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Family-based nutrition interventions for obesity prevention among school-aged children: a systematic review

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Megan Perdew, Sam Liu, Patti-Jean Naylor, Family-based nutrition interventions for obesity prevention among school-aged children: a systematic review, Translational Behavioral Medicine , Volume 11, Issue 3, March 2021, Pages 709–723, https://doi.org/10.1093/tbm/ibaa082

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Effective evidence-informed family-based nutrition interventions for childhood obesity management are needed. (a) To assess the number and quality of published randomized controlled trials incorporating family-based nutrition interventions for childhood obesity (ages 5–18 years) management and (b) to identify intervention attributes (e.g., contact time, nutrition curricula, and behavior change strategies) used in successful interventions. Studies that met eligibility criteria were randomized controlled trials and family-based childhood obesity management interventions for children and adolescents ages 5–18 years old that included a healthy eating component and measured child dietary behaviors and/or parent dietary feeding practices. Six databases were searched: CINAHL complete, Cochrane Central Register of Controlled Trials, Health Source: Nursing/Academic Edition, MEDLINE with full text (PubMed), PsycINFO, SPORTDiscus, and ERIC (EBSCO Host). The validated Quality Assessment Tool for Quantitative Studies was used to assess study quality. Eight studies met eligibility criteria. Study quality analysis showed that blinding of the research teams (e.g., analysts, and those focused on data collection) and the use of age appropriate, valid, and reliable instruments were areas of concern. Successful nutrition interventions targeting children 5–18 years old, appear to include setting family-based goals, modifying home food environment, hands-on approaches to teaching nutrition (games, group-based activities), and fruit and vegetable vouchers. This review highlighted a limited amount of moderate to high quality evidence to suggest that family-based nutrition interventions can be successful in improving dietary behaviors and that interventions with positive outcomes had some components of nutrition curricula and strategies in common.

Practice: Family-based nutrition interventions can be effective in improving dietary behaviors for obesity management among school-aged children.

Policy: Effective family-based nutrition interventions for childhood obesity management implemented behavior change techniques (e.g., family-based goal setting, modifying the home food environment), engaging activities and practical application components, and fruit and vegetable vouchers.

Research: Future family-based nutrition interventions should incorporate behavior change techniques, engaging activities with practical application components and strategies to increase accessibility to fruit and vegetables such as vouchers and examine how differences in contact time, intervention mode of delivery (i.e., in-person vs. technology-based) and families’ cultural and societal beliefs as potential mediators of intervention effectiveness.

Childhood obesity is one of the most common pediatric health problems and has been linked to multiple physiological and psychosocial problems throughout childhood [ 1–3 ]. Currently, it is estimated that 30% of children in North America are overweight or obese [ 4 ]. As of 2016, obesity prevalence in the USA was 18.4% among 6–11-year-olds and 20.6% among 12–19-year-olds [ 5 ]. In Canada, the prevalence of overweight and obesity appears to increase throughout childhood and adolescence; as of 2012/2013, 15.4% of 5–9-year-olds, 23% of 10–14-year-olds, and 17.1% of 15–17-year-olds were considered overweight [ 6 ]. Poor dietary quality is one of the leading lifestyle factors contributing to childhood obesity [ 7 ]. Approximately 60% of American youth consume below recommended daily servings of fruit, and 93% below the recommended daily servings of vegetables [ 8 ]. Greater than 12% of American youth reported drinking a sugary-sweetened beverage (SSB) (e.g., sports drink, soda, sweetened tea, etc.) at least once per day and 19% of youth had consumed a can, glass, or bottle of soda (i.e., Coke, Pepsi, non-diet sodas, or pop) one or more times per day [ 8 ]. Furthermore, data from the National Health and Nutrition Examination Survey (NHANES) showed that close to 40% of total calories consumed by children and adolescents (2–18 years of age) come from empty calories such as added fats and sugars [ 9 ].

Family-focused nutrition interventions have been recognized as a promising intervention approach to manage childhood obesity [ 4 , 10–13 ]. Families can play a critical role in developing a child’s dietary behaviors [ 14–16 ] because children often learn to model their dietary behaviors after their parents. The level of family cohesion and satisfaction largely influences child health behaviors, in turn, family dysfunction (i.e., lack of emotional bonding, insecurity) is related to childhood obesity risk factors [ 17 , 18 ]. Evidence suggests that socioeconomic adversity (i.e., low education, low social mobility, financial hardship) is closely associated with the onset of childhood obesity; particularly, low socioeconomic status (SES) negatively impacts families’ ability to provide healthy modeling of eating behaviors [ 18 ]. Families of low SES are often monetarily constrained which makes healthy lifestyle choices such as, purchasing fresh fruits and vegetables, less feasible [ 18 ]. Further, recent research has shown that family-based obesity interventions focusing on parent feeding practices and the home food environment are successful in improving child dietary behaviors [ 19–21 ]. However, substantial differences in study quality and nutrition curricula and strategies (e.g., contact hours, length, behavior techniques) exist across family-based nutrition interventions [ 22 , 23 ]. These differences in intervention content, duration, and dietary outcome measures make it challenging to specify components of the intervention that are critical to effectively managing childhood obesity. To our knowledge, no recent systematic review has examined the number and the quality of published randomized controlled trials incorporating family-based nutrition interventions. Thus, objectives of this systematic review were (a) to assess the number and quality of published randomized controlled trials incorporating family-based nutrition interventions for childhood obesity management (ages 5–18 years old) and (b) to identify intervention attributes (e.g., contact time, nutrition curricula, and behavior change strategies) used in successful interventions.

This review was registered electronically onto the International Prospective Register of Systematic Reviews (PROSPERO) on September 28, 2018. The review protocol can be accessed on the PROSPERO website by entering the following registration code into the search bar: CRD42018109587.

Search strategy

The search was limited to articles published from the year 2000 onward and the following databases were used: CINAHL complete, Cochrane Central Register of Controlled Trials, Health Source: Nursing/Academic Edition, MEDLINE with full text (PubMed), PsycINFO, SPORTDiscus, and ERIC (EBSCO Host). The following search terms were used in each database (family-based OR home OR parent) AND (nutrition OR food OR eating OR diet) AND (intervention OR program) AND (childhood OR kid OR youth) AND (obesity OR weight) AND (fruit OR vegetable) AND (intake OR consumption) AND (healthy eating) AND (obesity prevention).

Eligibility criteria

Eligible studies were randomized controlled trials evaluating family-based nutrition interventions targeting overweight, or obese (according to body mass index [BMI]-for-age and sex) children and adolescents (5–18 years of age). The interventions included a nutrition or healthy eating component. The terms “healthy eating” and “nutrition” were used interchangeably to describe intervention activities and curricula (i.e., setting family-based goals; cooking demonstrations; parental modeling at home; taste testing games, etc.). Additionally, the study assessed child dietary behaviors related to fruit and vegetable (FV), and/or SSB and/or fast-food consumption (food choices, 24-hr recall, 7-day recall, dietary quality), and/or frequency eating away from home.

Study selection

Two reviewers screened (MP and SL) all article titles; titles containing any obvious content that did not meet the inclusion criteria were removed. Abstracts were then screened and those not meeting the aforementioned inclusion criteria were removed. After filtering titles and abstracts, the remaining articles were reviewed in full to confirm they met the inclusion criteria. Additionally, the two reviewers completed a forward search of references for each eligible study and then screened them using the aforementioned procedure.

Quality assessment

Two reviewers (MP and SL) also assessed the study quality of all studies meeting the inclusion criteria using the validated Quality Assessment Tool for Quantitative Studies [ 24 ] created by the Effective Public Health Practice Project (EPHPP). The EPHPP Quality Assessment Tool is one of the most commonly used quality assessment tools [ 24 , 25 ]. The tool guides a reviewer to determine study quality (e.g., strong, moderate, or weak) based on six categories: (a) selection bias, (b) study design, (c) confounders, (d) blinding, (e) data collection methods, and (f) withdrawals and dropouts. If a study receives four strong ratings and no weak ratings out of the six categories, it earns a strong global rating. Studies receive a moderate global rating if the study has less than four strong ratings and one or more weak ratings. Finally, a study having two or more weak ratings out of the six categories receives a weak global quality rating. After both reviewers rated the studies, their results were compared and any discrepancies among their ratings were discussed until reaching an agreement.

Data extraction

One reviewer (MP) extracted the following data from each study that met inclusion criteria: author/country, sample size, intervention duration, intervention mode of delivery and contact time (in-person, telephone, and/or online contact), intervention components, behavior change curricula, positive intervention outcomes (i.e., nutrition-related outcomes such as FV and SSB consumption), behavior change techniques (BCTs), and data related to study quality (i.e., selection bias, confounders, data collection methods). Intervention components incorporated the behavior change curricula and nutrition activities whereby the BCTs were applied. A coding scheme developed by Michie et al. was used to categorize the BCTs techniques used in each study [ 26 ]. BCTs were examined as opposed to behavior theory because not all studies reported the use of behavior theory.

Data synthesis

The review used descriptive data synthesis to investigate the number and quality of published randomized controlled trials incorporating family-based nutrition interventions for childhood obesity and to identify the intervention attributes (e.g., contact time, nutrition curricula, and behavior change strategies) used in successful interventions. We identified the quality and intervention attributes (e.g., contact time, nutrition curricula, and behavior change strategies) and combined the observations into a matrix table. All intervention attributions were sorted by frequency to identify the most common intervention attributes used in effective interventions.

Figure 1 shows our screening process and rationale for excluding studies. During the initial database search, 461 relevant articles were identified; an additional ten articles were found while completing a forward reference search. After reviewing the abstracts and removing duplicate publications, 22 full-text articles were assessed and 8 met inclusion criteria.

PRISMA flow diagram.

PRISMA flow diagram.

Study characteristics

Study characteristics are displayed in Table 2 . The majority of studies were completed in the USA ( n = 7). Four studies targeted the intervention for minority populations (e.g., Latinos) [ 27–29 ] and families of SES [ 30 , 31 ]. The mean intervention length was 10.4 months [ SD = 7.00]. Six out of the eight studies had a duration of 6–12 months [ 14 , 28 , 30–33 ]. Interventions were primarily delivered in-person through home visits, or group sessions held at local schools, churches, or community centers. Specifically, five studies provided monthly in-person sessions or workshops [ 14 , 28 , 30 , 31 , 33 ]. The remaining three studies held weekly in-person sessions for at least the first month of the intervention and then transitioned to bi-weekly or monthly meetings [ 27 , 29 , 34 ]. Additionally, five studies also provided additional remote support via telephone calls that were intended to reinforce behavioral messages addressed at previous group sessions and allow time for setting behavioral goals [ 14 , 27–30 , 33 ].

Summary of interventions

CONT control; FV fruit and vegetables; HE healthy eating; HH household; INT intervention; PA physical activity; SD standard deviation; SSB sugary-sweetened beverage.

a BCT column: number in brackets corresponds to the hierarchical cluster analysis of behavior change techniques (grouping with 16 cluster solution) from Michie et al. [ 26 ]

b For the Epstein et al. [ 32 ] article there was not a true control, thus for the purpose of our review the “decreasing fat and sugar” group is represented as the control and the “increasing fruit and vegetable” group represented the intervention.

*These values represent postintervention means, data were not tabulated for mean Δ or baseline means.

Study quality

Scores for each quality assessment components (e.g., confounder, withdrawals and dropouts, data collection, and/or the analyses categories) for the eight studies are listed in Table 1 . Three studies received a “strong” global quality rating [ 14 , 30 , 32 ] and the remaining five studies were considered “moderate” [ 27–29 , 31 , 33 , 34 ]. Subratings of the quality assessment suggested that the study design and withdrawals and dropouts were an area of strength whereby the average dropout rate was 15.75% [ SD = 0.152]. However, blinding procedures were an area of concern as no studies received a “strong” rating. Data collection methods using valid and reliable dietary instrument were another area of concern as only three studies received strong subratings [ 30 , 32 , 33 ].

Intervention attributes used in successful interventions

A summary of intervention outcomes is shown in Table 2 . The average in-person contact hours per intervention were a minimum of 13 hr [ SD = 3.81], which included home visits and group sessions. An average of 1 hr [ SD = 2.24] remote contact hours were provided by five interventions via phone calls [ 14 , 27 , 28 , 30 , 33 ]. Half of the interventions offered 7–15 contact hours [ 28 , 32–34 ], while the remaining four provided up to 18–24 hr [ 14 , 27 , 29–31 ]. For the purpose of this review, we considered a “successful intervention” to include those that significantly improved at least one child or adolescent dietary outcome (i.e., FV intake, SSB, or fast-food/eating-out) among the intervention group relative to the control. Overall, no observable relationship was identified between number of contact hours (intervention intensity) and intervention success as there was considerable heterogeneity in the contact hours provided and subsequent dietary outcomes.

Four out of the eight studies evaluating child FV servings reported significant increases in children’s FV consumption [ 28 , 31–33 ]. The increase in FV servings ranged from 0.4 servings to 1.8 servings per day relative to control. However, two out of the four studies showed increases of 0.4–0.5 servings of FVs, which some authors suggest may have limited clinical impact [ 28 , 33 ]. These successful interventions implemented BCTs such as goal setting, restructuring the physical environment and social support (practical) and applied these BCTs using intervention components that emphasized setting family-based goals for healthy eating, modifying the home food environment and providing practical application components (e.g., preparing family meals together, planning and executing grocery shopping trips) [ 28 , 32 , 33 ]. Additionally, one of these studies used vouchers to facilitate the purchase of FV among low-income families for improving child FV [ 31 ] ( Table 2 ).

Out of the five studies that evaluated SSBs, two studies significantly decreased consumption relative to the control [ 14 , 29 ]. Specifically, Fulkerson et al. [ 14 ] reported that children in the control group (mean Δ = 0.45; SD = 0.34) were significantly more “likely to consume one SSB” compared with the intervention (mean Δ = −0.19; SD = 0.30; p = 0.04; see Table 2 ). Additionally, the Entre Familia trial stated that at 10-month postbaseline the intervention group had a significantly lower average of SSB (postintervention mean = 1.02; SD = 0.10) relative to the control group (postintervention mean = 1.38; SD = 0.10; p = 0.02; see Table 2 ) [ 29 ]. These successful interventions emphasized the role of family support (i.e., social support, restructuring the physical environment) while making changes to individual dietary behaviors encouraged families to regularly prepare and eat meals together and discuss barriers to achieving their weekly goals ( Table 2 ).

Out of the three studies that assessed fast-food/eating away from home, two studies were effective in decreasing the frequency of fast-food consumption and eating out [ 27 , 33 ]. Specifically, Horton et al. [ 27 ] showed that the intervention group consumed fast-food on average 1.09 (SE = 0.07) days per week, which was significantly lower than the control (postintervention mean = 1.33; SE = 0.06; p = 0.027; see Table 2 ). Further, French et al. [ 33 ] reported that per person dollars spent in each household on eating away from the home significantly decreased among intervention households compared with the controls (mean Δ = −4.6; SD = 1.75; p = 0.02; see Table 2 ). Both interventions emphasized setting household/family level goals for improving the availability and accessibility of FVs and setting appropriate limits on high-fat and sugary foods within the home food environment (Table 3). Each intervention also provided promotora home visits that focused on addressing barriers to healthy eating specific to families identifying as Latino and/or Mexican American. During home visits, the nutrition curricula promoted family goal setting as a strategy for improving the home food environment.

Overall, seven out of the eight studies in this review were successful in significantly improving at least one youth dietary behavior among the intervention group compared with the control. Wieland et al. [ 30 ] was the only study that did not show a significant improvement in any youth dietary behavior outcomes.

The purpose of this systematic review was to summarize the current evidence and inform future family-based childhood obesity management research and practice by assessing the number and quality of published randomized controlled trials incorporating family-based nutrition interventions and identifying nutrition curricula and strategies (e.g., BCTs, activities, and demonstrations) used in successful interventions. The review highlighted a limited amount of moderate to high quality evidence to suggest that family-based nutrition interventions can be successful in improving dietary behaviors and that successful interventions, despite heterogeneity, had some components of nutrition curricula and strategies in common. Specifically, we found three potential components that may be associated with intervention success.

First, we found that successful interventions may share a cluster of BCTs, particularly in intervention targeting children 5–12 years old. These BCTs emphasized intervention components such as setting family-based goals for healthy eating, modifying the home food environment and emphasizing the role of family support (i.e., social support, restructuring, the physical environment). However, future studies are needed to confirm this finding as only one study in this review reported none significant changes in dietary outcomes. Interestingly, similar BCTs were used in both successful and unsuccessful studies. The non-significant change observed in Wieland et al.’s [ 30 ] study may suggest that intervention components such as family-based goal setting and hands-on nutrition games may not be as successful among adolescents (>12 years of age). Younger children may be more flexible than adolescents in their ability to modify behaviors, since they are just beginning to develop self-regulation skills for healthy living [ 35 ]. In addition, parental support as an influence on child health behaviors tends to peak at age 12 [ 36 ]. Adolescents begin to distance themselves from family behaviors in free time and are influenced by their peers [ 37 ]. Thus, certain intervention components (i.e., family-based goals, modifying the home food environment, hands-on approaches to teaching nutrition, group-based activities, and FV vouchers) may be more successful among children ages 5–12 years old.

Second, we found that those studies that had hands on activities and practical application components, positively impacted children’s dietary behaviors [ 14 , 27–29 , 34 ]. This may be expected as hands on family activities can promote active learning and opportunities for families to practice meal preparation and cooking [ 14 , 31 , 34 ]. Additionally, encouraging parents and children to communicate and work together in nutrition education sessions focusing on cooking and planning family meals may make dietary behavior changes (i.e., increasing FV, decreasing SSB intake) more realistic and achievable for all family members [ 38–40 ].

Third, this review highlighted that facilitating the purchase of FVs through the use of vouchers may be a successful strategy [ 31 ]; however, more studies are needed in this population group. Previous studies have shown that food vouchers can be a successful strategy to improve dietary related outcomes in adults from lower social economic status [ 41–43 ]. Despite the potential success of food vouchers, it is important to consider implementation challenges such as difficulties in locating retail outlets that will accept vouchers and the rising price of produce compromising the value of FV vouchers [ 43 ]. It also may not always be feasible to provide vouchers due to budget limitations given that family and community-based interventions can be costly [ 44 , 45 ] and are often affected by fluctuations in government and provincial funding [ 46 ]. However, an increase in FV consumption does not always need to be costly for the families [ 47 ]. There may be a need for future family-based interventions to educate families regarding strategies used to reduce the cost of fresh produce, such as only buying FVs that are in season, or on sale, and in small quantities to reduce waste; and buying frozen vegetables that have no added fat or salt.

Family demographics including socioeconomic status and ethnicity may impact children’s risk for obesity. Specifically, children of low-SES and ethnic minorities are at a higher risk of developing obesity [ 18 ]. Thus, some of the studies in this review targeted these vulnerable populations. For instance, Horton et al. [ 27 ] and Crespo et al. [ 28 ] targeted Mexican-American youth and their families by providing culturally sensitive interventions. Further, Buscail et al. [ 31 ] addressed low-income families and aimed to increase FV consumption among this population. There is a growing body of literature showing that low-SES is associated with less healthy dietary behaviors, such as lower consumption of fruits and vegetables [ 48 ]. There is also higher prevalence of obesity among ethnic minority and immigrant populations [ 49 ]. Research shows that certain aspects of sociocultural environments across ethnic minority groups may favor childhood obesity [ 18 , 50 ]. For example, indulgent feeding practices such as lack of structure in the timing of meals and snacks, are more common among Latin immigrant mothers [ 51 ]. Additionally, there are other lifestyle factors that contribute to the risk of childhood obesity including physical activity and sedentary behavior [ 52 ]; often, racial/ethnic minorities and immigrant populations have demonstrated limited participation in physical activity, which can partially be attributed to low-SES and acculturation processes [ 53 ]. Therefore, it is important to tailor future interventions to the needs of low-SES and ethnic minority families in order to reduce the health disparities that exist among these vulnerable populations.

A pattern whereby greater contact time was consistently related to achieving intervention outcomes was not observed in this review. However, a previous meta-analysis reported that family-based lifestyle interventions addressing childhood obesity providing 26 or more contact hours resulted in moderate to large effects on children’s BMI compared with interventions providing fewer contact hours [ 21 ]. This inconsistency may be due to the primary outcome selected it may be that health behavior change occurs more immediately than BMI. It may also be due to the lack of studies and variability in program delivery, measurement tools used to assess behavioral outcomes and possible response bias. More research is needed to better understand the intervention dose–response relationship.

The findings of this systematic review have also uncovered gaps in the family-based nutrition intervention literature. Currently, there are a limited number of randomized control trials examining the effectiveness of family-based childhood obesity interventions targeting nutrition-related outcomes. There is a need for future interventions to address cultural and societal beliefs that can influence families’ preferences for meal preparation and family meal-time. With emerging evidence that innovative digital technologies can successfully deliver scalable population-based lifestyle modification it appears that there is a paucity of evidence about their effectiveness in family interventions [ 54–58 ]. Thus, there is a need to evaluate optimal strategies to incorporate digital technology (web-based, mobile applications) to help further improve the program personalization and scalability. Methodological issues that limit quality ratings for studies were a challenge that needs to be addressed. Blinding participants in behavior community-based interventions may not be possible, but it is important to consider blinding the research team (e.g., analysts, and those focused on data collection) to minimize bias. Furthermore, researchers should consider using validated instruments (e.g., 7-day recalls, food frequency questionnaires) to assess dietary intake among school-aged children.

There are several limitations to this review. First, there was a lack of consistency between studies with regard to the methods used to measure and report child FV consumption. Thus, a meta-analysis was not feasible due to the diversity of measurement tools used to evaluate and report children’s daily FV consumption. Second, the ability to generalize our findings may be limited due to the small number of studies and our specific search terms focusing on childhood obesity. Further, many of the studies in this review had small sample sizes (<100 participants); thus, a lack of statistical power may have impacted the ability to demonstrate statistically significant differences. For instance, Fulkerson et al. [ 34 ] and Wieland et al. [ 30 ] appeared to have meaningful outcomes for FV intake whereby there was approximately one serving greater observed in the intervention relative to the control group, but these samples were under-powered to detect significant differences with small sample sizes. Third, not all studies used validated and reliable nutrition-related measures. Therefore, this may introduce bias in our results. Fourth, this review did not discuss the importance of other lifestyle behaviors that contribute to childhood obesity such as physical activity and sedentary behavior. Finally, because BCTs were coded based on each study’s description of intervention components, it is possible that some BCTs may not have been appropriately described. Therefore, it is critical for future studies to utilize common language according to Michie et al.’s [ 26 ] behavior change taxonomy [ 26 ].

There are currently relatively few randomized controlled trials testing the efficacy of family-based childhood obesity nutrition interventions aiming to improve children’s dietary behaviors. Successful interventions targeted children ages 5–12 years old included intervention components such as family-based goal setting, modifying the home food environment, hands-on approaches to teaching nutrition (games, group-based activities) and FV vouchers. However, more studies are needed. Future research should target ethnically and culturally diverse sample populations as well as other vulnerable populations, such as low-income families. Furthermore, enhanced methodological rigor is needed; specifically, blinding of the research teams and the use of age appropriate, valid, and reliable instruments.

Funding: This project was funded by Mitacs and any additional funding that Megan Perdew received during her MSc at the University of Victoria.

Conflicts of Interest: There are no conflicts of interest to report for this research.

Author M.P. contributed to the study selection, quality assessment, data extraction and analysis for this systematic review. Author S.L. contributed to the study selection and quality assessment for this review. Authors M.P., S.L., and P.J.N. all contributed to the writing and editing for this systematic review.

Ethical Approval: There are no ethical applications to report for this research given that it is a systematic review.

Informed Consent: None.

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Lucas PJ , Curtis-Tyler K , Arai L , Stapley S , Fagg J , Roberts H . What works in practice: user and provider perspectives on the acceptability, affordability, implementation, and impact of a family-based intervention for child overweight and obesity delivered at scale . BMC Public Health. 2014 ; 14 : 614 .

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.

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StatPearls [Internet].

Obesity effects on child health.

Palanikumar Balasundaram ; Sunil Krishna .


Last Update: April 10, 2023 .

  • Continuing Education Activity

Obesity in childhood is the most challenging public health issue in the twenty-first century. Childhood obesity is associated with increased morbidity and premature death. Prevention of obesity in children is a high priority in the current situation. This activity reviews the etiology, pathophysiology, and consequence of childhood obesity and also highlights the role of the interprofessional team in the prevention and management of childhood obesity.

  • Outline the definition of childhood obesity.
  • Describe the etiology and pathophysiology of childhood obesity.
  • Summarize the consequences of childhood obesity.
  • Explain how interprofessional teamwork can improve effective management interventions for childhood obesity.
  • Introduction

Obesity in childhood is the most challenging public health issue in the twenty-first century. It has emerged as a pandemic health problem worldwide. The children who are obese tend to stay obese in adulthood and prone to increased risk for diabetes and cardiac problems at a younger age. Childhood obesity is associated with increased morbidity and premature death. [1] Prevention of obesity in children is a high priority in the current situation.


The prevalence of childhood obesity has alarmingly increased. The overall burden of obesity has almost tripled since 1975. However, an eightfold increase in obesity burden in the 5 to 19 years age group has been noted between 1975 and 2016. [2] Though childhood obesity is more prevalent in developed countries, the prevalence is increasing even in developing countries. [3] Currently, about 18.5% of US children present with obesity. Among boys, obesity is more prevalent in the school-age group (6 to 11 years), whereas in girls, it is more prevalent in adolescents (12 to 19 years). The prevalence of childhood obesity among boys and girls was not significantly different overall or by age groups. [4]

The word obesity infers the deposition of excessive fat in the body. Different methods can directly measure body fat like skinfold thickness, hydro densitometry, bioelectrical impedance, and air displacement plethysmography. [5] These methods are not readily available in the clinical setting and are expensive. Body mass index (BMI) provides an economical method to assess body fat indirectly. BMI is measured using a formula [BMI = weight (kg)/ height (m)^2]. [6] [7] As growth in children varies with age and sex, so do the norms for BMI. The following definitions are used to classify weight status based on BMI for children from 2 to 20 years of age. [8] [9]

  • Overweight – 85th to less than the 95th percentile.
  • Obese (class 1) – 95th percentile or greater
  • Severe (class II) obesity – ≥ 120% of 95th percentile (99th percentile) or ≥ 35 kg/m^2 (whichever is lower)
  • Class III obesity is a subcategory of severe obesity and is defined as BMI ≥140 % of 95th percentile or ≥ 40 kg/m^2. 

The World Health Organization (WHO) recommends using BMI Z-score cut-offs of >1, > 2, and > 3 to define at risk of overweight, overweight, and obesity, respectively. [7] Z-score is measured in terms of standard deviations from the mean.

  • Issues of Concern

Etiology and Pathophysiology

The complex interaction of individual and environmental factors plays a crucial role in developing obesity. The most important factors contributing to childhood obesity are summarized below. 

Environmental Factors

Changes in the environment in the past few decades in terms of easy access/ affordability of high-calorie fast food, increased portion size, increased intake of sugary beverages, and sedentary lifestyles are associated with increased incidence of obesity. [10] Increasing use of electronic devices [television, tablets, smartphone, videogames] by children has led to limited physical activity, disruption of the sleep-wake cycle, depression of metabolic rate, and poor eating patterns. [11]

Feeding patterns in infancy have a long-term effect on developing obesity later on in life. It has been shown that breastfeeding in the first year of life is inversely associated with weight gain and obesity. [12] This association was much more significant if the child was exclusively breastfed compared to having added formula or solid food. Despite concerns about the risk for obesity in preterm and SGA infants receiving calorie and protein supplementation, it has been shown to improve catch-up growth without increasing the risk of obesity. [13] High protein intake in the initial two years of life has also been postulated to increase weight gain later in childhood. 

Biological Factors

There is a complex interaction between the neural, hormonal, and gut-brain axis affecting hunger and satiety. Hypothalamus regulates appetite and is influenced by key hormones, ghrelin, and leptin. Ghrelin is released from the stomach and stimulates hunger (orexigenic), whereas leptin is mainly secreted from adipose tissue and suppresses appetite (anorexigenic). Several other hormones like neuropeptide Y and agouti-related peptide stimulate hunger, while pro-melanocortin and α-melanocyte-stimulating hormone suppress hunger. [14] These hormones control energy balance by stimulating the hunger and satiety centers in the arcuate nucleus of the hypothalamus through various signaling pathways. Stress-related psychiatric disorders with associated abnormal sleep-wake cycles can also lead to increased ghrelin levels and, in turn, increase appetite.

The gut microbiome includes the trillions of microorganisms that inhabit the human gut. Alterations in the gut microbiome can lead to weight gain through numerous pathways. [15] The dominant gut florae are Firmicutes and Bacteroidetes (90%), Proteobacteria , Actinobacteria , and Fusobacteria . These bacteria have a symbiotic relationship with their host. They can be affected by various factors, such as gestational age at birth, premature rupture of membranes, mode of delivery of the infant, type of feeding, feeding practices, and antibiotics usage. The maturation of gut flora occurs from birth to adulthood and is determined by various genetic factors, diet, lifestyle, and environment. Gut microbiota helps maintain the mucosal barrier, nutrient digestion (especially the synthesis of short-chain fatty acids), and immune response against pathogens. The imbalance of the gut microbiome (dysbiosis), leading to increased production of short-chain fatty acids, has been linked to developing obesity and other medical conditions, such as type 2 Diabetes Mellitus, Metabolic syndrome, anxiety, and depression. [16]

Genetic Factors

Obesity can be either monogenic, syndromic, or polygenic types. Monogenic obesity is uncommon, occurring in 3% to 5% of obese children. [17] Mutations in genes for leptin, leptin receptor, proopiomelanocortin, and melanocortin-4 receptor can lead to obesity. Monogenic type presents in early childhood with unusual feeding behaviors and severe obesity.

