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Effectiveness of weight management interventions for adults delivered in primary care: systematic review and meta-analysis of randomised controlled trials

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• Peer review
• 1 Centre for Lifestyle Medicine and Behaviour (CLiMB), The School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
• 2 School of Primary and Allied Health Care, Monash University, Melbourne, Australia
• 3 Department of Psychology, Addiction and Mental Health Group, University of Bath, Bath, UK
• Correspondence to: C D Madigan c.madigan{at}lboro.ac.uk (or @claire_wm and @lboroclimb on Twitter)
• Accepted 26 April 2022

Objective To examine the effectiveness of behavioural weight management interventions for adults with obesity delivered in primary care.

Design Systematic review and meta-analysis of randomised controlled trials.

Eligibility criteria for selection of studies Randomised controlled trials of behavioural weight management interventions for adults with a body mass index ≥25 delivered in primary care compared with no treatment, attention control, or minimal intervention and weight change at ≥12 months follow-up.

Data sources Trials from a previous systematic review were extracted and the search completed using the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021.

Data extraction and synthesis Two reviewers independently identified eligible studies, extracted data, and assessed risk of bias using the Cochrane risk of bias tool. Meta-analyses were conducted with random effects models, and a pooled mean difference for both weight (kg) and waist circumference (cm) were calculated.

Main outcome measures Primary outcome was weight change from baseline to 12 months. Secondary outcome was weight change from baseline to ≥24 months. Change in waist circumference was assessed at 12 months.

Results 34 trials were included: 14 were additional, from a previous review. 27 trials (n=8000) were included in the primary outcome of weight change at 12 month follow-up. The mean difference between the intervention and comparator groups at 12 months was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, P<0.001), favouring the intervention group. At ≥24 months (13 trials, n=5011) the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, P<0.001) favouring the intervention. The mean difference in waist circumference (18 trials, n=5288) was −2.5 cm (−3.2 to −1.8 cm, I 2 =69%, P<0.001) in favour of the intervention at 12 months.

Conclusions Behavioural weight management interventions for adults with obesity delivered in primary care are effective for weight loss and could be offered to members of the public.

Systematic review registration PROSPERO CRD42021275529.

Introduction

Obesity is associated with an increased risk of diseases such as cancer, type 2 diabetes, and heart disease, leading to early mortality. 1 2 3 More recently, obesity is a risk factor for worse outcomes with covid-19. 4 5 Because of this increased risk, health agencies and governments worldwide are focused on finding effective ways to help people lose weight. 6

Primary care is an ideal setting for delivering weight management services, and international guidelines recommend that doctors should opportunistically screen and encourage patients to lose weight. 7 8 On average, most people consult a primary care doctor four times yearly, providing opportunities for weight management interventions. 9 10 A systematic review of randomised controlled trials by LeBlanc et al identified behavioural interventions that could potentially be delivered in primary care, or involved referral of patients by primary care professionals, were effective for weight loss at 12-18 months follow-up (−2.4 kg, 95% confidence interval −2.9 to−1.9 kg). 11 However, this review included trials with interventions that the review authors considered directly transferrable to primary care, but not all interventions involved primary care practitioners. The review included interventions that were entirely delivered by university research employees, meaning implementation of these interventions might differ if offered in primary care, as has been the case in other implementation research of weight management interventions, where effects were smaller. 12 As many similar trials have been published after this review, an updated review would be useful to guide health policy.

We examined the effectiveness of weight loss interventions delivered in primary care on measures of body composition (weight and waist circumference). We also identified characteristics of effective weight management programmes for policy makers to consider.

This systematic review was registered on PROSPERO and is reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. 13 14

Eligibility criteria

We considered studies to be eligible for inclusion if they were randomised controlled trials, comprised adult participants (≥18 years), and evaluated behavioural weight management interventions delivered in primary care that focused on weight loss. A primary care setting was broadly defined as the first point of contact with the healthcare system, providing accessible, continued, comprehensive, and coordinated care, focused on long term health. 15 Delivery in primary care was defined as the majority of the intervention being delivered by medical and non-medical clinicians within the primary care setting. Table 1 lists the inclusion and exclusion criteria.

Study inclusion and exclusion criteria

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We extracted studies from the systematic review by LeBlanc et al that met our inclusion criteria. 11 We also searched the exclusions in this review because the researchers excluded interventions specifically for diabetes management, low quality trials, and only included studies from an Organisation for Economic Co-operation and Development country, limiting the scope of the findings.

We searched for studies in the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021 (see supplementary file 1). Reference lists of previous reviews 16 17 18 19 20 21 and included trials were hand searched.

Data extraction

Results were uploaded to Covidence, 22 a software platform used for screening, and duplicates removed. Two independent reviewers screened study titles, abstracts, and full texts. Disagreements were discussed and resolved by a third reviewer. All decisions were recorded in Covidence, and reviewers were blinded to each other’s decisions. Covidence calculates proportionate agreement as a measure of inter-rater reliability, and data are reported separately by title or abstract screening and full text screening. One reviewer extracted data on study characteristics (see supplementary table 1) and two authors independently extracted data on weight outcomes. We contacted the authors of four included trials (from the updated search) for further information. 23 24 25 26

Outcomes, summary measures, and synthesis of results

The primary outcome was weight change from baseline to 12 months. Secondary outcomes were weight change from baseline to ≥24 months and from baseline to last follow-up (to include as many trials as possible), and waist circumference from baseline to 12 months. Supplementary file 2 details the prespecified subgroup analysis that we were unable to complete. The prespecified subgroup analyses that could be completed were type of healthcare professional who delivered the intervention, country, intensity of the intervention, and risk of bias rating.

Healthcare professional delivering intervention —From the data we were able to compare subgroups by type of healthcare professional: nurses, 24 26 27 28 general practitioners, 23 29 30 31 and non-medical practitioners (eg, health coaches). 32 33 34 35 36 37 38 39 Some of the interventions delivered by non-medical practitioners were supported, but not predominantly delivered, by GPs. Other interventions were delivered by a combination of several different practitioners—for example, it was not possible to determine whether a nurse or dietitian delivered the intervention. In the subgroup analysis of practitioner delivery, we refer to this group as “other.”

Country —We explored the effectiveness of interventions by country. Only countries with three or more trials were included in subgroup analyses (United Kingdom, United States, and Spain).

Intensity of interventions —As the median number of contacts was 12, we categorised intervention groups according to whether ≤11 or ≥12 contacts were required.

Risk of bias rating —Studies were classified as being at low, unclear, and high risk of bias. Risk of bias was explored as a potential influence on the results.

Meta-analyses

Meta-analyses were conducted using Review Manager 5.4. 40 As we expected the treatment effects to differ because of the diversity of intervention components and comparator conditions, we used random effects models. A pooled mean difference was calculated for each analysis, and variance in heterogeneity between studies was compared using the I 2 and τ 2 statistics. We generated funnel plots to evaluate small study effects. If more than two intervention groups existed, we divided the number of participants in the comparator group by the number of intervention groups and analysed each individually. Nine trials were cluster randomised controlled trials. The trials had adjusted their results for clustering, or adjustment had been made in the previous systematic review by LeBlanc et al. 11 One trial did not report change in weight by group. 26 We calculated the mean weight change and standard deviation using a standard formula, which imputes a correlation for the baseline and follow-up weights. 41 42 In a non-prespecified analysis, we conducted univariate and multivariable metaregression (in Stata) using a random effects model to examine the association between number of sessions and type of interventionalist on study effect estimates.

Risk of bias

Two authors independently assessed the risk of bias using the Cochrane risk of bias tool v2. 43 For incomplete outcome data we defined a high risk of bias as ≥20% attrition. Disagreements were resolved by discussion or consultation with a third author.

Patient and public involvement

The study idea was discussed with patients and members of the public. They were not, however, included in discussions about the design or conduct of the study.

The search identified 11 609 unique study titles or abstracts after duplicates were removed ( fig 1 ). After screening, 97 full text articles were assessed for eligibility. The proportionate agreement ranged from 0.94 to 1.0 for screening of titles or abstracts and was 0.84 for full text screening. Fourteen new trials met the inclusion criteria. Twenty one studies from the review by LeBlanc et al met our eligibility criteria and one study from another systematic review was considered eligible and included. 44 Some studies had follow-up studies (ie, two publications) that were found in both the second and the first search; hence the total number of trials was 34 and not 36. Of the 34 trials, 27 (n=8000 participants) were included in the primary outcome meta-analysis of weight change from baseline to 12 months, 13 (n=5011) in the secondary outcome from baseline to ≥24 months, and 30 (n=8938) in the secondary outcome for weight change from baseline to last follow-up. Baseline weight was accounted for in 18 of these trials, but in 14 24 26 29 30 31 32 44 45 46 47 48 49 50 51 it was unclear or the trials did not consider baseline weight. Eighteen trials (n=5288) were included in the analysis of change in waist circumference at 12 months.

Studies included in systematic review of effectiveness of behavioural weight management interventions in primary care. *Studies were merged in Covidence if they were from same trial

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Study characteristics

Included trials (see supplementary table 1) were individual randomised controlled trials (n=25) 24 25 26 27 28 29 32 33 34 35 38 39 41 44 45 46 47 50 51 52 53 54 55 56 59 or cluster randomised controlled trials (n=9). 23 30 31 36 37 48 49 57 58 Most were conducted in the US (n=14), 29 30 31 32 33 34 35 36 37 45 48 51 54 55 UK (n=7), 27 28 38 41 47 57 58 and Spain (n=4). 25 44 46 49 The median number of participants was 276 (range 50-864).

Four trials included only women (average 65.9% of women). 31 48 51 59 The mean BMI at baseline was 35.2 (SD 4.2) and mean age was 48 (SD 9.7) years. The interventions lasted between one session (with participants subsequently following the programme unassisted for three months) and several sessions over three years (median 12 months). The follow-up period ranged from 12 months to three years (median 12 months). Most trials excluded participants who had lost weight in the past six months and were taking drugs that affected weight.

Meta-analysis

Overall, 27 trials were included in the primary meta-analysis of weight change from baseline to 12 months. Three trials could not be included in the primary analysis as data on weight were only available at two and three years and not 12 months follow-up, but we included these trials in the secondary analyses of last follow-up and ≥24 months follow-up. 26 44 50 Four trials could not be included in the meta-analysis as they did not present data in a way that could be synthesised (ie, measures of dispersion). 25 52 53 58 The mean difference was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, τ 2 =3.38; P<0.001) in favour of the intervention group ( fig 2 ). We found no evidence of publication bias (see supplementary fig 1). Absolute weight change was −3.7 (SD 6.1) kg in the intervention group and −1.4 (SD 5.5) kg in the comparator group.

Mean difference in weight at 12 months by weight management programme in primary care (intervention) or no treatment, different content, or minimal intervention (control). SD=standard deviation

Supplementary file 2 provides a summary of the main subgroup analyses.

Weight change

The mean difference in weight change at the last follow-up was −1.9 kg (95% confidence interval −2.5 to −1.3 kg, I 2 =81%, τ 2 =2.15; P<0.001). Absolute weight change was −3.2 (SD 6.4) kg in the intervention group and −1.2 (SD 6.0) kg in the comparator group (see supplementary figs 2 and 3).

At the 24 month follow-up the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, τ 2 =3.13; P<0.001) (see supplementary fig 4). As the weight change data did not differ between the last follow-up and ≥24 months, we used the weight data from the last follow-up in subgroup analyses.

In subgroup analyses of type of interventionalist, differences were significant (P=0.005) between non-medical practitioners, GPs, nurses, and other people who delivered interventions (see supplementary fig 2).

Participants who had ≥12 contacts during interventions lost significantly more weight than those with fewer contacts (see supplementary fig 6). The association remained after adjustment for type of interventionalist.

Waist circumference

The mean difference in waist circumference was −2.5 cm (95% confidence interval −3.2 to −1.8 cm, I 2 =69%, τ 2 =1.73; P<0.001) in favour of the intervention at 12 months ( fig 3 ). Absolute changes were −3.7 cm (SD 7.8 cm) in the intervention group and −1.3 cm (SD 7.3) in the comparator group.

Mean difference in waist circumference at 12 months. SD=standard deviation

Risk of bias was considered to be low in nine trials, 24 33 34 35 39 41 47 55 56 unclear in 12 trials, 25 27 28 29 32 45 46 50 51 52 54 59 and high in 13 trials 23 26 30 31 36 37 38 44 48 49 53 57 58 ( fig 4 ). No significant (P=0.65) differences were found in subgroup analyses according to level of risk of bias from baseline to 12 months (see supplementary fig 7).

Risk of bias in included studies

Worldwide, governments are trying to find the most effective services to help people lose weight to improve the health of populations. We found weight management interventions delivered by primary care practitioners result in effective weight loss and reduction in waist circumference and these interventions should be considered part of the services offered to help people manage their weight. A greater number of contacts between patients and healthcare professionals led to more weight loss, and interventions should be designed to include at least 12 contacts (face-to-face or by telephone, or both). Evidence suggests that interventions delivered by non-medical practitioners were as effective as those delivered by GPs (both showed statistically significant weight loss). It is also possible that more contacts were made with non-medical interventionalists, which might partially explain this result, although the metaregression analysis suggested the effect remained after adjustment for type of interventionalist. Because most comparator groups had fewer contacts than intervention groups, it is not known whether the effects of the interventions are related to contact with interventionalists or to the content of the intervention itself.

Although we did not determine the costs of the programme, it is likely that interventions delivered by non-medical practitioners would be cheaper than GP and nurse led programmes. 41 Most of the interventions delivered by non-medical practitioners involved endorsement and supervision from GPs (ie, a recommendation or checking in to see how patients were progressing), and these should be considered when implementing these types of weight management interventions in primary care settings. Our findings suggest that a combination of practitioners would be most effective because GPs might not have the time for 12 consultations to support weight management.

Although the 2.3 kg greater weight loss in the intervention group may seem modest, just 2-5% in weight loss is associated with improvements in systolic blood pressure and glucose and triglyceride levels. 60 The confidence intervals suggest a potential range of weight loss and that these interventions might not provide as much benefit to those with a higher BMI. Patients might not find an average weight loss of 3.7 kg attractive, as many would prefer to lose more weight; explaining to patients the benefits of small weight losses to health would be important.

Strengths and limitations of this review

Our conclusions are based on a large sample of about 8000 participants, and 12 of these trials were published since 2018. It was occasionally difficult to distinguish who delivered the interventions and how they were implemented. We therefore made some assumptions at the screening stage about whether the interventionalists were primary care practitioners or if most of the interventions were delivered in primary care. These discussions were resolved by consensus. All included trials measured weight, and we excluded those that used self-reported data. Dropout rates are important in weight management interventions as those who do less well are less likely to be followed-up. We found that participants in trials with an attrition rate of 20% or more lost less weight and we are confident that those with high attrition rates have not inflated the results. Trials were mainly conducted in socially economic developed countries, so our findings might not be applicable to all countries. The meta-analyses showed statistically significant heterogeneity, and our prespecified subgroups analysis explained some, but not all, of the variance.

