• Research article
  • Open access
  • Published: 16 April 2015

Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators

  • Pedro J Teixeira 1 ,
  • Eliana V Carraça 1 ,
  • Marta M Marques 1 ,
  • Harry Rutter 2 ,
  • Jean-Michel Oppert 3 , 4 ,
  • Ilse De Bourdeaudhuij 5 ,
  • Jeroen Lakerveld 6 &
  • Johannes Brug 7  

BMC Medicine volume  13 , Article number:  84 ( 2015 ) Cite this article

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Relapse is high in lifestyle obesity interventions involving behavior and weight change. Identifying mediators of successful outcomes in these interventions is critical to improve effectiveness and to guide approaches to obesity treatment, including resource allocation. This article reviews the most consistent self-regulation mediators of medium- and long-term weight control, physical activity, and dietary intake in clinical and community behavior change interventions targeting overweight/obese adults.

A comprehensive search of peer-reviewed articles, published since 2000, was conducted on electronic databases (for example, MEDLINE) and journal reference lists. Experimental studies were eligible if they reported intervention effects on hypothesized mediators (self-regulatory and psychological mechanisms) and the association between these and the outcomes of interest (weight change, physical activity, and dietary intake). Quality and content of selected studies were analyzed and findings summarized. Studies with formal mediation analyses were reported separately.

Thirty-five studies were included testing 42 putative mediators. Ten studies used formal mediation analyses. Twenty-eight studies were randomized controlled trials, mainly aiming at weight loss or maintenance (n = 21). Targeted participants were obese (n = 26) or overweight individuals, aged between 25 to 44 years (n = 23), and 13 studies targeted women only. In terms of study quality, 13 trials were rated as “strong”, 15 as “moderate”, and 7 studies as “weak”. In addition, methodological quality of formal mediation analyses was “medium”. Identified mediators for medium-/long-term weight control were higher levels of autonomous motivation, self-efficacy/barriers, self-regulation skills (such as self-monitoring), flexible eating restraint, and positive body image. For physical activity, significant putative mediators were high autonomous motivation, self-efficacy, and use of self-regulation skills. For dietary intake, the evidence was much less clear, and no consistent mediators were identified.

Conclusions

This is the first systematic review of mediational psychological mechanisms of successful outcomes in obesity-related lifestyle change interventions. Despite limited evidence, higher autonomous motivation, self-efficacy, and self-regulation skills emerged as the best predictors of beneficial weight and physical activity outcomes; for weight control, positive body image and flexible eating restraint may additionally improve outcomes. These variables represent possible targets for future lifestyle interventions in overweight/obese populations.

Peer Review reports

Lifestyle treatment interventions for obesity typically target changes in diet and physical activity through strategies like setting adequate goals and enhancing patients’ motivation, changing their beliefs and expectations, and providing guidance in the use of a variety of self-regulation skills (such as self-monitoring), all of which are thought to influence behavior change and maintenance [ 1 - 4 ]. A wide variety of health behavior change theories has been employed to provide conceptual organization of these determinants, including social cognitive theories such as the theory of planned behavior [ 5 ], theories of motivation such as self-determination theory [ 6 ], theories distinguishing between motivational and post-motivational or volitional phases [ 7 ] such as the health action process approach (HAPA) [ 8 ], and self-regulation models such as control theory [ 9 ]. Since all these theories address the regulation of a person’s behavior in the service of some goal or desired outcome, through intrapersonal factors, in this paper we broadly refer to intervening variables in this process as self-regulation factors .

Behavior modification in general, and “comprehensive lifestyle interventions” in particular [ 10 ] are currently the first recommended step in obesity management. However, so far, randomized controlled trials evaluating the effectiveness of programs that target lifestyle behavior have shown mixed effects and, if effective, they have generally resulted in only small changes in target behaviors [ 11 - 15 ]. In addition, the evidence shows that relatively little if any weight loss accomplished in treatment programs is maintained over the long term [ 16 ]. Furthermore, few studies have analyzed why, or by which mechanisms, interventions are successful for some individuals and not for others. Clearly, there is a need for research that identifies causal predictors of long-term weight control, including successful weight loss and maintenance [ 17 ].

Despite the limited success of available interventions in reversing the current trends in obesity prevalence, approaches focusing on individual behavior change remain an important topic of interest in obesity research. Several reasons justify this assertion. First, these interventions typically focus on behaviors (for example, diet and physical activity), which have widespread consequences for health, with or without weight loss. Second, if and when individuals are able to successfully self-regulate their behaviors, these effects tend to be sustainable, which is essential for having a lasting impact on health; moreover, this successful self-regulation may also “transfer” to, and help change, other health behaviors [ 18 ]. Third, although some interventions targeting individuals may be ineffective on their own, they might be able to contribute to the effectiveness of strategies that integrate multiple levels (that is, strategies that include individual-level and environmental-level approaches) [ 19 ]. Finally, the potential for dissemination of individual-level intervention approaches is considerable, given that a sizable number of overweight and obese persons will seek professional help at some point in their lives. Consequently, improving the efficacy of such interventions has substantial clinical as well as public health relevance.

One recent development in studies testing lifestyle interventions for obesity is their ability to identify the mechanisms or processes by which interventions induce meaningful and lasting change in their (most successful) participants. These mechanisms can generally be named predictors (or determinants ) of success, and some studies have gone one step further to evaluate the extent to which they may be causal mediators of intervention effects. Testing of mediation, using appropriate methods, is a critical step in this field; it provides the strongest possible inference for the identification of elements in interventions which are causally “responsible” for achieving desired outcomes [ 20 ].

Success and failure in the self-regulation of health behaviors involve multiple psychological and behavioral aspects. The aim of this review was to identify and summarize psychological self-regulation mediators of successful weight change, or change in energy balance-related behavior (physical activity and diet), in clinical and community behavior change obesity interventions. Because eventual weight regain is frequent after behavior and/or weight change interventions, particular attention was given to studies reporting long-term outcomes, that is, one year or more after the beginning of the intervention.

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 21 ].

Eligibility criteria

Studies were included in this review if they were intervention studies published since 2000 in the English language, used experimental designs, and referred to clinical or community behavior change interventions with overweight/obese adults (≥18 years old) aiming to reduce overweight/obesity. This review was limited to “lifestyle interventions” defined as interventions that promote change in energy balance-related behaviors (such as diet and physical activity, as the outcomes) and self-regulatory factors (such as motivation and self-monitoring, as the potential mediators) relevant for overweight/obesity treatment, typically in settings involving personal contact between interventionists and participants. There were no restrictions with respect to the format and duration of the intervention. To be eligible, studies should also report outcomes assessed at least 6 months after the start of the intervention; include a quantitative assessment of change in weight/BMI, physical activity (for example, self-reported or accelerometer-derived minutes of moderate and vigorous physical activity, daily pedometer steps, attendance to PA sessions), or dietary intake (for example, energy intake, fat intake, fruit and vegetable intake) as well as a quantitative assessment of potential mediators of successful behavior change. We decided not to distinguish predictors of weight loss and predictors of weight loss maintenance, choosing instead to divide the studies according to the length of measurement periods (shorter versus longer than 12 months). While it is possible that predictors of those two processes differ, to appropriately evaluate predictors of weight loss maintenance, we would have to rely on studies of successful weight losers, and preferably including psychological measures before and after the maintenance period. Only one intervention study fit both criteria.

An a priori list of mediators was used for study inclusion/exclusion, based on previous work in this area (for example, [ 2 , 22 ]). Only mediators representing individual-level self-regulatory processes were considered (that is, those related to skills, motivation, competence , coping mechanisms, beliefs, physical self-perceptions, and eating regulation factors such as disinhibition, restraint, and perceived hunger). Mediators associated with personality factors, social support, and health-related outcomes (such as psychological distress, quality of life, and well-being) were excluded. Finally, eligible studies were required to report the effect of the intervention on hypothesized mediator(s) and the association of the putative mediator with the outcomes of interest.

Search strategy and study selection

A comprehensive search of peer-reviewed articles published between January 2000 and February 2014 (including online ahead of print publication) was conducted in six electronic databases (Pubmed, MEDLINE, PsycINFO, the Cochrane Library, Web of Knowledge, and SPORTDiscus). The decision to restrict the selection to studies published since 2000 is based on the fact that recent development in studies testing the effectiveness of lifestyle interventions for obesity makes older studies less externally valid. For instance, in 1995, Friedman and Brownell [ 23 ] alerted for the need of a “third generation” of obesity treatment studies analyzing causal mechanisms between psychosocial variables and weight change. Despite this, one decade later, it has been observed that very few studies had investigated such mechanisms, and even fewer looked into long-term changes [ 2 ].

Searches included various combinations of four sets of terms: i) terms concerning the health condition or population of interest (overweight/obesity); ii) terms concerning the intervention(s)/exposure(s) evaluated (for example, behavior change/lifestyle obesity interventions); iii) terms respecting the outcomes of interest (weight change, physical activity, and dietary intake); iv) terms concerning the predictors/mediators of interest (psychological, self-regulation); and v) terms concerning the type of analyses of relevance (for example, mediation, correlates, predictors). (See Additional file 1 for a search example; complete search strategies can be obtained from the authors). Other sources included manual cross-referencing of bibliographies cited in previous reviews [ 2 , 22 , 24 - 26 ] and included studies, as well as manual searches of the content of key scientific journals ( Obesity Reviews; International Journal of Obesity; Obesity (Silver Spring); International Journal of Behavioral Nutrition and Physical Activity; Journal of the American Dietetic Association; Psychology of Sports and Exercise; Health Psychology; Journal of Behavioral Medicine; Preventive Medicine ).

Titles, abstracts, and references of potential articles were reviewed by two authors (EVC, MM) to identify studies that met the eligibility criteria. Duplicate entries were manually removed. Relevant articles were then retrieved for a full read. The same two authors reviewed the full text of potential studies, and decisions to include or exclude studies in the review were made by consensus.

Data coding and extraction

A data extraction form was developed, informed by the PRISMA statement for reporting systematic reviews [ 21 ] and the Cochrane Collaboration’s tool for assessing risk of bias [ 27 ]. Data extraction included information about study details (authors, year, country of publication, affiliations, and funding), participants (characteristics, recruitment, setting, attrition, compliance, and blinding), study design and setting, outcomes of interest, mediators/predictors (in/out list), intervention length and characteristics, psychosocial instruments, and statistical analysis, including mediation techniques (a complete coding form can be obtained from the authors). Authors of included studies were contacted when necessary to retrieve missing data in published reports.

Considering that the main focus of this review was the identification of mediators, data extraction was performed separately, starting with the studies formally testing mediation (see Additional file 2 ), followed by those that reported both the effect of the intervention on hypothesized mediators ( path a ) and the association of the putative mediator with the outcomes of interest ( path b ), but did not test mediation (see Additional file 3 ). Regarding mediation and specifically in studies with formal mediation tests, researchers could use Baron and Kenny’s approach [ 28 ] and check whether the main effects were reduced in the presence of the mediator, or employ more sophisticated techniques to directly test the significance of the indirect effect through the mediator (for instance, by following MacKinnon’s approach [ 29 ], and using Preacher and Hayes mediation procedures or structural equation modeling). Additional file 4 presents a detailed description of the mediation analyses procedures and estimates for each study. In the latter (that is, predictor studies), we generally looked at a) whether significant intervention-control differences existed for a given variable (or pre-post change in non-controlled designs); b) whether there was an association between these changes (in intervention group only) and changes in the outcome (weight/PA/diet) in this group. If both were present, results were deemed consistent with mediation.

Quality assessment

The quality of included studies was assessed using an adapted version of the Quality Assessment Tool for Quantitative Studies, developed by the Effective Public Health Practice Project [ 30 ], and recommended for use by the Cochrane Public Health Review Group [ 27 ]. The current adaptation was based on recommendations from several authors [ 31 , 32 ], and has been used in a previous systematic review conducted as part of the SPOTLIGHT project [ 33 ]. This tool was adapted to allow the evaluation of both experimental and observational studies and contains 19 items, guiding the assessment of eight key methodological domains – 1) study design, 2) blinding, 3) representativeness (selection bias), 4) representativeness (withdrawals/dropouts), 5) confounders, 6) data collection, 7) data analysis, and 8) reporting. Each domain is classified as Strong (low risk of bias/high methodological quality), Moderate , or Low (high risk of bias/low methodological quality) methodological quality. A global rating is determined based on the scores of each component (see Additional file 5 for a full description of the Assessment Tool components and scoring system). Two researchers independently rated each of the eight domains and overall quality (EVC, MM). Discrepancies were resolved by consensus.

For studies employing formal tests of mediation, assessment of methodological quality was complemented with a checklist tool developed specifically for mediation analysis by Lubans, Foster, and Biddle [ 34 ], and subsequently adapted by Rhodes and Pfaeffli [ 35 ]. This tool includes 11 questions answered with a yes (1) or no (0) format, whose scores are added to generate a global score. High quality is represented by scores between 9 and 11, moderate quality ranges between 5 and 8, and low quality is considered when scores are below 5 (see Additional file 5 for a full description of the Checklist components and scoring system). Methodological quality of the mediation analyses was also rated by two authors (EVC, MM), with conflicting judgments discussed to reach agreement. Inter-rater agreement was good (Cohen’s kappa = 0.78).

Data synthesis

This review analyzed psychological and self-regulation mediators and predictors of change in body weight or BMI (primary outcome), physical activity, and dietary intake, separately (Note: we will use the term predictors when studies did not test for formal mediation, and mediators when they did). Intervention effects on the outcomes of interest were included in Additional files 2 and 3 . Results were divided according to the length of assessment of the outcomes, into short-term (<12 months from the start of the intervention) and long-term (≥12 months), so that those variables mediating/predicting more sustainable outcomes (the main focus of this review) could be more easily identified. Twelve months has been indicated by an expert panel on obesity as an appropriate threshold between weight loss and the maintenance of the weight lost [ 10 ]. In the synthesis of data derived from studies formally testing mediation, only controlled trials were included, to further strengthen inference regarding intervention effects on mediators and outcomes. In the case of prediction studies (not formally testing mediation), we included both controlled and uncontrolled trials, to capitalize on the (relatively) larger number of studies available, which would otherwise be excluded using the more stringent criteria. Table  1 describes the 35 included studies. In Tables  2 , 3 , 4 , 5 , 6 , and 7 , mediation-specific results are discriminated from the general results, provided that the main goal of this review was the identification of self-regulation mediators in behavior change obesity interventions. The overall results (considering multivariate, bivariate/correlational, and mediation analyses) are also presented in each table (Tables  2 , 3 , 4 , 5 , 6 , and 7 ). A total of 42 mediators/predictors were identified across outcomes. To facilitate data interpretation, considering the very large number of individual variables, these were grouped together by similarity into categories. Categorization was done through the extraction of information from primary studies on the definition and operationalization of the constructs. The following 12 categories were formed: Self-regulatory skills use, Processes of change, Coping mechanisms, Self-efficacy/barriers, Autonomous motivation, Controlled motivation, Decisional balance, Outcomes expectations/beliefs, Body image/physical self-worth, Cognitive restraint, Eating disinhibition, Perceived hunger. Please refer to Additional file 6 for full details regarding the mediators/predictors identified per outcome.

Finally, Tables  2 , 3 , 4 , 5 , 6 , and 7 show, separately for each mediator/predictor, the number of studies that have analyzed it, the number of times it was tested (some of them within the same study), and the number of times a significant effect was found. Results are presented for mediation-specific results and for the overall results.

Study selection

The literature search yielded a total of 1,394 potentially relevant records. Eight additional articles that were identified through manual searches and cross-referencing were added, bringing the total number of potential articles to 1,404. Of these, 770 abstracts were assessed for eligibility (634 duplicates removed). After the initial screening of titles and abstracts, 692 articles were excluded (Figure  1 ). Some articles were excluded for multiple reasons. Thirty-five articles describing 32 unique lifestyle interventions met the eligibility criteria and were therefore included [ 36 - 70 ]. Papers reporting on the same intervention are identified in Additional files 2 and 3 .

Flow diagram of studies.