Genetic syndromes causing severe obesity include

  • Prader Willi syndrome:  Early growth faltering followed by hyperphagia and increased weight gain by 2 to 3 years. The mild or moderate cognitive deficit, microcephaly, short stature, hypotonia, almond-shaped eyes, high-arched palate, narrow hands/feet, delayed puberty are common features.
  • Alstrom syndrome:  Blindness, deafness, acanthosis nigricans, chronic nephropathy, type 2 diabetes, cirrhosis, primary hypogonadism in males, and normal cognition are common features in Alstrom syndrome.
  • Bardet Biedl syndrome: Intellectual disability, hypotonia, retinitis pigmentosa, polydactyly, hypogonadism, glucose intolerance, deafness, and renal disease are the features in Bardet Biedl syndrome.
  • Other syndromes include Beckwith-Weideman syndrome and Cohen syndrome.

Polygenic obesity is much more common and is caused by a complex interaction between multiple genetic variants and the environment known as gene-environment interaction (GEI). When a child with genotype variants conferring risk for obesity interacts with various environmental factors predisposing to obesity, there is a tendency for decreased physical activity, increased food intake, and body fat storage. Early life environment starting with maternal nutrition during the prenatal or early postnatal period and early childhood adverse environmental or psychosocial stressors can lead to epigenetic changes leading to obesity.

Endocrine Factors

Endocrine causes constitute less than 1% of cases of obesity in children. [18] It is usually associated with mild to moderate obesity, short stature, or hypogonadism. These include cortisol excess [steroid medications or Cushing syndrome], hypothyroidism, growth hormone deficiency, and pseudohypoparathyroidism.


Numerous medications can cause weight gain. These include antiepileptics, antidepressants, antipsychotics, diabetes medications [insulin, sulfonylureas, thiazolidinediones], glucocorticoids, progestins, antihistamines [cyproheptadine], alpha-blockers [terazosin], and beta-blockers [propranolol]. Close monitoring for excessive weight gain should be done when any of these medications are used in children.

Endocrine-disrupting chemicals, such as bisphenol A and dichlorodiphenyltrichloroethane, have been hypothesized to predispose to obesity by modulating estrogen receptors and possibly metabolic programming. [19]

Few studies in animal models have proven that obesity can be triggered by infection with adenovirus. However, human studies have found conflicting results.

  • Clinical Significance

Childhood obesity significantly impacts both physical and psychological health. Obesity can lead to severe health conditions, including non-insulin-dependent diabetes, cardiovascular problems, bronchial asthma, obstructive sleep apnea (OSA), hypertension, hepatic steatosis, gastroesophageal reflux (GER), and psychosocial issues. The preventive and therapeutic interventions in childhood obesity are crucial in decreasing the burden of comorbid health conditions.

Metabolic Syndrome

Metabolic syndrome, also named syndrome X, is a cluster of risk factors specific for cardiovascular diseases such as hypertension, glucose intolerance, dyslipidemia, and abdominal obesity that commonly occur in obese children or adolescents. Insulin resistance, hyperinsulinemia, and oxidative stress are the underlying factors contributing to metabolic syndrome. [20]  


Atherogenic dyslipidemia is common in obese children and adolescents. A fasting lipoprotein level needs to be obtained in all children with obesity. Elevated triglycerides (TG) and Free fatty acid (FFA) levels, decreased HDL (high-density lipoprotein) cholesterol levels, and normal or mildly increased serum LDL (low-density lipoprotein) cholesterol levels are common findings in childhood obesity. [21] Hyperinsulinemia and insulin resistance in childhood obesity promotes hepatic delivery of FFA for triglyceride synthesis and sequestration into TG-rich lipoproteins. [22]  

Glucose Intolerance

Childhood obesity quadruples the risk of developing glucose intolerance and non-insulin-dependent diabetes mellitus (NIDDM or Type 2 diabetes). Over 85% of children with NIDDM are either overweight or obese at diagnosis. [23] Acanthosis nigricans is an increased pigmentation and thickness of the skin in intertriginous folds, and it is usually associated with glucose intolerance in children and adolescents. Fasting insulin and glucose should be included in the evaluation of childhood obesity. The risk factors for type 2 non-insulin-dependent diabetes and metabolic syndrome include, 

  • children with BMI 85th to 95th percentile along with,
  • immediate family history of type 2 diabetes 
  • signs of insulin resistance such as acanthosis nigricans, dyslipidemia, hypertension, and polycystic ovarian syndrome.
  • Children with BMI >95th percentile regardless of family history or associated features. [24]  


The most significant risk factor for pediatric hypertension is the high body mass index. One-fourth of obese children can have hypertension. Adipocyte is not only a storage depot for fat but is also an active endocrinological cell. The pro-inflammatory adipokines (leptin, resistin, and IL-6) lead to an increase in sympathetic nervous system (SNS) activation, which preferentially impacts the renal vascular beds. [25] Hypertension risk in childhood obesity can also be explained due to hyperinsulinemia. Hyperinsulinemia causes hypertension through secondary mechanisms such as increased renal sodium retention, increased intracellular free calcium, and increased SNS activity. [26] Dietary therapy, along with exercise, effectively decreases blood pressure. 

Hepatic Steatosis  

Pediatric liver disease is a severe complication of childhood obesity. Obesity-related non-alcoholic fatty liver disease (NAFLD) spectrum includes fatty liver, steatohepatitis, cirrhosis, and hepatocellular carcinoma. [27] Hyperinsulinemia in childhood obesity plays a significant role in contributing to hepatic steatosis. Gradual weight loss with regular exercise and diet with less refined carbohydrates and low-fat help normalize hepatic enzymes and resolve hepatic steatosis. [28]   


The prevalence of cholelithiasis is high among adolescents with obesity, and the association is more robust in girls than in boys. Increased cholesterol synthesis and cholesterol saturation of bile contribute to cholelithiasis among adolescents with obesity. [29] [29]  Cholelithiasis occurs even more frequently with weight reduction. Almost half of the cases of cholecystitis in adolescents may be associated with obesity. 

Overweight or obese children have been observed to have a higher prevalence of asthma and asthma exacerbations. The link between asthma and obesity is mediated through abnormal inflammatory and oxidant stress, chest restriction with airway narrowing, and obesity-related comorbidities such as obstructive sleep apnea and gastroesophageal reflux. [30]  

Idiopathic Intracranial Hypertension 

Idiopathic intracranial hypertension (IIH) is an uncommon disease of childhood and adolescence characterized by increased intracranial pressure without any identifiable cause. Almost half of the children who present with this syndrome may be obese and also have more IIH symptoms at onset. [31]  The disease is characterized by elevated intracranial pressure. IIH presents with headaches and may lead to severe visual impairment or blindness. The potential for visual impairment indicates the need for aggressive treatment of obesity in patients with IIH.

Sleep Apnea

Obesity and overweight are crucial risk factors for obstructive sleep apnea (OSA). Neurocognitive deficits and excessive daytime sleepiness are common among obese children with sleep apnea. [32] Obesity hypoventilation syndrome may represent a long-term consequence of sleep apnea and is associated with a high mortality rate. Aggressive therapy is warranted for obese children with this syndrome. Obesity management such as increased physical activity and a healthy diet are recommended for OSA treatment, as well as surgical procedures, if appropriate. 

Orthopedic Complications

Fractures, musculoskeletal discomfort, and lower extremity malalignment such as Blount disease and slipped capital femoral epiphyses are more common in overweight than non-overweight children and adolescents. [33]  Blount disease is a disorder of the proximal tibial growth plate, which results in progressive bowing of the tibia. Although the prevalence of Blount disease is low, approximately two-thirds of Blount disease patients may be obese. Slipped capital femoral epiphysis occurs due to epiphyseal plate disruption. Between 30% and 50% of patients with slipped capital femoral epiphysis are overweight.  

Polycystic Ovary Disease 

Obesity is frequently associated with polycystic ovary disease (PCOD). Up to 30% of women with PCOD may be obese. Hyperandrogenism and hyperinsulinemia often accompany PCOD. Obesity increases the risk of PCOD through insulin resistance and compensatory hyperinsulinemia, which increases androgen production and decreases sex hormone-binding globulin, thereby increasing the bioavailability of androgen. Adolescents with PCOD are at increased risk for metabolic syndrome and glucose intolerance. Weight loss represents an important therapeutic target in obese adolescents with PCOD.  

Persistence of obesity into adulthood

About 15% to 30% of adults with obesity were also obese in their childhood or adolescence. [34]  The cardiovascular risk factors present in obese children or adolescents usually persist into adulthood. The change in body fat in obese adolescents can be a reasonable mediator contributing to the excess morbidity and mortality in later adulthood. 

Psychosocial impact 

Children with obesity or overweight are more likely to experience low self-esteem and depression during adolescence. Negative psychological experiences trigger emotional eating, leading to an ongoing obesity-depression cycle. Children who are overweight or obese face bullying at school and are excluded from competitive physical activities. Overall, children with obesity have less social interaction and spend more time in sedentary activities. Numerous studies have confirmed the association of childhood obesity with ADHD and anxiety disorders. [35]

Eating Disorders

Children with overweight or obesity have a high prevalence of disordered eating behaviors, increasing the risk of developing eating disorders. The majority of adolescents with restrictive eating disorders report a history of obesity in the past. Binge eating increases the risk of obesity and type 2 diabetes. [36]  Appropriate evaluation for eating disorders should be performed during the treatment planning of childhood obesity. 

Academic Performance 

Children who are obese and have comorbid health problems like diabetes, asthma, or sleep apnea miss school more frequently, thereby affecting their school performance negatively.

  • Enhancing Healthcare Team Outcomes

Prevention is the best intervention to decrease the prevalence of obesity. The pediatrician should explore the risk of obesity and overweight during every clinical visit for all children.  

  • Both bottle-fed and breastfed infants are at risk of overfeeding. However, overfeeding is more prevalent among bottle-fed infants. Exclusive breastfeeding and delayed initiation of solid foods may reduce the future risk of overweight. 
  • Skim milk is a safe replacement for whole milk after two years of age. Parents or caretakers should never use food like sweets for a reward. The entire family should have a balanced diet that comprises less than 30 percent of calories from fat. AAP recommends consuming a variety of vegetables and fruits, whole grains, proteins, low-fat dairies and decreasing the intake of sodium, saturated fats, and refined sugars beginning at the age of two years. [37]
  • An essential step in preventing obesity is reducing sedentary time. Limit the screen time, including television, video games, or mobile, not more than 2 hours per day for more than six-year-old children and not more than 1 hour per day for 2-6 years of age group. AAP strongly recommends not allowing kids less than two years to have screen time. [38]
  • Encourage physical activity for children. Children aged 3 to 5 years should be active throughout the day. Children and adolescents ages 6 to 17 years should be physically active for at least 60 minutes every day. [39]
  • As per CDC, 60% of middle school kids and 70% of high school kids do not meet the standard sleep recommendations. AAP recommends that children aged 1 to 2 years sleep 11 to 14 hours per day, children 3 to 5 years sleep 10 to 13 hours, children 6 to 12 years sleep 9 to 12 hours, and adolescents aged 13 to 18 years should regularly sleep 8 to 10 hours. [40]  Avoiding heavy meals close to bedtime, being physically active throughout the day, and removing electronic devices in the bedroom will help to get better sleep.  

The pediatrician should explore for associated morbidity in all obese children. The detailed assessment in obese children should include assessing cardiac comorbidities, orthopedic complications, and psycho-social complications.

  • Reasonable weight-loss goals should be initially 5 to 10 pounds (2 kg to 4.5 kg) or a rate of 1 to 4 pounds (0.5 to 2 kg) per month.
  • Dietary management:  Dieticians provide dietary prescriptions mentioning the total calories per day and recommended percentage of calories from carbohydrates, protein, and fat. The Traffic Light Plan is one method of providing dietary management. The Traffic Light Plan classifies foods as green (low energy density), yellow (moderate energy density), and red (high energy density). These categories help children in adopting healthier eating patterns.[41] The dietician plays a significant role in guiding the diet plan for the patients.
  • Physical activity:  As per the fitness level, begin the physical activity with the goal of 30 minutes/day in addition to any school activity. Treatment should target gradually increasing the activity to 60 minutes per day. An exercise physiologist, along with the physician, can help the patients to achieve their target physical activity.
  • Behavior modification:  Primary care-based behavioral interventions such as self-monitoring, nutritional education, improvement of eating habits, increasing physical activity, attitude change, and rewards help manage childhood obesity.
  • Family involvement:  Review overall family activity and television viewing patterns and always involve parents in nutrition counseling. Family-based behavioral treatment is the most robust intervention for childhood obesity. [41]
  • Psychotherapy:   Behavioral therapy and Cognitive therapy are commonly used by the psychologist in the management of obesity. Behavioral therapy trains patients to act differently around food, and cognitive therapy trains patients how to change their thoughts and emotions related to food.
  • None of the anorexiant medications are FDA approved for use in childhood obesity. Orlistat is the only FDA-approved medication for use in adolescents. 
  • Surgical procedures like gastric bypass have not been studied sufficiently in children to advise their use. 

An interprofessional team that provides a holistic and integrated approach can help achieve the best possible outcomes. Collaboration, shared decision making, and communication are key elements for a good outcome. Multidisciplinary teams include a primary physician, a dietician, a nurse or nurse practitioner, a clinical exercise physiologist, and a psychologist. The interprofessional team can provide a comprehensive weight loss program that benefits the patients.

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Disclosure: Palanikumar Balasundaram declares no relevant financial relationships with ineligible companies.

Disclosure: Sunil Krishna declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

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  • Open access
  • Published: 24 August 2017

Family-based childhood obesity prevention interventions: a systematic review and quantitative content analysis

  • Tayla Ash   ORCID: orcid.org/0000-0001-7621-3545 1 , 2 ,
  • Alen Agaronov 1 ,
  • Ta’Loria Young 3 ,
  • Alyssa Aftosmes-Tobio 2 &
  • Kirsten K. Davison 1 , 2  

International Journal of Behavioral Nutrition and Physical Activity volume  14 , Article number:  113 ( 2017 ) Cite this article

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A wide range of interventions has been implemented and tested to prevent obesity in children. Given parents’ influence and control over children’s energy-balance behaviors, including diet, physical activity, media use, and sleep, family interventions are a key strategy in this effort. The objective of this study was to profile the field of recent family-based childhood obesity prevention interventions by employing systematic review and quantitative content analysis methods to identify gaps in the knowledge base.

Using a comprehensive search strategy, we searched the PubMed, PsycIFO, and CINAHL databases to identify eligible interventions aimed at preventing childhood obesity with an active family component published between 2008 and 2015. Characteristics of study design, behavioral domains targeted, and sample demographics were extracted from eligible articles using a comprehensive codebook.

More than 90% of the 119 eligible interventions were based in the United States, Europe, or Australia. Most interventions targeted children 2–5 years of age (43%) or 6–10 years of age (35%), with few studies targeting the prenatal period (8%) or children 14–17 years of age (7%). The home (28%), primary health care (27%), and community (33%) were the most common intervention settings. Diet (90%) and physical activity (82%) were more frequently targeted in interventions than media use (55%) and sleep (20%). Only 16% of interventions targeted all four behavioral domains. In addition to studies in developing countries, racial minorities and non-traditional families were also underrepresented. Hispanic/Latino and families of low socioeconomic status were highly represented.


The limited number of interventions targeting diverse populations and obesity risk behaviors beyond diet and physical activity inhibit the development of comprehensive, tailored interventions. To ensure a broad evidence base, more interventions implemented in developing countries and targeting racial minorities, children at both ends of the age spectrum, and media and sleep behaviors would be beneficial. This study can help inform future decision-making around the design and funding of family-based interventions to prevent childhood obesity.

Childhood obesity continues to be a pervasive global public health issue as children worldwide are significantly heavier than prior generations [ 1 ]. Over the past few decades, the prevalence of obesity among children and adolescents has risen by 47% [ 2 ]. Increases have been seen in both developed and developing countries, with recent prevalence estimates of 23 and 13%, respectively [ 2 ]. Despite evidence of a plateau in the rates of obesity, at least among young children in developed countries, current levels are still too high, posing short- and long-term impacts on children’s physical, psychological, social, and economic well-being [ 2 , 3 , 4 , 5 ]. Of equal, if not greater concern, racial/ethnic and socioeconomic disparities appear to be widening in some countries [ 5 , 6 , 7 , 8 ]. Given the extensive disease burden, treatment resistance of obesity, and lack of signs of attenuation for rates in the developing world, scientists, clinicians, and practitioners are working hard to devise and test interventions to prevent childhood obesity and reduce associated disparities [ 2 , 9 ].

One category of interventions to prevent childhood obesity that has grown considerably in recent years is family-based interventions. This was in part due to a number of key reports published in 2007, including an Institute of Medicine (IOM) report on the recent progress of childhood obesity prevention [ 10 ] and a report from a committee of experts representing 15 professional organizations appointed to make evidence-based recommendations for the prevention, assessment, and treatment of childhood obesity [ 11 , 12 ]. In both reports, parents are described as integral targets in interventions, given their highly influential role in supporting and managing the four behaviors that affect children’s energy balance (diet, physical activity, media use, and sleep) [ 13 , 14 , 15 ]. This includes not only parenting practices and rules, but also the environments to which children are exposed, and the adoption of parents’ own behavioral habits by children [ 15 , 16 , 17 , 18 , 19 ].

Since the release of these reports, there has been a proliferation of family-based interventions to prevent and treat childhood obesity as documented in at least five published reviews of this literature in the past decade [ 20 , 21 , 22 , 23 , 24 ]. While these reviews convey extensive information around intervention effectiveness, they cannot reveal gaps in the knowledge base. Quantitative content analysis [ 25 , 26 , 27 ] can be used to code intervention and participant characteristics, and a review of the resulting data can reveal areas and populations receiving a great deal of attention, as well as those where few or no studies exist, thereby highlighting knowledge gaps. With a focus on childhood obesity interventions, pertinent questions to address include: whether interventions have continued to focus primarily on diet and physical activity, neglecting the more recently established predictors of media use and sleep [ 28 , 29 , 30 ]; whether some behaviors are more likely to be targeted among certain age groups or settings than others; and whether there are gaps with regard to the populations targeted by interventions to date, in particular, the representation of vulnerable populations (e.g. families living in developing countries, those of low socioeconomic status, racial and ethnic minorities, immigrants, and non-traditional families) [ 2 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. In addition to ethical reasons, from a pragmatic viewpoint, it is difficult to identify best practices to prevent childhood obesity in vulnerable populations when few interventions have focused on that population [ 38 , 39 ].

The goal of this study is to profile family-based interventions to prevent childhood obesity published since 2008 to identify gaps in intervention design and methodology. In particular, we use quantitative content analysis to systematically document intervention and sample characteristics with the goal of directing future research to address the identified knowledge gaps.

We used a multistage process informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify family-based childhood obesity prevention interventions that were written in English and published between January 1, 2008 and December 31, 2015 [ 40 ]. Using an a priori defined protocol, we identified relevant articles and systematically screened articles against inclusion and exclusion criteria. The systematic review protocol was registered in the PROSPERO database (CRD42016042009).

Following the identification of eligible studies, we conducted a quantitative content analysis to profile recent interventions for childhood obesity prevention. Content analysis, originally used in communication sciences but increasingly utilized in public health, is a research method used to generate objective, systematic, and quantitative descriptions of a topic of interest [ 25 , 26 , 27 ]. Our research team has previously employed this technique to survey observational studies on parenting and childhood obesity published between 2009 and 2015 [ 41 , 42 ].

Search strategy and initial screening

With the help of a research librarian, two authors (TA, AA) searched three databases (PubMed, PsycINFO, and CINAHL) using individually tailored search strategies most appropriate for each database. The selected databases are the three most common databases used in recent systematic reviews. Our search strategy consisted of search strings composed of terms targeting four concepts: (1) family (e.g. family, mother, father, home), (2) intervention (e.g. prevention, promotion), (3) children (e.g. child, infant, youth), and (4) obesity (e.g. overweight, body mass) (see Additional file 1 for full search strategy for one database). We searched title, abstract, and medical subject headings (MeSH) or descriptor subjects (DE) term fields. Animal studies (e.g. rats), non-original research articles (e.g. commentaries, editorials, case reports), studies written in languages other than English and studies focused on populations older than 18 years were excluded using search limits and NOT terms. We restricted the search to articles published since January 1, 2008, to capture interventions implemented after the release of the IOM and expert committee reports. Furthermore, a start point of January 2008 ensured the feasibility of this study given the labor and time intensive process to screen and code studies. In a recent systematic review of family-based interventions for the treatment and prevention of childhood obesity, more than 80% of eligible studies were published since 2008 [ 43 ]. Thus, a start date of 2008 appropriately balances feasibility of implementation and the validity of the resulting information. The search end date was December 31, 2015.

The search yielded 12,274 hits, representing 9152 unique articles after removing duplicates (see Fig. 1 ). Following a review of titles by three authors (TA, AA, TY) and one research assistant, 7451 articles were removed based on exclusion criteria, resulting in 1701 articles that proceeded to abstract review. Articles were removed during title review if they were not written in English or published in the designated time frame, were not original research articles, did not include human subjects, did not target children, were observational studies, were not relevant to the topic of childhood obesity (e.g. papers about Anorexia Nervosa), or included special clinical populations.

PRISMA flow diagram for identifying and screening eligible family-based childhood obesity prevention interventions

Application of eligibility criteria

Three authors (TA, AA, TY) and one research assistant screened articles against the eligibility criteria during abstract review, while two authors (TA, AA) screened during full-text review, applying the aforementioned exclusion criteria. Eligible studies included family-based interventions for childhood obesity prevention published since 2008. We defined family-based interventions as those involving active and repeated involvement in intervention activities from at least one parent or guardian [ 19 ]. Examples of intervention activities that qualify as active parent involvement include workshops and counseling. Examples of passive involvement, which were excluded, include sending home brochures for parents, or simply inviting parents to a single event, but not involving them in the intervention in an integral way. We defined obesity interventions as those that reported at least one weight-related outcome (weight, body mass index, etc.) or which self-identified as an obesity intervention. We defined interventions as preventive if they did not explicitly focus on weight loss or management, or if they did not recruit only children with obesity. The final inclusion criterion was that the intervention was designed with the intent of benefiting children (child being defined as <18 years of age), excluded interventions in which the objective was to better parent health outcomes.

Of the 1701 articles screened at the abstract level, 329 proceeded to full-text screening, of which 159 articles met the eligibility criteria and were included in the final pool of eligible papers (see Additional file 2 for a list of eligible articles). We examined intervention name, trial number, the last name of the first author, and the last name of the last author to identify articles that originated from the same intervention. After collating, 119 unique interventions were identified, which included interventions with published outcome data, and interventions for which only a protocol was published. Percent agreement for all screening criteria ranged between 86 and 98%. Discrepancies were discussed and resolved.

To ensure a fully inclusive search strategy, we also reviewed the references of a random subset of the articles meeting the inclusion criteria. A subset of 5% was chosen given the large sample size. No additional studies meeting the eligibility criteria were identified in the process, suggesting that the employed search was exhaustive.

Data extraction

For all eligible articles, we used conventional content analysis methodology [ 25 , 26 , 27 ] to extract and analyze article, intervention, and participant characteristics. We developed a comprehensive codebook to standardize the coding process. Multiple authors (TA, AA, AA-T) tested the codebook by coding five articles not included in the final pool of studies. An additional round of testing included 10 randomly selected articles from the study pool. After pilot testing the codebook and establishing reliability (see intercoder reliability), two trained coders (TA, AA) each coded half of the 159 eligible articles.

Article characteristics

We coded publication year, journal, funding sources, and type of paper. All specific funding sources for a given intervention were extracted and classified after web-based searching. Funding sources were categorized as federal, foundation, corporate, or university, and then further coded based on the specific federal, foundation or corporate agency. For type of paper, articles were coded as an intervention protocol or outcome evaluation. Articles that reported any intervention outcomes were coded as outcome evaluations; interventions that only described the intervention (or provided only baseline data) were coded as protocols. Because a seemingly large number of protocols were discovered among the final pool of articles, we elected to include them in the study. Interventions in which only a protocol has been published tend to represent the next generation of intervention studies and thus lend to a better understanding of the field’s trajectory.

Intervention characteristics

We coded a wide range of intervention characteristics including geographic region of the study, age of target child, intervention setting, length of intervention, delivery mode, evaluation design, intervention recipient, behavioral domains targeted, and theory used. Age of the target child at baseline was coded as prenatal (i.e., the intervention started before birth), 0–1 years, 2–5 years, 6–10 years, 11–13 years, and 14–17 years. If the age range fell predominantly into one category, any subsequent categories were only coded affirmative if the ages of participants crossed at least 2 years into a given range. Intervention setting was coded as home, primary care or health clinic, community-based, school, and childcare/preschool. Community-based interventions included those taking place in community gardens, parks, or recreational facilities. Interventions taking place at universities were also coded as community-based. In cases where intervention setting was ambiguous, or the intervention was not setting specific, we coded the intervention setting as unclear.

Intervention length was coded as less than 13 weeks (3 months), 13–51 weeks (3–11.9 months), or 52 weeks (12 months) or more. Two different types of intervention delivery modes were coded: in-person and technology-based. Technology-based approaches included those using computers, social media, text messages, or anything else involving the Internet. Evaluation design was coded as either randomized-controlled trial or quasi-experimental trial. We also extracted data on intervention recipients (i.e. those who directly received the intervention program or materials). This was coded as adults, children, or both. Behavioral domains targeted included diet, physical activity, media use, and sleep. Finally, we coded use of theory. Theories were specified using the following categories: social cognitive theory, parenting styles, ecological frameworks, transtheoretical model of behavior change, health belief model, theory of planned behavior, or other. For age category, intervention setting, delivery mode, intervention recipients, and theory, multiple categories could be selected.

Sample characteristics

Sample characteristics were coded for the inclusion of participants from underserved populations and non-traditional families, and racial/ethnic composition of the sample. We coded sample characteristics for outcome evaluations only ( n  = 84 studies) because intervention protocols generally do not include this information. We coded whether the intervention included any participants from the following underserved or non-traditional groups: low socioeconomic status (SES), racial/ethnic minorities (i.e., Black/African American, Hispanic/Latino, Indigenous), immigrant families, single parents, non-biological parents, and non-residential parents. Low SES was defined as either low income (self-identified by the study) or low education (high school diploma or less). Families participating in low-income qualifying programs (Women, Infants, and Children services, Supplemental Nutrition Assistance Program, free or reduced school lunch, Head Start, etc.) were considered low SES. We coded parents as single if they self-identified as such, were not cohabitating, or were widowed or divorced. In studies where limited information was provided and marital status was simply dichotomized as married or not married, not married was used as a proxy for single. Finally, we coded whether the sample included participants from each racial/ethnic group (i.e. White, Black/African American, Hispanic/Latino, Asian, Indigenous, and multiracial/other). For all sample characteristics, in addition to coding whether families belonging to each of the groups were included, we also coded whether they made up at least 50% of the sample, as well as 90% of the sample. The purpose of these categories was to distinguish between studies that included only a few families from a given category and those in which at least half the sample belonged to the category. If at least 90% of the families included in a sample belonged to a given category, the sample was considered to be predominantly that category (e.g. predominantly-Hispanic). Samples coded affirmative for 90% criteria were also coded affirmative for the 50% criteria.

Inter-rater reliability

Both coders coded randomly selected articles from the final study pool until reliability was sufficiently established. Ultimately, this included four rounds of coding a total of 55 articles. We computed Cohen’s kappa as a measure of agreement between the coders, using weighted kappas for ordinal variables [ 44 ]. The final average kappa across all variables was 0.87, and the average percent agreement was 92%. Three variables had kappas below 0.70, the conservative threshold for adequate inter-rater reliability [ 45 ]. These variables included the following: inclusion of children 11–13 years old (kappa 0.36), inclusion of children 14–17 years old (kappa 0.65), and childcare/preschool setting (kappa 0.46). Because percent agreement for each of these variables was high (>89%), and given that kappa coefficients are difficult to interpret when variability is low [ 45 , 46 ], which would result from a category (e.g. inclusion of children 14–17 years) being infrequently coded or endorsed, they were retained in the analyses. Coders were retrained on the three variables prior to coding the remainder of the articles.

Data synthesis and analysis

Both inter-rater reliability and all other analyses were conducted in STATA 13 [StataCorp LP, College Station, TX, USA]. One coder (TA) cleaned the data. The majority of missing data was not reported (i.e., were missing by design) and therefore coded as ‘0’ (no/not sure). Where data were missing, one of the coders (TA) returned to the full-text article to confirm and correct any errors.

