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Exploring the Benefits of Playing Online Games for Mental Health

In recent years, online games have gained immense popularity across all age groups. While some may argue that spending time playing games online is a waste of time, research suggests that there are actually numerous mental health benefits associated with engaging in this activity. From improving cognitive abilities to reducing stress and anxiety, let’s explore the positive effects of playing online games on mental well-being.

Enhancing Cognitive Abilities

Online games often require players to think critically, solve problems, and make quick decisions. These activities can significantly enhance cognitive abilities such as memory, attention span, and problem-solving skills. For instance, strategy-based games like chess or puzzle-solving games like Sudoku can help sharpen analytical thinking and logical reasoning.

Furthermore, many online games involve complex narratives that require players to follow the storyline, remember important details, and make connections between different elements. This kind of mental exercise can improve memory retention and overall cognitive functioning.

Stress Relief and Relaxation

In today’s fast-paced world, stress has become a common issue affecting individuals of all ages. Engaging in online games can provide a much-needed escape from daily life stressors and offer a sense of relaxation. When playing online games, people often get absorbed in the virtual world, temporarily forgetting their worries and responsibilities.

Moreover, participating in challenging game levels or competing against other players can release endorphins – natural mood-boosting chemicals in the brain – leading to a feeling of happiness and contentment. This positive emotional state helps alleviate stress levels and promotes overall well-being.

Social Interaction and Connection

Contrary to popular belief that online gaming isolates individuals from real-life social interactions, many multiplayer online games actually foster social connections among players. Online gaming communities provide platforms for individuals with common interests to engage in teamwork or compete against each other while simultaneously building relationships.

Through voice chats or messaging systems within these gaming platforms, players can communicate and collaborate with others, forming friendships and even virtual communities. This social interaction can have a positive impact on mental health by reducing feelings of loneliness and promoting a sense of belonging.

Cognitive Distraction and Anxiety Reduction

For individuals dealing with anxiety or other mental health conditions, online games can serve as a cognitive distraction tool. Engaging in an immersive gaming experience diverts attention away from anxious thoughts or intrusive worries, allowing the mind to focus on the game’s challenges instead.

Additionally, online games that incorporate relaxation techniques such as soothing music or calming visuals can help induce a state of relaxation and tranquility. By redirecting attention towards these calming elements, players can experience temporary relief from anxiety symptoms.

In conclusion, playing online games can have significant mental health benefits. From enhancing cognitive abilities to providing stress relief and promoting social interaction, the positive effects of engaging in this activity should not be overlooked. However, it is important to maintain a healthy balance between gaming and other aspects of life to ensure overall well-being. So go ahead, enjoy your favorite online game guilt-free knowing that it’s not just entertainment but also beneficial for your mental health.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.

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• v.8(7); 2018 Jul

Health Benefits of Exercise

Gregory n. ruegsegger.

1 Department of Biomedical Sciences, University of Missouri, Columbia, Missouri 65211

Frank W. Booth

2 Department of Medical Pharmacology and Physiology, University of Missouri, Columbia, Missouri 65211

3 Department of Nutrition and Exercise Physiology, University of Missouri, Columbia, Missouri 65211

4 Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211

Overwhelming evidence exists that lifelong exercise is associated with a longer health span, delaying the onset of 40 chronic conditions/diseases. What is beginning to be learned is the molecular mechanisms by which exercise sustains and improves quality of life. The current review begins with two short considerations. The first short presentation concerns the effects of endurance exercise training on cardiovascular fitness, and how it relates to improved health outcomes. The second short section contemplates emerging molecular connections from endurance training to mental health. Finally, approximately half of the remaining review concentrates on the relationships between type 2 diabetes, mitochondria, and endurance training. It is now clear that physical training is complex biology, invoking polygenic interactions within cells, tissues/organs, systems, with remarkable cross talk occurring among the former list.

The aim of this introduction is briefly to document facts that health benefits of physical activity predate its readers. In the 5th century BC, the ancient physician Hippocrates stated: “All parts of the body, if used in moderation and exercised in labors to which each is accustomed, become thereby healthy and well developed and age slowly; but if they are unused and left idle, they become liable to disease, defective in growth and age quickly.” However, by the 21st century, the belief in the value of exercise for health has faded so considerably, the lack of exercise now presents a major public health problem ( Fig. 1 ) ( Booth et al. 2012 ). Similarly, the lack of exercise was classified as an actual cause of chronic diseases and death ( Mokdad et al. 2004 ).

Simplistic overview of how physical activity can prevent the development of type 2 diabetes and one of its complications, cardiovascular disease. Physical inactivity is an actual cause of type 2 diabetes, cardiovascular disease, and tens of other chronic conditions ( Table 1 ) via interaction with other factors (e.g., age, diet, gender, and genetics) to increase disease risk factors. This leads to chronic disease, reduced quality of life, and premature death. However, physical activity can prevent and, in some cases, treat disease progression associated with physical inactivity and other genetic and environmental factors.

Published in 1953, Jeremy N. Morris and colleagues conducted the first rigorous epidemiological study investigating physical activity and chronic disease risk, in which coronary heart disease (CHD) rates were increased in physically inactive bus drivers versus active conductors ( Morris et al. 1953 ). Since this pioneering report, a plethora of evidence shows that physical inactivity is associated with the development of 40 chronic diseases ( Table 1 ), including major noncommunicable diseases such as type 2 diabetes (T2D) and CHD, and as premature mortality ( Booth et al. 2012 ).

Worsening of 40 conditions caused by the lack of physical activity with growth, maturation, and aging throughout life span

The breadth of the list implies that a single molecular target will not substitute for appropriate daily physical activity to prevent the loss of all listed items.

In this review, we highlight the far-reaching health benefits of physical activity. However, note that the studies cited here represent only a fraction of the >100,000 studies showing positive associations between the terms “exercise” and “health.” In addition, we discuss how exercise promotes complex integrative responses that lead to multisystem responses to exercise, an underappreciated area of medical research. Finally, we consider how strategies that “mimic” parts of exercise training compare with physical exercise for their potential to combat metabolic disease.

EXERCISE IMPROVES CARDIORESPIRATORY FITNESS

There is arguably no measure more important for health than cardiorespiratory fitness (CRF) (commonly measured by maximal oxygen uptake, VO 2max ) ( Blair et al. 1989 ). For example, Myers et al. (2002 ) showed that each 1 metabolic equivalent (1 MET) increase in exercise-test performance conferred a 12% improvement in survival, stating that “VO 2max is a more powerful predictor of mortality among men than other established risk factors for cardiovascular disease (CVD).” Low CRF is also well established as an independent risk factor of T2D ( Booth et al. 2002 ) and CVD morbidity and mortality ( Kodama et al. 2009 ; Gupta et al. 2011 ). Similarly, Kokkinos et al. (2010) reported that men who transitioned from having low to high CRF decreased their mortality risk by ∼50% over an 8-yr period, whereas men who transitioned from having high to low CRF increased their mortality risk by ∼50%.

Importantly then, from the above paragraph, physical activity and inactivity are major environmental modulators of CRF, increasing and decreasing it, respectively, often through independent pathways. Findings from rats selectively bred for high or low intrinsic aerobic capacity show that rats bred for high capacity, which are also more physically active, have 28%–42% increases in life span compared to low-capacity rats ( Koch et al. 2011 ). Endurance exercise is well recognized to improve CRF and cardiometabolic risk factors. Exercise improves numerous factors speculated to limit VO 2max including, but not restricted to, the capacity to transport oxygen (e.g., cardiac output), oxygen diffusion to working muscles (e.g., capillary density, membrane permeability, muscle myoglobin content), and adenosine triphosphate (ATP) generation (e.g., mitochondrial density, protein concentrations).

Data from the HERITAGE Family Study has provided some of the first knowledge of genes associated with VO 2max plasticity because of endurance-exercise training. Following 6 wk of cycling training at 70% of pretraining VO 2max , Timmons et al. (2010) performed messenger RNA (mRNA) expression microarray profiling to identify molecules potentially predicting VO 2max training responses, and then assessed these molecular predictors to determine whether DNA variants in these genes correlated with VO 2max training responses. This approach identified 29 mRNAs in skeletal muscle and 11 single-nucleotide polymorphisms (SNPs) that predicted ∼50% and ∼23%, respectively, of the variability in VO 2max plasticity following aerobic training ( Timmons et al. 2010 ). Intriguingly, pretraining levels of these mRNAs were greater in subjects that achieved greater increases in VO 2max following aerobic training, and of the 29 mRNAs, >90% were unchanged with aerobic training, suggesting that alternative exercise intervention paradigms or pharmacological strategies may be needed to improve VO 2max in individuals with a low responder profile for the identified predictor genes ( Timmons et al. 2010 ). Keller et al. (2011) found that, in response to endurance training, improvements in VO 2max were associated with effectively up-regulating proangiogenic gene networks and miRNAs influencing the transcription factor–directed networks for runt-related transcription factor 1 (RUNX1), paired box gene 3 (PAC3), and sex-determining region Y box 9 (SOX9). Collectively, these results led the investigators to speculate that improvements in skeletal muscle oxygen sensing and angiogenesis are primary determinates in training responses in VO 2max ( Keller et al. 2011 ).

Clinically important concepts have emerged from the pioneering HERITAGE Family Study. One new clinical concept is that a threshold dose–response relationship influences the percentage of subjects responding with an increase in VO 2max to endurance training volumes (with volume being defined here as the product of intensity × duration), as previously published ( Slentz et al. 2005 , 2007 ). Ross et al. (2015) later extended the aforementioned Slentz et al. studies. After a 24-wk-long endurance training study ( Ross et al. 2015 ), percentages of women and men identified as nonresponders to the training (i.e., defined as not increasing their VO 2peak ) progressively fell inversely to a two stepwise progressive increase in endurance-exercise training volume, as described next. Thirty-nine percent (15 of 39) of training subjects did not increase their VO 2peak in response to the low-amount, low-intensity training; 18% (9 of 51) had no increase in VO 2peak in the group having high-amount, low-intensity training; and 0% (0 of 31) who underwent high-amount, high-intensity training did not increase their VO 2peak . A biological basis for the dose–response relationship in the previous sentence could be made from an analysis of interval training (IT) and IT/continuous-training studies published from 1965 to 2012 ( Bacon et al. 2013 ). A second older concept is being reinvigorated; Bacon et al. (2013) indicate that different endurance-exercise intensities and durations are needed for different systems in the body. They suggest that very short periods of high-intensity endurance-type exercise may be needed to reach a threshold for peripheral metabolic adaptations, but that longer training durations at lower intensities are required to see large changes in maximal cardiac output and VO 2max .

A comparable example exists for resistance training. Maximal resistance loads require a minimum of 2 min/per wk for each muscle group recruited by a specific maneuver to obtain a strength training adaptation [(8 contractions/set × 2 sec/contraction × 3 sets/day) × 2 days/wk) = 96 sec]. As of 2016, one opinion from Sarzynski et al. (2016) for the molecular mechanisms by which endurance exercise drives VO 2max include, but are not limited to, calcium signaling, energy sensing and partitioning, mitochondrial biogenesis, angiogenesis, immune functions, and regulation of autophagy and apoptosis.

Perhaps more importantly, lifelong aerobic exercise training preserves VO 2max into old age. CRF generally increases until early adulthood, then declines the remainder of life in sedentary humans ( Astrand 1956 ). The age-related decline in VO 2max is not trivial, as Schneider (2013) reported a ∼40% decline in healthy males and females spanning from 20 to 70 yr of age. However, cross-sectional data show that with lifelong aerobic exercise training, trained individuals often have the same VO 2max as a sedentary individual four decades younger ( Booth et al. 2012 ). Myers et al. (2002) found that low estimated VO 2max increases mortality 4.5-fold compared to high estimated VO 2max . They concluded, “Exercise capacity is a more powerful predictor of mortality among men than other established risk factors for cardiovascular disease.” Given the strong association between CRF, chronic disease, and mortality, we feel identifying the molecular transducers that cause age-related reductions in CRF may have profound implications for improving health span and delaying the onset of chronic disease. In two of our recent papers, transcriptomics was performed on the triceps muscle ( Toedebusch et al. 2016 ) and on the cardiac left ventricle ( Ruegsegger et al. 2017 ). We were addressing the question of what molecule initiates the beginning of the lifelong decline in aerobic capacity with aging. Aerobic capacity (VO 2max ) involves, at a minimum, the next systems/tissues, as oxygen travels through the mouth, airways, pulmonary membrane, pulmonary circulation, left heart, aorta/arteries/capillaries, and sarcoplasm/myoglobin to mitochondria. We allowed female rats access, or no access, to running wheels from 5 to 27 wk of age. Surprisingly, voluntary running had no effect on the delay in the beginning of the lifetime decrease in VO 2max . Our skeletal muscle transcriptomics elicited no molecular targets, whereas gene networks suggestive of influencing maximal stroke volume were identified in the left ventricle transcriptomics ( Ruegsegger et al. 2017 ).

Publications concerning the effects of exercise on the brain (from 54 to 216 papers listed on PubMed from 2007 to 2016) have increased 400%. In addition, a 2016 study ( Schuch et al. 2016 ) of three previous papers reported that humans with low- and moderate-CRF had 76% and 23%, respectively, increased risk of developing depression compared to high CRF in three publications. With this forming trend, the next section will consider exercise and brain health.

EXERCISE IMPROVES MENTAL HEALTH

Many studies support physical activity as a noninvasive therapy for mental health improvements in cognition ( Beier et al. 2014 ; Bielak et al. 2014 ; Tian et al. 2014 ), depression ( Kratz et al. 2014 ; McKercher et al. 2014 ; Mura et al. 2014 ), anxiety ( Greenwood et al. 2012 ; Nishijima et al. 2013 ; Schoenfeld et al. 2013 ), neurodegenerative diseases (i.e., Alzheimer’s and Parkinson’s disease) ( Bjerring and Arendt-Nielsen 1990 ; Mattson 2014 ), and drug addiction ( Zlebnik et al. 2012 ; Lynch et al. 2013 ; Peterson et al. 2014 ). In 1999, van Praag et al. (1999) showed the survival of newborn cells in the adult mouse dentate gyrus, a hippocampal region important for spatial recognition, is enhanced by voluntary wheel running. Similarly, spatial pattern separation and neurogenesis in the dentate gyrus are strongly correlated in 3-mo-old mice following 10 wk of voluntary wheel running ( Creer et al. 2010 ), and the development of new neurons in the dentate gyrus is coupled with the formation of new blood vessels ( Pereira et al. 2007 ). Many exercise-related improvements in cognitive function have been associated with local and systemic expression of growth factors in the hippocampus, notably, brain-derived neurotrophic factor (BDNF) ( Neeper et al. 1995 ; Cotman and Berchtold 2002 ). BDNF promotes many developmental functions in the brain, including neuronal cell survival, differentiation, migration, dendritic arborization, and synaptic plasticity ( Park and Poo 2013 ). In rat hippocampus, regular exercise promotes a progressive increase in BDNF protein for up to at least 3 mo ( Berchtold et al. 2005 ). In an opposite manner, BDNF mRNA in the hippocampus is rapidly decreased by the cessation of wheel running, suggesting BDNF expression is tightly related to exercise volume ( Widenfalk et al. 1999 ).

Findings by Wrann et al. (2013) highlight one mechanism by which endurance exercise may up-regulate BDNF expression. To summarize, Wrann et al. (2013) noted that exercise increases the activity of the estrogen-related receptor α (ERRα)/peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) complex, in turn increasing levels of the exercise-secreted factor FNDC5 in skeletal muscle and the hippocampus, whose cleavage products provide beneficial effects in the hippocampus by increasing BDNF gene expression. While future research should determine whether the FNDC5 cleavage-product was produced locally in hippocampal neurons or was secreted into the circulation, this finding eloquently displays one mechanism responsible for brain health benefits following exercise. Similarly, work by van Praag and colleagues suggests that exercise or pharmacological activation of AMP-activated protein kinase (AMPK) in skeletal muscle enhances indices of learning and memory, neurogenesis, and gene expression related to mitochondrial function in the hippocampus ( Kobilo et al. 2011 , 2014 ; Guerrieri and van Praag 2015 ).