Study characteristics

The characteristics of included studies are summarized in Table  1 (for further details, see Additional files 2 and 3 ). Most studies (n = 28) were randomized controlled trials, mainly aiming at weight loss or weight loss maintenance (n = 21). Most interventions took place in university (n = 15) or fitness club settings (n = 12), and most lasted more than 6 months (n = 29). However, only 11 trials included a follow-up assessment period and, of these, more than half were shorter than 12 months. Most studies were based on, or at least informed by, one or more theories of behavior change; the most frequent being social cognitive theory (n = 23), the transtheoretical model (n = 5), and self-determination theory (n = 3). Eight interventions were grounded in other theories, including group dynamics theory, problem solving model, theory of planned behavior, health belief model, and self-regulation theory. Four studies did not report using any theoretical framework. Samples were mostly composed of obese individuals (n = 26), aged between 25 to 44 years old (n = 23), and 13 studies targeted women only.

Twenty-six studies evaluated mediators/predictors of weight change, of which 17 reported medium/long-term outcomes; 19 studies evaluated mediators/predictors of physical activity, with 8 of them reporting medium/long-term outcomes; finally, 11 studies investigated dietary intake as the outcome measure, 4 of them in the medium/long-term. Weight-related measurements were performed with calibrated digital scales, and weight changes were expressed in weight change percent from baseline (n = 9), in kilograms (n = 10), as residualized scores regressed on the baseline scores (n = 6), or as BMI changes (n = 3). Regarding physical activity, objective measures were employed in 4 studies (for example, accelerometry, pedometry) and self-reported instruments in 17 studies; of these, 6 used the Seven-Day Physical Activity Recall [ 71 ] and 6 studies used the Godin Leisure-Time Exercise Questionnaire [ 72 ]. Dietary and caloric intake, indirectly assessed through the number of servings, was collected with the Food Intake Questionnaire in most studies (n = 5), followed by the Three-Day Food Records in most of the studies (n = 3), and the Block Food-Frequency Questionnaire (n = 1).

The overall results of the quality assessment can be found in Table  1 and the total quality score for each study in Additional files 2 and 3 (for a detailed classification of each item and study see Additional file 7 ). Regarding the overall methodological quality of the studies, 13 studies were rated as ”strong”, 15 were classified as ”moderate”, and 7 were rated as ”weak”. All included studies scored strong on the Study design , as they were experimental. Thirteen studies were rated as weak regarding Blinding of participants (during recruitment) and outcome assessors, 13 were rated as moderate, 8 as strong, and 1 did not receive a rating, as it was a non-randomized trial. All studies except two (one scored weak and the other scored strong) scored moderate regarding Representativeness (selection bias). Regarding reporting of Withdrawals and dropouts , 5 studies were rated as weak, 16 as moderate, and 14 as strong. Four studies scored weak in the adjustment of analysis for Confounders , 10 scored moderate, and 21 strong. In terms of Data collection tools, 4 studies were rated as weak as they did not provide information on the validity or reliability of the measures used, 11 were classified as moderate, and 17 as strong. Three studies were not rated as they used a larger dataset for which information on psychometric properties of the measures is already provided. All studies scored strong in the use of Appropriate statistical analyses . In terms of Reporting, 30 studies were rated as strong, and 5 studies as moderate.

In addition, studies including formal tests of mediation (n = 10) were classified as of moderate (n = 10) quality on the mediation analysis checklist. None of the studies reported conducting pilot studies to test mediation, and in all except two studies, there was no specific information regarding the power of the analysis to detect mediation. In only six studies were the outcomes controlled for baseline values.

Mediators/predictors tested in studies with weak methodological quality are identified in Tables  2 , 3 , 4 , 5 , 6 , and 7 . Overall, there appeared to be no association between the methodological quality of the studies and the results of the mediation analyses. Only 2 out of the 7 studies with a global weak score reported significant results for all mediator/predictors.

Mediators/predictors of weight control

Of the total number of studies investigating mediators/predictors of weight control (n = 26), 9 looked into short-term outcomes (<12 months) [ 47 - 49 , 51 , 52 , 54 , 57 , 62 , 70 ]. Twenty-one variables, grouped into nine categories, were tested as mediators/predictors of short-term weight control (Table  2 ). None of the studies performed formal tests of mediation. In the overall analyses (in this case, all were multivariate), self-regulation skill use emerged as the most consistent predictor of short-term weight control (consistent with mediation in 92% of the times it was tested [12 times in 6 studies]). Other variables that appear promising as mediators of short-term weight control were higher self-efficacy (and/or lower perceived barriers) and more positive body image, both consistent with mediation in 67% of the times they were tested (a total of 9 and 6 times, respectively). In the case of self-efficacy, 2 (out of 6) studies presented with low methodological quality. Although lower eating disinhibition also appears to find empirical support in non-formal mediation analyses, these results come from a single, weak quality study, and are correlational in nature. There were no other consistent mediators/predictors of short-term weight control.

Seventeen studies investigated potential mediators/predictors of long-term (≥12 months) weight outcomes, the main focus of the review [ 36 , 39 , 40 , 43 - 45 , 55 , 56 , 58 , 59 , 63 - 66 ]. Of these, six were RCTs that included formal tests of mediation [ 36 , 39 , 40 , 43 - 45 ]. Thirty variables, grouped in 12 categories, were tested as potential mediators/predictors (Table  3 ). The variables with stronger empirical support in formal mediation studies were body image, which was significant in all the times it was tested (3 times), and self-regulation skills, which was identified as a mediator in 67% of the times it was tested (2 times out of 3 studies). Self-efficacy was a significant mediator in 2 of the 3 times it was tested. For autonomous motivation and flexible eating restraint, results appear promising but derive from a single study in each case. Results observed in non-mediation analyses were consistent with the most stringent analyses, especially those concerning self-regulation skill use, autonomous motivation, and self-efficacy. For self-regulation skill use, significant effects were found in 83% of the 6 times it was tested, and every time in multivariate analyses. For autonomous motivation, results were consistent with mediation in all cases, but they originate from only two studies. On the other hand, empirical support from non-mediation analyses for other variables like body image and self-efficacy appears comparatively weaker and correlation-based; yet, the number of times each of these variables was tested is substantially higher (34 and 28 times, respectively). Eating disinhibition, which appeared to be an additional predictor in the short-term, does not seem to be consistent in the long-term provided that it was significant only in 38% of the 16 times it was tested. There were no other consistent mediators/predictors of long-term weight control.

Mediators/predictors of physical activity

Of the total number of studies investigating mediators/predictors of physical activity (n = 19), 11 looked into short-term outcomes (less than 6 months beyond the start of the intervention) [ 37 , 46 , 51 - 53 , 60 , 61 , 67 - 70 ]. Of these, only one formally tested mediation [ 37 ]. Fourteen variables, grouped in seven categories, were tested as mediators/predictors of short-term weight control (Table  4 ). Regarding mediation-specific results, body image emerged as a significant mediator only in one of the two times it was tested. In non-mediation studies, stronger empirical support was found for self-regulation skill use, which was significant in 11 of the 13 times it was tested (corresponding to 7 different studies). Body image and self-efficacy appear to be promising as mediators of short-term physical activity, showing significant results in 4 (out of 6) and 10 (out of 15) times they were tested, respectively. No other consistent mediators/predictors of short-term physical activity were identified.

Eight studies analyzed mediators/predictors of long-term physical activity [ 36 , 38 , 41 - 43 , 50 , 64 , 65 ], of which five used formal tests of mediation [ 36 , 38 , 41 - 43 ]. Twenty-three variables, grouped in nine categories, were tested as predictors (Table  5 ). The main predictors of long-term physical activity were autonomous motivation and self-efficacy, considering both mediation-specific analyses and the overall analyses. For autonomous motivation, results from two studies showed that mediation analyses were significant in 83% of the times and overall analyses showed consistency with mediation in 93% of the times (out of 14). For self-efficacy, results originated from 6 different studies. Mediation analyses were significant in 67% of the times self-efficacy was tested (6 times); and in the overall analyses, results were consistent with mediation in 75% of the times (out of 12). Controlled motivation was also consistently unrelated with physical activity outcomes, independent of the type of analyses performed. Finally, self-regulation skill use appears to mediate long-term physical activity in one out of two (formal mediation) and two out of the three (all analyses) times tested, but these results derive from two studies with low methodological quality.

Mediators/predictors of dietary intake

Of the total number of studies (n = 11) investigating mediators/predictors of dietary intake, seven looked into short-term outcomes [ 46 , 53 , 61 , 67 - 70 ] and four into long-term outcomes [ 41 , 50 , 64 , 65 ]. Only one study formally tested mediation [ 41 ]. Seven variables (grouped in three categories) were tested as mediators/predictors of short-term dietary intake, while 12 variables (grouped in seven categories) were tested in the long-term (Tables  6 and 7 ). Self-efficacy/barriers and self-regulation skill use appear promising as mediators of dietary intake in the short-term, both showing results consistent with mediation in 75% of the times they were tested (12 times out of 6 studies for self-efficacy, and 12 times out of 5 studies for self-regulation skills). No consistent mediators/predictors were identified in the longer time frame. Yet, self-efficacy was consistently unrelated with long-term dietary intake, looking less promising as a mediator (results were consistent with mediation only in 2 of the 8 times it was tested).

This review sought to identify the most consistent individual-level mediators of weight change, physical activity, and obesity-related dietary variables, in the context of lifestyle obesity interventions aimed at overweight and obese adults. These mediators or predictors of intervention effects were assessed by self-report, and are thought to represent psychological mechanisms or processes by which interventions affect body weight, through changes in energy-balance related behaviors. Note that this review did not focus on the efficacy of the interventions’ main effects per se. However, mediation mechanisms can be evaluated even in the absence of main significant effects of interventions, particularly in controlled trials [ 20 ].

Special emphasis was given to variables tested as formal mediators of changes in the outcomes of interest, as this provides the best possible inferences regarding causal determinants of behavior change [ 73 ]; to the extent a consistent mediator is identified, it can more confidently be targeted in future interventions of comparable characteristics. Moreover, because it is unlikely that any single factor (self-regulatory or otherwise) by itself will explain a large share of variance of change in complex behaviors such as physical activity and diet (as a result of an intervention), the identification of groups of significant predictors, which can be then discussed in the context of current theories of behavior change, can additionally contribute to understanding the role of theory in health behavior change [ 74 , 75 ].

As in many systematic reviews of behavior change interventions, the diversity of studies available - reflected on a similarly diverse set of independent (and dependent) variables, study designs, measurement methods, populations represented, and so forth - is a substantial limitation. In the present review, the large number of predictors per study, combined with substantial heterogeneity in study length, type and format of interventions (for example, web-based, face to face, group-based), and assessments employed for each variable made the task at hand especially difficult. In this scenario, the fact that several variables were identified as predictors or, in some cases, actual mediators of intervention-related change in weight control and physical activity is encouraging. Specifically, the present review shows that positive changes in body image, in autonomous motivation for physical activity, in self-efficacy (and fewer perceived barriers), and in the use of self-regulation skills (such as self-monitoring) are promising aspects that may explain the variability of results in current lifestyle obesity treatment interventions. Increases in flexible restraint could also be in this group with respect to weight outcomes, but with lower inference. Therefore, these are currently the best evidence-based candidates to target in future individual-level, real-world interventions in this domain.

Some qualifications to the previous conclusions are of note. First, for short-term results, formal tests of mediation were only reported for one of the outcomes of interest (physical activity) and taking into account only two mediators (body image/self-worth and self-efficacy/barriers). Second, there are currently too few studies using dietary variables as outcomes to allow us to draw meaningful conclusions, and only one study tested formal mediation for both time frames. Third, the external validity of some of the reported findings, such as regarding self-regulation skills and autonomous motivation, may be limited, because these findings were derived from few studies conducted by a small number of research groups, using similar study designs.

Body image appeared important as a mechanism leading to change in body weight in several studies. Body image is a multidimensional concept [ 76 ] that depicts attitudes, perceptions, and in some cases behaviors associated with mental representations of one’s body (or some of its parts) [ 77 , 78 ]. Poor body image often reflects a high level of concern with body weight or shape, what is known as dysfunctional investment in body image, when body esteem occupies an excessive role as a determinant of overall self-esteem [ 79 ]. Previous reviews [ 2 , 22 ] have identified poor body image as a predictor of less success at body weight loss (or, conversely, better body image as a positive factor in obesity treatment interventions). Potential reasons for this association range from excessive psychological pressure leading to more rigid and inconsistent eating regulation [ 80 - 82 ] - poor body image being associated with a history of failed attempts and thus being a marker for other physiological, psychological, or socio-environmental risk factors for weight gain/regain [ 83 , 84 ] - to motivational factors in which external pressures and goals predominate but tend not to produce behavior change in consistent or healthy ways (for example, wanting to be thin for reasons related to social comparison and perceived desirability) [ 85 - 87 ].

Autonomous motivation, a concept derived from self-determination theory (SDT, [ 88 ]), generally indicates the degree to which individuals self-endorse, feel that they have a choice about, and attribute deeply reflected value to a certain behavior. In contrast with the most common quantitative view of motivation (how much?), the level of autonomy represents a qualitative analysis of people’s psychological energy to act, which is perceived as internal (reflecting a sense of “ownership” over the behavior). Autonomous motivation is often associated with goals such as pursuing positive personal challenges, attaining/preserving health and well-being, social affiliation, personal development, and self-expression [ 89 ]. Additionally, because self-determined, well-internalized behaviors are associated with the satisfaction of the needs for autonomy, competence, and relatedness - and with the feelings of internal coherence and well-being that are thought to emerge from those experiences - this provides an explanation for the behavior to be pursued consistently [ 89 ]. A recent meta-analysis [ 90 ] and other reviews provide empirical support for both the SDT motivation model and the association of autonomous motivation with health behavior change in different areas [ 91 ].

Self-efficacy and perceived barriers are common variables in several theoretical frameworks concerned with health behaviors [ 92 , 93 ]. Self-efficacy measures one’s confidence to successfully implement a course of action by successfully organizing internal and external resources [ 94 ]. Although efficacy can be assessed towards other aspects of behavior regulation, it is commonly conceptualized and assessed as “barriers efficacy” or confidence to overcome internal or external obstacles that may stand in the way of one’s actions. Indeed, the correlation between self-efficacy and perceived barriers is usually high [ 56 ] (which explains our decision to group these variables in the same category). Although conceptual differences exist, self-efficacy is often equated to the concept of perceived behavior control (from the theory of planned behavior) or perceived competence (as used in self-determination theory). In practical and simple terms, enhancing confidence and competence about a given health behavior appears to be helpful in overcoming barriers - namely in initial stages of adoption - and is often a first step to increase and improve motivation for change.

Flexible eating restraint involves regulating one’s food intake so that no particular behavior is forbidden and thus subject to rigid control and scrutiny [ 95 ]. Flexible restraint is generally associated with less internal pressure to diet and a more gradual understanding of the diet’s impact on energy balance. It stands in opposition to rigid restraint [ 96 ]. Although, in the past, cognitive restraint was measured as a unified concept, its separation into flexible and rigid dimensions is increasingly frequent in obesity studies and has proven useful in understanding diet and weight regulation, particularly in the long-term. For example, we found that flexible, but not rigid or total restraint, mediated 24-month weight loss in overweight women [ 39 ] and, in the present review, results for the total restraint scale and the flexible scale also differed, as in other studies [ 97 ]. More broadly, psychological flexibility appears to predict health and psychological well-being [ 97 ], is thought to reflect more committed, values-based goal pursuit [ 98 , 99 ] and is considered a hallmark of self-determination [ 89 ], factors which may help explain successful health behavior self-regulation.

Finally, the use of certain self-regulation skills, for instance, monitoring weight, diet, and activity, as well as employing goal setting and planning techniques, was also identified as a relatively consistent predictor of successful outcomes, most especially in the shorter-term analyses. In brief, some of these skills may be important for people to ultimately act on their positive intentions. Sometimes associated with self-regulation theories ( cf . [ 100 ]) these variables are more skill-based (in some cases, they are discrete behaviors in themselves) and somewhat different than the previous set of predictors, more intrapsychic. Notably, recent behavior change models focusing on the “intention-behavior gap” (see, for example, [ 7 , 101 , 102 ]) make the distinction between motivational and implementation phases (sometimes referred to as “volitional” or “post-motivational”), with self-regulation skills reviewed in the present study falling in the latter phase [ 103 ]. Results from the present review suggest that some combination of motivational and implementation factors is important. Although this needs confirmation, there is some indication that the latter may be especially useful in early stages of behavioral adoption, whereas motivational factors may be operative along the entire continuum from adoption to maintenance, as highlighted recently in a separate study [ 104 ].