Comparison with other studies

The mean difference of −2.3 kg in favour of the intervention group at 12 months is similar to the findings in the review by LeBlanc et al, who reported a reduction of −2.4 kg in participants who received a weight management intervention in a range of settings, including primary care, universities, and the community. 11 61 This is important because the review by LeBlanc et al included interventions that were not exclusively conducted in primary care or by primary care practitioners. Trials conducted in university or hospital settings are not typically representative of primary care populations and are often more intensive than trials conducted in primary care as a result of less constraints on time. Thus, our review provides encouraging findings for the implementation of weight management interventions delivered in primary care. The findings are of a similar magnitude to those found in a trial by Ahern et al that tested primary care referral to a commercial programme, with a difference of −2.7 kg (95% confidence interval −3.9 to −1.5 kg) reported at 12 month follow-up. 62 The trial by Ahern et al also found a difference in waist circumference of −4.1 cm (95% confidence interval −5.5 to −2.3 cm) in favour of the intervention group at 12 months. Our finding was smaller at −2.5 cm (95% confidence interval −3.2 to −1.8 cm). Some evidence suggests clinical benefits from a reduction of 3 cm in waist circumference, particularly in decreased glucose levels, and the intervention groups showed a 3.7 cm absolute change in waist circumference. 63

Policy implications and conclusions

Weight management interventions delivered in primary care are effective and should be part of services offered to members of the public to help them manage weight. As about 39% of the world’s population is living with obesity, helping people to manage their weight is an enormous task. 64 Primary care offers good reach into the community as the first point of contact in the healthcare system and the remit to provide whole person care across the life course. 65 When developing weight management interventions, it is important to reflect on resource availability within primary care settings to ensure patients’ needs can be met within existing healthcare systems. 66

We did not examine the equity of interventions, but primary care interventions may offer an additional service and potentially help those who would not attend a programme delivered outside of primary care. Interventions should consist of 12 or more contacts, and these findings are based on a mixture of telephone and face-to-face sessions. Previous evidence suggests that GPs find it difficult to raise the issue of weight with patients and are pessimistic about the success of weight loss interventions. 67 Therefore, interventions should be implemented with appropriate training for primary care practitioners so that they feel confident about helping patients to manage their weight. 68

Unanswered questions and future research

A range of effective interventions are available in primary care settings to help people manage their weight, but we found substantial heterogeneity. It was beyond the scope of this systematic review to examine the specific components of the interventions that may be associated with greater weight loss, but this could be investigated by future research. We do not know whether these interventions are universally suitable and will decrease or increase health inequalities. As the data are most likely collected in trials, an individual patient meta-analysis is now needed to explore characteristics or factors that might explain the variance. Most of the interventions excluded people prescribed drugs that affect weight gain, such as antipsychotics, glucocorticoids, and some antidepressants. This population might benefit from help with managing their weight owing to the side effects of these drug classes on weight gain, although we do not know whether the weight management interventions we investigated would be effective in this population. 69

What is already known on this topic

Referral by primary care to behavioural weight management programmes is effective, but the effectiveness of weight management interventions delivered by primary care is not known

Systematic reviews have provided evidence for weight management interventions, but the latest review of primary care delivered interventions was published in 2014

Factors such as intensity and delivery mechanisms have not been investigated and could influence the effectiveness of weight management interventions delivered by primary care

What this study adds

Weight management interventions delivered by primary care are effective and can help patients to better manage their weight

At least 12 contacts (telephone or face to face) are needed to deliver weight management programmes in primary care

Some evidence suggests that weight loss after weight management interventions delivered by non-medical practitioners in primary care (often endorsed and supervised by doctors) is similar to that delivered by clinician led programmes

Ethics statements

Ethical approval.

Not required.

Data availability statement

Additional data are available in the supplementary files.

Contributors: CDM and AJD conceived the study, with support from ES. CDM conducted the search with support from HEG. CDM, AJD, ES, HEG, KG, GB, and VEK completed the screening and full text identification. CDM and VEK completed the risk of bias assessment. CDM extracted data for the primary outcome and study characteristics. HEJ, GB, and KG extracted primary outcome data. CDM completed the analysis in RevMan, and GMJT completed the metaregression analysis in Stata. CDM drafted the paper with AJD. All authors provided comments on the paper. CDM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: AJD is supported by a National Institute for Health and Care Research (NIHR) research professorship award. This research was supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ES’s salary is supported by an investigator grant (National Health and Medical Research Council, Australia). GT is supported by a Cancer Research UK fellowship. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: This research was supported by the National Institute for Health and Care Research Leicester Biomedical Research Centre; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

The lead author (CDM) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: We plan to disseminate these research findings to a wider community through press releases, featuring on the Centre for Lifestyle Medicine and Behaviour website ( www.lboro.ac.uk/research/climb/ ) via our policy networks, through social media platforms, and presentation at conferences.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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Obesity research: Moving from bench to bedside to population

* E-mail: [email protected]

Affiliation Diabetes Research Program, Department of Medicine, New York University Grossman School of Medicine, New York, New York, United States of America

• Ann Marie Schmidt

Published: December 4, 2023

• https://doi.org/10.1371/journal.pbio.3002448
• Reader Comments

Globally, obesity is on the rise. Research over the past 20 years has highlighted the far-reaching multisystem complications of obesity, but a better understanding of its complex pathogenesis is needed to identify safe and lasting solutions.

Citation: Schmidt AM (2023) Obesity research: Moving from bench to bedside to population. PLoS Biol 21(12): e3002448. https://doi.org/10.1371/journal.pbio.3002448

Copyright: © 2023 Ann Marie Schmidt. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: AMS received funding from U.S. Public Health Service (grants 2P01HL131481 and P01HL146367). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Abbreviations: EDC, endocrine disruptor chemical; GIP, gastric inhibitory polypeptide; GLP1, glucagon-like peptide 1; HFCS, high-fructose corn syrup

This article is part of the PLOS Biology 20th anniversary collection.

Obesity is a multifaceted disorder, affecting individuals across their life span, with increased prevalence in persons from underrepresented groups. The complexity of obesity is underscored by the multiple hypotheses proposed to pinpoint its seminal mechanisms, such as the “energy balance” hypothesis and the “carbohydrate–insulin” model. It is generally accepted that host (including genetic factors)–environment interactions have critical roles in this disease. The recently framed “fructose survival hypothesis” proposes that high-fructose corn syrup (HFCS), through reduction in the cellular content of ATP, stimulates glycolysis and reduces mitochondrial oxidative phosphorylation, processes that stimulate hunger, foraging, weight gain, and fat accumulation [ 1 ]. The marked upswing in the use of HFCS in beverages and foods, beginning in the 1980s, has coincided with the rising prevalence of obesity.

The past few decades of scientific progress have dramatically transformed our understanding of pathogenic mechanisms of obesity ( Fig 1 ). Fundamental roles for inflammation were unveiled by the discovery that tumor necrosis factor-α contributed to insulin resistance and the risk for type 2 diabetes in obesity [ 2 ]. Recent work has ascribed contributory roles for multiple immune cell types, such as monocytes/macrophages, neutrophils, T cells, B cells, dendritic cells, and mast cells, in disturbances in glucose and insulin homeostasis in obesity. In the central nervous system, microglia and their interactions with hypothalamic neurons affect food intake, energy expenditure, and insulin sensitivity. In addition to cell-specific contributions of central and peripheral immune cells in obesity, roles for interorgan communication have been described. Extracellular vesicles emitted from immune cells and from adipocytes, as examples, are potent transmitters of obesogenic species that transfer diverse cargo, including microRNAs, proteins, metabolites, lipids, and organelles (such as mitochondria) to distant organs, affecting functions such as insulin sensitivity and, strikingly, cognition, through connections to the brain [ 3 ].

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Basic, clinical/translational, and epidemiological research has made great strides in the past few decades in uncovering novel components of cell-intrinsic, intercellular, and interorgan communications that contribute to the pathogenesis of obesity. Both endogenous and exogenous (environmental) stressors contribute to the myriad of metabolic perturbations that impact energy intake and expenditure; mediate innate disturbances in the multiple cell types affected in obesity in metabolic organelles and organs, including in immune cells; and impair beneficial interkingdom interactions of the mammalian host with the gut microbiome. The past few decades have also witnessed remarkable efforts to successfully treat obesity, such as the use of the incretin agonists and bariatric surgery. Yet, these and other strategies may be accompanied by resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation. Hence, through leveraging novel discoveries from the bench to the bedside to the population, additional strategies to prevent obesity and weight regain post-weight loss, such as the use of “wearables,” with potential for implementation of immediate and personalized behavior modifications, may hold great promise as complementary strategies to prevent and identify lasting treatments for obesity. Figure created with BioRender.

https://doi.org/10.1371/journal.pbio.3002448.g001

Beyond intercellular communication mediated by extracellular vesicles, the discovery of interactions between the host and the gut microbiome has suggested important roles for this interkingdom axis in obesity. Although disturbances in commensal gut microbiota species and their causal links to obesity are still debated, transplantation studies have demonstrated relationships between Firmicutes/Bacteroidetes ratios and obesity [ 4 ]. Evidence supports the concept that modulation of gut microbiota phyla modulates fundamental activities, such as thermogenesis and bile acid and lipid metabolism. Furthermore, compelling discoveries during the past few decades have illustrated specific mechanisms within adipocytes that exert profound effects on organismal homeostasis, such as adipose creatine metabolism, transforming growth factor/SMAD signaling, fibrosis [ 5 ], hypoxia and angiogenesis, mitochondrial dysfunction, cellular senescence, impairments in autophagy, and modulation of the circadian rhythm. Collectively, these recent discoveries set the stage for the identification of potential new therapeutic approaches in obesity.

Although the above discoveries focus largely on perturbations in energy metabolism (energy intake and expenditure) as drivers of obesity, a recently published study suggests that revisiting the timeline of obesogenic forces in 20th and 21st century society may be required. The authors tracked 320,962 Danish schoolchildren (born during 1930 to 1976) and 205,153 Danish male military conscripts (born during 1939 to 1959). Although the overall trend of the percentiles of the distributions of body mass index were linear across the years of birth, with percentiles below the 75th being nearly stable, those above the 75th percentile demonstrated a steadily steeper rise the more extreme the percentile; this was noted in the schoolchildren and the military conscripts [ 6 ]. The authors concluded that the emergence of the obesity epidemic might have preceded the appearance of the factors typically ascribed to mediating the obesogenic transformation of society by several decades. What are these underlying factors and their yet-to-be-discovered mechanisms?

First, in terms of endogenous factors relevant to individuals, stressors such as insufficient sleep and psychosocial stress may impact substrate metabolism, circulating appetite hormones, hunger, satiety, and weight gain [ 7 ]. Reduced access to healthy foods rich in vegetables and fruits but easy access to ultraprocessed ingredients in “food deserts” and “food swamps” caused excessive caloric intake and weight gain in clinical studies [ 8 ]. Second, exogenous environmental stresses have been associated with obesity. For example, air pollution has been directly linked to adipose tissue dysfunction [ 9 ], and ubiquitous endocrine disruptor chemicals (EDCs) such as bisphenols and phthalates (found in many items of daily life including plastics, food, clothing, cosmetics, and paper) are linked to metabolic dysfunction and the development of obesity [ 10 ]. Hence, factors specific to individuals and their environment may exacerbate their predisposition to obesity.

In addition to the effects of exposure to endogenous and exogenous stressors on the risk of obesity, transgenerational (passed through generations without direct exposure of stimulant) and intergenerational (direct exposure across generations) transmission of these stressors has also been demonstrated. A leading proposed mechanism is through epigenetic modulation of the genome, which then predisposes affected offspring to exacerbated responses to obesogenic conditions such as diet. A recent study suggested that transmission of disease risk might be mediated through transfer of maternal oocyte-derived dysfunctional mitochondria from mothers with obesity [ 11 ]. Additional mechanisms imparting obesogenic “memory” may be evoked through “trained immunity.”

Strikingly, the work of the past few decades has resulted in profound triumphs in the treatment of obesity. Multiple approved glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP) agonists [ 12 ] (alone or in combinations) induce highly significant weight loss in persons with obesity [ 13 ]. However, adverse effects of these agents, such as pancreatitis and biliary disorders, have been reported [ 14 ]. Therefore, the long-term safety and tolerability of these drugs is yet to be determined. In addition to pharmacological agents, bariatric surgery has led to significant weight loss as well. However, efforts to induce weight loss through reduction in caloric intake and increased physical activity, pharmacological approaches, and bariatric surgery may not mediate long-term cures in obesity on account of resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation of these measures.

Where might efforts in combating obesity lie in the next decades? At the level of basic and translational science, the heterogeneity of metabolic organs could be uncovered through state-of-the-art spatial “omics” and single-cell RNA sequencing approaches. For example, analogous to the deepening understanding of the great diversity in immune cell subsets in homeostasis and disease, adipocyte heterogeneity has also been suggested, which may reflect nuances in pathogenesis and treatment approaches. Further, approaches to bolster brown fat and thermogenesis may offer promise to combat evolutionary forces to hoard and store fat. A better understanding of which interorgan communications may drive obesity will require intensive profiling of extracellular vesicles shed from multiple metabolic organs to identify their cargo and, critically, their destinations. In the three-dimensional space, the generation of organs-on-a-chip may facilitate the discovery of intermetabolic organ communications and their perturbations in the pathogenesis of obesity and the screening of new therapies.

Looking to prevention, recent epidemiological studies suggest that efforts to tackle obesity require intervention at multiple levels. The institution of public health policies to reduce air pollution and the vast employment of EDCs in common household products could impact the obesity epidemic. Where possible, the availability of fresh, healthy foods in lieu of highly processed foods may be of benefit. At the individual level, focused attention on day-to-day behaviors may yield long-term benefit in stemming the tide of obesity. “Wearable” devices that continuously monitor the quantity, timing, and patterns of food intake, physical activity, sleep duration and quality, and glycemic variability might stimulate on-the-spot and personalized behavior modulation to contribute to the prevention of obesity or of maintenance of the weight-reduced state.

Given the involvement of experts with wide-ranging expertise in the science of obesity, from basic science, through clinical/translational research to epidemiology and public health, it is reasonable to anticipate that the work of the next 2 decades will integrate burgeoning multidisciplinary discoveries to drive improved efforts to treat and prevent obesity.

Acknowledgments

The author is grateful to Ms. Latoya Woods of the Diabetes Research Program for assistance with the preparation of the manuscript and to Ms. Kristen Dancel-Manning for preparation of the Figure accompanying the manuscript.

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A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Affiliations.