Insulin-like growth factor 1 (IGF-1), is central to many exercise-induced adaptations in the brain. Like BDNF, physical activity increases circulatory IGF-1 levels and both exercise and infusion of IGF-1 increase BrdU + cell number and survivability in the hippocampus ( Trejo et al. 2001 ). Similarly, the protective effects of exercise on various brain lesions are nullified by anti-IGF-1 antibody ( Carro et al. 2001 ).

In 1979, Greist et al. (1979) provided evidence that running reduced depression symptoms similarly to psychotherapy. However, the precise mechanisms by which exercise prevents and/or treats depression remain largely unknown. Of the proposed mechanisms, increases in the availability of brain neurotransmitters and neurotrophic factors (e.g., BDNF, dopamine, glutamate, norepinephrine, serotonin) are perhaps the best studied. For example, tyrosine hydroxylase (TH) activity, the rate-limiting enzyme in dopamine formation, in the striatum, an area of the brain's reward system, is increased following 7 days of treadmill running in an intensity-dependent manner ( Hattori et al. 1994 ). Voluntary wheel running is also highly rewarding in rats, and voluntary wheel running in rats lowers the motivation to self-administer cocaine, suggesting exercise may be a viable strategy in the fight against drug addiction ( Larson and Carroll 2005 ).

Similar to the above examples, secreted factors from skeletal muscle have been linked to the regulation of depression. Agudelo et al. (2014) showed that exercise training in mice and humans, and overexpression of skeletal muscle PGC-1α1, leads to robust increases in kynurenine amino transferase (KAT) expression in skeletal muscle, an enzyme whose activity protects from stress-induced increases in depression in the brain by converting kynurenine into kynurenic acid. Additionally, overexpression of PGC-1α1 in skeletal muscle left mice resistant to stress, as evaluated by various behavioral assays indicative of depression ( Agudelo et al. 2014 ). Simultaneously, they report gene expression related to synaptic plasticity in the hippocampus, such as BDNF and CamkII, were unaffected by chronic mild stress compared to wild-type mice. Collectively, these findings suggest exercise-induced increases in skeletal muscle PGC-1α1 may be an important regulator of KAT expression in skeletal muscle, which, via modulation in plasma kynurenine levels, may alleviate stress-induced depression and promote hippocampal neuronal plasticity.

TYPE 2 DIABETES, MITOCHONDRIA, AND EXERCISE

T2d predictions show a pandemic.

In a 2001 Diabetes Care article ( Boyle et al. 2001 ), investigators at the U.S. Centers for Disease Control (CDC) predicted 29 million U.S. cases of T2D would be present in 2050. Unfortunately, the 2001 prediction of 29 million was reached in 2012! For 2012, the American Diabetes Association reported that 29 million Americans had diagnosed and undiagnosed T2D, which was 9% of the American population ( Dwyer-Lindgren et al. 2016 ). More rapid increases in T2D are now predicted by the CDC than in the previous estimate. The CDC now predicts a doubling or tripling in T2D in 2050. The tripling would mean that one out of three U.S. adults would have T2D in their lifetime by 2050 ( Boyle et al. 2010 ), which would be >100 million U.S. cases. The International Diabetes Federation (IDF) reports T2D cases worldwide. In 2015, the IDF reported that 344 and 416 million North American (including Caribbean) and worldwide adults, respectively, had T2D. Furthermore, the IDF predicts for 2040 that 413 and 642 million, respectively, will have T2D. In sum, T2D is now pandemic, and the pandemic will increase in numbers without current apparent action within the general public.

Type 2 Diabetes Prevalence Is Based on a Strong Genetic Predisposition

The Framingham study found that T2D risk in offspring was 3.5-fold and sixfold higher for a single and two diabetic parent(s), respectively, as compared to nondiabetic offspring ( Meigs et al. 2000 ). Thus, T2D is gene-based.

Noncoding regions of the human genome contain >90% of the >100 variants associated with both T2D and related traits that were observed in genome-wide association studies ( Scott et al. 2016 ). Another 2016 paper ( Kwak and Park 2016 ) lists at least 75 independent genetic loci that are associated with T2D. Taken together, T2D is a complex genetic disease ( Scott et al. 2016 ).

Type 2 Diabetes Is Modulated by Lifestyle, with Exercise as the More Powerful Lifestyle Factor

Three large-scale epidemiological studies have been performed on prediabetics, each in a different geographical location. The first study, and only study to have separate study arms for diet and exercise, was in China. The pure exercise intervention group had a 46% reduction in the onset of T2D, relative to the nontreated group, after 6 yr of the study ( Pan et al. 1997 ). Diet alone reduced T2D by 31% in the Chinese study. The second study on T2D was the Finnish Diabetes Prevention Study. It found a 58% reduction in T2D in the lifestyle intervention (combined diet and exercise) in its 522 prediabetic subjects after a mean study duration of 3.2 yr ( Tuomilehto et al. 2001 ). The latest of the three studies was in the U.S. Diabetes Prevention Program. The large randomized trial ( n = 3150 prediabetics) was stopped after 2.8 yr, because of harm to the control group. T2D prevalence in the high-risk adults was reduced by 58% with intensive lifestyle (diet and exercise) intervention, whereas the drug arm (metformin) of the study only reduced T2D by 31%, both compared to the noninnervation group ( Knowler et al. 2002 ). Thus, if differences in genetics in the above three differing ethnicities are not a factor, combined exercise and diet remain more effective in T2D prevention than the drug metformin two decades ago.

Exercise Increases Glucose by Signaling Independent of the Insulin Receptor

A single exercise bout increases glucose uptake by skeletal muscle, sidestepping the insulin receptor and thus insulin resistance in T2D patients ( Holloszy and Narahara 1965 ; Goodyear and Kahn 1998 ; Holloszy 2005 ). After insulin binding to its receptor, insulin initiates a downstream signaling cascade of tyrosine autophosphorylation of insulin receptor, insulin receptor substrate 1 (IRS-1) binding and phosphorylation, activation of a PI3K-dependent pathway, including key downstream regulators protein kinase B (Akt) and the Akt substrate of 160 kDa (AS160), ultimately promoting glucose transporter 4 (GLUT4) translocation to the plasma membrane ( Rockl et al. 2008 ; Stanford and Goodyear 2014 ). Despite normal GLUT4 levels, insulin fails to induce GLUT4 translocation in T2D ( Zierath et al. 2000 ). However, exercise activates a downstream insulin-signaling pathway at AS160 and TBC1 domain family member 1 (TBC1D1) ( Deshmukh et al. 2006 ; Maarbjerg et al. 2011 ), facilitating GLUT4 expression translocation to the plasma membrane independent of the insulin receptor. We contend that exercise could be considered as a very powerful tool to primarily attenuate the T2D pandemic.

Complex Biology of T2D Interactions with the Complex Biology of Exercise

An important consideration from the above is that T2D is such a genetically complex disease that a single gene has not been proven to be sufficiently causal to be effective, at this stage in time, to be a successful target for pharmacological treatment. The expectation for a single molecule target has been met for infectious diseases, which are often monogenic diseases. For example, a vaccine against smallpox was highly successful. Edward Jenner in 1796 produced the first successful vaccine. An important fact is that exercise is genetically complex. The literature allows us to speculate that exercise is at least as genetically complex as the approximately 75 genes associated with T2D ( Kwak and Park 2016 ). An example indicating that exercise is complex biology follows. RNA sequencing analysis of all 119 vastus lateralis muscle biopsies found that endurance training for 4 days/wk for 12 wk produced the differential expression of 3404 putative isoforms, belonging to 2624 different genes, many associated with oxidative ATP production in 23 women and men aged 29 yr old ( Lindholm et al. 2016 ). Our notion is that over 2600 genes suggests complex biology.

A “Case-Type” Study of the Molecular Underpinnings of Exercise, Mitochondria, and T2D Interactions

A PubMed search for the terms “diabetes mitochondria exercise molecular” elicited 74 papers. We arbitrarily selected some of the most recent 50 (spanning from mid-2014 into January 2017), with the assumption they would be representative of any other papers that we did not find in our search. Papers fell into our two arbitrary categories of single gene studies versus “omic”-type studies. First, subcategories of studies that develop themes will be arbitrarily presented.

Recent Studies Show Single Gene Manipulation Alters Mitochondrial Level and Running Performance

Numerous reports in the past couple of years observed that single gene manipulations increase mitochondrial gene expression and activity, which was also associated with increased exercise performance/capacity. A few of these are presented below:

• Irisin was shown to increase oxidative metabolism in myocytes and increase PGC-1α mRNA and protein ( Vaughan et al. 2014 ), which extends the first observation made earlier in adipose tissue by Spiegelman ( Bostrom et al. 2012 ).
• Patients with impaired glucose tolerance underwent low-intensity exercise training. Patients whose mitochondrial markers increased to levels that were measured in a separate cohort of nonexercised healthy individuals recovered normal glucose tolerance ( Osler et al. 2015 ). In opposition, those patients whose mitochondria markers did not improve, remained with impaired glucose tolerance.
• In 2003, muscle PGC-1α mRNA was shown to be induced by endurance-exercise training in human skeletal muscle ( Short et al. 2003 ). PGC-1α was shown to have multiple isoforms ( Lin et al. 2002 ). After a 60-min cycling bout, human vastus lateralis biopsies were taken from both sexes in their mid-20s. Additional biopsies were taken 30 min, and at 2, 6, and 24 hr postexercise. At 30 min postexercise, PGC-1α-ex1b mRNA and PGC-1α mRNA increased 468- and 2.4-fold, respectively, whereas PGC-1α-ex1b protein and PGC-1α protein increased 3.1-fold and no change, respectively. Gidlund et al. (2015 ) interprets the above data as implying PGC-1α-ex1b could be responsible for other changes that have previously been recorded before the increase in total PGC-1α postexercise.
• Mice with knockout of the kinin B1 receptor gene had higher mitochondrial DNA quantification and of mRNA levels of genes related to mitochondrial biogenesis in soleus and gastrocnemius muscles and had higher exercise times to exhaustion, but did not have higher VO 2max ( Reis et al. 2015 ).
• Mice do not normally express cholesteryl ester transfer protein (CETP), which is a lipid transfer protein that shuttles lipids between serum lipoproteins and tissues. Overexpression of CETP in mice after 6 wk on a high-fat diet increased treadmill running duration and distance, mitochondrial oxidation of glutamate/malate, but not palmitoylcarnitine oxidation, and doubled PGC-1α mRNA concentration ( Cappel et al. 2015 ).
• The myokine musclin is a peptide secreted from exercising muscle during treadmill running. Removal of musclin release during running results in lowered VO 2max , lower skeletal muscle mitochondrial content and respiratory complex protein expression, and reduced exercise tolerance ( Subbotina et al. 2015 ).
• Lactate dehydrogenase B (LDHB), which produces pyruvate from lactate, was overexpressed in mouse skeletal muscle. Increases in markers of skeletal muscle mitochondria were associated with increased running distance in a progressive speed test, and increased peak VO 2 ( Liang et al. 2016 ).
• Another example of endurance-type exercise adaptations is the 2016 paper that transcription factor EB (TFEB) regulates metabolic flexibility in skeletal muscle independent of PGC-1α during endurance-type exercise ( Mansueto et al. 2017 ). Lack of metabolic flexibility, termed “metabolic inflexibility,” is important because it is common in T2D. One definition of metabolic inflexibility is its inability to rapidly switch between glucose and fatty acid substrates for ATP production when nutrient availability changes from high blood glucose levels immediately after a meal to decreasing below 100 mg/dl when not eating for hours after a meal. A clinical consequence of T2D-induced metabolic inflexibility is prolonged periods of hyperglycemia, because skeletal muscle is more insulin insensitive in T2D. In contrast, after sufficient endurance exercise, skeletal muscle increases its insulin sensitivity by a second pathway that is independent of proximal postreceptor insulin signaling (see Stephenson et al. 2014 for further discussion).

Studies Showing that Manipulation of One Signaling Molecule Does Not Alter Expression of All Genes with Mitochondrial Functions Found in Skeletal Muscles of Wild-Type Animals to Exercise Training

A 2010 review article ( Lira et al. 2010 ) concludes from gene-deletion studies that p38γ MAPK/PGC-1α signaling controls mitochondrial biogenesis’ adaptation to endurance exercise in skeletal muscle. Two studies do not completely agree with the conclusion in the review article. The Pilegaard laboratory published a 2008 study ( Leick et al. 2008 ) that did not confirm their hypothesis that PGC-1α was required for every metabolic protein adaptive increase after endurance-exercise training by skeletal muscle. They reported that PGC-1α was not required for endurance-training-induced increases in ALAS1, COXI, and cytochrome c expression ( Leick et al. 2008 ). Their interpretation, at that time, was that molecules other than PGC-1α can exert exercise-induced mitochondrial adaptations. A second study published in 2012 rendered a similar verdict. A 12-day program of endurance training led to the middle portion of the gastrocnemius muscle demonstrating a similar 60% increase in mitochondrial density in both wild-type and PGC-1α muscle-specific knockout mice (Myo-PGC-1αKO) ( Rowe et al. 2012 ). The paper concludes that PGC-1α is dispensable for endurance-exercise’s induction of skeletal muscle mitochondrial adaptations.

Exercise signaling targets have actions that are independent of PGC-1α, which is specific to endurance exercise. In 2002, two groups identified PGC-1β, a transcriptional coactivator closely related to PGC-1α ( Kressler et al. 2002 ; Lin et al. 2002 ). Later in 2012, the PGC-1α4 variant of PGC-1α was found to induce skeletal muscle hypertrophy and strength ( Ruas et al. 2012 ). The importance of the finding of a PGC-1α variant is that it partially explains the phenotypic variation for differing types of exercise. Since the 1970s ( Holloszy and Booth 1976 ), it has been appreciated that the biochemical and anatomical observations between endurance and resistance differed. For example, Holloszy and Booth (1976) noted in 1976 that, whereas endurance-type exercise markedly increased skeletal muscle mitochondrial density with very minor increases in muscle fiber diameter, strength-type exercise, in contrast, increased muscle fiber diameter without increases in skeletal muscle mitochondrial density. Taken together, a drug specific for PGC-1α will not likely mimic separate physical training for endurance, strength/resistance, and coordination types of exercise in the same subject. Thus, the common usage of the term exercise capacity is a misnomer because endurance training and resistance training were shown to have different exercise capacity phenotypes very long ago.

In a 2015 Diabetes paper ( Wong et al. 2015 ), Muoio’s laboratory concluded that changes in glucose tolerance and total body fat depended upon how much energy is expended in contracting muscle rather than muscle mitochondrial content or substrate selection. A finding to support the previous sentence was the glucose tolerance tests (GTTs). MCK-PGC-1α mice and their nontransgenic (NT) littermates were not different in GTT, with both being the most glucose intolerant after 10 wk of high-fat feeding. Adding 10 wk of voluntary wheel running to the two high-fat-feed groups during the next 10-wk period (weeks 11–20 of the experiment) lowered the glucose intolerance, and then during weeks 21–30 of the experiment, glucose intolerance was further lowered by adding 25% caloric restriction with the high-fat food and running during the final 10 wk. The percentage weight lost after 30 wk of high-fat feeding was positively related to greater running distances. No single front-runner gene candidate could be identified by principle component analysis. Taken together, the paper suggests “doubts” that pharmacological exercise mimetics that increase muscle oxidative capacity will be effective antiobesity and/or antidiabetic agents. Rather, Muoio and investigators suggest energy expenditure by muscle contraction induces localized shifts in energy balance inside the muscle fiber, which then initiates a broad network of metabolic intermediates regulating nutrient sensing and insulin action. A further discussion of complex biology produced by polygenicity continues next.