For article characteristics (e.g. publication year, journal), the unit of analysis is article, with a denominator of 159 articles. For intervention and sample characteristics, which are presented in Tables 1 - 3 , the unit of analysis is intervention. In instances where multiple studies were published on the same intervention, the data extracted from each study were synthesized into a single entry [ 47 ]. For example, if both a protocol and outcome evaluation were published for an intervention, the intervention was marked as having an outcome evaluation. As a result, a denominator of 119 interventions was used to assess intervention characteristics. Interventions with a protocol only were not included in the assessment of sample characteristics because sample information is infrequently reported in such papers. Thus the denominator for sample characteristics was 85 interventions with published outcome data.

We also examined article and intervention characteristics separately for protocols and outcome evaluations. Given that few differences were identified, this information is presented in Additional file 3 : Table S1 to streamline the presentation of results.

The number of eligible articles published each year was as follows: 2008 = 6 (4%), 2009 = 5 (3%), 2010 = 14 (9%), 2011 = 15 (9%), 2012 = 33 (21%), 2013 = 35 (22%), 2014 = 23 (14%), and 2015 = 28 (18%). The predominant journals in which articles were published included BioMed Central Public Health ( n  = 28, 18%), Contemporary Clinical Trials ( n  = 12, 8%), Childhood Obesity ( n  = 9, 6%), Pediatrics ( n  = 7, 4%), Pediatric Obesity ( n  = 6, 4%), and Preventive Medicine ( n  = 6, 4%).

Eligible articles described 119 unique interventions. Table 1 summarizes additional intervention characteristics for eligible interventions. For more than a fourth of these interventions ( n  = 34, 29%), only an intervention protocol was identified (i.e., no published outcomes were available). More than half ( n  = 66, 56%) of the interventions were based in the U.S. Studies based in Europe/United Kingdom ( n  = 30, 25%), Australia/New Zealand ( n  = 10, 8%), and Canada ( n  = 6, 5%) comprised 38%. Few interventions were conducted in countries in Central America, South America, Asia, Africa, the Middle East, or the Caribbean.

Less than a third of interventions were implemented for a year or more ( n  = 33, 28%). Interventions that were implemented in-person ( n  = 101, 85%) were more common than those delivered using technology ( n  = 27, 23%). Fourteen (12%) of interventions had both in-person and technology components. Five interventions (4%) had neither an in-person nor a technology component; these interventions consisted of printed materials and phone calls. Nearly three out of four interventions utilized a randomized controlled trial design ( n  = 87, 73%). Because active parent engagement was a requirement for eligibility in this review, parents were intervention recipients in all interventions. Children were also intervention recipients in approximately half of the interventions ( n  = 65, 55%).

A slight majority of interventions were federally funded ( n  = 75, 63%). Of these, about half ( n  = 34, 29% of the 119 eligible interventions) received funding from the National Institutes of Health, with the National Institute of Diabetes and Digestive and Kidney Diseases ( n  = 14, 12%) and the National Heart, Lung, and Blood Institute ( n  = 7, 6%) being the two leading funding institutes (data not shown). The United States Department of Agriculture funded 10 (8%) interventions. Twenty-three (19%) interventions received federal funding from countries other than the United States, with Australia funding the most ( n  = 6, 5%). Of the 50 (42%) interventions funded by foundations, the Robert Woods Johnson Foundation was the leading funder ( n  = 5, 4%). A similar proportion of interventions received corporate ( n  = 21, 18%) or university funding ( n  = 23, 19%). Many interventions ( n  = 46, 39%) received multiple types of funding, and funding source was not listed in 8 (7%) of interventions.

A majority of interventions mentioned theory ( n  = 85, 71%), with many ( n  = 34, 29%) using multiple theories. However, interventions varied greatly with respect to how heavily theory was emphasized. Social cognitive theory was the most widely noted theory ( n  = 49, 41%).

Approximately 40% of interventions targeted families with children ages 2–5 years ( n  = 51, 43%) or 6–10 years ( n  = 42, 35%), whereas fewer than 10% of interventions targeted families during the prenatal period ( n  = 10, 8%) or families of children with 14–17-year-olds ( n  = 8, 7%). One in three interventions were implemented in a home setting ( n  = 33, 28%), a primary care/health clinic ( n  = 32, 27%) or in the community ( n  = 39, 33%), and one in five ( n  = 24) were implemented in multiple settings. Finally, just over half ( n  = 69, 58%) of studies targeted a behavioral domain beyond diet and physical activity (i.e., they targeted media use and/or sleep in addition to diet and physical activity), and only a few ( n  = 3, 3%) interventions did not target either diet or physical activity.

Table 2 provides a cross tabulation of age of target child, setting, and behavioral domains. A number of patterns are apparent. First, interventions that targeted children in the earlier years of life (prenatal to age 5 years) tended to be focused in the home ( n  = 28, 31%) and primary care settings ( n  = 30, 33%), whereas interventions that targeted older children occurred most frequently in community ( n  = 40, 53%) and school ( n  = 20, 27%) settings. Second, media use was least frequently included in school-based interventions ( n  = 9, 43%). Physical activity was most frequently targeted in a school setting ( n  = 21, 100%), and least likely to be targeted in homes ( n  = 23, 70%). Sleep was most often included in home-based ( n  = 8, 24%), health-based ( n  = 8, 25%), and childcare-based ( n  = 3, 27%) interventions; it was seldom targeted in families with school-age children ( n  = 4, 10%) and has not been targeted in families with children older than 10 years of age.

Sample characteristics are summarized in Table 3 . Underserved families appeared well-represented, particularly low SES families ( n  = 62, 73%). A slight majority of samples included at least some racial or ethnic minority families ( n  = 46, 54%), and just over a quarter included immigrant families ( n  = 24, 28%). Ethnic minorities (i.e., Hispanics) were better represented than racial minorities. About half of the interventions included families identifying as Hispanic/Latino ( n  = 40, 47%).

The most frequently represented racial group was White ( n  = 30, 35%), followed by Black/African American ( n  = 26, 31%), Asian ( n  = 20, 24%), and then Indigenous ( n  = 12, 14%). Notably, many interventions ( n  = 29, 34%) did not specify the racial/ethnic background of families. Fig. 2 provides a more detailed assessment of the racial/ethnic composition of U.S.-based interventions (non-U.S. interventions infrequently reported participant race or ethnicity and were therefore not included). In 42% ( n  = 21) of U.S.-based interventions, Hispanic/Latino families made up at least half of the sample, and in 30% ( n  = 15) of interventions they made up at least 90% of the sample. Again, families identifying as White were the most represented racial group ( n  = 24, 48%). Less than 20% of studies included a sample that was at least half Black/African American ( n  = 5, 10%), Asian ( n  = 2, 4%), or Indigenous ( n  = 1, 2%).

Inclusion and representation for racial/ethnic groups in U.S. family-based childhood obesity prevention interventions ( n  = 50)

Few studies included non-traditional families; less than a third of interventions included any single parent households ( n  = 23, 27%) and less than 5% included non-biological parents ( n  = 2, 2%) or non-residential parents ( n  = 0, 0%).

Comparing protocols to outcome evaluations

When comparing interventions with evaluations to those with protocols only, a proxy for more recent interventions, interventions with protocols targeted more domains than those with evaluations. The proportion of evaluation and protocols that targeted just one behavioral domain was 20 and 12%, respectively, while the proportion targeting all four behavioral domains was 13 and 24%, respectively. Other notable differences were that interventions with protocols only were more likely to be of longer duration, utilize technology, adopt a randomized controlled trial design, target parents exclusively, receive federal funding, and use theory (see Additional file 3 : Table S1).

Parents are important agents of change in the childhood obesity epidemic [ 20 , 22 , 48 , 49 ]. This study used rigorous systematic methods to conduct a quantitative content analysis of family-based interventions to prevent childhood published between 2008 and 2015 to profile the field of recent family-based childhood obesity prevention interventions and identify knowledge gaps. We identified gaps in both intervention content and sample demographics. Key research gaps include studies in low-income countries, interventions for children on both the lower and higher ends of the age spectrum, and interventions targeting media use and sleep. Racial minorities and children from non-traditional families have also been underrepresented.

Intervention gaps and implications

The vast majority of studies were conducted in developed, or high-income, countries. Given the rapid increase of obesity as a significant public health burden in developing countries, this study demonstrates a need for further intervention efforts in low- and middle-income countries [ 50 , 51 ]. Although obesity rates are lower in low- and middle-income countries than developed countries, two-thirds of people with obesity worldwide live in developing countries where rates of obesity are increasing [ 2 ]. The small number of studies in these geographic regions limits the development of locally relevant programs and policies aiming to address the growing problem of obesity in these regions.

Non-traditional families were underrepresented in interventions. This is concerning given that children from non-traditional families have an elevated risk for obesity [ 31 , 32 , 33 , 34 , 35 , 36 ]. The changing nature of family structures, including the increasing number of single-parent households over time, [ 52 ] calls for a more inclusive approach to defining what is considered a family in research. Like non-traditional families, Black/African American, Asian, and Indigenous families have been underrepresented. Racial and ethnic minorities are vulnerable populations who experience elevated risk for obesity [ 33 , 34 ]. Initiatives to fund interventions specifically targeted at racial and ethnic minorities may have increased the number of interventions targeting Hispanics, but not racial minorities. Thus, more efforts are needed that specifically target families identifying as races other than White. The lack of studies including adequate representation of these groups limits the scientific community’s understanding of effective strategies in high-risk communities and fails to fully address noted health disparities.

Family-based childhood obesity prevention interventions have focused heavily on children 2–10 years of age, despite the robust evidence demonstrating the importance of prevention efforts as early as infancy and the prenatal period [ 53 , 54 ]. Establishing healthy habits early in life is critical given the difficulty of changing energy-balance behaviors later on. While it has been established that prenatal life influences childhood obesity risk, the low number of interventions beginning in the prenatal period, in particular, may be due to a general lack of understanding of the mechanisms responsible for this association, and general debate in the field about how early intervention efforts should begin [ 55 , 56 ].

This study also revealed gaps in behavioral domains targeted, as interventions have not adequately targeted media use and sleep. Moreover, only 16% of interventions targeted all four behavioral domains. The emphasis of interventions on diet and physical activity may reflect their relative contribution to obesity risk. However, behavioral risk factors for obesity are interconnected, and thus may be better addressed by considering complimentary and supplementary behaviors [ 57 , 58 , 59 ]. While it can be argued that targeted messages may have a greater impact, the research gaps identified in this study (e.g. the lack of interventions targeting sleep among older children) highlight areas of needed research in the field. It is worth acknowledging how varied intervention length was across studies, with about a third of interventions being less than 3 months long. This is important given the difficulty in making and sustaining lifestyle changes.

Comparisons with observational studies

The results of this study are consistent with findings from a content analysis by Gicevic et al. on observational research on parenting and childhood obesity published over a similar time frame [ 41 ]. The majority of studies were conducted in developed countries; diet and physical activity were the most heavily targeted behavioral domains; most studies targeted children ages 2–10; and there was a low representation, or at least specification, of non-traditional families. Also consistent with Gicevic et al., non-U.S. studies seldom reported the racial/ethnic composition of the sample [ 41 ].


There are several limitations to this study that are worth noting. First, this study focused on articles published over a relatively narrow time-period. Given the immense number of records initially identified, we needed to consider the feasibility of screening and then thoroughly coding eligible articles. Thus we decided to focus on recent literature. Additionally, it was not a focus of this study to look at time trends. Future studies that wish to see how the field is changing should do time-trend analyses, ideally taking into account a longer period of time. Another limitation of this study is that we did not assess intervention effectiveness or quality. While this may limit the potential utility of this review, we chose to focus on the results of the content analysis and not include this information because it is included in prior reviews of family-based interventions for childhood obesity prevention published in the past 10 years [ 20 , 21 , 22 , 23 , 24 , 60 ]. Although systematic reviews can identify effective intervention strategies, they cannot identify the absence of information or gaps in the literature. This study explicitly addressed this shortfall in prior reviews. Lastly, the results of this study may be influenced by the number and choice of databases searched, and may be subject to publication bias. Given the large volume of studies (~7000) obtained by searching PubMed, and the considerable overlap with other databases (i.e. the number of duplicates), we limited our search to the three most commonly searched databases in previous reviews [ 20 , 21 , 22 , 23 , 24 , 41 , 60 ]. By limiting our search, it is possible that a few otherwise eligible studies were missed. It is also possible that including other databases (e.g. EMBASE, Dissertation Abstracts International) would have slightly increased the proportion of non-U.S. based interventions.

Despite limitations, this study used a novel approach to synthesize and profile the recent literature on family-based childhood obesity prevention interventions. Results demonstrate the current emphasis in interventions, and lack of adequate representation of various groups. More interventions that recruit diverse populations, and target behaviors beyond diet and physical activity, are needed to better understand the influence of these characteristics when designing and implementing family-based childhood obesity prevention interventions. The results of this study can be used to inform decision-making around intervention design and funding aimed at filling gaps in the knowledge base. Filling these gaps will lead to a better understanding of how best to target a wide range of behaviors in diverse populations.


Institute of Medicine

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Socioeconomic status

United States

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We would like to acknowledge Carol Mita and Selma Gicevic for their assistance in constructing the search strategy. We would also like to acknowledge Martina Sepulveda for assisting with screening.

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TA and AA developed the search strategy, performed the literature search, conducted article screening, and data extraction, and drafted the manuscript. In addition, TA cleaned the data, ran the analyses, and generated the Tables. TY assisted with article screening and drafted a portion of the manuscript. AAT created the codebook, assisted with screening and coding training, provided input on result interpretation, and edited the manuscript. KKD conceptualized the study, supervised the systematic review process, provided input on coding categories, helped generate the tables, and critically reviewed the manuscript. All authors read and approved the final manuscript.

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Additional files

Additional file 1:.

Full search strategy for PubMed database to identify eligible family-based childhood obesity prevention interventions published between 2008 and 2015. (DOCX 135 kb)

Additional file 2:

List of eligible articles published between 2008 and 2015 detailing a family-based childhood obesity prevention intervention. (DOCX 210 kb)

Additional file 3: Table S1.

Intervention characteristics of family-based childhood obesity prevention interventions separating studies with evaluations from protocols. (DOCX 116 kb)

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Ash, T., Agaronov, A., Young, T. et al. Family-based childhood obesity prevention interventions: a systematic review and quantitative content analysis. Int J Behav Nutr Phys Act 14 , 113 (2017). https://doi.org/10.1186/s12966-017-0571-2

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A randomized controlled trial for overweight and obesity in preschoolers: the More and Less Europe study - an intervention within the STOP project

  • Anna Ek   ORCID: orcid.org/0000-0002-2179-8408 1   na1 ,
  • Christine Delisle Nyström 2   na1 ,
  • Adela Chirita-Emandi 3 , 4 ,
  • Josep A. Tur 5 , 6 ,
  • Karin Nordin 1 ,
  • Cristina Bouzas 5 , 6 ,
  • Emma Argelich 5 , 6 ,
  • J. Alfredo Martínez 6 , 7 , 8 ,
  • Gary Frost 9 ,
  • Isabel Garcia-Perez 10 ,
  • Marc Saez 11 , 12 ,
  • Corina Paul 13 , 14 ,
  • Marie Löf 2 , 15 &
  • Paulina Nowicka 1 , 16  

BMC Public Health volume  19 , Article number:  945 ( 2019 ) Cite this article

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Childhood overweight and obesity is a serious public health issue with an increase being observed in preschool-aged children. Treating childhood obesity is difficult and few countries use standardized treatments. Therefore, there is a need to find effective approaches that are feasible for both health care providers and families. Thus, the overall aim of this study is to assess the acceptance and effectiveness of a parent support program (the More and Less, ML) for the management of overweight and obesity followed by a mobile health (mHealth) program (the MINISTOP application) in a socially diverse population of families.


A two-arm, parallel design randomized controlled trial in 300 2-to 6-year-old children with overweight and obesity from Romania, Spain and Sweden ( n  = 100 from each). Following baseline assessments children are randomized into the intervention or control group in a 1:1 ratio. The intervention, the ML program, consists of 10-weekly group sessions which focus on evidence-based parenting practices, followed by the previously validated MINISTOP application for 6-months to support healthy eating and physical activity behaviors. The primary outcome is change in body mass index (BMI) z-score after 9-months and secondary outcomes include: waist circumference, eating behavior (Child Eating Behavior Questionnaire), parenting behavior (Comprehensive Feeding Practices Questionnaire), physical activity (ActiGraph wGT3x-BT), dietary patterns (based on metabolic markers from urine and 24 h dietary recalls), epigenetic and gut hormones (fasting blood samples), and the overall acceptance of the overweight and obesity management in young children (semi-structured interviews). Outcomes are measured at baseline and after: 10-weeks (only BMI z-score, waist circumference), 9-months (all outcomes), 15- and 21-months (all outcomes except physical activity, dietary patterns, epigenetics and gut hormones) post-baseline.

This study will evaluate a parent support program for weight management in young children in three European countries. To boost the effect of the ML program the families will be supported by an app for 6-months. If the program is found to be effective, it has the potential to be implemented into routine care to reduce overweight and obesity in young children and the app could prove to be a viable option for sustained effects of the care provided.

Trial registration

ClinicalTrials.gov NCT03800823; 11 Jan 2019.

Peer Review reports

According to the World Health Organization childhood obesity is one of the gravest public health challenges of today’s society [ 1 ], with approximately 108 million 2- to 19-year-old children being classified as having obesity [ 2 ]. More specifically, in children less than 5 years, there has been a swift increase in childhood overweight and obesity and if these trends continue it is predicted that 70 million children will be overweight or obese by 2025 [ 3 ]. These statistics are concerning as Geserick et al. [ 4 ] found that 90% of 3 year olds with obesity still had overweight or obesity in adolescence. Furthermore, for those adolescents with overweight or obesity, the majority of weight gain happened between two and 6 years of age [ 4 ]. Thus, this demonstrates the need for evidence-based treatment programs in the pre-school years in order to attempt to rectify the increased prevalence of childhood overweight and obesity.

According to Colquitt et al. [ 5 ] for children under 6 years of age multicomponent interventions (i.e., diet, physical activity, and behavioral interventions) seem to be effective at treating overweight and obesity. However, the authors did state that evidence is limited [ 5 ]. To date, the majority of the treatment interventions for overweight and obesity use face-to-face delivery methods [ 6 ]. A recent meta-analysis by Ling et al. [ 6 ] found small effect sizes on treatment interventions for preschool-aged children for body mass index (BMI) (− 0.28 kg/m 2 , p  < 0.001) using various in person delivery methods. Furthermore, the More and Less (ML) study found that at the 12-month follow-up, a 10-week group treatment program focusing on parenting practices had a greater reduction in BMI z-scores than standard treatment in health care (− 0.30 vs. -0.07, p  < 0.05). An even greater reduction was observed in the intervention group who received booster sessions (a 30-min phone call every 4 to 6 weeks over a 9-month period) [ 7 ]. These results are promising; however, sustained contacts with families after treatment programs are burdensome on both health care providers and participants, which makes it difficult to scale-up. Therefore, different types of boosters need to be used in order to reduce the burden on both health care and participants.

The universal use of smartphones makes the use of mobile health (mHealth) an option for boosting the effects of treatment programs. mHealth is increasingly being used for promoting healthy habits and as treatment of many types of health conditions and diseases. In adults, two meta-analyses have found that mHealth interventions focusing on weight loss significantly decreased participants’ weight in the intervention groups compared to the control groups [ 8 , 9 ]. In children and adolescents few studies have utilized mHealth in the prevention or treatment of obesity [ 10 , 11 , 12 , 13 , 14 ] and hardly any have been conducted in the preschool-age group [ 15 , 16 ]. The Mobile-based Intervention Intended to Stop Obesity in Preschoolers (MINISTOP) trial was a mHealth obesity prevention intervention that was developed and led by Marie Löf and her team to improve 4-year-old children’s body composition, dietary, physical activity, and sedentary behaviors [ 17 , 18 ]. The MINISTOP intervention had a significant effect on a composite score composed of body composition, diet, and physical activity variables, with this effect being more evident among children with a higher fat mass index [ 18 ]. There are numerous advantages of mHealth over conventional intervention approaches such as: the programs can be delivered any time and place; are interactive; can be tailored to different groups (e.g., translated into multiple languages); and reduces burden on health care professionals and participants. These advantages further motivates the use of mHealth in families with young children with overweight and obesity.

The mechanisms that drive weight gain such as epigenetics and gut hormones are still unclear [ 19 , 20 ]. Epigenetics has received attention during the recent years for the putative involvement in transmitting obesity risk to offspring and in the heritable regulation of gene expression without altering their coding sequence [ 21 ]. The most relevant epigenetic mechanisms involved in gene activity control are histone modifications, non-coding RNAs (ncRNA) and DNA methylation [ 20 ]. Further, obesity has been associated with the epigenetic modulation of several genes. For example, a relationship has been reported between increased BMI and adiposity as well as higher DNA methylation levels at the hypoxia-inducible transcription factor 3A (HIF3A) gene [ 22 ]. Moreover, an increased methylation in the gene RXRA measured at birth has been associated with greater adiposity in later childhood [ 23 ]. Two other investigations identified a strong correlation between obesity and serum levels of micro RNA (miR)-122 and miR-519d [ 24 ] and found DNA methylation to be related to insulin resistance [ 25 ]. However, these findings need to be confirmed and further explored in young children.

Another field of interest for obesity is the gastrointestinal tract (GIT) [ 26 ]. The GIT plays an important role in acute appetite regulation through a number of mechanisms: (1) the release of hormones that play a role in appetite regulation such as anorectic hormones (Peptide YY, PYY, and glucagon-like peptide, GLP-1) and orexogenic gut hormones (e.g., ghrelin), (2) the enteric nervous system and signals through the vagus to the brain to influence appetite and (3) secondary to stimulating signals from other organs such as liver adipose. Previous research in adults has demonstrated that the infusion of the GIT anorectic hormones PYY and GLP-1 at physiological doses has profound effects to suppress appetite [ 26 ]. Also weight loss appears to lead to a suppression of PYY and GLP-1 suggesting a role in the feelings of hunger during weight reduction. However, evidence of the role of GIT hormones in overweight and obesity among young children is sparse.

A major challenge in the management of obesity in both adults and children is understanding what people eat. Most dietary assessment methodologies use methods of self-reported food intake which is a subject to large misreporting error [ 27 , 28 ]. It is therefore impossible to understand what children eat. Garcia et al. has developed a new metabolomic methodology of dietary assessment using urine, which is not subject to the same misreporting errors [ 29 ]. This method has been validated in adults. Our aim is to do this is children.

To the best of our knowledge there is no study to date that has the ambition to assess a broad array of key biological and social determinants of obesity in young children. This study protocol outlines the design of a multi-country study that incorporates both a parent support program and mHealth in an overweight and obesity intervention in 2- to 6-year-old children with overweight and obesity.

The overall aim of this study is to assess the feasibility, acceptance and effectiveness of an overweight and obesity intervention in a socially diverse population of families. The specific aims are:

To determine the effectiveness on child weight status (BMI z-score) of a 10-week parent support program delivered in groups focusing on evidence-based parenting practices (the ML program) followed by a mHealth component for 6-months (the MINISTOP application, app) for overweight and obesity in preschool-aged children.

To assess change in secondary outcomes, which are: waist circumference, child eating behavior, parental feeding practices, and physical activity.

To assess epigenetic mechanisms and physio pathological processes underlying childhood obesity including the role of gut hormones.

To assess and validate child food intake with metabolic markers in urine metabolomics.

To evaluate the feasibility of recruitment (facilitators and barriers), attrition and acceptability of the ML program, the standard treatment and the overall acceptance of overweight and obesity management according to patients and care providers.

Our central hypothesis is that the intervention (the ML program followed by the MINISTOP app for boosting) will be more effective in decreasing children’s BMI z-score (primary outcome), improving eating and feeding behaviors, and physical activity (secondary outcomes) compared to standard care. Another study hypothesis is that the intervention will produce changes in urinary metabolites, which will serve as biomarkers of the nutritional outcomes or as targets for application. We also hypothesize that the parent program and the mHealth intervention will be well accepted by families and caregivers.

Study design

ML Europe is a two-arm parallel design randomized controlled trial (RCT) comparing overweight and obesity treatments in 2-to 6-year-old children in three countries (Romania, Spain, and Sweden). Following baseline assessments, participants will be randomized into the intervention and control group in a 1:1 ratio. The intervention group receives a 10-week parent support program (the ML program) which focuses on evidence-based parenting practices [ 7 , 30 ] followed by a previously validated 6-month mHealth program (the MINISTOP app, PI: M Löf) to support healthy lifestyle changes [ 17 , 18 ]. The control group receives standard treatment as offered in the country of participation. The different interventions are described in greater detail below. Assessments will be conducted at 10 weeks, 9 months, 15 months, and 21 months post-baseline (see Fig.  1 for study outline). This study protocol follows the SPIRIT 2013 statement [ 31 , 32 ].

figure 1

Flow-chart of the More and Less Europe trial design

Sample size and power calculation

Based on power calculations, 75 children are needed in each group (adjusted for drop-out) to detect a difference of 0.3 BMI z-score with 85% power at the 9-month follow-up between the intervention and control group. These calculations are based on a previous study in this age group [ 33 ]. Thus, each site aims to recruit 100 participants to ensure adequate power.

Participants, eligibility, and recruitment

In total, we aim to include 300 families ( n  = 100 in Romania, Spain, and Sweden, respectively). To be included in this study: children must be between 2 and 6 years old and have overweight or obesity as classified by international cut-offs [ 34 ]; have no other underlying medical condition(s); the child has not started any treatment for overweight or obesity; and at least one parent has to have the ability to communicate in Romanian, Spanish, or Swedish depending on the country of participation. Parents who do not own a smartphone compatible with the MINISTOP app will be excluded from this study (i.e., version 10.0 or higher for iOS or version 5.0 or higher for Android).

Recruitment will follow a standardized protocol for all countries. In Romania, family physicians and pediatricians will be involved to hand out information regarding the study to families with 2- to 6-year-olds with overweight or obesity. Parents who want to learn more about the study are provided with a phone number, email address, web page and Facebook page with information of how to contact the research group. Participants will also be recruited, as self-referrals, using an official page for the study on Facebook to be shared with specialized groups.

In Spain, families with children who attend weight and height assessments at their pediatricians at primary care health centers and hospitals will be asked to participate in the study. If the parents are interested in participating, the pediatrician will schedule a visit within a maximum of 7 days to provide them with more detailed information regarding the study and for them to sign the informed consent.

Finally, in Sweden, the recruitment methods have been previously described in detail [ 7 , 30 ]. Briefly, recruitment is done primarily at primary child health care centers, where all parents of children from birth to 5 years of age are offered free, yearly check-ups. If overweight or obesity is detected the nurse provides a verbal and a short one-page explanation of the study. If the parent(s) are interested in participating the nurse sends a referral to the research group that will send out more detailed information regarding the study together with a consent letter. After 1 week, a member from the research team will contact the families to answer any questions that they have. Recruitment is also conducted at secondary health care (i.e., out-patient pediatric clinics). Additionally, self-recruitment is being done through newspaper ads as well as by placing posters on primary health care bulletin boards.

For all countries, after fully informing the families, if they still want to participate they send back the signed consent letter, which is subsequently signed by a member of the research team and a copy is sent back to the family. A time for baseline assessments is then scheduled with the research group.

Randomization and blinding

After the consent form has been signed, the participants are randomly allocated to either the intervention group (parent support program and mHealth booster) or the control group (standard care as per country) at a 1:1 ratio via a random allocation sequence list (in blocks of three). The sequence list was generated using free software environment for statistical computing and graphics R (version 3.5.1) [ 35 ]. The random allocation sequence is managed by a person who has no relationship with recruitment or treatment and opaque envelopes are used to ensure concealment. Those assessing the outcomes are blinded to the treatment allocation; however, owing to the nature of the intervention participants are not blind to their allocation.


The more and less program.

The ML program is based on the Keeping Foster and Kin Parents Supported and Trained (KEEP) parenting program, which has been tested in multiple settings [ 36 , 37 , 38 , 39 ]. KEEP is based on Bandura’s Social Learning Theory [ 40 ] and Patterson’s Social Interaction Learning Theory [ 41 , 42 ]. The key concept of the programs is to support parents in evidence-based parenting practices, especially regarding positive reinforcement and limit setting, in order to improve parent and child communication. In ML, the improved communication lays the foundation for parents to support a healthy lifestyle for the child.

The ML program is comprised of 10 weekly sessions (1.5 h/week) and is culturally adapted for Romanian, Spanish, and Swedish families with preschool aged children with overweight or obesity. Table  1 displays the content of the ML program [ 7 , 30 ]. Beyond the evidence-based parenting practices, the program includes content regarding healthy food habits, physical activity habits, as well as techniques to help parents regulate emotional control. Each session begins with a theoretical introduction to a parenting skill, the focus of the session is then discussed and practice is done through role play and homework assignments. To facilitate the implementation of the ML program it follows a manual where the sessions are described with precise instructions to the group leaders (2 per group). The parents receive a manual which summarizes what has been discussed during each session. For parents who are unable to attend sessions, the parental manual is sent home to the family and the family is contacted by phone for a brief review of the session. To facilitate session attendance the time and location for the groups are planned to suit the parents. Child care is also provided during the sessions.

The ML group leaders received an initial 4 day training in child overweight and obesity management and in the ML program content. The training was provided by the ML program developers PN and AE. During the training the sessions of the program were thoroughly discussed and the group leaders were trained in how to deliver the program by acting as group leaders while the other participants acted as parents. The training of group leaders will continue by external supervision after each weekly session for the first group in all countries. The group leaders will be asked to watch the filmed sessions and reflect on how they delivered the program. In Sweden and in Spain, groups will be held in health care facilities and in Romania in university facilities.


