote glycemic control and adherence to self-management.

Healthy coping strategies to identify and manage the impact of stress on diabetes management may be a difficult aspect of diabetes care because the participants in this study scored lowest in this area for both the skills and self-efficacy domains. All forms of stress either physical or mental, negatively impact blood glucose levels in those with diabetes [ 54 ] and it is a potential obstacle to attaining effective self-management and optimal health outcomes [ 55 ]. Patients’ understanding of dimensions of diabetes related stress is a clinically important factor and forms of stress that are potentially modifiable should be prioritized to guide clinical and educational interventions. This can include regular educational information on the impact of stress on health of diabetes patients and suggestions to reduce it.

Contrary to the findings of a previous study [ 56 ] that reported people with type 1 diabetes as having poorer self-management; our study participants who had type 1 diabetes scored higher than those with type 2 diabetes in skills and self-efficacy to care for their diabetes. Additionally, there was a significant positive relationship between the duration of diabetes and both skills and confidence for self-management. Patients with type 1 diabetes are typically diagnosed at an early age that may correspond to longer duration of diabetes. This pattern might have afforded them prolonged and regular exposure to health education, which is a significant predictor of successful diabetes self-management [ 20 ].

Overall, the strong correlation between the level of skills and self-efficacy found in this study strengthens the body of evidence supporting this link [ 32 ]. This pattern may be related to high level of education among most of the study respondents as also observed in a previous study [ 57 ]. Patients who possess higher skills usually have higher perceived level of efficacy and are most likely to actually engage in their self-management [ 25 , 32 ]. Building patients’ skills and confidence in their ability to self-manage diabetes is therefore imperative. Regular encouragement which could either be provided verbally or through other means of contact (e.g text messages through phones or emails) could be beneficial to patients [ 58 ]. While for those with limited educational backgrounds, the use of clear and simple communication styles when providing diabetes education to them will be essential to foster their skills and confidence [ 57 ].

Based on the results of the interviews, the most commonly perceived factor that fostered regular self-management was the will to prevent the development of diabetes complications. This result corroborates previous findings [ 12 , 59 ] and indicates that the participants in this study took responsibility for their choices and respective consequences. Discipline and proactive approaches to self-management are essential to reducing or preventing the development of diabetes complications. Regular reinforcement of education and motivation of patients could provide in-depth information about the disease and foster the will to mitigate its’ clinical course.

Furthermore, our study findings confirm those of other studies that the use of mobile technologies such as smartphone applications [ 19 ], insulin pump [ 60 ] and continuous glucose monitor [ 61 ] could enhance diabetes self-management in patients. Technology interventions have positive impact on diabetes outcomes such as adherence to self-management activities, glycosylated hemoglobin and diabetes self-efficacy [ 19 ]. Therefore, health professionals could recommend the use of mobile health technologies to patients who are capable of using them as they benefit from them.

The lack of enthusiasm towards regular self-management due to the chronic and dynamic nature of diabetes was not entirely unexpected. This phenomenon could be referred to as diabetes distress which is the emotional stress resulting from living with diabetes and the ‘‘burden of relentless management” [ 62 ]. High diabetes distress results in sub-optimal diabetes management and compromised quality of life [ 3 , 63 ]. Diabetes distress is common among patients and impacts on their self-management and health outcomes. Therefore, the importance of providing appropriate regular support to all patients in this regard cannot be overemphasized. Health professionals could ask patients at every consultation about how they are coping with diabetes, encourage them to express particular diabetes related issues causing them distress and offer encouragement and suggestions on ways to deal with it on a daily basis.

For many of the respondents in this study, the need to meet up with job requirements especially frequent travelling, makes adherence to healthy eating difficult. Additionally, work related stress impacts greatly on their blood glucose levels. These findings echo the results of Chao et al. [ 39 ]. Recommendations to patients to engage in creative planning and social support are strategies to help address this barrier. Social support from families are essential. Families should be encouraged to attend educational training sessions with patients so as to offer appropriate support which can assist patients to make healthy food choices and decisions regarding their diabetes management [ 12 , 13 ].

Furthermore, financial burden associated with diabetes could be a hindrance to self-management especially those associated with out-of-pocket expenditure for medical needs. Campbell et al., (2017) observed that the predominant area of management where patients experience financial burdens are medications, diabetes supply and healthy food [ 37 ]. People with diabetes require regular self-management and clinical monitoring to prevent the development of complications and foster optimal health outcomes; hence the associated financial demand. Health care providers could inform patients about resources available to them to buffer financial constraints that limit adherence to treatment plans. Such resources may include referring patients to specific social programs or compassionate relief programs to support financial burdens and enable easier access to necessary services.

Differences in patients’ and health care professionals’ (HCP) views of what constitutes the best approach to care was also identified as a barrier to self-management. This may be due to gaps in the way treatment recommendations were communicated to patients. Often times, HCPs’ view of good care are based on adhering to stipulated biomedical care model, structured communication and central decision making [ 64 ], whereas patients perceived quality health care is how the scientific knowledge of HCPs’ aligns with their own experiential knowledge and personal preferences [ 65 ]. Therefore, patients are always seeking exhaustive information about their diagnosis and treatment [ 65 ]. There is responsibility on the part of HCP’s to advice and educate their patients on different treatment options and the reasons they are placed on a particular option and not the other. This patient centered-approach will empower patients and foster their health outcomes.

Integration of findings and recommendations for future interventions

The survey results show that many patients have limited capacity for healthy coping strategies to identify and manage the impact of diabetes related stress. This finding was confirmed in the interviews where diabetes distress was reported as a major barrier to self-management. Given that stress is a potential contributor to chronic elevated blood glucose levels, it is essential for health care professionals to assist patients with identifying approaches to reducing diabetes distress. Additionally, increased access to healthcare providers through expanded clinic hours could be a means of easing the burden of diabetes diagnosis [ 41 ].