POLYGENICITY OF EXERCISE LEADS TO COMPLEX MULTISYSTEM RESPONSES TO IMPROVE HEALTH OUTCOMES

Multiples tissues, organs, and systems are influenced by physical activity, or the lack thereof ( Table 2 ).

Worsening of maximal functioning in selected major organ/tissue/systems that are caused by the lack of physical activity with growth, maturation, and aging

The higher their maximal function is before the end of each item’s maturation, the longer chances are that the quality of life will remain optimal. The breadth of the list implies that a single molecular target will not substitute for appropriate daily physical activity to prevent the loss of all listed items.

To present one extreme, that most will agree, one molecule will not describe the 1000s of molecules adapting to aerobic, resistance, and coordination exercise training. On the opposite extreme, many could likely agree that usage of the various “omics” underlying all adaptations to physical activity will differ (i.e., not be identical in most aspects) among the next list: various cell types within a tissue/organ, tissues/organs, and various intensities of physical activity (i.e., the thresholds among gene responses for health benefits will differ because of the presence of responders and nonresponders, or protein isoform type); during various types cycling (circadian or menstrual); postprandial versus fasting between meals; male and female; child, adult, and the elderly; trained and untrained; aerobic- and resistance-exercise types; and so forth. Others have repetitively written that only ∼59% of the risk reduction for all forms of CVD have been shown to be caused by effects through traditional factors ( Mora et al. 2007 ; Joyner and Green 2009 ). Thus, we pose the next question: what is the identity of all molecules in the yet-to-be-discovered gap between our knowledge of single gene functions and the totality of personalized prescription of physical activity to maximize the period of life free of any chronic disease, termed health span?

While approaches using single-gene manipulations are valuable tools, research must also focus on integrating exercise-responsive molecules into networks that maintain or improve health. This process will reveal complex, multisystem, polygenic networking essential for the advancement of many goals pertaining to exercise physiology, such as tailoring exercise prescriptions and implementing personalized medicine. One example is the developing myokine network with auto-, para-, and endocrine molecules. The first myokine interleukin (IL)-6 began to be described as early as 1994 by the Pedersen laboratory ( Ullum et al. 1994 ), with a history of its development as the first exercise myokine recounted in 2007 ( Pedersen et al. 2007 ). Since their discovery, myokine action within and at a distance from their origins in skeletal muscle have been increasingly studied, as schematically illustrated by Schnyder and Handschin (2015) ( Fig. 2 ).

Figure provides an illustration of myokine production by skeletal muscle for actions within or at a distance. Myokine release promotes a high degree of intertissue cross talk. CNTF, Ciliary neurotrophic factor; OSM, oncostatin M; IL, interleukin; BDNF, brain-derived neurotrophic factor; VEGF, vascular endothelial growth factor. (From Schnyder and Handschin 2015 ; reprinted, with permission, courtesy of PMC Open Access.)

Similarly, maximal aerobic exercise is accompanied by tremendous stress on many systems, yet whole-body homeostasis is remarkably maintained. For example, world-class endurance athletes can increase whole-body energy production well over 20-fold ( Joyner and Coyle 2008 ), whereas maintaining blood glucose concentrations at resting levels ( Wasserman 2009 ). Intuitively, such effort would require sophisticated interorgan cross talk and polygenic integration of numerous functions.

Exercise Provides Too Many Benefits to “Fit into a Single Pill”

Despite the well-known benefits of exercise, most adults and many children lead relatively sedentary lifestyles and are not active enough to achieve the health benefits of exercise ( Warburton et al. 2006 ; Fried 2016 ). Accelerometry measurements suggest that >90% of U.S. individuals >12 yr of age and ∼50% of children aged 6–11 yr old fail to meet U.S. Federal physical activity guidelines ( Troiano et al. 2008 ). Given this incredibly low compliance, the identification of genetic and/or orally active agents that mimic the effects of endurance exercise might have high appeal for a majority of sedentary individuals. This high appeal has led to recent identification/development of exercise “mimetics.” In 2009, we set criteria for proper usage of the term “exercise mimetic,” based upon its common usage ( Booth and Laye 2009 ). We gave the Oxford English Dictionary’s definition of mimetic, “A synthetic compound that produces the same (or a very similar) effect as another (especially a naturally occurring) compound.” While many exercise “mimetics” activate signaling pathways commonly associated with muscle endurance, these agents have not completely mimicked all effects for all types of exercise. For example, the AMPK activator 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), when given daily to rats over a 5-wk-period, did not increase maximal oxygen consumption (VO 2peak ) in the sedentary group of rats that were forced to run to VO 2peak on treadmills, as compared to sedentary rats receiving the vehicle ( Toedebusch et al. 2016 ). Thus, in our opinion, the published claim ( Narkar et al. 2008 ) that AICAR is an exercise mimetic is invalidated because it did not increase VO 2peak . While these agents may undoubtedly have specific health benefits, it is currently impractical to assume that all of the benefits of exercise can be replaced by “exercise mimetics.”

CONCLUDING REMARKS

Exercise is a powerful tool in the fight to prevent and treat numerous chronic diseases ( Table 1 ). Given its whole-body, health-promoting nature, the integrative responses to exercise should surely attract a great detail of interest as the notion of “exercise is medicine” continues to its integration into clinical settings.

ACKNOWLEDGMENTS

The authors disclose no conflicts of interest. Partial funding for this project was obtained from grants awarded to G.N.R. (AHA 16PRE2715005).

Editors: Juleen R. Zierath, Michael J. Joyner, and John A. Hawley

Additional Perspectives on The Biology of Exercise available at www.perspectivesinmedicine.org

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Health research improves healthcare: now we have the evidence and the chance to help the WHO spread such benefits globally

• Stephen R Hanney 1 &
• Miguel A González-Block 2  

Health Research Policy and Systems volume  13 , Article number:  12 ( 2015 ) Cite this article

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There has been a dramatic increase in the body of evidence demonstrating the benefits that come from health research. In 2014, the funding bodies for higher education in the UK conducted an assessment of research using an approach termed the Research Excellence Framework (REF). As one element of the REF, universities and medical schools in the UK submitted 1,621 case studies claiming to show the impact of their health and other life sciences research conducted over the last 20 years. The recently published results show many case studies were judged positively as providing examples of the wide range and extensive nature of the benefits from such research, including the development of new treatments and screening programmes that resulted in considerable reductions in mortality and morbidity.

Analysis of specific case studies yet again illustrates the international dimension of progress in health research; however, as has also long been argued, not all populations fully share the benefits. In recognition of this, in May 2013 the World Health Assembly requested the World Health Organization (WHO) to establish a Global Observatory on Health Research and Development (R&D) as part of a strategic work-plan to promote innovation, build capacity, improve access, and mobilise resources to address diseases that disproportionately affect the world’s poorest countries.

As editors of Health Research Policy and Systems ( HARPS ), we are delighted that our journal has been invited to help inform the establishment of the WHO Global Observatory through a Call for Papers covering a range of topics relevant to the Observatory, including topics on which HARPS has published articles over the last few months, such as approaches to assessing research results, measuring expenditure data with a focus on R&D, and landscape analyses of platforms for implementing R&D. Topics related to research capacity building may also be considered. The task of establishing a Global Observatory on Health R&D to achieve the specified objectives will not be easy; nevertheless, this Call for Papers is well timed – it comes just at the point where the evidence of the benefits from health research has been considerably strengthened.

The start of 2015 sees a dramatic increase in the body of evidence demonstrating the benefits arising from health research. Throughout 2014, the higher education funding bodies in the UK conducted an assessment of research, termed the Research Excellence Framework (REF), in which, for the first time, account was taken of the impact on society of the research undertaken. As part of this, UK universities and medical schools produced 1,621 case studies that aimed to show the benefits, such as improved healthcare, arising from examples of their health and other life sciences research conducted over the last 20 years. Panels of experts, including leading academics from many countries, published their assessments of these case studies in December 2014 [ 1 ], with the full case studies and an analysis of the results being made public in January 2015 [ 2 , 3 ].

As we recently anticipated [ 4 ], the expert panels concluded that the case studies did indeed overwhelmingly illustrate the wide range and extensive nature of the benefits from health research. Main Panel A covered the range of life sciences and its overview report states: “ MPA [Main Panel A] believes that the collection of impact case studies provide a unique and powerful illustration of the outstanding contribution that research in the fields covered by this panel is making to health, wellbeing, wealth creation and society within and beyond the UK ” [ 3 ], p. 1. The section of the report covering public health and health services research also notes that: “ Outstanding examples included cases focused on national screening programmes for the selection and early diagnosis of conditions ” [ 3 ], p. 30. In their section of the report, the international experts say of the REF2014: “ It is the boldest, largest, and most comprehensive exercise of its kind of any country’s assessment of its science ” [ 3 ], p. 20.

The REF2014 is therefore attracting wide international attention. Indeed, some of the methods used are already informing studies in other countries, including, for example, an innovative assessment recently published in Health Research Policy and Systems ( HARPS ) identifying the beneficial effects made on healthcare policies and practice in Australia by intervention studies funded by the National Health and Medical Research Council [ 5 ].

The REF also illustrates that, even when focusing on the research from one country, there are examples of studies in which there has been international collaboration and which have built on research conducted elsewhere. For example, one REF case study on screening describes how a major UK randomised controlled trial of screening for abdominal aortic aneurysms (AAA) involving 67,800 men [ 6 , 7 ] was the most significant trial globally. The trial provided the main evidence for the policy to introduce national screening programmes for AAA for men reaching 65 throughout the UK [ 2 ]. The importance of this trial lay partly in its size, given that it accounted for over 50% of the men included in the meta-analyses performed in the 2007 Cochrane review [ 8 ] and the 2009 practice guideline from the US Society for Vascular Surgery [ 9 ]. Nevertheless, two of the three smaller studies that were also included in these two meta-analyses came from outside the UK, specifically from Denmark [ 10 ] and Australia [ 11 ].

Moreover, a recent paper published in HARPS also included descriptions of how the research contributing to new interventions often comes from more than one country. These accounts are included in a separate set of seven extensive case studies constructed to illustrate innovative ways to measure the time that can elapse between research being conducted and its translation into improved health [ 12 ]. While being a separate set of case studies, one of them does, nevertheless, explore the international timelines involved in research on screening for AAA, and, in addition to highlighting the key role of the UK research, it also highlights that the pioneering first screening study using ultrasound had been conducted in 1983 on 73 patients in a US Army medical base [ 13 ].

These case studies therefore further reinforce the well-established argument that health research progress often involves contributions from various countries. However, as has long been argued, not all populations fully share the benefits. In recognition of this, in May 2013, the World Health Assembly requested the World Health Organization (WHO), in its resolution 66.22, to establish a Global Observatory on Health Research and Development as part of a strategic work-plan to promote innovation, build capacity, improve access, and mobilise resources to address diseases that disproportionately affect the world’s poorest countries [ 14 ].

As editors of HARPS , we are delighted that our journal has been invited to help inform the establishment of the WHO Global Observatory by publishing a series of papers whose publication costs will be funded by the WHO. In support of this WHO initiative, Taghreed Adam, John-Arne Røttingen, and Marie-Paule Kieny recently published a Call for Papers for this series [ 15 ], which can be accessed through the HARPS webpage.

The aim of the series is “ to contribute state-of-the-art knowledge and innovative approaches to analyse, interpret, and report on health R&D information… [and] to serve as a key resource to inform the future WHO-convened coordination mechanism, which will be utilized to generate evidence-informed priorities for new R&D investments to be financed through a proposed new global financing and coordination mechanism for health R&D ” [ 15 ], p. 1. The Call for Papers covers a range of topics relevant to the aims of the Global Observatory. These include ones on which HARPS has published articles in the last few months, such as approaches to assessing research results, as seen in the Australian article described above [ 5 ]; papers measuring expenditure data with a focus on R&D, as described in a recent Commentary by Young et al. [ 16 ]; and landscape analyses of platforms for implementing R&D, as described in the article by Ongolo-Zogo et al. [ 17 ], analysing knowledge translation platforms in Cameroon and Uganda, and partially in the article by Yazdizadeh et al. [ 18 ], relaying lessons learnt from knowledge networks in Iran.

Adam et al. also make clear that the topics listed in the Call for Papers are examples and that the series editors are also willing to consider other areas [ 15 ]. Indeed, in the Introduction to the Call for Papers, the importance of capacity building is highlighted. This, too, is a topic described in recent papers in HARPS , such as those by Ager and Zarowsky [ 19 ], analysing the experiences of the Health Research Capacity Strengthening initiative’s Global Learning program of work across sub-Saharan Africa, and by Hunter et al. [ 20 ], describing needs assessment to strengthen capacity in water and sanitation research in Africa.

Finally, as we noted in our earlier editorial [ 4 ], the World Health Report 2013: Health Research for Universal Coverage showed how the demonstration of the benefits from health research could be a strong motivation for further funding of such research. As the Report states, “ adding impetus to do more research is a growing body of evidence on the returns on investments … there is mounting quantitative proof of the benefits of research to health, society and the economy ” [ 21 ]. We noted, too, that since the Report’s publication in 2013, there had been further examples from many countries of the benefits from medical research. The REF2014 in the UK signifies an additional major boost to the evidence that a wide range of health research does contribute to improved health and other social benefits. The results of such evaluations highlight the appropriateness of the WHO’s actions in attempting to ensure all populations share the benefits of health research endeavours by creating the Global Observatory on Health Research and Development. This will not be an easy task, but we welcome the opportunity afforded by the current Call for Papers for researchers and other stakeholders to engage with this process and influence it [ 15 ].

Abbreviations

Abdominal aortic aneurysms

Health Research Policy and Systems

Main Panel A

Research and development

Research Excellence Framework

• World Health Organization

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The authors thank Bryony Soper for most helpful comments on an earlier draft.

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Stephen R Hanney

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Hanney, S.R., González-Block, M.A. Health research improves healthcare: now we have the evidence and the chance to help the WHO spread such benefits globally. Health Res Policy Sys 13 , 12 (2015). https://doi.org/10.1186/s12961-015-0006-y

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• Volume 5, Issue 8
• Practical metrics for establishing the health benefits of research to support research prioritisation
• Article Text
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• Beth Woods 1 ,
• Laetitia Schmitt 1 ,
• Claire Rothery 1 ,
• Andrew Phillips 2 ,
• Timothy B Hallett 3 ,
• Paul Revill 1 ,
• Karl Claxton 1
• 1 Centre for Health Economics , University of York , York , Yorkshire , UK
• 2 Institute for Global Health , University College London , London , UK
• 3 Department of Infectious Disease Epidemiology , Imperial College London , London , London , UK
• Correspondence to Beth Woods; beth.woods{at}york.ac.uk

Introduction We present practical metrics for estimating the expected health benefits of specific research proposals. These can be used by research funders, researchers and healthcare decision-makers within low-income and middle-income countries to support evidence-based research prioritisation.

Methods The methods require three key assessments: (1) the current level of uncertainty around the endpoints the proposed study will measure; (2) how uncertainty impacts on the health benefits and costs of healthcare programmes and (3) the health opportunity costs imposed by programme costs. Research is valuable because it can improve health by informing the choice of which programmes should be implemented. We provide a Microsoft Excel tool to allow readers to generate estimates of the health benefits of research studies based on these three assessments. The tool can be populated using existing studies, existing cost-effectiveness models and expert opinion. Where such evidence is not available, the tool can quantify the value of research under different assumptions. Estimates of the health benefits of research can be considered alongside research costs, and the consequences of delaying implementation until research reports, to determine whether research is worthwhile. We illustrate the method using a case study of research on HIV self-testing programmes in Malawi. This analysis combines data from the literature with outputs from the HIV synthesis model.