The MINISTOP app was developed and evaluated in a population based study with preschool aged children (PI: Marie Löf) and has been previously described in detail [ 17 , 18 ]. Briefly, MINISTOP comprises of an extensive program of information and push notifications built using current guidelines for a healthy diet and physical activity in pre-school aged children [ 43 ]. Over the 6-month period 12 themes will be covered (Table 1 ). A new theme is introduced bi-weekly, with parents being alerted by a push notification when this happens. Every theme is split into three parts (general information; advice; and strategies to change unwanted behavior). Through the app, parents have the ability to register their child’s consumption of sugar sweetened beverages, candy, fruits and vegetables, and physical activity and sedentary behavior. Parents then receive feedback on the registered parameters at the end of every week. Reminder messages are sent out to parents if they have not been in the app after a couple of days [ 17 ].

Two days before the tenth and final session of the ML program, parents receive an email with a username and password for the MINISTOP app as well as a text message with a link to download the app. At the final session, the ML program leaders will ensure that all parents were able to download the app and sign in. Thereafter, they will explain how the app works to the parents and answer any questions that they may have.

The weight management offered to the control group follows the standard care procedure for each country of participation. In Romania and Spain, the control group receives an evaluation of a one-day food frequency questionnaire as well as a 30-min consultation with a doctor that is a specialist in childhood nutrition, where healthy lifestyle recommendations are made. The parents also receive a hand-out which provides general recommendations for healthy food and physical activity in 2 to 6 year olds. Furthermore, in Romania the children are re-evaluated after 3 months during a 15-min consultation. In Sweden, the control group receives standard care according to the Action plan for overweight and obesity for Stockholm County [ 44 ]. Children with overweight and children with obesity younger than 4 years receive support from their child health care nurse. Children older than for 4 years with obesity are followed in an outpatient pediatric clinic with yearly visits to a pediatrician and follow-up visits to a pediatric nurse, approximately 5 visits (30 min in duration) per year [ 7 ]. The treatment centers around supporting the family in creating healthy diet and physical activity habits for the child. Children may also be referred to dieticians, psychologists or physiotherapists.

Outcome measures are collected at baseline, 10 weeks, 9 months, 15 months, and 21 months post baseline. Table  2 presents when outcome measures are assessed and the instruments used to assess child and parental behaviors are displayed in Table  3 .

Primary outcome

BMI z-score is the primary outcome measure which is the most commonly used indicator of weight change in pediatric obesity studies [ 47 ]. The children’s weight and height will be measured to the nearest 0.1 kg and 0.1 cm, respectively. A fixed stadiometer is used to assess height and weight will be measured with the children wearing only underwear. BMI is derived as weight (kg) divided by height (m) squared. BMI z-scores are then calculated using age and gender specific reference values [ 34 ].

Secondary outcomes

Waist circumference.

Waist circumference is measured at the mid-point between the lower rib and iliac crest to the nearest 0.1 cm using a non-elastic tape measurer.

Weight, height and waist circumference are measured three times and mean values are then calculated. All children are measured in a standardized manner by trained health care professionals using calibrated instruments.

Eating behavior

The children’s eating behavior is assessed using the Child Eating Behavior Questionnaire (CEBQ) [ 45 ]. It includes 35 items on eating styles comprising eight factors related to the risk of obesity. Parents rate each behavior on a five-point Likert scale (`never´, `rarely´, `sometimes´, `mostly´, and `always´ for items 1 to 13 and `disagree´, `slightly disagree´, `neutral´, `slightly agree´, and `agree´ for items 14 to 49). Mean scores for each sub-scale are calculated. This questionnaire has been found to have high internal reliability and good validity [ 45 , 48 , 49 , 50 , 51 , 52 , 53 ].

Parenting behavior

The Comprehensive Feeding Practices Questionnaire (CFPQ) is used to measure parenting behavior [ 46 ]. The CFPQ is a parent-report instrument, designed to measure feeding practices of parents of children aged 2–8 years. It contains 49 items comprising 12 factors, where parents rate each behavior on a five-point Likert scale (`never´, `rarely´, `sometimes´, `mostly´, and `always´). The CFPQ has previously been validated in Brazilian preschoolers [ 54 ].

Physical activity and sedentary behavior

The ActiGraph wGT3x-BT accelerometer (ActiGraph Corp, Pensacola, USA, www.actiGraphcorp.com ) is used to assess physical activity and sedentary behavior over seven consecutive 24 h periods. The ActiGraph will be attached the child’s non-dominant wrist and be worn at all times, except for water-based activities (e.g., showering/bathing or swimming). The recorded movements will be used to estimate time in various activity levels based on appropriate cut-points.

Metabolites of food intake

First void urinary samples will be collected from the children and will be used to assess metabolites of food intake. Two urine samples from the child will be collected by the parents at home six and 3 days before the visit to the research group. The third urine sample is collected on the morning of the visit to the research group. The urine metabolite analysis will be carried out as previously described [ 29 ]. In brief, urine samples will be measured by proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy. Global urinary 1 H-NMR profiles will be used to predict the quality of the diet using the World Health Organization guidelines as a reference. Individual urinary metabolites associated with the intake of foods will be used to assess the dietary profile of the child. The Dietary Metabotype Score that embodies concentrations of urinary metabolites related to food components and adherence to diet will be developed and validated against one 24-h dietary recall with a parent. The 24-h recall will cover the day before the visit to the research group. For children attending preschool a food diary for teachers to fill out will be collected to cover the food intake not provided by the parent.

Epigenetic markers and gut hormones

Fasting blood samples are collected to assess reversibility of metabolic markers through epigenetic markers and the role of gut hormones.

Epigenetic markers

The epigenetic analysis is carried out in white blood cells, which require DNA extraction, bisulphite transformation, and analysis with Polymerase chain reactions (PCRs) or other technologies involving hypothesis driven methylation (CpGs). The unit of measurement/criteria is changes in percentage CpGs. The methodology has been explained in detail elsewhere [ 55 , 56 ]. Methylation levels will be analyzed following standardized epigenetic methods after bisulphite conversion as described previously [ 55 , 56 ] in hypothesis- driven specific CpGs.

Gut hormones

PYY concentrations will be measured using an in-house radioimmunoassay (RIA). The assays are highly sensitive and do not cross-react with other gut hormones. Separation of the antibody-antigen complexes from the free antigen is achieved by secondary antibody. The reported intra- and inter-assay variation is 5.8 and 9.8% respectively.

GLP-1 concentrations will be measured using an in-house RIA. This assay is highly specific and sensitive with the antibody cross reacting with 100% of all amidated forms of GLP-1. The assay does not cross react with glycine extended forms (GLP1–37 and GLP9–37) or any other gut hormones. The lowest level of GLP-1 that can be detected by this assay is 7.5 pmol/l. Separation of the antibody-antigen complexes from the free antigen is achieved by charcoal adsorption. The reported in-house intra- and inter-assay variation is 5.4 and 11.5% respectively.

Feasibility, attrition, and acceptability

Using semi-structured interviews, the facilitators and barriers of recruitment as well as attrition to the intervention (first 10 weeks, i.e., the ML parent program) and feasibility and acceptability of the MINISTOP app and of the standard care offered are assessed. Both parents and healthcare professionals are interviewed by trained research staff. During the interviews a set of questions are asked to all participants follow-up questions are however based on individual responses. The questions have been tested in pilot interviews with both parents and health care professionals. The interviews are recorded and fully transcribed. Interviews will be conducted before and after the intervention.

Sociodemographic data

At baseline parents are asked to fill out a background questionnaire for the child and themselves. Questions for the parent include: health status, sociodemographic factors and social support. For the child, questions include: country of birth, health status, family structure and lifestyle related questions such as food and screen time behaviors.

Adverse events

Adverse events will be monitored, reported and handled appropriately. The risks imposed by this research project are deemed to be low, i.e., the burden of the experiments for the research subjects is limited. It is important to note that blood samples collected in the study are optional and not a criteria for participation. However, blood samples are taken by experienced nurses and a pain reducing cream is used to reduce any discomfort. Urinary samples are none invasive and thus cause no risk to the participants. In addition, the investigators have extensive experience conducting behavioral weight control studies, and active efforts will be taken by the research staff to ensure the participating families’ safety. Other adverse events may include psychosocial burden that parents may experience when made aware about their child’s weight status and the sense of guilt that may arise. To handle that, already in the first session of the ML program causes and consequences are reviewed in a non-judgmental way. Also, potential impact on the child’s self-esteem and the way to talk about body weight and obesity with children, if necessary, are addressed.

Data management

All collected data will be handled as approved by the ethical boards to protect confidentiality. Data is de-identified and entered manually into a database by research staff at the participating site where the data originated from. An identical database is used at each site. To ensure data quality and validity the researchers follow standard operation procedure protocols when entering data. The entered data will be double checked by the person entering the data and random checks will be performed regularly to ensure data validity. The database will be password protected and access is restricted to researchers with passwords. Original data forms will be stored in a secure place at each study site.

Statistical analysis

Intention-to-treat analysis using generalized linear mixed models with repeated measures will be used to examine the effects of the intervention on primary (aim 1) and secondary outcomes (aim 2) for the total study population (i.e., all three sites). The link function for the primary outcome (BMI z-score) will be the identity and the Gaussian family (equivalent to a linear regression). In secondary outcomes we will use a Gaussian identity and family link function for waist circumference, physical activity and sedentary behavior, and a logarithmic link and Poisson family function (equivalent to a Poisson regression) for child eating behavior and parental feeding practices. A random effect for country will be used to account for the clustered study design. In the models, we will control for relevant covariates such as sex, age, parental weight status, education level, income and foreign background. Random intercept and a random slope for time will be included in the model to control those non-observed confounders specific to each child that could be constant or vary in time, respectively. Furthermore, interactions between variables will be estimated. If missing values in the outcomes (primary and secondary) are more than 10%, these will be imputed through a two-part model (also known as a model for semi continuous data). In this model, we would simultaneously estimate the probability of not being missing (first part) and the outcome (second part), using a mixed generalized linear model, in which we would include, as explanatory variables: age, sex, parental weight status, foreign background, educational level, and the random effects which are aforementioned.

Statistical tests and analyses of the interaction of phenotypical outcomes with epigenetics will include Manhattan plots, volcano plots, principal component analysis (PCA)/cluster, heatmaps, partial least square-discriminant analysis (PLS-DA), correlations and association studies, linear regression models, receiver operating characteristic (ROC) curves and these will be implemented as appropriate.

The means and medians for the gut hormone values before and after the intervention will be compared, using Student’s t test and Mann-Whitney U test, respectively. The differences will be adjusted in a generalized linear mixed model, with an identity link and Gaussian family, including the confounders, both observed and unobserved, indicated above.

For the validation of child food intake with metabolic markers in urine, the urinary dietary model will be derived using previously described methodology [ 29 ]. Comparison between the study groups will be carried out using PCA and Monte Carlo cross-validated partial least square-discriminant analysis (MCCV-PLS-DA) methodology. The relationship between dietary biomarkers and the dietary metabolite profile will be carried out using a generalized linear mixed model, with an identity link and Gaussian family, including, again, the confounders.

The semi-structured interviews with parents and health care professional will be fully transcribed verbatim and analyzed using thematic analysis [ 57 ].

For our analyses we will use R [ 35 ], STATA version 12.1 (StataCorp 2011, College Station, TX, USA) and SPSS Statistics (IBM, Armonk, NY, USA).

Ethics approval

This trial was approved by: the Ethics Committee of Scientific Research in University of Medicine and Pharmacy “Victor Babes”, Timisoara, Romania, October 31st, 2018 (25/31.10.2018), the Balearic Islands Ethics Committee, Mallorca, Spain, February 13th, 2019 (IB 3814/18 PI), and the Research Ethics Committee, Stockholm, Sweden, December 11th, 2018 (2018/2082–31/1). Written informed consent is obtained from all parents/caregivers. The ethics committees approved the consent procedure.

Trial status

In Sweden and Romania recruitment began in January 2019 and Spain began to recruit in February 2019. Recruitment is expected to last until 2020.

The ML Europe trial will assess the impact of parent support group sessions (the ML program) followed by a mHealth program (the MINISTOP app) to treat overweight and obesity in 2- to 6-year-old children from three European countries. Globally, there has been very few overweight and obesity treatment interventions targeted to pre-school aged children [ 6 ] and to date no intervention has coupled face-to-face delivery with mHealth to boost the effect of the intervention.

In this trial we aim to recruit a representative sample of the study population in each participating country. In Sweden this will be done by inviting all primary and secondary health care centers in Stockholm County to participate in recruitment, with a similar process being done in Spain (all primary health care centers and hospitals in Mallorca were invited to participate). However, the ability to get a representative sample of the study population in Romania might be more difficult as recruitment relies on families contacting the research team themselves through contacts with physicians and pediatricians and Facebook announcements. Therefore, certain parts of the population may be missed, e.g., those not likely to contact the research team and those who do not use Facebook.

Additionally, there are a few other factors that should be considered with regards to recruitment, which have the possibility to influence the representativeness of the overall sample. Firstly, the participating families need to be able to understand, speak, and read Romanian, Spanish, or Swedish sufficiently well (depending on the country of participation) in order to participate. Secondly, families with low socioeconomic status and parents with a lower educational background have been found to be less likely to participate in research [ 58 , 59 ]. The inability to speak the language the intervention is being conducted in coupled with the possibility of low participation rates in families with low socioeconomic status is of concern. This is due to the fact that children of migrant parents and those of low socioeconomic status are more likely to have overweight or obesity [ 60 , 61 ]. Furthermore, families will only be included if they own a smartphone compatible with the MINISTOP app, which could affect recruitment of low socioeconomic families; however, we believe this risk to be quite small as smartphones are so commonly used in most populations. Finally, we foresee that recruitment for this study will be a challenge as was found in the ML trial [ 7 ]. In the ML trial parents decided not to participate for various reasons, with the most common being parents’ work schedules or family situation [ 7 ]. When recruiting for ML Europe we used our experiences from previous clinical RCTs to ensure that recruitment and patient participation are organized in the most feasible way, e.g., time, date and place for the parent groups will be adjusted to suit as many families as possible. We anticipate the recruitment to be influenced by target population size which varies between the countries (330,000 in Timisoara, 860,000 in Mallorca and 2.3 million in Stockholm County). Also, we are aware that the prevalence of overweight and obesity among children differs in each site. While recent national data are yet to be published, in Romania, a study including 6-year-old children found the prevalence for overweight and obesity to be 19% [ 62 ]. In Spain, the prevalence of overweight and obesity was 21% in 3 to 5-year-old children [ 63 ]. In the Stockholm County, the prevalence of overweight and obesity among 4-year-old children is on average 11% ranging from 4% in the more affluent areas to over 15% in less affluent areas [ 64 ]. Thus, although the prevalence and obesity seems to be lowest in the Swedish site the larger population may compensate this challenge. It remains to be elucidated what the largest barrier in the recruitment process will be: not sufficiently large targeted population or low prevalence of overweight and obesity.

The randomized controlled design and multi-site recruitment (i.e., Timisoara, Romania; Mallorca, Spain; and Stockholm, Sweden) are strengths of this study. Furthermore, the fairly large sample size ( n  = 300) will allow us to assess the intervention’s effectiveness in samples within and across three very different European countries. With regards to the intervention, both components are based on behavior change theories (i.e., Bandura’s Social Learning Theory [ 40 ] and Patterson’s Social Interaction Learning Theory [ 41 , 42 ] for the ML program and Social Cognitive Theory [ 65 ] for the MINISTOP program). Furthermore, the combination of group sessions followed by a previously evaluated mHealth app is a further strength, as it will allow for the reiteration of the material taught during the group sessions to be explained in different ways with different examples. This is important as the booster group in the ML study had a mean change in BMI z-score from baseline which was significantly larger in comparison to standard treatment and the group without boosters (− 0.54, p  < 0.001; − 0.11, p  = 0.551; and − 0.04 for the booster, without boosters, and standard treatment groups, respectively) [ 7 ]. In today’s society, telephone based booster sessions after an intervention such as ML is difficult to sustain due to parents’ busy schedules. Therefore, a mHealth solution such as MINISTOP to booster the effect of treatment may be a more feasible approach as it allows parents to work through the material at their own pace, when they have time. Finally, this study is limited by the fact that there is no standard overweight and obesity treatment across Europe. Therefore, the control group will receive different treatment depending on the country of participation, which could influence the results. However, standard treatment as per country is the best possible control as it would be considered unethical to withhold treatment for a condition if a treatment exists [ 66 ].

The use of objective assessments for anthropometrics and body composition, physical activity and sedentary behavior, food intake, as well as epigenetic and metabolic markers is a further strength of this study. Additionally, the use of qualitative methods, i.e., semi-structured interviews with health care professionals and parents from all sites will allow us to assess the feasibility of this new overweight and obesity management intervention in three European countries.

In conclusion, in the majority of countries, there is no standard management of overweight and obesity in the pre-school years. As overweight and obesity in this age may track into adolescence and adulthood, causing psychological and physical consequences, families should receive support as early as possible. Feasible and effective approaches for families with pre-school aged children are yet to be developed. If the ML Europe intervention is found to be effective, it has the potential to be implemented into routine care for overweight and obesity across Europe.

Availability of data and materials

Not applicable.


proton nuclear magnetic resonance


Body mass index

Child eating behaviour questionnaire

Comprehensive feeding practices questionnaire

Deoxyribonucleic acid

Gastrointestinal tract

Glucagon-like peptide

Hypoxia-inducible transcription factor 3A

Keeping foster and kin parents supported and trained

Monte Carlo cross-validated partial least square-discriminant analysis

Mobile health

Mobile-based intervention intended to stop obesity in preschoolers

Micro Ribonucleic acid

More and Less

Non-coding Ribonucleic acid

Principal component analysis

Polymerase chain reaction

Partial least square-discriminant analysis

Randomized controlled trial


receiver operating characteristic curves

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Many people have contributed with valuable ideas and practical support to this study. Among these are: (in Romania) Prof. Dr. Maria Puiu, Prof. Dr. Mihai Gafencu, Dr. Iulia Teodora Perva, Dr. Iulia Jurca Simina, Dr. Anamaria Dragomir, Casandra Chera (psychologist), Meda Bugi (dietician), (in Sweden) Fredika Gauffin (Head of the Astrid Lindgren’s Children’s Hospital’s out-patient pediatric clinics in Stockholm), Ola Eklund (Assisting Head of the Astrid Lindgren’s Children’s Hospital’s out-patient pediatric clinics in Stockholm), Christina Norling (Nursing Manager of the Astrid Lindgren’s Children’s Hospital’s out-patient pediatric clinics South Stockholm), Annelie Täppmark (Nursing Manager of the Astrid Lindgren’s Children’s Hospital’s out-patient pediatric clinics North Stockholm), Helena Martin (Head of the Stockholm County Child Health Care), Catharina Neovius (Child Health Care Developer in Stockholm County), Emmie Söderström (research assistant), My Sjunnestrand (research assistant) all child health care nurses, pediatricians and pediatric nurses that are involved in recruitment and providing standard care, (in Spain) David Mateos (nurse, Son Espases University Hospital), Diego de Sotto (Head of Pediatric Service, Rotger Clinics), Helena Corral (pediatrician, Hospital of Inca), Maria Àngels Martínez (pediatrician, Hospital of Inca), Maria Caimari (pediatric endocrinologist, Son Espases University Hospital), Marta Minguez (pediatrician, Hospital of Inca) for support and involvement in recruitment and providing standard care. The authors thank Nils Lidström and Jan Fjellström for help with the technical development of the MINISTOP app and Hanna Hennriksson for her work and support during the translation process of the app.

This study is funded through the STOP project,  http://www.stopchildobesity.eu/ . The STOP project received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 774548 [ 67 ]. The content of this document reflects only the authors’ views and the European Commission is not liable for any use that may be made of the information it contains. JAT, CB, EA and JAM are also funded by CIBEROBN (CB12/03/30038), Instituto de Salud Carlos III and the European Regional Development Fund. CB is also funded by a Fernando Tarongí Bauzà Grant. MS was also funded by CIBERESP, Instituto de Salud Carlos III. The funders had no role in the design; collection, analysis, and interpretation of data; or in writing the manuscript.

Author information

Anna Ek and Christine Delisle Nyström contributed equally to this work.

Authors and Affiliations

Division of Pediatrics, Department of Clinical Science Intervention and Technology, Karolinska Institutet, Stockholm, Sweden

Anna Ek, Karin Nordin & Paulina Nowicka

Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden

Christine Delisle Nyström & Marie Löf

Genetics Department, University of Medicine and Pharmacy “Victor Babes”, Timisoara, Romania

Adela Chirita-Emandi

“Louis Turcanu” Clinical Emergency Hospital for Children, Timisoara, Romania

Research Group on Community Nutrition & Oxidative Stress, University of the Balearic Islands, Palma de Mallorca, Spain

Josep A. Tur, Cristina Bouzas & Emma Argelich

CIBER of Physiology of Obesity and Nutrition (CIBEROBN), Instituto Carlos III, Madrid, Spain

Josep A. Tur, Cristina Bouzas, Emma Argelich & J. Alfredo Martínez

Department of Nutrition, Food Science, and Physiology, Centre for Nutrition Research, University of Navarra, Pamplona, Spain

J. Alfredo Martínez

IMDEA Food Precision Nutrition, Madrid, Spain

Section for Nutrition Research, Department of Medicine, Imperial College London, Hammersmith Campus, London, UK

Division of Systems and Digestive Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington Campus, London, UK

Isabel Garcia-Perez

Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Campus de Montilivi, Girona, Spain

CIBER of Epidemiology and Public Health (CIBERESP), Instituto Carlos III, Madrid, Spain

Pediatrics Department, University of Medicine and Pharmacy “Victor Babes”, Timisoara, Romania

Corina Paul

2nd Pediatrics Clinic, Clinical Emergency County Hospital Timisoara, Timisoara, Romania

Department of Medical and Health Sciences, Linköping University, Linköping, Sweden

Department of Food Studies, Nutrition, and Dietetics, Uppsala University, Uppsala, Sweden

Paulina Nowicka

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All authors were involved in the study design for More and Less Europe. AE is the project coordinator for the three sites and drafted the manuscript together with CDN who also aided in the development of the MINISTOP app’s content. ACE is the primary investigator for the Romanian site and JAT is the primary investigator for the Spanish site. Regarding recruitment and data collection KN is the coordinator in Sweden, CP in Romania and EA and CB in Spain. JAM is responsible for analyses of epigenetic and metabolic markers and their interpretations. GF and IGP are responsible for the analyses of gut hormones and validation of food intake through urine. MS is responsible for statistical analysis. ML created the original MINISTOP program and led the work when it was modified for ML Europe. PN is responsible for the Swedish site and the primary investigator of the ML Europe. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Anna Ek .

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Ethics approval was obtained from: the Ethics Committee of Scientific Research in University of Medicine and Pharmacy “Victor Babes”, Timisoara, Romania, October 31st, 2018 (25/31.10.2018), the Balearic Islands Ethics Committee, Mallorca, Spain, February 13th, 2019 (IB 3814/18 PI), and the Research Ethics Committee, Stockholm, Sweden, December 11th, 2018 (2018/2082–31/1). Written informed consent is obtained from all parents or caregivers.

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Ek, A., Delisle Nyström, C., Chirita-Emandi, A. et al. A randomized controlled trial for overweight and obesity in preschoolers: the More and Less Europe study - an intervention within the STOP project. BMC Public Health 19 , 945 (2019). https://doi.org/10.1186/s12889-019-7161-y

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quantitative research article on childhood obesity

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The effectiveness of pediatric obesity prevention policies: a comprehensive systematic review and dose–response meta-analysis of controlled clinical trials

  • Shahnaz Taghizadeh 1 &
  • Mahdieh Abbasalizad Farhangi 2  

Journal of Translational Medicine volume  18 , Article number:  480 ( 2020 ) Cite this article

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Childhood obesity persists as a serious public health problem. In the current meta-analysis, we summarized the results of controlled trials that evaluated the effect of obesity prevention policies in children and adolescents.

Three databases (SCOPUS, PubMed and Embase) were searched for studies published before the 6th April 2020, by reported outcome measures of body mass index (BMI) and BMI-Z score . Forty-seven studies reported BMI, while 45 studies reported BMI-Z score as final outcome.

The results showed that the obesity-prevention policies had significant effect in reducing BMI (WMD: − 0.127; CI − 0.198, − 0.056; P < 0.001). These changes were not significant for BMI-Z score (WMD: − 0.020; CI − 0.061, 0.021; P = 0.340). In dose–response meta-analysis, a non-linear association was reported between the duration of intervention and BMI (P nonlinearity  < 0.001) as well as BMI-Z score (P nonlinearity  = 0.023). In subgroup analysis, the more favorite results were observed for 5–10 years old, with combination of physical activity and diet as intervention materials.

In conclusion, the obesity prevention policies in short-term periods of less than 2 years, in rather early age of school with approaches of change in both of diet and physical activity, could be more effective in prevention of childhood obesity.

Trial registration PROSPERO registration number: CRD42019138359

Overweight and obese children persist as a serious health problem and a public challenge of the twenty-first century. Obesity among children and adolescents is a leading cause of health and contributes to cardiovascular disease, cerebrovascular disease, and metabolic diseases [ 1 ]. Nearly one in five children and adolescents are overweight or obese [ 2 ], and the growing prevalence of obesity in youth has led to an alarming increase of 18.5% in children and adolescents between the ages of 2–19 years [ 3 ]. Obese children are at greater risk of obesity in adulthood; a recent study of 200,777 participants showed that 80% of teens with obesity remained obese in adulthood and this continued with a prevalence of 70% past the age of 30 [ 4 ]. According to a recent study in the United States comparing the cost–benefit of prevention versus treatment interventions in youth, preventive interventions in the early stages of life were found to be more beneficial than in adulthood, and addressing childhood obesity as early as possible is an effective strategy against obesity in later ages [ 5 ]. Although the underlying reasons of genetics and individual behavior for being overweight in adults and young people are almost the same [ 6 ], obesity prevention policies in the younger age group are different from those adopted in adulthood. Developing and implementing effective strategies to prevent childhood obesity is difficult at the population level. The National Academy of Sciences recommended that more attention should be paid to providing opportunities to choose healthy foods in society [ 7 ]. Obesity prevention is a public health priority around the world. The effectiveness of childhood obesity prevention programs has been shown by previous Cochrane reviews [ 8 ]. Some previous systematic reviews have focused on childhood obesity prevention programs that were not at national, governmental or macro-population level policies or that focused on some specific interventional approaches, including changes in physical activity (PA), diet and education [ 9 , 10 , 11 , 12 , 13 ]. Although there is evidence to support the beneficial effects of increased PA and diet as a basic and early strategy at any time and for any age against obesity [ 14 , 15 ], no summarized study is available to critically evaluate the effectiveness of different policies with different interventional approaches in prevention of childhood obesity considering the role of setting, age, geographical distribution, and intervention type or strategy. Therefore, the aim of the current study was to systematically search controlled trials that evaluated the effectiveness of pediatric obesity prevention policies among children and adolescents and to analyze the effectiveness of these policies on the study outcomes of body mass index (BMI) and BMI-Z score (BMI-Z) measurements while considering a possible dose–response association with preventive tools.

Methods and materials

The current systematic review and meta-analysis was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement for reporting systematic reviews and meta-analyses [ 16 ] (checklist is provided in Additional file 1 : Table S1). The study protocol was registered in PROSPERO (identifier: CRD42019138359) and was approved by the Research Undersecretary of the Tabriz University of Medical Sciences as the Ph.D. thesis of SHT (Registration number: IR.TBZMED.REC.1398.840).

Data sources and search strategy

Searches were conducted using SCOPUS, PubMed and Embase. All articles were considered eligible, if published before April 6, 2020. Additional file 1 : Table S2 shows the full search strategy in PubMed. Four concept groups were organized according to the search terms: (a) Population (pediatric, children, or adolescents); (b) Health problem under consideration (obesity, pediatric obesity); (c) Intervention (policy, program, strategy); and (d) Relevant outcomes of interest (BMI, BMI-Z score) . The reference lists of all related and available articles were reviewed to reduce the possibility of missing articles. The selection criteria for this review were independently verified by two researchers (SHT, MAF).

Study selection

Relevant studies conducting a community approach that evaluated policies to prevent obesity in children and adolescents aged 0–18 years were included in the current review. Studies were excluded if they were aimed to treat childhood overweight/obesity), were performed in children with other diseases, or if their full text was not available. Detailed exclusion and inclusion criteria are shown in Table 1 .

Quality assessment and data extraction

Study quality was assessed using the Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies, a useful tool for quality assessment of randomized and non-randomized intervention trials [ 17 , 18 ]. This tool is comprised of six components that include selection bias, study design, confounders, blinding, data collection methods (validity/reliability), and withdrawals and dropouts. The overall quality rating and the components are scored as strong, moderate and weak according to the tool’s instructions. Individual component quality rankings are shown in Additional file 1 : Table S3. General study characteristics (author, year of publication, country, sample size, number of intervention and control, type of study (randomized or non-randomized), duration of intervention, follow-up from baseline, follow-up from end of intervention, participant characteristics, outcomes (BMI, BMI-Z score ), and policy characteristics were extracted for included studies. Effect size was defined as changes in BMI and BMI-Z score compared with control group. Two researchers (SHT, MAF) independently extracted the data from all studies.