• 1 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• 2 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia. Electronic address: [email protected].
• 3 RIADI Laboratory, University of Manouba, Manouba, Tunisia; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
• 4 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• PMID: 34426171
• DOI: 10.1016/j.compbiomed.2021.104754

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Keywords: Diseases; Machine learning; Obesity; Overweight; Risk factors.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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Home > Books > Role of Obesity in Human Health and Disease

Top 100 Most Cited Studies in Obesity Research: A Bibliometric Analysis

Submitted: 07 June 2021 Reviewed: 14 June 2021 Published: 22 December 2021

DOI: 10.5772/intechopen.98877

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Obesity represents a major global public health problem. In the past few decades the prevalence of obesity has increased worldwide. In 2016, an estimated 1.9 billion adults were overweight; of these more than 650 million were obese. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity. A bibliometric analysis study was designed to provide a comprehensive overview of top 100 most cited studies on obesity indexed in Web of Science database. The online search was conducted on June 6, 2021 using the keywords “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on document types or languages. The top 100 cited studies were selected in descending order based on number of citations. The obtained data were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the data were also imported in to HistCite™ for further citation analysis, and VOSviewer software for windows to plot the data for network visualization mapping. The initial search retrieved a total of 167,553 documents on obesity. Of the total retrieved documents, only top 100 most cited studies on obesity were included for further analysis. These studies were published from 1982 to 2017 in English language. Most of the studies were published as an article (n = 84). The highly cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal (Impact Factor 39.890, Incites Journal Citation Reports, 2021) in 2000 cited 10,543 times. The average number of citations per study was 2,947.22 (ranging from 1,566 to 10,543 citations). Two studies had more than 10,000 citations. A total of 2,272 authors from 111 countries were involved. The most prolific author was Flegal KM authored 14 studies with 53,558 citations. The highly active country in obesity research was United States of America. The included studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association (Impact Factor 56.272) published 17 studies and cited globally 51,853 times. The most frequently used keywords were obesity (n = 87) and overweight (n = 22). The countries with highest total link strength was United States of America (n = 155), followed by England (n = 140), and Scotland (n = 130). Our results show that most number of highly cited studies were published in developed countries. The findings of this study can serve as a standard benchmark for researchers to provide the quality bibliographic references and insights into the future research trends and scientific cooperation in obesity research.

• bibliometric analysis

Author Information

Tauseef ahmad *.

• Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China

*Address all correspondence to: [email protected];, [email protected]

1. Introduction

Obesity represents a major public health challenge, in the past few decades the prevalence of obesity has increased worldwide and associated with serious adverse health outcomes [ 1 , 2 ]. According to the statistics of World Health Organization, in 2016, an estimated 1.9 billion adults (18 years and older) were overweight, of these more than 650 million were obese. In 2019, 38 million children (under age of 5 years) were overweight or obese [ 3 ].

Obesity associated comorbidities including certain cancer, depression, fatty liver disease, hepatic steatosis, hyperlipidemia, hypertension, obstructive sleep apnea, orthopedic conditions, type 2 diabetes mellitus and social isolation [ 1 , 4 , 5 ]. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity.

Bibliometric type studies are of great interest, conducted not only to present an overall overview of the published scientific literature but also critical and subjective summarization of the most influential scientific studies [ 6 , 7 , 8 ].

This study aimed to provide a comprehensive overview of top 100 most cited studies on obesity. The finding can serve as a standard benchmark for researchers and to provide the quality bibliographic references.

3.1 Study design

Bibliometric citation analysis study.

3.2 Searching strategy and database

On June 6, 2021 the online search was conducted on Web of Science, Core Collection database (Philadelphia, Pennsylvania, United State of America). The search keywords used were “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on documents types or languages. The top 100 cited studies were selected in descending order based on number of citations.

3.3 Data extraction

The obtained studies were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the downloaded dataset were imported in to HistCite™ for further citation analysis.

3.4 Visualization network

Visualization network co-authorship countries and co-occurrence all keywords were plotted by using VOSviewer software version 1.6.15 ( https://www.vosviewer.com/ ) for windows.

4. Ethical approval

This study did not involve any human or animal subjects, thus, ethical approval was not required.

The initial search retrieved a total of 167,553 documents on obesity indexed in Web of Science database. Of the total retrieved documents, only top 100 most studies on obesity were included in this study. The included studies were published in English language. Most of the studies were published as an article (n = 84) followed by review (n = 14) and letter (n = 1). The average number of citations per study was 2,947.22, ranging from 1,566 to 10,543 citations.

The most cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal in 2000 cited 10,543 times. Another study “Positional cloning of the mouse obese gene and its human homolog” published in Nature in 1994 was cited 10,214 times. A total of 10 studies were cited more than 5,000 times. Furthermore, 52 studies were cited at least 2,000 times, while the remaining studies were cited more than 1,500 times. The top 100 studies on obesity is presented in Table 1 .

5.1 Most prolific authors

A total of 2,272 authors contributed to top 100 most cited studies. The most prolific author was Flegal KM authored 14 studies with 53,558 citations, followed by followed by Carroll MD (n = 10, citations = 36,950), and Ogden CL (n = 9, citations = 34,784). Only nine authors authored at least five studies as shown in Table 2 . In addition, only 22 authors contributed in at least three studies.

Top 100 most cited studies on obesity.

Note: LCS: Local citation score; LCS/t: Local citation score per year; GCS: Global citation score; GCS/t: Global citation score per year.

Authors with at least 4 studies.

5.2 Most active countries

A total 111 countries were involved in top 100 most cited studies on obesity. The most active country was United States of America (studies contributed: 75, citations: 217,788), followed by United Kingdom (studies contributed: 18, citations: 57,015), Canada (studies contributed: 9, citations: 17,920), Japan (studies contributed: 9, citations: 26,695), France (studies contributed: 8, citations: 21,228), Sweden (studies contributed: 8, citations: 20,632), and Netherlands (studies contributed: 7, citations: 13,018) as shown in Table 3 . Only 21 countries were involved at least in four studies.

Country with at least 3 studies.

Note: LCS: Local citation score; GCS: Global citation score.

5.3 Journals

The top 100 most cited studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association published 17 studies and cited globally 51,853 times as shown in Table 4 . Only seven journals published at least 4 studies, six journals published two studies each, while the remaining journals published a single study each.

Journals published at least 4 studies.

Note: IF: Impact Factor, Incites Journal Citation Reports, 2021; Q: Quartile; LCS: Local citation score; LCS/t: Local citation score per year; GCS: Global citation score; GCS/t: Global citation score per year.

5.4 Commonly used keywords

A total of 366 keywords were used in the top 100 most cited studies. The most widely used keywords were obesity (n = 87) and overweight (n = 22) as shown in Table 5 .

The keywords used at least ten times.

5.5 Year of publication

The top 100 most cited on obesity were published from 1982 to 2017 as shown in Figure 1 . The highest number of studies were published in 2006 (n = 9, citations = 29,552) and 2007 (n = 7, citations = 19,035) as presented in Figures 1 and 2 .

Publication years of top 100 most cited studies in obesity research.

Total global citation score per year of top 100 most cited studies in obesity research.

5.6 Co-authorship countries network visualization

The minimum number of studies for a country was fixed at 3. Of the total countries, only 38 countries were plotted based on total link strength (TLS) as shown in Figure 3 . The countries with highest TLS were United States of America (155), England (140), and Scotland (130).

Co-authorship countries network visualization. Two clusters are formed; red color represents cluster 1 (24 items), and green color represents cluster 2 (14 items).

5.7 Co-occurrence all keywords network visualization

Of the total keywords, only 69 were plotted as shown in Figure 4 . The keyword body-mass index has the highest TLS 117, followed by overweight (65), adipose-tissue (56), prevalence (53), weight (52), and obesity (49).

Co-occurrence all keywords network visualization. Three clusters are formed; red color represents cluster 1 (29 items), green color represents cluster 2 (26 items), and blue color represents cluster 3 (14 items).

6. Discussion

In recent years, bibliometric type studies have been increased significantly, these studies not only recognize the most influential studies in certain area but also determine the research shift and other important insights into the bibliometric parameters. Globally, obesity is a major public health problem and the prevalence has increased in the past few decades. Therefore, this study was undertaken to recognize the most influential studies in obesity research and provide essential bibliographic information. To the best of our knowledge this is the first bibliometric analysis on top 100 most cited studies on obesity indexed in Web of Science database. The highly cited study in obesity research received a total of 10,543 citations. The study published in a highly rated journal in medicine had an impact factor of 39.890 and placed in quartile 1 (Q1) category. The study entitled “Establishing a standard definition for child overweight and obesity worldwide: international survey” provides cut off points for body mass index in childhood of six large nationally representative cross sectional growth studies [ 9 ].

Another study received a total of 10,218 citations. The study titled “Positional cloning of the mouse obese gene and its human homologue” discusses the potential role of obese gene and these genes may function as part of a signaling pathway from adipose tissue that acts to regulate the size of the body fat depot [ 10 ].

The top 100 most cited were published in 33 journals. The most attractive and core journals in obesity research were JAMA-Journal of the American Medical Association (n = 17), and Nature (n = 14) had an impact factor of 56.272, and 49.962 respectively. A total of 31 studies were published in these two journals with a total citations of 100,377, thus representing the quality of work and aiming of the authors for high impact factor journals. Influential studies on obesity were published in higher impact factor journals. Furthermore, studies published in higher impact factor journals are more likely to be cited by the scientific community. The impact factor shows importance and quality of a journal [ 109 ]. The top three authors based on number of studies in obesity research were Flegal KM (n = 14, citations = 53,558), followed by Carroll MD (n = 10, citations = 36,950), and Ogden CL (n = 9, citations = 34,784). In our study, the leading country was United States of America contributed in a total of 75 studies with a total citations of 217,788. The finding is in line with studies in other research areas [ 110 , 111 , 112 , 113 ].

7. Conclusion

This study provides a comprehensive information of the most cited studies in obesity research. Majority of the most cited studies were published by developed countries in higher impact factor journals. The current study might be helpful to researchers for insights into the future research trends and scientific cooperation.

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Epidemiology and Population Health

How has big data contributed to obesity research? A review of the literature

• Kate A. Timmins   ORCID: orcid.org/0000-0002-7643-7319 1 ,
• Mark A. Green 2 ,
• Duncan Radley 3 ,
• Michelle A. Morris   ORCID: orcid.org/0000-0002-9325-619X 4 &
• Jamie Pearce 5  

International Journal of Obesity volume  42 ,  pages 1951–1962 ( 2018 ) Cite this article

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There has been growing interest in the potential of ‘big data’ to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are ‘found’ (as opposed to ‘made’), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of ‘found’ data to obesity research to date, and describe the benefits and challenges encountered. A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which ‘found’ data can complement conventional ‘made’ data: firstly, in moving beyond constraints in scope (coverage, size and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard-to-access population groups; and lastly in the potential for evaluating real-world interventions. Alongside these opportunities, ‘found’ data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage. Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.

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Introduction.

There has been growing interest in the potential of ‘big data’ for enhancing our understanding of a wide array of societal challenges including in medicine and public health. Facilitated by advances in computing hardware, software and networking, big data have been heralded as a powerful new resource that can provide novel insights into human behaviour and social phenomena. Despite the broad excitement and interest, there is no single agreed definition of big data. However, it is widely accepted that the greater volume, complexity, coverage and speed of availability of the observations and variables are critical components [ 1 , 2 ]. In contrast, conventional, or ‘small’, data (e.g. from trials, cohorts or surveys), tend to be produced in more constrained ways using sampling strategies that restrict the scope (e.g. number of questions), size (e.g. number of respondents) or temporality (e.g. number of time points).

Big data generation tends to strive to: be comprehensive, often capturing full populations; have high temporal and/or spatial resolution; be interlinked and connected across different data resources with common fields to enable unique identification; and be dynamic and adaptive to allow new and greater quantities of data to be readily appended [ 3 ]. Connelly et al. [ 2 ] make the useful distinction between data that are ‘made’ and that which are ‘found’. ‘Made’ data include information collected to investigate a defined hypotheses; whereas ‘found’ data have been collected for alternative (often non-research) purposes, but could include potentially valuable information for research. The sources and production of ‘found’ data include, but are not limited to, online activities (e.g. social media, web searches), commercial transactions (e.g. in-store purchase from supermarkets or bank transactions), remote physiological sensors (e.g. heart-rate monitors) or environmental sensors (e.g. GPS, satellite data).

With increasing volumes and greater access to data in electronic formats, it is unsurprising that researchers are beginning to apply big data to key concerns including mental health [ 4 ], infectious disease [ 5 ] and healthcare [ 6 ]. In the field of obesity research, there is a long history of using routine data sources to track the prevalence of the disease, as well as identify risk factors. Supplementing this with new forms of data has potential to broaden our understanding of obesity, bringing together information from different facets of environment and behaviours. Although obtaining, analysing and disseminating big data has potential to benefit society, there are also a number of possible risks [ 3 , 7 ], including challenges relating to data governance and methodological robustness. There has not yet been an attempt to review the current applications of big data to obesity-related research.

The aim of this paper is to review the contribution of ‘found’ data (adopting Connelley et al’s distinction) to obesity research, and consider the implications for the future of big data in this field. We focus on data that have been repurposed for research, rather than data originally designed for research or health monitoring purposes (such as health register or birth cohorts), because these sources of data offer new opportunities and challenges compared to conventional ‘made’ research data. Our intention is to review the nature and scope of the research that is emerging, and describe the benefits and challenges encountered.

The aim of this review was illustrative, rather than to provide an exhaustive examination of obesity research examples. We developed a narrative, rather than systematic, review that identifies and collates research in which ‘found’ data have been adopted to address obesity-related concerns. From a scoping of the literature in November 2016, informed by activities within the ESRC Strategic Network for Obesity meetings (reference pending), we identified six categories of data: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. These data categories are described in the Results.

Database searches were conducted between January and April 2017 (MEDLINE, PsycINFO, SPORTDiscus) using search terms such as: obesity, diet*, physical activity, body mass index, big data, commercial data, loyalty card, smart ticket, smart metr*, point of sale, tax*, purchas*, social media, crowd sourc*, app, mobile phone, cell phone. We only considered articles published in English in peer-reviewed academic literature, which described original research, and that used data sets not originally intended for research purposes. Outcomes considered relevant included measures of obesity, as well as dietary or physical activity outcomes. Search updates were run in September 2017, and articles were also found through citations and expert recommendation.

For each data category, we collated details from relevant studies to describe the data used, how and why they had been used, and the benefits and limitations of using them. We then considered as a whole the extent to which these data had contributed to obesity research to date.

An overview of the examples found in the literature can be seen in Table  1 , including a brief summary of the added value and limitations of each data type. These are described in more detail below.

Retail sales data

What are the data.

Perhaps the earliest usage of ‘found’ data for obesity research involves the examination of retail sales data. Product sales data have long been collected by retailers to monitor transactions. Data can be taken directly from barcode scanners [ 8 , 9 ], consumer marketing panels [ 10 ], retailer data sets [ 11 , 12 , 13 , 14 , 15 ] or national-level industry data [ 16 , 17 ]. More recently, these data have been linked to individual-level information (e.g. age, sex, address) using store loyalty cards [ 18 ].

What has the data been used for?