Results For this case study, we found a costing study that could be completed and inform decision making within 1 year offered the highest health benefits (67 000 disability-adjusted life years (DALYs) averted). Research on outcomes improved population health to a lesser extent (12 000 DALYs averted) and only if carried out alongside programme implementation.

Conclusion Our work provides a method for estimating the health benefits of research in a practical and timely fashion. This can be used to support accountable use of research funds.

• health economics
• health services research

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

http://dx.doi.org/10.1136/bmjgh-2019-002152

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Key questions

What is already known.

Methods are available to estimate the value of research studies but are not widely understood, appreciated or applied.

What are the new findings?

We provide a method and companion Microsoft Excel tool that can be used to estimate the health benefits of research studies without using advanced value of information methods.

The tool can be populated using a range of evidence or used to test how different assumptions affect the value of research.

We illustrate the method by applying it to estimate the value of research studies on HIV self-testing programmes.

What do the new findings imply?

Our work provides a method for estimating the health benefits of research in a practical and timely fashion; these estimates can be considered alongside research costs to prioritise research studies for funding.

Introduction

Globally, significant resource and effort is spent on health-related research with the 10 largest public and philanthropic funders spending US$37.1 billion in 2013. 1 An important component of this funding is dedicated to basic science and preclinical research. However, much research aims to better understand current epidemiological patterns, healthcare provision and patient outcomes, and how they would be impacted by alternative interventions with a view to informing healthcare investments in the near-term. Clinical trials, surveillance programmes, cost studies, morbidity surveys and implementation studies all serve this purpose. By improving the information available to support investment decisions, they have the potential to improve population health. However, research is costly and those funding research have constraints on their ability to expand research budgets. This raises the question of which research activities should be prioritised.

To answer this question there is a need to understand why evidence is valuable to healthcare systems and the populations they serve, and how to assess the value of specific research proposals. This has been recognised by a number of stakeholders and a set of methods called value of information analysis allow the value of specific research proposals to be quantified. 2–4 Value of information analysis has been applied in a range of contexts in high-income settings, for example, to assess the value of clinical trials of interventions for which limited evidence exists. 5 6 Previous studies have also estimated the value of further research in low-income and middle-income countries (LMICs). 4 7 8 These studies used advanced methods 4 7 8 that require specific types of analyses to have been conducted (probabilistic analyses of a model already addressing the policy question of interest). 9 10 The application of value of information analysis to help prioritise research has, therefore, been limited as the advanced methods required are often not practical given time and resource constraints, and computation may be impractical where transmission models are required to represent disease dynamics.

In this paper, we use a graphical method and simple metrics to show how the principles of value of information analysis can be applied in these common but challenging circumstances. We provide a simple excel tool to facilitate use of the method and explain how the method can be applied using different types of evidence including typical outputs from existing cost-effectiveness models. We also discuss how this type of analysis can inform key policy questions relating to the allocation of research funds. We then apply this method in a case study assessing the value of research in HIV self-testing programmes in Malawi.

The methods presented are relevant to any party with a stake in ensuring health research funds are used in a way that is expected to improve population health. This includes research funders, researchers and healthcare decision-makers within LMICs who rely on robust evidence to make investment decisions. The latter group includes individuals within ministries of health charged with prioritising health programmes (including designing health benefits packages), and other decision-makers at a regional and national level who are responsible for healthcare resource allocation. The methods presented apply where a single budget is used to fund research and service provision, and to the more common situation where budgets for these activities are separate.

Graphical illustration using a simple quantitative tool to quantify the value of research

Cost-effectiveness analyses are routinely used to assess whether a programme is expected to improve population health once the health opportunity costs imposed by additional programme spending are accounted for. This assessment can be summarised using an estimate of the net disability-adjusted life years (DALYs) averted by the programme. This reflects both the health benefits of the programme and an assessment of the health forgone as funding a programme means that resources will be unavailable for the delivery of other programmes. This is calculated as the DALYs directly averted via the programme minus the DALYs incurred elsewhere in the health system due to the additional programme funding required.

In the same way, we can quantify the net DALY impact of investing in healthcare provision, we can also quantify the net DALY impact of investing in research. This idea is the basis for value of information analysis.

To assess the value of a research study or other data collection or evidence gathering activities, we need to understand the types of uncertainty that we could examine in a study with particular endpoints. These endpoints may be epidemiological, clinical, patient reported, process related or economic. For example, we might be uncertain about the effectiveness of a drug, the uptake of a rural community-based prevention programme, the quality of life of people with different treatment outcomes, or the cost of implementing a new diagnostic pathway. To assess the value of improving information relating to an endpoint, we need to understand our current level of uncertainty about the endpoint given existing evidence. This uncertainty can be described by a probability distribution showing the likelihood that the endpoint takes different values. This distribution is often called a prior, since it is based on existing knowledge of uncertainty about the specific endpoint. Figure 1 B shows the prior on an uncertain endpoint as a histogram.

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Calculating the net health effects of research. Legend: (A) shows net disability-adjusted life years (DALYs) averted by the programme for different values of the endpoint of interest when the programme is expected to be cost-effective based on current evidence; (B and D) show the prior on the uncertain endpoint; (C) shows net DALYs averted by the programme for different values of the endpoint of interest when the programme is not expected to be cost-effective based on current evidence.

Uncertainty about the endpoint alone is not sufficient to justify expenditure on research. For research to deliver value, the uncertainty in the endpoint must translate to uncertainty about whether the programme is cost-effective. For example, we might be highly uncertain about a programme’s effects on clinical outcomes. However, if the programme is cost-effective across the range of plausible clinical outcomes then further research on this endpoint may not deliver value in this setting as it would not change the decision about funding the intervention.

We can assess whether uncertainty in the endpoint is likely to translate to uncertainty about cost-effectiveness by estimating the net DALYs we would expect to avert if the endpoint was found to take the different values reflected in the prior. This is shown in figure 1 A. In this illustration, as the endpoint increases, the net DALYs averted increase. This reflects estimates of how both DALYs averted and additional costs (or cost savings) change with the value of the endpoint. It also reflects a measure of the health opportunity cost of financing the programme, as this allows the additional costs of the programme to be converted to health foregone.

The mean value of the endpoint represents our ‘best guess’ of the value the endpoint takes given currently available information. At this value the net DALYs averted by the programme are positive and the programme would be considered cost-effective. However, below a certain ‘trigger’ value of the endpoint, the net health effects of the programme become negative, that is, the programme is not cost-effective. The shaded area of the prior histogram ( figure 1 B) indicates the probability that the endpoint will fall below the trigger point. This is the probability that the intervention will turn out not to be cost-effective and that implementation will reduce population health. However, if we conduct research to improve our understanding of the endpoint this is the probability that the research could change the implementation decision. If it is considered implausible that the endpoint could take a value as extreme as the trigger point then further research will not result in a change in decision and, therefore, based on the available evidence, may not be considered an appropriate use of resources. This emphasises that we should care about uncertainty in endpoints when it leads to uncertainty in decisions.

Without additional research, on average implementation averts DALYs but if low values of the endpoint are realised, implementation reduces population health. If research is conducted and indicates that the endpoint falls below the trigger point (i.e. the programme is not cost-effective), then the programme will not be implemented. Research therefore avoids the health losses associated with programme implementation under these conditions as shown by the grey bars in figure 1 A. These bars, therefore, represent the potential health gains from research. The expected net DALYs averted via research are calculated as the health gains (resulting from avoided health losses) when the endpoint takes values below the trigger point, that is, the shaded bars in figure 1 A weighted by the probability of the quantity taking each value below the trigger point, that is, the shaded bars in figure 1 B.

Figure 1 C, D show how the value of research can be calculated when the programme is not expected to be cost-effective based on current information. Without further research the programme is not implemented and no population health gains are generated. With further research, there is a possibility that the endpoint will take values sufficiently high to support implementation and net DALYs are averted. The possible health gains from research are again shown by the grey bars ( figure 1 C).

This method shows the value of completely eliminating the uncertainty around the endpoint. Although in reality further research will not resolve all uncertainty, the estimates generated provide an expected upper bound for the population health benefits from research for the setting of interest.

We express the value of the research proposals using two different metrics. The first is the net DALYs averted by using the research to improve decision making. Where a research study is expected to be used in a number of countries, the approach described above can be applied for each country and the net DALYs averted across countries can be calculated. Individual country estimates of the net DALYs averted by research are likely to differ for a range of reasons including differences in the size of the population that stand to benefit from research, the costs and health benefits of the programme and the health opportunity costs of healthcare funds.

The net DALYs averted by research provides an estimate of the expected maximum population health gains from research accounting for both health gains and programme costs, but it does not consider research costs. Funding a specific research proposal has opportunity costs, which are the health gains that could be generated by using this funding for other research studies.

The second metric is, therefore, the maximum amount a research funder should be willing to spend on the research, given its estimated net health effects. This metric is estimated by multiplying the net DALYs averted by research by a measure of the opportunity cost of research funds. We assume that research funds have similar levels of opportunity costs as funds for service provision. For example, if a research study is expected to avert 1000 DALYs and our measure of opportunity costs indicates that every US$500 of expenditure results in an additional 1 DALY being incurred elsewhere in the health system, then the maximum a research funder should be willing to spend on the research would be US$500 000. If they spend more than this the health opportunity costs of funding the research would exceed 1000 DALYs and thus, more than outweigh the net health gains from research. Given the very different sources of funding that typically underpin service provision and research, the opportunity cost of research funds may differ from the opportunity cost of service funding. We will return to the question of how the opportunity cost of research funds could be estimated in the discussion.

To illustrate the approach, we use a numeric example where we are interested in an outcomes endpoint that can, in principle, take different values between 0 and 1 (eg, the probability of treatment response). Our existing knowledge of the endpoint indicates it is expected to take a value of 0.10 (SE 0.04, 95% CI 0.04, 0.19), which allows us to define its prior (we apply a beta distribution here). In a second step, we make use of existing information about how different values of the endpoint influence health effects and costs of the programme. In the present example, we know that if the endpoint takes the average value, the programme is expected to avert 2000 DALYs. If the endpoint takes the value at the lower bound of the CI the programme is expected to avert 1000 DALYs, whereas if the endpoint takes the value at the higher bound of the CI, the programme is expected to avert 3000 DALYs. The expected additional long-term cost associated with the programme is US$450 000 and is not expected to vary with the endpoint. Lastly, we evaluate the health opportunity cost associated with funding the intervention. This is 1500 DALYs based on additional costs of US$450 000 and an estimate of health opportunity cost of US$300/DALY. This information about the DALYs averted at different values of the endpoint, and about opportunity costs, allows us to estimate the net DALYs averted at different values of the endpoint. We provide a simple Microsoft Excel tool to allow users to review the numeric example and apply the approach to their own contexts. This tool is available in the online supplementary material , for the most up to date version of the tool see https://www.york.ac.uk/che/research/global-health/methods-guidelines/%23tab-4 . The tool provides a graphical summary of the prior information and the relationship between net health effects and the endpoint of interest as shown in figure 2 .

Supplemental material

Output of quantitative excel tool for calculating the net health effects of research. DALYs, disability-adjusted life years.

The tool uses regression methods to generate estimates of the net health effects of a programme at all plausible values of the endpoint. The regression uses estimates of DALYs averted and additional costs at different values of the endpoint that are entered by the user. Two regressions are then fitted, one regressing DALYs averted on the endpoint and the other regressing additional costs on the endpoint. Options are available to use linear regression, or to assume range of non-linear relationships between the endpoint and DALYs averted or additional costs.

The tool uses the data entered to generate estimates of the benefits of research. The tool shows the implications of making decisions based on current evidence, and the potential benefits of making decisions on the basis of further research as shown in figure 2 . Without further research we can only base our decision on what we expect to occur. We expect that the programme averts 1868 DALYs (the expected health benefits (1868) are not identical to the health benefits at the mean value of the endpoint (2000) as the beta distribution used to describe the endpoint is not symmetrical) with a health opportunity cost of 1500 DALYs, that is, 368 net DALYs averted. On this basis, we implement the programme based on current evidence. If we conduct research, we will gain more information about which value the endpoint takes. If the endpoint is as expected or higher, there is no change to the decision. If the endpoint is lower than the trigger point of 0.07, the net DALYs averted become negative and we choose not to implement the intervention. Weighting the probability of observing values of the endpoint below 0.07 by the net DALYs averted by avoiding implementation, we expect the research to avert 59 DALYs. If the research is only considered relevant in this context then the maximum a research funder should be willing to spend on the research is US$17 800, suggesting that this may not be a high priority area for research. If the research is expected to inform decision making in other countries, then the process can be repeated for each country, and the value of research across countries can be calculated.

Guidance for gathering evidence to inform estimates of the value of research

As shown above, a necessary part of any assessment of the value of research is formulating a view on the current level of uncertainty about the endpoints the research will examine. This uncertainty can be represented as a prior distribution. Evidence from existing studies including pilot studies or systematic reviews can be used to formulate priors. In practice, however, many research studies examine combinations of interventions and contexts which have not previously been studied. When evaluating a specific research proposal formally elicited expert opinion 11 12 may, therefore, be valuable to complement quantitative and qualitative information to formulate priors.

It is also necessary to estimate how the health benefits and additional costs of the programme change with the endpoint. Where a cost-effectiveness model is available, this can be obtained by conducting one-way sensitivity analysis, that is, varying the values taken by the endpoint of interest and recording the corresponding variations in health benefits and additional long-term costs associated with the intervention. If a cost-effectiveness model is not available for the context of interest, or existing models cannot be easily adapted, then formal expert elicitation can be used to quantify the magnitude of health benefits and additional costs at different levels of the endpoint.

In order to estimate the net health effects of programmes, we require an understanding of how additional programme costs translate to health opportunity costs. Recent work has estimated the opportunity cost of domestic healthcare spending in a wide range of LMICs. 13 Where programmes are funded via overseas aid the opportunity costs of this funding will depend on the remit of the funder. An understanding of the potential health opportunity cost of an overseas aid funding stream can be garnered by reviewing the cost-effectiveness of those interventions that are and are not currently funded, and potentially developing a cost-effectiveness league table of funded programmes.

Specification of each element described above is likely to require judgements regarding which evidence is relevant and how to use that evidence. By using the tool provided, users can explore the sensitivity of their results to each of these elements. In some contexts, the time-sensitive nature of a research-funding decision, analyst capacity or funding availability, may make it infeasible to assemble these types of evidence. In these contexts, the tool can provide a quantitative basis for testing how different assumptions influence both the net DALYs averted by the research and the maximum amount a funder should be willing to spend on the research.

We now show how the approach can be applied to a specific example. In this example, evidence is available from a cost-effectiveness model but no probabilistic sensitivity analysis has been conducted thus prohibiting use of standard value of information methods.

Self-testing example using the HIV synthesis model

We show how these methods can be applied to assess the value of research in HIV self-testing programmes in Malawi. Self-testing programmes have been the subject of a number of recently published and ongoing research studies in sub-Saharan Africa (for some examples see refs. 14–18 ). We use the HIV synthesis model 19 20 which has been used to assess the cost-effectiveness of a range of HIV prevention and treatment investments in different settings. The self-testing programme under evaluation is not currently part of the HIV investment strategy. We assess two possible scenarios to estimate the population health benefits from research studies on self-testing programmes. Under the first scenario, no research is conducted and investment in self-testing is based on current evidence about the costs and benefits of the programme. Under the second scenario, research is commissioned and the results of the research inform the decision about investment in self-testing.

