obesity research case study

• - Google Chrome

Intended for healthcare professionals

• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Effectiveness of...

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

• Related content
• 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

• View inline

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

• Download figure
• Open in new tab
• Download powerpoint

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/ .

• Renehan AG ,
• Heller RF ,
• Bansback N ,
• Birmingham CL ,
• Abdullah A ,
• Peeters A ,
• de Courten M ,
• Stoelwinder J
• Aghili SMM ,
• Ebrahimpur M ,
• Arjmand B ,
• KETLAK Study Group
• ↵ Department of Health and Social Care. New specialised support to help those living with obesity to lose weight UK2021. www.gov.uk/government/news/new-specialised-support-to-help-those-living-with-obesity-to-lose-weight [accessed 08/02/2021].
• U.S. Preventive Services Task Force
• ↵ National Institute for Health and Care Excellence. Maintaining a Healthy Weight and Preventing Excess Weight Gain in Children and Adults. Cost Effectiveness Considerations from a Population Modelling Viewpoint. 2014, NICE. www.nice.org.uk/guidance/ng7/evidence/evidence-review-2-qualitative-evidence-review-of-the-most-acceptable-ways-to-communicate-information-about-individually-modifiable-behaviours-to-help-maintain-a-healthy-weight-or-prevent-excess-weigh-8733713.
• ↵ The Health Foundation. Use of primary care during the COVID-19 pandemic. 17/09/2020: The Health Foundation, 2020.
• ↵ Australian Bureau of Statistics. Patient Experiences in Australia: Summary of Findings, 2017-18. 2019 ed. Canberra, Australia, 2018. www.abs.gov.au/AUSSTATS/[email protected]/Lookup/4839.0Main+Features12017-18?OpenDocument.
• LeBlanc ES ,
• Patnode CD ,
• Webber EM ,
• Redmond N ,
• Rushkin M ,
• O’Connor EA
• Damschroder LJ ,
• Liberati A ,
• Tetzlaff J ,
• Altman DG ,
• PRISMA Group
• McKenzie JE ,
• Bossuyt PM ,
• ↵ WHO. Main terminology: World Health Organization; 2004. www.euro.who.int/en/health-topics/Health-systems/primary-health-care/main-terminology [accessed 09.12.21].
• Aceves-Martins M ,
• Robertson C ,
• REBALANCE team
• Glasziou P ,
• Isenring E ,
• Chisholm A ,
• Wakayama LN ,
• Kettle VE ,
• Madigan CD ,
• ↵ Covidence [program]. Melbourne, 2021.
• Welzel FD ,
• Carrington MJ ,
• Fernández-Ruiz VE ,
• Ramos-Morcillo AJ ,
• Solé-Agustí M ,
• Paniagua-Urbano JA ,
• Armero-Barranco D
• Bräutigam-Ewe M ,
• Hildingh C ,
• Yardley L ,
• Christian JG ,
• Bessesen DH ,
• Christian KK ,
• Goldstein MG ,
• Martin PD ,
• Dutton GR ,
• Horswell RL ,
• Brantley PJ
• Wadden TA ,
• Rogers MA ,
• Berkowitz RI ,
• Kumanyika SK ,
• Morales KH ,
• Allison KC ,
• Rozenblum R ,
• De La Cruz BA ,
• Katzmarzyk PT ,
• Martin CK ,
• Newton RL Jr . ,
• Nanchahal K ,
• Holdsworth E ,
• ↵ RevMan [program]. 5.4 version: Copenhagen, 2014.
• Sterne JAC ,
• Savović J ,
• Gomez-Huelgas R ,
• Jansen-Chaparro S ,
• Baca-Osorio AJ ,
• Mancera-Romero J ,
• Tinahones FJ ,
• Bernal-López MR
• Delahanty LM ,
• Tárraga Marcos ML ,
• Panisello Royo JM ,
• Carbayo Herencia JA ,
• Beeken RJ ,
• Leurent B ,
• Vickerstaff V ,
• Hagobian T ,
• Brannen A ,
• Rodriguez-Cristobal JJ ,
• Alonso-Villaverde C ,
• Panisello JM ,
• Conroy MB ,
• Spadaro KC ,
• Takasawa N ,
• Mashiyama Y ,
• Pritchard DA ,
• Hyndman J ,
• Jarjoura D ,
• Smucker W ,
• Baughman K ,
• Bennett GG ,
• Steinberg D ,
• Zaghloul H ,
• Chagoury O ,
• Leslie WS ,
• Barnes AC ,
• Summerbell CD ,
• Greenwood DC ,
• Huseinovic E ,
• Leu Agelii M ,
• Hellebö Johansson E ,
• Winkvist A ,
• Look AHEAD Research Group
• LeBlanc EL ,
• Wheeler GM ,
• Aveyard P ,
• de Koning L ,
• Chiuve SE ,
• Willett WC ,
• ↵ World Health Organization. Obesity and Overweight, 2021, www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
• Starfield B ,
• Sturgiss E ,
• Dewhurst A ,
• Devereux-Fitzgerald A ,
• Haesler E ,
• van Weel C ,
• Gulliford MC
• Fassbender JE ,
• Sarwer DB ,
• Brekke HK ,

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Heart-Healthy Living
• High Blood Pressure
• Sickle Cell Disease
• Sleep Apnea
• Information & Resources on COVID-19
• The Heart Truth®
• Learn More Breathe Better®
• Blood Diseases & Disorders Education Program
• Publications and Resources
• Clinical Trials
• Blood Disorders and Blood Safety
• Sleep Science and Sleep Disorders
• Lung Diseases
• Health Disparities and Inequities
• Heart and Vascular Diseases
• Precision Medicine Activities
• Obesity, Nutrition, and Physical Activity
• Population and Epidemiology Studies
• Women’s Health
• Research Topics
• All Science A-Z
• Grants and Training Home
• Policies and Guidelines
• Funding Opportunities and Contacts
• Training and Career Development
• Email Alerts
• NHLBI in the Press
• Research Features
• Ask a Scientist
• Past Events
• Upcoming Events
• Mission and Strategic Vision
• Divisions, Offices and Centers
• Advisory Committees
• Budget and Legislative Information
• Jobs and Working at the NHLBI
• Contact and FAQs
• NIH Sleep Research Plan
• < Back To Research Topics

Obesity Research

Language switcher.

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:

• Type your search words into the  Quick Search  box and press enter. 
• Check  Active Projects  if you want current research.
• Select the  Agencies  arrow, then the  NIH  arrow, then check  NHLBI .

If you want to sort the projects by budget size — from the biggest to the smallest — click on the  FY Total Cost by IC  column heading.

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Entire Site
• Research & Funding
• Health Information
• About NIDDK
• Weight Management
• Understanding Adult Overweight & Obesity
• Clinical Trials
• Español

Clinical Trials for Overweight & Obesity

NIDDK conducts and supports clinical trials in many diseases and conditions, including overweight and obesity. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for overweight and obesity?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of overweight and obesity, such as

• why some people find it harder than other people to maintain weight loss over time
• new medicines that could help people with obesity lose weight and keep it off
• different treatments that may prevent weight regain after weight-loss surgery, also called metabolic and bariatric surgery
• why storing excess fat in some parts of your body—such as in your abdomen, or belly—rather than other parts of your body may increase the risk of developing health problems such as type 2 diabetes
• how weight-loss surgery may lower long-term health costs for adults with obesity

Find out if clinical studies are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical studies for overweight or obesity are looking for participants?

You can view a filtered list of clinical studies on overweight or obesity that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe. Always talk with your health care provider before you participate in a clinical study.

What have we learned about overweight and obesity from NIDDK-funded research?

NIDDK has supported many research projects to learn more about overweight and obesity.

The Look AHEAD: Action for Health in Diabetes  study showed that people who had type 2 diabetes  and were overweight or had obesity can lose weight and maintain that weight loss through a program of healthy eating and increased physical activity. The study also showed that weight loss provides other health benefits, such as better physical mobility and improved blood glucose, blood pressure, and cholesterol levels. The trial was extended to study the long-term effects in older adults with type 2 diabetes.

The Longitudinal Assessment of Bariatric Surgery  (LABS) study looked at the effects of two types of weight-loss surgery in adults, gastric bypass and adjustable gastric band. LABS found that weight-loss surgery is relatively safe when performed by experienced surgeons. It can also lead to significant weight loss and may improve many weight-related health problems. After 7 years, the average weight loss of patients who had gastric bypass surgery was 84 pounds, or about 28% of their starting weight. The average weight loss of patients who had gastric band surgery was 41 pounds, or about 15% of their starting weight. Because gastric band surgery is less effective than other types of weight-loss surgery , it is not often performed.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 27 January 2020

Epidemiology and Population Health

Evidence from big data in obesity research: international case studies

• Emma Wilkins 1 ,
• Ariadni Aravani 1 ,
• Amy Downing 1 ,
• Adam Drewnowski 2 ,
• Claire Griffiths 3 ,
• Stephen Zwolinsky 3 ,
• Mark Birkin 4 ,
• Seraphim Alvanides 5 , 6 &
• Michelle A. Morris   ORCID: orcid.org/0000-0002-9325-619X 1  

International Journal of Obesity volume  44 ,  pages 1028–1040 ( 2020 ) Cite this article

979 Accesses

4 Citations

8 Altmetric

Metrics details

• Risk factors
• Signs and symptoms

Background/objective

Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of ‘big data’ presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital , has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). ‘Additional computing power’ introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.

Methods and results

Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.

Conclusions

The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 print issues and online access

251,40 € per year

only 20,95 € per issue

Buy this article

• Purchase on SpringerLink
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments

Trends in the prevalence of adult overweight and obesity in Australia, and its association with geographic remoteness

Cross-sectional evidence of the cardiometabolic health benefits of urban liveability in Australia

Davison KK, Birch LL. Childhood overweight: a contextual model and recommendations for future research. Obes Rev. 2001;2:159–71.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Egger G, Swinburn B. An “ecological” approach to the obesity pandemic. BMJ. 1997;315:477–80.

Harrison K, Bost KK, McBride BA, Donovan SM, Grigsby-Toussaint DS, Kim J, et al. Toward a developmental conceptualization of contributors to overweight and obesity in childhood: the six-Cs model. Child Dev Perspect. 2011;5:50–8.

Article   Google Scholar  

Butland B, Jebb S, Kopelman P, McPherson K, Thomas S, Mardell J et al. Foresight. Tackling obesities: future choices—project report. Government Office for Science; 2007.

Rutter HR, Bes-Rastrollo M, de Henauw S, Lahti-Koski M, Lehtinen-Jacks S, Mullerova D, et al. Balancing upstream and downstream measures to tackle the obesity epidemic: a position statement from the European association for the study of obesity. Obes Facts. 2017;10:61–3.

Article   PubMed   PubMed Central   Google Scholar  

Mittelstadt BD, Floridi L. The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics. 2016;22:303–41.

Article   PubMed   Google Scholar  

Kaisler S, Armour F, Espinosa JA, Money W. Big data: issues and challenges moving forward. In: Proceedings of the 46th Hawaii International Conference on System Sciences. Association for Computing Machinery Digital Library; 2013. p. 995–1004.

Herland M, Khoshgoftaar TM, Wald R. A review of data mining using big data in health informatics. J Big Data. 2014;1: https://doi.org/10.1186/2196-1115-1-2 .

Vogel C, Zwolinsky S, Griffiths C, Hobbs M, Henderson E, Wilkins E. A Delphi study to build consensus on the definition and use of big data in obesity research. Int J Obes. 2019. https://doi.org/10.1038/s41366-018-0313-9 .

Morris M, Birkin M. The ESRC strategic network for obesity: tackling obesity with big data. Int J Obes. 2018;42:1948–50.

Timmins K, Green M, Radley D, Morris M, Pearce J. How has big data contributed to obesity research? A review of the literature. Int J Obes. 2018;42:1951–62.

Monsivais P, Francis O, Lovelace R, Chang M, Strachan E, Burgoine T. Data visualisation to support obesity policy: case studies of data tools for planning and transport policy in the UK. Int J Obes. 2018;42:1977–86.

Morris M, Wilkins E, Timmins K, Bryant M, Birkin M, Griffiths C. Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. Int J Obes. 2018;42:1963–76.

Vayena E, Salathé M, Madoff LC, Brownstein JS. Ethical challenges of big data in public health. PLOS Comput Biol. 2015;11:e1003904.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Silver LD, Ng SW, Ryan-Ibarra S, Taillie LS, Induni M, Miles DR, et al. Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: a before-and-after study. PLoS Med. 2017;14:e1002283.