In looking at the collective findings from the present analysis, the temptation to interpret them in an integrative way is unavoidable. In principle, there should be “a logic” as to why this set of predictors emerged and not a different one, even considering the intrinsic limitations of the available data (see below). In this exercise, we are informed by our own research, for instance, linking improved eating regulation, including flexible eating, with improved body image [ 105 ] and with exercise autonomous motivation [ 18 ] and also by other studies. For example, recently, in a large dataset of women in New Zealand, autonomous motivation for eating was associated with less binge eating and slower speed of eating (and a much healthier diet), indicative of improved eating self-regulation [ 106 ]. The literature looking at relations between body image and eating behavior is also fertile in suggesting a close association between improved body image, improved eating regulation, and better weight control (see, for example, [ 55 , 87 ]). In this respect, an attempt was recently made to provide an integrative view of eating regulation and weight maintenance, which also includes an explanation for the etiology and role of body image concerns and disordered eating, while considering motivational and self-regulatory aspects [ 80 ]. It links goals (such as appearance versus health focus) and the predominant approach to eating regulation (such as rigid versus flexible restraint; focus on quality versus quantity) with the satisfaction of the needs for competence, autonomy, and relatedness, resulting in more or less adaptive diet and weight regulation (see Figures one and two in [ 80 ] for more details). The evidence from other recent systematic reviews and meta-analyses, showing that more autonomous forms of health behavior regulation, in physical activity [ 91 ], weight control [ 2 ], and in health more generally [ 90 ] are predictive of better adherence and improved outcomes, is also consistent with the relationships found in the present study.

While some limitations of the present work have been presented above, others need to be considered when interpreting the findings of this review. The large heterogeneity in the study-specific mediation methods and estimates reported in the primary studies prevented us from deriving a single comparable estimate for each variable and reporting on the pooled magnitude of mediation effects. This variability, as well as the limited number of studies for each mediator, did not recommend the use of meta-analytical techniques to pool data across studies. In this review, we used a narrative synthesis approach including vote counting of the number of significant mediation effects for a given variable in relation to all tests of mediation available for that variable. Although this method is not as robust as other quantitative approaches to synthesize data, it provides a reasonably good indication of whether that variable can be identified as a formal mediator (or a variable consistent with mediation) of each intervention, for each specific outcome. It should also be considered that in the primary studies included in this review, statistical significance of the mediation effects was typically the parameter used to infer that a given variable mediated the intervention effect.

Some studies were characterized by poor methodological quality, and none of the studies employing formal mediation analysis presented strong methodological quality. Nonetheless, for most mediators we did not find an association between the methodological quality of the studies and the direction/strength of the effects reported. As exceptions, we did find that in the analyses in which eating disinhibition had consistent significant effects, this was tested mostly in studies of poor quality. A similar result was found for self-regulation skills for the long-term effects in physical activity and dietary intake. Future reviews would benefit from sensitivity analyses. The diversity of outcome measures, especially for physical activity, is also a limitation, as different types of physical activity may be predicted by different factors. The fact that the coding of study characteristics was based on the description provided in the articles is also limiting, given that in many cases these descriptions did not provide enough information regarding mediation analysis, which measures were used, or the content of the interventions. Future studies should provide more detail on the content of the interventions and self-regulation factors addressed to facilitate data interpretation and inference. The choice of the year 2000 to start our search was largely arbitrary and could be seen as a limitation. Finally, the inclusion of non-controlled trials in some of the analyses could be viewed as a limiting factor; on balance, we found this an acceptable compromise (for non-mediation studies only) against the prospect of altogether excluding several studies from this review.

These limitations notwithstanding, this study identified a small number of intervention-related aspects with supporting evidence for an important role played in the difficult path of successful weight control. Since all evidence was derived from intervention studies and independent variables were analyzed as to their mediating position in the behavioral causal chain (although with variable levels of inference), we believe this is a first step leading to their formal inclusion in recommendations for lifestyle programs aiming at weight control. In practical terms, this could mean that strategies or “behavior change techniques” [ 107 ] identified as the most effective to specifically change these variables (for example, self-efficacy [ 108 ] or autonomous motivation [ 109 ]) would be integrated into future interventions in a widespread fashion, and that health professionals would be appropriately trained on how to target them regularly in their practices. It could also mean that bedside instruments (such as brief questionnaires or interview items) would be made available for professionals to quickly assess their patients for these variables (for example, to assess their body image or level of self-regulation skill use [ 1 , 110 ]) and tailor interventions to the most relevant targets for each person. In the area of motivation enhancement, the techniques and instruments used in motivational interviewing [ 111 , 112 ] are a good example of such potential application in medicine and health care.

In conclusion, based on the scientific literature to date, autonomous motivation, self-efficacy, and self-regulation skill use emerge as the most promising individual-level mediators of positive weight outcomes and increased physical activity. For long-term weight control, promoting a positive body image and flexible eating control may also be important. These aspects represent potential entry points for future lifestyle obesity interventions in adults.

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Acknowledgements

This work is part of the SPOTLIGHT project and is funded by the Seventh Framework Program (CORDIS FP7) of the European Commission, HEALTH (FP7-HEALTH-2011-two-stage), Grant agreement no. 278186. The content of this article reflects only the authors’ views, and the European Commission is not liable for any use that may be made of the information contained therein.

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This systematic review is reported in accordance with the PRISMA statement for reporting systematic reviews [ 21 ].

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Pedro J Teixeira, Eliana V Carraça & Marta M Marques

European Centre on Health of Societies in Transition, London School of Hygiene and Tropical Medicine, London, UK

Harry Rutter

Department of Nutrition Pitié-Salpetrière (AP-HP), Université Pierre et Marie Curie-Paris 6, Paris, France

Jean-Michel Oppert

UREN (Nutritional Epidemiology Research Unit), Université Paris 13, Sorbonne Paris Cité, Inserm (U557), Inra (U1125), Cnam, F-93017, Bobigny, France

Department of Movement and Sport Sciences, Ghent University, Ghent, Belgium

Ilse De Bourdeaudhuij

Department of General Practice and Elderly Care, The EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands

Jeroen Lakerveld

Department of Epidemiology and Biostatistics, The EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands

Johannes Brug

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Correspondence to Pedro J Teixeira .

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Competing interests.

The authors declare that they have no competing interests.

Authors’ contributions

PJT, JB, JL, JMO, HR, and IB conceived the study, and PJT and EVC developed a systematic review protocol. EVC and MM conducted the literature search and selected the studies based on the title and the abstract. EVC and MM extracted and coded the data from all studies. Study outcomes were summarized by PJT, EVC, and MM. They wrote the initial draft of the manuscript, and JB, JL, JMO, HR, and IB made significant revisions and contributions. All authors read and approved the final manuscript.

Additional files

Additional file 1:.

An Example of the Conducted Search (Medline).

Additional file 2:

Characteristics of Included Studies With Formal Mediation Analyses.

Additional file 3:

Characteristics of Included Studies Without Formal Mediation Analyses.

Additional file 4:

Indirect effects’ estimates in studies with formal mediation analysis.

Additional file 5:

EPHPP Quality Assessment Tool (adapted by the SPOTLIGHT Consortium).

Additional file 6:

Complete results for weight change, physical activity and dietary behaviors.

Additional file 7:

Consensus Ratings of Methodological Study Quality.

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Teixeira, P.J., Carraça, E.V., Marques, M.M. et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 13 , 84 (2015). https://doi.org/10.1186/s12916-015-0323-6

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  • Lifestyle interventions
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The lived experience of people with obesity: study protocol for a systematic review and synthesis of qualitative studies

  • Emma Farrell   ORCID: orcid.org/0000-0002-7780-9428 1 ,
  • Marta Bustillo 2 ,
  • Carel W. le Roux 3 ,
  • Joe Nadglowski 4 ,
  • Eva Hollmann 1 &
  • Deirdre McGillicuddy 1  

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Obesity is a prevalent, complex, progressive and relapsing chronic disease characterised by abnormal or excessive body fat that impairs health and quality of life. It affects more than 650 million adults worldwide and is associated with a range of health complications. Qualitative research plays a key role in understanding patient experiences and the factors that facilitate or hinder the effectiveness of health interventions. This review aims to systematically locate, assess and synthesise qualitative studies in order to develop a more comprehensive understanding of the lived experience of people with obesity.

This is a protocol for a qualitative evidence synthesis of the lived experience of people with obesity. A defined search strategy will be employed in conducting a comprehensive literature search of the following databases: PubMed, Embase, PsycInfo, PsycArticles and Dimensions (from 2011 onwards). Qualitative studies focusing on the lived experience of adults with obesity (BMI >30) will be included. Two reviewers will independently screen all citations, abstracts and full-text articles and abstract data. The quality of included studies will be appraised using the critical appraisal skills programme (CASP) criteria. Thematic synthesis will be conducted on all of the included studies. Confidence in the review findings will be assessed using GRADE CERQual.

The findings from this synthesis will be used to inform the EU Innovative Medicines Initiative (IMI)-funded SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) study. The objective of SOPHIA is to optimise future obesity treatment and stimulate a new narrative, understanding and vocabulary around obesity as a set of complex and chronic diseases. The findings will also be useful to health care providers and policy makers who seek to understand the experience of those with obesity.

Systematic review registration

PROSPERO CRD42020214560 .

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Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health and quality of life, increases the risk of long-term medical complications and reduces lifespan [ 1 ]. Operationally defined in epidemiological and population studies as a body mass index (BMI) greater than or equal to 30, obesity affects more than 650 million adults worldwide [ 2 ]. Its prevalence has almost tripled between 1975 and 2016, and, globally, there are now more people with obesity than people classified as underweight [ 2 ].

Obesity is caused by the complex interplay of multiple genetic, metabolic, behavioural and environmental factors, with the latter thought to be the proximate factor which enabled the substantial rise in the prevalence of obesity in recent decades [ 3 , 4 ]. This increased prevalence has resulted in obesity becoming a major public health issue with a resulting growth in health care and economic costs [ 5 , 6 ]. At a population level, health complications from excess body fat increase as BMI increases [ 7 ]. At the individual level, health complications occur due to a variety of factors such as distribution of adiposity, environment, genetic, biologic and socioeconomic factors [ 8 ]. These health complications include type 2 diabetes [ 9 ], gallbladder disease [ 10 ] and non-alcoholic fatty liver disease [ 11 ]. Excess body fat can also place an individual at increased cardiometabolic and cancer risk [ 12 , 13 , 14 ] with an estimated 20% of all cancers attributed to obesity [ 15 ].

Although first recognised as a disease by the American Medical Association in 2013 [ 16 ], the dominant cultural narrative continues to present obesity as a failure of willpower. People with obesity are positioned as personally responsible for their weight. This, combined with the moralisation of health behaviours and the widespread association between thinness, self-control and success, has resulted in those who fail to live up to this cultural ideal being subject to weight bias, stigma and discrimination [ 17 , 18 , 19 ]. Weight bias, stigma and discrimination have been found to contribute, independent of weight or BMI, to increased morbidity or mortality [ 20 ].

Thomas et al. [ 21 ] highlighted, more than a decade ago, the need to rethink how we approach obesity so as not to perpetuate damaging stereotypes at a societal level. Obesity research then, as now, largely focused on measurable outcomes and quantifiable terms such as body mass index [ 22 , 23 ]. Qualitative research approaches play a key role in understanding patient experiences, how factors facilitate or hinder the effectiveness of interventions and how the processes of interventions are perceived and implemented by users [ 24 ]. Studies adopting qualitative approaches have been shown to deliver a greater depth of understanding of complex and socially mediated diseases such as obesity [ 25 ]. In spite of an increasing recognition of the integral role of patient experience in health research [ 25 , 26 ], the voices of patients remain largely underrepresented in obesity research [ 27 , 28 ].

Systematic reviews and syntheses of qualitative studies are recognised as a useful contribution to evidence and policy development [ 29 ]. To the best of the authors’ knowledge, this will be the first systematic review and synthesis of qualitative studies focusing on the lived experience of people with obesity. While systematic reviews have been carried out on patient experiences of treatments such as behavioural management [ 30 ] and bariatric surgery [ 31 ], this review and synthesis will be the first to focus on the experience of living with obesity rather than patient experiences of particular treatments or interventions. This focus represents a growing awareness that ‘patients have a specific expertise and knowledge derived from lived experience’ and that understanding lived experience can help ‘make healthcare both effective and more efficient’ [ 32 ].

This paper outlines a protocol for the systematic review of qualitative studies based on the lived experience of people with obesity. The findings of this review will be synthesised in order to develop an overview of the lived experience of patients with obesity. It will look, in particular, at patient concerns around the risks of obesity and their aspirations for response to obesity treatment.

The review protocol has been registered within the PROSPERO database (registration number: CRD42020214560) and is being reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement [ 33 , 34 ] (see checklist in Additional file  1 ).

Information sources and search strategy

The primary source of literature will be a structured search of the following electronic databases (from January 2011 onwards—to encompass the increase in research focused on patient experience observed over the last 10 years): PubMed, Embase, PsycInfo, PsycArticles and Dimensions. There is no methodological agreement as to how many search terms or databases out to be searched as part of a ‘good’ qualitative synthesis (Toye et al. [ 35 ]). However, the breadth and depth of the search terms, the inclusion of clinical and personal language and the variety within the selected databases, which cover areas such as medicine, nursing, psychology and sociology, will position this qualitative synthesis as comprehensive. Grey literature will not be included in this study as its purpose is to conduct a comprehensive review of peer-reviewed primary research. The study’s patient advisory board will be consulted at each stage of the review process, and content experts and authors who are prolific in the field will be contacted. The literature searches will be designed and conducted by the review team which includes an experienced university librarian (MB) following the methodological guidance of chapter two of the JBI Manual for Evidence Synthesis [ 36 ]. The search will include a broad range of terms and keywords related to obesity and qualitative research. A full draft search strategy for PubMed is provided in Additional file  2 .

Eligibility criteria

Studies based on primary data generated with adults with obesity (operationally defined as BMI >30) and focusing on their lived experience will be eligible for inclusion in this synthesis (Table  1 ). The context can include any country and all three levels of care provision (primary, secondary and tertiary). Only peer-reviewed, English language, articles will be included. Studies adopting a qualitative design, such as phenomenology, grounded theory or ethnography, and employing qualitative methods of data collection and analysis, such as interviews, focus groups, life histories and thematic analysis, will be included. Publications with a specific focus, for example, patient’s experience of bariatric surgery, will be included, as well as studies adopting a more general view of the experience of obesity.

Screening and study selection process

Search results will be imported to Endnote X9, and duplicate entries will be removed. Covidence [ 38 ] will be used to screen references with two reviewers (EF and EH) removing entries that are clearly unrelated to the research question. Titles and abstracts will then be independently screened by two reviewers (EF and EH) according to the inclusion criteria (Table  1 ). Any disagreements will be resolved through a third reviewer (DMcG). This layer of screening will determine which publications will be eligible for independent full-text review by two reviewers (EF and EH) with disagreements again being resolved by a third reviewer (DMcG).

Data extraction

Data will be extracted independently by two researchers (EF and EH) and combined in table format using the following headings: author, year, title, country, research aims, participant characteristics, method of data collection, method of data analysis, author conclusions and qualitative themes. In the case of insufficient or unclear information in a potentially eligible article, the authors will be contacted by email to obtain or confirm data, and a timeframe of 3 weeks to reply will be offered before article exclusion.

Quality appraisal of included studies

This qualitative synthesis will facilitate the development of a conceptual understanding of obesity and will be used to inform the development of policy and practice. As such, it is important that the studies included are themselves of suitable quality. The methodological quality of all included studies will be assessed using the critical appraisal skills programme (CASP) checklist, and studies that are deemed of insufficient quality will be excluded. The CASP checklist for qualitative research comprises ten questions that cover three main issues: Are the results of the study under review valid? What are the results? Will the results help locally? Two reviewers (EF and EH) will independently evaluate each study using the checklist with a third and fourth reviewer (DMcG and MB) available for consultation in the event of disagreement.