Studies of HIV testing have included a range of endpoints measuring intervention effectiveness and costs at different points in the cascade of care. Frequently reported endpoints include coverage and uptake, HIV positivity, linkage and retention in care, and programme costs. 18 The cost-effectiveness of self-testing is strongly linked to the cost per new HIV diagnosis 21 which is calculated as the programme cost per person divided by the proportion of people diagnosed with HIV as a result of the programme. This suggests that two endpoints: programme costs and the proportion of people diagnosed with HIV, are likely to be important determinants of whether testing is cost-effective and therefore important targets for further research. The proportion of people diagnosed with HIV within facility-based care as a proportion of those targeted for testing reflects the combined effect of multiple endpoints collected within testing studies such as uptake, HIV positivity within those tested and linkage to facility-based care. We, therefore, examine a cost study focused on the cost of the self-testing programme per individual eligible for testing; and an outcomes study estimating the proportion of the eligible population who are diagnosed with HIV in facility-based care.

To evaluate the research proposals, we require priors describing the uncertainty about both programme costs and the proportion of the eligible population who are diagnosed with HIV in facility-based care. These priors will depend on the characteristics of the target population and implementation setting, the details of the testing programme such as whether measures to enhance linkage are proposed (eg, financial incentives, community-based support) and other contextual factors. The priors will, therefore, depend on the exact details of a specific research proposal and are most likely best formulated by combining available data, qualitative information and expert opinion. For the purposes of this demonstration, we use only data from the literature to inform the priors. We use data from a systematic review and meta-analysis, 18 focusing on those data relating to self-testing. This work reflects the fairly limited data on self-testing available in 2015, when many of the self-testing studies were designed. For further details see online supplementary material S1 .

Estimates of the additional costs and DALYs averted by a self-testing programme were derived from the HIV synthesis model. This is an individual-based stochastic model of heterosexual transmission, progression and treatment of HIV infection. We used outputs from the model generated by the ‘Working group on cost effectiveness of HIV testing in low income settings in sub-Saharan Africa’ 21 which examined the effects of expanding HIV testing beyond a core testing programme considered to represent current standard of care in many countries. This core testing programme included testing for: pregnant women, symptomatic individuals, female sex workers (although this is not fully implemented in many countries) and men coming forward for circumcision. This work examined the relationship between cost per HIV diagnosis and long-term cost effectiveness. The demographics of the population and the HIV epidemic features were based on those for Malawi and the model is calibrated to data that are representative of this setting. This work examined the cost-effectiveness of testing for a wide range of scenarios. The scenarios reflect variation in the expanded testing programme testing rates, how well the programme targets HIV positive individuals and cost per test. The scenarios also reflect uncertainty about the context in which the programme is implemented in terms of the nature of the epidemic, ART programme characteristics and the core testing programme. The model time horizon was 50 years and a discount rate of 3% was used for costs and outcomes.

We used the scenario analysis outputs from the model to estimate the relationship between costs and DALYs averted and both endpoints of interest (the proportion of the targeted population diagnosed with HIV in facility-based care and programme costs). For further details, see online supplementary material S2 .

Estimating the net DALYs averted by self-testing, requires a measure of the health opportunity cost of the funds used to pay for self-testing. We have used a measure of opportunity cost of US$500/DALY. This represents the cost per DALY averted of those services we expect to be displaced by investments in self-testing. US$500/DALY is considered a relevant cost-effectiveness threshold for resource allocation within the HIV programme which is overwhelmingly reliant on overseas aid. 21 22 Additionally, HIV investments which Malawi and other countries in sub-Saharan Africa have struggled to scale up often have incremental cost-effectiveness ratio (ICERs) around US$500/DALY, and HIV budgets have been shown to be exhausted in South Africa after funding interventions with ICERs around US$500/DALY. 23 Where delivery of HIV interventions draws on resources that would otherwise be used for non-HIV health activities a lower threshold is more appropriate, we return to this in the discussion.

The analysis of the outputs from the HIV synthesis model were conducted in the statistical software R and associated packages. 24–39

The implications of making decisions about the self-testing programme based on current evidence are shown in table 1 . The self-testing programme is cost-effective as indicated by the ICER falling below the cost-effectiveness threshold of US$500/DALY averted and positive values for net DALYs averted. These results represent the expected net health effects of the programme. However, due to uncertainties in the evidence base, there is a possibility that self-testing is not cost-effective and in this case, implementing it will reduce net population health. Further research may, therefore, be of value to better understand the cost-effectiveness of the self-testing programme. Figure 3 shows how the principles outlined in figures 1–2 can be applied to quantify the implications of decision making based on further research on outcomes. The net DALYs averted by the self-testing programme increase as the proportion of people diagnosed with HIV increases ( figure 3 A). At the mean value of the outcome endpoint, the programme delivers net health gains (108 400 averted DALYs). If the proportion of people diagnosed with HIV is less than the trigger point of 0.05, then self-testing is no longer cost-effective. The probability that the outcome endpoint is below this trigger value is shown by the shaded area in figure 3 B (probability of 0.33). If the outcome study is commissioned then this would avoid the programme being implemented in these circumstances. The avoided potential waste of healthcare resources can be translated into population health gain as indicated by the grey shaded area in figure 3 A. Weighting these gains from research ( figure 3 A grey area) by the likelihood that the outcome endpoint takes these values ( figure 3 B red bars) shows that research could potentially avert an additional 41 700 DALYs compared with implementation without research.

Calculating the value of a study of self-testing in women and men focused on outcomes. (A) shows the net health effects of the self- testing programme for different values of the outcome endpoint; (B) shows the prior on the outcome endpoint.

• View inline

Population health consequences of implementation without and with additional research for the HIV self-testing case study

The results of conducting this analysis for both the outcomes and cost endpoints are shown in table 1 . The additional health benefits of research are 41 700 DALYs averted by the outcomes study and 89 400 DALYs averted by the cost study. The maximum amount a research funder should be willing to spend is US$20.9 million for the outcomes study and US$44.7 million for the cost study, this suggests that further research is potentially valuable in this setting.

So far we have shown how the population health benefits of research into self-testing can be quantified using existing models and model outputs and without use of advanced value of information methods. This does not answer a key question facing healthcare decision-makers, which is, when is the right time to implement a programme if we are uncertain about its net health effects?

Three different policy choices are available to healthcare decision-makers:

Implementation without research: implement programme without further research if current evidence indicates it is cost-effective.

Implementation alongside research: implement programme while conducting research, and consider scaling back programme if research shows programme does not improve net population health.

Research then implementation: delay decision about implementation until research reports.

There are trade-offs to consider when choosing between these policies. If we wait until the research reports before implementing the programme, we forego the benefits of implementation in the meantime. On the other hand, if the programme is implemented while research is conducted there is a risk that the programme is found not to have been worthwhile once research findings emerge and is scaled back. This also risks the loss of resources where irrecoverable programme setup costs are high. In some cases, implementation alongside research may not be feasible.

We, therefore, quantified the net health consequences of each available choice open to policy-makers, assuming the outcomes study takes 3 years to report and the cost study 1 year to report. This analysis reflects that under the research then implementation policy there will be no access to self-testing in the research period, and reflects that under both policies involving a research component, the benefits of research do not emerge until the research reports. The methods for this part of the analysis are shown in the online supplementary material (see online supplementary material S3 ) and results are shown in table 2 .

The outcomes study offers the potential to avert 12 400 additional DALYs if the programme is implemented alongside research. If programme implementation is delayed until research findings emerge then the net DALYs averted are 19 900 lower than if self-testing was implemented without further research. The benefits of having a programme up and running straight away (ie, implementation) exceed the benefits from making a more informed decision based on improved outcome data but delaying availability of the intervention by 3 years.

A cost study is expected to avert approximately 67 000 DALYs regardless of whether it is implemented alongside research or implementation is delayed until the cost study reports.

Our analysis underestimates the benefits of implementation alongside research for both the outcomes and the cost study. Without conducting additional analyses using the transmission model, we could not fully simulate the consequences of discontinuing the self-testing programme when research did not support continued implementation. This would have allowed the long-term benefits of the self-testing conducted in the 1 or 3 year research period to have been captured. Instead, we assumed that there were no further benefits of self-testing when the programme was discontinued. However, these potential missed benefits need to be weighed against set-up costs which will represent irrecoverable expenditures in the event that research suggests self-testing should be scaled back.

Population health consequences of implementation and research policy choices

In this paper, we have shown how a graphical method can be used to estimate the value of research studies without advanced value of information methods. We provide a simple excel tool to allow readers to use the method. Where time and resources allow, information from existing studies, expert elicitation and outputs generated from existing cost-effectiveness models can be used to inform the calculations. Where the assembly of such information is not feasible, the method and tool can be used to test how different assumptions influence estimates of the value of research, identify the assumptions under which a proposed research study appears worthwhile, and allow decision-makers to consider their plausibility. These methods apply to a wide range of research studies aimed to inform programme design in the near-term (see online supplementary material S4 ) and can be used to quantify the value of collecting data on different endpoints and in different populations. The methods are relevant where evidence is expected to be considered relevant for decision making in multiple countries. The net health benefits of the research can be calculated for each country and considered collectively when assessing the value of the study.

When evaluating a specific research proposal, it may be important to consider, quantitatively or qualitatively, other factors that may modify the value of research. These include future changes that would modify the net DALYs averted by the intervention (eg, anticipated price reductions for health technologies), uncertainty around whether the research completes and is used to inform decisions, the degree to which uncertainty is reduced, and the potential for the study to generate additional secondary outcome data which may be used in a range of ways. 4 In this example, we assumed the research study was small relative to the population that will benefit from the research and did not therefore account for the benefits of self-testing for those enrolled in the study. In some contexts, the population health benefits for this group are significant (eg, in the case of large studies) and could be included in the calculations. 40

Findings from the HIV self-testing case study

We applied the graphical method to a case study of HIV self-testing. This allowed us to show how existing evidence can be used to inform an assessment of the value of a future study, and how an assessment of the value of further research can be used to guide policy decisions relating to programme implementation and research.

This showed that a 1-year cost study is likely to be of high value, whereas a 3-year outcomes study offered more modest value. The outcomes study is only worth conducting if it is run in parallel with implementation. Delaying implementation until the outcomes study is complete results in worse outcomes than implementing self-testing without further research. Overall the results suggest that if a decision maker considered setup costs to be significant, they may wish to commission a cost study and delay implementation of self-testing until it reports. If setup costs are not considered significant, running a cost and outcomes study alongside implementation may be the preferred option.

The value of research is fundamentally an economic question, as research that aims to inform programme design can only deliver value if there is a chance that its results could change the assessment about whether a programme’s benefits outweigh the opportunity costs. The cost per DALY averted threshold used to determine the health opportunity costs imposed by programme costs is a key driver of this assessment. We used a value of US$500/DALY to reflect the opportunity cost of HIV service funding. This value is subject to uncertainty and our conclusions will differ if a different estimate of opportunity cost is used. This emphasises the need for both resource allocation and research prioritisation decisions to be based on a robust assessment of the opportunity cost of healthcare funds. Recent work has estimated the cost per DALY averted for general (ie, not HIV specific) healthcare spending in a range of countries. 13 Using the estimate generated for Malawi of US$138/DALY 13 within our analysis results in research no longer generating value. The health opportunity costs of dedicating funding to the self-testing programme become so high that even under optimistic scenarios about the outcomes and costs of testing, the programme will not produce positive net population health benefits. This may become relevant as funding of HIV services becomes more reliant on domestic rather than overseas funding.

The estimates presented reflect the impact of the self-testing studies for population health in Malawi. It is possible that the research could be used to inform resource allocation decisions in additional countries with similar local epidemiology and healthcare seeking behaviours. If this is the case, we will underestimate the value of the studies. Where a research study is expected to be used in a number of countries the approach described above can be extended to reflect the total global value of the research. The value of the study in each country can be estimated accounting for differences in the size of the population that stand to benefit from research, the costs and health benefits of the intervention and the cost-effectiveness threshold. This will generate estimate of the value of research in each country which can then be aggregated to estimate the global value of research. 4 41 A worked example of this is provided in Woods et al. 42

Using estimates of the net DALYs averted by research to inform research prioritisation

Using estimates of the net DALYs averted by research to inform research prioritisation as suggested here is likely to require substantive changes to how evidence is used to support research funding decisions.

Currently, research funding decisions do not routinely use the type of evidence discussed in this paper. Institutional changes are required to facilitate use of the methods. This could include requiring funding bids to include these types of analyses, funders themselves conducting the analyses for submitted research bids, or decision-makers within LMICs conducting analyses to inform the specification of research calls. Research to explore how this might work in practise is ongoing in high-income settings 2 6 43 and further work to assess this in LMICs is warranted.

Our case study focused on HIV where both evidence and detailed cost-effectiveness models are often well developed. In many contexts, models used to assess cost-effectiveness will be available and can be used and extended to make the value of information assessments described here. For decisions where available evidence is sparse, cost-effectiveness analyses unavailable, or collation of such evidence is not feasible, our work can be used to test the sensitivity of the value of research to different plausible assumptions. This may be sufficient to determine whether research should be funded. If decisions about research appear sensitive to different plausible assumptions then there may be value in low-level initial research funding to assemble existing evidence, conduct expert elicitation and develop basic cost-effectiveness analysis and make a more informed assessment of the value of research.

The robustness of any estimates of value of information will depend on the use of appropriate priors to represent uncertainties in the available evidence, the credibility of the underlying cost-effectiveness model, and use of an appropriate measure of health opportunity costs. Specification of each element is likely to require judgements regarding approach and input parameters. By using quantitative methods such as those set out here, the judgements are open to empirical challenge thus allowing for more accountable decision making. When cost-effectiveness analyses are used to support service investments this often involves an iterative process whereby relevant stakeholders review key judgements, and scenarios are presented exploring the implications of different judgements. We envisage a similar deliberative decision-making process could be usefully implemented when using value of information estimates to inform research prioritisation.

Ultimately, once an assessment of the potential population health benefits of a research study has been made, a research funder will have to assess whether the value offered by the research is sufficient to justify the opportunity costs imposed by funding the research. These opportunity costs depend on potential alternative uses for those research funds which may include other research and non-research investments. This raises the question of how the research funder should assess the opportunity costs of their research funds when prioritising between funding applications. One way of doing this is to ensure only those proposals with the lowest research cost per net DALY averted are funded, that is, a cost-effectiveness league table approach for research proposals. The net DALYs averted by research estimated using the methods presented here could be used alongside the research costs to generate this information. In the absence of this evidence, a useful but imperfect starting point is to assume that the opportunity cost of research funds and service funds is similar. We have used this assumption within this work to estimate the maximum a research funder should be willing to spend on a study. Where research costs are known this assumption can be used to translate research costs to health opportunity costs which can be directly compared with the net health benefits of research. Those proposals offering the largest difference between net health benefits of research and health opportunity costs of research funding may be considered particularly attractive to research funders.

Our work provides a method for estimating the health benefits of research in a practical and timely fashion. This can be used to prioritise funding of those research and evidence generation activities that offer real potential to improve population health.

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Handling editor Seye Abimbola

Twitter @bs_woods

Contributors All authors conceived of and planned the paper, reviewed analysis results, and reviewed and edited the manuscript. BW and LS conducted the analysis. BW wrote the manuscript.

Funding This study was funded by the HIV Modelling Consortium, which is funded by a grant from the Bill & Melinda Gates Foundation to Imperial College London and by the Thanzi la Onse project, which is funded through the Medical Research Council Global Challenges Research Fund. In addition, TBH acknowledges joint Centre funding from the UK Medical Research Council and Department for International Development.

Competing interests All authors had financial support from the Bill & Melinda Gates Foundation and Medical Research Council Global Challenges Research Fund. TBH received joint Centre funding from the UK Medical Research Council and Department for International Development. In addition, TBH reports grants from the Bill & Melinda Gates Foundation, WHO and Medical Research Council, and personal fees from the Bill & Melinda Gates Foundation, the Global Fund and World Health Organisation, outside the submitted work.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement No additional data are available as this study is based on an analysis of transmission model outputs.