Statistical analysis

The data were analyzed using STATA version 15 (STATA Corp, College Station, TX, USA), and p- values of less than 0.05 were considered statistically significant.

Two-class meta-analysis of continuous variable

The studies that reported BMI and BMI-Z score as primary or secondary outcomes in intervention and control groups were included for two-class meta-analysis synthesis. The means and standard deviations (SD) of variables were used to compute standardized mean differences as effect size computed by pooled estimate of weighted mean difference (WMD) at a 95% confidence interval (CI). Subgroup analyses were conducted to explore sources of heterogeneity. Due to high heterogeneity values (i.e., above 50%), the random effects model was used. Between-study heterogeneity was identified using Cochran's Q and I-squared tests as follows: I 2  < 25%, no heterogeneity; I 2 25% to 50%, moderate heterogeneity; I 2  > 50%, large heterogeneity [ 19 ]. Studies that reported separate results for both sexes, in different age categories, or at different time periods of follow-up were included as individual studies. Publication bias was examined using Begg’s funnel plots, followed by Egger's regression asymmetry test and Begg's rank correlation for formal statistical assessment of funnel plot asymmetry. For missing SDs, the method described by Walter and Yao was used to calculate SD [ 20 ]. Studies were excluded from the analysis if they (a) were not controlled trials or (b) did not report sufficient data of outcome variables.

Dose–response meta-analysis of continuous variables

For dose–response meta-analysis of variables, variables of duration of intervention and PA time and training sessions (as education time) were included. The mean difference of variables in each study was also identified. A dose–response meta-analysis of BMI and BMI-Z score was performed using fractional polynomial modeling [ 21 ] to explore nonlinear potential effects of duration of intervention (year), PA and education time and study-specific parameters.

Literature search and study characteristics

A search of electronic data bases retrieved 30,719 records. After removing duplicates, 20,686 items were screened by title/abstract (Fig.  1 ) and selected according the criteria identified above. The remaining 224 full text articles were screened and 49 publications were selected in a qualitative synthesis; finally, 38 publications were included in a quantitative synthesis, which contained outcomes for 64 individual studies as described above.

figure 1

Flow chart of study selection

Grey literature searches identified no published results for policies in scope. Study, participant, and program characteristics of the quantitative synthesis (meta-analysis) are presented in Table 2 with additional information including the full name of the studies shown in Additional file 1 : Table S4. Studies were performed in various settings of school (n = 16) [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], community and school (n = 10) [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], school and home (n = 1) [ 48 ], community, school, and home (n = 2) [ 49 , 50 ], community, school, home, and primary care clinic (n = 5) [ 51 , 52 , 53 , 54 , 55 ], community and home (n = 2) [ 56 , 57 ], primary care clinic (n = 1) [ 58 ], and cyberspace/online (n = 1) [ 59 ]. In all, 64 individual studies were obtained from 38 publications included in the quantitative synthesis. Twelve studies were performed as combinations of different follow-up times, age groups, genders, or different durations or populations; therefore each was included as two [ 23 , 24 , 25 , 31 , 35 , 36 , 42 , 44 , 48 , 50 , 54 , 55 , 56 , 59 ], three [ 41 , 49 , 52 ], or four individual studies [ 30 , 51 ]. The rationale for extracting several studies from these publications and additional information about the policies are shown in Table 2 and Additional file 1 : Table S5). Characteristics of studies that were not included in the meta-analysis with the exclusion reasons are shown in Additional file 1 : Table S6.

Approximately 35% of programs were carried out in the United States (n = 13) [ 29 , 31 , 32 , 33 , 34 , 37 , 40 , 42 , 49 , 50 , 51 , 52 , 57 ], and 31% (n = 12) studies in Australia [ 24 , 25 , 26 , 27 , 28 , 38 , 43 , 44 , 47 , 53 , 54 , 59 ]. Other studies took place in China (n = 1) [ 22 ], Brazil (n = 1) [ 23 ], New Zealand (n = 3) [ 30 , 35 , 55 ], Spain (n = 2) [ 36 , 39 ], the United Kingdom (n = 1) [ 41 ], Fiji (n = 1) [ 45 ], Tonga (n = 1) [ 46 ], France (n = 1) [ 48 ], Sweden (n = 1) [ 58 ], and one study which was conducted in eight European countries (Belgium, Cyprus, Estonia, Germany, Hungary, Italy, Spain and Sweden) [ 56 ].

Thirty studies reported BMI [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 35 , 36 , 38 , 39 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 52 , 53 , 54 , 55 , 57 , 58 , 59 ] and 27 studies reported BMI-Z score [ 22 , 23 , 24 , 25 , 27 , 28 , 32 , 33 , 35 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 49 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. The total number of participants in the systematic reviews was 200,255; 178,017 participants were included in the meta-analysis, ranging from 86 [ 59 ] to 35,157 [ 54 ], with an average sample size of 2849. Nine studies were carried out among girls, [ 23 , 27 , 30 , 31 , 36 , 42 , 48 , 51 , 56 ], eight studies among boys [ 24 , 26 , 30 , 36 , 42 , 48 , 51 , 56 ] and 21 studies were performed with both genders. The majority of policies (n = 33) examined combined diet and PA interventions, with five studies that consisted of only PA [ 22 , 26 , 30 , 34 , 36 ] and no study focused only on diet. The majority of studies (n = 31) were conducted as randomized controlled trials (81.5%), and seven [ 35 , 47 , 48 , 51 , 55 , 56 , 58 ] were non-randomized controlled trials (18.4%). BMI or BMI-Z score as outcomes were reported at the end of the intervention in 31 studies [ 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 55 , 57 ], and 14 programs had follow-up periods after the end of the intervention [ 23 , 24 , 26 , 28 , 31 , 35 , 41 , 42 , 49 , 52 , 54 , 56 , 58 , 59 ]. The length of follow-up ranged from 6 weeks [ 52 ] to 3 years [ 54 ].

Dose–response meta-analysis of the association between education time, PA, duration of intervention and BMI or BMI-Z score

The non-linear dose–response association between the study outcomes of BMI or BMI-Z score and education time, PA, and duration of intervention was performed using fractional polynomial (FP) modelling. Thirteen studies were assessed for a dose–response association between BMI and education time [ 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 37 , 39 , 52 , 57 ], and 12 studies for BMI-Z score and education time [ 23 , 24 , 25 , 27 , 33 , 37 , 39 , 41 , 49 , 51 , 52 , 57 ] (Figs.  2 a, 3 a). There was no evidence for nonlinear association between BMI (P- for nonlinearity = 0.163) or BMI-Z score (P- for nonlinearity = 0.270) with education time. Ten studies were assessed for a dose–response association between BMI and PA [ 24 , 25 , 26 , 27 , 30 , 31 , 33 , 34 , 36 , 52 ] and 8 studies for BMI-Z score [ 24 , 25 , 27 , 33 , 41 , 42 , 51 , 52 ] (Figs.  2 b, 3 b). No evidence of nonlinearity association was observed between BMI (P- for nonlinearity = 0.254) or BMI-Z score (P- for nonlinearity = 0.452) and PA. All 30 studies of BMI and 27 studies of BMI-Z score were included for calculating the dose–response association between changes in BMI or BMI-Z score with duration of intervention, respectively (Figs.  2 c, 3 c). There was evidence of a nonlinear association between the duration of intervention and BMI (P- for nonlinearity < 0.001) as well as BMI-Z score (P- for nonlinearity = 0.023).

figure 2

Dose–response association between duration of intervention, PA, education time and body mass index (BMI). Linear relation (solid line) and 95% confidence interval (CI) (gray area) of mean difference in BMI. This figure indicates the association between mean difference of BMI and a education time, b PA, c duration of intervention

figure 3

Dose–response association between duration of intervention, PA, education time and BMI-Z score . Linear relation (solid line) and 95% confidence interval (CI) (gray area) of mean difference in BMI-Z. This figure indicates the association between mean difference of BMI-Z and a education time, b PA, c duration of intervention

Details of the dose–response association between duration of intervention, PA, education time and BMI and BMI-Z score are shown in Table 3 .

Two-class meta-analysis of the comparison of effectiveness of childhood obesity prevention policies on BMI and BMI-Z score

A total of 38 publications [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ] were included in the two-class meta-analysis of the effects of obesity prevention policies on BMI (Fig.  4 ) and BMI-Z score (Fig.  5 ).

figure 4

The forest plot showing the weighted mean difference (WMD) of the effect of childhood obesity prevention policies on body mass index (BMI) [weighted mean difference (WMD): − 0.127; confidence interval (CI) − 0.198, − 0.056; P < 0.001]

figure 5

The forest plot showing the weighted mean difference (WMD) of the effect of childhood obesity prevention policies on body mass index Z score (BMI-Z score ) [weighted mean difference (WMD): − 0.020; confidence interval (CI) − 0.061, − 0.021; P = 0.340]

The results showed that obesity-prevention policies had a significant effect in reducing BMI (WMD: − 0.127; CI − 0.198, − 0.056; P  < 0.001; I 2  = 99.7%; P-heterogeneity < 0.001) and a non-significant reduction in BMI-Z score (WMD, − 0.020; CI − 0.061, − 0.021; P  = 0.340; I 2  = 99.8). A subgrouping meta-analysis (shown in Tables 4 and 5 ) and a meta-regression (Table 6 ) were also performed to assess the source of heterogeneity for the included studies. According to the subgroup meta-analysis, school-based policies in children aged 5–10 years, in relatively short period of time (less or equal to 2 years), with approaches to practical changes in diet and PA (i.e., not consisting of education only) and the policies that were performed in combination with both genders seemed to be more effective in reducing BMI and BMI-Z score with more favorable changes. Subgrouping also revealed that the heterogeneity level for BMI was reduced in subgrouping according to target group (e.g., for the parent group it was reduced from 99.7 to 49.8%), type of intervention (e.g., for only education it was reduced from 99.7 to 30.9%), study focus (e.g., for PA it was reduced from 99.7 to 35.7%), and frequency of intervention (e.g., for monthly it was reduced from 99.7 to 13.4%). In examining setting, the setting of community, school, and home and school, home and cyberspace and continent as US, the frequency of intervention as weekly, baseline BMI as a range of 22–25 and ≥ 25 kg/m 2 , and gender as male, heterogeneity disappeared. For BMI-Z score , the target group, the continent, the gender, and the setting were the primary sources of heterogeneity.

Quality assessment of included studies

The Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies was used for quality assessment of the studies. Study quality [ 17 , 18 ] was evaluated as “weak” for 15 studies [ 22 , 24 , 27 , 28 , 29 , 30 , 31 , 34 , 42 , 43 , 48 , 52 , 54 , 55 , 56 ], “moderate” for 10 studies [ 25 , 26 , 36 , 37 , 40 , 44 , 45 , 47 , 50 , 51 ], and “strong” for 13 studies [ 23 , 32 , 33 , 35 , 38 , 39 , 41 , 46 , 49 , 53 , 57 , 58 , 59 ]. Quality assessment results also showed that the average change in BMI or BMI-Z score in the follow-up compared to baseline was 0.5401 and − 0.0054 in the intervention groups and 0.7291 and 0.5401 in the control groups (Additional file 1 : Table S3).

Publication bias

Publication bias was determined using the funnel plot of BMI and BMI-Z score (Additional file 1 : Figure S1). Begg's and Egger's regression tests were used to further illustrate publication bias (Additional file 1 : Table S7). No evidence of publication bias was seen for BMI in Begg's ( P  = 0.08) or Egger's regression tests ( P  = 0.54) or for BMI-Z score in Begg's ( P  = 0.89) or Egger's regression test ( P  = 0.65).

Sensitivity analysis

A sensitivity analysis was performed to obtain the effects of individual studies on the BMI-Z score results and the results of the sensitivity analysis is presented as a plot in Additional file 1 : Figure S2. By removing the studies of Kremer et al. [ 45 ] and de Silva-Sanigorsk et al. [ 54 ] a significant change in the results occurred (WMD: − 0.036; CI − 0.068, − 0.005; P  = 0.025; I 2  = 72.4; P  < 0.005). When Sadeghi et al. [ 42 ] among boys was also removed, the changes were even more pronounced (WMD: − 0.042; CI − 0.073, − 0.010; P  = 0.009; I 2  = 71.5; P  < 0.001).

This systematic review and meta-analysis is the first, to our knowledge, to evaluate the quantitative effects of various childhood obesity prevention policies on children's BMI and BMI-Z score in an interventional design. There are many systematic reviews or meta-analysis studies that have been performed in specific settings such as schools only [ 12 , 13 , 60 ] or were performed for single-axis interventions such as physical activity only [ 10 , 61 ], diet only [ 13 ] or with limited duration of intervention [ 62 ] or follow-up [ 63 , 64 ] and different age ranges [ 9 , 10 , 60 , 64 ]. The current comprehensive meta-analysis evaluated the isolated effects of settings, intervention materials, duration and length of follow up, with a focus on the adiposity-related outcome of BMI or BMI-Z score . The key findings of the current study were as follows. First, obesity prevention policies were associated with 0.127 kg/m 2 reduction in BMI but with no significant change in BMI-Z score . Second, there was a nonlinear dose–response association between duration of intervention and reduction in BMI and BMI-Z score in studies with duration of intervention of ≤ 2 years.

In a meta-analysis by Stice et al. [ 65 ], no statistically significant effects on prevention or treatment of obesity were reported in a large percentage of studies (79%). In the current meta-analysis childhood obesity prevention policies were associated with 0.127 kg/m 2 decrease in BMI. This BMI reduction due to weight control programs in the present study was similar to Peirson et al. [ 63 ], who assessed 76 studies for normal, overweight and obese children. In contrast in a study by Harris et al., in a systematic review of 18 interventions studies, no significant effects on BMI were found [ 61 ]. Another finding in the current study was a small but non-significant change in BMI-Z score in intervention groups (e.g., 0.0054 units’ reduction of BMI-Z score in the intervention vs 0.5401 units’ increase in the control). On the other hand, Peirson et al. [ 63 ] found a significant reduction in BMI-Z score in their study. These inconsistencies might be due to differences in inclusion criteria. A nonlinear dose–response association between the duration of intervention (less than 2 years) and decrease in BMI and BMI-Z score indicated long-term duration of intervention reduces the efficacy of weight management policies. As shown in Fig.  2 c, for interventions longer than 2 years, the increase in intervention time reduced the mean change in BMI between the intervention and control groups. Consistent with our findings, Stice et al. also found that the weight reducing effects of weight management programs disappeared after a 3-year follow-up, suggesting that short-term obesity prevention programs are more effective than long-term ones in obesity management [ 65 ]. These findings were not similar for adults; for example, in a study of adults with an intervention duration that ranged from 6 weeks to 2 years, it was reported that obesity prevention programs could be effective for more than 4 months [ 66 ]. Some studies have found no association between the duration of the intervention and weight change [ 63 ]. These differences could be due to different populations, age groups, or settings. Stone et al. in a study conducted in Italy to evaluate the effectiveness of the recommended activities in schools, with at least 20 min’ physical activity in a day, reported that less than half of children (49%) took part in the physical activity, while after 7 years follow-up none of the children were engaged in physical activity schedules of more than 20 min [ 67 ]. Although we did not show the minimum possible time for the interventions to be effective in this study, the theory of Prochaska and DiClemente [ 68 ], recommended that 6 months is the minimum time for stabilizing behavior change involving PA practice. We were not able to assess the long-term sustainability of obesity prevention policies, because there was a limited number of studies that included long-term follow-up after the end of the intervention [ 54 , 69 , 70 ]. From the perspective of the frequency of intervention, optimal frequencies seemed to be daily or weekly schedules, with little effectiveness seen at monthly intervals. It has been established that integration of obesity prevention interventions in the classroom is difficult to achieve [ 65 ] and their long-term effectiveness is negligible [ 67 ]. Another finding of this study was that school-based programs had the most favorable results in prevention of obesity, which was consistent with the results of some previous studies [ 64 ] supporting Centers for Disease Control and Prevention (CDC) [ 71 ] and World Health Organization (WHO) [ 72 ] recommendations that schools are the best place for obesity prevention in children and adolescents. Wang et al. found that multi-setting trials had beneficial and significant effects compared to single-setting interventions against pediatric obesity [ 9 ]. Since most studies of the studies in pediatrics are conducted in schools, further investigations in other settings are indicated to elucidate their effectiveness in pediatric obesity prevention. In our finding, the integration of education alongside changes in the school environment had more favorable results compared with education only. Similarly, Sbruzzi et al. [ 73 ] reported that education-only interventions are effective the obesity treatment but not prevention. The heterogeneity of educational materials that are provided in different studies make it difficult to achieve a unique finding about their effectiveness [ 74 ]. Most studies (65%) were carried out in either Australia or the United States. Wang et al., in a meta-analysis across high-income countries, found similar results [ 9 ]. In subgrouping according to age, reductions in BMI and BMI-Z score were observed in children aged 5–10 years old; similarly, in one study conducted by Peirson et al. in 2013 [ 63 ] among 0–18 years old children, beneficial results were observed in the same age range. Richards et al. showed that the strongest effect of PA intervention was found in the youngest children (grade 3 learners compared to the grade 4–6 learners). This was interpreted to be because the intervention promoted PA in the form of playing may have been more attractive and suitable for the younger children [ 75 ], or maybe it is because of the ease of interventions in this age groups [ 76 ]. On the other hand, high schools and middle schools were more likely to sell competitive foods than were elementary schools [ 77 ], which can have a negative impact on the implementation of obesity prevention policies. Finkelstein et al. in their study demonstrated that the consumption of unhealthy foods were high in the high schools children than in elementary school children [ 78 ], which is probably due to the fact that the behavior of buying fast food and soft drinks is not fully formed at this age group of children. Finally , most of the childhood obesity prevention studies used diet and physical activity combined as an intervention strategy. The result of the current study showed that diet and physical activity-based policies were more effective regarding BMI and BMI-Z score reduction while studies with physical activity-only interventions were not effective. The results of studies by Katz et al. [ 79 ], Peirson et al. [ 63 ] and Wang et al. [ 9 ] found that a combination of diet and physical activity compared to diet-only or physical activity-only interventions had the most favorable results in pediatric obesity prevention. Our sensitivity analysis showed that by removing the studies of Kremer et al. [ 45 ], de Silva-Sanigorsk et al. [ 54 ] and Sadeghi et al. [ 42 ], a significant reduction in BMI-Z score was observed. One of the most important features that these three studies had in common was poor management of selection bias in the quality assessment. As shown by Munafò et al., selection bias can considerably influence observed associations in large-scale cross-sectional studies [ 80 ].

Strengths and limitations

The principal strength of the current study is a comprehensive assessment of obesity prevention policies with an emphasis on different settings, age ranges, and interventional materials and content with BMI and BMI-Z score as target outcomes. We also considered the possible role of the intervention duration, follow-up time and the amount of physical activity by including both randomized and non-randomized controlled clinical trials. Some of the limitations of the current meta-analysis should also be noted; for example, we were not able to obtain the education time and physical activity from all included articles because some of the articles did not specify these. Physical activity and nutrition education interventions are complex and, in each study, different approaches and theories may be used, which in all studies didn’t mention the approach and method of them, therefore, different approaches in educational methods and physical activities made it difficult to classify.

In conclusion, childhood obesity prevention (a) in school-based policies (b) between the ages of 5–10 years old children, (c) in short-term periods (less than 2 years) at more frequent intervals, (d) with a dual approach of diet and physical activity, can be effective in preventing childhood obesity. These findings can be used by health policymakers and policy providers to apply more effective strategies for obesity prevention in this age group.

Availability of data and materials

The data are available with reasonable request from corresponding authors.


Body mass index

Physical activity

BMI-Z score

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Weighted mean difference

Confidence interval

Fractional polynomial

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: Table S1. PRISMA checklist. Table S2 . Search strategies and the number of records according to different electronic database. Table S3 . Study quality of final studies, assessed by Effective Public Health Practice Project Quality Assessment Tool for quantitative studies. Table S4. Full name of studies. Table S5. Summary of study findings and additional information of some studies. Table S6. The general characteristics of the studies that not include in the meta-analysis. Table S7. Publication bias checked by the Begg’s and Egger test in the BMI a and BMI-Z score . Figure S1. Begg's funnel plot (with pseudo 95% CIs) of the WMD versus the se (WMD) for studies evaluating the effects of obesity preventive policies in children and adolescents and (A) body mass index (BMI) (B) BMI-Z score . Figure S2. Sensitivity analysis for the effects of childhood obesity prevention policies on BMI-Z score .

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Taghizadeh, S., Farhangi, M.A. The effectiveness of pediatric obesity prevention policies: a comprehensive systematic review and dose–response meta-analysis of controlled clinical trials. J Transl Med 18 , 480 (2020). https://doi.org/10.1186/s12967-020-02640-1

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quantitative research article on childhood obesity

Parenting and childhood obesity research: a quantitative content analysis of published research 2009-2015


  • 1 Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • 2 School of Public Health, Department of Health Policy, Management and Behavior, One University Place, University at Albany, Rensselaer, NY, USA.
  • 3 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • 4 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • PMID: 27125603
  • DOI: 10.1111/obr.12416

Background: A quantitative content analysis of research on parenting and childhood obesity was conducted to describe the recent literature and to identify gaps to address in future research.

Methods: Studies were identified from multiple databases and screened according to an a priori defined protocol. Eligible studies included non-intervention studies, published in English (January 2009-December 2015) that focused on parenting and childhood obesity and included parent participants.

Results: Studies eligible for inclusion (N = 667) focused on diet (57%), physical activity (23%) and sedentary behaviours (12%). The vast majority of studies used quantitative methods (80%) and a cross-sectional design (86%). Few studies focused exclusively on fathers (1%) or included non-residential (1%), non-biological (4%), indigenous (1%), immigrant (7%), ethnic/racial minority (15%) or low-socioeconomic status (19%) parents.

Discussion: While results illustrate that parenting in the context of childhood obesity is a robust, global and multidisciplinary area of inquiry, it is also evident that the vast majority of studies are conducted among Caucasian, female, biological caregivers living in westernized countries. Expansion of study foci and design is recommended to capture a wider range of caregiver types and obesity-related parenting constructs, improve the validity and generalizability of findings and inform the development of culture-specific childhood obesity prevention interventions and policies. © 2016 World Obesity.

Keywords: Childhood obesity; diet; parenting; physical activity.

© 2016 World Obesity.

Publication types

  • Evaluation Studies as Topic
  • Health Behavior
  • Parenting / psychology*
  • Pediatric Obesity / prevention & control
  • Pediatric Obesity / psychology*
  • Sedentary Behavior

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  • Published: 18 May 2023

Child and adolescent obesity

  • Natalie B. Lister   ORCID: orcid.org/0000-0002-9148-8632 1 , 2 ,
  • Louise A. Baur   ORCID: orcid.org/0000-0002-4521-9482 1 , 3 , 4 ,
  • Janine F. Felix 5 , 6 ,
  • Andrew J. Hill   ORCID: orcid.org/0000-0003-3192-0427 7 ,
  • Claude Marcus   ORCID: orcid.org/0000-0003-0890-2650 8 ,
  • Thomas Reinehr   ORCID: orcid.org/0000-0002-4351-1834 9 ,
  • Carolyn Summerbell 10 &
  • Martin Wabitsch   ORCID: orcid.org/0000-0001-6795-8430 11  

Nature Reviews Disease Primers volume  9 , Article number:  24 ( 2023 ) Cite this article

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  • Paediatric research

The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. Obesity arises when a mix of genetic and epigenetic factors, behavioural risk patterns and broader environmental and sociocultural influences affect the two body weight regulation systems: energy homeostasis, including leptin and gastrointestinal tract signals, operating predominantly at an unconscious level, and cognitive–emotional control that is regulated by higher brain centres, operating at a conscious level. Health-related quality of life is reduced in those with obesity. Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. Treatment incorporates a respectful, stigma-free and family-based approach involving multiple components, and addresses dietary, physical activity, sedentary and sleep behaviours. In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. Prevention of obesity requires a whole-system approach and joined-up policy initiatives across government departments. Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities.


The prevalence of child and adolescent obesity remains high and continues to rise in low-income and middle-income countries (LMICs) at a time when these regions are also contending with under-nutrition in its various forms 1 , 2 . In addition, during the COVID-19 pandemic, children and adolescents with obesity have been more likely to have severe COVID-19 requiring hospitalization and mechanical ventilation 3 . At the same time, the pandemic was associated with rising levels of childhood obesity in many countries. These developments are concerning, considering that recognition is also growing that paediatric obesity is associated with a range of immediate and long-term negative health outcomes, a decreased quality of life 4 , 5 , an increased presentation to health services 6 and increased economic costs to individuals and society 7 .

Body weight is regulated by a range of energy homeostatic and cognitive–emotional processes and a multifactorial interplay of complex regulatory circuits 8 . Paediatric obesity arises when multiple environmental factors — covering preconception and prenatal exposures, as well as broader changes in the food and physical activity environments — disturb these regulatory processes; these influences are now widespread in most countries 9 .

The treatment of obesity includes management of obesity-associated complications, a developmentally sensitive approach, family engagement, and support for long-term behaviour changes in diet, physical activity, sedentary behaviours and sleep 10 . New evidence highlights the role, in adolescents with more severe obesity, of bariatric surgery 11 and pharmacotherapy, particularly the potential for glucagon-like peptide 1 (GLP1) receptor agonists 12 .

Obesity prevention requires a whole-system approach, with policies across all government and community sectors systematically taking health into account, avoiding harmful health impacts and decreasing inequity. Programmatic prevention interventions operating ‘downstream’ at the level of the child and family, as well as ‘upstream’ interventions at the level of the community and broader society, are required if a step change in tackling childhood obesity is to be realized 13 , 14 .

In this Primer, we provide an overview of the epidemiology, causes, pathophysiology and consequences of child and adolescent obesity. We discuss diagnostic considerations, as well as approaches to its prevention and management. Furthermore, we summarize effects of paediatric obesity on quality of life, and open research questions.


Definition and prevalence.

The World Health Organization (WHO) defines obesity as “abnormal or excessive fat accumulation that presents a risk to health” 15 . Paediatric obesity is defined epidemiologically using BMI, which is adjusted for age and sex because of the physiological changes in BMI during growth 16 . Global prevalence of paediatric obesity has risen markedly over the past four decades, initially in high-income countries (HICs), but now also in many LMICs 1 .

Despite attempts to standardize the epidemiological classification, several definitions of paediatric obesity are in use; hence, care is needed when comparing prevalence rates. The 2006 WHO Child Growth Standard, for children aged 0 to 5 years, is based on longitudinal observations of multiethnic populations of children with optimal infant feeding and child-rearing conditions 17 . The 2007 WHO Growth Reference is used for the age group 5–19 years 18 , and the 2000 US Centers for Disease Control and Prevention (CDC) Growth Charts for the age group 2–20 years 19 . The WHO and CDC definitions based on BMI-for-age charts are widely used, including in clinical practice. By contrast, the International Obesity Task Force (IOTF) definition, developed from nationally representative BMI data for the age group 2–18 years from six countries, is used exclusively for epidemiological studies 20 .

For the age group 5–19 years, between 1975 and 2016, the global prevalence of obesity (BMI >2 standard deviations (SD) above the median of the WHO growth reference) increased around eightfold to 5.6% in girls and 7.8% in boys 1 . Rates have plateaued at high levels in many HICs but have accelerated in other regions, particularly in parts of Asia. For the age group 2–4 years, between 1980 and 2015, obesity prevalence (IOTF definition, equivalent to an adult BMI of ≥30 kg/m 2 ) increased from 3.9% to 7.2% in boys and from 3.7% to 6.4% in girls 21 . Obesity prevalence is highest in Polynesia and Micronesia, the Middle East and North Africa, the Caribbean and the USA (Fig.  1 ). Variations in prevalence probably reflect different background levels of obesogenic environments, or the sum total of the physical, economic, policy, social and cultural factors that promote obesity 22 . Obesogenic environments include those with decreased active transport options, a ubiquity of food marketing directed towards children, and reduced costs and increased availability of nutrient-poor, energy-dense foods. Particularly in LMICs, the growth of urbanization, new forms of technology and global trade have led to reduced physical activity at work and leisure, a shift towards Western diets, and the expansion of transnational food and beverage companies to shape local food systems 23 .

figure 1

Maps showing the proportions of children and adolescents living with overweight or obesity (part  a , boys; part b , girls) according to latest available data from the Global Obesity Observatory . Data might not be comparable between countries owing to differences in survey methodology.

The reasons for varying sex differences in prevalence in different countries are unclear but may relate to cultural variations in parental feeding practices for boys and girls and societal ideals of body size 24 . In 2016, obesity in the age group 5–19 years was more prevalent in girls than in boys in sub-Saharan Africa, Oceania and some middle-income countries in other regions, whereas it was more prevalent in boys than in girls in all HICs, and in East and South-East Asia 21 . Ethnic and racial differences in obesity prevalence within countries are often assumed to mirror variations in social deprivation and other social determinants of obesity. However, an independent effect of ethnicity even after adjustment for socioeconomic status has been documented in the UK, with Black and Asian boys in primary school having higher prevalence of obesity than white boys 25 .