Gore RJ, Diallo S, Padilla J. You are what you tweet: connecting the geographic variation in america’s obesity rate to Twitter content. PLoS ONE. 2015;10:e0133505.

Nguyen QC, Li D, Meng H-W, Kath S, Nsoesie E, Li F, et al. Building a national neighborhood dataset from geotagged Twitter data for indicators of happiness, diet, and physical activity. JMIR Public Health Surveill. 2016;2:e158.

Hirsch JA, James P, Robinson JR, Eastman KM, Conley KD, Evenson KR, et al. Using MapMyFitness to place physical activity into neighborhood context. Front Public Health. 2014;2:1–9.

Althoff T, Hicks JL, King AC, Delp SL, Leskovec J. Large-scale physical activity data reveal worldwide activity inequality. Nature. 2017;547:336–9.

Kerr NL. HARKing: hypothesizing after the results are known. Pers Soc Psychol Rev. 1998;2:196–217.

Article   CAS   PubMed   Google Scholar  

Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–29.

Bennett JE, Li G, Foreman K, Best N, Kontis V, Pearson C, et al. The future of life expectancy and life expectancy inequalities in England and Wales: Bayesian spatiotemporal forecasting. Lancet. 2015;386:163–70.

World Health Organisation. Report of the Commission on ending childhood obesity. Geneva, Switzerland: World Health Organisation; 2016.

Centers for Disease Control and Prevention. Recommended community strategies and measurements to prevent obesity in the United States. Atlanta, GA, U.S.: Centers for Disease Control and Prevention; 2009.

Local Government Association. Building the foundations: tackling obesity through planning and development. London, UK: Local Government Association; 2016.

Burgoine T, Alvanides S, Lake AA. Creating ‘obesogenic realities’; Do our methodological choices make a difference when measuring the food environment? Int J Health Geogr. 2013;12. https://doi.org/10.1186/1476-072X-12-33 .

Wilkins E, Morris M, Radley D, Griffiths C. Methods of measuring associations between the Retail Food Environment and weight status: Importance of classifications and metrics. SSM Popul Health. 2019. https://doi.org/10.1016/j.ssmph.2019.100404 .

Bardou M, Barkun AN, Martel M. Obesity and colorectal cancer. Gut. 2013;62:933–47.

Siegel R, Desantis C, Jemal A. Colorectal cancer statistics, 2014. CA Cancer J Clin. 2014;64:104–17.

Derogar M, Hull MA, Kant P, Östlund M, Lu Y, Lagergren J. Increased risk of colorectal cancer after obesity surgery. Ann Surg. 2013;258:983–8.

Kant P, Hull MA. Excess body weight and obesity—the link with gastrointestinal and hepatobiliary cancer. Nat Rev Gastroenterol Hepatol. 2011;8:224–38.

Östlund MP, Lu Y, Lagergren J. Risk of obesity-related cancer after obesity surgery in a population-based cohort study. Ann Surg. 2010;252:972–6.

Sainsbury A, Goodlad RA, Perry SL, Pollard SG, Robins GG, Hull MA. Increased colorectal epithelial cell proliferation and crypt fission associated with obesity and roux-en-Y gastric bypass. Cancer Epidemiol Biomark Prev. 2008;17:1401–10.

Article   CAS   Google Scholar  

Aravani A, Downing A, Thomas JD, Lagergren J, Morris EJA, Hull MA. Obesity surgery and risk of colorectal and other obesity-related cancers: an English population-based cohort study. Cancer Epidemiol. 2018;53:99–104.

Openshaw S. The modifiable areal unit problem. In: Concepts and techniques in modern geography. Norwich: Geo Books; 1984. p. 1–41.

Kwan M-P. The uncertain geographic context problem. Ann Assoc Am Geogr. 2012;102:958–68.

Di Zhu X, Yang Y, Liu X. The importance of housing to the accumulation of household net wealth. Harvard, USA: Joint Center for Housing Studies, Harvard University; 2003.

Rehm CD, Moudon AV, Hurvitz PM, Drewnowski A. Residential property values are associated with obesity among women in King County, WA, USA. Soc Sci Med. 2012;75:491–5.

Drewnowski A, Buszkiewicz J, Aggarwal A. Soda, salad, and socioeconomic status: findings from the Seattle Obesity Study (SOS). SSM Popul Health. 2019;7:e100339.

Birkin M, Morris MA, Birkin TM, Lovelace R. Using census data in microsimulation modelling. In: Stillwell J, Duke-Williams O, editors. The Routledge handbook of census resources, methods and applications. 1st ed. Routledge: IJO publication; 2018.

Jiao J, Drewnowski A, Moudon AV, Aggarwal A, Oppert J-M, Charreire H, et al. The impact of area residential property values on self-rated health: a cross-sectional comparative study of Seattle and Paris. Prev Med Rep. 2016;4:68–74.

Nguyen DM, El-Serag HB. The epidemiology of obesity. Gastroenterol Clinics. 2010;39:1–7.

Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Commun Health. 2001;55:111–22.

Timperio A, Salmon J, Telford A, Crawford D. Perceptions of local neighbourhood environments and their relationship to childhood overweight and obesity. Int J Obes. 2005;29:170–5.

Roda C, Charreire H, Feuillet T, Mackenbach JD, Compernolle S, Glonti K, et al. Mismatch between perceived and objectively measured environmental obesogenic features in European neighbourhoods. Obes Rev. 2016;17 S1:31–41.

Drewnowski A, Arterburn D, Zane J, Aggarwal A, Gupta S, Hurvitz PM, et al. The Moving to Health (M2H) approach to natural experiment research: a paradigm shift for studies on built environment and health. SSM Popul Health. 2019;7:100345.

Bourassa SC, Cantoni E, Hoesli M. Predicting house prices with spatial dependence a comparison of alternative methods. J Real Estate Res. 2010;32:139–60.

Google Scholar  

Wilkins EL, Radley D, Morris MA, Griffiths C. Examining the validity and utility of two secondary sources of food environment data against street audits in England. Nutr J. 2017;16:1–13.

Nevalainen J, Erkkola M, Saarijarvi H, Nappila T, Fogelholm M. Large-scale loyalty card data in health research. Digit Health. 2018;4:2055207618816898.

PubMed   PubMed Central   Google Scholar  

Aiello L, Schifanello R, Quercia D, Del Prete L. Large-scale and high-resolution analysis of food purchases and health outcomes. EPJ Data Sci. 2019;8:14.

Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–95.

Zwolinsky S, McKenna J, Pringle A, Widdop P, Griffiths C, Mellis M, et al. Physical activity and sedentary behavior clustering: segmentation to optimize active lifestyles. J Phys Act Health. 2016;13:921–8.

Bauman A, Ainsworth BE, Sallis JF, Hagströmer M, Craig CL, Bull FC, et al. The descriptive epidemiology of sitting: a 20-country comparison using the International Physical Activity Questionnaire (IPAQ). Am J Prev Med. 2011;41:228–35.

Guerin PB, Diiriye RO, Corrigan C, Guerin B. Physical activity programs for refugee somali women: working out in a new country. Women & Health. 2003;38:83–99.

Pope L, Harvey J. The efficacy of incentives to motivate continued fitness-center attendance in college first-year students: a randomized controlled trial. J Am Coll Health. 2014;62:81–90.

Cetateanu A, Jones A. Understanding the relationship between food environments, deprivation and childhood overweight and obesity: evidence from a cross sectional England-wide study. Health Place. 2014;27:68–76.

Harrison F, Burgoine T, Corder K, van Sluijs EM, Jones A. How well do modelled routes to school record the environments children are exposed to? A cross-sectional comparison of GIS-modelled and GPS-measured routes to school. Int J Health Geogr. 2014;13:5.

Ells LJ, Macknight N, Wilkinson JR. Obesity surgery in England: an examination of the health episode statistics 1996–2005. Obes Surg. 2007;17:400–5.

Nielsen JDJ, Laverty AA, Millett C, Mainous AG, Majeed A, Saxena S. Rising obesity-related hospital admissions among children and young people in England: National time trends study. PLoS ONE. 2013;8:e65764.

Smittenaar C, Petersen K, Stewart K, Moitt N. Cancer incidence and mortality projections in the UK until 2035. Br J Cancer. 2016;115:1147–55.

Wallington M, Saxon EB, Bomb M, Smittenaar R, Wickenden M, McPhail S, et al. 30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study. The Lancet Oncol. 2016;17:1203–16.

Smolina K, Wright FL, Rayner M, Goldacre MJ. Determinants of the decline in mortality from acute myocardial infarction in England between 2002 and 2010: Linked national database study. BMJ. 2012;344:d8059.

Hanratty B, Lowson E, Grande G, Payne S, Addington-Hall J, Valtorta N, et al. Transitions at the end of life for older adults–patient, carer and professional perspectives: A mixed-methods study. Health Serv Deliv Res. 2014. https://doi.org/10.3310/hsdr02170 .

Aggarwal A, Monsivais P, Cook AJ, Drewnowski A. Does diet cost mediate the relation between socioeconomic position and diet quality? Eur J Clin Nutr. 2011;65:1059–66.

Drewnowski A, Aggarwal A, Tang W, Moudon AV. Residential property values predict prevalent obesity but do not predict 1-year weight change. Obesity. 2015;23:671–6.

Download references

Acknowledgements

The ESRC Strategic Network for Obesity was funded via ESRC grant number ES/N00941X/1. The authors would like to thank all of the network investigators ( https://www.cdrc.ac.uk/research/obesity/investigators/ ) and members ( https://www.cdrc.ac.uk/research/obesity/network-members/ ) for their participation in network meetings and discussion which contributed to the development of this paper.

Author information

Authors and affiliations.

Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds, UK

Emma Wilkins, Ariadni Aravani, Amy Downing & Michelle A. Morris

Center for Public Health Nutrition, University of Washington, Seattle, WA, USA

Adam Drewnowski

School of Sport, Leeds Beckett University, Leeds, UK

Claire Griffiths & Stephen Zwolinsky

Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds, UK

• Mark Birkin

Engineering and Environment, Northumbria University, Newcastle, UK

Seraphim Alvanides

GESIS—Leibniz Institute for the Social Sciences, Cologne, Germany

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Michelle A. Morris .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Wilkins, E., Aravani, A., Downing, A. et al. Evidence from big data in obesity research: international case studies. Int J Obes 44 , 1028–1040 (2020). https://doi.org/10.1038/s41366-020-0532-8

Download citation

Received : 23 May 2019

Revised : 20 December 2019

Accepted : 07 January 2020

Published : 27 January 2020

Issue Date : May 2020

DOI : https://doi.org/10.1038/s41366-020-0532-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

• Julia Mariel Wirtz Baker
• Sonia Alejandra Pou
• Laura Rosana Aballay

International Journal of Obesity (2023)

Creating a long-term future for big data in obesity research

• Emma Wilkins
• Michelle A. Morris

International Journal of Obesity (2019)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Search Menu

Sign in through your institution

• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Numismatics
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Social History
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Acquisition
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Religion
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Legal System - Costs and Funding
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Restitution
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business Strategy
• Business History
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Social Issues in Business and Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Systems
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Management of Land and Natural Resources (Social Science)
• Natural Disasters (Environment)
• Pollution and Threats to the Environment (Social Science)
• Social Impact of Environmental Issues (Social Science)
• Sustainability
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• Ethnic Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Politics and Law
• Politics of Development
• Public Policy
• Public Administration
• Qualitative Political Methodology
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Disability Studies
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

11 Case Study: Can We Reduce Obesity by Encouraging People to Eat Healthy Food?

• Published: June 2016
• Cite Icon Cite
• Permissions Icon Permissions

In the United States, an estimated 17% of children age 2 to 19 years are considered obese; 32% are overweight. Worldwide, 12.9% to 23.8% of children are obese, and the prevalence is increasing. Preventing the onset of obesity remains a critical public health goal of the next decade. Population health science approaches to reducing the prevalence of obesity are presented: one that focuses on coaching individuals to change their behaviors related to food and exercise, and another that focuses on changing the food environment (ubiquitous exposure). An illustration is provided of how to conceptualize the limits of individual-level behavioral interventions on the population distributions of obesity incidence using basic assumptions and data simulation. The effect of individual motivation to prevent obesity is bounded by the prevalence of unhealthy environments in which children are living, which affects the number of incident obesity cases observed and the proportion attributable to individual determinants.