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• Research article
• Open access
• Published: 08 September 2013

Long-term health benefits of physical activity – a systematic review of longitudinal studies

• Miriam Reiner 1 ,
• Christina Niermann 2 ,
• Darko Jekauc 2 &
• Alexander Woll 1  

BMC Public Health volume  13 , Article number:  813 ( 2013 ) Cite this article

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The treatment of noncommunicable diseases (NCD), like coronary heart disease or type 2 diabetes mellitus, causes rising costs for the health system. Physical activity is supposed to reduce the risk for these diseases. Results of cross-sectional studies showed that physical activity is associated with better health, and that physical activity could prevent the development of these diseases. The purpose of this review is to summarize existing evidence for the long-term (>5 years) relationship between physical activity and weight gain, obesity, coronary heart disease, type 2 diabetes mellitus, Alzheimer’s disease and dementia.

Fifteen longitudinal studies with at least 5-year follow up times and a total of 288,724 subjects (>500 participants in each study), aged between 18 and 85 years, were identified using digital databases. Only studies published in English, about healthy adults at baseline, intentional physical activity and the listed NCDs were included.

The results of these studies show that physical activity appears to have a positive long-term influence on all selected diseases.

Conclusions

This review revealed a paucity of long-term studies on the relationship between physical activity and the incidence of NCD.

Peer Review reports

Especially in the last century, most Western countries have experienced significant demographic changes with a continuing increase in the number of older people who face medical and functional challenges, as well as diseases that are age-specific but have often originated in people’s younger years [ 1 – 4 ]. Most of these diseases including obesity, cardiovascular heart diseases (CHD) or type 2 diabetes mellitus are caused by civilisation [ 1 , 2 , 5 ]. The World Health Organisation has identified these three diseases as the most severe noncommunicable diseases (NCD) causing problems in today’s Western world [ 6 ]. Noncommunicable diseases are mostly diseases of slow progression and normally of long duration. The WHO identified for main types of NCDs: cardiovascular diseases, cancer, chronic respiratory diseases and diabetes [ 7 ].

Most NCDs primarily result from unhealthy lifestyles including the consumption of too much or unhealthy food [ 1 , 6 , 8 , 9 ], too much alcohol [ 1 , 8 , 10 ] and excessive smoking habits [ 1 , 8 , 11 ], combined with physical inactivity [ 1 , 2 , 8 , 12 ]. More specifically, inactivity and unhealthy eating habits are associated with weight gain, overweight and obesity are the major underlying causes for modern diseases such as CHD or type 2 diabetes mellitus [ 13 – 15 ]. Many cross-sectional and intervention studies have focused on the relationship between an unhealthy lifestyle, e.g. physical inactivity, unhealthy eating behaviour, smoking and alcohol consumption, and diseases in different study groups, e.g. high risk groups or different age groups [ 14 ]. All in all, cross-sectional studies suggest that physical activity may be an important factor for improving the general health and preventing the development of among others the above mentioned NCDs [ 1 ]. Because NCDs develop, not only by definition, over a long period of time and may have many causes, understanding the development of these diseases and their association with habitual factors such as physical activity is important for developing long-term prevention programs and guidelines. To investigate the development of these diseases, longitudinal studies with healthy persons, i.e. persons without obvious diseases at baseline examination, and a long term epidemiological view are necessary. It is important to follow the general population and not specific subgroups, e.g. high risk groups, persons with indications of NCD (e.g. hypertension or obesity / high body weight) or top athletes, to discover the general progression of the researched complaints in the general population.

Although these diseases are very prominent in many western countries, only few longitudinal studies exist that focus on their development during a person’s lifetime and their association with other habitual factors such as physical activity.

Many cross sectional studies have researched the relationship between physical activity and health outcomes - these results are summarized in quite a number of reviews. As opposed to this, only few long-term studies about the effect of physical activity on diseases exist, and to date there are no reviews that concentrate on long-term results in an epidemiologic view.

Therefore, the purpose of this article was to review long-term effects of physical activity on the development of weight gain and obesity, CHD and type 2 diabetes mellitus in healthy adults.

Furthermore, dementia and Alzheimer’s disease, two diseases which are of rising importance in modern societies and which develop over a long period of time, are regarded in the context of the long-term influences of physical activity. There is some evidence which indicates that physical activity has a positive effect against the development and progress of these two diseases.

To determine the importance of physical activity for the above described common health problems [ 8 ], only studies investigating the effect of physical activity on weight gain and obesity , CHD , type 2 diabetes mellitus and dementia and Alzheimer’s disease were included in this review. We searched the electronic databases Pubmed, BASE and OVID for articles published between January 1980 and May 2012 using the following search terms (without “and” or “or” and with longitudinal as well as long-term as a keyword to reduce the selection to such studies alone): “longitudinal / long-term, physical activity, adult” (3708 articles); “longitudinal / long-term, physical activity, adult, weight gain” (180 articles); “longitudinal / long-term, physical activity, adult, obesity” (483 articles); “longitudinal / long-term, physical activity, adult, CHD / coronary heart disease” (224 articles); “longitudinal / long-term, physical activity, adult, t2dm / type 2 diabetes mellitus” (87 articles); “longitudinal / long-term, physical activity, adult, dementia” (103 articles); and “longitudinal / long-term, physical activity, adult, Alzheimer’s disease” (60 articles) (Figure  1 Selection criteria and number of excluded and included papers / studies.).

Selection criteria and number of excluded and included papers/studies.

From these studies, only longitudinal studies with five or more years of follow-up time were included to show the intermediate to long-term effects of physical activity rather than short-term effects of physical activity. In addition, only studies involving adults were included to show the disease development in adulthood and old age. To show the development in the general population, not in subgroups, only large epidemiological studies with more than 500 participants were included.

Further, only epidemiologic longitudinal studies involving healthy adult participants at the baseline examination were included to determine the impact of normal daily activities performed by the general population. Clinical trials, cross-sectional studies, studies involving patients, and reviews and overviews were excluded. Publications using the same study population were included as long as they held more information or investigated other topics as well.

Only those studies were included that referred to intentional physical activity , e.g. playing soccer, or intentional activities of daily living, e.g. take the bike for shopping, to determine the impact of leisure time physical activity in the general population. Instead of this, activities of daily living, that are necessary to live a normal self-determined life, e.g. getting up from a chair or climbing stairs, are excluded.

Finally only studies published in English were included in this review.

Overall, 4,845 articles were identified with our search strategy; of these, 4,827 were excluded from the review (Figure  1 ) because of the above mentioned reasons. A total of 292,278 subjects were involved at baseline (268,885 subjects at follow-up). Four publications, involving 17,329 subjects, studied the effect of physical activity on weight gain and obesity [ 16 – 19 ]. Six publications, involving 134,188 subjects, investigated the effect of physical activity on CHD [ 20 – 26 ]. Five publications, involving 84,647 subjects, studied the effect of physical activity on type 2 diabetes mellitus [ 27 – 31 ]. Six publications, involving 15,006 subjects, investigated the effect of physical activity on Alzheimer’s disease and dementia [ 32 – 37 ]. Some studies included more than one disease accounting for the discrepancy in the overall number of subjects and included studies. The maximum follow-up time ranged from 6 to 60 years.

Effect of physical activity on weight gain and obesity

Overall, the studies included in this review showed a negative relationship between physical activity and weight gain or obesity over time. Additional file 1 : Table S1 summarises the examination data and the used survey sizes for the included studies on the long-term relationship between physical activity and weight gain and obesity.

An important study analysing the development of obesity depending on physical activity is the Aerobics Center Longitudinal Study (ACLS) conducted by the Cooper Clinic, Texas [ 16 ]. Between 1970 and 1998, DiPietro et al. [ 16 ] examined 2,501 healthy men aged between 22 and 55 years at baseline and five years later. The daily physical activity level was negatively related to the weight gain during the follow-up time. Those people who reduced their daily physical activity level gained a considerable amount of weight, while those people who maintained the same level of activity during the study did not gain weight. Further, those people who increased their physical activity level during the study experienced weight loss. DiPietro et al. [ 16 ] reported that a daily physical activity level with a metabolic rate of at least 60% above the resting metabolic rate is necessary for losing weight. Hence 45 to 60 minutes of brisk walking, gardening or cycling should be included in the daily routine to maintain weight in middle-aged men.

Gordon-Larsen et al. [ 17 ] investigated the relationship between walking and weight gain. In the Coronary Artery Risk Development in Young Adults (CARDIA) Study, they examined 4,995 women and men aged between 18 and 30 years at baseline (1985/1986) who were re-examined 2, 5, 7, 10 and 15 years later. After 15 years, there was a negative association between 30 minutes walking per day and weight gain depending on the percentile of baseline weight. Data for people in the 25 th percentile of baseline weight showed no significant relation between walking duration and weight gain. In contrast, data for people in the 50 th percentile of baseline weight revealed that for every 30 minutes of daily walking the weight gain was 0.15 kg per year less for men and 0.29 kg per year less for women. Finally, data for people in the 75 th percentile of baseline weight showed the smallest weight gain: for every 30 minutes of walking per day, men reduced their weight gain by about 0.25 kg per year and women by about 0.53 kg per year without making any other changes to their habitual lifestyle. Hence, the results of this study indicate that participants with a higher baseline weight benefit more from being physically active (for instance, for women: the total weight gain in 15 years was 13 kg for inactive women compared to only 5 kg for active women).

Hankinson et al. [ 18 ] used the same study population (CARDIA) to investigate the physical activity level in relation to a 20-year weight gain. Of 1,561 men and women, those with high habitual activity at the 20-year follow-up had a smaller increase in mean BMI, waist circumference and weight per year compared than those with low habitual activity. Men and women maintaining higher activity gained 2.6 and 6.1 kg less weight over the 20-year period than men and women with low activity, respectively. In addition, the results of that study indicated that women benefit more from maintaining a higher physical activity level than men and that maintaining higher activity levels during adulthood may lessen weight gain during the course of their life.

The Copenhagen City Heart Study by Petersen, Schnohr and Sorensen [ 19 ] linked cross-sectional and 10 year long-term analyses, to determine the development of weight gain. They examined 3,653 women and 2,626 men at three measurement points at 5-year intervals. The participants were aged between 20 and 78 years at baseline. Results of the three cross-sectional examinations (1 st at baseline, 2 nd after five years, 3 rd after 10 years) also showed a negative relationship between physical activity and weight. The preventing effects of medium leisure time physical activity (LTPA) on obesity were lower than those of high LTPA for both genders. The longitudinal analysis revealed a significant direct correlation between the level of LTPA and the risk of becoming obese for men but not for women. In contrast to the results of the cross-sectional analysis, the more active participants had a higher risk of becoming obese. Moreover, the results of that study indicate that obesity may lead to physical inactivity.

Therefore, the results of the first three studies [ 16 – 18 ] suggest a negative correlation between physical activity and weight gain after several years of follow-up (greater physical activity leads to less weight gain). In contrast, the fourth study [ 19 ] provided evidence that being more physically active leads to a greater risk of becoming obese. They suggest that obesity influences the development of physical inactivity; however they did not discuss possible causes and effect relations. These results raise the question of the causality of the relationship between physical activity and weight gain. Detailed information, results and limitations of each study are presented in Additional file 1 : Table S1.

Effect of physical activity on coronary heart disease (CHD)

Of all modern diseases, coronary heart disease (CHD) has received the most scientific scrutiny. Overall, most studies reported a negative relationship between physical activity and the occurrence of CHD for physical activity levels above the minimum energy expenditure. Additional file 2 : Table S2 summarises the examination data and the used survey sizes of the included studies addressing the longitudinal relationship between physical activity and coronary heart diseases.

In 1948, the National Heart, Lung and Blood Institute founded by Kannel et al. established the Framingham Heart Study. This research group investigated the general causes and the development of coronary heart disease in 5,209 men and women, aged 30 to 62 years at baseline [ 38 ]. The results revealed a negative association between the physical activity level and the emergence of CHD events and overall cardiovascular mortality [ 38 – 40 ]. Lee and Paffenbarger [ 20 ] compared the results of the Framingham Heart Study with data for 18,835 men who graduated from Harvard University between 1916 and 1950 and established the Harvard Alumni Health Study. In five mail-back surveys, researchers investigated the association between physical activity and stroke [ 20 ] and other CHD [ 21 ].

The relationship between energy expenditure and the incidence of stroke showed a u-shape pattern [ 20 ]. Specifically, spending at least 2,000 to 3,000 kcal additional energy per week on physical activity was necessary for reducing the risk of stroke. These results were reassessed for all CHD [ 21 ] in 12,516 Harvard Alumni over the course of 16 years (from 1977 through 1996). For CHD in general, the relationship between energy expenditure and the incidence of CHD showed the same u-shape pattern but the curve was shifted towards lower additional energy expenditure: spending at least 1,000 kcal additional energy per week on physical activity was necessary to reduce the risk of CHD. Hence, moderate to vigorous additional physical activity of about 2,000 to 3,000 kcal (min. 1,000 to 2,000 kcal) per week appear to reduce the overall risk for CHD, stroke and other diseases (e.g. hypertension).

Comparable results were also reported by the Honolulu Heart Program [ 22 , 23 ] including 8,006 men of Japanese ancestry aged 45 to 68 years at baseline who lived in Oahu, Hawaii. After 16 years, the physical activity reported at baseline was negatively related to CHD events and mortality. However, it is important to note that these results were partially mediated through the effects of hypertension, diabetes mellitus, cholesterol and BMI.

The studies cited in the next section had similar results but also featured the following additional findings.

The Alameda County Health Study by Kaplan et al. [ 24 ] reported the dependency of CHD mortality on several health factors and behaviour by quantifying the relative risks of various covariates (age, sex, perceived health, mobility impairment, heart problems, high blood pressure, diabetes mellitus, shortness of breath, current smoking, low BMI and social isolation) in 6928 men and women. After including all covariates, a protective effect of LTPA is still noticeable.

Gillum et al. [ 25 ] investigated the relationship between physical activity and stroke incidence in The National Health and Nutrition Examination Study I Epidemic Follow-Up Study on 5,852 persons aged 24 to 74 years at baseline and reported comparable results as above studies [ 20 – 23 ]. However, while the u-shaped relationship between physical activity and the incidence of stroke was confirmed for men, for women greater physical activity was negatively linearly associated with the incidence of stroke. In addition, recreational physical activity was not associated with the incidence of stroke in African American subjects, yet a significant interaction between heart rate and the incidence of stroke was observed only for African American subjects. The authors provided limited discussion of these differing results between Caucasian and African Americans.

To investigate the link between obesity and associated diseases, Li et al. [ 26 ] quantified the relative risk of developing CHD dependent on obesity and physical activity. They followed 88,393 Nurses aged 34 to 59 in their Nurses’ Health Study from 1980 to 2000. Being overweight and obese was significantly associated with increased risk of CHD. In addition, increased levels of physical activity were related to a graded reduction in CHD risk. Further, greater absolute mass (in kg) gained during adulthood predicted a higher CHD risk. The study concluded that obesity and physical inactivity contribute independently to the development of CHD in women.

Overall, all studies included in this review section showed a predicted negative relation between physical activity and the risk of CHD over time. Two studies [ 20 , 21 ] showed that a minimum additional energy expenditure of 1,000 to 2,000 kcal per week is necessary to achieve health related results. Limitations of these studies comprise the inclusion of very specific and selected participants (e.g. Harvard Alumni in the Harvard Alumni Heart Study and Nurses in the Nurses’ Health Study). In addition, these results cannot be generalized for the general public because of the selected social and ethnic backgrounds of participants and unbalanced gender distributions. In addition, most studies used Caucasian subjects alone. Hence, additional research on other ethnicities is necessary to obtain generalizable results. Moreover, the summarized studies were not designed to clarify the causality of the relationship between physical activity and CHD events. Additional research on the impact of other lifestyle factors as mediators or moderators of the relationship between physical activity and CHD is necessary. Detailed information, results and limitations of each study are presented in Additional file 2 : Table S2.