Among individuals with obesity, very high BMI values have become more common in the past 15 years. The prevalence of severe obesity (BMI ≥120% of the 95th percentile (CDC definition), or ≥35 kg/m 2 at any age 26 , 27 ) has increased in many HICs, accounting for one-quarter to one-third of those with obesity 28 , 29 . Future health risks of paediatric obesity in adulthood are well documented. For example, in a data linkage prospective study in Israel with 2.3 million participants who had BMI measured at age 17 years, those with obesity (≥95th percentile BMI for age) had a much higher risk of death from coronary heart disease (HR 4.9, 95% CI 3.9–6.1), stroke (HR 2.6, 95% CI 1.7–4.1) and sudden death (HR 2.1, 95% CI 1.5–2.9) compared with those whose BMI fell between the 5th and 24th percentiles 30 .

Causes and risk factors

Early life is a critical period for childhood obesity development 9 , 31 , 32 , 33 . According to the Developmental Origins of Health and Disease framework, the early life environment may affect organ structure and function and influence health in later life 34 , 35 . Meta-analyses have shown that preconception and prenatal environmental exposures, including high maternal pre-pregnancy BMI and, to a lesser extent, gestational weight gain, as well as gestational diabetes and maternal smoking, are associated with childhood obesity, potentially through effects on the in utero environment 33 , 36 , 37 , 38 . Paternal obesity is also associated with childhood obesity 33 . Birthweight, reflecting fetal growth, is a proxy for in utero exposures. Both low and high birthweights are associated with later adiposity, with high birthweight linked to increased BMI and low birthweight to central obesity 33 , 39 .

Growth trajectories in early life are important determinants of later adiposity. Rapid weight gain in early childhood is associated with obesity in adolescence 32 . Also, later age and higher BMI at adiposity peak (the usual peak in BMI around 9 months of age), as well as earlier age at adiposity rebound (the lowest BMI reached between 4 and 7 years of age), are associated with increased adolescent and adult BMI 40 , 41 . Specific early life nutritional factors, including a lower protein content in formula food, are consistently associated with a lower risk of childhood obesity 42 , 43 . These also include longer breastfeeding duration, which is generally associated with a lower risk of childhood obesity 42 . However, some controversy exists, as these effects are affected by multiple sociodemographic confounding factors and their underlying mechanisms remain uncertain 44 . Some studies comparing higher and lower infant formula protein content have reported that the higher protein group have a greater risk of subsequent obesity, especially in early childhood 41 , 42 ; however, one study with a follow-up period until age 11 years found no significant difference in the risk of obesity, but an increased risk of overweight in the high protein group was still observed 42 , 43 , 45 . A high intake of sugar-sweetened beverages is associated with childhood obesity 33 , 46 .

Many other behavioural factors are associated with an increased risk of childhood obesity, including increased screen time, short sleep duration and poor sleep quality 33 , 47 , reductions in physical activity 48 and increased intake of energy-dense micronutrient-poor foods 49 . These have been influenced by multiple changes in the past few decades in the broader social, economic, political and physical environments, including the widespread marketing of food and beverages to children, the loss of walkable green spaces in many urban environments, the rise in motorized transport, rapid changes in the use of technology, and the move away from traditional foods to ultraprocessed foods.

Obesity prevalence is inextricably linked to relative social inequality, with data suggesting a shift in prevalence over time towards those living with socioeconomic disadvantage, and thus contributes to social inequalities. In HICs, being in lower social strata is associated with a higher risk of obesity, even in infants and young children 50 , whereas the opposite relationship occurs in middle-income countries 51 . In low-income countries, the relationship is variable, and the obesity burden seems to be across socioeconomic groups 52 , 53 .

Overall, many environmental, lifestyle, behavioural and social factors in early life are associated with childhood obesity. These factors cannot be seen in isolation but are part of a complex interplay of exposures that jointly contribute to increased obesity risk. In addition to multiple prenatal and postnatal environmental factors, genetic variants also have a role in the development of childhood obesity (see section Mechanisms/pathophysiology).

Comorbidities and complications

Childhood obesity is associated with a wide range of short-term comorbidities (Fig.  2 ). In addition, childhood obesity tracks into adolescence and adulthood and is associated with complications across the life course 32 , 41 , 54 , 55 .

figure 2

Obesity in children and adolescents can be accompanied by various other pathologies. In addition, childhood obesity is associated with complications and disorders that manifest in adulthood (red box).

Increased BMI, especially in adolescence, is linked to a higher risk of many health outcomes, including metabolic disorders, such as raised fasting glucose, impaired glucose tolerance, type 2 diabetes mellitus (T2DM), metabolic syndrome and fatty liver disease 56 , 57 , 58 , 59 . Other well-recognized obesity-associated complications include coronary heart disease, asthma, obstructive sleep apnoea syndrome (itself associated with metabolic dysfunction and inflammation) 60 , orthopaedic complications and a range of mental health outcomes including depression and low self-esteem 27 , 55 , 57 , 61 , 62 , 63 .

A 2019 systematic review showed that children and adolescents with obesity are 1.4 times more likely to have prediabetes, 1.7 times more likely to have asthma, 4.4 times more likely to have high blood pressure and 26.1 times more likely to have fatty liver disease than those with a healthy weight 64 . In 2016, it was estimated that, at a global level by 2025, childhood obesity would lead to 12 million children aged 5–17 years with glucose intolerance, 4 million with T2DM, 27 million with hypertension and 38 million with fatty liver disease 65 . These high prevalence rates have implications for both paediatric and adult health services.


Body weight regulation.

Body weight is regulated within narrow limits by homeostatic and cognitive–emotional processes and a multifactorial interplay of hormones and messenger substances in complex regulatory circuits (Fig.  3 ). When these regulatory circuits are disturbed, an imbalance between energy intake and expenditure leads to obesity or to poor weight gain. As weight loss is much harder to achieve than weight gain in the long term due to the regulation circuits discussed below, the development of obesity is encouraged by modern living conditions, which enable underlying predispositions for obesity to become manifest 8 , 66 .

figure 3

Body weight is predominantly regulated by two systems: energy homeostasis and cognitive–emotional control. Both homeostatic and non-homeostatic signals are processed in the brain, involving multiple hormone and receptor cascades 217 , 218 , 219 . This overview depicts the best-known regulatory pathways. The homeostatic system, which is mainly regulated by brain centres in the hypothalamus and brainstem, operates on an unconscious level. Both long-term signals from the energy store in adipose tissue (for example, leptin) and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status. During gastric distension or after the release of gastrointestinal hormones (multiple receptors are involved) and insulin, a temporary feeling of fullness is induced. The non-homeostatic or hedonic system is regulated by higher-level brain centres and operates at the conscious level. After integration in the thalamus, homeostatic signals are combined with stimuli from the environment, experiences and emotions; emotional and cognitive impulses are then induced to control food intake. Regulation of energy homeostasis in the hypothalamus involves two neuron types of the arcuate nucleus: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). Leptin stimulates these neurons via specific leptin receptors (LEPR) inducing anabolic effects in case of decreasing leptin levels and catabolic effects in case of increasing leptin levels. Leptin inhibits the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production resulting in the feeling of hunger. Leptin directly stimulates POMC production in POMC neurons. POMC is cleaved into different hormone polypeptides including α-melanocyte-stimulating hormone which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. CART, cocaine and amphetamine responsive transcript; IR, insulin receptor.

In principle, there are two main systems in the brain which regulate body weight 8 , 66 (Fig.  3 ): energy homeostasis and cognitive–emotional control. Energy homeostasis is predominantly regulated by brain centres in the hypothalamus and brainstem and operates at an unconscious level. Both long-term signals from the adipose tissue energy stores and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status 8 , 66 . For example, negative energy balance leading to reduced fat mass results in reduced leptin levels, a permanently reduced urge to exercise and an increased feeling of hunger. During gastric distension or after the release of gastrointestinal hormones and insulin, a temporary feeling of fullness is induced 8 , 66 . Cognitive–emotional control is regulated by higher brain centres and operates at a conscious level. Here, the homeostatic signals are combined with stimuli from the environment (sight, smell and taste of food), experiences and emotions 8 , 66 . Disorders at the level of cognitive–emotional control mechanisms include emotional eating as well as eating disorders. For example, the reward areas in the brain of people with overweight are more strongly activated by high-calorie foods than those in the brain of people with normal weight 67 . Both systems interact with each other, and the cognitive–emotional system is strongly influenced by the homeostatic control circuits.

Disturbances in the regulatory circuits of energy homeostasis can be genetically determined, can result from disease or injury to the regulatory centres involved, or can be caused by prenatal programming 8 , 66 . If the target value of body weight has been shifted, the organism tries by all means (hunger, drive) to reach the desired higher weight. These disturbed signals of the homeostatic system can have an imperative, irresistible character, so that a conscious influence on food intake is no longer effectively possible 8 , 66 . The most important disturbances of energy homeostasis are listed in Table  1 .

The leptin pathway

The peptide hormone leptin is primarily produced by fat cells. Its production depends on the amount of adipose tissue and the energy balance. A negative energy balance during fasting results in a reduction of circulating leptin levels by 50% after 24 h (ref. 68 ). In a state of weight loss, leptin production is reduced 69 . In the brain, leptin stimulates two neuron types of the arcuate nucleus in the hypothalamus via specific leptin receptors: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). High leptin levels inhibit the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production. By contrast, leptin directly stimulates POMC production in POMC neurons (Fig.  3 ). POMC is a hormone precursor that is cleaved into different hormone polypeptides by specific enzymes, such as prohormone convertase 1 (PCSK1). This releases α-melanocyte-stimulating hormone (α-MSH) which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. Rare, functionally relevant mutations in the genes for leptin and leptin receptor, POMC , PCSK1/3 or MC4R lead to extreme obesity in early childhood. These forms of obesity are potential indications for specific pharmacological treatments, for example setmelanotide 70 , 71 . MC4R mutations are the most common cause of monogenic obesity, as heterozygous mutations can be symptomatic depending on the functional impairment and with variable penetrance and expression. Other genes have been identified, in which rare heterozygous pathological variants are also associated with early onset obesity (Table  1 ).

Pathological changes in adipose tissue

Adipose tissue can be classified into two types, white and brown adipose tissue. White adipose tissue comprises unilocular fat cells and brown adipose tissue contains multilocular fat cells, which are rich in mitochondria 72 . A third type of adipocyte, beige adipocytes, within the white adipose tissue are induced by prolonged exposure to cold or adrenergic signalling, and show a brown adipocyte-like morphology 72 . White adipose tissue has a large potential to change its volume to store energy and meet the metabolic demands of the body. The storage capacity and metabolic function of adipose tissue depend on the anatomical location of the adipose tissue depot. Predominant enlargement of white adipose tissue in the visceral, intra-abdominal area (central obesity) is associated with insulin resistance and an increased risk of metabolic disease development before puberty. Accumulation of adipose tissue in the hips and flanks has no adverse effect and may be protective against metabolic syndrome. In those with obesity, adipose tissue is characterized by an increased number of adipocytes (hyperplasia), which originate from tissue-resident mesenchymal stem cells, and by enlarged adipocytes (hypertrophy) 73 . Adipocytes with a very large diameter reach the limit of the maximal oxygen diffusion distance, resulting in hypoxia, the development of an inflammatory expression profile (characterized by, for example, leptin, TNF and IL-6) and adipocyte necrosis, triggering the recruitment of leukocytes. Resident macrophages switch from the anti-inflammatory M2 phenotype to a pro-inflammatory M1 phenotype, which is associated with insulin resistance, further promoting local sterile inflammation and the development of fibrotic adipose tissue. This process limits the expandability of the adipose tissue for further storage of triglycerides. In the patient, the increase in fat mass in obesity is associated with insulin resistance and systemic low-grade inflammation characterized by elevated serum levels of C-reactive protein and pro-inflammatory cytokines. The limitation of adipose tissue expandability results in storage of triglycerides in other organs, such as the liver, muscle and pancreas 74 .

Genetics and epigenetics in the general population

Twin studies have found heritability estimates for BMI of up to 70% 75 , 76 . In contrast to rare monogenic forms of obesity, which are often caused by a single genetic defect with a large effect, the genetic background of childhood obesity in the general population is shaped by the joint effects of many common genetic variants, each of which individually makes a small contribution to the phenotype. For adult BMI, genome-wide association studies, which examine associations of millions of such variants across the genome at the same time, have identified around 1,000 genetic loci 77 . The largest genome-wide association studies in children, which include much smaller sample sizes of up to 60,000 children, have identified 25 genetic loci for childhood BMI and 18 for childhood obesity, the majority of which overlap 78 , 79 . There is also a clear overlap with genetic loci identified in adults, for example for FTO , MC4R and TMEM18 , but this overlap is not complete, some loci are specific to early life BMI, or have a relatively larger contribution in childhood 78 , 79 , 80 . These findings suggest that biological mechanisms underlying obesity in childhood are mostly similar to those in adulthood, but the relative influence of these mechanisms may differ at different phases of life.

The role of epigenetic processes in childhood and adolescent obesity has gained increasing attention. In children, several studies found associations between DNA methylation and BMI 81 , 82 , 83 , 84 , but a meta-analysis including data from >4,000 children identified only minimal associations 85 . Most studies support the hypothesis that DNA methylation changes are predominantly a consequence rather than a cause of obesity, which may explain the lower number of identified (up to 12) associations in children, in whom duration of exposure to a higher BMI is shorter than in adults, in whom associations with DNA methylation at hundreds of sites have been identified 85 , 86 , 87 . In addition to DNA methylation, some specific circulating microRNAs have been found to be associated with obesity in childhood 84 .

The field of epigenetic studies in childhood obesity is relatively young and evolving quickly. Future studies will need to focus on defining robust associations in blood as well as other tissues and on identifying cause-and-effect relationships. In addition, other omics, such as metabolomics and proteomics, are promising areas that may contribute to an improved aetiological understanding or may provide biological signatures that can be used as predictive or prognostic markers of childhood obesity and its comorbidities.

Parental obesity and childhood obesity

There is an established link between increased parental BMI and increased childhood BMI 88 , 89 . This link may be due to shared genetics, shared environment, a direct intrauterine effect of maternal BMI or a combination of these factors. In the case of shared genetics, the child inherits BMI-increasing genetic variants from one or both parents. Shared environmental factors, such as diet or lifestyle, may also contribute to an increased BMI in both parents and child. In addition, maternal obesity might create an intrauterine environment that programmes metabolic processes in the fetus, which increases the risk of childhood obesity. Some studies show larger effects of maternal than paternal BMI, indicating a potential causal intrauterine mechanism of maternal obesity, but evidence showing similar maternal and paternal effects is increasing. The data may indicate that there is only a limited direct intrauterine effect of maternal obesity on childhood obesity; rather, genetic effects inherited from the mother or father, or both, and/or shared environmental factors may contribute to childhood obesity risk 90 , 91 , 92 , 93 , 94 , 95 .

Diagnosis, screening and prevention

Diagnostic work-up.

The extent of overweight in clinical practice is estimated using BMI based on national charts 96 , 97 , 98 , 99 , 100 . Of note, the clinical classification of overweight or obesity differ depending on the BMI charts used and national recommendations; hence, local guidelines should be referred to. For example, the US CDC Growth Charts and several others use the 85th and 95th centile cut-points to denote overweight and obesity, respectively 19 . The WHO Growth Reference for children aged 5–19 years defines cut-points for overweight and obesity as a BMI-for-age greater than +1 and +2 SDs for BMI for age, respectively 18 . For children <5 years of age, overweight and obesity are defined as weight-for-height greater than +2 and +3 SDs, respectively, above the WHO Child Growth Standards median 17 . The IOTF and many countries in Europe use cut-points of 85th, 90th and 97th to define overweight, obesity and extreme obesity 26 .

BMI as an indirect measurement of body fat has some limitations; for example, pronounced muscle tissue leads to an increase in BMI, and BMI is not independent of height. In addition, people of different ethnicities may have different cut-points for obesity risk; for example, cardiometabolic risk occurs at lower BMI values in individuals with south Asian than in those with European ancestry 101 . Thus, BMI is best seen as a convenient screening tool that is supplemented by clinical assessment and investigations.

Other measures of body fat may help differentiate between fat mass and other tissues. Some of these tools are prone to low reliability, such as body impedance analyses (high day-to-day variation and dependent on level of fluid consumption) or skinfold thickness (high inter-observer variation), or are more expensive or invasive, such as MRI, CT or dual-energy X-ray absorptiometry, than simpler measures of body composition or BMI assessment.

Primary diseases rarely cause obesity in children and adolescents (<2%) 102 . However, treatable diseases should be excluded in those with obesity. A suggested diagnostic work-up is summarized in Fig.  4 . Routine measurement of thyroid-stimulating hormone (TSH) is not recommended 96 . Moderately elevated TSH levels (usually <10 IU/l) are frequently observed in obesity and are a consequence, and not a cause, of obesity 103 . In a growing child with normal height velocity, a normal BMI at the age of 2 years and normal cognitive development, no further diagnostic steps are necessary to exclude primary diseases 96 , 104 .

figure 4

Concerning findings from a detailed medical history and physical examination will lead to further examinations. In individuals with early onset, extreme obesity (before age 3 years) and signs of hyperphagia, serum leptin level should be measured to rule out the extremely rare condition of congenital leptin deficiency. In individuals with normal or high leptin levels, genetic testing is indicated to search for monogenetic obesity. In individuals with intellectual disability, a syndromic disease may be present. Signs of impaired growth velocity or the history of central nervous system trauma or surgery will result in deeper endocrine evaluation and/or brain MRI. BDNF , brain-derived neurotropic factor; FT4, free thyroxin; KSR2 , kinase suppressor of ras 2; MC4R , melanocortin 4 receptor; POMC , pro-opiomelanocortin; SH2B1 , Src-homology 2 (SH2) B adapter protein 1; SIM1 , single-minded homologue 1; TSH, thyroid-stimulating hormone.

Clinical findings which need no further examination include pseudogynaecomastia (adipose tissue mimicking breast development; differentiated from breast tissue by ultrasonography), striae (caused by rapid weight increase) and a hidden penis in suprapubic adipose tissue (differentiated from micropenis by measurement of stretched penis length while pressing down on the suprapubic adipose tissue) 96 , 105 . Girls with obesity tend to have an earlier puberty onset (usually at around 8–9 years of age) and boys with severe obesity may have a delayed puberty onset (usually at around 13–14 years of age) 106 . Thus, if pubertal onset is slightly premature in girls or slightly delayed in boys, no further endocrine assessment is necessary.

Assessment of obesity-associated comorbidities

A waist to height ratio of >0.5 is a simple tool to identify central obesity 107 , 108 . Screening for cardiometabolic risk factors and fatty liver disease is recommended, especially in adolescents, and in those with more severe obesity or central adiposity, a strong family history of T2DM or premature heart disease, or relevant clinical symptoms, such as high blood pressure or acanthosis nigricans 96 , 97 , 98 , 99 , 109 . Investigations generally include fasting glucose levels, lipid profile, liver function and glycated haemoglobin, and might include an oral glucose tolerance test, polysomnography, and additional endocrine tests for polycystic ovary syndrome 96 , 97 , 98 , 99 .

T2DM in children and adolescents often occurs in the presence of a strong family history and may not be related to obesity severity 110 . T2DM onset usually occurs during puberty, a physiological state associated with increased insulin resistance 111 and, therefore, screening for T2DM should be considered in children and adolescents with obesity and at least one risk factor (family history of T2DM or features of metabolic syndrome) starting at pubertal onset 112 . As maturity-onset diabetes of the young (MODY) type II and type III are more frequent than T2DM in children and adolescents in many ethnicities, genetic screening for MODY may be appropriate 112 . Furthermore, type 1 diabetes mellitus (T1DM) should be excluded by measurement of autoantibodies in any individual with suspected diabetes with obesity. The differentiation of T2DM from MODY and T1DM is important as the diabetes treatment approaches differ 112 .

Several comorbidities of obesity should be considered if specific symptoms occur 96 , 109 . For polycystic ovary syndrome in hirsute adolescent girls with oligomenorrhoea or amenorrhoea, moderately increased testosterone levels and decreased sex hormone binding globulin levels are typical laboratory findings 113 . Obstructive sleep apnoea can occur in those with more severe obesity and who snore, have daytime somnolence or witnessed apnoeas. Diagnosis is made by polysomnography 114 . Minor orthopaedic disorders, such as flat feet and genu valgum, are frequent in children and adolescents with obesity and may cause pain. Major orthopaedic complications include slipped capital femoral epiphyses (acute and chronic), which manifest with hip and knee pain in young adolescents and are characterized by reduced range of hip rotation and waddling gait; and Blount disease (tibia vara), typically occurring in children aged 2–5 years 105 , 115 . In addition, children and adolescents with extreme obesity frequently have increased dyspnoea and decreased exercise capacity. A heightened demand for ventilation, elevated work of breathing, respiratory muscle inefficiency and diminished respiratory compliance are caused by increased truncal fat mass. This may result in a decreased functional residual capacity and expiratory reserve volume, ventilation to perfusion ratio abnormalities and hypoxaemia, especially when supine. However, conventional respiratory function tests are only mildly affected by obesity except in extreme cases 116 . Furthermore, gallstones should be suspected in the context of abdominal pain after rapid weight loss, which can be readily diagnosed via abdominal ultrasonography 105 . Finally, pseudotumor cerebri may present with chronic headache, and depression may present with flat affect, chronic fatigue and sleep problems 105 .

Obesity in adolescents can also be associated with disordered eating, eating disorders and other psychological disorders 117 , 118 . If suspected, assessment by a mental health professional is recommended.

A comprehensive approach

The 2016 report of the WHO Commission on Ending Childhood Obesity stated that progress in tackling childhood obesity has been slow and inconsistent, with obesity prevention requiring a whole-of-government approach in which policies across all sectors systematically take health into account, avoiding harmful health impacts and, therefore, improving population health and health equity 13 , 119 . The focus in developing and implementing interventions to prevent obesity in children should be on interventions that are feasible, effective and likely to reduce health inequalities 14 . Importantly, the voices of children and adolescents living with social disadvantage and those from minority groups must be heard if such interventions are to be effective and reduce inequalities 120 .

Figure  5 presents a system for the prevention of childhood obesity within different domains of the socioecological model 121 and highlights opportunities for interventions. These domains can be described on a continuum, from (most downstream) individual and interpersonal (including parents, peers and wider family) through to organizational (including health care and schools), community (including food, activity and environment), society (including media and finally cultural norms) and (most upstream) public policy (from local to national level). Interventions to prevent childhood obesity can be classified on the Nuffield intervention ladder 122 . This framework was proposed by the Nuffield Council on Bioethics in 2007 (ref. 122 ) and distributes interventions on the ladder steps depending on the degree of agency required by the individual to make the behavioural changes that are the aim of the intervention. The bottom step of the ladder includes interventions that provide information, which requires the highest agency and relies on a child, adolescent and/or family choosing (and their ability to choose) to act on that information and change behaviour. The next steps of the ladder are interventions that enable choice, guide choice through changing the default policy, guide choice through incentives, guide choice through disincentives, or restrict choice. On the top-most step of the ladder (lowest agency required) are interventions that eliminate choice.

figure 5

This schematic integrates interventions that were included in a Cochrane review 127 of 153 randomized controlled trials of interventions to prevent obesity in children and are high on the Nuffield intervention ladder 122 . The Nuffield intervention ladder distributes interventions depending on the degree of agency required for the behavioural changes that are the aim of the intervention. The socioecological model 121 comprises different domains (or levels) from the individual up to public policy. Interventions targeting the individual and interpersonal domains can be described as downstream interventions, and interventions within public policy can be described as the highest level of upstream interventions. Within each of these domains, arrow symbols with colours corresponding to the Nuffield intervention ladder category are used to show interventions that were both included in the Cochrane review 127 and that guide, restrict or eliminate choice as defined by the Nuffield intervention ladder 122 . Upstream interventions, and interventions on the top steps of the Nuffield ladder, are more likely to reduce inequalities. NGO, non-governmental organization.

Downstream and high-agency interventions (on the bottom steps of the Nuffield ladder) are more likely to result in intervention-generated inequalities 123 . This has been elegantly described and evidenced, with examples from the obesity prevention literature 124 , 125 . A particularly strong example is a systematic review of 38 interventions to promote healthy eating that showed that food price (an upstream and low-agency intervention) seemed to decrease inequalities, all interventions that combined taxes and subsidies consistently decreased inequalities, and downstream high-agency interventions, especially dietary counselling, seemed to increase inequalities 126 .

Effectiveness of prevention interventions

A 2019 Cochrane review of interventions to prevent obesity in children 127 included 153 randomized controlled trials (RCTs), mainly in HICs (12% were from middle-income countries). Of these RCTs, 56% tested interventions in children aged 6–12 years, 24% in children aged 0–5 years, and 20% in adolescents aged 13–18 years. The review showed that diet-only interventions to prevent obesity in children were generally ineffective across all ages. Interventions combining diet and physical activity resulted in modest benefits in children aged 0–12 years but not in adolescents. However, physical activity-only interventions to prevent obesity were effective in school-age children (aged 5–18 years). Whether the interventions were likely to work equitably in all children was investigated in 13 RCTs. These RCTs did not indicate that the strategies increased inequalities, although most of the 13 RCTs included relatively homogeneous groups of children from disadvantaged backgrounds.

The potential for negative unintended consequences of obesity prevention interventions has received much attention 128 . The Cochrane review 127 investigated whether children were harmed by any of the strategies; for example, by having injuries, losing too much weight or developing damaging views about themselves and their weight. Of the few RCTs that did monitor these outcomes, none found any harms in participants.

Intervention levels

Most interventions (58%) of RCTs in the Cochrane review aimed to change individual lifestyle factors via education-based approaches (that is, simply provide information) 129 . In relation to the socioecological model, only 11 RCTs were set in the food and physical activity environment domain, and child care, preschools and schools were the most common targets for interventions. Of note, no RCTs were conducted in a faith-based setting 130 . Table  2 highlights examples of upstream interventions that involve more than simply providing information and their classification on the Nuffield intervention ladder.

Different settings for interventions to prevent childhood obesity, including preschools and schools, primary health care, community settings and national policy, offer different opportunities for reach and effectiveness, and a reduction in inequalities.

Preschools and schools are key settings for public policy interventions for childhood obesity prevention, and mandatory and voluntary food standards and guidance on physical education are in place in many countries. Individual schools are tasked with translating and implementing these standards and guidance for their local context. Successful implementation of a whole-school approach, such as that used in the WHO Nutrition-Friendly Schools Initiative 131 , is a key factor in the effectiveness of interventions. Careful consideration should be given to how school culture can, and needs to, be shifted by working with schools to tailor the approach and manage possible staff capacity issues, and by building relationships within and outside the school gates to enhance sustainability 132 , 133 .

Primary health care offers opportunities for guidance for obesity prevention, especially from early childhood to puberty. Parent-targeted interventions conducted by clinicians in health-care or community settings have the strongest level of evidence for their effectiveness in reducing BMI z -score at age 2 years 134 . These interventions include group programmes, clinic nurse consultations, mobile phone text support or nurse home visiting, and focusing on healthy infant feeding, healthy childhood feeding behaviours and screen time.

A prospective individual participant data meta-analysis of four RCTs involving 2,196 mother–baby dyads, and involving nurse home visiting or group programmes, resulted in a small but significant reduction in BMI in infants in the intervention groups compared with control infants at age 18–24 months 134 . Improvements were also seen in television viewing time, breastfeeding duration and feeding practices. Interventions were more effective in settings with limited provision of maternal and child health services in the community. However, effectiveness diminished by age 5 years without further intervention, highlighting the need for ongoing interventions at each life stage 135 . Evidence exists that short-duration interventions targeting sleep in very early childhood may be more effective than nutrition-targeted interventions in influencing child BMI at age 5 years 136 .

Primary care clinicians can provide anticipatory guidance, as a form of primary prevention, to older children, adolescents and their families, aiming to support healthy weight and weight-related behaviours. Clinical guidelines recommend that clinicians monitor growth regularly, and provide guidance on healthy eating patterns, physical activity, sedentary behaviours and sleep patterns 97 , 100 . Very few paediatric trials have investigated whether this opportunistic screening and advice is effective in obesity prevention 100 . A 2021 review of registered RCTs for the prevention of obesity in infancy found 29 trials 137 , of which most were delivered, or were planned to be delivered, in community health-care settings, such as nurse-led clinics. At the time of publication, 11 trials had reported child weight-related outcomes, two of which showed a small but significant beneficial effect on BMI at age 2 years, and one found significant improvements in the prevalence of obesity but not BMI. Many of the trials showed improvements in practices, such as breastfeeding and screen time.

At the community level, local public policy should be mindful of the geography of the area (such as urban or rural) and population demographics. Adolescents usually have more freedom in food and beverage choices made outside the home than younger children. In addition, physical activity levels usually decline and sedentary behaviours rise during adolescence, particularly in girls 138 , 139 . These behavioural changes offer both opportunities and barriers for those developing community interventions. On a national societal level, public policies for interventions to prevent obesity in children include the control of advertising of foods and beverages high in fat, sugar and/or salt in some countries. Industry and the media, including social media, can have a considerable influence on the food and physical activity behaviours of children 13 , 119 .

Public policy may target interventions at all domains from the individual to the societal level. The main focus of interventions in most national public policies relies on the ability of individuals to make the behavioural changes that are the aim of the intervention (high-agency interventions) at the individual level (downstream interventions). An equal focus on low-agency and upstream interventions is required if a step change in tackling childhood obesity is to be realized 140 , 141 .