Published studies have had varied purposes: monitoring nutrient or food intakes at a population level [ 8 , 16 , 17 ], ascertaining national or regional nutrient availability [ 19 ], comparing ‘vice’ purchases online versus in store [ 15 ], or evaluating the impact of policies or interventions (e.g. changes to benefits (food stamps) [ 12 ], nutrition labelling [ 20 ], taxation [ 10 , 14 ] or public health campaigns [ 13 ]). Some studies have looked at the association between sales and aggregate-level outcomes (e.g. national-level BMI estimates [ 16 , 17 ]), or examined longitudinal patterns in sales [ 10 , 13 , 14 ].

What do they add over and above conventional data?

There appear to be three motivations for using this type of data: wide coverage (e.g. population level [ 16 , 17 ]); high ecological validity [ 14 , 15 ] and benefits of automation [ 8 , 21 ]. Conventional dietary assessment is often criticised as: burdensome, reliant on self-reports, expensive and typically only practical for use during a short window of time. Automatically collected sales data could reduce both respondent [ 22 ] and researcher [ 21 ] burden, and potentially minimise self-report errors [ 9 , 19 , 21 ]. Automation should also be considerably more cost-effective [ 8 , 9 , 11 , 21 , 22 ], enabling the collection of longitudinal and more timely data.

Sales data may be particularly useful for quasi-experimental evaluations of policy, where conventional randomised controlled trials (RCTs) may not be possible, and timely, longitudinal data are crucial. For example: Nikolova et al. [ 20 ] investigated the effect of point-of-sale nutritional information on consumer behaviour; Andreyeva et al [ 12 ] assessed the impact on nutrient purchases following revisions to federal food provision in the US; Colchero et al. [ 10 ] monitored panel members’ drinks purchases before and after the introduction of a tax on sugar-sweetened beverages in Mexico; Schwartz et al. [ 13 ] examined supermarket sales of sugary drinks before and during a campaign to reduce consumption and compared sales to those outside the community; and Silver et al. [ 14 ] looked at the impact of a tax on sugar-sweetened beverage consumption before and after a tax was implemented in Berkeley, California.

What are the limitations?

All studies identified issues in coverage, as they were only able to access data from certain supermarket chains [ 13 , 14 ] or panels, which were not representative [ 10 ]. In addition, purchases of food and drinks do not necessarily equate to dietary consumption [ 8 , 12 , 22 ]. Furthermore, no studies have yet been able to link to individual-level health outcomes. Several authors also described problems with the quality of the data, for example, missing data due to technical faults or inconsistencies in recording [ 9 , 14 , 19 , 21 ]. This is compounded by the dynamic nature of the retail food market [ 21 , 22 ]. Data linkage was one of the main challenges identified in this type of study.

Quasi-experimental studies, whilst high in ecological validity, are unable to isolate the causal mechanism given the many potential confounders, and researchers struggle to find appropriate comparison data; some studies compared to counterfactual data (i.e. consumption predicted on the basis of pre-tax trends), which come with a number of assumptions [ 10 , 14 ] and do not generate results demonstrating causal relationships.

A final challenge identified is the relationship with commercial partners. There is a concern that these data sets may prove cost-prohibitive for research purposes [ 22 ], and that their use may be restricted by non-disclosure agreements [ 22 ] or confidentiality worries [ 19 ]. Difficulties initiating partnerships or with finding partners with appropriate data collection were also described [ 14 ].

Transport monitoring has long involved the collection of data on mode and volume of transport to aid in planning and infrastructure. Collection of transport data is increasingly sophisticated and new technologies can offer novel insights into travel and lifestyle behaviour as well. For example, on-board sensors within vehicles to monitor vehicle performance can provide data on travel patterns. External sensors along transport networks such as roads or public transport are also increasingly more common both for monitoring transport flows and in the fields of urban informatics. The popularity of smart card systems for public transport systems also presents an opportunity for obtaining information on destinations, routes and transport modes, and may include additional information about individuals such as socio-demographic characteristics.

What have the data been used for?

There were few applications utilising such data within obesity-related research. Some studies have used aggregated data sources to explore patterns associated with obesity. For example, Lopez-Zetina et al. [ 23 ] used data collected from the ‘Highway Performance Monitoring System’ on traffic flow data for public roadways in the US to investigate the ecological association between areas with greater motorised transport usage (vehicle miles of travel) and obesity prevalence. US driver licence data have also been proposed as a potentially useful opportunity as they contain information on height and weight [ 24 ]. Other applications have compared the impacts from the introduction of city-based bicycle hire schemes, by analysing usage data from cycle hire stations [ 25 ]. Some studies have also used these data as inputs to simulation models to estimate the impacts on health outcomes [ 26 , 27 ].

Transport data often include explicit information about spatial location. We know little about the activity spaces and environments that individuals engage within their daily lives and these data can illuminate the role of urban structure, utilisation of services, or engagement with green space. Conventional research exploring their associations with obesity tend to rely on simple approximations of these concepts, whereas new forms of data can provide a more valid and objective picture of exposure. They additionally present greater detail on how individuals are engaging with different modes of transport. The rise of private motorised transport has been touted as one important driver of obesity trends [ 23 ]. These data can therefore help to improve our understanding of physical activity from transport options that conventional data are unable to cover.

A key criticism is that many data sources only contain journey information, with little additional information about lifestyle behaviours or socio-demographic characteristics. Similar to retail sales data (above), the link between what is measured and the relevant behaviour can only be assumed or extrapolated. For example, knowing that an individual travelled from point A to point B can only inform us about the direction of their travel, and not the impact of travel on physical activity or dietary behaviours, nor the wider impact of an intervention. Data linkage is therefore important to be able to unpick these complex interactions to provide robust explanations for obesity-related behaviour.

Commercial weight management data

This category refers to data that are provided by commercial weight management programmes. Weight management programmes routinely collect data not for research but as a standard part of their service provision. The intended use of the data may vary, possibilities including: client orientated feedback (e.g. self-monitoring), continuous service improvement (e.g. to monitor adaptations to programme content/delivery) and, if the service is being delivered as a procured provision, to monitor contractual targets (e.g. reporting key performance indicators). Data sets are often substantial in terms of participant numbers, and include information on individual characteristics (e.g. socio-demographic factors), engagement with the programme (e.g. enrolment, attrition or service usage) and weight outcomes.

Commercial data provide the opportunity for independent real-world service evaluations. For instance: Ahern et al. [ 28 ] reported outcomes for 29,326 participants attending Weight Watchers NHS Referral Scheme between April 2007 and October 2009; Finley et al. [ 29 ] examined 60,164 men and women, aged 18–79 years, who enrolled in the Jenny Craig Platinum programme between May 2001 and May 2002; Johnson et al. [ 30 ] investigated Nutracheck, a direct-to-consumer Internet weight-loss programme; Stubbs et al. [ 31 ] reported the short-term outcomes of 1,356,105 self-referred, fee-paying adult participants of Slimming World groups joining between January 2010 and April 2012; and Fagg et al. [ 32 ] assessed outcomes associated with participation in a family-based weight management programme (MEND 7–13, Mind, Exercise, Nutrition..Do it!) for childhood overweight in 21,132 referred or self-referred children.

These outcome evaluations provide important insight given that many large-scale programmes being used to treat obesity have not had their effectiveness formally evaluated using recognised research methodologies (e.g. RCTs). Further, even when programmes have been rigorously evaluated under trial conditions, programme effectiveness observed within controlled settings may differ to outcomes in real-world contexts [ 33 , 34 ].

The data also provide the opportunity to consider a variety of research questions that are commonly not addressed within conventional effectiveness trial research designs or are beyond the scope of such evaluations. For instance, the data collected are often substantial in terms of numbers of participants: Fagg et al. [ 32 , 35 ] were able to investigate: who is referred to, who started and who completed a child weight management intervention when delivered at scale; whether the socio-demographic characteristics of children attending the intervention matched those of the eligible population; changes in BMI observed under service conditions with those observed under research conditions; and how outcomes of the intervention varied by participant, family, neighbourhood and programme characteristics—all of which was enabled by the large-scale implementation of the intervention.

The wide-reaching scope of data in terms of participants also could allow investigation into hard-to-reach populations who are typically under-represented in conventional research. For example, Fagg et al. were able to explore patterns in programme usage by ethnicity and socioeconomic status—both of which are important to increase our understanding of health inequalities. Combining with other data sources, such as social media, transport and geospatial data, could present further useful insights, for example, by exploring relationships between the environment and programme outcomes.

Similar to the literature on retail sales data (see above), it is recognised that data accessibility, quality, completeness and representativeness must be addressed. Commercial sensitivities also need to be considered, as do ethical issues surrounding consent for data use and achieving appropriate levels of information security, confidentiality, and privacy, particularly given that individual-level data may be involved.

Geospatial refers to data in which the location of objects across environments are stored with a spatially explicit dimension. They include the location of services (e.g. healthcare facilities, restaurants), the layout of road networks, or features of the built environment (e.g. parks, woodland). Data may be accessed through retail databases, national mapping agencies, satellite technology or web mapping platforms (e.g. Google Maps, OpenStreetMap).

Geospatial data have been used to measure different features of the built and natural environment. Many studies have calculated simple counts of retail locations such as fast food outlets as a measure of exposure. For example, consumer and national agency data sources were used to create open access measures of accessibility to retail opportunities including fast food outlets or leisure services [ 36 ]. Other mapping services such as ‘Google Street View’ [ 37 , 38 ] and remote sensing [ 39 , 40 ] have also been used to develop virtual audits of environmental features which are then correlated to measures of obesity.

Where locational information has been collated using conventional approaches (e.g. field audits, surveys), they are often restricted in multiple ways. Data may be collected separately by locale, resulting in gaps in spatial coverage, discrepancies in the information provided by locale, or a lack of joined-up inclusion of data limiting the ability to undertake national-level analyses. They may appear temporally infrequent, and while annual data may be appropriate, services such as Google Maps can allow finer temporal resolution for nuanced analyses. Conventional data sources may also impose costs or licensing arrangements of use of data or in accessing data.

The main drawback is similar to that identified for transport data (above). Typically, geospatial data are fairly basic containing only the location and type of object. To build up a comprehensive view of how humans interact with these objects, we need to know much more. For example, while identifying the location of fast food outlets is valuable, also important are details on types of food sold, opening hours, business turnover, and the nature of in-store marketing and product placementLinkage of data to other sources may increase their usefulness in obesity research—for example, tracking individuals’ movements within and interactions with the environment using GPS-enabled smartphones (see below).

Social media

Social media are computer-assisted technologies that facilitate the creation of virtual networks connecting individuals and allowing the sharing of information. Their use has grown since the beginning of the twenty-first century and are embedded in the everyday lives of many people with, for example, 63% of UK adults using online social networks daily [ 41 ]. The ways in which individuals interact with these services are stored by their providers and can be made available to researchers.

Twitter data represented the majority of studies utilising social media sources. Twitter is an online platform where users can write and share short posts of (at the time of writing) 140 characters or fewer (and may include geographical location when sent using mobile devices). Unlike other social media platforms, Twitter makes a portion (~1%) of its data freely available. Studies typically focused on using descriptive statistics to examine patterns of what was posted. Some studies used geotagged tweets to produce geographical measures of behaviours including dietary behaviours [ 42 , 43 , 44 ], physical activity [ 44 , 45 ] or happiness/wellbeing [ 42 , 46 ]. These were then correlated with data on obesity rates or the density of fast food outlets. Other examples include using social network analysis to explore how messages about childhood obesity spread between individuals [ 47 ].

Other social media platforms have been less commonly utilised. Facebook data on posts shared and interests followed (identified using ‘likes’) were used as proxies for behaviours and opinions/perceptions surrounding obesity [ 48 , 49 , 50 ]. One study examined correlations between these data and ecological measures of obesity [ 51 ]. Other examples included using Reddit posts to characterise discussions about weight loss [ 52 ], utilisation of fast food outlets using Foresquare and Instagram [ 53 ], Strava data to explore physical activity behaviours [ 54 ] or self-reporting of body weight on an online forum [ 55 ].

With individuals opting to increasingly document their lives through digital platforms, social media data offer the potential to form intricate understandings of opinions, interactions with objects, locations and other individuals [ 56 ]. There is a paucity of data on social networks of individuals, and collecting ‘made’ data on the topic is both intensive and costly. Social media data offer cheaper and more comprehensive data on the issue, which can facilitate more in-depth studies on human interactions (particularly international interactions which are rarely considered). This is important given that it has been previously demonstrated that social networks have important roles in understanding obesity [ 57 ].

Few studies have engaged with the representativeness of social media data. For example, studies using Twitter data are purely describing patterns within Twitter users only, who disproportionately represent younger age groups [ 58 ], or even within just those Twitter users who allow geotagging (estimated at just over 1% [ 59 ]). Moving beyond single platforms will not only improve the generalisability of findings, but also open up opportunities for understanding how individuals engage with the increasing digitalisation of life. Linked to this notion of representativeness, we cannot ignore the increasing proportion of ‘bots’ among social media sites. Bots are automated social media accounts which post content with the aim of mimicking the behaviours of individuals. As such, they may contribute data to research, introducing bias to analyses [ 60 ]. Furthermore, our online personalities may not approximate who we are ‘offline’ [ 61 ].

Smartphones and wearable technologies

Smartphones are increasingly pervasive—estimates suggest almost 70% of US adults owned a smartphone in 2015 [ 62 ]. With ever more sophisticated technology, many smartphones now incorporate a range of sensors and logs that open up opportunities for continuous collection of data in free-living environments. Often used alongside smartphones, linked devices, such as wrist-worn activity monitors or heart-rate monitors (wearable technologies), are used to track a user’s behaviour and are often used to supplement ‘life-logs’. Data may be made available from device or app manufacturers.

Studies have typically used smartphone data to describe physical activity outcomes, such as step counts, GPS movements or logged journeys. In this way, activity patterns have been explored across populations, temporally or spatially [ 63 , 64 , 65 ]. There is some overlap here with geospatial data, where smartphone-integrated GPS can be triangulated with app data to describe the use of neighbourhoods or environments. As many smartphones and apps are widely utilised, the data can be used to make international comparisons, for example, correlating activity levels (using step counts) with national obesity trends [ 66 ]. Smartphone data have also been used to evaluate interventions: Heesch et al. [ 67 ] examine cycling behaviour before and after infrastructure changes. Other uses include assessing the influence of smartphone games on physical activity (Pokémon GO [ 68 , 69 ]), or characterising successful users of a weight-loss app (Lose It! [ 62 ]).

A key advantage of smartphone data is the wide-scale coverage, often international. This enables research that is broad in geographic scope, and large data sets offer additional analytical possibilities by being split into ‘training’ and ‘validation’ subsets [ 62 ]. In addition, where data recording is ‘passive’ and continuous, there is a lower respondent burden than many conventional methods, with potential benefits for participant adherence and longitudinal data collection. Apps which require users to actively log information (i.e. the data are non-passively generated) often include prompts and reminders, and thus may offer similar advantages as recognised for Ecological Momentary Assessment [ 70 ]. Incorporating GPS also allows the collection of geographically specific information. Several authors identified that sampling or inferential issues could be at least partially overcome by triangulating smartphone data with conventional research data to offer reassurances in terms of representativeness and validity.