Effect of physical activity on type 2 diabetes mellitus

While the incidence of type 2 diabetes mellitus in older people has increased rapidly [ 1 ], all studies reported a negative relation between physical activity and the risk of type 2 diabetes mellitus. Additional file 3 : Table S3 summarizes the results of the included studies that investigated the long-term relationship between physical activity and type 2 diabetes mellitus.

In their Nurses’ Health Study involving 70,120 nurses aged 40 to 64, which has been on-going since 1976, Hu et al. [ 27 ] investigated the relationship between participants’ physical activity level and the development of the relative risks for type 2 diabetes mellitus. Physical activity was negatively related to the incidence of type 2 diabetes mellitus even after adjusting for BMI where participants with higher physical activity levels had a lower relative risk of acquiring type 2 diabetes mellitus than those who with a lower physical activity level.

Berenzen et al. [ 28 ] and Demakakos et al. [ 29 ] reported generally comparable results in 653 men and women in the Copenhagen City Heart Study and in the English Longitudinal Study of Ageing covering different age groups, respectively. In addition to the negative relation between physical activity and the incidence of type 2 diabetes mellitus, Demakakos et al. [ 29 ] showed that moderate to vigorous physical activity (performed at least once per week) is necessary to achieve a positive effect on health and to reduce risk of type 2 diabetes mellitus. Stratifying their results by age revealed that with increasing age a higher intensity per training session or even several sessions per week are required to achieve the same risk reduction.

A high body weight or obesity, often described by the relation between body weight and body height (body mass index—BMI), and socioeconomic status are strong covariates for the relationship between physical activity and the incidence of type 2 diabetes mellitus. For instance, Katzmarzyk et al. [ 30 ] analysed the association between obesity, physical activity, cardiorespiratory fitness and the incidence of type 2 diabetes mellitus in their Physical Activity Longitudinal Study involving 1,543 men and women. Obesity and physical fitness, but not physical activity, were significant predictors of the incidence of type 2 diabetes mellitus. Mozaffarin et al. [ 31 ] added lifestyle factors in their analysis of the risk of type 2 diabetes mellitus in 4,883 participants of the Cardiovascular Health Study. Low-risk lifestyle factors included physical activity above the median level, dietary score in the upper two quintiles, having never smoked, no alcohol, a body mass index below 25 kg/m 2 and a waist circumference below 88 cm for women or below 92 cm for men. With every healthy lifestyle factor the incidence for type 2 diabetes mellitus decreased by 35%. For people scoring lowest (that is, were the healthiest) in every lifestyle factor, an 82% lower risk for type 2 diabetes mellitus was predicted compared to all other patients. In addition, it was predicted that if these associations were causal, 8 of 10 cases of type 2 diabetes mellitus could be prevented.

All studies [ 28 – 31 ] reported a negative relationship between physical activity and the incident risk of type 2 diabetes mellitus. However, there are other factors than physical activity that are important in the development of type 2 diabetes mellitus. For instance, the results of the Physical Activity Longitudinal Study by Katzmarzyk et al. [ 31 ] suggest that not only the presence or absence of physical activity is a determining health factor but that the level of obesity and physical fitness also has an influence on the relationship between physical activity and the state of health. However, it is difficult to confirm these conclusions because of the small number of longitudinal studies that consider physical fitness and other lifestyle factors. In addition, the precise mechanism of how physical activity acts to reduce the risk of type 2 diabetes mellitus, such as through altered insulin sensitivity or altered insulin production, is still unknown.

Effect of physical activity on Alzheimer’s disease and dementia

The relationship between physical activity and dementia, particularly Alzheimer’s disease, is important for the general public because the incidence of dementia increases with increasing age [ 1 ]. Additional file 4 : Table S4 summarizes the results of the included longitudinal studies on the relationship between physical activity and Alzheimer’s disease and dementia.

The few existing studies [ 32 – 37 ] found that physical activity is negatively related to the incidence of Alzheimer’s disease and dementia in healthy men and women. Physically active people are at a lower risk of developing cognitive impairment and have a higher cognitive ability score. Interestingly, activities with low intensity, such as walking, are negatively related to the incidence of dementia and Alzheimer’s disease [ 32 ]. These results indicate that regular physical activity may be an important and potent factor preventing cognitive decline and dementia in healthy older people. Most studies on Alzheimer’s disease and dementia originate in the field of Psychology. The link between physical activity and Alzheimer’s disease and dementia in healthy participants at baseline has only been reported in very few studies [ 32 – 37 ], further emphasizing the overall lack of studies and specifically the lack of long-term studies that include people without dementia or Alzheimer’s disease. Most studies included people who had already been diagnosed with dementia or Alzheimer’s disease to research the development of the diseases. Detailed information, results and limitations of all included studies on physical activity and Alzheimer’s disease and dementia are presented in additional file 4 : Table S4.

The results of the reviewed studies indicate that physical activity seems to be an important factor that can have beneficial effects for the reviewed noncommunicable diseases weight gain and obesity, CHD and type 2 diabetes mellitus, the risk factors weight gain and obesity and the age-related diseases dementia and Alzheimer’s disease.

Two of the three longitudinal studies with at least 5-year follow-up focusing on the development of obesity over time showed a negative relationship between physical activity and obesity [ 16 , 17 ]. Surprisingly, results of one study indicated that high leisure-time physical activity increased the risk of becoming obese in the following ten years for men [ 19 ]. The reason for this remains unexplained. Overall, the results of the studies included in this review are inconclusive regarding the required minimum level of physical activity for preventing obesity. There is no evidence for the type, intensity and frequency of activities that lead to positive health results.

Several studies [ 20 – 26 , 38 ] investigated the longitudinal effect of physical activity on the development of coronary heart diseases. Overall, the results showed a positive long-term effect where people who were physically active had a lower risk of suffering from a CHD later in their life. A minimum additional 1,000 kcal energy expenditure per week spent on physical activity has been found to be necessary for preventing overall CHD [ 21 ]. However, information on the type, intensity and frequency of activities necessary for reducing the incidence of CHD are unknown.

The results of studies [ 28 – 31 ] examining the effect of physical activity on the risk of suffering from type 2 diabetes mellitus showed a negative relation where higher rates of physical activity were associated with a lower risk of developing a type 2 diabetes mellitus. A higher level of physical activity appears to be required, that is a higher intensity per training session or even several sessions per week are needed, for achieving health benefits [ 29 ]. Presumably, not only physical activity level but also weight and fitness status, and their association, play a role in the development of type 2 diabetes mellitus [ 30 ].

Finally, six studies [ 32 – 37 ] focused on the relationship between physical activity and the incidence of dementia and Alzheimer’s disease. Results of these studies emphasized the importance of regular physical activity, but no information was provided about the type, intensity and frequency of physical activity that has the greatest health benefit.

However, several problems in the reviewed studies have become apparent.

First: There are only few long-term studies on the relationship between physical activity and the incidence of NCD, which stresses the general paucity of longitudinal research in this area. More long-term studies, following the development of diseases and the impact of lifestyle, especially physical activity, are needed. Further longitudinal studies are needed that differentiate between ethnic groups, genders and groups with different social backgrounds. The results presented in this review only encompass the relationship between physical activity and the incidence of NCD in western countries and mainly for Caucasian participants. In addition, only adults were included in these studies, and hence the results cannot be generalized to other groups. Many age related diseases, such as type 2 diabetes mellitus, CHD or certain types of cancer, develop over a long time before they are diagnosed by a physician. To identify this development in detail, longitudinal studies involving healthy participants at baseline should be conducted and these participants should be followed into older age when the disease occurs. To understand this lifelong development of NCD, studies following children throughout their lifespan are desirable. To the best of our knowledge, there are only very few studies that follow children through their adolescence into their adulthood [ 41 , 42 ]. The realization of this approach is very difficult, so the research should be as long as possible and about different groups, e.g. different age-related cohorts.

Second: However, more research is necessary on the clinical picture and the development of NCD. Clearly, a thorough insight into these aspects is a prerequisite for the design and development of effective prevention programs. In addition, the relationship between physical activity and the development of NCD must be better understood including the role of other parameters, such as, for instance, nutrition, body composition, alcohol consumption and smoking behaviour. Indeed, results of some of the included studies [ 17 , 18 , 20 , 21 , 23 , 24 , 26 , 27 ],[ 29 , 31 – 33 , 36 , 37 ] suggested that other factors including eating behaviour and food intake, smoking habits and a general activity level or disease specific risk factors such as hypertension are involved in the correlations of physical activity and health outcomes. It is almost impossible to explain the impact of just one factor, e.g. just physical activity, on the development of a lifestyle related complains like CHD - other factors are always involved, e.g. genetic constitution, other diseases, for instance obesity in the relationship with type 2 diabetes mellitus, personal behaviour or individual factors, like cognitive, motivational, volitional or emotional aspects.

Third: Most studies [ 16 – 24 , 26 , 27 , 29 , 31 – 38 ] only used self-reported/estimated physical activity for measuring participant’s physical activity. However, some studies [ 30 , 43 ] (this study was excluded from the review because they researched just physical fitness, not intentional physical activity), showed that the correlation of physical activity and health benefits are mediated through the physical fitness level. The quality and relevance of findings could be improved by the use of an objectively assessed variable, such as the physical fitness level measured by a fitness test or the physical activity level monitored by an accelerometer to become independent of subjective estimates and social desirability [ 44 ]. Another limitation of using self-reported physical activity alone is the fact that most questionnaires only feature the actual physical activity at the time of the examination, and hence are unable to assess physical activity performed between questionnaire administrations. However, this information is critical for determining the importance of continuous physical activity in a healthy and active lifestyle and its benefits for health [ 45 , 46 ].

The reviewed studies have shown that physical activity could help in the prevention of non-communicable and age-related diseases. The studies have shown that it is necessary to include physical activity into prevention programs for NCD and to inform the patients and the population in general about its virtues. To achieve this, a closer cooperation between physicians, research and sport facilities is needed. Research and physical activity service providers, e.g. gyms or sports clubs, health insurances or public providers (e.g. adult education centers) have to cooperate together to improve the general health. In addition, the knowledge about the causes and the development of modern diseases in the population should be improved. Instead of treating with medicine alone, physicians should advise patients to be more physically active within their limits. Children and adolescents should generally be encouraged to maintain a healthy lifestyle throughout their lives. In addition, public health projects that are targeted at improving the general health during adulthood and older age should focus on effective disease prevention starting during childhood.

It is important to highlight the limitations of this review in order to provide a context for the results. First, the assessment is limited to published work and may be subject to publication bias. Second, the influence of several confounders, as age, the lag between baseline and follow-up, or attrition rate, could affect conclusions of this review. Third, the work contained in this review is limited to English-written journals and thus the results cannot generalize to studies conducted and published in other languages. Fourth, we included only studies with more than 500 participants. Fifth, the literature reviewed consisted of self-reported physical activity. Finally, the review is limited to the search terms and data-bases contained in our “Methods” section. Studies that have not been abstracted with these key words will be missing from our review.

This review indicates the relative lack of epidemiologic longitudinal studies on the effects of physical activity in addition to non-communicable diseases. The presented studies exclusively illustrate positive results. To the best of our knowledge no other studies reporting no or negative results over time exist.

To show the longitudinal improvements of physical activity in addition to the presented non-communicable diseases of a large number of adults within normal communities, no studies with subsamples or unhealthy participants alone were considered. This review just focuses on studies with more than 500 healthy participants. Other studies [e.g. [ 47 ] following smaller samples of participants were not included in this review; however they too contribute to the long term understanding of the development of non-communicable diseases.

Overall, the results of the reviewed articles provide a general view about the longitudinal relationship between physical activity and the incidence of NCD and health problems. Physical activity seems to be a relevant factor for preventing age-related diseases; however more long-term research is necessary.

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We would also like to express our appreciation to PD Dr. Annegret Mündermann, Julia Everke-Buchanan, Sven Henrich and Andreas Nothardt for their writing assistance on behalf of the authors.

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Institute of Sport and Sport Science, Karlsruhe Institute of Technology, Engler-Bunte Ring 15, 76131, Karlsruhe, Germany

Miriam Reiner & Alexander Woll

Institute of Sport Science. University of Konstanz, Universitätsstr. 10, 78467, Konstanz, Germany

Christina Niermann & Darko Jekauc

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The authors declare that they have no competing interests.

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MR searched for relevant literature and wrote the manuscript. CN and DJ helped writing and drafting the manuscript. AW has given final approval of the version to be published. All authors read and appropriated the final manuscript.

Miriam Reiner, Christina Niermann, Darko Jekauc and Alexander Woll contributed equally to this work.

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12889_2012_5766_moesm1_esm.pdf.

Additional file 1: Table S1: Description of studies on the association between physical activity and weight gain / obesity. Description of dataset: Table S1 describes the included studies on the association between physical activity and weight gain or obesity (Author and year, Name of the study, Baseline and measuring points, Follow up time, Baseline sample and Age at baseline, Drop out, sample size in the survey, Operationalization of physical activity and the outcome variables, Results, Limitations). (PDF 81 KB)

12889_2012_5766_MOESM2_ESM.pdf

Additional file 2: Table S2: Description of studies of the association between physical activity and coronary heart diseases. Description of dataset: Table S2 describes the included studies on the association between physical activity and coronary heart diseases (Author and year, Name of the study, Baseline and measuring points, Follow up time, Baseline sample and Age at baseline, Drop out, sample size in the survey, Operationalization of physical activity and the outcome variables, Results, Limitations). (PDF 45 KB)

12889_2012_5766_MOESM3_ESM.pdf

Additional file 3: Table S3: Description of studies on the association between physical activity and type 2 diabetes mellitus. Description of dataset: Table S3 describes the included studies on the association between physical activity and type 2 diabetes mellitus (Author and year, Name of the study, Baseline and measuring points, Follow up time, Baseline sample and Age at baseline, Drop out, sample size in the survey, Operationalization of physical activity and the outcome variables, Results, Limitations). (PDF 58 KB)

12889_2012_5766_MOESM4_ESM.pdf

Additional file 4: Table S4: Description of studies on the association between physical activity and Alzheimer’s disease and dementia. Description of dataset: Table S4 describes the included studies on the association between physical activity and Alzheimer’s disease and dementia (Author and year, Name of the study, Baseline and measuring points, Follow up time, Baseline sample and Age at baseline, Drop out, sample size in the survey, Operationalization of physical activity and the outcome variables, Results, Limitations). (PDF 64 KB)

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Reiner, M., Niermann, C., Jekauc, D. et al. Long-term health benefits of physical activity – a systematic review of longitudinal studies. BMC Public Health 13 , 813 (2013). https://doi.org/10.1186/1471-2458-13-813

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Coffee lovers around the world who reach for their favorite morning brew probably aren’t thinking about its health benefits or risks. And yet this beverage has been subject to a long history of debate. In 1991 coffee was included in a list of possible carcinogens by the World Health Organization. By 2016 it was exonerated, as research found that the beverage was not associated with an increased risk of cancer; on the contrary, there was a decreased risk of certain cancers among those who drink coffee regularly once smoking history was properly accounted for. Additional accumulating research suggests that when consumed in moderation, coffee can be considered a healthy beverage . Why then in 2018 did one U.S. state pass legislation that coffee must bear a cancer warning label? Read on to explore the complexities of coffee.

• Vitamin B2 (riboflavin)
• Plant chemicals: polyphenols including chlorogenic acid and quinic acid, and diterpenes including cafestol and kahweol

One 8-ounce cup of brewed coffee contains about 95 mg of caffeine. A moderate amount of coffee is generally defined as 3-5 cups a day, or on average 400 mg of caffeine, according to the Dietary Guidelines for Americans.