The quantitative data also showed that higher educational level was the strongest predictor of better self-management skills in patients and this was affirmed by the highly skilled interviewees who identified the use of technological devices as an enabler to their self-management. This corroborates that higher educational level is a good predictor of eHealth usage [ 66 ]. In addition, in accordance with previous literature [ 67 ], good overall self-efficacy level observed in the survey might have influenced the positive report on the usefulness of technology in diabetes management. Therefore, given that use of health technologies provides both short and long term health improvement in diabetes patients [ 68 ], active usage should be encouraged where necessary especially among patients who are educated and have the ability to engage with them. Furthermore, it is important to device avenues to improve patients’ self-efficacy in their ability to manage the disease as this could increase their likelihood of engaging with technology for their self-management [ 69 ].

The interviews revealed that determination to prevent development of complications is one of the major enablers to diabetes-self-management. This might explain the overall high score in skills and self-efficacy observed in the survey. Therefore, we suggest that educators could focus on improving patients’ skills and self-efficacy for diabetes self-management thereby raising patients’ awareness of the negative effect of diabetes. This approach could in turn stimulate the patients’ determination to engage in diabetes self-management and thereby reduce their risk of developing complications.

A unique perspective from the qualitative results revealed that patients and HCPs have divergent views/opinions about what should constitute patient care. It is therefore, imperative that HCPs ensure that patients understand the reasons for the recommended treatments and engage them in shared decision making which is essential for patients’ satisfaction and engagement in self-management practices [ 70 ].

Lastly, it has been advocated that people with diabetes should receive self-management education and support in an ongoing and consistent manner [ 71 ], but the reality of facilitating face-to-face diabetes education between patients and HCPs on an ongoing basis is low due to limited human and organisational resources. Health behavioural treatments and therapies such as diabetes self-management education/support could be provided to patients on an on-going daily basis outside the clinical setting through the use of ecological momentary interventions such as mobile technologies [ 72 ]. Apart from the fact that apps were opined by patients to enable self-management in this study, the World Health Organisation (WHO) also confirmed that the use of mobile technologies (such as apps) can support attainment of health outcomes which could transform health service delivery globally [ 73 ]. Considering that Apps are cost effective avenues for providing ongoing delivery of care to patients outside the clinical environment [ 74 ], diabetes self-management educational (DSME) messages could be developed and integrated into apps for patients. Such DSME should be targeted at improving patients’ skills and self-efficacy capacity for effective self-management.

Strengths and limitations

The strength of this work is that it provides a multinational picture of skills and confidence for self-management in people with type 1 or type 2 diabetes. Such an elaborate and international approach to assessing the capacity and confidence levels for self-management is scanty in the literature. In addition, the data identified a number of factors serving as enablers and barriers to diabetes self-management emanated from patients’ perspectives and their lived experiences. Therefore, the results are tenable for providing immense insights into improved strategies for supporting patients in their self-management.

There are some limitations to this study. Firstly, the reliability and validity of the quantitative tool used have not been previously demonstrated at multinational/multicultural levels, therefore, this may limit the interpretation of our findings. Although, in a previous study [ 32 ], the construct validity of the scale was tested among type 1 and type 2 diabetic patients who were from different ethnic backgrounds (Asians, Caribbeans, Caucasians etc.), but living in the same regional location. The study reported that the scale was not influenced by ethnicity. Secondly, the small sample size/groups for the survey which mainly comprised of participants from three continents, may limit the generalization of our findings to other settings. Thirdly, the quantitative data were self-reported and therefore susceptible to bias, which may not reflect participants’ actual skills and confidence levels for self-management. Hence, under or over reporting could result in inaccurate identification of common gaps in skills and confidence requiring intervention. Nevertheless, self-report can be made more reliable when questions are asked in a non-judgmental manner as obtained in the SCPI tool used in this study. Lastly, the small number of interview participants is also acknowledged and the interview sessions were brief because additional compensation was not offered to interviewees. Short interview duration was utilised to foster increased participant numbers because long interviews may not be justifiable for participants’ time involvement in the study. Published literature has shown that the anonymity of telephone interview reduces interviewer bias which makes the interview setting more calming and forthcoming, thus fostering a more accurate and truthful data collection [ 75 ].

This study identified common gaps in the skills and self-efficacy of people with type 1 or type 2 diabetes mellitus as well as other perceived enablers of, and barriers to, self-management in this population. Diabetes health care stakeholders may consider strategies for regular educational reinforcement in patients in order to foster healthy coping with diabetes stress, exercise planning to avoid hypoglycemia, interpreting blood glucose patterns and adjusting medications or foods to reach the targeted blood glucose levels. Furthermore, designing of interventions that capitalize on how to improve patients’ desire to reduce the progression of diabetes and the use of relevant technological devices could enhance diabetes self-management. Improved approaches to address diabetes distress, financial burden, discrepancy between patients and their health professionals’ perception of care as well as work and environment related factors are essential to foster improved self-management in patients. Finally, attention should be paid to type of diabetes, level of education and duration of diagnosis when counselling patients on diabetes self-management. Consideration of these areas of educational reinforcement and interventions could enhance self-management in patients and consequently improve their health outcomes.

Supporting information

S1 checklist, s1 appendix, acknowledgments.

The authors would like to thank Mr Aaron Drovandi for his assistance with the study interview, and the health organisations who helped with the study advertisement. In addition, we sincerely appreciate the participants of this study.

Abbreviations

Funding statement.

The first author of this study (MDA) is funded by the Australian Government International Research Training Program Scholarship. The funder had no role in the study design, data collection and analysis, decision to publish or preparation of manuscript.

Data Availability

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