Personal account

• Sign in with email/username & password
• Get email alerts
• Save searches
• Purchase content
• Activate your purchase/trial code
• Add your ORCID iD

Institutional access

Sign in with a library card.

• Sign in with username/password
• Recommend to your librarian
• Institutional account management
• Get help with access

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:

IP based access

Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.

Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.

• Click Sign in through your institution.
• Select your institution from the list provided, which will take you to your institution's website to sign in.
• When on the institution site, please use the credentials provided by your institution. Do not use an Oxford Academic personal account.
• Following successful sign in, you will be returned to Oxford Academic.

If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.

Enter your library card number to sign in. If you cannot sign in, please contact your librarian.

Society Members

Society member access to a journal is achieved in one of the following ways:

Sign in through society site

Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:

• Click Sign in through society site.
• When on the society site, please use the credentials provided by that society. Do not use an Oxford Academic personal account.

If you do not have a society account or have forgotten your username or password, please contact your society.

Sign in using a personal account

Some societies use Oxford Academic personal accounts to provide access to their members. See below.

A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.

Some societies use Oxford Academic personal accounts to provide access to their members.

Viewing your signed in accounts

Click the account icon in the top right to:

• View your signed in personal account and access account management features.
• View the institutional accounts that are providing access.

Signed in but can't access content

Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.

For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.

Our books are available by subscription or purchase to libraries and institutions.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Rights and permissions
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Data and case studies

Resources Policy Dossiers Obesity & COVID-19 Data and case studies

• Sugar-Sweetened Beverage Tax
• Digital Marketing
• School-based interventions
• Community-level interventions
• Pregnancy & Obesity
• Childhood Obesity Treatment
• Front-of-pack nutrition labelling
• Obesity & COVID-19
• Physical Activity
• Food Systems
• Weight Stigma

World Obesity have collated some of the recent data and case studies available looking pertaining to obesity and the current outbreak of COVID-19. 

Researchers at Johns Hopkins University in the US examined 265 patients to determine if younger patients hospitalised with COVID-19 were more likely to be living with overweight and obesity. They found a correlation, which they hypothesise may be due to physiologic changes from obesity. Other comorbidities these patients may have had were not reported. Read the full study here .

Chinese researchers identified 66 patients with COVID-19 and fatty liver disease and compared the outcomes for those with and without obesity. They found obesity was a significant risk factor for severe illness in this population after accounting for other factors (age, gender, smoking, diabetes, high blood pressure, and dyslipidaemia). Read the full study here . 

The global rise in the prevalence of obesity and type 2 diabetes can be partially explained by a rise in diets high in fats, sugars and refined carbohydrates. Diets high in saturated fatty acids cause inflammation and immune disfunction, which may explain why minority groups (who experience disproportionate rates of diseases linked to nutrition, such as obesity and diabetes) are also hospitalised with COVID-19 at higher rates. Read the full study here .

MicroRNAs (abbreviated miRNAs) are produced in human cells to regulate gene expression. Some research has suggested that these may also defend against viruses. These researchers identified 848 miRNAs that are may be effective against SARS and 873 that could target COVID-19 using genome sequences of each of these viruses. Previous studies have suggested that the elderly and those with underlying conditions (including obesity) may produce less of these miRNAs, possibly explaining why these groups are at increased risk of severe illness from COVID-19. However, trials in human and animal subjects are needed to verify these theoretical results. Read the full study here .  

Given the importance of determining the risk factors for morbidity and mortality related to COVID-19, this retrospective study analysed the frequency and outcomes of COVID-19 patients in critical care who are living with overweight or obesity. “Of the 3,615 individuals who tested positive for COVID-19, 775 (21%) had a body mass index (BMI) 30-34, and 595 (16% of the total cohort) had a BMI >35.” While patients were separated into elderly (over 60) and younger (under 60) groups, it was not reported if the study controlled for other variables that may affect the course of COVID-19. Read the full study here .

This piece describes two patients with obesity that experienced damage to their airways while being intubated due to severe illness from COVID-19. The authors recommend videolaryngoscopy for intubation to protect both patients and healthcare workers. Read the full study here .

These researchers chose to specifically examine how many COVID-19 patients living with obesity or overweight were placed on ventilators. Based in Lille, France, the study included 124 patients, 68.8% of whom ultimately required ventilation. They established a dose-response relationship- increasing body max index (BMI) increased the risk of needing ventilation. This study found that BMI seemed to be associated with ventilator treatments independently of age, diabetes or high blood pressure. However, further research must be conducted before this relationship is proven. Read the full study here .

Researchers obtained medical records of 16,749 people hospitalised for COVID-19 to determine what were some of the factors that made patients more likely to experience severe cases of the illness. Slightly over half of patients had at least one underlying condition (including obesity) and these patients were more likely to die from COVID-19. The study found that obesity is linked to mortality, independently of age, gender and other associated conditions. Read the full study here .

Using a very large sample size of 17,425,455, this cohort study aimed to identify risk factors associated with mortality due to COVID-19 across the general population. Among the comorbidities, most of them were associated with increased risk, including obesity. Furthermore, deprivation was also identified as a major risk factor. Specifically, for patients with overweight and obesity, as their body mass index increased, so did their risk of dying from COVID-19. Read the full study here .

This study included 48 critically ill patients with COVID-19 treated with invasive ventilation in Spain. Of this population, 48% were living with obesity, 44% with hypertension, and 38% with chronic lung disease. Symptoms and patient outcomes were also described. Read the full study here .

This study examined the correlation between severe disease and body mass index (BMI) among 357 patients in France. People diagnosed with severe COVID-19 were 1.35 times more likely to also be living with obesity and people in critical care with COVID-19 were 1.89 times more likely to be living with obesity than the general public. This study adjusted for age and gender of patients but no other cofounding factors. Read the full study here .

Previous research has demonstrated that children tend to gain weight during when school is not in session, so experts have been concerned about the impact of lockdowns due to coronavirus on childhood obesity rates. This study observed lifestyle behaviours in 41 children living with obesity at baseline and then three weeks into quarantine. Scientists found that children reported eating more meals, as well as more potato chips, red meat, and sugar-sweetened beverages. They slept more, exercised less and spent much more time looking at screens. As a result, researchers recommend that lifestyle interventions be delivered through telemedicine while the lockdown lasts. Read the full study here .

A recent study from France examined 1317 COVID-19 patients living with diabetes. Of these, more than 10% passed away and almost 33% needed to be placed on a ventilator within a week of admission to the hospital. Obesity was found to be an independent risk factor for poor outcomes when other cofounding factors were accounted for. Read the full study here .

This study found that, of 5700 patients admitted to 12 selected New York hospitals with COVID-19, 56.6% had hypertension (high blood pressure), 41.7% were living with obesity and 33.8% had diabetes. It also reported data on patient outcomes. Read the full study here .  

Wuhan city, the capital of Hubei province in China, was for a long time the epicentre of the COVID-19 outbreak. This study presents information of patients admitted to two Wuhan hospitals with laboratory-confirmed COVID-19. 191 patients were included in order to determine what risk factors lead to fatalities, describe Covid-19 symptoms over time, determine how long patients are infectious after they recover and record what treatments were tried. It should be noted that almost half of patients had underlying health conditions such as hypertension or heart disease, although obesity was not measured. Read the full study here . 

This study examined 24 adults to determine which populations in the Seattle area were hospitalised with severe illness from COVID-19, what underlying conditions they had, the results of medical imaging tests and whether they recovered. Patients had an average body mass index of 33.2 (give or take 7.2 units) and over half (58%) of patients were diagnosed with diabetes. Scientists concluded that “patients with coexisting conditions and older age are at risk for severe disease and poor outcomes after ICU [intensive care unit] admission.” Read the full study here .

Looking at 383 patients in Shenzen, China, this study was the first to directly examine the correlation between obesity and severe illness from coronavirus. For this study, a person with a body mass index (BMI) between 24.0 - 27.9 was considered overweight and a person with a BMI greater than 28 was considered to be living with obesity. While people living with obesity generally experienced the same length of illness, they were significantly more likely to develop severe pneumonia, even when accounting for other risk factors. Read the full study here .

Based on a sample of 4,103 New York City residents, this paper evaluates what characteristics make people more likely to be admitted to the hospital and critical care.  Overall, it was observed that 39.8% of people living with obesity were hospitalised, compared to 14.5% without. Scientists found “particularly strong associations of older age, obesity, heart failure and chronic kidney disease with hospitalization risk, with much less influence of race, smoking status, chronic pulmonary disease and other forms of heart disease.” Read the full study here .

In order to ensure the proper monitoring of COVID-19-related hospitalisations across the United States, the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was developed. This report “presents age-stratified COVID-19-associated hospitalisation rates for patients admitted during March 1-28, 2020, and clinical data on patients admitted during March 1-30, 2020.” Among the 1,482 patients diagnosed and hospitalised with COVID-19, 90% had at least one comorbidity and 42% were living with obesity, with African Americans and the elderly disproportionately affected. Read the full study here .

This report examined demographic information of patients hospitalised with COVID-19 in China. Of these, older patients, diabetics and those living with obesity were significantly more likely to be considered “severely ill.” The study also looked at symptoms during admission at admission and treatment options. Read the full study here .

In this study, researchers used data from 103 consecutive patients hospitalized in the USA. There were two major findings- a correlation between critical care admissions due to COVID-19 and a body mass index greater than 35 in general, and a correlation between needing invasive mechanical ventilation and having both heart disease and obesity. These findings were adjusted for age, sex, and race. Read the full study here .

This article examined how SARS- CoV-2 impacts pregnancy using 46 patients in the USA. Almost all patients who developed severe disease were living with overweight and obesity. After diagnosis, 16% of patients were admitted to the hospital and 2% were placed in intensive care. Researchers believe this, along with the need to induce labour prematurely in some patients to improve breathing, may suggest that pregnant women should be classified as a vulnerable group. Read the full study here .

School and recreational space closures due to COVID-19 have reduced physical activity among children. Researchers used modeling software to simulate the following scenarios: 

• No school closures (control) 
• Schools closed for two months 
• Schools closed for two months and 10% reduction in physical activity over the summer break  
• Schools closed for four months (April through May and September through October) and 10% reduction in physical activity over the summer break 
• Schools closed for six months (April through May and September through December) and 10% reduction in physical activity over the summer break 

Overall, the pandemic is projected to increase mean standardised body mass index (BMI) between 0.056 (two-month closure) and 0.198 (six-month closure) units. It may also increase the percentage of children living with obesity in the USA by up to 2.373 percentage points. Read the full study here .

This study was conducted to examine the characteristics and course of disease in 50 New York children (under 21 years of age) hospitalised with COVID-19. Of the study population, 11 patients had obesity and 8 had overweight.  Obesity was found to be a significant risk factor for both severe disease and mechanical ventilation while immunosuppression was not.  Read the full study here .

Researchers at the University of Chicago Medical Center found that patients hospitalized with COVID-19 were more likely to die if they were also living with obesity, even when accounting for age, sex, and underlying conditions. 238 patients were included within the study. These researchers did not find a significant connection with admission to critical care units or mechanical ventilation in patients with obesity. Limitations included the makeup of the study population, as the sample size was small and the vast majority were African American, so the results may not be representative of all people. Read the full study here.  

This meta-analysis and systematic review found nine separate articles regarding the link between COVID-19, obesity and more severe diseases. Between all studies, 1817 patients were examined. Researchers found an odds ratio of 1.89 for poor outcomes in patients with obesity, especially among younger patients, which indicates that obesity increases the risk of severe diseases. Read the full study here . 

A meta-analysis concluded that people living with obesity were more likely to have worse outcomes if they also contracted COVID-19. Researchers identified nine articles (six of which were retrospective case-control studies, four of which were retrospective cohort studies, and one of which used both methods) and extracted data from each. Limitations included heterogeneity in study design (particularly regarding the definition of obesity), lack of comorbidity reporting, and low quantity of studies used. Read the full study here .

As almost 75% of American adults over the age of 20 are living with overweight or obesity, this disease should be considered a public health priority, especially given the increased likelihood of poor outcomes in COVID-19 patients with obesity. The paper outlines several mechanisms explaining why obesity may lead to more severe disease, including having more of the receptor the virus uses to enter cells, reduced lung function, chronic inflammation, endothelial disfunction, changes in blood clotting, and physiological changes related to common comorbidities of obesity. Finally, several compelling studies linking obesity to increased risk of complications are included. Read the full study here .