Data synthesis

The data generated through the systematic review outlined above will be synthesised using thematic synthesis as described by Thomas and Harden [ 39 ]. Thematic synthesis enables researchers to stay ‘close’ to the data of primary studies, synthesise them in a transparent way and produce new concepts and hypotheses. This inductive approach is useful for drawing inference based on common themes from studies with different designs and perspectives. Thematic synthesis is made up of a three-step process. Step one consists of line by line coding of the findings of primary studies. The second step involves organising these ‘free codes’ into related areas to construct ‘descriptive’ themes. In step three, the descriptive themes that emerged will be iteratively examined and compared to ‘go beyond’ the descriptive themes and the content of the initial studies. This step will generate analytical themes that will provide new insights related to the topic under review.

Data will be coded using NVivo 12. In order to increase the confirmability of the analysis, studies will be reviewed independently by two reviewers (EF and EH) following the three-step process outlined above. This process will be overseen by a third reviewer (DMcG). In order to increase the credibility of the findings, an overview of the results will be brought to a panel of patient representatives for discussion. Direct quotations from participants in the primary studies will be italicised and indented to distinguish them from author interpretations.

Assessment of confidence in the review findings

Confidence in the evidence generated as a result of this qualitative synthesis will be assessed using the Grading of Recommendations Assessment, Development and Evaluation Confidence in Evidence from Reviews of Qualitative Research (GRADE CERQual) [ 40 ] approach. Four components contribute to the assessment of confidence in the evidence: methodological limitations, relevance, coherence and adequacy of data. The methodological limitations of included studies will be examined using the CASP tool. Relevance assesses the degree to which the evidence from the primary studies applies to the synthesis question while coherence assesses how well the findings are supported by the primary studies. Adequacy of data assesses how much data supports a finding and how rich this data is. Confidence in the evidence will be independently assessed by two reviewers (EF and EH), graded as high, moderate or low, and discussed collectively amongst the research team.

Reflexivity

For the purposes of transparency and reflexivity, it will be important to consider the findings of the qualitative synthesis and how these are reached, in the context of researchers’ worldviews and experiences (Larkin et al, 2019). Authors have backgrounds in health science (EF and EH), education (DMcG and EF), nursing (EH), sociology (DMcG), philosophy (EF) and information science (MB). Prior to conducting the qualitative synthesis, the authors will examine and discuss their preconceptions and beliefs surrounding the subject under study and consider the relevance of these preconceptions during each stage of analysis.

Dissemination of findings

Findings from the qualitative synthesis will be disseminated through publications in peer-reviewed journals, a comprehensive and in-depth project report and presentation at peer-reviewed academic conferences (such as EASO) within the field of obesity research. It is also envisaged that the qualitative synthesis will contribute to the shared value analysis to be undertaken with key stakeholders (including patients, clinicians, payers, policy makers, regulators and industry) within the broader study which seeks to create a new narrative around obesity diagnosis and treatment by foregrounding patient experiences and voice(s). This synthesis will be disseminated to the 29 project partners through oral presentations at management board meetings and at the general assembly. It will also be presented as an educational resource for clinicians to contribute to an improved understanding of patient experience of living with obesity.

Obesity is a complex chronic disease which increases the risk of long-term medical complications and a reduced quality of life. It affects a significant proportion of the world’s population and is a major public health concern. Obesity is the result of a complex interplay of multiple factors including genetic, metabolic, behavioural and environmental factors. In spite of this complexity, obesity is often construed in simple terms as a failure of willpower. People with obesity are subject to weight bias, stigma and discrimination which in themselves result in increased risk of mobility or mortality. Research in the area of obesity has tended towards measurable outcomes and quantitative variables that fail to capture the complexity associated with the experience of obesity. A need to rethink how we approach obesity has been identified—one that represents the voices and experiences of people living with obesity. This paper outlines a protocol for the systematic review of available literature on the lived experience of people with obesity and the synthesis of these findings in order to develop an understanding of patient experiences, their concerns regarding the risks associated with obesity and their aspirations for response to obesity treatment. Its main strengths will be the breadth of its search remit—focusing on the experiences of people with obesity rather than their experience of a particular treatment or intervention. It will also involve people living with obesity and its findings disseminated amongst the 29 international partners SOPHIA research consortium, in peer reviewed journals and at academic conferences. Just as the study’s broad remit is its strength, it is also a potential challenge as it is anticipated that searchers will generate many thousands of results owing to the breadth of the search terms. However, to the best of the authors’ knowledge, this will be the first systematic review and synthesis of its kind, and its findings will contribute to shaping the optimisation of future obesity understanding and treatment.

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Abbreviations

Body mass index

Critical appraisal skills programme

Grading of Recommendations Assessment, Development and Evaluation Confidence in Evidence from Reviews of Qualitative Research

Innovative Medicines Initiative

Medical Subject Headings

Population, phenomenon of interest, context, study type

Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy

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Acknowledgements

Any amendments made to this protocol when conducting the study will be outlined in PROSPERO and reported in the final manuscript.

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 875534. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF and Obesity Action Coalition. The funding body had no role in the design of the study and will not have a role in collection, analysis and interpretation of data or in writing the manuscript.

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Marta Bustillo

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Contributions

EF conceptualised and designed the protocol with input from DMcG and MB. EF drafted the initial manuscript. EF and MB defined the concepts and search items with input from DmcG, CleR and JN. MB and EF designed and executed the search strategy. DMcG, CleR, JN and EH provided critical insights and reviewed and revised the protocol. All authors have approved and contributed to the final written manuscript.

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Supplementary Information

Additional file 1:..

PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol*.

Additional file 2: Table 1

. Search PubMed search string.

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Farrell, E., Bustillo, M., le Roux, C.W. et al. The lived experience of people with obesity: study protocol for a systematic review and synthesis of qualitative studies. Syst Rev 10 , 181 (2021). https://doi.org/10.1186/s13643-021-01706-5

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systematic review obesity interventions

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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 &
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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.

Conclusions

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 ].

Limitations

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.

Abbreviations

Institute of Medicine

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Socioeconomic status

United States

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Acknowledgments

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.

The authors received no funding for this study and have no relevant financial relationships to disclose.

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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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Influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors in children with low socioeconomic status: a systematic review

  • Friederike Butscher   ORCID: orcid.org/0000-0001-8204-6309 1 ,
  • Jan Ellinger 1 ,
  • Monika Singer 1 &
  • Christoph Mall 1  

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Health inequity (HI) remains a major challenge in public health. Improving the health of children with low socioeconomic status (SES) can help to reduce overall HI in children. Childhood obesity is a global problem, entailing several adverse health effects. It is crucial to assess the influencing factors for adoption, implementation, and sustainment of interventions. This review aims to identify articles reporting about influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors in children with low SES. It aims to critically appraise the articles’ quality, assess influencing factors, categorize and evaluate them, and to discuss possible implications.

A systematic search was conducted in 7 databases with the following main inclusion criteria: (1) school-based interventions and (2) target group aged 5–14 years. The Consolidated Framework for Implementation Research, its five domains (intervention characteristics, inner setting, outer setting, characteristics of individuals, process) along with 39 categories within these domains were used as deductive category system for data analysis. We grouped the articles with regard to the characteristics of the interventions in simple and complex interventions. For each domain, and for the groups of simple and complex interventions, the most commonly reported influencing factors are identified.

In total, 8111 articles were screened, and 17 met all eligibility criteria. Included articles applied mixed methods ( n =11), qualitative ( n =5), and quantitative design ( n =1). Of these, six were considered to report simple interventions and eleven were considered to report complex interventions. In total, 301 influencing factors were assessed. Aspects of the inner setting were reported in every study, aspects of the outer setting were the least reported domain. In the inner setting , most reported influencing factors were time ( n =8), scheduling ( n =6), and communication ( n =6).

This review found a wide range of influencing factors for implementation and contributes to existing literature regarding health equity as well as implementation science. Including all stakeholders involved in the implementation process and assessing the most important influencing factors in the specific setting, could enhance implementation and intervention effectiveness. More empirical research and practical guidance are needed to promote obesity prevention behaviors among children with low SES.

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Contributions to the literature

• The in-depth application of the Consolidated Framework for Implementation Research (CFIR) in this review facilitates comparability and transferability between findings of this review and other research findings.

• This review places a focus on the implementation of obesity prevention interventions for children with low socioeconomic status, thus expanding the literature related to health equity.

• The synthesis of the included papers in this review provides guidance that specifically addresses intervention developers, school staff, and researchers, respectively, and can therefore help to inform the selection of implementation strategies and planning.

“Implementation Science could, quite literally, put health equity back on the fast track.” Beryne Odeny [ 1 ]

Health inequalities are the difference between the health statuses of groups of people, they exist within and between populations [ 2 ]. One example is the difference in life expectancy within a population (e.g., between men and women) as well as between populations (e.g., between women in one population and another) [ 2 ]. Determinants of health include for example, fixed determinants like genes and age, and modifiable determinants like the individual lifestyle, social networks and broader aspects like the cultural, social, and physical environment [ 3 ]. Furthermore, interactions between determinants can occur, as for example, the wider sociocultural environment is linked to social norms, and social norms impact in turn individual lifestyles [ 3 ]. The social determinants of health, all (theoretically) modifiable determinants, are a powerful driver for health (in)equalities [ 4 , 5 , 6 ]. Under certain conditions, we no longer speak of health inequalities but of health inequity (HI) [ 7 , 8 ]. If health inequalities are avoidable and unfair [ 9 ], then we speak of health inequity. HI arises due to differences in opportunity, more specifically the unequal distribution of the social determinants of health, such as income, wealth, and access to health care [ 6 , 10 ]. Therefore, HI is social injustice in health [ 8 ] or vice versa: “Equity in health means that people’s needs guide the distribution of opportunities for well-being” [ 10 ].

HI follows a social gradient, as groups with a low socioeconomic status (SES) have poorer health (e.g., higher mortality and morbidity) than groups with high SES [ 6 , 7 , 11 ]. SES is a commonly used proxy for social determinants of health [ 12 , 13 ], as SES is a multidimensional concept and incorporates several socioeconomic factors. It can be described by past or current income, family wealth, educational level, occupation, and social standing within the community [ 14 ].

It is especially important to protect children’s health, as they have less control over their health and the circumstances influencing it than adults [ 15 ], as adults form the environment children live in (e.g., at home or school). Negative health influences in childhood can lead to health consequences throughout life [ 16 , 17 ]. Being overweight in childhood, for example, is associated with also being overweight as an adult [ 18 ], and diverse adverse health effects, such as cardiovascular diseases or mental disorders, can result from overweight and obesity in childhood [ 19 , 20 ]. The prevalence of obesity in children is increasing globally [ 21 , 22 ], and therefore, it is important to develop and implement interventions addressing childhood obesity.

In industrialized countries, childhood obesity exhibits HI: This is reflected, for example, in the fact that a low SES is associated with higher rates of obesity among children [ 23 ].

Furthermore, due to societal processes, low SES and poor health implicate and maintain each other [ 3 , 8 , 24 , 25 ]. In their model of child health inequalities, Pearce et al. [ 15 ] described those societal processes and showed that low SES and low child health status are in a mutually reinforcing cycle, conditioning and maintaining each other.

How child health status and SES condition and maintain each other, and therefore how HI is maintained, is described with five mechanisms. (I) Social stratification refers to all social structures that influence the SES of children (e.g., growing up in a low-income household compared to a high-income household). (II) Differential exposure describes how children living under different SES are exposed to different levels of health risks (e.g., living in a noisy/polluted area, because rents are lower in such areas). (III) Differential vulnerability means that exposure to a greater number of health risks and their interaction may increase vulnerability to adverse health outcomes (e.g., job loss of parents causes more mental health burden in a low-income household compared to a high-income household). The (III) differential vulnerability caused by the greater number of health risks influences the rest of the life, and thus also the future SES. This influence of (III) differential vulnerability on SES is referred to as (IV) differential consequences (e.g., child in a noisy household with mentally stressed parents could lead to a challenging learning atmosphere and could result in bad grades in school). Through these described four mechanisms, (V) further social stratification emerges (e.g., bad grades in school lead to a lower-payed job). From the mechanism of (V) further social stratification , it becomes clear that the SES not only has an impact on health, but that low SES and low child health status are in a mutually reinforcing cycle, conditioning and maintaining each other [ 15 ].

One suitable entry point to address (II) differential exposure and (III) differential vulnerability are health-promoting interventions. Health-promoting interventions can mitigate (IV) differential consequences and therefore mitigate (V) further social stratification. Health-promoting interventions that improve the health status of children can therefore help to reduce HI in children [ 15 ].

Many health-promoting interventions take place in schools, as in the school setting almost all children in society can be reached [ 26 ]. This also applies to obesity prevention interventions [ 27 ], for now moderate evidence has been found for school-based combined diet and physical activity (PA) interventions [ 28 , 29 , 30 ]. Furthermore, it is important to implement those interventions in real-world settings, as the implementation of an intervention influences its effectiveness [ 31 , 32 ]. Improving the reach and the adoption, delivery, and sustainment of effective interventions is the aim of implementation science [ 33 ]. Because several factors influence the speed and extent of the adoption, uptake, and use of an intervention (e.g., characteristics of the intervention like complexity or contextual factors like built environment) [ 34 ], a suggested first step in the implementation process is the identification of those influencing factors in order to address them [ 35 ].

The influencing factors for the implementation of interventions have been assessed in the school setting, both for PA-promoting interventions [ 32 ] and for interventions to promote PA and reducing sedentary behavior [ 36 ]. Barriers and facilitators were assessed for the sustainment of health behavior interventions in schools and childcare settings [ 37 ], for PA during school lessons [ 38 ], and for the provision of fruit and vegetable in kindergartens and schools [ 39 ].

Those reviews [ 32 , 36 , 37 , 38 , 39 ] present important results, but none of those reviews distinguished between different SES, although this factor is an important differentiator every study should take into account to approach health equity [ 40 ]. Furthermore, none of the existing reviews assessed the implementation of interventions addressing the combination of the two leading domains of behaviors in obesity development, namely, PA and nutrition [ 41 ], in the school setting. From these considerations, it seems essential that factors influencing the implementation of school-based interventions be systematically assessed to promote obesity prevention behaviors for children with low SES. These findings can help improving the understanding of specific needs, to guide practice, to improve implementation, and therefore, to enhance the sustainment of effective interventions. Effective interventions can contribute to prevent obesity, increase the health of children (with low SES) and reduce further social stratification . This could contribute to reducing HI in children. Therefore, this review aimed to identify articles reporting about influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors for children with low SES, to assess the methodological quality of the identified articles, to categorize and evaluate reported influencing factors, to analyze differences of reported influencing factors regarding simple and complex interventions, and to discuss possible implications.

We identified, critically appraised, and summarized the published evidence on influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors for children with low SES by means of a systematic review in accordance to PRISMA guidelines [ 42 ], the PRISMA Checklist can be found in the Additional file 1 . This review was previously registered at PROSPERO (ID: CRD42021281209).

Information sources and searches

The databases Scopus, PubMed, ERIC, SportDiscus, PsychArticles, Education Source, and SocINDEX were searched for relevant articles. The terms shown in Table 1 were used to construct the search term, following database specifications (see Additional file 2 ). There were no limitations with respect to the publication date of the articles, as no systematic review with the same aim had previously been conducted. The database search was completed on July 2, 2021. To ensure actuality, we conducted an update search on March 29, 2023.

Eligibility criteria

We adopted the following eligibility criteria:

Regarding the design, the article had to be an implementation evaluation or process evaluation study, or a hybrid process-effectiveness study as described by Curran et al. [ 43 ].

Qualitative, quantitative and mixed methods studies were eligible for inclusion.

The article had to investigate an intervention promoting obesity prevention behaviors (e.g., promotion of PA, promotion of health nutrition).

The intervention reported had to address children aged 5–14 years exclusively. The youngest age for beginning primary school is 5 years [ 44 ] and 14 years is the last year of childhood, before entering the youth category [ 45 ].

The intervention reported had to be conducted in a school setting.

The intervention reported had to be conducted in an area with population of low SES or address children with low SES in particular.

The article had to report influencing factors for the implementation of the intervention in their results section regarding children with low SES.