A key issue is sampling: only those individuals who own a particular app, device or model of smartphone will be included in the data. Furthermore, authors cited concerns about the lack of control on data generation, as participants may not consistently carry their phone with them and switched on [ 64 , 66 ]. Missing data due to technical reasons were also common, for example when signal or battery cut out [ 64 , 71 ]. Smartphones are also unable to capture activities where people are unlikely to have their phone on them, such as contact sports or swimming. Finally, user behaviour may be both measured by and influenced by the smartphone app or wearable device itself, with potential repercussions for the interpretation of findings.

This paper provides an overview of how ‘found’ data have been used in obesity research to date. The narrative review highlights the variety of uses in the literature, with contrasting types of data and varied research questions: from describing the built environment, to exploring social networks, estimating nutrient purchases or assessing the impact of interventions. Importantly, each of the described studies has attempted in some way to use this data to infer behaviours associated with energy balance (diet and physical activity) or to understand the context in which obesity-related behavioural decisions are made. In the ensuing discussion, we offer a summary of the opportunities highlighted by the literature. The intention is to illustrate areas of interest and promise, rather than attempt a full critical evaluation of the use of data in these studies.

Opportunities for big data research

The examples identified in this review demonstrate four significant ways in which ‘found’ data can complement the more conventional ‘made’ data: firstly, in moving beyond constraints in scope (in terms of coverage, size, and temporality); secondly, in providing objective, quantitative measures where conventional research has had to rely on self-reported data; thirdly, in reaching populations that have proven difficult to access with conventional research methods; and lastly in its potential for evaluating real-world interventions. We discuss each of these opportunities in turn.

Firstly, many of the examples of ‘found’ data described here are remarkable in their broad scope and coverage. The constraints of conventional ‘made’ data have provided much of the impetus for exploring the potential of repurposed data. Advocates of ‘found’ data suggest that automation could reduce the burden of data collection [ 8 , 21 ]. It follows that a reduction in burden would allow more data to be collected over a longer period, both because of reduced costs and also due to reduced participant burden. This was particularly evident in the retail sales literature. RCTs or evaluations could automatically be updated with long-term data without having to collect a lot of information from participants.

Secondly, automated data collection could make an important contribution where conventional methods rely on self-reported information. There is much research that has documented the systematic biases, which have plagued obesity-related research through individuals misreporting their weight, dietary intake, or physical activity [ 72 ]. Other important factors that have proven traditionally difficult to measure include environmental characteristics which are theorised to have a role in the aetiology of obesity [ 73 , 74 ]. Data from transport and geospatial sources, in particular, could offer a means of capturing environmental features, although work may still be needed to develop meaningful, validated metrics. Given the suspected multi-faceted influences on obesity [ 75 ], the ability to measure specific aspects of the aetiology of obesity will help to build a more complete picture of its determinants. Thus, the opportunities afforded through objective data automatically collected from ‘found’ data could revolutionise our understanding of many complex areas [ 56 ]. The ability to quantify increasingly complex scenarios could also prove invaluable for predictive explorations, such as investigating system dynamics or agent-based modelling [ 76 ].

Thirdly, we can leverage the broad scope of these big data to explore hard-to-reach populations that conventional data are unable to access or provide precise estimates on [ 56 , 77 ]. For example, the Health Survey for England 2014 [ 78 ], one of the largest and most comprehensive sources of data on health-related behaviours ( n  = 10, 041), included only 1332 non-White individuals. Understanding the role of ethnicity, a key non-modifiable factor in obesity research, becomes problematic here. Big data can help, and can be extended to smaller groups as well. Linked to this, the growing interest in understanding the heterogeneity of obesity [ 79 ] can be improved through capturing more nuanced data to examine the interactions between risk factors and behavioural characteristics.

Finally, ‘found’ data provide a key opportunity for quasi-experimental research, by which we mean natural experiments that assess the impact of a policy or intervention. Examples from our review included evaluations of commercial weight management programmes [ 28 , 29 , 30 , 31 , 35 ], and assessing the impacts of events as diverse as infrastructure changes (e.g. new cycle routes) [ 67 ], popular gaming apps [ 68 , 69 ], changes to taxation on obesity-related commodities (e.g. sugar-sweetened beverages) [ 10 , 14 ] or local campaigns [ 13 , 20 ]. These examples illustrate the value of repurposed data for assessing real-world change. For example, without ‘found’ data, conventional methods would have required a cohort recruited well before an intervention or policy was implemented, with longitudinal collection of data. Using repurposed data that have been collected consistently for an adequate period of time, on the other hand, means that timely, longitudinal patterns can be explored, without a costly and lengthy lead-in. Although necessarily observational, and whilst there may be difficulties in finding appropriate comparators, the implications for the evaluation of public health (and other) policies are obvious. A number of these quasi-experimental studies adopted a combined approach [ 14 , 67 ], complementing the use of ‘found’ data with a more conventional research design, which illustrates perhaps one of the ways the limitations of big data could be addressed.

Quasi-experimental studies were rare for some types of data—namely travel, geospatial and social media data—and published studies in these categories predominantly focussed on descriptive, rather than causal, questions. This could be a promising area for future research: if causal investigation could broaden across multiple levels of determinants, such as those described by the Social-Ecological Model [ 80 ], from the individual to the structural, the ability to look at multiple factors across multiple scales might better allow us to begin to unpack the complexity of obesity development and prevention. Mapping the possible data sources that would allow this is an important first step to realising multi-level research, and forms the basis of the subsequent paper from our network (reference pending).

These opportunities are not without challenges. Many of the limitations described in this review are not necessarily new. For example, ‘found’ data sets typically comprise convenience samples [ 56 ]. However, the use of ‘found’ data also throws up some distinct challenges, such as:

ethical and legal questions around access and ownership of data

commercial sensitivities and potential costs

lack of control over data acquisition

questions over attributional adequacy—big data are often mono-thematic with great depth but limited breadth—and the clinical relevance of measurements

finding appropriate comparators

new skills and capabilities necessary for data processing, management and linkage.

These challenges have been well described by colleagues in relation to other health outcomes [ 2 , 7 , 56 ], and a further detailed exposition of these limitations is not possible here. However, addressing these issues will be of vital importance to enable utilisation of these data as well as considering the profound implications in terms of validity.

Accessibility to each data type was a common barrier to the usage of big data in obesity-related research. Many data types were held by industrial partners who are not always willing to permit researchers to use this information (although there are numerous examples where commercial data are being utilised for research purposes) or the costs associated with usage were prohibitive. Recently, multiple trusted third parties have been established to provide indirect access to such data and help bridge such gaps between industry and researchers (e.g. Consumer Data Research Centre in UK). Social media and geospatial data were more often openly available, hence the preponderance of studies utilising this type of information. Time and cost were minimal issues in reducing access, and when compared to traditional data, found data can be more efficient in terms of time and cost for data collection [ 3 ]. While there is no natural order to the quality or reliability of found data, we advocate that the pitfalls of ‘big data research’ are no different from traditional research. Any data should be assessed for its representativeness or bias no matter how big or small. For example, while Twitter data were the most common data source encountered in the review, the key limitation of this information is that it is not generalisable to whole population [ 56 ].

It is perhaps as important to comment on the gaps in data usage. The literature described here demonstrate initial forays into big data usage in the field of obesity. However, there are examples of ‘found’ data usage in other research areas that were notably absent in the obesity literature. For example, we did not observe any studies, which made use of ‘found’ data in the form of physiological or biological measurements, although measurement is becoming possible through smartphone technologies (e.g. peripheral capillary oxygen saturation or heart rate) [ 81 ]. This highlights that there are many future opportunities in exploring untapped data sources.

Limitations of the review

This review was not intended as an exhaustive examination of obesity research using ‘found’ data; rather, the aim was to illustrate the opportunities afforded by such data. This was important to demonstrate how and why such forms of data have been used in obesity research to date, and provide some key opportunities as to what can be achieved with such data in the future. It is also important to note that the scope of this synthesis was limited to academic literature.

The focus here was on ‘found’ data, repurposed for research, rather than on ‘big data’. Big data are not synonymous with ‘found’ data. However, much of the data described as ‘big’ has been repurposed from non-research-specific sources. This, we believe, is where much of the opportunity of big data lies: where data are collected anyway, its scope in terms of coverage, timeliness and automation could make a real, fresh contribution to the ways we are able to measure behavioural and environmental variables. By focussing on ‘found’ data, we hoped to identify its potential as well as the concomitant challenges, regardless of size, ‘big’ or ‘small’. Some of the studies described would not be considered ‘big’ by most, yet these smaller examples help to reveal or address potential problems with validity or data processing. In many cases, it is apparent that these need to be resolved at this smaller scale before upscaling to larger data sets.

Our focus has meant that some undeniably ‘big’ data sets are absent from our narrative: health registers and genetic databases were beyond our scope, yet their potential in obesity research is apparent. Many of the advantages described for ‘found’ data also apply to these data types: for example, health registers offer great scope in terms of volume and longitudinal and geographical coverage. However, ‘found’ data are an as yet under-utilised source of information, and many of the opportunities have yet to be exploited. ‘Found’ data also come with unique challenges to processing, storage and interpretation, given that they are created outside a research environment, and are therefore worthy of separate attention.

Conclusions

This paper has shown the limited extent to which ‘found’ data have been employed in academic obesity research to date, as well as describing the unique contribution such data can add to conventional research. The examples from the literature demonstrate how the merit of such data lies not in their novelty, but in the benefits they add over and above, or in combination with, conventionally collected data. However, alongside these new opportunities, there are new and distinct challenges. There is still a need to investigate ways to combine these new forms of data with conventional research to increase confidence in their validity and interpretation.

Despite widespread recognition of the opportunities across a broad spectrum of disciplines and data types, the potential of ‘found’ data has not yet been fully realised, and the impact on academic obesity research has been limited. In part, this may be due to limited data access, or even a lack of awareness about the data that may be available. The aim of the next paper from the ESRC Strategic Network for Obesity (reference pending) is to highlight the potential sources of data for further research of this type, many of which are as yet untapped.

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Acknowledgements

The ESRC Strategic Network for Obesity was funded via Economic and Social Research Council grant number ES/N00941X/1. We would like to thank all of the network investigators ( www.cdrc.ac.uk/research/obesity/investigators/ ) and members ( www.cdrc.ac.uk/research/obesity/network-members/ ) for their participation in network meetings and discussion, which contributed to the development of this paper. Additional thanks are owed to Daniel Lewis for his insightful comments on the manuscript.

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Timmins, K.A., Green, M.A., Radley, D. et al. How has big data contributed to obesity research? A review of the literature. Int J Obes 42 , 1951–1962 (2018). https://doi.org/10.1038/s41366-018-0153-7

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Received : 08 October 2017

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Published : 18 July 2018

Issue Date : December 2018

DOI : https://doi.org/10.1038/s41366-018-0153-7

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Obesity Research

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Over the years, NHLBI-supported research on overweight and obesity has led to the development of evidence-based prevention and treatment guidelines for healthcare providers. NHLBI research has also led to guidance on how to choose a behavioral weight loss program.

Studies show that the skills learned and support offered by these programs can help most people make the necessary lifestyle changes for weight loss and reduce their risk of serious health conditions such as heart disease and diabetes.

Our research has also evaluated new community-based programs for various demographics, addressing the health disparities in overweight and obesity.

NHLBI research that really made a difference

• In 1991, the NHLBI developed an Obesity Education Initiative to educate the public and health professionals about obesity as an independent risk factor for cardiovascular disease and its relationship to other risk factors, such as high blood pressure and high blood cholesterol. The initiative led to the development of clinical guidelines for treating overweight and obesity.
• The NHLBI and other NIH Institutes funded the Obesity-Related Behavioral Intervention Trials (ORBIT) projects , which led to the ORBIT model for developing behavioral treatments to prevent or manage chronic diseases. These studies included families and a variety of demographic groups. A key finding from one study focuses on the importance of targeting psychological factors in obesity treatment.

Current research funded by the NHLBI

The Division of Cardiovascular Sciences , which includes the Clinical Applications and Prevention Branch, funds research to understand how obesity relates to heart disease. The Center for Translation Research and Implementation Science supports the translation and implementation of research, including obesity research, into clinical practice. The Division of Lung Diseases and its National Center on Sleep Disorders Research fund research on the impact of obesity on sleep-disordered breathing.

Find funding opportunities and program contacts for research related to obesity and its complications.

Current research on obesity and health disparities

Health disparities happen when members of a group experience negative impacts on their health because of where they live, their racial or ethnic background, how much money they make, or how much education they received. NHLBI-supported research aims to discover the factors that contribute to health disparities and test ways to eliminate them.

• NHLBI-funded researchers behind the RURAL: Risk Underlying Rural Areas Longitudinal Cohort Study want to discover why people in poor rural communities in the South have shorter, unhealthier lives on average. The study includes 4,000 diverse participants (ages 35–64 years, 50% women, 44% whites, 45% Blacks, 10% Hispanic) from 10 of the poorest rural counties in Kentucky, Alabama, Mississippi, and Louisiana. Their results will support future interventions and disease prevention efforts.
• The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is looking at what factors contribute to the higher-than-expected numbers of Hispanics/Latinos who suffer from metabolic diseases such as obesity and diabetes. The study includes more than 16,000 Hispanic/Latino adults across the nation.

Find more NHLBI-funded studies on obesity and health disparities at NIH RePORTER.

Read how African Americans are learning to transform soul food into healthy, delicious meals to prevent cardiovascular disease: Vegan soul food: Will it help fight heart disease, obesity?

Current research on obesity in pregnancy and childhood

• The NHLBI-supported Fragile Families Cardiovascular Health Follow-Up Study continues a study that began in 2000 with 5,000 American children born in large cities. The cohort was racially and ethnically diverse, with approximately 40% of the children living in poverty. Researchers collected socioeconomic, demographic, neighborhood, genetic, and developmental data from the participants. In this next phase, researchers will continue to collect similar data from the participants, who are now young adults.
• The NHLBI is supporting national adoption of the Bright Bodies program through Dissemination and Implementation of the Bright Bodies Intervention for Childhood Obesity . Bright Bodies is a high-intensity, family-based intervention for childhood obesity. In 2017, a U.S. Preventive Services Task Force found that Bright Bodies lowered children’s body mass index (BMI) more than other interventions did.
• The NHLBI supports the continuation of the nuMoM2b Heart Health Study , which has followed a diverse cohort of 4,475 women during their first pregnancy. The women provided data and specimens for up to 7 years after the birth of their children. Researchers are now conducting a follow-up study on the relationship between problems during pregnancy and future cardiovascular disease. Women who are pregnant and have obesity are at greater risk than other pregnant women for health problems that can affect mother and baby during pregnancy, at birth, and later in life.

Find more NHLBI-funded studies on obesity in pregnancy and childhood at NIH RePORTER.