Evidence shows that the impact of COVID-19 tends to be more serious in specific vulnerable groups, including people living with obesity. Furthermore, the pandemic also seems to have a number of indirect repercussions including on eating behaviour patterns among people with obesity. The objective of this study was “to examine the impact of the COVID-19 pandemic on patronage to unhealthy eating establishments in populations with obesity.”   

These researchers combined GPS data with known obesity rates to determine how many people with obesity entered unhealthy restaurants during the COVID-19 pandemic (December 2019- April 2020). Prior to lockdowns, more people in areas with high obesity rates entered fast food restaurants; in March, fewer people did across all areas; however, the numbers of patrons steadily increased during April, at a faster rate in areas with higher obesity rates. While informative, a number of limitations were observed, including the fact that not all consumers exactly match the demographics of the area they live in and that more variables may contribute to restaurant traffic than accounted for here. Read the full study here . 

Various studies over the past few months have linked obesity to a more serious course of illness from COVID-19. It is therefore essential that we improve our understanding of the possible reasons for the link and what it means for those living with obesity. This systematic review looks at the influence of obesity on COVID-19 outcomes and proposes biological mechanisms as to why a more severe courseof illness can occur. It also discusses the implications of COVID-19 for those living with obesity. Read the full study here .

Both COVID-19 and childhood obesity are pandemics raging across America today. Obesity is an independent risk factor for the severity of COVID-19, suggesting that children with obesity could see a more severe course of illness due to COVID-19. The stay-at-home mandates and physical distancing preventative measures have resulted in a lack of access to nutritious foods, physical activity, routines and social interactions, all of which could negatively impact children -especially those living with obesity. Read the full study here .

Obesity has been suggested as a risk factor for poor outcome in those with COVID-19. Studies show that patients with obesity are more likely to require mechanical ventilation. In fact, multiorgan failure in patients with COVID-19 and obesity could be dueto the chronic metabolic inflammation and predisposition to the “enhanced release of cytokines-pathophysiology accompanying severe obesity”. However, the association between body mass index (BMI) and COVID-19 outcomes has yet to be fully explored. This study intends to address that gap. Read the full study here .

Emerging evidence suggests that the severity of COVID-19 in a patient is associated with overweight and obesity. Patients with obesity are at risk for a number of other non-communicable diseases, including cardiovascular dysfunction and hypertension and diabetes. In individuals living with overweight and obesity, macronutrient excess in adipose tissue stimulates adipocytes “to release tumour necrosis factor α(TNF-α), interleukin-6 (IL-6) and other pro-inflammatory mediators and to reduce production of the anti-inflammatory adiponectin, thus predisposing to a proinflammatory state and oxidative stress”. Obesity also impairs immune responses; it has a negative impact on pathogen defences within the body. Therefore, the acceleration of viral inflammatory responses in COVID-19 and more unfavourable prognoses are associated with individuals living with obesity. Read the full study here .

Obesity has been identified as a comorbidity for severe outcomes in patients with COVID-19. In this study, comorbidities associated with increased risk of COVID-19 were determined in a population-based analysis of Mexicans with at least one comorbidity. Data was obtained from the COVID-19 database of the publicly available Mexican Ministry of Health “Dirección General de Epidemiología”. Variables of the patients’ heath were all noted, such as age, gender, smoking status, history of COVID-19 contact, comorbidities, etc. Patients with missing information were excluded in the analysis. To determine the independent effect of each comorbidityon COVID-19 and separate the effect of two or more, “analysis was limited to patients reporting only one comorbidity." Read the full study here .

Obesity has arisen as a major complication for the COVID-19 pandemic, which has been caused by the novel SARS-CoV-2 virus. The former is a major health concern due to its side-effects on human health and association with morbidity and mortality. Evidence points out that obesity can worsen patient prognosis due to COVID-19 infection. There may be a “pathophysiological link that could explain the fact that obese patients are prone to present with SARS-CoV-2 complications”. The authors present mechanistic obesity-related issues that aggravate the SARS-CoV-2 infection in patients living with obesity and the possible molecular links between obesity and SARS-CoV-2 infection. Read the full study here .

The highly infectious serious acute respiratory syndrome COVID-19 has caused high morbidity and mortality all over the world. It has been suggested that SARS-CoV-2, the pathogen of COVID-19, uses angiotensin-converting enzyme 2 (ACE2) as a cell receptor. This receptor is found in the lungs but also many other organs, including the adipose tissue, heart, and oral epithelium. Previous studies have identified obesity as a critical factor in the prognoses of COVID-19 patients, and that, in patients with COVID-19, non-survivors had a higher body mass index (BMI) than survivors. This study intended to “investigate the association between obesity and poor outcomes of COVID-19 patients." Read the full study here .

Approximately 45% of individuals worldwide have overweight or obesity. Obesity is characterized by its pro-inflammatory condition. The excess visceral and omental adiposity seen in individuals with obesity are linked with an increase in pro-inflammatory cytokines that affect systemic cellular processes. Importantly, they “change the nature and frequency of immune cells infiltration”. When a high percentage of a population have obesity, more virulent viral strains tend to develop, and the reach of a virus is wider. Furthermore, the state of obesity is correlated to the presence of comorbidities that are dangerous to human health, such as type 2 diabetes and hypertension. This systematic review includes articles from a myriad of databases in order to address how living with obesity impacts one’s reaction to the SARS-CoV-2 virus and course of COVID-19. Read the full study here .

The psychological impact of COVID-19 lockdown and quarantine on children has been documented to cause “anxiety, worrying, irritability, depressive symptoms, and even post-traumatic stress disorder symptoms”. In particular, children living with severe obesity may struggle with anxieties about the possibility of obesogenic issues that can arise during the course of illness due to COVID-19. In this study, 75 families (one child interviewed per family) were interviewed on anxiety that their child with severe obesity may have, and on what specific type anxieties they are. 24 of 75 children reported having COVID-19 related anxieties. Read the full study here . 

In this multi-centre study focused on retrospective observational data from eight hospitals throughout Greece, the data on 90 critically ill patients positive for COVID-19 is analysed. Those hospitalised due to COVID-19 reflect critically ill patients whodeveloped extremely severe acute respiratory syndrome (SARS) in elderly patients with COVID-19-related pneumonia and/or underlying chronic diseases. Many underlying chronic diseases have been identified as risk factors for developing more severe COVID-19. These include type-2 diabetes, cardiovascular diseases, and hypertension. Obesity has also been associated with disease severity. In this study the relation of comorbidities such as obesity and type-2 diabetes and COVID-19 disease severity is explored. Read the full study here .

According to the World Health Organisation, physical inactivity is the fourth leading cause of death, and increases the risk of a person contracting a “metabolic disease, including obesity and type 2 diabetes (T2D).” This article points out that those seeking treatment for obesity or T2D may find difficulty in doing so during the COVID-19 pandemic due to lockdowns. As it has been found that sedentary behaviour increases one's risk for many chronic diseases, the authors wished to explore hypothetical immunopathologyof COVID-19 in patients living with obesity and how the immune defences against COVID-19 may be related to the “immuno-metabolic dysregulations'' characterised by it. Furthermore, they explore the possibility of exercise as a counteractive measure due to its anti-inflammatory properties. Read the full study here .

Obesity has been linked to a less-efficient immune response in the human body as well as poorer outcomes for respiratory diseases. In this article, researchers hypothesised that a higher Body Mass Index is a risk factor for a more severe course of illness for COVID-19. They followed all patients hospitalised from 11 January to 16 February 2020 until March 26 2020 at the Third People’s Hospital of Shenzhen (China), which was dedicated to COVID-19 treatment. Read the full study here .

As reported by the World Health Organization, the global prevalence of obesity is still on the rise both across high-income as well as low-and middle-income countries. Obesity has been associated with an increase in mortality for patients fighting COVID-19. The authors suggest that the inflammatory profile associated with patients with obesity is conducive to a more severe course of illness in patients with COVID-19. Read the full study here .

Researchers studying COVID-19, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), have concluded that obesity, diabetes, hypertension or cardiovascular disease is correlated to an increased severity of illness due to COVID-19. Obesity has been associated with SARS-CoV-2 due to the “cytokine storm” of the latter; a number of the pro-inflammatory cytokines released in the “storm” which are detrimental to organ function are also found contributing to the chronic low-grade inflammation in patients with obesity. The authors wished to study a Middle Eastern population and assess the outcome of COVID-19 in relation to obesity. They observed clinical data from patients in the Al Kuwait Hospital in Dubai, UAE, to study the correlation between obesity and poor clinical outcomes of COVID-19. Read the full study here .

In many previous studies, underlying conditions such as obesity, hypertension and diabetes have been found to be correlated with an increased rate of hospitalisation and death due to SARS-CoV-2. Obesity is a non-communicable disease marked by an imbalanced energy state due to hypertrophy and hyperplasia of adipose tissue. Increased secretion of various cytokines and hormones, such as interleukin-6, tumour necrosis factor alpha and leptin, establishes a low-grade inflammatory state in patients with obesity. These pro-inflammatory cytokines predispose individuals “to increased risk for infection and adverse outcomes”. The metabolic disorders that are associated with obesity are numerous, including diabetes, hypertension and cardiovascular diseases. Most are associated with an increased risk of severe COVID-19. Due to this link, obesity is “an important factor in determining the morbidity and mortality risk in SARS CoV 2 patients” as well as the need for mechanical ventilation. Read the full study here .

Pulmonary consolidation is the most common complication of COVID-19. A high percentageof COVID-19 related pulmonary consolidationis due to extensive pulmonary fibrosis (PF). Viral infections have been shown to be a risk factor for PF, and both viral infections and aging were“strongly associated cofactors” for PF in this study. Infection with SARS-CoV-2, the virus responsible for COVID-19,suppresses the angiotensin-converting enzyme 1 (ACE2), which is a receptor exploited by the virus for cell entry; this receptor is “a negative regulator of” PF, which therefore links the virus to the progression of PF. Read the full study here .

Elevated body mass index has been marked as a risk factor for COVID-19 severity, hospital admissions and mortality. Diabetes and hypertension have also been associated with severe and fatal cases of COVID-19. Mendelian randomisation (MR) analyses the causal effect of an exposure risk factor on an outcome using genetic variants as instruments of estimation. In this study, the causal relationship between obesity traits (such as elevated BMI and metabolic disorders) and quantitative cardiometabolic biomarkers and COVID-19 susceptibility was examined by MR. Data was obtained from the UK Biobank. 1,211 individuals who had tested positive for COVID-19 and 387,079 individuals who were negativeor untestedwere analysed. Read the full study here .

Obesity and diabetes have both been identified in epidemiological reports as comorbidities “frequently associated with severe forms of COVID-19”. Both have also been identified as an independent risk factor for the severity of COVID-19 in a patient. The presence of these diseases is associated with each other; therefore, they could “confer a particularly high risk of severe COVID-19”. In previous analysis of the CORONAvirus-SARS-CoV-2 and Diabetes Outcomes (CORONADO) Study, it was shown “that body mass index (BMI) was positively and independently associated with severe COVID-19-related outcomes ... in patients with diabetes hospitalised for COVID-19”. In this analysis of the CORONADO data, the course of COVID-19 and its relationship to obesity in patients with type 2 diabetes hospitalised for this disease is explored. The influence of age on BMI and COVID-19 prognosis is also addressed due to the heightened impact of COVID-19 on the elderly population. Read the full study here .

Share this page

Training & Events

SCOPE E-Learning

We offer the only internationally recognised course on obesity management. Read more here.

Global Obesity Observatory

We offer various statistics, maps and key data around the topic of obesity. You can find all that and more here.

Policy & Advocacy

Our Policy Priorities

We have developed five key areas of policy that are a priority to us. Want to know more? Check them out here!

• Our Members
• Member Benefits
• Membership Application Form
• Partnerships
• Global Obesity Forum
• Patient Portal
• Finance Committee
• Annual Report and Financials
• Prevalence of Obesity
• Causes of Obesity
• Obesity Classification
• Prevention of Obesity
• Obesity as a disease
• Commercial determinants of obesity
• Childhood Obesity
• Obesity in Universal Health Coverage
• The ROOTS of Obesity
• World Obesity Day
• Healthy Venues
• Reinventing the Food System: A Report
• The Spotlight Project
• SCOPE Examination
• Guide to SCOPE Certification
• SCOPE Fellows
• Leadership Programme
• Accreditation
• SCOPE Schools
• SCOPE Pricing
• SCOPE Sessions
• SCOPE Accredited Events
• Event Archive
• International Congress on Obesity

Sign up for notifications

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Health Marketing for the Massachusetts Childhood Obesity Research Demonstration Study: A Case Study

Affiliations.