Operationalization of SES

Measuring the SES of children is challenging as they do not have their “own” SES. Parental income, parental education, and parental occupation are often used to measure children’s SES [ 13 , 46 ]. More broadly, children’s SES can be for example measured by the socioeconomic characteristics of the neighborhood [ 47 ]. Consequently, the parameters described previously that apply to the parents and their SES would also be applied to the child in question. These measures are correlated but not interchangeable [ 46 ]. Aggregated measures are also used to establish SES for school population or for regions or districts. Those aggregated measures would be drawn from administrative data and therefore depend on the institutional understanding of SES [ 46 ] and the availability of data. For this review, study authors reporting that low SES children had been focused upon in their research was considered sufficient, and a range of criteria and measures used to assess SES were accepted. The information on the criteria used to define low SES was extracted from the articles and is shown in the “ Results ” section.

Screening process

After deduplication, two reviewers (FB and JE) independently screened the articles on title and abstract level and in a second step on full text level using the software Rayyan to determine inclusion [ 48 ]. Conflicts were discussed and resolved between the reviewers. Additionally, all articles included in the review by Cassar et al. [ 36 ] were screened at full text level, as that review had a very similar aim, with the exception of the focus on SES.

Data extraction and synthesis

Article title, year of publication, country, aim of the evaluation, outcome variables assessed, means of data collection, criteria for low SES, and description of the intervention were extracted into one file (see Additional file 3 ) by MS and FB from each article. After the screening process, two reviewers (FB and JE) extracted barriers and facilitators for implementation from the results section of the articles into an Excel file. FB and JE extracted data from two articles independently and then matched their results through discussion. The remaining articles were split between FB and JE for data extraction. When any uncertainties arose about which details to extract from an article, a second reviewer extracted data from the same article and then any discrepancies were resolved through discussion.

The Excel file with extracted data was loaded into MAXQDA [ 49 ] for qualitative content analysis. The analysis was guided by the Consolidated Framework of Implementation Research (CFIR) [ 50 ]. This comprises the five domains (all expanded in detail below), along with 39 categories within these domains [ 50 ]. Intervention characteristics focus on the features of the intervention itself, for example, the source of the intervention or the design and packaging. Inner setting includes aspects of the setting, in which the intervention is being implemented, for example, the extent to which the intervention is prioritized compared to other activities within the setting. Outer setting is the setting, in which the inner setting exists in terms of structural, political, and cultural contexts, for example, policies, that must be adhered to. Characteristics of individuals include characteristics of people involved with the intervention, for example, their motivation or knowledge about the intervention. Process include all strategies and processes of implementing the intervention, for example, feedback processes, for example reflecting and evaluating on the quality during the implementation [ 50 ].

The 39 categories within these five domains from CFIR were used to deductively develop a category system for qualitative content analysis of the data. Each sense unit was coded into only one category. If reasonable due to different aspects within one category, subcategories were developed inductively. FB and JE coded 25% of the data independently, then the codes were reconciled, and the rest of the data was coded by FB. In the next step, all coded segments for each category were reviewed by FB. For a clear differentiation between certain categories for this review, additional specifications were developed (see Additional file 4 ) and, if necessary, the coding of the segments was adjusted according to the differentiations made between the categories. To test the final and refined category system, two reviewers (JE and a student assistant) coded 50% in total of the data again. The double-coded data were compared, and differences were discussed and resolved. All categories that caused more than one disagreement on the segment level were reviewed again for all data by FB. In the last step, all categories were reviewed. Content-related subcategories were developed inductively and coded again by a student assistant, and any disagreements were discussed and resolved.

For each of the five CFIR domains, the average number of articles per (sub)category (level above the barrier/facilitator) was calculated, by taking the total number of articles reporting on the categories within the domain and dividing it by the number of categories within the domain. This means, one articles could have been counted twice (or more), when it reported on two (or more) categories within the domain. For example, the domain of Intervention Characteristics includes 10 categories, and summed up, 33 articles reported factors related to this domain (see additional file 8 ). Dividing the total of 33 articles by the 10 categories results in an average article rate of 3.3 per category for this domain. In the “ Results ” section and in Figs. 2 , 3 , 4 , 5 , 6 , 7 , and 8 only (sub)categories are presented, which were reported above-average frequency for each domain respectively.

We grouped and then compared the articles with regard to the characteristics of the interventions, following the definition for complex interventions by Craig et al. [ 51 ]. If the intervention met two out of the three following aspects, it was considered to be complex , and otherwise it was considered to be simple : the intervention [ 1 ] addressed more than one obesity prevention behavior, [ 2 ] consisted of more than one component (e.g., classroom activities and teacher training), and [ 3 ] included parental involvement. We analyzed the most frequently reported influencing factors within those groups of simple and complex interventions.

Methodological quality assessment

We assessed the methodological quality of the articles using the Mixed Methods Appraisal Tool (MMAT) Version 2018 [ 52 , 53 ]. The MMAT allows the rating of quantitative and qualitative articles in the two separate corresponding categories, and mixed methods articles are rated in both, as well as an additional third mixed methods category.

Two reviewers (FB and JE) individually assessed the methodological quality of three articles, and the results were discussed with a third reviewer (CM). All of the remaining articles were split between two reviewers (FB and JE), and the methodological quality was individually assessed. If any uncertainties arose regarding the methodological quality of any particular article, it was assessed and evaluated by the reviewers individually, results of the individual assessments were discussed and the uncertainties resolved. No overall score was calculated, as recommended by the authors of MMAT [ 52 ].

Study selection

In total, 8111 articles were screened, 15 articles were identified as meeting all eligibility criteria from the initial search and screening of 6446 articles. Screening the articles included by Cassar et al. [ 36 ], one additional article met all eligibility criteria. The update search and screening of 1665 articles resulted in one additional article eligible for inclusion. In total, 17 articles were included in this systematic review [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] (see Fig. 1 ).

figure 1

Flow chart of screening

Study characteristics

Additional file 3 presents detailed information on the included articles, such as their aim and information on the intervention reported. The aims of eleven articles was to assess the implementation of the intervention and additional influencing factors for implementation ([ 54 , 56 , 57 , 63 , 64 , 65 , 68 , 70 , 59 , 60 , 61 ]). Five articles only assessed influencing factors for implementation [ 55 , 62 , 66 , 67 , 69 ], and one assessed the influencing factors for implementation, as well as the effectiveness of the intervention [ 58 ]. The articles applied mixed methods ( n =11), qualitative ( n =5), and quantitative design ( n =1). Data collection methods included interviews [ 54 , 56 , 57 , 60 , 64 , 65 , 68 , 69 ], questionnaires [ 54 , 56 , 57 , 58 , 59 , 60 , 64 , 65 , 68 , 70 ], focus groups [ 55 , 61 , 62 , 63 , 66 , 67 ], observations [ 54 , 60 , 64 , 69 ], document analyses [ 57 , 68 , 69 ], app usage [ 61 ], run tests [ 63 ], and accelerometers [ 70 ]. Data was collected from teachers or other school staff (head teachers, physical education teachers, program leaders) [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 63 , 64 , 65 , 66 , 67 , 69 , 70 ], students [ 54 , 57 , 58 , 60 , 61 , 62 , 63 , 64 , 69 , 70 ], parents [ 64 , 66 , 67 ], and externals like local sports coordinators [ 57 , 69 ].

The interventions reported by the articles promoted PA [ 57 , 58 , 59 , 63 , 68 , 70 ], healthy nutrition [ 60 , 64 , 65 ], PA and healthy nutrition [ 54 , 55 , 62 , 66 , 67 , 69 ], and PA, healthy nutrition and reducing screen time [ 61 ] and PA, healthy nutrition, healthy sleep, and reduce screen time [ 56 ]. In total, 15 independent interventions were reported, and three articles reported on the same intervention [ 62 , 66 , 67 ]. Five interventions were conducted in the USA [ 55 , 56 , 60 , 68 , 70 ], four in the Netherlands [ 54 , 57 , 64 , 69 ], two in Canada [ 58 , 59 ], one in Australia [ 61 ], one in Germany [ 65 ], one in Sweden [ 62 , 66 , 67 ], and one in the UK [ 63 ]. Six articles were considered reporting on simple interventions [ 58 , 59 , 63 , 65 , 68 , 70 ], and eleven articles were considered reporting on complex interventions [ 54 , 55 , 57 , 60 , 61 , 62 , 64 , 66 , 67 , 69 ] (see Additional file 3 ). Most frequent intervention components included classroom activities [ 55 , 56 , 57 , 60 , 62 , 66 , 67 , 69 ], physical activity lessons [ 54 , 58 , 63 , 69 , 70 ], financial support or materials provided to schools [ 54 , 56 , 59 , 61 , 64 , 65 , 68 ], and parental information [ 57 , 58 , 60 , 62 , 64 , 66 , 67 , 68 ].

Quality assessment results

Additional file 5 presents the ratings of the methodological quality assessment. Four [ 55 , 62 , 66 , 67 ] of the five qualitative articles received a “yes” for all criteria, and one article received a “no” for the criterion “Is the interpretation of results sufficiently substantiated by data?” [ 69 ]. The only quantitative article received a “can’t tell” for the criterion “Is the sampling strategy relevant to address the research question?” [ 65 ]. Of the eleven mixed methods articles, five [ 54 , 58 , 61 , 63 , 68 ] received a “yes” for all qualitative criteria, whereas only one of them received all quantitative criteria rated with “yes” [ 68 ]. Six [ 54 , 56 , 57 , 59 , 60 , 61 ] of the eleven mixed methods articles received a rating of “yes” for all mixed methods criteria, one article [ 63 ] received a “no” for the criterion “Is there an adequate rationale for using a mixed methods design to address the research question?”. None of the mixed methods articles received only “yes” ratings for all criteria. In the mixed methods articles, the qualitative items rated lower than the qualitative articles.

Influencing factors for implementation

In the following, selected results are presented to answer the research question what the influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors for children with low SES are.

Table 2 presents all included articles and their reporting of influencing factors in the five domains of CFIR, as well as the assignment to the groups of simple or complex interventions. The inner setting was reported in all articles ( n =17), and the least reported domain was the outer setting ( n =8). The outer setting was only reported in the group of complex interventions. Every article reported influencing factors in at least three different domains. In the 17 articles, 301 influencing factors were found across 89 (sub)categories, consisting of 35 categories (coming from the deductively developed categories) from CFIR and 55 categories (coming from the inductively developed categories). Of the 39 original CFIR categories, four categories were not reported at all. Additional file 6 presents all identified influencing barrier and facilitators for all (sub)categories for each domain. Additional file 7 presents the most commonly reported (sub)categories in the group of simple and complex interventions for each domain.

The results for each domain are presented below, with the number of articles reporting on the domain respectively, with the most reported influencing factors within each domain, and within the group of simple and complex interventions. Figs.  2 , 3 , 4 , 5 , 6 , 7 , and 8 show (sub)categories with above-average frequency for each domain, as well as the reported barriers and facilitators in those (sub)categories. Furthermore, the most reported barrier(s) or facilitator(s) for each group of interventions is marked. Additional file 8 presents all (sub)categories for each domain, and all the number of articles reporting the relevant barriers and facilitators.

figure 2

Intervention characteristics and most reported (sub)categories

figure 3

Inner setting (1) and most reported (sub)categories

figure 4

Inner setting (2) and most reported (sub)categories

figure 5

Outer setting and most reported (sub)categories

figure 6

Characteristics of Individuals and most reported (sub)categories

figure 7

Process (1) and most reported (sub)categories

figure 8

Process (2) most reported (sub)categories

Intervention characteristics

Intervention characteristics were reported in 14 of the included articles. The average number of articles reporting on the 10 (sub)categories was 3.3 per category. In total, four (sub)categories were reported with an above-average frequency and are displayed in Fig. 2 . The most reported influencing factors are described here.

Evidence strengths (perception of the quality and validity of evidence supporting the belief that the intervention will have desired outcomes) ( n =7) was reported as a barrier, because no short- or long-term effects of the intervention ( n =2) were seen. As facilitating for the implementation of the interventions, noticeable improvements in health (e.g., in the fitness level or self-confidence of children) ( n =3) and successful linking of intervention topics with the children’s everyday life ( n =3) were reported.

The preparation (perception of presentation and quality of intervention materials) ( n =7) of the intervention was reported as facilitator as good introduction to the intervention ( n =1) and intervention components with real-life relevance ( n =4) (e.g., hands-on sessions, real-life relevance of intervention components). In n =3 articles, barriers as inadequate intervention materials (e.g., wordiness of lessons) were reported.

Within the group of simple interventions, adaptability (the degree to which an intervention can be adapted, tailored, refined, or reinvented to meet local needs) ( n =3) was the most reported influencing factor as adapting the interventions as strategy for an improved fit (e.g., by trying different times during school day for extra PA lessons) ( n =3) facilitated implementation. Within the group of complex interventions, evidence strengths ( n =7) was the most reported influencing factor. Only studies reporting on complex interventions reported on evidence strengths.

Inner setting

Inner setting was reported in all 17 included articles. The average number of articles reporting on the 26 (sub)categories was 3.1 per category. In total, nine (sub)categories were reported with an above-average frequency and are displayed in Figs.  3 and 4 . The most reported influencing factors are described here.

The most reported influencing factor was time ( available time for implementation activities ) ( n =8), with sufficient time ( n =2) facilitating and insufficient time hindering ( n =6) (e.g., for meetings, for training, for the children, for implementation or insufficient time due to the evaluation timeline) was also reported as hindering implementation.

Scheduling (perceived compatibility of intervention activities with workflows) ( n =6) was reported as a barrier ( n =6), due to conflicts with scheduling ( n =4), insufficient fine-tuned organizational procedures ( n =1), and the school year was already planned, when the intervention was introduced ( n =1). As a facilitator, scheduling was reported ( n =2) in terms of a good fit of intervention in the work tasks ( n =1) and scheduling the intervention activity before school was successful ( n =1).

In the subcategory of communication (nature and quality of formal and informal communication with externals and within organization) ( n =6), good communication between stakeholders within school and with externals was reported as a facilitator ( n =5) and miscommunication between school stakeholders ( n =2) was considered a barrier for implementation.

Within the group of simple interventions, those time , scheduling , and communication ( n =2) were the most reported influencing factors, and within the group of complex interventions, time ( n =6) was the most commonly reported influencing factor.

Outer setting

Outer setting was reported in eight of the included articles. The average number of articles reporting on the 8 (sub)categories was 2.8 per category. In total, four (sub)categories were reported with an above-average frequency and are displayed in Fig. 5 . The most reported influencing factors are described here.

Abilities (abilities of parents) ( n =4) was reported as a barrier, because of lack of sufficient abilities among parents to conduct intervention / support children ( n =3), whereas recognized and considered parents’ abilities ( n =1) facilitated implementation.

Collaboration (status-quo of collaborations) ( n =4) was reported as barrier due to limited collaboration ( n =2) and as facilitator, because of various external partners supporting the intervention ( n =3) and widespread dissemination and intervention movement ( n =2).

Existing policy (a broad category including policy and regulations, external mandates, recommendations) ( n =4) were reported as barriers due to lack of policy/expectation from external for intervention implementation ( n =2) and lack of control (e.g., over administrative changes, food in cafeteria) ( n =2). Financial support for the intervention ( n =2) and fit between policies and intervention topics ( n =2) were reported as facilitators for implementation.

One of the articles within the group of simple interventions reported one influencing factor in the outer setting , therefore the subcategory collaboration ( n =1) is the only and the most reported influencing factor in the groups of simple interventions as presence of partnerships facilitating the implementation ( n =1). Within the group of complex interventions, abilities and existing policies ( n =4) were the most reported influencing factors (see paragraphs above).

Characteristics of individuals

Characteristics of individuals were reported in 14 of the included articles. The average number of articles reporting on the 14 (sub)categories was 3.4 per category. In total, five (sub)categories were reported with an above-average frequency and are displayed in Fig. 6 . The most reported influencing factors are described here.

Intervention strategy (strategies aiming to engage and motivate participants) ( n =6) was reported as a barrier due to competitive elements of the intervention for children activities ( n =1) and difficulty or ease of the intervention tasks ( n =1). As competitive, playful and applied intervention components ( n =5), and fitting the intervention to children’s abilities and leading to gradual improvement in fitness ( n =2) were reported as facilitators.

Interest in intervention (interest of stakeholders in the intervention components/topics) ( n =6) was reported as a barrier, due to a lack of interest by parents and children in intervention ( n =2) and teachers’ wish for additional training on topics other than the intervention topics ( n =1). Interest in, enthusiasm for, and commitment to the intervention from children ( n =1) and from teachers ( n =3) facilitated implementation.