Learn about the largest public health nonprofit for Black and African American women and girls in the United States: Empowering Women to Get Healthy, One Step at a Time .

Current research on obesity and sleep

• An NHLBI-funded study is looking at whether energy balance and obesity affect sleep in the same way that a lack of good-quality sleep affects obesity. The researchers are recruiting equal numbers of men and women to include sex differences in their study of how obesity affects sleep quality and circadian rhythms.
• NHLBI-funded researchers are studying metabolism and obstructive sleep apnea . Many people with obesity have sleep apnea. The researchers will look at the measurable metabolic changes in participants from a previous study. These participants were randomized to one of three treatments for sleep apnea: weight loss alone, positive airway pressure (PAP) alone, or combined weight loss and PAP. Researchers hope that the results of the study will allow a more personalized approach to diagnosing and treating sleep apnea.
• The NHLBI-funded Lipidomics Biomarkers Link Sleep Restriction to Adiposity Phenotype, Diabetes, and Cardiovascular Risk study explores the relationship between disrupted sleep patterns and diabetes. It uses data from the long-running Multiethnic Cohort Study, which has recruited more than 210,000 participants from five ethnic groups. Researchers are searching for a cellular-level change that can be measured and can predict the onset of diabetes in people who are chronically sleep deprived. Obesity is a common symptom that people with sleep issues have during the onset of diabetes.

Find more NHLBI-funded studies on obesity and sleep at NIH RePORTER.

Learn about a recent study that supports the need for healthy sleep habits from birth: Study finds link between sleep habits and weight gain in newborns .

Obesity research labs at the NHLBI

The Cardiovascular Branch and its Laboratory of Inflammation and Cardiometabolic Diseases conducts studies to understand the links between inflammation, atherosclerosis, and metabolic diseases.

NHLBI’s Division of Intramural Research , including its Laboratory of Obesity and Aging Research , seeks to understand how obesity induces metabolic disorders. The lab studies the “obesity-aging” paradox: how the average American gains more weight as they get older, even when food intake decreases.

Related obesity programs and guidelines

• Aim for a Healthy Weight is a self-guided weight-loss program led by the NHLBI that is based on the psychology of change. It includes tested strategies for eating right and moving more.
• The NHLBI developed the We Can! ® (Ways to Enhance Children’s Activity & Nutrition) program to help support parents in developing healthy habits for their children.
• The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project standardizes data collected from the various studies of obesity treatments so the data can be analyzed together. The bigger the dataset, the more confidence can be placed in the conclusions. The main goal of this project is to understand the individual differences between people who experience the same treatment.
• The NHLBI Director co-chairs the NIH Nutrition Research Task Force, which guided the development of the first NIH-wide strategic plan for nutrition research being conducted over the next 10 years. See the 2020–2030 Strategic Plan for NIH Nutrition Research .
• The NHLBI is an active member of the National Collaborative on Childhood Obesity (NCCOR) , which is a public–private partnership to accelerate progress in reducing childhood obesity.
• The NHLBI has been providing guidance to physicians on the diagnosis, prevention, and treatment of obesity since 1977. In 2017, the NHLBI convened a panel of experts to take on some of the pressing questions facing the obesity research community. See their responses: Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents (PDF, 3.69 MB).
• In 2021, the NHLBI held a Long Non-coding (lnc) RNAs Symposium to discuss research opportunities on lnc RNAs, which appear to play a role in the development of metabolic diseases such as obesity.
• The Muscatine Heart Study began enrolling children in 1970. By 1981, more than 11,000 students from Muscatine, Iowa, had taken surveys twice a year. The study is the longest-running study of cardiovascular risk factors in children in the United States. Today, many of the earliest participants and their children are still involved in the study, which has already shown that early habits affect cardiovascular health later in life.
• The Jackson Heart Study is a unique partnership of the NHLBI, three colleges and universities, and the Jackson, Miss., community. Its mission is to discover what factors contribute to the high prevalence of cardiovascular disease among African Americans. Researchers aim to test new approaches for reducing this health disparity. The study incudes more than 5,000 individuals. Among the study’s findings to date is a gene variant in African Americans that doubles the risk of heart disease.

Explore more NHLBI research on overweight and obesity

The sections above provide you with the highlights of NHLBI-supported research on overweight and obesity . You can explore the full list of NHLBI-funded studies on the NIH RePORTER .

To find more studies:

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• Published: 21 June 2021

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  

Systematic Reviews volume  10 , Article number:  181 ( 2021 ) Cite this article

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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 .

Peer Review reports

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.

Availability of data and materials

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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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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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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*.

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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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399 Obesity Essay Topics & Research Questions + Examples