• 1 Furman University, Greenville, SC, USA.
• 2 California State University, Long Beach, CA, USA.
• 3 MORE Advertising, Inc., Watertown, MA, USA.
• 4 Massachusetts General Hospital, Boston, MA, USA.
• 5 Harvard T. H. Chan School of Public Health, Boston, MA, USA.
• 6 Massachusetts Department of Public Health, Boston, MA, USA.
• PMID: 29566576
• DOI: 10.1177/1524839918760842

Introduction . This case study describes the Massachusetts Childhood Obesity Research Demonstration Study (MA-CORD) health marketing campaign, examines the strategies used in such campaigns, and offers lessons learned to improve health marketing for future interventions. MA-CORD Health Marketing Components and Implementation. The three main components were an outdoor printed advertisement and texting campaign, social media with a focus on Facebook, and the Summer Passport Program, an event-based initiative in parks for children. The advertisements consisted of billboards, bus advertisements, and handouts. The text messaging component, which required families to actively text a keyword to join, had a low opt-in rate. Facebook page "likes" increased from 1,024 to 1,453 in New Bedford and from 175 to 1,091 in Fitchburg. Fitchburg received technical assistance and paid for ads on Facebook. The Summer Passport participation in parks ranged from 120 to 875 children with participation in the free park lunch program doubling in Fitchburg. Discussion. Key lessons learned are engage communication experts from each community at the beginning of the project, use text messaging components with in-person staff onsite to assist participants in the opt-in process, build momentum for a Facebook presence through purchasing Facebook advertisements, and partner with local park departments for programming.

Keywords: advocacy; community intervention; health promotion; media advocacy.

PubMed Disclaimer

Similar articles

• Implementation of Text-Messaging and Social Media Strategies in a Multilevel Childhood Obesity Prevention Intervention: Process Evaluation Results. Loh IH, Schwendler T, Trude ACB, Anderson Steeves ET, Cheskin LJ, Lange S, Gittelsohn J. Loh IH, et al. Inquiry. 2018 Jan-Dec;55:46958018779189. doi: 10.1177/0046958018779189. Inquiry. 2018. PMID: 29865969 Free PMC article.
• Memphis FitKids: implementing a mobile-friendly web-based application to enhance parents' participation in improving child health. Ullmann G, Kedia SK, Homayouni R, Akkus C, Schmidt M, Klesges LM, Ward KD. Ullmann G, et al. BMC Public Health. 2018 Aug 29;18(1):1068. doi: 10.1186/s12889-018-5968-6. BMC Public Health. 2018. PMID: 30157819 Free PMC article.
• Using School Staff Members to Implement a Childhood Obesity Prevention Intervention in Low-Income School Districts: the Massachusetts Childhood Obesity Research Demonstration (MA-CORD Project), 2012-2014. Blaine RE, Franckle RL, Ganter C, Falbe J, Giles C, Criss S, Kwass JA, Land T, Gortmaker SL, Chuang E, Davison KK; MA-CORD Project Group. Blaine RE, et al. Prev Chronic Dis. 2017 Jan 12;14:E03. doi: 10.5888/pcd14.160381. Prev Chronic Dis. 2017. PMID: 28084989 Free PMC article.
• Development of a Teen-Friendly Health Education Program on Facebook: Lessons Learned. Park BK, Nahm ES, Rogers VE. Park BK, et al. J Pediatr Health Care. 2016 May-Jun;30(3):197-207. doi: 10.1016/j.pedhc.2015.06.011. Epub 2015 Aug 5. J Pediatr Health Care. 2016. PMID: 26254744 Review.
• Social Marketing as a Framework for Youth Physical Activity Initiatives: a 10-Year Retrospective on the Legacy of CDC's VERB Campaign. Huhman M, Kelly RP, Edgar T. Huhman M, et al. Curr Obes Rep. 2017 Jun;6(2):101-107. doi: 10.1007/s13679-017-0252-0. Curr Obes Rep. 2017. PMID: 28421471 Review.
• A Model of Social Media Effects in Public Health Communication Campaigns: Systematic Review. Kite J, Chan L, MacKay K, Corbett L, Reyes-Marcelino G, Nguyen B, Bellew W, Freeman B. Kite J, et al. J Med Internet Res. 2023 Jul 14;25:e46345. doi: 10.2196/46345. J Med Internet Res. 2023. PMID: 37450325 Free PMC article. Review.

Publication types

• Search in MeSH

Related information

Grants and funding.

• U18 DP003370/DP/NCCDPHP CDC HHS/United States
• R25 CA057711/CA/NCI NIH HHS/United States

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.

Other Literature Sources

• scite Smart Citations
• MedlinePlus Health Information

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Funding Opportunities

 Measures for Children at High Risk for Obesity

Case Study 3

How to adapt a measure for use in a different population.

Nutrition and dietary behaviors play an important role in the development of chronic diseases for Asian American adults, including those who are caregivers of Asian American children. However, food preferences and patterns are often different for Asian American sub-groups and thus require tailored instruments to capture nutrition and dietary behaviors. Developing high quality tools that capture culturally-specific and culturally-preferred foods among ethnic minority groups is important to reduce health inequities in the United States.

Glossary Adaptation refers to the process of making thoughtful and planned alterations to the design or delivery of an evidence-based intervention or tool, with the explicit goal of improving fit or effectiveness in a specific context. Cultural adaptation , more specifically, refers to adaptations that pay attention to the importance of cultural factors and accounts for cultural patterns, meanings, and values within that context.

This case study describes how one team approached the process of adapting the 26-item Dietary Screener Questionnaire (DSQ) from the 2009–2010 National Health and Nutrition Examination Survey (NHANES) to be both relevant and culturally appropriate for English-speaking Asian American families. Specifically, the research team adapted the DSQ to better reflect the diets of Chinese, South Asian, Filipino, Korean, Vietnamese, and Japanese Americans, which represent 85% of Asian Americans. The research team planned to adapt the DSQ after searching the peer-reviewed literature and “grey” literature on Google Scholar and identified that currently, no dietary screeners exist that capture the variety of food preferences and patterns specific to Asian American sub-groups.

Considerations and challenges

Why is it important to develop and culturally adapt measures to be relevant to asian american adults and children.

Asian Americans are an underserved yet understudied population. Applications of culturally adapted tools can help ensure more accurate data collection by providing specific examples of foods that are consumed by first-, second- and/or third- generation Asian ethnic subgroups. The overarching goal is to advance population health equity by filling gaps in knowledge about different Asian households and families.

What are the parameters for how the tool will be used?

In this case study, the selection of a specific tool is guided by 1) length—the tool must be short and validated, 2) delivery options—can it be delivered online, and 3) adaptability—does it allow for conversion of screener responses to dietary factors of interest. After examining the evidence-based tools listed in the National Cancer Institute’s Register of Validated Short Dietary Assessment Instruments , the research team makes the decision to adapt the DSQ. They recognize that the original DSQ did not capture foods typically consumed by Asian American families, including expectations of what a typical “plate” looks like in Asian American households—a lesson the research team learned from prior work to culturally adapt plate planning tools for multiple Asian subgroups. For example, among the many food options listed in the prompts/probes under each item in the DSQ, the majority 1) are not commonly found in Asian ethnic grocery stores, bakeries, coffeeshops, and household pantries, and 2) did not include traditional and popular items that are commonly found in Asian ethnic grocery stores, bakeries, coffeeshops, or household pantries.

Who is the target population, and what degree of adaptation is needed?

Practitioners and researchers should be specific in understanding who their target population is by age, ethnic subgroup, language preference, and acculturation level (i.e., adaptation to U.S. norms and values). These factors help determine the degree of cultural adaptation that is necessary and would be acceptable to the target population. For example, is a larger-scale “macro” adaptation required or would smaller-scale “micro” adaptation be more appropriate. For this case study, it is important to note that because the planned survey would be administered online and in English, the research team knows that they would likely be reaching Asian Americans with higher levels of acculturation. This bears mentioning, as the research team considers this level of acculturation when deciding how deeply to adapt some measures (e.g., consumption of fruits and vegetables did not include multiple ethnic options; retaining other common U.S. foods such as Mexican salsa).

Measure Selection

The research team’s adaptation process was driven by long-standing community partnerships, experience conducting community-based participatory research, previous survey work focused on diverse populations, and experience working with validated health status questions from national surveys. The community partnerships are sustained by the research team’s commitment to engage Asian American communities, mobilize large numbers of Asian Americans to participate in health research, and support the equitable translation of research findings into policy and practice recommendations. The research team’s key partnerships include those with a national partner, the Asian & Pacific Islander American Health Forum (APIAHF), a National Advisory Committee on Research and Development (NAC), and a Community Partner Network—comprised of more than 75 Asian American-serving community organizations, including public health departments, education, social service, and health care.

The first step in adapting the DSQ to be culturally relevant and culturally appropriate for English-speaking, Asian Americans was to identify the most popular Asian American foods in each food group listed in the DSQ for each Asian American subgroup. This involved 1) searching peer-reviewed literature for similar screeners that were adapted for specific Asian and/or Asian American subgroups; 2) searching for items included in food composition tables, including databases located here: http://www.fao.org/infoods/infoods/tables-and-databases/asia/en/ ; and 3) searching the Internet for relevant food blogs, social media accounts, etc.

The second step involved an iterative review process over multiple rounds to integrate critical feedback on the DSQ questions from a diverse group of fifteen Asian American-serving community leaders, partners, and staff, including several experienced bicultural community health workers. Key components of their feedback include whether the examples seemed appropriate and whether the research team missed any major examples or food groups/items.

During this phase, the research team had to decide which questions either should or should not be adapted, and whether to add questions. Not all items required adaptation, given the acculturation level anticipated for the sample. For example, no modifications were made to fruit or vegetable examples, or for chocolate and ice cream. Additionally, because seafood is such a prominent food group among Asian American caregivers and their children as indicated by the published literature, the team decided to add a question specifically addressing seafood intake. This added item mimics the pre-existing red and processed meat intake frequency questions, which also measures items per day. Decisions like these were then reviewed by community partners and leaders as member checks for relevance and accuracy.

To allow for broader applications of the adapted dietary screener, the team considers how to translate the instrument into multiple languages. Doing so would greatly enhance relevance and reach beyond just Asian Americans, who may be more acculturated, and would be an important next step towards advancing population health equity.

Lessons Learned 

• Throughout the cultural adaptation process, learning should be bi-directional between the research team and key community partners. This requires working closely together to co-learn and co-produce knowledge and solutions. The research team gleaned valuable cultural insights from the community for each subgroup, and community partners learned about some of the practical realities of data collection (e.g., limiting examples for question brevity).
• Relatedly, balance should be maintained between providing a comprehensive and representative list of food examples, without overwhelming respondents.
• Cultural adaptation can complement and bolster existing literature and online databases of evidence-based tools.
• Identify and specify when to prioritize food examples for one sub-group but not another.
• Determine the “depth” of adaptation required based on the characteristics of the setting and target population. In this particular case study, changes were more conservative because the research team targeted those who speak English and may be more acculturated, and given the online delivery of the adapted tool, the team had to decide how to best display examples.

Related Resources

• Kwon SC, Patel S, Choy C, Zanowiak J, Rideout C, Yi SS, et al. Implementing health promotion activities using community-engaged approaches in Asian American faith-based organizations in New York City and New Jersey. Transl Behav Med . 2017;7(3):444-66. doi:10.1007/s13142-017-0506-0.
• Gore R, Patel S, Choy C, Taher M, Garcia-Dia MJ, Singh H, et al. Influence of organizational and social contexts on the implementation of culturally adapted hypertension control programs in Asian American-serving grocery stores, restaurants, and faith-based community sites: a qualitative study. Transl Behav Med . 2019. doi:10.1093/tbm/ibz106.

Acknowledgments

The authors would like to thank Stella Chong, Lily Divino, Mary Joy Garcia, Alka Kanaya, Simona Kwon, Stephanie Liu, Binh Lu, Deborah Min, Rhea Naik, Chorong Park, MD Taher, Sameer Talegawkar, Kosuke Tamura, Tracy Vo, and Jennifer Wong for their feedback on our survey questions related to acculturation, diet, and grocery shopping.

Never miss a newsletter

We are social.