Coffee and Health

Coffee is an intricate mixture of more than a thousand chemicals. [1] The cup of coffee you order from a coffee shop is likely different from the coffee you brew at home. What defines a cup is the type of coffee bean used, how it is roasted, the amount of grind, and how it is brewed. Human response to coffee or caffeine can also vary substantially across individuals. Low to moderate doses of caffeine (50–300 mg) may cause increased alertness, energy, and ability to concentrate, while higher doses may have negative effects such as anxiety, restlessness, insomnia, and increased heart rate. [2] Still, the cumulative research on coffee points in the direction of a health benefit. [3,4] Does the benefit stem from the caffeine or plant compounds in the coffee bean? Is there a certain amount of coffee needed a day to produce a health benefit?

Coffee may affect how cancer develops, ranging from the initiation of a cancer cell to its death. For example, coffee may stimulate the production of bile acids and speed digestion through the colon, which can lower the amount of carcinogens to which colon tissue is exposed. Various polyphenols in coffee have been shown to prevent cancer cell growth in animal studies. Coffee has also been associated with decreased estrogen levels, a hormone linked to several types of cancer. [5] Caffeine itself may interfere with the growth and spread of cancer cells. [6] Coffee also appears to lower inflammation, a risk factor for many cancers.

The 2018 uproar in California due to warning labels placed on coffee products stemmed from a chemical in the beverage called acrylamide, which is formed when the beans are roasted. Acrylamide is also found in some starchy foods that are processed with high heat like French fries, cookies, crackers, and potato chips. It was classified in the National Toxicology Program’s 2014 Report on Carcinogens , as “reasonably anticipated to be a human carcinogen” based on studies in lab animals. However, there is not yet evidence of a health effect in humans from eating acrylamide in food. Regardless, in March 2018 a California judge ruled that all California coffee sellers must warn consumers about the “potential cancer risk” from drinking coffee, because coffee-selling companies failed to show that acrylamide did not pose a significant health risk. California’s law Proposition 65, or The Safe Drinking Water and Toxic Enforcement Act of 1986, fueled the ruling, which requires a warning label to be placed on any ingredient from a list of 900 confirmed or suspected carcinogens.

However, many cancer experts disputed the ruling , stating that the metabolism of acrylamide differs considerably in animals and humans, and the high amount of acrylamide used in animal research is not comparable to the amount present in food. They cited the beneficial health effects of coffee, with improved antioxidant responses and reduced inflammation, both factors important in cancer prevention. Evidence from the American Institute for Cancer Research concludes that drinking coffee may  reduce risk for endometrial and liver cancer , and based on a systematic review of a large body of research, it is not a risk for the cancers that were studied.

In June 2018, the California Office of Environmental Health Hazard Assessment (OEHHA) proposed a new regulation exempting coffee from displaying cancer warnings under Proposition 65. This proposal was based on a review of more than 1,000 studies published by the World Health Organization’s International Agency for Research on Cancer that found inadequate evidence that drinking coffee causes cancer. In January 2019, OEHHA completed its review and response to comments and submitted the regulation to the Office of Administrative Law (OAL) for final review.

Although ingestion of caffeine can increase blood sugar in the short-term, long-term studies have shown that habitual coffee drinkers have a lower risk of developing type 2 diabetes compared with non-drinkers. The polyphenols and minerals such as magnesium in coffee may improve the effectiveness of insulin and glucose metabolism in the body.

• In a meta-analysis of 45,335 people with type 2 diabetes followed for up to 20 years, an association was found with increasing cups of coffee and a lower risk of developing diabetes. Compared with no coffee, the decreased risk ranged from 8% with 1 cup a day to 33% for 6 cups a day. Caffeinated coffee showed a slightly greater benefit than decaffeinated coffee. [7]
• Another meta-analysis of prospective cohort studies showed similar associations. When comparing the highest intake of coffee (up to 10 cups a day) with the lowest (<1 cup), there was a 30% decreased risk of type 2 diabetes in those drinking the highest amounts of coffee and caffeine and a 20% decreased risk when drinking decaffeinated coffee. Further analysis showed that the incidence of diabetes decreased by 12% for every 2 extra cups of coffee a day, and 14% for every 200 mg a day increase in caffeine intake (up to 700 mg a day). [8]

Caffeine is a stimulant affecting the central nervous system that can cause different reactions in people. In sensitive individuals, it can irritate the stomach, increase anxiety or a jittery feeling, and disrupt sleep. Although many people appreciate the temporary energy boost after drinking an extra cup of coffee, high amounts of caffeine can cause unwanted heart palpitations in some.

Unfiltered coffee, such as French press and Turkish coffees, contains diterpenes, substances that can raise bad LDL cholesterol and triglycerides. Espresso coffee contains moderate amounts of diterpenes. Filtered coffee (drip-brewed coffee) and instant coffee contain almost no diterpenes as the filtering and processing of these coffee types removes the diterpenes.

Despite these factors, evidence suggests that drinking coffee regularly may lower the risk of heart disease and stroke :

• Among 83,076 women in the Nurses’ Health Study, drinking 4 or more cups of coffee each day was associated with a 20% lower risk of stroke compared with non-drinkers. Decaffeinated coffee also showed an association, with 2 or more cups daily and a 11% lower stroke risk. The authors found no such association with other caffeinated drinks such as tea and soda. These coffee-specific results suggest that components in coffee other than caffeine may be protective. [9]
• A large cohort of 37,514 women concluded that moderate coffee drinking of 2-3 cups a day was associated with a 21% reduced risk of heart disease. [10]
• In addition, a meta-analysis of 21 prospective studies of men and women looking at coffee consumption and death from chronic diseases found a link between moderate coffee consumption (3 cups per day) and a 21% lower risk of cardiovascular disease deaths compared with non-drinkers. [11]
• Another meta-analysis of 36 studies including men and women reviewed coffee consumption and risk of cardiovascular diseases (including heart disease, stroke, heart failure, and deaths from these conditions). It found that when compared with the lowest intakes of coffee (average 0 cups), a moderate coffee intake of 3-5 cups a day was linked with a 15% lower risk of cardiovascular disease. Heavier coffee intake of 6 or more cups daily was neither associated with a higher nor a lower risk of cardiovascular disease. [12]

Naturally occurring polyphenols in both caffeinated and decaffeinated coffee can act as antioxidants to reduce damaging oxidative stress and inflammation of cells. It may have neurological benefits in some people and act as an antidepressant. [13] Caffeine may affect mental states such as increasing alertness and attention, reducing anxiety, and improving mood. [14] A moderate caffeine intake of less than 6 cups of coffee per day has been associated with a lower risk of depression and suicide. However in a few cases of sensitive individuals, higher amounts of caffeine may increase anxiety, restlessness, and insomnia. Suddenly stopping caffeine intake can cause headache, fatigue, anxiety, and low mood for a few days and may persist for up to a week. [15]

• A prospective cohort study following 263,923 participants from the National Institutes of Health and American Association of Retired Persons found that those who drank 4 or more cups of coffee a day were almost 10% less likely to become depressed than those who drank none. [15]
• In a meta-analysis of observational studies including 330,677 participants, the authors found a 24% reduced risk of depression when comparing the highest (4.5 cups/day) to lowest (<1 cup) intakes of coffee. They found an 8% decreased risk of depression with each additional cup of coffee consumed. There was also a 28% reduced risk of depression comparing the highest to lowest intakes of caffeine, with the greatest benefit occurring with caffeine intakes between 68 and 509 mg a day (about 6 oz. to 2 cups of coffee). [16]
• A review looking at three large prospective cohorts of men and women in the U.S. found a decreasing risk of suicide with increasing coffee consumption. When compared with no-coffee drinkers, the pooled risk of suicide was 45% lower among those who drank 2-3 cups daily and 53% lower among those who drank 4 or more cups daily. There was no association between decaffeinated coffee and suicide risk, suggesting that caffeine was the key factor, rather than plant compounds in coffee. [17]
• A systematic review of 26 studies including cohort and case-control studies found a 25% lower risk of developing PD with higher intakes of caffeinated coffee. It also found a 24% decreased risk with every 300 mg increase in caffeine intake. [18]
• A Finnish cohort study tracked coffee consumption and PD development in 6,710 men and women over 22 years. In that time, after adjusting for known risks of PD, those who drank at least 10 cups of coffee a day had a significantly lower risk of developing the disease than non-drinkers. [19]
• A large cohort of men and women were followed for 10 and 16 years, respectively, to study caffeine and coffee intake on PD. The results showed an association in men drinking the most caffeine (6 or more cups of coffee daily) and a 58% lower risk of PD compared with men drinking no coffee. Women showed the lowest risk when drinking moderate intakes of 1-3 cups coffee daily. [20]
• However, three systematic reviews were inconclusive about coffee’s effect on Alzheimer’s disease due to a limited number of studies and a high variation in study types that produced mixed findings. Overall the results suggested a trend towards a protective effect of caffeine against late-life dementia and Alzheimer’s disease, but no definitive statements could be made. The authors stated the need for larger studies with longer follow-up periods. Randomized controlled trials studying a protective effect of coffee or caffeine on the progression of Alzheimer’s disease and dementia are not yet available. [21-23]

There are various proposed actions of caffeine or components in coffee that may prevent the formation of gallstones. The most common type of gallstone is made of cholesterol. Coffee may prevent cholesterol from forming into crystals in the gallbladder. It may stimulate contractions in the gallbladder and increase the flow of bile so that cholesterol does not collect. [24]

A study of 46,008 men tracked the development of gallstones and their coffee consumption for 10 years. After adjusting for other factors known to cause gallstones, the study concluded that men who consistently drank coffee were significantly less likely to develop gallstones compared to men who did not. [24] A similar large study found the same result in women. [25]

• In a large cohort of more than 200,000 participants followed for up to 30 years, an association was found between drinking moderate amounts of coffee and lower risk of early death. Compared with non-drinkers, those who drank 3-5 cups of coffee daily were 15% less likely to die early from all causes, including cardiovascular disease, suicide, and Parkinson’s disease. Both caffeinated and decaffeinated coffee provided benefits. The authors suggested that bioactive compounds in coffee may be responsible for interfering with disease development by reducing inflammation and insulin resistance. [26]
• In a large prospective cohort of more than 500,000 people followed for 10 years, an association was found between drinking higher amounts of coffee and lower rates of death from all causes. Compared with non-drinkers, those drinking 6-7 cups daily had a 16% lower risk of early death. [26] A protective association was also found in those who drank 8 or more cups daily. The protective effect was present regardless of a genetic predisposition to either faster or slower caffeine metabolism. Instant and decaffeinated coffee showed a similar health benefit.

The bottom line: A large body of evidence suggests that consumption of caffeinated coffee does not increase the risk of cardiovascular diseases and cancers. In fact, consumption of 3 to 5 standard cups of coffee daily has been consistently associated with a reduced risk of several chronic diseases. [4] However, some individuals may not tolerate higher amounts of caffeine due to symptoms of jitteriness, anxiety, and insomnia. Specifically, those who have difficulty controlling their blood pressure may want to moderate their coffee intake. Pregnant women are also advised to aim for less than 200 mg of caffeine daily, the amount in 2 cups of coffee, because caffeine passes through the placenta into the fetus and has been associated with pregnancy loss and low birth weight. [3, 27] Because of the potential negative side effects some people experience when drinking caffeinated coffee, it is not necessary to start drinking it if you do not already or to increase the amount you currently drink, as there are many other dietary strategies to improve your health. Decaffeinated coffee is a good option if one is sensitive to caffeine, and according to the research summarized above, it offers similar health benefits as caffeinated coffee. It’s also important to keep in mind how you enjoy your brew. The extra calories, sugar, and saturated fat in a coffee house beverage loaded with whipped cream and flavored syrup might offset any health benefits found in a basic black coffee.

What about iced coffee?

Coffee beans are the seeds of a fruit called a coffee cherry. Coffee cherries grow on coffee trees from a genus of plants called Coffea . There are a wide variety of species of coffee plants, ranging from shrubs to trees.

• Type of bean. There are two main types of coffee species, Arabica and Robusta. Arabica originates from Ethiopia and produces a mild, flavorful tasting coffee. It is the most popular type worldwide. However, it is expensive to grow because the Arabica plant is sensitive to the environment, requiring shade, humidity, and steady temperatures between 60-75 degrees Fahrenheit. The Robusta coffee plant is more economical to grow because it is resistant to disease and survives in a wider range of temperatures between 65-97 degrees Fahrenheit. It can also withstand harsh climate changes such as variations in rainfall and strong sunlight.
• Type of roast. Coffee beans start out green. They are roasted at a high heat to produce a chemical change that releases the rich aroma and flavor that we associate with coffee. They are then cooled and ground for brewing. Roasting levels range from light to medium to dark. The lighter the roast, the lighter the color and roasted flavor and the higher its acidity. Dark roasts produce a black bean with little acidity and a bitter roasted flavor. The popular French roast is medium-dark.
• Type of grind. A medium grind is the most common and used for automatic drip coffee makers. A fine grind is used for deeper flavors like espresso, which releases the oils, and a coarse grind is used in coffee presses.

Decaffeinated coffee . This is an option for those who experience unpleasant side effects from caffeine. The two most common methods used to remove caffeine from coffee is to apply chemical solvents (methylene chloride or ethyl acetate) or carbon dioxide gas. Both are applied to steamed or soaked beans, which are then allowed to dry. The solvents bind to caffeine and both evaporate when the beans are rinsed and/or dried. According to U.S. regulations, at least 97% of the caffeine must be removed to carry the decaffeinated label, so there may be trace residual amounts of caffeine. Both methods may cause some loss of flavor as other naturally occurring chemicals in coffee beans that impart their unique flavor and scent may be destroyed during processing.

A plain “black” cup of coffee is a very low calorie drink—8 ounces only contains 2 calories! However, adding sugar, cream, and milk can quickly bump up the calorie counts. A tablespoon of cream contains 52 calories, and a tablespoon of whole milk contains 9 calories. While 9 calories isn’t a lot, milk is often poured into coffee without measuring, so you may be getting several servings of milk or cream in your coffee. A tablespoon of sugar contains 48 calories, so if you take your coffee with cream and sugar, you’re adding over 100 extra calories to your daily cup.

However, the real caloric danger occurs in specialty mochas, lattes, or blended ice coffee drinks. These drinks are often super-sized and can contain anywhere from 200-500 calories, as well as an extremely large amount of sugar. With these drinks, it’s best to enjoy them as a treat or dessert, and stick with plain, minimally sweetened coffee on a regular basis

• Place beans or ground coffee in an airtight opaque container at room temperature away from sunlight. Inside a cool dark cabinet would be ideal. Exposure to moisture, air, heat, and light can strip coffee of its flavor. Coffee packaging does not preserve the coffee well for extended periods, so transfer larger amounts of coffee to airtight containers.
• Coffee can be frozen if stored in a very airtight container. Exposure to even small amounts of air in the freezer can lead to freezer burn.
• Follow directions on the coffee package and your coffee machine, but generally the ratio is 1-2 tablespoons of ground coffee per 6 ounces of water.
• For optimal coffee flavor, drink soon after brewing. The beverage will lose flavor with time.
• Use ground coffee within a few days and whole beans within two weeks.

Did You Know?

• It is a myth that darker roasts contain a higher level of caffeine than lighter roasts. Lighter roasts actually have a slightly higher concentration!
• Coffee grinds should not be brewed more than once. Brewed grinds taste bitter and may no longer produce a pleasant coffee flavor.
• While water is always the best choice for quenching your thirst, coffee can count towards your daily fluid goals. Although caffeine has a mild diuretic effect, it is offset by the total amount of fluid from the coffee.

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• Ding M, Satija A, Bhupathiraju SN, Hu Y, Sun Q, Han J, Lopez-Garcia E, Willett W, van Dam RM, Hu FB. Association of Coffee Consumption With Total and Cause-Specific Mortality in 3 Large Prospective Cohorts. Circulation . 2015 Dec 15;132(24):2305-15.
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