Within the group of simple interventions, effect of stage (the effect of the individual stage of attitude towards an enthusiastic and sustainable usage of the intervention) ( n =2) as disengaged teachers resulted in disengaged students and vice versa ( n =2) was the most reported influencing factor hindering implementation. Within the group of complex interventions, the most reported influencing factor was character (other character traits influencing implementation) ( n =5) as, for example, forgetting about intervention ( n =1) as barrier, or girls feeling more comfortable in activities where they outnumbered boys ( n =1) as facilitator for implementation.

The process domain was reported in 15 of the included articles. The average number of articles reporting on the 16 (sub)categories was 3.8 per category. In total, seven (sub)categories were reported with an above-average frequency and are displayed in Figs.  7 and 8 . The most reported influencing factors are described here. Influence on executing (aspects affecting the execution of the intervention) ( n =8) was reported as lack of support for children to finish intervention activities ( n =1), in-class lessons that were too long or too diverse ( n =2), parents lacking structure regarding the intervention ( n =1), teachers conducting home activities in school due to lack of parental ability ( n =1) and sticking to intervention guidelines leading to lack of enthusiasm ( n =1). However, practice-oriented thinking of stakeholders ( n =1), children receiving support to finish intervention activities ( n =1), and following the intervention guidelines ( n =1) were reported as facilitators.

Within the group of simple interventions, influence on executing ( n =3) was the most reported influencing factor as sticking to intervention guidelines and tracking lead to reduced enthusiasm ( n =2) and hindered implementation. Within the group of complex interventions, the outcome of parental engagement ( n =6) as for example, lack of parental communication and engagement ( n =4) hindered implementation, and influence on executing ( n =5) were the most reported influencing factor.

This review identified, categorized, and evaluated influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors in children with low SES. We identified 301 influencing factors reported in 17 articles across 89 (sub)categories in the five domains of CFIR. The articles examined were grouped in a set of six simple and eleven complex interventions.

Aspects of the inner setting (also referred to as organizational ) were reported in every article, and aspects of the outer setting (also referred to as context ) constituted the least reported domain. These findings are consistent with the results of comparable reviews that assessed influencing factors on interventions promoting PA [ 32 , 38 ] and reducing sedentary behavior in school settings [ 36 ], as well as the sustainment of health behavior interventions in school settings and childcare services [ 37 ]. Comparable reviews [ 32 , 36 , 37 , 38 ] did not specifically address children with low SES. Although the present review and comparison reviews analyzed different target groups, the results are still comparable in the domain level. Therefore, one can consider that the presented results on this higher level are independent of the SES of children.

The inner setting is the most comprehensive domain of the CFIR, which likely have led to the accumulation of identified influencing factors. Although the outer setting is of great importance for implementation [ 31 , 71 , 72 , 73 ], it is the least reported domain.

Due to the huge variety of identified influencing factors, in the following sections, selected aspect, relevant for intervention developers, school staff, and researchers, will be discussed. All findings can be found in the supplementary files and we are happy to provide additional information upon request.

Influencing factors—for intervention developers

We grouped the articles in a set of simple and complex interventions, because complex interventions might entail a wider range of influencing factors than simple interventions (e.g., implementing an additional sport lessons, as simple intervention, is influenced by less factors than implementing additional sport lessons and healthy lunchboxes, as complex intervention). Furthermore, there is moderate evidence that complex interventions are more effective than simple interventions [ 28 , 29 , 30 ]. Comparing the groups of simple and complex interventions, one of the most reported influencing factors was executing in both groups.

Executing (also referred to as fidelity [ 74 ]) defined as carrying out or accomplishing the implementation according to plan [ 50 ] and adaptability ( the degree to which an intervention can be adapted, tailored, refined, or reinvented to meet local needs ) are highly connected. Adaptability was indeed the most reported influencing factor within the group of simple interventions.

There are examples in both groups of interventions for adaptability of the interventions, adaptations made, and their influence on executing . For example, in the group of simple intervention for example, having running routes inside, instead of outside, caused challenges [ 63 ]. Furthermore, sticking to the original principles and monitoring the intervention can lead to lack of enthusiasm [ 59 , 68 ]. In the group of complex interventions, for example, lack of support for children to complete intervention activities [ 62 , 66 ] or large variation of the time spent on activities [ 60 ] were reported as barriers for implementation. Children receiving support [ 69 ] and practice-oriented thinking by the executers [ 69 ] facilitated implementation. The examples mentioned in the groups of simple and complex intervention are different, though we cannot evaluate what reason for this is.

Adaptability is important to meet local needs, but adaptations mostly decrease the executing of an intervention. Adaptations are quite relevant for implementation [ 75 ] and executing is often used as outcome for measuring the degree of implementation [ 74 ]. To analyze the influence of adaptability , adaptations made, and executing on health outcome, it is important to document and consider both [ 75 ].

We grouped the articles according to the intervention characteristics, following the criteria for complex interventions by Craig [ 51 ]. This is one option for grouping the interventions, it could be argued that every intervention itself can be considered a complex intervention, following different criteria (e.g., synergies between intervention components, degree of flexibility, and multiplicity of mediators or moderators) [ 76 ]. Furthermore, if the intervention itself is not complex, one could argue that the school setting, with its context and stakeholders, and the interactions between them certainly can be considered complex (regardless of how simple or complicated the intervention is) [ 77 , 78 ]. Those different options for grouping interventions and furthermore different perspectives regarding school as a setting reflect on the complexity of (evaluations of) interventions in the real world.

Compared to other reviews [ 32 , 36 , 37 , 38 ], similar but also different influencing factors for the implementation of interventions were found on the subcategory level. For example, the subcategory insufficient time: insufficient time was also found as barrier for implementation by Naylor et al. [ 32 ]. If we consider the aspects reported in this review in the subcategory insufficient time , the six articles that reported this barrier indicated four different aspects where insufficient time was felt: insufficient time for implementation itself [ 54 , 59 ], for teachers to participate in trainings regarding the intervention [ 56 ], for formal meetings on the intervention [ 54 , 67 ] and for planning the implementation [ 54 ]. The aspects identified by Naylor et al. [ 32 ] for insufficient time were, for example, lack of time for planning, for training or for notifying parents on family events. These aspects are as various as the aspects identified in this study. For practical application, this means that even though there are consistent results on an aggregated level ( insufficient time ), the underlying aspects can be very diverse.

Influencing factors that have not been identified by similar reviews without the focus on children with a low SES were, for example, that girls felt not enough privacy in the locker rooms [ 55 ] or that it requires diverse efforts to achieve parental involvement, for example, language courses, and a personal approach, coffee meeting [ 57 ], or a holistic cooperation between the school and parents [ 66 ].

No direct comparison has been made between the influencing factors for implementation addressing low SES versus high SES samples, and so it is possible that these influencing factors are not unique to low SES samples. However, it is possible that these influencing factors may be particularly relevant to low SES samples. School buildings and sport facilities, like locker rooms, might be less maintained in underserved areas, leading to a lower feeling of comfort when using them. Furthermore, schools in low SES areas face additional challenges compared to high SES areas, which might negatively affect students’ academic development [ 79 ], which might reinforce the cycle between low SES and low child health [ 15 ].

The question now arises, which categories or aspects should be considered when developing new or adapting existing interventions for new settings and scaling them up to better reach children with low SES and therefore to contribute in address HI. Every setting and organization has different needs and resources [ 50 ]. This is also reflected by the different contexts and characteristics of the interventions identified in this review. The results showed no pattern or influencing factors standing out; however, the results show the breadth of existing influencing factors. Implementation aspects, like influencing factors or implementation outcomes, always depend on the specific intervention, which is being implemented as well as on the setting and context [ 50 , 78 ]. It is possible that the interventions and their context [ 72 ] included in this review were too diverse to find patterns or differences between them. There are recommendations on what to do to decrease HI [ 1 , 76 , 80 , 81 , 82 , 83 ], which are partly reflected by the results of this review as well. The intervention materials and personnel conducting the intervention should be culturally appropriate to the target population to build trust, as trust is very important during the whole intervention process [ 80 ]. Purposely including strategies on how to reach underserved groups can help addressing HI [ 1 ]. The groups that receive interventions, such as those of a low SES, must be involved in the process of implementing effective health-promoting interventions [ 76 ]. Participatory approaches can increase the likelihood of successful implementation, and improving the sustainability of interventions and can help balance top-down and bottom-up approaches [ 82 , 83 ]. These recommendations might seem non-specific, but are worth considering for application in conducting interventions. The intervention mapping approach [ 84 ], implementation mapping [ 35 ], and the closely related method of co-creation [ 85 ] offer guidance for a participatory intervention development and the implementation process.

School setting—for school staff

All organizations require resources to conduct health-promoting activities. A team for implementation, a health supporting culture, and a head teacher, who supports the intervention are likely to be important factors for successful implementation [ 86 ]. These aspects were also reported by articles included in this review in the subcategories organization and communication in the inner setting domain . For example, support from the whole school staff and principal [ 56 , 69 ], good coordination [ 54 ], clear protocols [ 57 ], efficient work between stakeholders [ 69 ], and clear hierarchical structure [ 57 ] and being a small school [ 59 ] were reported as facilitators for implementation.

In the following, we would like to give some suggestions for school (head) teachers, school health-promoters, and social workers: Networks and collaboration can facilitate implementation [ 57 , 69 ]. It is important to be open, persistent and willing to try different things, and to be ready to adjust aspects of the intervention, as each institution has different preconditions, needs, and resources. Working with the community is a promising opportunity, as those collaborations can improve the community networks and benefit school and students [ 87 ].

For children outside the school, the family is a very important setting and might be crucial in school-based obesity prevention [ 88 ] and is therefore worth considering in implementation activities as well. Regarding school-based nutrition and PA interventions with direct parental involvement (e.g., completing a questionnaire would not count as direct involvement), Verjans-Janssen et al. [ 89 ] found mainly positive effects. This may indicate the influence and importance of direct parental involvement in school-based interventions [ 89 ], especially in obesity prevention interventions [ 39 ] and for children with low SES [ 90 , 91 ].

Guidelines have been developed for schools on how to implement health-promoting activities [ 86 , 87 ]. Evidence-informed guidance is of great importance; however, those guidance tend to exhibit a quite theoretical perspective. Building on this foundation, there is still a need for empirical tested and actionable strategies the theory practice translation.

Methodological considerations—for researcher

Because a wide variety of implementation frameworks exists [ 92 , 93 ], this review also facilitates standardization and an increase in comparability of results in implementation research in general [ 94 ] and for school-based obesity-targeting interventions specifically, using CFIR. CFIR offers several advantages, due to its constant development [ 95 ], its method of rating determinants [ 96 ], the CFIR outcome addendum [ 97 ], and the CFIR-ERIC (Expert Recommendations for Implementation Change: a summary of 73 implementation strategies) matching tool [ 98 , 99 ].

In identifying and reporting influencing factors using CFIR, very detailed information can be presented, using the categories, as well as more generally by using the domains. This offers comparability on different levels. On the other hand, and this might also be the case for this review, by categorizing all aspects with such many (sub)categories might lead to a reduced applicability for practice. An alternative analytic option for similar investigations would be to conduct an inductive approach for developing a category system using qualitative content analysis and then compare the category system with the CFIR domains and categories. Regarding the field of implementation science, it is rather young [ 100 ] and therefore still evolving. This is reflected by, for example, the updated version of CFIR and the various and partly overlapping theories, models, and frameworks [ 92 ]. Different terms and definitions are used for similar/the same aspects, for example, fidelity as mentioned above or as evaluated by Schaap et al. [ 101 ]. This inconsistency in terms and definitions makes a comparison with existing literature somewhat challenging.

In this review and in many other instances, the application of CFIR is descriptive and linear. This review focused on identifying and evaluating influencing factors for implementation of school-based interventions preventing obesity prevention behaviors for children with low SES. The search term and the eligibility criteria were chosen accordingly. The eligibility criteria of articles was to report on interventions addressing children aged 5 or older, which refers to the youngest age for beginning school [ 44 ]. However, this may have led to exclusion of articles on interventions also addressing children entering school before the age of five. There are issues this review could not answer, but future research should address: It can be helpful to quantify the strengths of the influencing factors on the implementation [ 96 , 102 ], and to analyze which influencing factors are interconnected. It is not only essential to analyze the factors that have an influence on implementation, like this review did, but also on health outcomes [ 103 ]. Choosing appropriate implementation strategies [ 104 ] and organizing the whole process with an overall evaluation plan [ 35 ] should be the standard in implementation evaluation in general and in children with low SES in particular. Furthermore, we want to emphasize the importance of measuring and analyzing different effects of an intervention in groups with different SES [ 104 ].

Limitations

There are several limitations to the results of this review. Every intervention inherently conducts implementation by being performed in real-world settings. The corresponding articles might also report about implementation aspects. Articles in which reported aspects are not labeled as implementation evaluation or process evaluation results though, were not found with the search term and therefore not included in this review, although they might have contained important findings.

All included articles measured the SES on an aggregated school or area level, presumably, because measuring SES for children is challenging and often performed using aggregated proxy measures as described in the “ Methods ” section [ 47 ]. Due to the variety of measuring SES and different available data on an aggregated level, the comparability of included studies might be limited.

Due to a lack of transparency regarding how the influencing factors were identified, influencing factors must have been reported in the empirical results section. Articles reporting influencing factors only as part of the discussion were excluded. In addition, some clustering of influencing factors may not have occurred, because the number of 17 articles in total, six in the group of simple interventions, and eleven in the group of complex interventions, was too small.

This review is the first assessing influencing factors for the implementation of interventions promoting obesity prevention behaviors in children with low SES specifically. We identified influencing factors for the implementation of school-based obesity prevention interventions and presented them on a detailed level. This enhances the presentation of results at the most applicable level and contributes to the translation between theory and practice. The detailed reporting shows the tremendous variety of influencing factors for the implementation of obesity prevention interventions for children with low SES. This review could not find striking differences regarding influencing factors for implementation between existing literature without specific target groups and the focus on children with low SES. Still, this review highlights the need of empirical research investigating the processes and dynamics during the adoption, implementation, and sustainment of an intervention as a whole as well as possible differences between groups and settings. Health-promoting interventions for children (with low SES) can lead to less social stratification and can therefore add one piece to the puzzle in the bigger picture of increasing health equity.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Consolidated Framework of Implementation Research

Health inequity

Mixed Methods Appraisal Tool

Physical activity

Socioeconomic status

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Acknowledgements

We would like to thank Elaine Fischer and Julia Frank for supporting the qualitative analysis for this article as well as Antonia Frank for supporting the preparation of the graphics. Furthermore, we would like to thank Doris Gebhard for providing important advice in the course of shaping this article.

Open Access funding enabled and organized by Projekt DEAL. This review was funded by the German Federal Ministry of Health in the Project “Familie + – living healthy together in family and school” (ZMVI1-2519KIG006). Open Access funding by Technical University of Munich enabled and organized by Project DEAL. English lecture service funding by the Graduate School of the Technical University of Munich.

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FB directed the planning of this review. FB developed and refined the search strategy. FB conducted the database search, the screening, methodological quality assessment, data extraction, and data analysis and synthesis. JE contributed to the screening, methodological quality assessment, data extraction, and data analysis. MS supported the screening process and contributed to the data extraction and preparation of supplementary files. CM contributed to developing and refining the search strategy and to the methodological quality assessment. FB wrote the manuscript, CM and JE commented on the manuscript. CM advised throughout the review process and contributed to and commented on the manuscript. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1..

PRISMA guidelines for reporting. Checklist of PRISMA guidelines for reporting.

Additional file 2.

Search strategy. Search strategy and search term used in the different data bases.

Additional file 3.

List of included articles. Description of included articles, with, e.g., information about article design, data collection and intervention’s description.

Additional file 4.

Consolidated Framework for Implementation Research Categories with specifications. Consolidated Framework for Implementation Research Categories with specifications made for this review.

Additional file 5.

Mixed Methods Appraisal Tool ratings. Methodological quality assessment ratings for each article using the Mixed Methods Appraisal Tool items.

Additional file 6.

Extracted and categorized influencing factors. Extracted and categorized influencing factors for all domains and categories of the Consolidated Framework for Implementation Research Categories.

Additional file 7.