Check us out on Facebook, LinkedIn, Twitter and YouTube

• Open access
• Published: 07 September 2024

A case study of the informative value of risk of bias and reporting quality assessments for systematic reviews

• Cathalijn H. C. Leenaars   ORCID: orcid.org/0000-0002-8212-7632 1 ,
• Frans R. Stafleu 2 ,
• Christine Häger 1 &
• André Bleich 1  

Systematic Reviews volume  13 , Article number:  230 ( 2024 ) Cite this article

Metrics details

While undisputedly important, and part of any systematic review (SR) by definition, evaluation of the risk of bias within the included studies is one of the most time-consuming parts of performing an SR. In this paper, we describe a case study comprising an extensive analysis of risk of bias (RoB) and reporting quality (RQ) assessment from a previously published review (CRD42021236047). It included both animal and human studies, and the included studies compared baseline diseased subjects with controls, assessed the effects of investigational treatments, or both. We compared RoB and RQ between the different types of included primary studies. We also assessed the “informative value” of each of the separate elements for meta-researchers, based on the notion that variation in reporting may be more interesting for the meta-researcher than consistently high/low or reported/non-reported scores. In general, reporting of experimental details was low. This resulted in frequent unclear risk-of-bias scores. We observed this both for animal and for human studies and both for disease-control comparisons and investigations of experimental treatments. Plots and explorative chi-square tests showed that reporting was slightly better for human studies of investigational treatments than for the other study types. With the evidence reported as is, risk-of-bias assessments for systematic reviews have low informative value other than repeatedly showing that reporting of experimental details needs to improve in all kinds of in vivo research. Particularly for reviews that do not directly inform treatment decisions, it could be efficient to perform a thorough but partial assessment of the quality of the included studies, either of a random subset of the included publications or of a subset of relatively informative elements, comprising, e.g. ethics evaluation, conflicts of interest statements, study limitations, baseline characteristics, and the unit of analysis. This publication suggests several potential procedures.

Peer Review reports

Introduction

Researchers performing systematic reviews (SRs) face bias at two potential levels: first, at the level of the SR methods themselves, and second, at the level of the included primary studies [ 1 ]. To safeguard correct interpretation of the review’s results, transparency is required at both levels. For bias at the level of the SR methods, this is ensured by transparent reporting of the full SR methods, at least to the level of detail as required by the PRISMA statement [ 2 ]. For bias at the level of the included studies, study reporting quality (RQ) and/or risk of bias (RoB) are evaluated at the level of the individual included study. Specific tools are available to evaluate RoB in different study types [ 3 ]. Also, for reporting of primary studies, multiple guidelines and checklists are available to prevent missing important experimental details and more become available for different types of studies over time [ 4 , 5 ]. Journal endorsement of these types of guidelines has been shown to improve study reporting quality [ 6 ].

While undisputedly important, evaluation of the RoB and/or RQ of the included studies is one of the most time-consuming parts of an SR. Experienced reviewers need 10 min to an hour to complete an individual RoB assessment [ 7 ], and every included study needs to be evaluated by two reviewers. Besides spending substantial amounts of time on RoB or RQ assessments, reviewers tend to become frustrated because of the scores frequently being unclear or not reported (personal experience from the authors, colleagues and students). While automation of RoB seems to be possible without loss of accuracy [ 8 , 9 ], so far, this automation has not had significant impact on the speed; in a noninferiority randomised controlled trial of the effect of automation on person-time spent on RoB assessment, the confidence interval for the time saved ranged from − 5.20 to + 2.41 min [ 8 ].

In any scientific endeavour, there is a balance between reliability and speed; to guarantee reliability of a study, time investments are necessary. RoB or RQ assessment is generally considered to be an essential part of the systematic review process to warrant correct interpretation of the findings, but with so many studies scoring “unclear” or “not reported”, we wondered if all this time spent on RoB assessments is resulting in increased reliability of reviews.

Overall unclear risk of bias in the included primary studies is a conclusion of multiple reviews, and these assessments are useful in pinpointing problems in reporting, thereby potentially improving the quality of future publications of primary studies. However, the direct goal of most SRs is to answer a specific review question, and in that respect, unclear RoB/not reported RQ scores contribute little to the validity of the review’s results. If all included studies score “unclear” or “high” RoB on at least one of the analysed elements, the overall effect should be interpreted as inconclusive.

While it is challenging to properly evaluate the added validity value of a methodological step, we had data available allowing for an explorative case study to assess the informative value of various RoB and RQ elements in different types of studies. We previously performed an SR of the nasal potential difference (nPD) for cystic fibrosis (CF) in animals and humans, aiming to quantify the predictive value of animal models for people with CF [ 10 , 11 ]. That review comprised between-subject comparisons of both baseline versus disease-control and treatment versus treatment control. For that review, we performed full RoB and RQ analyses. This resulted in data allowing for comparisons of RoB and RQ between animal and human studies, but also between baseline and treatment studies, which are both presented in this manuscript. RoB evaluations were based on the Cochrane collaboration’s tool [ 12 ] for human studies and SYRCLE’s tool [ 13 ] for animal studies. RQ was tested based on the ARRIVE guidelines [ 14 ] for animal studies and the 2010 CONSORT guidelines [ 15 ] for human studies. Brief descriptions of these tools are provided in Table  1 .

All these tools are focussed on interventional studies. Lacking more specific tools for baseline disease-control comparisons, we applied them as far as relevant for the baseline comparisons. We performed additional analyses on our RQ and RoB assessments to assess the amount of distinctive information gained from them.

The analyses described in this manuscript are based on a case study SR of the nPD related to cystic fibrosis (CF). That review was preregistered on PROSPERO (CRD42021236047) on 5 March 2021 [ 16 ]. Part of the results were published previously [ 10 ]. The main review questions are answered in a manuscript that has more recently been published [ 11 ]. Both publications show a simple RoB plot corresponding to the publication-specific results.

For the ease of the reader, we provide a brief summary of the overall review methods. The full methods have been described in our posted protocol [ 16 ] and the earlier publications [ 10 , 11 ]. Comprehensive searches were performed in PubMed and Embase, unrestricted for publication date or language, on 23 March 2021. Title-abstract screening and full-text screening were performed by two independent reviewers blinded to the other’s decision (FS and CL) using Rayyan [ 17 ]. We included animal and/or human studies describing nPD in CF patients and/or CF animal models. We restricted to between-subject comparisons, either CF versus healthy controls or experimental CF treatments versus CF controls. Reference lists of relevant reviews and included studies were screened (single level) for snowballing. Discrepancies were all resolved by discussions between the reviewers.

Data were extracted by two independent reviewers per reference in several distinct phases. Relevant to this manuscript, FS and CL extracted RoB and RQ data in Covidence [ 18 ], in two separate projects using the same list of 48 questions for studies assessing treatment effects and studies assessing CF-control differences. The k  = 11 studies that were included in both parts of the overarching SR were included twice in the current data set, as RoB was separately scored for each comparison. Discrepancies were all resolved by discussions between the reviewers. In violation of the protocol, no third reviewer was involved.

RoB and SQ data extraction followed our review protocol, which states the following: “For human studies, risk of bias will be assessed with the Cochrane Collaboration’s tool for assessing risk of bias. For animal studies, risk of bias will be assessed with SYRCLE’s RoB tool. Besides, we will check compliance with the ARRIVE and CONSORT guidelines for reporting quality”. The four tools contain overlapping questions. To prevent unnecessary repetition of our own work, we created a single list of 48 items, which were ordered by topic for ease of extraction. For RoB, this list contains the same elements as the original tools, with the same response options (high/unclear/low RoB). For RQ, we created checklists with all elements as listed in the original tools, with the response options reported yes/no. For (RQ and RoB) elements specific to some of the included studies, the response option “irrelevant” was added. We combined these lists, only changing the order and merging duplicate elements. We do not intend this list to replace the individual tools; it was created for this specific study only.

In our list, each question was preceded by a short code indicating the tool it was derived from (A for ARRIVE, C for CONSORT, and S for SYRCLE’s) to aid later analyses. When setting up, we started with the animal-specific tools, with which the authors are more familiar. After preparing data extraction for those, we observed that all elements from the Cochrane tool had already been addressed. Therefore, this list was not explicit in our extractions. The extraction form always allowed free text to support the response. Our extraction list is provided with our supplementary data.

For RoB, the tools provide relatively clear suggestions for which level to score and when, with signalling questions and examples [ 12 , 13 ]. However, this still leaves some room for interpretation, and while the signalling questions are very educative, there are situations where the response would in our opinion not correspond to the actual bias. The RQ tools have been developed as guidelines on what to report when writing a manuscript, and not as a tool to assess RQ [ 14 , 15 ]. This means we had to operationalise upfront which level we would find sufficient to score “reported”. Our operationalisations and corrections of the tools are detailed in Table  2 .

Data were exported from Covidence into Microsoft’s Excel, where the two projects were merged and spelling and capitalisation were harmonised. Subsequent analyses were performed in R [ 21 ] version 4.3.1 (“Beagle Scouts”) via RStudio [ 22 ], using the following packages: readxl [ 23 ], dplyr [ 24 ], tidyr [ 25 ], ggplot2 [ 26 ], and crosstable [ 27 ].

Separate analyses were performed for RQ (with two levels per element) and RoB (with three levels per element). For both RoB and RQ, we first counted the numbers of irrelevant scores overall and per item. Next, irrelevant scores were deleted from further analyses. We then ranked the items by percentages for reported/not reported, or for high/unclear/low scores, and reported the top and bottom 3 (RoB) or 5 (RQ) elements.

While 100% reported is most informative to understand what actually happened in the included studies, if all authors continuously report a specific element, scoring of this element for an SR is not the most informative for meta-researchers. If an element is not reported at all, this is bad news for the overall level of confidence in an SR, but evaluating it per included study is also not too efficient except for highlighting problems in reporting, which may help to improve the quality of future (publications of) primary studies. For meta-researchers, elements with variation in reporting may be considered most interesting because these elements highlight differences between the included studies. Subgroup analyses based on specific RQ/RoB scores can help to estimate the effects of specific types of bias on the overall effect size observed in meta-analyses, as has been done for example randomisation and blinding [ 28 ]. However, these types of subgroup analyses are only possible if there is some variation in the reporting. Based on this idea, we defined a “distinctive informative value” (DIV) for RQ elements, based on the optimal variation being 50% reported and either 0% or 100% reporting being minimally informative. Thus, this “DIV” was calculated as follows:

Thus, the DIV could range from 0 (no informative value) to 50 (maximally informative), visualised in Fig.  1 .

Visual explanation of the DIV value

The DIV value was only used for ranking. The results were visualised in a heatmap, in which the intermediate shades correspond to high DIV values.

For RoB, no comparable measure was calculated. With only 10 elements but at 3 distinct levels, we thought a comparable measure would sooner hinder interpretation of informative value than help it. Instead, we show the results in an RoB plot split by population and study design type.

Because we are interested in quantifying the predictive value of animal models for human patients, we commonly perform SRs including both animal and human data (e.g. [ 29 , 30 ]). The dataset described in the current manuscript contained baseline and intervention studies in animals and humans. Because animal studies are often held responsible for the reproducibility crisis, but also to increase the external validity of this work, explorative chi-square tests (the standard statistical test for comparing percentages for binary variables) were performed to compare RQ and RoB between animal and human studies and between studies comparing baselines and treatment effects. They were performed with the base R “chisq.test” function. No power calculations were performed, as these analyses were not planned.

Literature sample

We extracted RoB and RQ data from 164 studies that were described in 151 manuscripts. These manuscripts were published from 1981 through 2020. Overall, 164 studies comprised 78 animal studies and 86 human studies, 130 comparisons of CF versus non-CF control, and 34 studies assessing experimental treatments. These numbers are detailed in a crosstable (Table  3 ).

The 48 elements in our template were completed for these 164 studies, which results in 7872 assessed elements. In total, 954 elements (12.1%) were irrelevant for various reasons (mainly for noninterventional studies and for human studies). The 7872 individual scores per study are available from the data file on OSF.

Of the 48 questions in our extraction template, 38 addressed RQ, and 10 addressed RoB.

Overall reporting quality

Of the 6232 elements related to RQ, 611 (9.8%) were deemed irrelevant. Of the remainder, 1493 (26.6% of 5621) were reported. The most reported elements were background of the research question (100% reported), objectives (98.8% reported), interpretation of the results (98.2% reported), generalisability (86.0% reported), and the experimental groups (83.5% reported). The least-reported elements were protocol violations, interim analyses + stopping rules and when the experiments were performed (all 0% reported), where the experiments were performed (0.6% reported), and all assessed outcome measures (1.2% reported).