COVID-19 and obesity

Early indications in several countries show rising levels of childhood obesity, and an increase in inequalities in childhood obesity during the COVID-19 pandemic 142 . The substantial disruptions in nutrition and lifestyle habits of children during and since the pandemic include social isolation and addiction to screens 143 . Under-nutrition is expected to worsen in poor countries, but obesity rates could increase in middle-income countries and HICs, especially among vulnerable groups, widening the gap in health and social inequalities 143 . Public health approaches at national, regional and local levels should include strategies that not only prevent obesity and under-nutrition, but also reduce health inequalities.

In summary, although most trials of obesity prevention have occurred at the level of the individual, the immediate family, school or community, effective prevention of obesity will require greater investment in upstream, low-agency interventions.

Treatment goals

Treatment should be centred on the individual and stigma-free (Box  1 ) and may aim for a reduction in overweight and improvement in associated comorbidities and health behaviours. Clinical considerations when determining a treatment approach should include age, severity of overweight and the presence of associated complications 144 , 145 .

Box 1 Strategies for minimizing weight stigma in health care 220 , 221 , 222

Minimizing weight bias in the education of health-care professionals

Improved education of health professionals:

pay attention to the implicit and explicit communication of social norms

include coverage of the broader determinants of obesity

include discussion of harms caused by social and cultural norms and messages concerning body weight

provide opportunities to practise non-stigmatizing care throughout education

Provide causal information focusing on the genetic and/or socioenvironmental determinants of weight.

Provide empathy-invoking interventions, emphasizing size acceptance, respect and human dignity.

Provide a weight-inclusive approach, by emphasizing that all individuals, regardless of size, have the right to equal health care.

Addressing health facility infrastructure and processes

Provide appropriately sized chairs, blood pressure cuffs, weight scales, beds, toilets, showers and gowns.

Use non-stigmatizing language in signage, descriptions of clinical services and other documentation.

Providing clinical leadership and using appropriate language within health-care settings

Senior clinicians and managers should role-model supportive and non-biased behaviours towards people with obesity and indicate that they do not tolerate weight-based discrimination in any form.

Staff should identify the language that individuals prefer in referring to obesity.

Use person-first language, for example a ‘person with obesity’ rather than ‘an obese person’.

Treatment guidelines

Clinical guidelines advise that first-line management incorporates a family-based multicomponent approach that addresses dietary, physical activity, sedentary and sleep behaviours 97 , 99 , 109 , 146 . This approach is foundational, with adjunctive therapies, especially pharmacotherapy and bariatric surgery, indicated under specific circumstances, usually in adolescents with more severe obesity 144 , 145 . Guideline recommendations vary greatly among countries and are influenced by current evidence, and functionality and resourcing of local health systems. Hence, availability and feasibility of therapies differs internationally. In usual clinical practice, interventions may have poorer outcomes than is observed in original studies or anticipated in evidence-based guidelines 147 because implementation of guidelines is more challenging in resource-constrained environments 148 . In addition, clinical trials are less likely to include patients with specialized needs, such as children from culturally diverse populations, those living with social disadvantage, children with complex health problems, and those with severe obesity 149 , 150 .

Behavioural interventions

There are marked differences in individual responses to behavioural interventions, and overall weight change outcomes are often modest. In children aged 6–11 years, a 2017 Cochrane review 150 found that mean BMI z -scores were reduced in those involved in behaviour-changing interventions compared with those receiving usual care or no treatment by only 0.06 units (37 trials; 4,019 participants; low-quality evidence) at the latest follow-up (median 10 months after the end of active intervention). In adolescents aged 12–17 years, another 2017 Cochrane review 149 found that multicomponent behavioural interventions resulted in a mean reduction in weight of 3.67 kg (20 trials; 1,993 participants) and reduction in BMI of 1.18 kg/m 2 (28 trials; 2,774 participants). These effects were maintained at the 24-month follow-up. A 2012 systematic review found significant improvements in LDL cholesterol triglycerides and blood pressure up to 1 year from baseline following lifestyle interventions in children and adolescents 151 .

Family-based behavioural interventions are recommended in national level clinical practice guidelines 97 , 100 , 146 , 152 . They are an important element of intensive health behaviour and lifestyle treatments (IHBLTs) 109 . Family-based approaches use behavioural techniques, such as goal setting, parental monitoring or modelling, taught in family sessions or in individual sessions separately to children and care givers, depending on the child’s developmental level. The priority is to encourage the whole family to engage in healthier behaviours that result in dietary improvement, greater physical activity, and less sedentariness. This includes making changes to the family food environment and requires parental monitoring.

Family-based interventions differ in philosophy and implementation from those based on family systems theory and therapy 153 . All are intensive interventions that require multiple contact hours (26 or more) with trained specialists delivered over an extended period of time (6–12 months) 10 . Changing family lifestyle habits is challenging and expensive, and the therapeutic expertise is not widely available. Moving interventions to primary care settings, delivered by trained health coaches, and supplemented by remote contact (for example by phone), will improve access and equity 154 .

Very few interventions use single psychological approaches. Most effective IHBLTs are multicomponent and intensive (many sessions), and include face-to-face contact. There has been interest in motivational interviewing as an approach to delivery 155 . As client-centred counselling, this places the young person at the centre of their behaviour change. Fundamental to motivational interviewing is the practitioner partnership that helps the young person and/or parents to explore ambivalence to change, consolidate commitment to change, and develop a plan based on their own insights and expertise. Evidence reviews generally support the view that motivational interviewing reduces BMI. Longer interventions (>4 months), those that assess and report on intervention fidelity, and those that target both diet and physical activity are most effective 155 , 156 .

More intensive dietary interventions

Some individuals benefit from more intensive interventions 98 , 144 , 157 , 158 , which include very low-energy diets, very low-carbohydrate diets and intermittent energy restriction 159 . These interventions usually aim for weight loss and are only recommended for adolescents who have reached their final height. These diets are not recommended for long periods of time due to challenges in achieving nutritional adequacy 158 , 160 , and lack of long-term safety data 158 , 161 . However, intensive dietary interventions may be considered when conventional treatment is unsuccessful, or when adolescents with comorbidities or severe obesity require rapid or substantial weight loss 98 . A 2019 systematic review of very low-energy diets in children and adolescents found a mean reduction in body weight of −5.3 kg (seven studies) at the latest follow‐up, ranging from 5 to 14.5 months from baseline 161 .

Pharmacological treatment

Until the early 2020s the only drug approved in many jurisdictions for the treatment of obesity in adolescents was orlistat, a gastrointestinal lipase inhibitor resulting in reduced uptake of lipids and, thereby, a reduced total energy intake 162 . However, the modest effect on weight in combination with gastrointestinal adverse effects limit its usefulness overall 163 .

A new generation of drugs has been developed for the treatment of both T2DM and obesity. These drugs are based on gastrointestinal peptides with effects both locally and in the central nervous system. GLP1 is an incretin that reduces appetite and slows gastric motility. The GLP1 receptor agonist liraglutide is approved for the treatment of obesity in those aged 12 years and older both in the USA and Europe 164 , 165 . Liraglutide, delivered subcutaneously daily at a higher dose than used for T2DM resulted in a 5% better BMI reduction than placebo after 12 months 166 . A 2022 trial of semaglutide, another GLP1 receptor agonist, delivered subcutaneously weekly in adolescents demonstrated 16% weight loss after 68 weeks of treatment, with modest adverse events and a low drop-out rate 12 . Tirzepatide, an agonist of both GLP1 and glucose-dependent insulinotropic polypeptide (GIP), is approved by the FDA for the treatment of T2DM in adults 167 . Subcutaneous tirzepatide weekly in adults with obesity resulted in ~20% weight loss over 72 weeks 168 . Of note, GIP alone increases appetite, but the complex receptor–agonist interaction results in downregulation of the GIP receptors 169 , illustrating why slightly modified agonists exert different effects. A study of the use of tirzepatide in adolescents with T2DM has been initiated but results are not expected before 2027 (ref. 170 ). No trials of tirzepatide are currently underway in adolescents with obesity but without T2DM.

Hypothalamic obesity is difficult to treat. Setmelanotide is a MC4R agonist that reduces weight and improves quality of life in most people with LEPR and POMC mutations 71 . In trials of setmelanotide, 8 of 10 participants with POMC deficiency and 5 of 11 with LEPR deficiency had weight loss of at least 10% at ~1 year. The mean percentage change in most hunger score from baseline was −27.1% and −43.7% in those with POMC deficiency and leptin receptor deficiency, respectively 71 .

In the near future, effective new drugs with, hopefully, an acceptable safety profile will be available that will change the way we treat and set goals for paediatric obesity treatment 171 .

Bariatric surgery

Bariatric surgery is the most potent treatment for obesity in adolescents with severe obesity. The types of surgery most frequently used are sleeve gastrectomy and gastric bypass, both of which reduce appetite 172 . Mechanisms of action are complex, involving changes in gastrointestinal hormones, neural signalling, bile acid metabolism and gut microbiota 173 . Sleeve gastrectomy is a more straightforward procedure and the need for vitamin supplementation is lower than with gastric bypass. However, long-term weight loss may be greater after gastric bypass surgery 174 .

Prospective long-term studies demonstrate beneficial effects of both sleeve gastrectomy and gastric bypass on weight loss and comorbidities in adolescents with severe obesity 175 , 176 . In a 5-year follow-up period, in 161 participants in the US TEEN-LABS study who underwent gastric bypass, mean BMI declined from 50 to 37 kg/m 2 (ref. 11 ). In a Swedish prospective study in 81 adolescents who underwent gastric bypass, the mean decrease in BMI at 5 years was 13.1 kg/m 2 (baseline BMI 45.5 kg/m 2 ) compared with a BMI increase of 3.1 kg/m 2 in the control group 176 . Both studies showed marked inter-individual variations. Negative adverse effects, including gastrointestinal problems, vitamin deficits and reduction in lean body mass, are similar in adults and adolescents. Most surgical complications following bariatric surgery in the paediatric population are minor, occurring in the early postoperative time frame, but 8% of patients may have major perioperative complications 177 . Up to one-quarter of patients may require subsequent related procedures within 5 years 109 . However, many adolescents with severe obesity also have social and psychological problems, highlighting the need for routine and long-term monitoring 109 , 178 .

Recommendations for bariatric surgery in adolescents differ considerably among countries, with information on long-term outcomes emerging rapidly. In many countries, bariatric surgery is recommended only from Tanner pubertal stage 3–4 and beyond, and only in children with severe obesity and cardiometabolic comorbidities 177 . The 2023 American Academy of Pediatrics clinical practice guidelines recommend that bariatric surgery be considered in adolescents ≥13 years of age with a BMI of ≥35 kg/m 2 or 120% of the 95th percentile for age and sex, whichever is lower, as well as clinically significant disease, such as T2DM, non-alcoholic fatty liver disease, major orthopaedic complications, obstructive sleep apnoea, the presence of cardiometabolic risk, or depressed quality of life 109 . For those with a BMI of ≥40 kg/m 2 or 140% of the 95th percentile for age and sex, bariatric surgery is indicated regardless of the presence of comorbidities. Potential contraindications to surgery include correctable causes of obesity, pregnancy and ongoing substance use disorder. The guidelines comment that further evaluation, undertaken by multidisciplinary centres that offer bariatric surgery for adolescents, should determine the capacity of the patient and family to understand the risks and benefits of surgery and to adhere to the required lifestyle changes before and after surgery.

Long-term weight outcomes

Few paediatric studies have investigated long-term weight maintenance after the initial, more intensive, weight loss phase. A 2018 systematic review of 11 studies in children and adolescents showed that a diverse range of maintenance interventions, including support via face-to-face psychobehavioural therapies, individual physician consultations, or adjunctive therapeutic contact via newsletters, mobile phone text or e-mail, led to stabilization of BMI z -score compared with control participants, who had increases in BMI z -score 179 . Interventions that are web-based or use mobile devices may be particularly useful in young people 180 .

One concern is weight regain which occurs after bariatric surgery in general 181 but may be more prevalent in adolescents 176 . For example, in a Swedish prospective study, after 5 years, 25–30% of participants fulfilled the definitions of low surgical treatment effectiveness, which was associated with poorer metabolic outcomes 176 . As with adults, prevention of weight regain for most at-risk individuals might be possible with the combination of lifestyle support and pharmacological treatment 182 . Further weight maintenance strategies and long-term outcomes are discussed in the 2023 American Academy of Pediatrics clinical practice guidelines 109 . The appropriate role and timing of other therapies for long-term weight loss maintenance, such as anti-obesity medications, more intensive dietary interventions and bariatric surgery, are areas for future research.

In summary, management of obesity in childhood and adolescence requires intensive interventions. Emerging pharmacological therapies demonstrate greater short-term effectiveness than behavioural interventions; however, long-term outcomes at ≥2 years remain an important area for future research.

Quality of life

Weight bias describes the negative attitudes to, beliefs about and behaviour towards people with obesity 183 . It can lead to stigma causing exclusion, and discrimination in work, school and health care, and contributes to the inequities common in people with obesity 184 . Weight bias also affects social engagement and psychological well-being of children.

Children and adolescents with obesity score lower overall on health-related quality of life (HRQoL) 4 , 5 . In measures that assess domains of functioning, most score lower in physical functioning, physical/general health and psychosocial areas, such as appearance, and social acceptance and functioning. HRQoL is lowest in treatment-seeking children and in those with more extreme obesity 185 . Weight loss interventions generally increase HRQoL independent of the extent of weight loss 186 , especially in the domains most affected. However, changes in weight and HRQoL are often not strongly correlated. This may reflect a lag in the physical and/or psychosocial benefit from weight change, or the extent of change that is needed to drive change in a child’s self-perception.

Similar observations apply to the literature on self-esteem. Global self-worth is reduced in children and adolescents with obesity, as is satisfaction with physical appearance, athletic competence and social acceptance 187 . Data from intensive interventions suggest the psychological benefit of weight loss may be as dependent on some feature of the treatment environment or supportive social network as the weight loss itself 188 . This may include the daily company of others with obesity, making new friendships, and experienced improvements in newly prioritized competences.

There is a bidirectional relationship between HRQoL and obesity 189 , something also accepted in the relationship with mood disorder. Obesity increases the risk of depression and vice versa, albeit over a longer period of time and which may only become apparent in adulthood 190 . Obesity also presents an increased risk of anxiety 191 .

Structured and professionally delivered weight management interventions ameliorate mood disorder symptoms 192 and improve self-esteem 193 . Regular and extended support are important components beyond losing weight. Such interventions do not increase the risk of eating disorders 194 . This is despite a recognition that binge eating disorder is present in up to 5% of adolescents with overweight or obesity 195 . They are five times more likely to have binge eating symptoms than those with average weight. Importantly, adolescents who do not have access to professionally delivered weight management may be more likely to engage in self-directed dieting, which is implicated in eating disorder development 196 .

The literature linking childhood obesity with either attention deficit hyperactivity disorder or autism spectrum disorder is complex and the relationship is uncertain. The association seems to be clearer in adults but the mechanisms and their causal directions remain unclear 109 , 197 . Young children with obesity, especially boys, are more likely to be parent-rated as having behavioural problems 198 . This may be a response to the behaviour of others rather than reflect clinical diagnoses such as attention deficit hyperactivity disorder or autism spectrum disorder. Conduct and peer relationship problems co-occur in children, regardless of their weight.

Children with obesity experience more social rejection. They receive fewer friendship nominations and more peer rejections, most pronounced in those with severe obesity 199 . This continues through adolescence and beyond. Children with obesity are more likely to report being victimized 200 . Younger children may respond by being perpetrators themselves. While it is assumed that children are victimized because of their weight, very few studies have looked at the nature or reason behind victimization. A substantial proportion of children with obesity fail to identify themselves as being fat-teased 187 . Although the stigma associated with obesity should be anticipated in children, especially in those most overweight, it would be inappropriate to see all as victims. A better understanding of children’s resilience is needed.

Many gaps remain in basic, translational and clinical research in child and adolescent obesity. The mechanisms (genetic, epigenetic, environmental and social) behind the overwhelming association between parental obesity and child and adolescent obesity are still unclear given the paradoxically weak association in BMI between adopted children and their parents in combination with the modest effect size of known genetic loci associated with obesity 201 .

Early manifestation of extreme obesity in childhood suggests a strong biological basis for disturbances of homeostatic weight regulation. Deep genotyping (including next-generation sequencing) and epigenetic analyses in these patients will reveal new genetic causes and causal pathways as a basis for the development of mechanism-based treatments. Future work aiming to understand the mechanisms underlying the development of childhood obesity should consider the complex biopsychosocial interactions and take a systems approach to understanding causal pathways leading to childhood obesity to contribute to evidence-based prevention and treatment strategies.

Long-term outcome data to better determine the risks of eating disorders are required. Although symptoms improve during obesity treatment in most adolescents, screening and monitoring for disordered eating is recommended in those presenting for treatment 202 and effective tools for use in clinical practice are required. A limited number of tools are validated to identify binge eating disorder in youth with obesity 203 but further research is needed to screen appropriately for the full spectrum of eating disorder diagnoses in obesity treatment seeking youth 203 . Recent reviews provide additional detail regarding eating disorder risk in child and adolescent obesity 117 , 202 , 204 .

Most studies of paediatric obesity treatment have been undertaken in HICs and predominantly middle-class populations. However, research is needed to determine which strategies are best suited for those in LMICs and low-resource settings, for priority population groups including indigenous peoples, migrant populations and those living with social disadvantage, and for children with neurobehavioural and psychiatric disorders. We currently have a limited understanding of how best to target treatment pathways for different levels of genetic risk, age, developmental level, obesity severity, and cardiometabolic and psychological risk. Current outcomes for behavioural interventions are relatively modest and improved treatment outcomes are needed to address the potentially severe long-term health outcomes of paediatric obesity. Studies also need to include longer follow-up periods after an intervention, record all adverse events, incorporate cost-effectiveness analyses and have improved process evaluation.

Other areas in need of research include the role of new anti-obesity medications especially in adolescents, long-term outcomes following bariatric surgery and implementation of digital support systems to optimize outcomes and reduce costs of behavioural change interventions 205 . We must also better understand and tackle the barriers to implementation of treatment in real-life clinical settings, including the role of training of health professionals. Importantly, treatment studies of all kinds must engage people with lived experience — adolescents, parents and families — to understand what outcomes and elements of treatment are most valued.

Obesity prevention is challenging because it requires a multilevel, multisectoral approach that addresses inequity, involves many stakeholders and addresses both the upstream and the downstream factors influencing obesity risk. Some evidence exists of effectiveness of prevention interventions operating at the level of the child, family and school, but the very poor progress overall in modifying obesity prevalence globally highlights many areas in need of research and evidence implementation. Studies are needed especially in LMICs, particularly in the context of the nutrition transition and the double burden of malnutrition. A focus on intergenerational research, rather than the age-based focus of current work, is also needed. Systems research approaches should be used, addressing the broader food and physical activity environments, and links to climate change 206 . In all studies, strategies are needed that enable co-production with relevant communities, long-term follow-up, process evaluation and cost-effectiveness analyses. In the next few years, research and practice priorities must include a focus on intervention strategies in the earliest phases of life, including during pregnancy. The effects of COVID-19 and cost of living crises in many countries are leading to widening health inequalities 207 and this will further challenge obesity prevention interventions. Available resourcing for prevention interventions may become further constrained, requiring innovative solutions across agendas, with clear identification of co-benefits. For example, public health interventions for other diseases, such as dental caries or depression, or other societal concerns, such as urban congestion or climate change, may also act as obesity prevention strategies. Ultimately, to implement obesity prevention, societal changes are needed in terms of urban planning, social structures and health-care access.

Future high-quality paediatric obesity research can be enabled through strategies that support data sharing, which avoids research waste and bias, and enables new research questions to be addressed. Such approaches require leadership, careful engagement of multiple research teams, and resourcing. Four national or regional level paediatric weight registries exist 208 , 209 , 210 , 211 , which are all based in North America or Europe. Such registries should be established in other countries, especially in low-resource settings, even if challenging 208 . Another data-sharing approach is through individual participant data meta-analyses of intervention trials, which can include prospectively collected data 212 and are quite distinct from systematic reviews of aggregate data. Two recent examples are the Transforming Obesity Prevention in Childhood (TOPCHILD) Collaboration, which includes early interventions to prevent obesity in the first 2 years of life 213 , and the Eating Disorders in Weight-Related Therapy (EDIT) Collaboration, which aims to identify characteristics of individuals or trials that increase or protect against eating disorder risk following obesity treatment 214 . Formal data linkage studies, especially those joining up routine administrative datasets, enable longer-term and broader outcome measures to be assessed than is possible with standard clinical or public health intervention studies.

Collaborative research will also be enhanced through the use of agreed core outcome sets, supporting data harmonization. The Edmonton Obesity Staging System – Paediatric 215 is one option for paediatric obesity treatment. A core outcome set for early intervention trials to prevent obesity in childhood (COS-EPOCH) has been recently established 216 . These efforts incorporate a balance between wanting and needing to share data and adhering to privacy protection regulations. Objective end points are ideal, including directly measured physical activity and body composition.

Collaborative efforts and a systems approach are paramount to understand, prevent and manage child and adolescent obesity. Research funding and health policies should focus on feasible, effective and equitable interventions.

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Natalie B. Lister & Louise A. Baur

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Natalie B. Lister

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Louise A. Baur

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Introduction (L.A.B., J.F.F. and N.B.L.); Epidemiology (L.A.B. and J.F.F.); Mechanisms/pathophysiology (L.A.B., J.F.F., T.R. and M.W.); Diagnosis, screening and prevention (L.A.B., N.B.L., T.R., C.S. and M.W.); Management (L.A.B., N.B.L., A.J.H., C.M. and T.R.); Quality of life (L.A.B., N.B.L. and A.J.H.); Outlook (L.A.B., N.B.L., J.F.F., A.J.H., C.M., T.R., C.S. and M.W.); Overview of the Primer (L.A.B. and N.B.L.).

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A.J.H. reports receiving payment for consultancy advice for Slimming World (UK). L.A.B. reports receiving honoraria for speaking in forums organized by Novo Nordisk in relation to management of adolescent obesity and the ACTION-Teens study, which is sponsored by Novo Nordisk. L.A.B. is the Australian lead of the study. T.R. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). T.R. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk and Lilly, as well as honoraria for lectures in symposia organized by Novo Nordisk, Novartis and Merck. C.M. receives payments for consultancy advice and advisory board participation from Novo Nordisk, Oriflame Wellness, DeFaire AB and Itrim AB. C.M. also receives honoraria for speaking at meetings organized by Novo Nordisk and Astra Zeneca. C.M. is a shareholder and founder of Evira AB, a company that develops and sells systems for digital support for weight loss, and receives grants from Novo Nordisk for epidemiological studies of the effects of weight loss on future heath. M.W. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). M.W. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk, Regeneron, Boehringer Ingelheim and LG Chem, as well as honoraria for speaking in symposia organized by Novo Nordisk, Rhythm Pharmaceuticals and Infectopharm. M.W. is principal investigator in phase II and phase III studies of setmelanotide sponsored by Rhythm Pharmaceuticals. N.B.L., J.F.F. and C.S. declare no competing interests.

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Lister, N.B., Baur, L.A., Felix, J.F. et al. Child and adolescent obesity. Nat Rev Dis Primers 9 , 24 (2023). https://doi.org/10.1038/s41572-023-00435-4

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Original research article, making childhood obesity a priority: a qualitative study of healthcare professionals' perspectives on facilitating communication and improving treatment.

quantitative research article on childhood obesity

  • 1 Functional Sciences Department, “Victor Babes” University of Medicine and Pharmacy Timişoara, Timişoara, Romania
  • 2 Microbiology Department, Centre for Studies in Preventive Medicine, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania
  • 3 Division of Pediatrics, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
  • 4 Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
  • 5 School of Anthropology and Museum Ethnography, University of Oxford, Oxford, United Kingdom
  • 6 Department of Food Studies, Nutrition, and Dietetics, Uppsala University, Uppsala, Sweden
  • 7 Department of Microscopic Morphology Genetics Discipline, Center of Genomic Medicine, Regional Center of Medical Genetics Timis, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania
  • 8 Regional Center of Medical Genetics, “Louis Turcanu” Clinical Emergency Hospital for Children, Timişoara, Romania

In Romania, one in four children has excess weight. Because childhood obesity is a sensitive topic, many healthcare professionals find it difficult to discuss children's excess weight with parents. This study aims to identify barriers and facilitators in childhood obesity-related communication, as perceived by healthcare professionals in Romania. As part of the STOP project, healthcare professionals (family physicians, pediatricians, and dieticians) who treat children with excess weight were invited to a telephone interview. The semi-structured questions were translated from a questionnaire previously used at the Swedish study site of the STOP project. Interviews were transcribed and then used for thematic analysis. Fifteen doctors and three dieticians (16 females and 2 males), with average 18.2 ± 10.1 years of experience, were interviewed. Four main themes were identified. Professionals reported that when children began experiencing obesity-related stigma or comorbidities, this became the tipping point of weight excess, where parents felt motivated to begin treatment. Barriers in communication were part of several layers of distrust, recognized as tension between professionals and caregivers due to conflicting beliefs about excess weight, as well as lack of trust in medical studies. Most respondents felt confident using models of good practice, consisting of a gentle approach and patient-centered care. Nonetheless, professionals noted systemic barriers due to a referral system and allocation of clinical time that hinder obesity treatment. They suggested that lack of specialized centers and inadequate education of healthcare professional conveys the system does not prioritize obesity treatment and prevention. The interviewed Romanian doctors and dieticians identified patient-centered care as key to treating children with obesity and building trust with their caregivers. However their efforts are hindered by healthcare system barriers, including the lack of specialized centers, training, and a referral system. The findings therefore suggest that, to improve childhood obesity prevention and treatment, systemic barriers should be addressed.

Trial Registration: ClinicalTrials.gov , NCT03800823; 11 Jan 2019.


Excess weight in children is prevalent worldwide. Although plateauing trends were observed in many high-income countries in Europe, in medium and low-income countries childhood obesity prevalence has increased in the last decade ( 1 ). In Romania, almost one in four children has overweight or obesity, as shown in a pooled analysis of more than 25,000 school age Romanian children ( 2 ) and the European Childhood Obesity Surveillance Initiative (COSI) ( 3 ).

Childhood obesity is recognized as a significant concern in the 2014–2020 Romanian National Health Strategy (HG, No.1028/18.11.2014) and efforts have been made to implement policies supporting healthy food and activities in the school setting. Additionally, media campaigns were coordinated to promote a healthy lifestyle. The Health Ministry's strategy did not formally name the healthcare professionals who should treat childhood obesity; however, family physicians, general, and specialist pediatricians (endocrinologists and specialists in diabetes and metabolic diseases), together with dieticians, are key to childhood obesity treatment.

Communicating with children and families about excess weight represents a crucial clinical skill for healthcare professionals in the prevention and management of childhood obesity ( 4 ). Focusing on obesity is difficult for several reasons, including time constraints and a concern about how the message is perceived ( 5 ). Previous studies have shown that many healthcare providers hold negative attitudes toward people with obesity ( 6 ). Such stigma can lead to reduced quality of care for people with obesity despite the best intentions of healthcare professionals ( 6 , 7 ). A joint international consensus statement for ending weight stigma was recently published, in an attempt to raise awareness of the negative consequences of weight stigma among a broad group of stakeholders, including healthcare providers, researchers, the media, policymakers, and patients ( 8 ). However, even when healthcare professionals do not endorse weight stigma, the social stigmatizing of obesity may affect the quality of care. Healthcare professionals are often avoiding discussing obesity with patients and their families, for fear of offending them and losing their trust ( 9 ).

The success of childhood obesity treatment depends on improving healthcare professionals' education, attitudes, and practices related to communication about obesity, as well as coordinated efforts to reduce weight stigma on the community level ( 10 , 11 ). A recent meta-analysis has shown that numerous studies explored weight stigma and healthcare communication in North America and Western Europe ( 12 ). However, similar studies have not been performed in Central/Eastern Europe, and no study has investigated how Romanian healthcare professionals perceive communication about excess weight. Overall, research on childhood obesity treatment in Central/Eastern Europe is limited, as shown by a recently updated Cochrane review ( 13 ).

This study presents the first analysis of barriers and facilitators Romanian healthcare professionals face when communicating with and treating families of children with excess weight. Through this, we aim to develop an in-depth understanding of the locally-specific conditions healthcare professionals face, and thereby contribute to the improvement of childhood obesity treatment in Romania.


Healthcare professionals (family physicians, pediatricians, and dieticians) who treat children with excess weight were recruited for the study. Doctors were identified through a public registry of 242 family physicians and 54 pediatricians. A formal list of pediatric dieticians was not available, therefore, a sample of the author's professional contacts were invited to participate. All healthcare professionals were recruited in Timisoara, a metropolitan area in western Romania. Timisoara's metropolitan area is home to almost half a million inhabitants and is one of Romania's economic hubs, as shown by gross domestic product per region ( 14 ). Potential participants were initially invited by email. The invitation emails explained the purpose and procedure of the current study. Interviews were scheduled with those who responded within a 3-week timeframe. The number of participants has met the criteria for data saturation established by Guest et al. ( 15 ).