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• Preventing Obesity Health Issues From Childhood The selected problem is childhood obesity, the rates of which increase nationwide yearly and require the attention of the government, society, and parents.
• Childhood Obesity: Causes and Effects Childhood obesity has many causes and effects, which denotes that parents and teachers should make children with obesity engage in regular physical exercise in school and at home.
• Describing the Problem of Childhood Obesity Childhood obesity is a problem that affects many children. If individuals experience a health issue in their childhood, it is going to lead to negative consequences.
• Researching of Obesity in Florida It is important to note that Florida does not elicit the only state with an obesity problem, as the nation’s obesity prevalence stood at 42.4% in 2018.
• Preventing Obesity Health Issues From the Childhood The paper is valuable for parents of children who are subject to gaining excess weight because the report offers how to solve the issue.
• Obesity and Health Outcomes in COVID-19 Patients The COVID-19 pandemic has posed many challenges over the last three years, and significant research has been done regarding its health effects and factors.
• Childhood Obesity in the US from Economic Perspective The economic explanation for the problem of childhood obesity refers to the inability of a part of the population to provide themselves and their children with healthy food.
• Addressing Teenage Obesity in America The paper states that adolescence is one of the most crucial developmental phases of human life during which the issue of obesity must be solved.
• Obesity in the United States of America The article discusses the causes of the obesity pandemic in the United States of America, which has been recognized as a pandemic due to its scope, and high prevalence.
• The Problem of Childhood Obesity Obesity in childhood is a great concern of current medicine as the habits of healthy eating and lifestyle are taught by parents at an early age.
• Should fast-food restaurants be liable for increasing obesity rates?
• Does public education on healthy eating reduce obesity prevalence?
• Is obesity a result of personal choices or socioeconomic circumstances?
• Should the government impose taxes on soda and junk food?
• Weight loss surgery for obesity: pros and cons.
• Should restaurants be required to display the caloric content of every menu item?
• Genetics and the environment: which is a more significant contributor to obesity?
• Should parents be held accountable for their children’s obesity?
• Does weight stigmatization affect obesity treatment outcomes?
• Does the fashion industry contribute to obesity among women?
• Oral Health and Obesity Among Adolescents This research paper developed the idea of using dental offices as the primary gateway to detect potential obesity among Texas adolescents.
• Obesity, Weight Loss Programs and Nutrition The article addresses issues that can help increase access to information related to the provision of weight loss programs and nutrition.
• Childhood Obesity in the US From an Economic Perspective Looking at the problem of childhood obesity from an economic point of view offers an understanding of a wider range of causes and the definition of government intervention.
• The Science Behind Obesity and Its Impact on Cancer The paper addresses the connection between cancer and physical activity, diet, and obesity in Latin America and the USA. The transitions in dietary practices may be observed.
• The Current Problem of Obesity in the United States The paper raises the current problem of obesity in the United States and informs people about the issue, as well as what effect obesity can have on health.
• Childhood and Adolescent Obesity and Its Reasons Various socio-economic, health-related, biological, and behavioral factors may cause childhood obesity. They include an unhealthy diet and insufficient physical activity and sleep.
• Pediatric Obesity and Its Treatment Pediatric obesity is often the result of unhealthy nutrition and the lack of control from parents but not of health issues or hormonal imbalance.
• Impact of Obesity on Healthcare System Patients suffering from obesity suffer immensely from stigma during the process of care due to avoidance which ultimately affects the quality of care.
• Trending Diets to Curb Obesity There are many trending diets that have significant effects on shedding pounds; however, the discourse will focus on the Mediterranean diet.
• Issues of Obesity and Food Addiction Obesity and food addiction have become widespread and significant problems in modern society, both health-related and social.
• Diet, Physical Activity, Obesity, and Related Cancer Risk One’s health is affected by their lifestyle, which should be well managed since childhood to set a basis for a healthier adulthood.
• Articles About Childhood Obesity The most straightforward technique to diagnose childhood obesity is to measure the child’s weight and height and compare them to conventional height and weight charts.
• Childhood Obesity and Overweight Issues The paper discusses childhood obesity. It has been shown to have a negative influence on both physical health and mental well-being.
• Obesity: Causes, Consequences, and Care Nowadays, an increasing number of people suffer from having excess weight. This paper analyzes the relationship between obesity and other diseases.
• Obesity Prevention Policy Making in Texas Obesity is a national health problem, especially in Texas; therefore, the state immediately needed to launch a policy to combat and prevent obesity in the population.
• Childhood Obesity: Quantitative Annotated Bibliography Childhood obesity is a problem that stands especially acute today, in the era of consumerism. Children now have immense access to the Internet.
• Obesity and How It Can Cause Chronic Diseases Obesity is associated with increased cardiovascular diseases, and cancer risks. The modifications in nutrition patterns and physical activity are effective methods to manage them.
• Physical Wellness to Prevent Obesity Heart Diseases Heart disease remains to be one of the most severe health concerns around the world. One of the leading causes of the condition is obesity.
• Obesity and General State of Public Health Obesity is a condition caused by an abnormal or excessive buildup of fat that poses a health concern. It raises the risk of developing various diseases and health issues.
• Ways of Obesity Interventions The paper discusses ways of obesity interventions. It includes diet and exercise, patient education, adherence to medication, and social justice.
• Obesity, Cardiovascular and Inflammatory Condition Under Hormones The essay discusses heart-related diseases and obesity conditions in the human body. The essay also explains the ghrelin hormone and how it affects the cardiovascular system.
• Aspects of Obesity Risk Factors Obesity is one of the most pressing concerns in recent years. Most studies attribute the rising cases of obesity to economic development.
• Obesity in Adolescence in the Hispanic Community The health risks linked to Hispanic community adolescent obesity range from diabetes, heart problems, sleep disorders, asthma, and joint pain.
• Obesity as a Wellness Concern in the Nursing Field A critical analysis of wellness can provide an understanding of why people make specific health-related choices.
• Physio- and Psychological Causes of Obesity The paper states that obesity is a complex problem in the formation of which many physiological and psychological factors are involved.
• How Junk Diets Can Reduce Obesity To control obesity there is a need to ensure that the junk foods produced are safe for consumption before being released into the foods market.
• The Problem of Obesity: Weight Management Obesity is now a significant public health issue around the world. The type 2 diabetes, cardiac conditions, stroke, and metabolism are the main risk factors.
• Behavioral Modifications for Patients With Obesity This paper aims to find out in obese patients, do lifestyle and behavioral changes, compared to weight loss surgery, improve patients’ health and reduce complications.
• Sleep Deprivation Effects on Adolescents Who Suffer From Obesity The academic literature on sleep deprivation argues that it has a number of adverse health effects on children and adolescents, with obesity being one of them.
• Hypertensive Patients Will Maintain Healthy Blood Pressure and Prevent Obesity Despite hypertension and obesity are being major life threats, there are safer lifeways that one can use to combat the problem.
• Evolving Societal Norms of Obesity The primary individual factors that lead to overeating include limited self-control, peer pressure, and automatic functioning.
• Obesity: Racial and Ethnicity Disparities in West Virginia Numerous social, economic, and environmental factors contribute to racial disparities in obesity. The rates of obesity vary depending on race and ethnicity in West Virginia.
• The Worldwide Health Problem: Obesity in Children The paper touch upon the main causes of obesity, its spread throughout the world, the major effects of the condition and ways of prevention.
• Mental Stability and Obesity Interrelation The study aims to conduct an integrative review synthesizing and interpreting existing research results on the interrelation between mental stability and obesity.
• Obesity in Low-Income Community: Diet and Physical Activity The research evaluates the relationship between family earnings and physical activity and overweight rates of children in 8 different communities divided by race or ethnicity.
• Dealing with Obesity as a Societal Concern This essay shall discuss the health issue of obesity, a social health problem that is, unfortunately, growing at a rapid rate.
• Adolescent Obesity in the United States The article reflects the problem of overweight in the use, a consideration which the authors blame on influential factors such as age and body mass index.
• Obesity Problem Solved by Proper Nutrition and Exercise Most people who suffer from obesity are often discouraged to pursue nutrition and exercise because their bodies cannot achieve a particular look.
• Hispanic Obesity in the Context of Cultural Empowerment This paper identifies negative factors directly causing obesity within the Hispanic people while distinguishing positive effects upon which potential interventions should be based.
• Health Psychology and Activists’ Views on Obesity This paper examines obesity from the psychological and activists’ perspectives while highlighting some of the steps to be taken in the prevention and curbing of the disease.
• The link between excess weight and chronic diseases.
• The role of genetics in obesity.
• The impact on income and education on obesity risks.
• The influence of food advertising on consumer choices.
• Debunking the myths related to weight loss.
• Obesity during pregnancy: risks and complications.
• Cultural influences on eating patterns and obesity prevalence.
• Community initiatives for obesity prevention.
• The healthcare and societal costs of obesity.
• The bidirectional relationship between sleep disorders and obesity.
• Childhood Obesity Teaching Experience and Observations The proposed teaching plan aimed at introducing the importance of healthy eating habits to children between the ages of 6 and 11.
• Care Plan: Quincy Town, Massachusetts With Childhood Obesity This study will develop a community assessment program based on the city with the aim of creating a care plan for tackling the issue of child obesity in the town.
• Exercise for Obesity Description There are numerous methods by which obesity can be controlled and one of the most effective ways is through exercising.
• Obesity and Disparity in African American Women Several studies indicate that the rate of developing obesity is the highest in African American populations in the US.
• Factors Increasing the Risk of Obesity The consumption of fast food or processed products is one of the major factors increasing the risk of obesity and associated health outcomes.
• Obesity in Hispanic American Citizens The issue of obesity anong Hispanic Americans occurs as a result of poor dieting choices caused by misinformed perceptions of proper eating.
• Effectiveness of a Diet and Physical Activity on the Prevention of Obesity Research indicates that obesity is the global epidemic of the 21st century, especially due to its prevalent growth and health implications.
• Community Obesity and Diabetes: Mississippi Focus Study The paper provides a detailed discussion of the correct method to be used in the state of Mississippi to control and avoid obesity and diabetes issues.
• Multicausality: Reserpine, Breast Cancer, and Obesity All the factors are not significant in the context of the liability to breast cancer development, though their minor influence is undeniable.
• The Home Food Environment and Obesity-Promoting Eating Behaviours Campbell, Crawford, Salmon, Carver, Garnett, and Baur conducted a study to determine the associations between the home food environment and obesity.
• The Problem of Childhood Obesity in the United States Childhood obesity is one of the reasons for the development of chronic diseases. In the US the problem is quite burning as the percentage of obese children increased significantly.
• The Situation of Obesity in Children in the U.S. The paper will discuss the situation of obesity in Children in the U.S. while giving the associated outcomes and consequences.
• Childhood Obesity and Healthy Lifestyles The purpose of this paper is to discuss childhood obesity and the various ways of fostering good eating habits and healthy lifestyles.
• Screen Time and Pediatric Obesity Among School-Aged Children Increased screen time raises the likelihood of children becoming overweight/obese because of the deficiency of physical exercise and the consumption of high-calorie foods.
• Eating Fast Food and Obesity Correlation Analysis The proposed study will attempt to answer the question of what is the relationship between eating fast food and obesity, using correlation analysis.
• Policymaker Visit About the Childhood Obesity Problem The policy issue of childhood obesity continues to be burning in American society. It causes a variety of concurrent problems including mental disorders.
• Public Health Interventions and Economics: Obesity The purpose of this article is to consider the economic feasibility of public health interventions to prevent the emergence of the problem of obesity.
• Obesity Overview and Ways to Improve Health The main focus of this paper is to analyze the problems of vice marketing and some unhealthy products to teens and children.
• Nursing: Issue of Obesity, Impact of Food Obesity is a pandemic problem in America. The fast food industry is under pressure from critics about the Americans weight gain problem.
• Childhood Overweight and Obesity Childhood overweight and obesity have increased in the US. Effective transportation systems and planning decisions could eliminate such overweight-related challenges.
• Childhood Obesity as an International Problem This paper explores the significance of using the web-based technological approach in combating obesity among Jewish children.
• Obesity Negative Influence on Public Health In recent years the increased attention has been paid to the growing obesity trends in connection to a possible negative influence on public health.
• The Effects of Gender on Child Obesity The high percentage of women’s obesity prevalence is a result of poor nutrition in childhood and access to greater resources in adulthood.
• Problematic of Obesity in Mexican Americans With this strategy, patients and guardians will embrace the best habits and eventually address the problem of obesity among Mexican Americans.
• Child Obesity Problem in the United States Obesity is a disease commonly associated with children in most countries in the world. Obesity means weighing much more than is healthy for someone.
• Obesity Rates and Global Economy The process of obesity in modern society is undoubtedly a severe obstacle to the development of the global economy, as well as to the achievement of its sustainability.
• Screen Time and Pediatric Obesity in School-Aged Children Obesity in school-aged children negatively influences their health, educational accomplishment, and quality of life.
• Obesity Treatment – More Than Food Researchers concluded that due to underlying issues, obese adolescents failed to achieve their goals in terms of losing weight.
• Effects of Exercise on Obesity Reduction in Adults One of the most effective methods of managing obesity is physical exercise. Physical exercise promotes weight loss and helps individuals to manage obesity.
• The Problem of Obesity in the Latin Community The purpose of this paper is to discuss the matter of a large number of overweight people in the Latin community of Florida and how the situation can be improved.
• Obesity Prevention in Ramsey County, Minnesota The problem of obesity has risen among working-class people but declined barely among children and senior adults. Ramsey has a low level of obesity relative to the national level.
• Childhood Obesity and Its Potential Prevention The paper delves into the use of early onset obesity detection in children and suggests methods of potentially preventing childhood obesity later on in the child’s life.
• Non-Surgical Reduction of Obesity and Overweight in Young Adults This paper review exercise, behavioral therapy, and good dietary habit as non-surgical means of managing obesity.
• Obesity Prevention Due to Education For obesity prevention, the current study will focus on patient education as an initiative that can potentially decrease the incidence of this disease.
• Physical Activity and Obesity in Children by Hills et al. Obesity has become one of the most significant health issues for high-income countries. Living standards are rising; people can afford to buy more while working less.
• The Best Way to Address Obesity in the United States This article examines the types of questions and notes the importance of being able to identify the type of question to answer it correctly.
• The Issues with Obesity of Children and Adolescents One of the primary concerns of medical specialists is the increasing rates of childhood obesity. It is linked to numerous health issues that occur among people with early obesity.
• Obesity in People with Intellectual Disabilities’: The Article Review Mashall, McConkey, and Moore, in the ‘Obesity in People with Intellectual Disabilities’ article, seek to assess obese and overweight individuals.
• Non-Surgical Reduction of Obesity in Young Adults The proposal analyzes the multifaceted approach that involves behavioral therapy and good dietary habit as non-surgical means of managing the obesity pandemic among young adults.
• Obesity in Children in the United States The present paper discusses the most promising practices for planning and managing the question of childhood obesity.
• Childhood Obesity in Ocean Springs Mississippi
• The Problem of Children Obesity
• “Physical Activity and Obesity in Children” by A. P. Hills
• “Physical Activity and Obesity in Children” by Hills
• The Current State of Obesity in Children Issue
• Effects of Obesity on Human Lifespan Development
• Obesity and High Blood Pressure as Health Issues
• The Prevention of Childhood Obesity in Children of 1 to 10 Years of Age
• Obesity as a Major Health Concern in the United States
• Screen Time and Pediatric Obesity
• Janet Tomiyama’s “Stress and Obesity” Summary
• A Dissemination Plan on Adolescent Obesity and Falls in Elderly Population
• The Issue of Obesity: Reasons and Consequences
• “Obesity and the Growing Brain” by Stacy Lu
• Obesity Disease: Symptoms and Causes
• Obesity Among Mexican-American School-Age Children in the US
• Obesity as a One of the Major Health Concerns
• Obesity: Diet Management in Adult Patients
• Children’s Obesity in the Hispanic Population
• Childhood Obesity: Problem Analysis
• Prevention of Childhood Obesity
• Assessing Inputs and Outputs of a Summer Obesity Prevention Program
• Designing a Program to Address Obesity in Florida
• Widespread Obesity in Low-Income Societies
• Health Policy: Obesity in Children
• Youth Obesity In Clark County in Vancouver Washington
• Obesity in Clark County and Health Policy Proposal
• Obesity: Is It a Disease?
• Clark County Obesity Problem
• Obesity Action Coalition Website Promoting Health
• How to Reduce Obesity and Maintain Health?
• Childhood Obesity: Medical Complications and Social Problems
• How to Address Obesity in the United States
• The Epidemic of Obesity: Issue Analysis
• Eating Healthy and Its Link to Obesity
• Child Obesity in North America
• Personal Issues: Marriage, Obesity, and Alcohol Abuse
• Obesity in Children: Relevance of School-Based BMI Reporting Policy
• Obesity in the United States: Defining the Problem
• Depression and Other Antecedents of Obesity
• Obesity in Children in the US
• Childhood Obesity: Issue Analysis
• Data Mining Techniques for African American Childhood Obesity Factors
• Approaches to Childhood Obesity Treatment
• Researching Childhood Obesity Issues
• Infant Feeding Practices and Early Childhood Obesity
• Prevalence of Obesity and Severe Obesity in U.S. Children
• Problem of Obesity: Analytic Method
• Obesity as National Practice Problem
• Obesity Management: Hypothesis Test Study
• Practice Problem of the Obesity in United States
• Exercise for Obesity Management: Evidence-Based Project
• Obesity in African-American Women: Methodology
• The Epidemiology of Obesity
• Community Health: Obesity Prevention
• Obesity Treatment in Primary Care: Evidence-Based Guide
• Childhood Obesity and Mothers’ Education Project
• Childhood Obesity Research Critiques
• Childhood Obesity: Medication and Parent Education
• Childhood Obesity Study: Literature Review
• Motivational Interviewing in Obesity Reduction: Statistical Analysis
• Obesity Among the Adult Population: Research Planning
• Research and Global Health: Obesity and Overweight
• Adolescent Obesity Treatment in Primary Care
• Obesity in School-Aged Children as a Social Burden
• The Issues of Childhood Obesity: Overweight and Parent Education
• Childhood Obesity and Parent Education: Ethical Issues
• Childhood Obesity Prevention: Physical Education and Nutrition
• Obesity Reduction and Effectiveness of Interventions
• Childhood and Adult Obesity in the US in 2011-12
• Obesity Prevention Advocacy Campaigns
• Childhood Obesity Study, Ethics, and Human Rights
• Childhood Obesity, Demographics and Environment
• Childhood Obesity and Self-Care Deficit Theory
• Overweight and Obesity in 195 Countries Since 1980
• Childhood Obesity and American Policy Intervention
• Obesity in Miami as a Policy-Priority Issue
• Childhood Obesity and Public Health Intervention
• Childhood Obesity and Healtcare Spending in the US
• Childhood Obesity, Medical and Parental Education
• Nursing Role in Tackling Youth Obesity
• Childhood Obesity Causes: Junk Food and Video Games
• Childhood Obesity: Problem Issues
• Adolescent Obesity and Parental Education Study
• Childhood Obesity: Data Management
• Obesity Prevention and Patient Teaching Plan
• “Management of Obesity” by Dietz et al.
• Obesity, Diet Modification and Physical Exercises
• Obesity, Its Definition, Treatment and Prevention
• Childhood Obesity and Eating Habits in Low-Income Families
• Diet and Lifestyle vs Surgery in Obesity Treatment
• Obesity: Society’s Attitude and Media Profiling
• Childhood Obesity and Family’s Responsibility
• Childhood Obesity: Parental Education vs. Medicaments
• Childhood Obesity and Healthy Lifestyle Education
• Childhood Obesity and Health Promoting Schools Program
• Childhood Obesity Risks, Reasons, Prevention
• Fast Food as a Cause of Obesity in the US and World
• Obesity Prevention and Education in Young Children
• Childhood Obesity: The Relationships Between Overweight and Parental Education
• Obesity, Its Demographics and Health Effects
• Obesity Treatment: Surgery vs. Diet and Exercises
• Child Obesity as London’s Urban Health Issue
• Childhood Obesity Problem Solution
• Treat and Reduce Obesity Act and Its Potential
• Obesity Prevention in Young Children: Evidence-Based Project
• Advocacy Campaign: Childhood Obesity
• Obesity as American Social Health Issue
• Prevalence of Childhood and Adult Obesity in the US
• The Role of Nurses in the Obesity Problem
• The Issue of Obesity in Youth in the U.S.
• Obesity Among Children of London Borough of Southwark
• Childhood Obesity Risks and Preventive Measures
• Ways of Treating Obesity in Older Patients
• Life Expectancy and Obesity Health Indicators
• The Overuse of Antibiotics and Its Role in Child Obesity
• Children and Adolescents With Obesity: Physical Examination
• Obesity in the United States: Learning Process
• Pharmacotherapy for Childhood Obesity
• “Let’s Move” Intervention for Childhood Obesity
• Obesity Prevention in Childhood
• Patient Education for Obesity Treatment
• Childhood Obesity Prevention Trends
• Obesity Prevention in Young Children in US
• Wellness, Academics & You: Obesity Intervention
• Evidence Based Practice Related to Patient Obesity
• Childhood Obesity and Its Solutions
• Obesity Problem among the Adult Population
• Obesity Education in Social Media for Children
• Childhood Obesity and Governmental Measures
• Childhood Obesity Research and Ethical Concerns
• Obesity, Its Contributing Factors and Consequences
• Obesity Education Plan for Older Adults
• Obesity: Causes, Consequences, and Prevention
• Multimodal-Lifestyle Intervention for Obesity
• Technological Education Programs and Obesity Prevention
• Childhood Obesity and Independent Variable in Parents
• Childhood Obesity: A Global Public Health Crisis
• Childhood Obesity, Its Definition and Causes
• Public Health Initiative for Childhood Obesity
• Childhood Obesity and Health Promotion
• Childhood Obesity in the US: Factors and Challenges
• Obesity: Genetic, Hormonal and Environmental Influences
• The Problem of Obesity in the USA
• Childhood Obesity in the USA
• Racial and Ethnic Trends in Childhood Obesity in the US
• Diabetic Patients with Obesity or Overweight
• Childhood Obesity and Public Health Interventions
• Obesity in Florida and Prevention Programs
• Obesity in Afro-Americans: Ethics of Intervention
• Food Ads Ban for Childhood Obesity Prevention
• Helping Children with Obesity and Health Risks
• The Role of Nurses in the Problem of Obesity
• Healthy Nutrition: Obesity Prevention in Young Children
• Myocardial Infarction, Obesity and Hypertension
• Childhood Obesity and Parent Education
• Obesity’s Effect on Children and Elderly People
• Childhood Obesity and Community Nursing Intervention
• Obesity Trends Among Non-Hispanic Whites and Blacks
• Family-Based Childhood Obesity and Parental Weight
• Childhood Obesity and Depression Intervention
• Problem of the Childhood Obesity
• Advocacy Campaign: the Problem of Childhood Obesity
• Obesity in African Americans: Prevention and Therapy
• Childhood Obesity, Social Actions and Intervention
• Childhood Obesity and Control Measures in the US
• Decreasing Obesity in Jewish Children
• Nutrition: Obesity Epidemics in America
• Fast Food and Obesity Link – Nutrition
• Dairy Products Consumption and Obesity – Nutrition
• Nutrition Issues: Obesity and Breastfeeding
• The Evidence of Association between Iron Deficiency and Childhood Obesity
• Food Allergies and Obesity
• Childhood Obesity: a Population Health Issue
• Nutrition: Fighting the Childhood Obesity Epidemic
• What Factors Causes Obesity?
• What Are Five Problems With Obesity?
• Can the Government Help the Obesity Issue?
• What Are the Three Dangers of Obesity?
• What Are Ten Health Problems Associated With Obesity?
• Are the Parents to Blame for Childhood Obesity?
• What Are the Social Effects of Obesity?
• Does Adolescent Media Use Cause Obesity and Eating Disorders?
• How Is Obesity Affecting the World?
• How Does Obesity Impact Quality of Life?
• Does Society Affect America’s Obesity Crisis?
• How Does Obesity Affect You Mentally?
• How Does Obesity Impact Children?
• How Does Obesity Affect Self-Esteem?
• How Does Obesity Cause Depression?
• Are First Generation Mexican Children More Prone to Obesity Than Their Second Generation Counterparts?
• Should Fast Food Companies Be Held Responsibility for Children’s Obesity?
• Does Obesity Cause Mood Swings?
• What Are the Causes and Effects of Childhood Obesity?
• Is Obesity a Mental or Physical Illness?
• What Comes First: Depression or Obesity?
• What Makes Obesity Dangerous?
• Which European Country Has the Highest Rate of Obesity?
• What Is the Obesity Rate in Africa?

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StudyCorgi . "399 Obesity Essay Topics & Research Questions + Examples." September 9, 2021. https://studycorgi.com/ideas/obesity-essay-topics/.

StudyCorgi . 2021. "399 Obesity Essay Topics & Research Questions + Examples." September 9, 2021. https://studycorgi.com/ideas/obesity-essay-topics/.

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Original quantitative research - Discrimination in the health care system among higher-weight adults: evidence from a Canadian national cross-sectional survey

Neeru gupta.

1 Department of Sociology, University of New Brunswick, Fredericton, New Brunswick, Canada

Andrea Bombak

Ismael foroughi, natalie riediger.