The elements with most distinctive variation in reporting (highest DIV, refer to the “ Methods ” section for further information) were as follows: ethics evaluation (64.6% reported), conflicts of interest (34.8% reported), study limitations (29.3% reported), baseline characteristics (26.2% reported), and the unit of analysis (26.2% reported). RQ elements with DIV values over 10 are shown in Table  4 .

Overall risk of bias

Of the 1640 elements related to RoB, 343 (20.9%) were deemed irrelevant. Of the remainder, 219 (16.9%) scored high RoB, and 68 (5.2%) scored low RoB. The overall RoB scores were highest for selective outcome reporting (97.6% high), baseline group differences (19.5% high), and other biases (9.8% high); lowest for blinding of participants, caregivers, and investigators (13.4% low); blinding of outcome assessors (11.6% low) and baseline group differences (8.5% low); and most unclear for bias due to animal housing (100% unclear), detection bias due to the order of outcome measurements (99.4% unclear), and selection bias in sequence generation (97.1% unclear). The baseline group differences being both in the highest and the lowest RoB score are explained by the baseline values being reported better than the other measures, resulting in fewer unclear scores.

Variation in reporting is relatively high for most of the elements scoring high or low. Overall distinctive value of the RoB elements is low, with most scores being unclear (or, for selective outcome reporting, most scores being high).

Animal versus human studies

For RQ, the explorative chi-square tests indicated differences in reporting between animal and human studies for baseline values ( Χ 1  = 50.3, p  < 0.001), ethical review ( Χ 1  = 5.1, p  = 0.02), type of study ( Χ 1  = 11.2, p  < 0.001), experimental groups ( Χ 1  = 3.9, p  = 0.050), inclusion criteria ( Χ 1  = 24.6, p  < 0.001), the exact n value per group and in total ( Χ 1  = 26.0, p  < 0.001), (absence of) excluded datapoints ( Χ 1  = 4.5, p  = 0.03), adverse events ( Χ 1  = 5.5, p  = 0.02), and study limitations ( Χ 1  = 8.2, p  = 0.004). These explorative findings are visualised in a heatmap (Fig.  2 ).

Heatmap of reporting by type of study. Refer to Table  3 for absolute numbers of studies per category

For RoB, the explorative chi-square tests indicated differences in risk of bias between animal and human studies for baseline differences between the groups ( Χ 2  = 34.6, p  < 0.001) and incomplete outcome data ( Χ 2  = 7.6, p  = 0.02). These explorative findings are visualised in Fig.  3 .

Risk of bias by type of study. Refer to Table  3 for absolute numbers of studies per category. Note that the data shown in these plots overlap with those in the two preceding publications [ 10 , 11 ]

Studies assessing treatment effects versus studies assessing baseline differences

For RQ, the explorative chi-square tests indicated differences in reporting between comparisons of disease with control versus comparisons of treatment effects for the title listing the type of study ( X 1  = 5.0, p  = 0.03), the full paper explicitly mentioning the type of study ( X 1  = 14.0, p  < 0.001), explicit reporting of the primary outcome ( X 1  = 11.7, p  < 0.001), and reporting of adverse events X 1  = 25.4, p  < 0.001). These explorative findings are visualised in Fig.  2 .

For RoB, the explorative chi-square tests indicated differences in risk of bias between comparisons of disease with control versus comparisons of treatment effects for baseline differences between the groups ( Χ 2  = 11.4, p  = 0.003), blinding of investigators and caretakers ( Χ 2  = 29.1, p  < 0.001), blinding of outcome assessors ( Χ 2  = 6.2, p  = 0.046), and selective outcome reporting ( Χ 2  = 8.9, p  = 0.01). These explorative findings are visualised in Fig.  3 .

Overall, our results suggest lower RoB and higher RQ for human treatment studies compared to the other study types.

This literature study shows that reporting of experimental details is low, frequently resulting in unclear risk-of-bias assessments. We observed this both for animal and for human studies, with two main study designs: disease-control comparisons and, in a smaller sample, investigations of experimental treatments. Overall reporting is slightly better for elements that contribute to the “story” of a publication, such as the background of the research question, interpretation of the results and generalisability, and worst for experimental details that relate to differences between what was planned and what was actually done, such as protocol violations, interim analyses, and assessed outcome measures. The latter also results in overall high RoB scores for selective outcome reporting.

Of note, we scored this more stringently than SYRCLE’s RoB tool [ 13 ] suggests and always scored a high RoB if no protocol was posted, because only comparing the “Methods” and “Results” sections within a publication would, in our opinion, result in an overly optimistic view. Within this sample, only human treatment studies reported posting protocols upfront [ 31 , 32 ]. In contrast to selective outcome reporting, we would have scored selection, performance, and detection bias due to sequence generation more liberally for counterbalanced designs (Table  2 ), because randomisation is not the only appropriate method for preventing these types of bias. Particularly when blinding is not possible, counterbalancing [ 33 , 34 ] and Latin-square like designs [ 35 ] can decrease these biases, while randomisation would risk imbalance between groups due to “randomisation failure” [ 36 , 37 ]. We would have scored high risk of bias for blinding for these types of designs, because of increased sequence predictability. However, in practice, we did not include any studies reporting Latin-square-like or other counterbalancing designs.

One of the “non-story” elements that is reported relatively well, particularly for human treatment studies, is the blinding of participants, investigators, and caretakers. This might relate to scientists being more aware of potential bias of participants; they may consider themselves to be more objective than the general population, while the risk of influencing patients could be considered more relevant.

The main strength of this work is that it is a full formal analysis of RoB and RQ in different study types: animal and human, baseline comparisons, and treatment studies. The main limitation is that it is a single case study from a specific topic: the nPD test in CF. The results shown in this paper are not necessarily valid for other fields, particularly as we hypothesise that differences in scientific practice between medical fields relate to differences in translational success [ 38 ]. Thus, it is worth to investigate field-specific informative values before selecting which elements to score and analyse in detail.

Our comparisons of different study and population types show lower RoB and higher RQ for human treatment studies compared to the other study types for certain elements. Concerning RQ, the effects were most pronounced for the type of experimental design being explicitly mentioned and the reporting of adverse events. Concerning RoB, the effects were most pronounced for baseline differences between the groups, blinding of investigators and caretakers, and selective outcome reporting. Note, however, that the number of included treatment studies is a lot lower than the number of included baseline studies, and that the comparisons were based on only k  = 12 human treatment studies. Refer to Table  3 for absolute numbers of studies per category. Besides, our comparisons may be confounded to some extent by the publication date. The nPD was originally developed for human diagnostics [ 39 , 40 ], and animal studies only started to be reported at a later date [ 41 ]. Also, the use of the nPD as an outcome in (pre)clinical trials of investigational treatments originated at a later date [ 42 , 43 ].

Because we did not collect our data to assess time effects, we did not formally analyse them. However, we had an informal look at the publication dates by RoB score for blinding of the investigators and caretakers, and by RQ score for ethics evaluation (in box plots with dot overlay), showing more reported and fewer unclear scores in the more recent publications (data not shown). While we thus cannot rule out confounding of our results by publication date, the results are suggestive of mildly improved reporting of experimental details over time.

This study is a formal comparison of RoB and RQ scoring for two main study types (baseline comparisons and investigational treatment studies), for both animals and humans. Performing these comparisons within the context of a single SR [ 16 ] resulted in a small, but relatively homogeneous sample of primary studies about the nPD in relation to CF. On conferences and from colleagues in the animal SR field, we heard that reporting would be worse for animal than for human studies. Our comparisons allowed us to show that particularly for baseline comparisons of the nPD in CF versus control, this is not the case.

The analysed tools [ 12 , 13 , 15 ] were developed for experimental interventional studies. While some of the elements are less appropriate for other types of studies, such as animal model comparisons, our results show that many of the elements can be used and could still be useful, particularly if the reporting quality of the included studies would be better.

Implications

To correctly interpret the findings of a meta-analysis, awareness of the RoB in the included studies is more relevant than the RQ on its own. However, it is impossible to evaluate the RoB if the experimental details have not been reported, resulting in many unclear scores. With at least one unclear or high RoB score per included study, the overall conclusions of the review become inconclusive. For SRs of overall treatment effects that are performed to inform evidence-based treatment guidelines, RoB analyses remain crucial, even though the scores will often be unclear. Ideally, especially for SRs that will be used to plan future experiments/develop treatment guidelines, analyses should only include those studies consistently showing low risk of bias (i.e. low risk on all elements). However, in practice, consistently low RoB studies in our included literature samples (> 20 SRs to date) are too scarce for meaningful analyses. For other types of reviews, we think it is time to consider if complete RoB assessment is the most efficient use of limited resources. While these assessments regularly show problems in reporting, which may help to improve the quality of future primary studies, the unclear scores do not contribute much to understanding the effects observed in meta-analyses.

With PubMed already indexing nearly 300,000 mentioning the term “systematic review” in the title, abstract, or keywords, we can assume that many scientists are spending substantial amounts of time and resources on RoB and RQ assessments. Particularly for larger reviews, it could be worthwhile to restrict RoB assessment to either a random subset of the included publications or a subset of relatively informative elements. Even a combination of these two strategies may be sufficiently informative if the results of the review are not directly used to guide treatment decisions. The subset could give a reasonable indication of the overall level of evidence of the SR while saving resources. Different suggested procedures are provided in Table  5 . The authors of this work would probably have changed to such a strategy during their early data extraction phase, if the funder would not have stipulated full RoB assessment in their funding conditions.

We previously created a brief and simple taxonomy of systematised review types [ 44 ], in which we advocate RoB assessments to be a mandatory part of any SR. We would still urge anyone calling their review “systematic” to stick to this definition and perform some kind of RoB and/or RQ assessment, but two independent scientists following a lengthy and complex tool for all included publications, resulting in 74.6% of the assessed elements not being reported, or 77.9% unclear RoB, can, in our opinion, in most cases be considered inefficient and unnecessary.

Our results show that there is plenty of room for improvement in the reporting of experimental details in medical scientific literature, both for animal and for human studies. With the current status of the primary literature as it is, full RoB assessment may not be the most efficient use of limited resources, particularly for SRs that are not directly used as the basis for treatment guidelines or future experiments.

Availability of data and materials

The data described in this study are available from the Open Science Platform ( https://osf.io/fmhcq/ ) in the form of a spreadsheet file. In the data file, the first tab shows the list of questions that were used for data extraction with their respective short codes. The second tab shows the full individual study-level scores, with lines per study and columns per short code.

Abbreviations

• Cystic fibrosis

High risk of bias

Low risk of bias

No, not reported

• Nasal potential difference
• Risk of bias
• Reporting quality

Systematic review

Unclear risk of bias

Yes, reported

Drucker AM, Fleming P, Chan AW. Research techniques made simple: assessing risk of bias in systematic reviews. J Invest Dermatol. 2016;136(11):e109–14.

Article   PubMed   CAS   Google Scholar  

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Article   PubMed   PubMed Central   Google Scholar  

Page MJ, McKenzie JE, Higgins JPT. Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review. BMJ Open. 2018;8(3):e019703.

Wang X, Chen Y, Yang N, Deng W, Wang Q, Li N, et al. Methodology and reporting quality of reporting guidelines: systematic review. BMC Med Res Methodol. 2015;15:74.

Zeng X, Zhang Y, Kwong JS, Zhang C, Li S, Sun F, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med. 2015;8(1):2–10.

Article   PubMed   Google Scholar  

Turner L, Shamseer L, Altman DG, Schulz KF, Moher D. Does use of the CONSORT statement impact the completeness of reporting of randomised controlled trials published in medical journals? A Cochrane review Syst Rev. 2012;1:60.

Savovic J, Weeks L, Sterne JA, Turner L, Altman DG, Moher D, et al. Evaluation of the Cochrane collaboration’s tool for assessing the risk of bias in randomized trials: focus groups, online survey, proposed recommendations and their implementation. Syst Rev. 2014;3:37.

Arno A, Thomas J, Wallace B, Marshall IJ, McKenzie JE, Elliott JH. Accuracy and efficiency of machine learning-assisted risk-of-bias assessments in “real-world” systematic reviews : a noninferiority randomized controlled trial. Ann Intern Med. 2022;175(7):1001–9.