Most commonly reported (sub)categories by group of simple and complex interventions. The most commonly reported (sub)categories in the group of simple and complex interventions for each CFIR domain.

Additional file 8.

Number of reported barriers/facilitators and (sub)categories for each CFIR domain. All (sub)categories for each of the five CFIR domains, and all the number of articles reporting the relevant barriers and facilitators.

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Butscher, F., Ellinger, J., Singer, M. et al. Influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors in children with low socioeconomic status: a systematic review. Implement Sci Commun 5 , 12 (2024). https://doi.org/10.1186/s43058-024-00548-1

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systematic review obesity interventions

Adherence and dropout in exercise-based interventions in childhood obesity: A systematic review of randomized trials

Affiliations.

  • 1 Post-Graduate Program in Human Movement Sciences and Rehabilitation, Federal University of São Paulo (UNIFESP) Campus Baixada Santista, Santos, Brazil.
  • 2 Department of Kinesiology, California State University San Bernardino, San Bernardino, California, USA.
  • 3 Department of Physical Therapy, University of Pernambuco, Petrolina, Brazil.
  • 4 Department of Health Science and Human Ecology, California State University San Bernardino, San Bernardino, California, USA.
  • 5 Department of Human Movement Sciences and Rehabilitation, Federal University of São Paulo (UNIFESP) Campus Baixada Santista, Santos, Brazil.
  • PMID: 38359911
  • DOI: 10.1111/obr.13721

Our objective was to systematically examine the characteristics of exercise interventions on adherence and dropout in children and adolescents with obesity. PubMed, Embase, PsycINFO, Lilacs, Scielo, and The Cochrane Central Register of Controlled Trials and reference lists of relevant articles were searched. We included randomized controlled trials with exercise interventions for pediatric patients with obesity presenting data on dropout and/or adherence. Two reviewers screened the records independently for eligibility with disagreements being resolved by a third reviewer. Twenty-seven studies with 1268 participants were included. Because of high heterogeneity and poor reporting of adherence, it was not possible to perform a meta-analysis. Dropout prevalence was calculated, and subgroup analyses comparing different types of exercise and a meta-regression with potential moderators were performed. We found a dropout rate of 13%. Subgroup analyses did not identify significant differences. The duration of the exercise presented a moderating effect on dropout, suggesting that longer exercise sessions may lead to higher dropout in children and adolescents with obesity. Because of the poor adherence data, it is not clear which exercise characteristics may moderate adherence. To improve the quality of childhood obesity care, it is mandatory that future studies present adherence data. Systematic review registration: PROSPERO CRD42021290700.

Keywords: attendance; children; pediatric obesity; physical activity.

© 2024 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

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Association between metabolic syndrome and myocardial infarction among patients with excess body weight: a systematic review and meta-analysis

  • Zahra Sedaghat 1 ,
  • Soheila Khodakarim 2 ,
  • Seyed Aria Nejadghaderi 3 , 4 &
  • Siamak Sabour 5  

BMC Public Health volume  24 , Article number:  444 ( 2024 ) Cite this article

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Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Controversial views exist over the effects of metabolically unhealthy obesity phenotypes on CVDs. This study aimed to perform a meta-analysis to assess the association between metabolic syndrome and myocardial infarction (MI) among individuals with excess body weight (EBW).

We searched PubMed/Medline, Scopus, and Web of Science databases as of December 9, 2023. Cohort studies involving patients with overweight or obesity that reported the relevant effect measures for the association between metabolic syndrome and MI were included. We excluded studies with incomplete or unavailable original data, reanalysis of previously published data, and those that did not report the adjusted effect sizes. We used the Newcastle Ottawa Scale for quality assessment. Random-effect model meta-analysis was performed. Publication bias was assessed by Begg’s test.

Overall, nine studies comprising a total of 61,104 participants were included. There was a significant positive association between metabolic syndrome and MI among those with obesity (hazard ratio (HR): 1.68; 95% confidence interval (CI): 1.27, 2.22). Subgroup analysis showed higher HRs for obesity (1.72; 1.03, 2.88) than overweight (1.58; 1.-13-2.21). Meta-regression revealed no significant association between nationality and risk of MI ( p  = 0.75). All studies had high qualities. There was no significant publication bias ( p  = 0.42).

Conclusions

Metabolic syndrome increased the risk of MI in those with EBW. Further studies are recommended to investigate other risk factors of CVDs in EBW, in order to implement preventive programs to reduce the burden of CVD in obesity.

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Introduction

Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality in developed and developing countries and are accounting for 46.2% of total deaths worldwide [ 1 ]. As a risk factor for CVDs, metabolic syndrome is a disorder defined by the co-occurrence of at least three of five medical conditions, which are hyperglycemia, elevated triglyceride (TG), hypertension, low high-density lipoprotein (HDL), and obesity [ 2 ]. Along with lifestyle changes, metabolic syndrome is becoming a more serious health issue as the number of obese patients constantly increases among children and adults [ 3 , 4 ]. Metabolic syndrome is associated with several debilitating outcomes, such as myocardial infarction (MI), diabetes, and stroke [ 5 ]. Additionally, metabolically healthy obese individuals are at a higher risk of MI than metabolically healthy individuals with normal weight [ 6 ].

Several prior studies have been conducted to identify the association between metabolic syndrome and MI, all of which have shown that metabolic syndrome is an important risk factor for MI [ 1 , 7 , 8 ]. It is believed that lifestyles and nutritional factors, especially excess body weight (EBW) and insufficient physical activity play important roles in hypertension, hyperglycemia, dyslipidemia, and ultimately, MI development [ 1 , 9 ].

However, there are controversial findings in the studies regarding the association between metabolic syndrome and CVDs. Moreover, studies were conducted on different populations and in different settings [ 1 , 10 , 11 ]. Although several studies suggested a positive association between metabolic syndrome and MI in individuals with obesity [ 12 , 13 ], others reported contradictory results [ 14 , 15 ]. So, opinions regarding the impact of metabolic syndrome on MI in people with EBW or metabolically unhealthy obese patients are debatable. It is important to note that while meta-analyses are carried out to examine the association between metabolic syndrome and CVDs [ 16 , 17 ], none have examined the association between metabolically unhealthy obesity and MI, nor have they been published in recent years. Therefore, there is a need to conduct a pooled analysis to make a conclusive statement about the association between metabolic syndrome and CVDs in those with EBW. This systematic review and meta-analysis aimed to investigate both whether there is an association between metabolic syndrome and MI in individuals with EBW and to investigate the strength of the association using meta-analysis while reporting the pooled effect size of the association.

The study was conducted according to the guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 [ 18 ].

Study design and eligibility criteria

We included data from studies evaluated the association between metabolic syndrome and MI among participants with overweight or obesity, collectively mentioned as EBW. The PICO framework was as follow: Population: Individuals with EBW; Intervention/exposure: Diagnosis of metabolic syndrome using valid criteria; Comparison: Individuals with normal body mass index (BMI); and Outcomes: MI.

Cohort studies that evaluated the association between metabolic syndrome and MI in individuals with EBW without applying any limitation on age, sex, language, and ethnicity were included. Studies with incomplete or unavailable original data, reanalysis of previously published data, and those that did not report the adjusted effect size of the association between metabolic syndrome and outcomes of interest were excluded. Moreover, clinical trials, case reports, editorials, reviews, news, book chapters, and retracted articles were excluded. In the cases where outcomes were published at different time points, the last evaluation was considered.

Database searching and study selection

We searched electronic databases, including PubMed/Medline, Scopus, and Web of Science. Initially, keywords were selected using medical subject headings and screening of related articles and journals. Then, searches were performed separately in the databases from January 1, 2010 to June 30, 2021. We also updated the search on December 9, 2023. The detailed search quaery for each database is presented in Table S1 .

The search records were imported into the Mendeley software and deduplicated using that software. Then, two independent reviewers screened the titles and abstract. In the next step, the full-texts of the articles were retrieved and evaluated by the same reviewers. Discrepancies were resolved by consultation with the principal investigator. If the data could not be extracted from the study, we emailed the corresponding authors three times with a one week interval and asked to provide the data. If we did not receive a response or they did not provide such results, we excluded those studies.

Data extraction and risk of bias assessment

Data were extracted and summarized in a predefined data extraction form in Microsoft Excel software. In case of disagreement between the two reviewers, the third reviewer was consulted. The extracted data included study characteristics (i.e., first author’s name, publication year, follow-up, country, and study type), population characteristics (i.e., sample size, sex, age, systolic and diastolic blood pressures, fasting blood sugar (FBS), TG, HDL, low-density lipoprotein (LDL), waist circumferences, BMI, and history of smoking) and outcomes. If a study reported the results as a graph, data were extracted by “data extraction from graph method” explained by Sistrom and Mergo [ 19 ].

The risk of bias assessment was performed using the nine-star Newcastle Ottawa scale (NOS), including selection (representativeness of the population), comparability of groups (adjustment for confounders such as age and sex), and ascertainment of outcomes [ 20 ]. The NOS assigns four stars for selection, two for comparability, and three for outcome. The NOS scores of more than seven were acknowledged as high quality [ 20 ].

Statistical analysis

The STATA version 14.0 (Stata Corporation, College Station, TX) was used for statistical analysis. We used the “metan” command to perform a pooled analysis (a random or fixed effect analysis based on the heterogeneity among studies). Findings were presented as an overall hazard ratio (HR) with a 95% confidence interval (95% CI). Heterogeneity among studies was assessed using the Q-statistic and I-square test, and p -values less than 0.05 or I-square > 50% were considered as high heterogeneity. In case of high heterogeneity, subgroup analysis and meta-regression were used to investigate the potential source of heterogeneity. Funnel plot was only used to evaluate publication bias if at least ten studies were included [ 21 ]. Also, Begg's test was used to identify publication bias [ 22 ].

The search found 2898 results. Following removing 963 duplicates, 1935 articles were included for the title/abstract screening. Then, 113 studies were included for the full-text reviewing. Finally, the data from nine studies were included in the meta-analysis [ 6 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Eighty studies were excluded because they were not conducted on individuals with EBW and 24 studies were excluded because the adjusted effect sizes were not reported (Fig.  1 ).

figure 1

Study selection process

Study characteristics

These studies included 61104 participants from eight different countries and regions. The follow-up duration ranged from one to 11.6 years. The studies were published between 2010 and 2023. Three studies used adult treatment panel III (ATP-III) [ 25 , 26 , 27 ], while others used other definitions like Japanese society of internal medicine [ 6 ], American heart association/National heart, lung, and blood institute [ 24 ], harmonized international diabetes federation (IDF) [ 23 ], World Health Organization [ 28 ], joint interim statement [ 29 ], and IDF [ 30 ] (Table  1 ).

The study by Ogorodnikova et al. [ 25 ] was conducted on obese participants (BMI: 33.7 kg/m2) compared to the study by Lee et al. which was conducted on people with overweight [ 27 ]. The average of TG was lower in the study by Ogorodnikova et al. than Lee et al. (96.0 mg/dl vs. 189.9 mg/dl). Systolic blood pressures (SBPs) were 122.5, 131.8, and 150.0 mmHg in the studies by Ogorodnikova et al., Lee et al., and Thomsen et al., respectively [ 25 , 26 , 27 ]. FBS was 95.3 mg/dl in the Ogorodnikova’s study [ 25 ] compared to 179.2 mg/dl in Lee’s study [ 27 ] and 97.0 mg/dl in Thomsen’s study [ 26 ]. In addition, HDL values were 56.8, 39.6, and 46.0 mg/dl in Ogorodnikova et al., Lee et al., and Thomsen et al., respectively [ 25 , 26 , 27 ] (Table  2 ).

Quality assessment and publication bias

All studies had a high quality. The quality assessment scores were seven in three studies, eight in five studies, and nine in one study [ 27 ]. All studies had a high quality regarding selection of non-exposed cohorts, ascertainment of exposure, controlling for confounders, and duration of follow-up. The risk of bias assessment showed that seven studies did not report data regarding report the adequacy of a follow-up cohort (Table  3 ).

The Begg's test showed no significant publication bias ( p  = 0.42).

Overall meta-analysis results

We found a significant positive association between metabolic syndrome and MI among obese patients (HR = 1.68; 95% CI: 1.27, 2.22). Among nine studies included in the analysis, only one study showed a significant negative association between metabolic syndrome and MI (HR = 0.59; 95% CI: 0.47, 0.73) (Fig.  2 ).

figure 2

Forest plots of the association between metabolic syndrome and myocardial infarction among individuals with excess body weight. ES: effect size; CI: confidence interval

Subgroup analysis and meta-regression

We performed subgroup analysis by quality assessment scores and BMI values. The pooled HRs for overweight (25 < BMI ≤ 29.9 kg/m2) and obesity (BMI ≥ 30 kg/m2) were 1.58 (95% CI: 1.13, 2.21) and 1.72 (95% CI: 1.03, 2.88), respectively (Fig.  3 A). Subgroup analysis by quality assessment scores showed higher pooled HRs for score eight (1.72; 95% CI: 1.03, 2.88) than score seven (1.66; 95% CI: 1.31, 2.09) (Fig.  3 B). The meta-regression showed no significant association between nationality and risk of MI ( p  = 0.75).

figure 3

Forest plots of the association between metabolic syndrome and myocardial infarction among individuals with excess body weight by body mass index values ( A ) and quality assessment scores ( B ). ES: effect size; CI: confidence interval

To the best of our knowledge, no previous meta-analyses have assessed the association between metabolic syndrome and MI among individuals with EBW. Our results suggested that metabolic syndrome increased the risk of MI by 1.68 times among patients with EBW. The effect size was higher for obesity compared with overweight.

Among the nine studies included, only one study reported a negative association between metabolic syndrome and MI in patients with EBW [ 25 ]. In this regard, the article by Lavie and colleagues proposed a debate that some studies showed a better prognosis for CVDs in people with EBW than those with normal weights [ 31 ]. Nevertheless, the overall findings of our meta-analysis showed a significant higher risk of MI in people with EBW and metabolic syndrome. Also, previous studies showed adverse effects of metabolic syndrome. Accordingly, metabolic syndrome increased the risk of major adverse cardiovascular events by 1.55 times (95% CI: 1.28, 1.87) in patients with hypertension [ 32 ]. Another meta-analysis on eight studies showed that patients with end-stage renal disease and metabolic syndrome had an increased risk of mortality (risk ratio (RR): 1.92; 95% CI: 1.15, 3.21) and CVDs (RR: 6.42; 95% CI: 2.00, 20.58) compared to those without metabolic syndrome [ 33 ]. Therefore, it appears that metabolic syndrome has remarkable negative effects on risk of MI. Nevertheless, other large scale studies on people with EBW are recommended.

We found a high heterogeneity between studies (I-square: 92.7%). To account for the source of heterogeneity, we performed meta-regression and subgroup analysis. Meta-regressions showed no significant association with nationality. Also, subgroup analysis by quality assessment and BMI determined no source for heterogeneity. So, this heterogeneity might be related to the received treatments and relevant drugs that were not specifically reported in the primary studies. In this regard, the paper by Ogorodnikova et al. mentioned that the components of metabolic syndrome were controlled through medications [ 25 ].

It is worth noticing that people who are involved in the cohort studies might be different from healthy people in the general population because those who participated in the cohort study are under both drug and non-drug treatment, especially in obese patients. In addition, people with obesity are more taken under control, and their disease is under treatment. Due to this fact, metabolic syndrome is a protective factor for CVDs in this study. Interestingly, among different factors, the country is considered an important special contributor to that protective association. It is noticeable that the pattern of obesity is different among different countries [ 34 ]. For example, the average BMI in the United States is higher than other countries [ 35 ]. In that regard, patients with metabolic syndrome who reside in China, Japan, and Korea may not need any treatment although they have symptoms of metabolic syndrome. As a result, the severity of metabolic syndrome varies from one country to another [ 36 ]. Considering all these explanations, they did not require any drug treatments due to the early diagnosis of participants’ metabolic syndrome at the primary stages. On the other hand, the severity of metabolic syndrome in the United States was high, and all patients underwent drug treatments. Accordingly, this might explain the reasons for the protective results found in the study by Ogorodnikova et al., which was conducted in the United States [ 25 ].

Strengths and limitations

The strength of the study lies in that it is one of the pioneer studies that was focused on people with EBW and evaluated the association between metabolic syndrome and MI among them. We used a robust meta-analytical approach to report the pooled effect size for this association. Also, our included cohort studies were of high quality.