Interview Guide

The present study is part of the EU funded project “Science and Technology in childhood Obesity Policy” (STOP) (Grant Agreement No. 774548). We used the questionnaire and interview guide developed by Sjunnestrand et al. ( 9 ) at the STOP project's Swedish study site. The semi-structured interview questions aimed to capture respondents' experiences of communicating with parents about their children's excess weight and to identify the perceived barriers and facilitators for inception of obesity treatment. The Swedish guide was translated into English and then to Romanian with minor reformulations and cultural adaptations.

The interviews were conducted one-on-one by a female researcher (SP), with professional experience in medicine and dietetics (MD, Ph.D.) and teaching experience of 26 years. She asked all respondents the same set of core questions, as well as individualized follow-up questions based on the responses, using think-aloud and verbal probing techniques ( 16 ). Both techniques provide a better understanding of the cognitive processes induced by the questions, enabling participants to express their own thought processes and raise additional issues. All interviews were conducted via telephone. The interviews were audio recorded and then transcribed verbatim by members of the research team (IJS, CLS, MB, AD). Field notes were made after the interviews.

Thematic Analysis

The interview transcripts were analyzed in Romanian, using thematic analysis. The transcribed interviews were read, re-read and then coded by ACE and CLS, using an inductive approach (data driven), rather than being limited to a pre-existing coding frame stipulated by the initial hypothesis ( 17 ). Thus, identified themes related to the responses rather than the specific interview questions. In using an inductive approach, we did not employ a predetermined theoretical framework; however, the coders were particularly interested in data that addressed barriers and facilitators to communication. All codes were defined in a codebook following a template ( 18 ). ACE and CLS had several meetings to follow the progress of analysis and to discuss the coding. Themes and subthemes were developed from the codes, following wider team discussions between ACE, CLS, KE and PN, in which a few disagreements were resolved and consensus achieved. Relevant quotes for the subthemes were translated from Romanian to English by ACE and CLS.

The study received ethics approval from the Scientific Research Ethics Committee Board of the “Victor Babes” University of Medicine and Pharmacy Timisoara (no.06/02.03.2020). Participant confidentiality is maintained throughout the manuscript. Each quote is labeled to indicate the position of each respondent and the interview number, as follows: (1) gender (F -female/M-male), (2) number within group, (3) group: primary care (PC) represented by family physicians and general pediatricians; secondary care (SC) represented by specialist pediatricians (gastroenterology, cardiology, nephrology, orthopedics, genetics); tertiary care (TC) represented by pediatric specialists designated for obesity healthcare (endocrinology, diabetes) and dieticians; (4) years of experience. The COnsolidated criteria for REporting Qualitative research (COREQ) checklist ( Supplement 1 ) was used in creating the report.

Initially, 21 participants agreed to be interviewed; however, three of them were not available to schedule an interview in the proposed timeframe due to time constraints (2 persons) and health issues (1 person). The final analysis included interviews with 18 respondents, all of whom verbally expressed informed consent and provided answers to all questions.

In total, 18 physicians and dieticians (16 females and 2 males), with average 18.2 ± 10.1 years of experience (range 5–35 years), were included in the analysis. Physicians in primary care (2 family physicians, 6 general pediatricians) had on average 17.75 ± 10.4 years of experience. Physicians in secondary care (5 specialist pediatricians in cardiology, nephrology, genetics, orthopedics, and gastro-enterology) had on average 21.2 ± 9.0 years of experience. Healthcare professionals in tertiary care (one pediatric specialist in endocrinology, one specialist in diabetes care and 3 dieticians) had on average 15.2 ± 12.0 years of experience. The average interview time was 26 min (range 14–49 min). Four main themes and eight sub-themes were identified (presented in Figure 1 ).


Figure 1 . Main themes and sub-themes identified.

Tipping Point of Weight Excess

The first theme, “Tipping point of weight excess,” captured the triggers that motivated families to seek obesity treatment. The respondents noted that families did not perceive excess weight as a problem until a tipping point was reached and obesity became a priority. The determining moment for families/individuals was associated with the burden of stigma (first subtheme) and emerging comorbidities (second subtheme).

Burden of Stigma

Although some physicians described excess weight discussions as routine, the majority felt that children's excess weight was a sensitive subject for caregivers. This was especially the case for physicians who were the first to raise the topic. On the contrary, dieticians did not consider excess weight as a particularly sensitive topic, as they saw families after they had been self-referred or referred by a physician, and were therefore already prepared for discussions of excess weight.

Most healthcare professionals deliberately used non-offensive words in raising the issue of childhood excess weight:

“ I try to be very careful, I don't use the words obesity or fat, I may say that he/she weighs a few extra kilograms and that he/she should take some measures to prevent worse situations, (…) I try an approach with kid gloves, more gentle, to see how parents react” (F5PC30).

Practitioners were aware of the effects obesity stigma can have on families, and actively tried to avoid connotations of stigma when speaking to patients and parents:

“ trying to keep a balance between avoiding stigma for the patient and explaining that excess weight is a problem,. it is not about aesthetics or trying to fit into a body ideal, it is about the health consequences of obesity (.) To stigmatize is not in our interest, as we try to obtain the patient's compliance” (F3TC33).

However, children and families often encountered stigma before meeting the physician. Parent and family awareness was sometimes triggered by a shocking event in a social context or a school setting, such as an incident of bullying. For example, one physician said:

“ I think there is a context, in school, or somewhere else, a situation that triggers this, and all of a sudden the parent understands -.Ooo! my child is…- even though in the last years the child was chubby and nothing happened”(F2TC6) .

Stigma burden in the school setting was reported in almost all interviews. “ Older children, girls, are generally bothered by being overweight because they have problems at school. They are bullied (…)” (F2TC6). In school, stigmatization by peers was worse in sports contexts, where physical capabilities and endurance were evaluated: “ the child cannot integrate in the sports class because he cannot cope and his peers laugh or the teacher points out the difference in performance” (F2TC6). According to most respondents, stigmatization at school was a key tipping point that urged children and parents to seek help. For example, one physician spoke about a patient who said “ that she really wants to lose weight because she gets tired too easily and is ashamed during sport classes and in school” (F4PC9). Another respondent also mentioned: “ When children are socially discriminated against, the parents are starting to feel that something is wrong and want to make a change” (F1TC12) .

Emerging Comorbidities

Respondents reported that some families approached healthcare practitioners only when the child's excess weight became moderate or severe: “ Never with small excess (weight), always when the excess outruns some threshold, when they feel it's beyond their control (.) in most, when the excess is moderate to severe” (F5TC23). In many cases, families contacted physicians due to comorbidities of excess weight rather than due to the excess weight itself:

“ Most do not contact us due to being overweight, rather (.) for other ( causes ). asthma, hypertension or diabetes or something else, but do not come to the hospital due to obesity, do not come to ask for help in losing weight” (F7PC15).

In preschoolers, orthopedic complaints, gastroenterological issues, recurrent respiratory infections, and sleep disturbance were mentioned as drivers to seek medical care. As one respondent described:

“ A very high percentage asks for help when other health problems arise, that can be felt. That is, concretely, the child feels ill, the child does not breathe well, the child snores, does not sleep at night, does not rest” (F2PC7).

Another respondent noted:

“ They decide [to seek help] when their children have an orthopedic problem, they have joint pain, pain in their lower limbs or back or when they have respiratory infections, (…) more frequently compared to their friends” (F1PC30).

In adolescent patients, comorbidities usually found in adults, such as arterial hypertension, metabolic syndrome, and endocrine dysfunctions were reported.

Physicians reported that, even after seeking help for a child's obesity-related illness, parents still did not recognize obesity as the underlying issue, and sometimes did not understand that excess weight could contribute to the condition for which they seek treatment:

“ Most of them come for gastroenterology consultation, due to high levels of transaminase or irritable bowel syndrome or dyspepsia, which are clearly connected with obesity. But they (the caregivers) do not see obesity as a problem, they only see these symptoms and focus on them” (F4SC12).

Nonetheless, physicians explained that, even when parents did not recognize obesity as a problem, they used the child's referral for another condition to initiate a conversation about obesity: “ They come for other pathologies - breathing problems, most often, and then, we take the opportunity to talk about obesity” (F2PC7).

Layers of Distrust

The second overarching theme identified in the interviews was layers of distrust . This theme captures disagreements about excess weight and food practices in the family, alongside an intrafamilal “blame game.” It also captures conflicting views of obesity, as expressed by families and healthcare professionals, and families' lack of trust in the medical system. Two subthemes were identified: family in disagreement and tension between doctors and families.

Family in Disagreement

Respondents noted that family members often disagreed about the meaning and severity of excess weight, and that this had implications for children's treatment. As one respondent said,“ (.) most unsuccessful outcomes are due to family disagreeing views” (F3PC10). According to the respondents, the presence of both parents in the clinical consultation increased the level of agreement and strengthened the chances of treatment success. When children were accompanied by one caregiver, usually the mother, she disclosed that other member(s) of the family did not agree with seeking help for the child's excess weight, usually because they felt excess weight was not urgently problematic. One respondent explained that “ [m]ost of the time both parents agree that there is a problem, (but) the way they respond to the problems is different. At least one of them tends to neglect or minimize the importance of this problem ” (M2SC18).

When children were accompanied by only one caregiver, absent family members were frequently blamed for the child's obesity: “ (.) the guilt is usually assigned to grandparents, or someone else than the person that accompanies the child. (.) Others are (accused of) secretly giving chocolate, snacks and potato chips ” (F7PC15). When possible, a more complete picture of the familial situation was ascertained by conducting separate discussions with different caregivers involved in the child's care: “ things are not exactly as the parents present them. When we talk to grandparents, we learn that the parents are also buying and eating unhealthy foods and then things are more complicated” (F3TC33).

Tension Between Doctors and Families

The gap between healthcare professionals' and families' understandings of childhood excess weight was another source of tension. Respondents said that communication barriers occurred more often in meetings with low-income families or with grandparents. Among poorer and older family members, the respondents explained, children's excess weight was associated with higher social status, health and beauty. For example, one respondent said a grandmother confronted her, saying “ [y]ou (the doctor) cannot tell me that something is wrong with my grandchild, that he is too fat. He is a child that eats well” (F1SC16). Another respondent described remarks such as: “ - Ma'am, this is not a problem, I mean, I am also white, fat and beautiful”; this practitioner explained that “ [t]his creates a communication barrier, that I felt that I will have no success, no matter how I approach it” (F2PC7).

Respondents noted that families often refused to participate in studies concerning excess weight. A recurrent opinion was that parents worried their children would be treated as “guinea pigs” in clinical studies: “ I don't know what it's like in other countries, but in Romania they don't like to be studied. They fear they will become a guinea pig. I think it's a matter of perception and I don't know how it could be changed” (F4SC12). Respondents suggested that families' concerns were driven by not understanding the benefits of research and the belief that no feedback will be provided, as well as low parental education level, the socio-cultural valuation of children's excess weight, and the additional effort that research participation involves: “ The complexity of these studies and the fact that we ask parents and children make an extra effort, efforts that I do not think they are willing to make (…) Somewhere here is the barrier” (M2SC18). A potentially key reason for refusal to participate in studies was lack of trust in the healthcare system. One respondent reported that she encountered similar refusal to participate in studies on diabetes and obesity across Romania:

“ Because there is a general distrust in the medical staff in Romania (.). Distrust is also showed by the refusal to vaccinate children (.) We did not experience this 10-12 years ago, instead, recently, for a study regarding diabetes, two years ago it was a mass refusal, and not only in Timisoara but also in other parts of the country. (…) In studies that require blood sampling, interventional studies or studies that include medication, there are already more reluctant. I can't say that these refusals are justified, but unfortunately I found refusals like that … without any a justification” (F3TC33).

Models of Good Practice

Most respondents reported they use various models of good practice to ensure appropriate communication, aiming to empower patients and their families to start treatment. This theme consisted of the two subthemes: gentle approach and patient-centered care.

Gentle Approach

Respondents recognized the crucial role of language in the clinical encounter: “ Words, including gentle words, can have an impact. We must not be brutal in the way we communicate with the families, in any form” (F4PC9). Many practitioners reported using a probing interview approach to assess caregivers' awareness of excess weight: “ after I have taken care of the acute pathology that brings them to me, I ask questions like what age did the child start to gain weight, and so, I find out if it is a problem for them, because for many it is not a problem” (F6PC30). Other respondents reported using a direct approach without labeling the child obese. This was usually achieved by highlighting children's deviations from the normal growth curve: “ based on measurements, we show them exactly the situation of their child versus normal growth line for age and gender (.). On a reference chart we can show the family (.) the healthy weight for the height of their children” (F3TC33).

When approaching families who were not aware of the risks associated with excess weight, respondents described using a gentle approach that included seeking a “ level of awareness, to be sure that they understand what I want to say to them, (.) that I am not talking about aesthetics, rather, thinking about possible risks that they undertake if they continue with increasing weight” (F5TC23).

Healthcare professionals recognized that trust could be built step-by-step only when a channel of communication had been opened, with recommendations delineated gradually: “ I think, foremost, you have to earn their cooperation and trust and then recommendations can be enacted progressively, but with their consent. First they must understand and then they will accept” (F5TC23). Respondents also frequently reported focusing on what could be done and achieved, using positive language. For example, one physician describes speaking to families as follows:

“ you have to look at the positive side all the time: your child has a few extra kilograms, but let's look at the good side, he/she is in a growing period and it's much easier to do something now, as the child is much more physically active and then let's see how we can improve in the future (…) and that kind of works. So, I always tell them that it is much easier for the child to lose weight during periods of growth” (F7PC15).

To improve communication, respondents made efforts to empower patients and families. For example, one respondent said

“ [i]t helps to communicate and listen to their problems actively. Many times, they just need to be listened to. When they feel understood, they have even more confidence; somehow, they feel that they can do it. They often need encouragement and self-confidence that they can make changes” (F1TC12).

Another respondent said she made sure to praise her patients: “ I praise the child directly. I ask him if some things have improved (…), I ask how he feels, toward maintaining or losing weight” (F1PC30).

Patient-Centered Care

Respondents communicated to families that they were on their side: “ I think it matters a lot to be perceived as an ally, as a person who wants what's best for them and not as an extremely authoritarian figure. As an ally who knows what she is talking about and who gives them informed advice, having experience in this field” (F2PC7). Respondents also said they adapted to the families' concerns by offering personalized advice, taking into account families' wishes and their financial and social needs. For example, a dietician trying to dismiss a family's perception that healthy food was expensive said:

“ I gave them information about the price of foods a child can eat. To prove that it is not so expensive for a child to eat healthy (.) I give them options and examples - Look, instead, he can eat something that is four times cheaper and healthier. I even give them variants of shopping lists, including stores where to do grocery shopping. (.) From the moment I explain the cost (of healthy foods), that it's not so expensive, they have less preconceptions” (F2TC6).

According to the respondents, good practice entailed the successful involvement of the family in providing supportive environment for the child. They encouraged family members to share home cooked meals and act as role models of healthy eating. When grandparents were in charge of cooking for the child, the respondents invited them to the clinic to attend discussions regarding healthy food choices. For example, one respondent noted, “ [i]n a subsequent conversation we invite the grandparents, (.) and then the grandparents have to support the treatment plan by sharing the same meals as the grandchildren” (F3TC33).

Almost all respondents agreed that children's presence in clinical visits was beneficial, except if the children were too young to join the discussions. However, even if young children were kept busy with other activities during the visit, the respondents thought it was good for them to be present and to hear the discussion, as one physician pointed out “ [t]hey ( the small children ) understand more than we think” (F4SC12). If the children were older, respondents said that compliance and outcomes were improved if the child was engaged in the treatment plan. Indeed, one respondent noted that “ doctors can sometimes work better with the child than with the parent” (F7PC15). Some practitioners reported using an age-and gender-specific approach:

“ If the child is small, I do not approach the child, I approach the parent. If the child is a female preadolescent, I try to approach it differently. I tell the parent very clearly that if she does not control her excess weight at this age, she is at risk to have distorted body image perception that might lead to eating disorders in adolescence. In boys, I mainly focus on sports and mention that social success with the group of young people is also related to the physical aspect” (M3SC25) .

Systemic Barriers

Although most respondents felt confident using models of good practice, they noted systemic barriers to good practice, specifically a referral system, and time constraints that did not accommodate obesity treatment, showing that obesity does not fit the system. Additionally, respondents suggested that lack of specialized centers and poor education of healthcare professionals indicated the system doesn't prioritize obesity treatment and prevention.

Obesity Does Not Fit the System

Respondents frequently noted that clinical consultations should empower families. For this to be achieved, patients needed time to build trust, and healthcare practitioners needed time to adapt to each family's needs; however, time constraints sometimes hindered this. As one respondent described: “ You have to have a lot of time for explanations with these families, (…) you gain their trust this way (.) however doctor's consultation time is shorter than I would need (.) honestly with a patient with obesity, I consume time for 3 consultations” (F5TC23). Another respondent explained: “ One cannot do a consultation for obesity in 15 or 20 minutes, as for acute pathologies” (F3TC33).

Healthcare settings and processes were also cited as barriers. One respondent noted that a barrier in seeking treatment for the child's excess weight might be related to the hospital setting:

“ It is a cultural fear of being in a physician's office or in hospital. Parents say: “I don't want to take my child to the hospital because he gets a disease there." Perhaps, de-medicalization of the subject would be a solution, in the sense of not being addressed in a clinical setting as a medical treatment, rather as a behavioral treatment” (F1TC12).

Some respondents worried that adolescents with risk for morbid obesity were lost in the transition to adult care: “ we lose them there because when they turn 18, they no longer have a trusting vote with us (pediatricians) … . They see themselves as adults and as an adult they do not respect what they are told.unfortunately.” (F3TC33).

The System Does Not Prioritize Obesity

Several respondents noted that, as opposed to diabetes or cardiovascular diseases, the Romanian medical system does not consider obesity prevention or treatment a priority. As one respondent said: “ Obesity is not seen as a medical condition, it is not seen as a condition that needs treatment. That's why the (healthcare) system works poorly here” (M2SC18). Respondents mentioned lack of training as one example of the deprioritizing of obesity. One pediatrician said: “ I am not very convinced that we, the physicians, are properly educated to understand that obesity is a disease.” (F2PC7). Another aspect of the deprioritizing of obesity was the delay in referrals, with specialist doctors saying that general practitioners did not address childhood obesity in time:

“ (.) the family physician, or the physicians in schools should have observed that the child has excess weight and they should have addressed it, and get in contact with specialists to receive adequate monitoring. Practically, the system (the medical network for management of obesity) does not exist” (M2SC18).

Respondents suggested that multiple professionals should be involved in obesity care, including dieticians, psychologists and social services, as needed. However, consultations could rarely be scheduled in 1 day and respondents cited multiple visits as a contributor to families' reduced treatment participation. To increase coordination amongst multiple professionals, respondents underlined the need for specialized centers for childhood obesity treatment, as one respondent noted:

“ Medical barriers are organizational. (.) all consultations should be scheduled and well-coordinated. I'm talking about a consultation with the medical team, followed by a meeting with the dietician and with the psychologist, so that all this can be performed conveniently during one morning. Then the child does not have to miss school once for the doctor appointment, once for the dietician, once for the psychologist” (F3TC33).

All respondents underlined the need for a systemic approach, with childhood obesity prevention and intervention promoted by government policies, media, kindergartens and schools, family physicians, and general pediatricians.

This is the first study in Romania and in Central/Eastern Europe to investigate the barriers and facilitators healthcare professionals face when communicating with and treating families of children with obesity. The analysis shows that healthcare professionals identified the burden of stigma and the presence of comorbidities as the “tipping points” that lead families to seek help. However, they found that disagreements between family members about the seriousness of obesity, alongside families' distrust of the healthcare system, posed barriers to treatment. To build trust and engage families in treatment, the participating healthcare professionals followed models of good practice, consisting of a gentle approach and patient-centered care. Yet the respondents argued that treatment was often limited due to systematic barriers, including the referral system and time constraints, as well as the lack of specialized centers and poor education of healthcare professional. These were cited as examples of the healthcare system's deprioritizing of childhood obesity treatment and prevention.

The respondents' observation that children's experiences of weight stigma were the main motivation for treatment seeking suggests that treatment was initiated too late, and that children starting treatment had already experienced emotional and social hurt related to obesity. Children and adolescents with obesity who experience stigma suffer from psychological, physical, and behavioral difficulties that increase the risk of social isolation and weight gain ( 19 ). In addition, experiences of stigma have detrimental effects on healthy eating and engaging in physical activity ( 20 – 22 ), access to treatment ( 23 ), and adherence to weight loss related treatment ( 21 , 24 ), suggesting that children who begin treatment after having experienced stigma might not fully benefit from it.

The respondents characterized childhood obesity an “invisible disease,” with family members seeking treatment only after observing comorbid conditions. Delayed treatment seeking, until childhood obesity becomes severe or complicated by other diseases, has been observed in other studies ( 25 – 28 ). Moreover, in a United States based study, Eli et al. ( 29 ) found that parents of young children believe that obesity becomes a problem only at the beginning of primary school, when children might face bullying, or when comorbidities occur.

To make the “invisible” nature of excess weight concrete, the healthcare professionals in our study reported that they used growth charts to objectively compare the child's weight status to with a healthy weight reference population. Growth charts are useful and acceptable tools that professionals commonly use to improve counseling and facilitate behavior change ( 30 ). Therefore, these charts should be shown to parents before children develop obesity, to assist early prevention efforts. The recent study by Sjunnestrand et al. conveys the importance of teaching parents to understand child growth charts early on, and thus prevent obesity and associated comorbidities ( 9 ).

Several layers of distrust within the family and between the family and the medical practitioners were noted in the interviews. Within the family, respondents said that often only one parent or some family members recognized the excess weight, while others did not, leading to disagreements on how to help the child. Similar findings were reported in an Australian interview study by Jackson et al. ( 31 ), who found that, in almost half of the families, fathers either did not recognize the child's excess weight or were in denial about it. The authors therefore proposed addressing children's excess weight as a familial issue, rather than as an individual's problem, in order to get the family to work together ( 31 ). Between the family and the medical practitioners, tension arose around definitions of childhood obesity. Moreover, respondents underscored that families were reluctant to participate in clinical trials for obesity treatment, suggesting distrusts of the healthcare system. This reluctance, according to the respondents, could indicate fear of being experimented on. At the time of publication there were no studies in Romania investigating communication between healthcare practitioners and families of children with obesity in relation to clinical studies. However, studies investigating patient's satisfaction with the Romanian healthcare system, showed high levels of dissatisfaction, and lack of trust in medical services and healthcare professionals ( 32 , 33 ). Patient dissatisfaction was related to communication barriers with healthcare professionals, as well as low quality accommodation, food and hygiene in hospitals, which thus failed to provide patients with a sense of security ( 32 – 34 ). Distrust toward medical services might lead to distrust toward medical studies. The local STOP project team in Romania has observed that recruitment to the study ( 35 ) has been limited. Similar concerns were reported in a US-based study which evaluate clinical trial acceptance ( 36 ). It has been suggested that addressing patients' fears with detailed explanations of methodology and patient safety measures could improve recruitment. Davidson and Vigden have recently evaluated the acceptance of participation in childhood obesity studies in Australia ( 37 ). They found that parents' decision to enroll in obesity programs was influenced by experiences with previous attempts to lose weight and their child's emotional state ( 37 ).

In our study, tension between families and healthcare professionals created barriers in communication. This is in line with a meta-synthesis of qualitative studies showing that different perceptions of excess weight between families and doctors are common barriers ( 12 ). These barriers, unless understood and addressed, might limit treatment success ( 38 ). In this study, the respondents addressed these barriers by using models of good practice when communicating with families about children's excess weight. A gentle approach, which increased parental awareness about childhood obesity while avoiding offense, was key. Previous research has shown the importance of avoiding judgment in developing successful communication with families of preschoolers with obesity ( 39 ). However, clinical practice alone does not improve communication skills, and when communication skills are lacking, practitioners usually avoid difficult conversations ( 38 ). Therefore, formal training is crucial in improving communication with families ( 40 ). In the present study, the respondents identified lack of training as a barrier to communicating with families. This barrier should be addressed by governmental policies to improve clinical continuing education.

Models of good practice reported by the respondents also included a patient centered care approach. Practitioners adapted treatment recommendations to the possibilities and needs of patients and families. Promotion of informed choice as part of the patient centered care approach has been emphasized in different medical fields ( 41 , 42 ). It includes a stepwise approach culminating with patients expressing their own choice, after the healthcare professional has presented different treatment pathways with their pros and cons ( 41 , 42 ).

A key finding was that respondents identified changes in the healthcare system as crucial to improving capacity for childhood obesity interventions. They noted that primary care physicians were often slow to refer families, and that families initiated treatment in secondary and tertiary care, only after comorbidities were observed. If obesity treatment were promoted in primary care, excess weight could be addressed earlier, with benefits to both patients and the healthcare system, as shown by a recent guideline issued by World Health Organization ( 43 ). Interestingly, while Romania and Sweden have different healthcare systems, Sjunnestrand et al. also reported that healthcare practitioners felt systematic changes were needed to improve treatment referral and uptake ( 9 ). Additionally, because childhood obesity treatment in Romania is associated with secondary and tertiary care, the respondents noted that families might refuse or postpone obesity treatment due to fears related to hospital settings. Offering a community-based, multi-disciplinary setting for childhood obesity treatment might increase the acceptability of treatment. The healthcare system could also benefit by limiting overcrowding in secondary and tertiary care and encouraging earlier treatment initiation ( 44 ).

Strengths and Limitations

This is the first study to investigate Romanian healthcare professionals' perspectives on communicating with families about childhood obesity. The study used the interview guide developed by Sjunnestrand et al. ( 9 ), who interviewed a homogenous group of pediatric nurses working in primary care centers in Sweden. In the present study, the interviews were conducted with a heterogeneous group of healthcare professionals from primary, secondary and tertiary care, in order to present various perspectives on communication with families of children with excess weight in Romania. Thus, while the study benefited from using an established interview guide, the different categories of healthcare professionals interviewed precluded a meaningful comparison with the Swedish dataset. A formal list of pediatric dieticians was not available, as at the time of the study a national association of dieticians did not exist. Therefore, we invited the authors' personal contacts to participate, and we recognize this may have limited the diversity of dietician participants. The dieticians invited could not provide contact for other colleagues involved pediatric nutrition, possibly because of low numbers or lack of formal association. Future research involving dieticians would be strengthened by the use of snowball sampling, which could provide a more diverse sample. The study was potentially limited by selection bias, since participants who agreed to be interviewed might have had a particular interest in childhood obesity. The study was also limited by the low response rate, likely due to the interview timeframe, which overlapped with the COVID-19 pandemic lockdown.

Future Directions

While this study has focused on healthcare professionals' perceptions of communicating about and treating childhood obesity, it would be important to understand families' experiences, as well. We plan to investigate the experiences of parents and children as part of the More and Less Study Europe ( 35 ). The study's findings convey the importance of establishing a professional network in Romania for the management of childhood obesity. Moreover, services provided by dieticians should be included in the Romanian Health Insurance network, to facilitate the integration of dietetics service into standard childhood obesity treatment. In addition, the primary medical care in kindergarten and schools in Romania, might be used, in a step-by-step approach, for raising awareness about childhood obesity, reducing obesity stigma, and encouraging treatment initiation.


The interviewed doctors and dieticians in Romania identified patient-centered care as key to treating children with obesity and building trust with their caregivers. However, their efforts are hindered by healthcare system barriers, including the lack of specialized centers, training and a referral system. The findings therefore suggest that, to improve childhood obesity prevention and treatment, systematic barriers should be addressed.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors.

Ethics Statement

The studies involving human participants were reviewed and approved by Scientific Research Ethics Committee Board of the Victor Babes University of Medicine and Pharmacy Timisoara (no.06/02.03.2020). The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AC-E, PN, and AE developed the interview guide. SP conducted the interviews. AC-E and CLS wrote the first draft of the manuscript under supervision of PN. KE contributed to the analysis and to the writing of the manuscript. All authors contributed to final editing of the document and approved of the last version of the manuscript.

The STOP project received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 774548.

The content of this document reflects only the authors' views and the European Commission is not liable for any use that may be made of the information it contains.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The authors want to thank the physicians and dieticians that participated in the interviews. We acknowledge Iulia Jurca-Simina (IJS), Costela Lacrimioara Serban (CLS), Meda Bugi (MB), and Ana-Maria Dragomir (AD), who transcribed the interviews.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2021.652491/full#supplementary-material

Supplement 1. COREQ checklist. Includes the COREQ checklist for this submission.


COREQ, COnsolidated criteria for REporting Qualitative research; COSI, European Childhood Obesity Surveillance Initiative; F, female; M, male; PC, primary care; SC, secondary care; STOP, Science and Technology in childhood Obesity Policy; TC, tertiary care.

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Keywords: children, family, overweight, obesity, parents, stigma, STOP project, thematic analysis and constructs

Citation: Serban CL, Putnoky S, Ek A, Eli K, Nowicka P and Chirita-Emandi A (2021) Making Childhood Obesity a Priority: A Qualitative Study of Healthcare Professionals' Perspectives on Facilitating Communication and Improving Treatment. Front. Public Health 9:652491. doi: 10.3389/fpubh.2021.652491

Received: 13 January 2021; Accepted: 21 June 2021; Published: 15 July 2021.

Reviewed by:

Copyright © 2021 Serban, Putnoky, Ek, Eli, Nowicka and Chirita-Emandi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Adela Chirita-Emandi, adela.chirita@umft.ro


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