2 Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, Manitoba, Canada

Introduction:

Weight-related social stigma is associated with adverse health outcomes. Health care systems are not exempt of weight stigma, which includes stereotyping, prejudice and discrimination. The objective of this study was to examine the association between body mass index (BMI) class and experiencing discrimination in health care.

We used data from the 2013 Canadian Community Health Survey, which included measurements of discrimination never collected previously on a national scale. Logistic regression analysis was used to assess the risk of self-reported discrimination in health care in adults (≥18 years) across weight categories: not obese (BMI < 30 kg/m 2 ), obese class I (BMI = 30–<35 kg/m 2 ) and obese class II or III (BMI ≥ 35 kg/m 2 ).

One in 15 (6.4%; 95% CI: 5.7–7.0%) of the adult population reported discrimination in a health care setting (e.g. physician’s office, clinic or hospital). Compared with those in the not obese group, the risk of discrimination in health care was somewhat higher among those in the class I obesity category (odds ratio [OR] = 1.20; 95% CI: 1.00–1.44) and significantly higher among those in class II/III (OR = 1.52; 95% CI: 1.21–1.91), after controlling for sex, age and other socioeconomic characteristics.

Conclusion:

Quantified experiences of weight-related discrimination underscore the need to change practitioner attitudes and practices as well as the policies and procedures of the health care system. More research is needed on the social and economic impacts of weight stigma to inform focused investments for reducing discrimination in the health care system as a microcosm of the society it reflects.

• Weight stigma is associated with adverse physical and mental health outcomes.
• Based on data from the first nationally representative survey on every day and medical discrimination, we found that 6.4% of Canadian adults experienced discrimination in a health care setting.
• Higher-weight people were significantly more likely to report discrimination in health care, after adjusting for sex, income group and other social and demographic characteristics, than those whose body mass index was in the not obese category.
• More research is needed to inform interventions to reduce weight stigma in the health care system.

Introduction

A small but growing body of literature suggests that weight stigma is directly associated with adverse physiological and psychological outcomes. 1 Stigma and discrimination have a spectrum of effects that can lead to negative health outcomes by creating and reinforcing social inequalities. 2 These inequalities, in turn, limit access to resources and opportunities. 3

Stigma in health care undermines diagnosis, treatment and optimal health. 3 Consequences of weight stigma may include avoidance of medical care, provider distrust, medication nonadherence, disordered eating, physical inactivity and poorer mental health. 4 - 9 Experiencing weight stigma has been associated with numerous cardiometabolic disturbances including atherosclerosis, cardiovascular conditions, diabetes and biological stress. 10 - 13

A longitudinal assessment from the United States associated weight discrimination with increased mortality risk, after adjustment for frequently related morbidities and behaviours. 14 The World Health Organization recognizes that many individuals and groups face discrimination in health care settings on the basis of their sex, age, ethnicity, gender identity, vulnerability to ill health and/or other characteristics— and that such discrimination does not occur in a vacuum. 15 An enhanced evidence base is needed to support accountability and policy development. 15

The implications of stigma and discrimination for population health and health inequities are increasingly acknowledged in Canada and elsewhere. 16 - 18 Data from a national household survey indicate that everyday discrimination persists across multiple social groups in Canada. 19 , 20 Discrimination is often attributed to gender and physical characteristics such as weight, although the intergroup empirical patterns of chronic subtle mistreatments do not necessarily follow a straightforward socialization theory trajectory. 19 , 20

In particular, weight stigmatization is a commonly used umbrella term in the literature. 21 It can be defined as “negative weight-related attitudes and beliefs that are manifested by stereotypes, rejection and prejudice towards individuals because they are overweight or obese.” 22 Some studies found that substantial proportions of clinicians hold prejudiced beliefs about higherweight patients, including that they are less motivated, noncompliant, awkward and lack will power. 23 - 25 In a sample of family physicians practising in Canada (n = 400), large proportions gave responses suggestive of weight bias: 49% agreed that “people with obesity increase demand on the public health care system”; 33% stated they “often feel frustrated with patients who have obesity”; 28% stated they felt “patients with obesity are often noncompliant with treatment recommendations”; 19% said “I feel disgust when treating a patient with obesity”; and 17% indicated that “sometimes I think that people with obesity are dishonest.” 26

Under-explored in Canada is the prevalence of weight-based stigma in different settings, despite its pernicious effects. 27 This study aims to address this knowledge gap by assessing the association of higher body weight with self-reported discrimination in health care among Canadian women and men.

We used information from a national data collection on stigma and discrimination as an emerging population health issue to support evidence-based health promotion in this context of publicly funded universal health care coverage. The goal is to inform policy actions for enhanced accountability and reduction of stigma in the health care system as a microcosm of the society it reflects.

Study design

We analyzed data from the 2013 Canadian Community Health Survey (CCHS) and, specifically, its rapid response module on everyday discrimination. The CCHS is an annual cross-sectional survey administered by Statistics Canada that collects information on health determinants, health status and health care from a nationally representative sample of the communitydwelling population aged 12 years and over. The 2013 CCHS included a unique module that captured data to measure discrimination never collected previously on a national scale. 28 The original sample for the CCHS “everyday discrimination” module included 19 876 respondents. 29 We limited the sample to adults aged 18 years and over with valid responses to all variables of interest (n = 16 340).

Discrimination in health care

Respondents were asked questions about their perception of discrimination in their day-to-day life and in their experiences with health care services. Previous studies have found itemized measures of perceived discrimination to have consistent predictive validity. 30 The outcome variable for this analysis was based on valid answers to the question, “Have you received poorer service than other people in any of the following situations?” The settings included a physician’s office, a community health centre, a walk-in clinic, a hospital emergency room or another health care service. 31 We measured our outcome dichotomously, that is, whether or not the respondent reported receiving poorer service in any physical health care setting.

Weight category

Our main independent variable was derived from self-reported height and weight. We grouped weight status from calculated body mass index (BMI) based on the standard Health Canada framework for classifying body weight: not categorized as obese (BMI < 30 kg/m 2 ); categorized as obese class I (BMI = 30–<35 kg/m 2 ); and categorized as obese class II or III (BMI ≥ 35 kg/m 2 ). Women who were pregnant at the time of the survey were excluded.

Statistical analysis

We conducted multiple logistic regression analysis to assess the independent association of weight status with stigma in health care, adjusting for other socioeconomic characteristics: sex (male or female); age group (18–29 years, 30–44 years, 45–64 years or ≥ 65 years); marital status (whether or not currently in a marital or common-law union); educational attainment (whether or not a household member had attained a postsecondary level of schooling); and income group. We dichotomized individuals’ income group into lower-range versus higher-range categories based on the total annual household income from all sources ($0–29 999 versus ≥ 30 000). 32

Bootstrapped survey weights were applied to the descriptive statistics to ensure population representation given the CCHS complex sampling design. Rounding algorithms were further applied to the descriptive counts in respect of data privacy protocols. To ease interpretation of the results from the logistic model, coefficients were converted to odds ratios (ORs) with 95% confidence intervals (CIs) (α = 0.05) using statistical software STATA version 15 (StataCorp LP, College Station, TX, USA).

We accessed the confidential survey microdata used in the analysis in the secure environment of the Statistics Canada Research Data Centre (RDC) at the University of New Brunswick in Fredericton, Canada. The study complied with the University of New Brunswick’s Research Ethics Board, which does not require an internal institutional review for research projects using data accessed through the RDC, in accordance with the Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans . 33

Based on data from the CCHS, 32.7% (95% CI: 31.0–34.5%) of the adult population reported experiencing discrimination in their everyday life and 6.4% (5.7–7.0%) reported discrimination in a health care setting. The number reporting discrimination in a health care setting represented 1 616 700 (1 453 400–1 780 000) Canadians. Of these people, 29% (24–33%) specifically reported poorer service in the health care sector, but did not also report everyday discrimination in the previous year.

One in five (19.4%) adults were classified with obesity. Specifically, 13.5% (95% CI: 12.6–14.4%) were categorized with class I obesity and 5.9% (5.4–6.5%) with class II or III ( Table 1 ). Reflecting the aging of the population, there were more adults aged 45 years and over (54.8%; 54.2–55.4%) than those aged 18 to 44 years (45.2%; 44.3–46.0%). Fifteen per cent (15.7%; 95% CI: 14.8–16.6%) were in the lowest household- income range (<$30,000 annually).

Results from the multiple logistic regression showed that, compared with those whose BMI was categorized as not obese, the odds of reporting discrimination in a health care setting was somewhat higher among those with class I obesity (OR = 1.20, 95% CI: 1.00–1.44, p = .05) and significantly higher among those with class II/III obesity (1.52, 1.21–1.91, p < .05), after controlling for other sociodemographic characteristics ( Table 2 ).

All else being equal, women had significantly higher odds than men of reporting discrimination in health care (OR = 1.48, 95% CI: 1.29–1.70, p < .05). People not currently married or living in union had higher odds of reporting discrimination in health care than those who were married (1.18, 1.03–1.38, p < .05). The odds of those in the lowest household-income group reporting discrimination were higher than those of their higher-income counterparts (1.69, 1.44–2.00, p < .05). Individuals aged 45 years and over were less likely to report discrimination in health care than those aged 18 to 29 years. People living in a household of at most secondary-level educational attainment were also less likely to report discrimination than those in households where a postsecondary level had been attained.

The need to pay attention to the consequences of systemic weight bias is increasingly advocated in policy and practice recommendations made through the lens of health promotion, equity and social determinants. 34

This study is, to our knowledge, the first national investigation quantifying experiences of discrimination in health care among higher-weight persons using data representative of the Canadian population. A non-negligible proportion (6.4%) of adults reported discrimination in a health care setting. Compared with those in the not obese group, the risk of discrimination in health care was approaching statistical significance among those in the class I obesity category (OR = 1.20, 95% CI: 1.00–1.44, p = .05) and was significantly higher among those in the class II or III obesity category (1.52, 1.21–1.91, p < .05), after controlling for other sociodemographic characteristics.

Being male was found to be independently protective of the risk of experiencing discrimination in a health care setting. Previous studies have found perceived weight discrimination, including in health care contexts, to be more prevalent among women than men. 35 , 36 Being in a higher household-income group was associated with a significantly lower risk of experiencing discrimination in health care, whereas being in a household with higher educational attainment was associated with a significantly higher risk. These potentially contradictory patterns of self-reported discriminatory experiences depending on the measure of socioeconomic status examined may reflect, on the one hand, underreporting due to minimization bias (e.g. lack of awareness), or on the other hand, overreporting due to vigilance bias (heightened focus on their social identity status). 19

These results underscore the need to change practitioner attitudes and practices that may be detrimental to health. One in 15 Canadian adults report discrimination in a health care setting, an indicator suggestive of more overt forms of discrimination compared with global discrimination measures. 20 However, weight bias has been a neglected issue in health professional education and training. 37 Despite the critical importance of an effective provider– patient relationship for achieving positive outcomes, there is little empirical evidence about the pathways to valuing trust and managing the power imbalance. 38

More research is needed to address the negative attitudes health care professionals may have towards higher-weight patients and the underlying causes of weight stigma, as few intervention strategies have proven especially effective to date. 39 , 40 A qualitative study of stigmareduction interventions prioritized better education on the etiology of body size, the difficulty of losing weight and the falsity of common weight-based stereotypes. 22 Appropriate interventions need to extend beyond issues of controllability of weight and address the negative value of fatness— such as unwarranted assumptions and judgements regarding higher-weight persons’ health status or attractiveness. 37 , 40 As the science of anti-weight stigma intervention expands, to ensure lasting and noticeable impacts, anti-stigma education strategies must be supported through antiweight discrimination legislation, antibullying policies and culture change. 41 In line with this, favouring neutral terminology such as “higher-weight” in health promotion, research and provider–patient communications has been identified among the evidence-based means of fostering safe and respectful dialogue towards the ultimate goal of eliminating weight-stigmatizing attitudes and practices in health care. 42 - 44

Strengths and limitations

Strengths of the study include the nationally representative nature of the data. While the “true” extent of discrimination may be impossible to determine, as it may be underreported in a survey, the observational data reflect differences between members of Canadian society in judgements of disparate treatment. 20

Limitations include the relatively small sample size of the CCHS rapid response module, which was not designed to produce high quality estimates at detailed levels, 29 hindering our ability to tease associations between specific health care settings (such as a hospital emergency department versus a physician’s office) or across provinces. In particular, we were unable to retain the statistical power to comprehensively investigate other individual-level characteristics potentially intersecting with weight-based social identity, such as ethnicity, Indigenous identity, immigration status, occupational type, racialization, language, sexual identity, physical disability status or mental health status.

Given the cross-sectional nature of the data, causality cannot be inferred. It is possible, for example, that individuals’ past experiences of discrimination may have led to changes in weight and BMI categorization. 1 , 8 Using data on selfreported weight is known to underestimate BMI compared with measured weight; however, such misreporting is statistically predictable and does not necessarily lead to exaggerated bias in studies aiming to estimate effects of BMI on health-related outcomes (such as, in this case, on weight stigma). 45 Lastly, while BMI is an expedient measure to collect in national household surveys, it remains an imprecise means of assessing morbidity or mortality risk. 46 , 47

Quantifying experiences of stigma and discrimination in health care settings as related to higher-weight status and other individual characteristics is an important prerequisite to developing and implementing interventions that achieve better population health and equity in the health care system, including in the Canadian context of publicly funded universal coverage. Weight stigma may be exacerbated in the era of the COVID-19 pandemic, when increasing media and social media attention may be paid to weight gain during associated lockdowns. 48 International consultations have highlighted concerns among higher-weight individuals of scrutiny while eating, exercising and grocery shopping and of being stigmatized by health practitioners as a negative and lasting barrier to accessing care. 48

The starting points for focused investment in health-care stigma reduction are standardized stigma measures and rigorous evaluation. 3 Results from this research, which revealed the persistence of weight stigma in health services delivery, are expected to help support evidence-informed decisions targeting the individual level, to change practitioner attitudes and practices, and the structural level, to change the policies and procedures of the health system environment that guide the delivery of care.

Acknowledgements

The data analysis for this study was conducted at the New Brunswick Research Data Centre (NB-RDC), which is part of the Canadian Research Data Centre Network (CRDCN). The services and activities provided by the NB-RDC are made possible by the financial or in-kind support of the Social Sciences and Humanities Research Council, the Canadian Institutes of Health Research, the Canadian Foundation for Innovation, Statistics Canada and the University of New Brunswick. Selected results were presented at the 2019 CRDCN National Conference (24–25 October 2019, Halifax, Canada).

Conflicts of interest

The authors declare they have no competing interests.

Authors’ contributions and statement

NG, AB, IF and NR contributed to the design of the work and interpretation of the data. NG effected data acquisition. IF conducted formal data analysis. NG and AB prepared the first draft of the manuscript. All the authors critically reviewed the final version.

The content and views expressed in this article are those of the authors and do not necessarily reflect those of the Government of Canada.

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