Additionally, the issue of confounders was controlled by including only cohort studies and using adjusted HRs in the analysis. So, the findings can be valuable for health policymaking and clinicians for prevention and reduction the mortality and morbidity of CVDs, particularly MI, in individuals with EBW.

Nevertheless, this systematic review and meta-analysis has some limitations that need to be taken into consideration when interpreting the results. First, the number of studies included in this meta–analysis was low. Therefore, we could not assess the publication bias using a funnel plot. Moreover, there was a high heterogeneity. To find the potential sources of heterogeneity, we performed subgroup analysis and meta-regression. However, due to the small sample number of included studies, the heterogeneities remained high. Second, a large proportion of studies did not provide sufficient information about the effect sizes among participants, leading to their exclusion. Third, although the included studies performed adjusted analysis based on several factors, there is still a possibility of biases due to inadequate adjustment for confounders. Fourth, in most primary studies, medical records were used for data gathering, raising the possibility of misclassification. Although we searched three major online databases, we did not perform grey literature search, thus potentially missing unpublished data.

Overall, metabolic syndrome significantly increased the risk of MI by 68% among individuals with EBW. Therefore, the findings of the study can be used by health policymakers to develop preventive programs for patients with EBW. Also, physicians should control the relevant risk factors, especially metabolic syndrome, in order to prevent from MI in individuals with EBW. Further large-scale observational studies and meta-analyses are needed to determine other risk factors of CVDs in patients with EBW, especially in other countries and populations like African countries and the African American race.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to decision of the research team but are available from the corresponding author on reasonable request.

Abbreviations

Cardiovascular disease

High-density lipoprotein

  • Myocardial infarction

Excess body weight

Body mass index

Fasting blood sugar

Triglyceride

Low-density lipoprotein

Newcastle Ottawa scale

Hazard ratio

Confidence interval

Adult treatment panel III

International diabetes federation

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Acknowledgements

This study is related to the project of a student from Shahid Beheshti University of Medical Sciences, Tehran, Iran.

The present study was financially supported by Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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Zahra Sedaghat

Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Soheila Khodakarim

School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Seyed Aria Nejadghaderi

Systematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran

Department of Clinical Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

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S. Khodakarim and S. Sabour contributed in conception and design of the work; data analysis was performed by Z. Sedaghat. The first draft of the manuscript was written by Z. Sedaghat and S.A. Nejadghaderi. It was critically revised by S.A. Nejadghaderi. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Sedaghat, Z., Khodakarim, S., Nejadghaderi, S. et al. Association between metabolic syndrome and myocardial infarction among patients with excess body weight: a systematic review and meta-analysis. BMC Public Health 24 , 444 (2024). https://doi.org/10.1186/s12889-024-17707-7

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Controlling childhood obesity: A systematic review on strategies and challenges

Roya kelishadi.

Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primary Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran

Fatemeh Azizi-Soleiman

1 School of Nutrition and Food Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

Background:

Childhood obesity is a global health problem with short- and long-term health consequences. This systematic review presents a summary of the experiences on different family-, school-, and clinic-based interventions.

Materials and Methods:

Electronic search was conducted in MEDLINE, PubMed, ISI Web of Science, and Scopus scientific databases. We included those studies conducted among obese individuals aged up to 18 years. Our search yielded 105 relevant papers, 70 of them were conducted as high quality clinical trials.

Our findings propose that school-based programs can have long-term effects in a large target group. This can be related to this fact that children spend a considerable part of their time in school, and adopt some parts of lifestyle there. They have remarkable consequences on health behaviors, but as there are some common limitations, their effects on anthropometric measures are not clear. Due to the crucial role of parents in development of children's behaviors, family-based interventions are reported to have successful effects in some aspects; but selection bias and high dropout rate can confound their results. Clinic-based interventions revealed favorable effects. They include dietary or other lifestyle changes like increasing physical activity or behavior therapy. It seems that a comprehensive intervention including diet and exercise are more practical. When they have different designs, results are controversial.

Conclusion:

We suggest that among different types of interventional programs, a multidisciplinary approach in schools in which children's family are involved, can be the best and most sustainable approach for management of childhood obesity.

INTRODUCTION

The epidemic of childhood obesity is no more limited to high-income countries,[ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] and has become as one of the most important global health problems of the 21 th century.[ 9 ] The World Health Organization (WHO) experts have estimated that there are 43 million overweight children under the age of 5 and by 2020 more than 60% of global disease burden will be the result of obesity related disorders.[ 2 , 10 ] Childhood obesity is associated with several short term and long-term health hazards as cardiovascular diseases, hypertension, type 2 diabetes, fatty liver disease, orthopedic problems, low self-esteem, etc.[ 11 , 12 ] Childhood obesity can reduce life expectancy by 2-5 years.[ 2 ] Moreover, the increasing trend of obesity has enormous economic outcomes.[ 13 ] Two main underlying causes of excess weight are genes and environment.[ 14 , 15 ] Although both genes and environment have a role in an obesity epidemic, gene defects needs to time to show their phenotype; so obesogenic environment is responsible for obesity.[ 11 ]

Primordial/primary prevention of pediatrics obesity and establishment of a healthy lifestyle behaviors from early life are the favored against the epidemic of obesity at the global level.[ 16 ]

Effective interventions for prevention and control of childhood obesity should be considered for different aspects.[ 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] Experts recommend specific eating and physical activity (PA) behaviors through counseling.[ 14 ] Along with clinic-based interventions, researchers have attempted to manage obesity by virtue of family, community, school, and after school programs. Based on Cochrane review of obesity prevention programs in children, most of the well-designed interventions had positive results especially in 6-12-year-old children.[ 25 ] Clearly targeted interventions for children and population-based approach for adolescents may be useful and make economic sense. The purpose of this investigation was to systematically review the effects of various clinical-, family-, and community-based interventions targeting the control of childhood obesity and make a suggestion for future interventions.

MATERIALS AND METHODS

Literature search.

Relevant literature reporting the interventions for controlling excess weight in children and adolescents was identified through electronic search of papers published from 2000 to 2012 in MEDLINE, PubMed, ISI Web of Science, and Scopus. Keywords such as “childhood obesity”, “overweight,” “weight disorder,” “intervention,” “treatment,” “management,” “control,” “PA,” “nutrition,” “behavior therapy,” and “diet therapy” were used. The searches yielded 1768 articles.

Study selection and eligibility criteria

Having removed duplicates, the relevant papers were selected in three phases. In the first and second phases, titles and abstracts of papers were screened and irrelevant papers were excluded. In the last phase, the full text of recruited papers was explored deeply to select only relevant papers. All these three screening phases were done by two independent reviewers (RK and FA). Discrepancies were resolved by consultation and consensus.

Studies were included if they met the following criteria: Studies on 2-18-year-old children; community, family, school, and clinic interventions or a combination of them; English language; and conducted among obese or overweight children and adolescents. Systematic reviews, meta-analysis, and editorials were excluded. Articles were firstly assessed on their abstracts and 234 were removed.

Data extraction and abstraction

The required information that was extracted from all eligible papers was as follow:

  • (i) General characteristics of the study (first author's name, publication year, study year, study design, sampling method,
  • (ii) Characteristics of the study population (age and sex of studied participants and sample size, follow-up),
  • (iii) Type and duration of the intervention, measure(s) used to assess child weight, and
  • (iv) Main finding. One reviewer (FA) extracted the data while another (RK) randomly selected 10% of them and checked their extracted data.

The selection process of our systematic review is presented in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is JRMS-19-993-g001.jpg

Flow chart of study selection process

The interventions were categorized as school-based, family-based, and clinic-based programs as described below:

School-based programs

A summary of the school-based obesity prevention and control programs is presented in Table 1 . In brief, such interventions are suggested to be feasible and effective;[ 26 ] because students spend a considerable part of their time in school,[ 27 ] moreover teachers and peers can be engaged in such programs.[ 28 ] These kinds of programs can improve health behaviors in a large target group. They are characterized by nutritional education and changes in dietary habits, as well as increase in PA through structured programs.[ 29 ] Findings of various studies proposed that the effects of such interventions will be preserved for several years after intervention.[ 30 , 31 , 32 ] This effect has been of special concern about consuming fruits and vegetables, and healthy snacks, as well as increased PA. Nevertheless, the impact of school-based programs on obesity prevention is controversial and remains to be determined by large studies with long-term follow-up research. Some studies have not evaluated the effect of intervention on anthropometric measures,[ 27 , 33 , 34 ] but they have shown positive impacts on eating and activity behaviors. The most common limitation of these studies is presenting self-reported data, non-randomized selection of schools, short duration of study, and not masking the interventional groups.

School-based weight control studies

An external file that holds a picture, illustration, etc.
Object name is JRMS-19-993-g002.jpg

Family-based programs

Reaching a healthy weight is not successful unless children have support for making healthy behavior choices; obviously, providers of this support are families. Family is an applicable target for health promoting interventions. Family-based intervention programs are considered as one of the most successful methods for obesity treatment or prevention.[ 59 ] Engaging parents in childhood obesity prevention programs may make weight loss easier for children; because they can provide confirmatory conditions to help their children to choose healthy behaviors, furthermore they are important role models for their children.[ 60 ] It is difficult for parents to know and accept that their child has excess weight, and that recommended diets would not have adverse health effect for their children;[ 61 ] therefore, they often do not comprehend the necessity of obesity prevention. Families are able to construct children's lifestyle habits, perhaps through their “parenting style” and management of “family functioning.”[ 62 ] Table 2 shows family-based interventions for management of childhood obesity. As it demonstrates, most of these programs were successful in decreasing body mass index (BMI) z-score and some health consequences of overweight. After participation of parents in these kinds of programs, their children consumed more fiber and were less sedentary. In some cases, significant decrease in fat mass is documented, as well.[ 63 , 64 ] It has shown that low parental confidence predicts dropout rate from family-based behavioral treatment.[ 65 ] The main limitation of family-based studies is the small sample size, high dropout rate, no follow-up data, and selection of motivated families.

Family-based studies for controlling childhood obesity

An external file that holds a picture, illustration, etc.
Object name is JRMS-19-993-g003.jpg

Clinic-based programs

Table 3 presents a summary of clinic-based weight management programs conducted in the pediatric age group. Although most researchers have tried low calorie-low fat diets for treating obesity, experts have recommended to consider a diet with balanced macronutrients.[ 14 ] Nevertheless, different dietary changes have been tried to control excess weight in children and adolescents. High protein (HP) diets seems to make more satiety, but two studies did not confirm their advantage versus standard diets.[ 90 , 91 ]

Clinic-based weight control studies for children and adolescents

An external file that holds a picture, illustration, etc.
Object name is JRMS-19-993-g004.jpg

In studies in which diet, exercise or both of them were taken into account, nutrition plus PA had more effect on anthropometric indices.[ 99 , 103 , 124 ] One study showed that combination of aerobic and strength training along with diet therapy results in BMI decrease in comparison with strength training plus diet recommendation.[ 127 ] A successful experience is reported about the favorable effects of zinc supplementation on anthropometric and metabolic indices.[ 102 , 133 ]

Obesity behavioral therapy has different parts such as motivational interviewing, goal setting, positive reinforcement, monitoring, and cognitive restructuring.[ 134 ] Most of behavioral therapies had positive consequences on weight, BMI, or dietary and PA habits.[ 92 , 98 , 107 , 108 , 116 ]

All interventions that consisted of nutrition, exercise, and counseling had significant effects on body weight or other obesity-related factors[ 84 , 93 , 96 , 100 , 101 , 105 , 109 , 111 , 112 , 113 , 114 , 115 , 117 , 118 , 120 , 128 , 135 ] except for a study, which had beneficial effects only on obesity related behaviors.[ 97 ] The main limitation of some of these studies is lack of comparison with the control group, and short-term follow-up of participants, and the uncertain sustainability of such kinds of interventions.

This review evaluated three different approaches in childhood obesity management. As the design of most studies is a clinical trial, it makes their comparison easier. Schools are a safe place for learning healthy skills and continuing them during life. Most (29/32) of the papers reported a positive effect of school-based intervention on dietary habits or anthropometric measures. One of negative effects of this kind intervention is discrimination resulted from stigmatization. This may persuade them to get involved in healthier lifestyle or might have opposite results. All of the studies conducted in the family setting ( n = 26), had favorable results on obesity criteria. Although some of them had negligible effects. Clinic-based intervention had different methods but almost the same results.

Some studies had no effects on anthropometric index. However, they had resulted in dietary habits or physical fitness improvement.[ 35 , 36 , 46 , 55 , 72 , 97 , 78 , 132 ] One explanation for this can be self-reported dietary intake and PA data. On the other words, children may not pay attention to the instruction they were given.

Teachers can train students how to choose nutritious and low-calorie foods. In addition, exercise training can be reinforced in the school curriculum.[ 14 ] Most students with excess weight prefer to eat fatty, sweetened, and salty snacks; they also choose fast foods as their first meal preference. If attendants get involved in obesity prevention programs, they can provide an environment for children to purchase healthy snacks and foods. Families can also make a circumstance which facilitates dietary and behavioral changes. Furthermore, if parents recognize the importance of weight control, they will be motivated to persuade their children for weight control. Families, especially mothers, are the best paradigm for children to learn a healthful eating pattern and activity habits.[ 136 ] Through family meals, children can eat more whole grains, fruits, vegetables, low fat milk, and consume less sweets and unhealthy fats. Parents should involve kids in preparing food to make a positive effect on their attitudes toward obesity prevention. It seems that the family has a key role in long-term weight control.[ 71 ] It has been shown that if family confidence is low, rate of dropout from weight loss programs will increase.[ 65 ] In this regard, providing parenting styles and skills as well as child management strategies are really critical.[ 81 , 137 ] Principally clinic-setting programs have brought nutrition, PA, and education or counseling together to achieve their goals and they have demonstrated long lasting results.[ 138 ] Most experts advise a low calorie low fat diet for obesity management; but they may have side-effects such as binge eating.[ 139 ] Actually weight loss is allowed in severe obesity and in other cases weight maintenance is an appropriate policy.[ 114 ] Some studies recommend HP or low carbohydrate diets because they cause more satiety.[ 140 ] A review article revealed that low carbohydrate ad libitum diets are as effective as calorie restricted diets.[ 140 ] In addition, a Cochrane review showed that low fat diets have no extra advantages in comparison with other diets with calorie restriction.[ 141 ] Another review article revealed moderate effect of exercise on adiposity and not on BMI.[ 142 ] Clearly, PA is efficient when lasts for more than 60 min, is moderate to vigorous, and is done in all weekdays.[ 134 ] As low calorie diets are harmful for growth, and complying with them is difficult, some studies suggested that vigorous exercise can be a suitable substitute for diet therapy.[ 137 , 139 ] As always emphasized, to be effective, PA should be considered as an enjoyable fun, and should be integrated into daily lifestyle. Obesity causes mental problems in children and adolescents,[ 118 ] so behavior therapy seems to be vital. It sounds that group treatment is more successful than individual ones;[ 75 , 79 ] specifically when parents are engaged. Counselors should persuade children and adolescents to eat breakfast, to have structured meal plan to increase consumption of fruits, vegetables, and family meals, as well as to decrease the intake of sweetened beverages, calorie-dense foods, and eating out, as well as reducing the sedentary behaviors and the screen time.[ 14 , 91 ] Counselors also need to teach families about healthy shopping and cooking habits. Unfortunately, most studies did not show favorable effects, many of them had small sample sizes or had short-term follow-up or lacked of the control group. Managing extra group support sessions or using technologies such as E-mail or SMS for monitoring weight losers can be a good idea.[ 63 , 92 , 143 ]

The findings suggest that among different types of interventional programs for management of childhood obesity, a multidisciplinary approach in schools in which children's family are involved, can be the most feasible and effective approach. As teachers and parents are the best role models, it will be easier to accustom children with healthy dietary, PA, and behavioral habits. Future studies are needed to determine the long-term effects and sustainability of different programs.

AUTHORS’ CONTRIBUTION

FAS contributed in the conception of the work, conducting the review, revising the draft, approval of the final version of the manuscript, and agreed for all aspects of the work. RK contributed in the conception and design of the work, drafting and revising the draft, approval of the final version of the manuscript, and agreed for all aspects of the work.

Source of Support: Nil

Conflict of Interest: None declared.

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