Jardim PSJ, Rose CJ, Ames HM, Echavez JFM, Van de Velde S, Muller AE. Automating risk of bias assessment in systematic reviews: a real-time mixed methods comparison of human researchers to a machine learning system. BMC Med Res Methodol. 2022;22(1):167.

Leenaars C, Hager C, Stafleu F, Nieraad H, Bleich A. A systematic review of the effect of cystic fibrosis treatments on the nasal potential difference test in animals and humans. Diagnostics (Basel). 2023;13(19):3098.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Leenaars CHC, Stafleu FR, Hager C, Nieraad H, Bleich A. A systematic review of animal and human data comparing the nasal potential difference test between cystic fibrosis and control. Sci Rep. 2024;14(1):9664.

Higgins JPT, Savović J, Page MJ, Elbers RG, Sterne JAC. Chapter 8: Assessing risk of bias in a randomized trial. Cochrane Handbook for Systematic Reviews of Interventions. 2022.

Google Scholar  

Hooijmans CR, Rovers MM, de Vries RB, Leenaars M, Ritskes-Hoitinga M, Langendam MW. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol. 2014;14:43.

Kilkenny C, Browne WJ, Cuthill IC, Emerson M, Altman DG. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 2010;8(6):e1000412.

Begg C, Cho M, Eastwood S, Horton R, Moher D, Olkin I, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement JAMA. 1996;276(8):637–9.

CAS   Google Scholar  

Leenaars C, Stafleu F, Bleich A. The nasal potential difference test for diagnosing cystic fibrosis and assessing disease severity: a systematic review. 2021.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.

Covidence systematic review software Melbourne, Australia: Veritas Health Innovation. Available from: www.covidence.org .

Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 20: updated guidelines for reporting animal research. J Cereb Blood Flow Metab. 2020;40(9):1769–77.

Knowles MR, Gatzy JT, Boucher RC. Aldosterone metabolism and transepithelial potential difference in normal and cystic fibrosis subjects. Pediatr Res. 1985;19(7):676–9.

Team RC. a language and environment for statistical computing. R Foundation for Statistical Computing. 2021.

RStudio_Team. RStudio: integrated development for R. Boston, MA.: RStudio, Inc.; 2019. Available from: http://www.rstudio.com/ .

Wickham H, Bryan J. readxl: read Excel files. R package version 1.3.1. 2019.

Wickham H, François R, Henry L, Müller K. dplyr: a grammar of data manipulation. R package version 1.0.3. 2021.

Wickham H, Girlich M. tidyr: tidy messy data. R package version 1.2.0. 2022.

Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.

Book   Google Scholar  

Chaltiel D. Crosstable: crosstables for descriptive analyses. R package version 0.5.0. 2022.

Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U, Donnan GA. Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke. 2008;39(10):2824–9.

Leenaars C, Stafleu F, de Jong D, van Berlo M, Geurts T, Coenen-de Roo T, et al. A systematic review comparing experimental design of animal and human methotrexate efficacy studies for rheumatoid arthritis: lessons for the translational value of animal studies. Animals (Basel). 2020;10(6):1047.

Leenaars CHC, Kouwenaar C, Stafleu FR, Bleich A, Ritskes-Hoitinga M, De Vries RBM, et al. Animal to human translation: a systematic scoping review of reported concordance rates. J Transl Med. 2019;17(1):223.

Kerem E, Konstan MW, De Boeck K, Accurso FJ, Sermet-Gaudelus I, Wilschanski M, et al. Ataluren for the treatment of nonsense-mutation cystic fibrosis: a randomised, double-blind, placebo-controlled phase 3 trial. Lancet Respir Med. 2014;2(7):539–47.

Rowe SM, Liu B, Hill A, Hathorne H, Cohen M, Beamer JR, et al. Optimizing nasal potential difference analysis for CFTR modulator development: assessment of ivacaftor in CF subjects with the G551D-CFTR mutation. PLoS ONE. 2013;8(7): e66955.

Reese HW. Counterbalancing and other uses of repeated-measures Latin-square designs: analyses and interpretations. J Exp Child Psychol. 1997;64(1):137–58.

Article   Google Scholar  

Zeelenberg R, Pecher D. A method for simultaneously counterbalancing condition order and assignment of stimulus materials to conditions. Behav Res Methods. 2015;47(1):127–33.

Richardson JTE. The use of Latin-square designs in educational and psychological research. Educ Res Rev. 2018;24:84–97.

King G, Nielsen R, Coberley C, Pope JE, Wells A. Avoiding randomization failure in program evaluation, with application to the Medicare Health Support program. Popul Health Manag. 2011;14(Suppl 1):S11-22.

Meier B, Nietlispach F. Fallacies of evidence-based medicine in cardiovascular medicine. Am J Cardiol. 2019;123(4):690–4.

Van de Wall G, Van Hattem A, Timmermans J, Ritskes-Hoitinga M, Bleich A, Leenaars C. Comparing translational success rates across medical research fields - a combined analysis of literature and clinical trial data. Altex. 2023;40(4):584–94.

PubMed   Google Scholar  

Knowles MR, Gatzy JT, Boucher RC. Increased bioelectric potential differences across respiratory epithelia in cystic fibrosis. N Engl Med. 1981;305:1489–95.

Article   CAS   Google Scholar  

Unal-Maelger OH, Urbanek R. Status of determining the transepithelial potential difference (PD) of the respiratory epithelium in the diagnosis of mucoviscidosis. Monatsschr Kinderheilkd. 1988;136(2):76–80.

PubMed   CAS   Google Scholar  

Dorin JR, Dickinson P, Alton EW, Smith SN, Geddes DM, Stevenson BJ, et al. Cystic fibrosis in the mouse by targeted insertional mutagenesis. Nature. 1992;359(6392):211–5.

Alton EW, Middleton PG, Caplen NJ, Smith SN, Steel DM, Munkonge FM, et al. Non-invasive liposome-mediated gene delivery can correct the ion transport defect in cystic fibrosis mutant mice. Nat Genet. 1993;5(2):135–42.

Caplen NJ, Alton EW, Middleton PG, Dorin JR, Stevenson BJ, Gao X, et al. Liposome-mediated CFTR gene transfer to the nasal epithelium of patients with cystic fibrosis. Nat Med. 1995;1(1):39–46.

Leenaars C, Tsaioun K, Stafleu F, Rooney K, Meijboom F, Ritskes-Hoitinga M, et al. Reviewing the animal literature: how to describe and choose between different types of literature reviews. Lab Anim. 2021;55(2):129–41.

Download references

Acknowledgements

The authors kindly acknowledge Dr. Hendrik Nieraad for his help in study classification.

Open Access funding enabled and organized by Projekt DEAL. This research was funded by the BMBF, grant number 01KC1904. During grant review, the BMBF asked for changes in the review design which we incorporated. Publication of the review results was a condition of the call. Otherwise, the BMBF had no role in the collection, analysis and interpretation of data, or in writing the manuscript.

Author information

Authors and affiliations.

Institute for Laboratory Animal Science, Hannover Medical School, Carl Neubergstrasse 1, 30625, Hannover, Germany

Cathalijn H. C. Leenaars, Christine Häger & André Bleich

Department of Animals in Science and Society, Utrecht University, Yalelaan 2, Utrecht, 3584 CM, the Netherlands

Frans R. Stafleu

You can also search for this author in PubMed   Google Scholar

Contributions

CL and AB acquired the grant to perform this work and designed the study. CL performed the searches. FS and CL extracted the data. CL performed the analyses. CH performed quality controls for the data and analyses. CL drafted the manuscript. All authors revised the manuscript and approved of the final version.

Corresponding author

Correspondence to Cathalijn H. C. Leenaars .

Ethics declarations

Declarations.

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Leenaars, C.H.C., Stafleu, F.R., Häger, C. et al. A case study of the informative value of risk of bias and reporting quality assessments for systematic reviews. Syst Rev 13 , 230 (2024). https://doi.org/10.1186/s13643-024-02650-w

Download citation

Received : 10 April 2024

Accepted : 28 August 2024

Published : 07 September 2024

DOI : https://doi.org/10.1186/s13643-024-02650-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Systematic reviews
• Informative value

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Search Please fill out this field.
• Newsletters
• Sweepstakes

This Pew Research Study Says Teens Think Their Lives are Harder Than Ever–Do They Have a Point?

It's (still) not easy being a teen.

If you've ever rolled your eyes at your teen complaining their life is harder than ever , well, they now have some actual data to back it up.

In a recent study conducted by the Pew Research Center, researchers asked 1,453 parents of teens whether they felt it was harder to be a teen 20 years ago or today. 69% of parents surveyed felt that today's world is tougher on teens. 44% of teens said the same. 

The study examined many different aspects of teenage life, such as social pressures , violence , the state of the world, and more. But the biggest reason why parents felt that teenage life is harder now was social media , with 41% saying it was the biggest stressor. The second was technology in general at 26%.

Getty Images/VioletaStoimenova

Teens Say the Internet is Both Invaluable and an Instigator

Being a teenager has never been exactly easy. Between shifting hormones , navigating complicated friend dynamics , and wrestling with their own identities (while living in a house with parents who may not share all the same values), is a gargantuan task. However, the world looks a lot different now than it did two decades ago—mainly due to the internet.

Jeffrey Gottfried , the associate director of research at Pew Research Center and one of the researchers on the new study, tells Parents that it's part of a series the researchers are conducting on tech adoption among teens.

"Adolescents are the trailblazers when it comes to technology, and new devices, and online platforms," he shares, making them an essential group to study.

Gottfried says it was important to put teens' voices in the study. In doing so, some themes about negative experiences arose, most about the pressure that comes with technology and social media. Whether it's "the pressure to look a certain way, or act or a certain way, or present themselves a certain way" it was evident in the research.

A teen girl who was part of the study succinctly summed that feeling up, telling researchers, “Social media tells kids what to do and say. And if you aren’t up on it, you look like the fool and become like an outcast from lots of people.”

Still, the internet has perks. I mean, no one is driving around with printed-out Mapquest directions on their laps anymore. Kids can Google answers to questions in seconds, or talk to their grandparents across the globe. Teens acknowledge that in some ways, they do have it easier.

While the results of the study weren't surprising to Gottfried, the fact that teens pointed to technology as something that made life both harder and easier was a point of interest that may show how ingrained technology use is in their lives–for better and for worse. Though answers were divided, the overall sentiment among the teens is they feel technology makes their lives more difficult.

Social Media is a Mental Battlefield

Observing my own teen's relationship with social media, it does appear to make conflicts more difficult and certainly more drawn out. If friends get into an argument or have a falling out, online attacks seem to perpetuate the issue. Conflicts no longer stay at school, the sports field, or at the mall. With the reach of online interaction, teens who are having conflict with others may feel like there is no real safe space to go for a reprieve.

Some studies have even pointed to the epidemic of online bullying as being a culprit for troubling suicide rates and a teen mental health crisis .

Susanna Park , public health expert at the wellness app Skylight, tells Parents that cyberbullying is rampant, with 46% of teens ages 13 to 17 reporting that they have experienced it.

“This is a public health issue as victims of cyberbullying were nearly 2.5 times more likely to report psychosomatic complaints (physical ailments caused or worsened by psychological factors like stress, anxiety, etc.) than those who never experienced cyberbullying,” Park explains.

Titania Jordan , the author of Parenting in a Tech World and Chief Parent Officer of Bark, a resource designed to help protect kids online, agrees, saying cruel online behavior happens because kids come of age in “a world where hiding behind a veil of anonymity online is both pervasive and accepted.”

But dealing with the consequences of something online–even if they may feel small at the time–is actually massive, especially for teens, who Jordan explains are already dealing with tough transitions and insecurities.

Jordan also doesn't believe teens necessarily intend to be mean online, rather social media is set up to drive this kind of behavior.

“Additionally, the very nature of social media and group texts lends itself to exclusion (close friends lists, getting kicked out of the group, following and unfollowing)," she says. "We have essentially created a ripe landscape for cyberbullying at this stage of development, which deeply affects tweens’ and teens’ mental health.”

Picking and Fighting the Internet Battle

While the internet is pretty much impossible for today's teen to avoid, Jordan says there are ways to help them navigate the traps that add negativity to their lives. First, remember just because many kids are exposed to social media at an early age, there's no harm in setting the pace for your own kid . 

She also suggests making your child aware that everything that happens online can be saved, screenshotted, or shared, and that using caution is extremely important. Expressing the impact of cyberbullying can't be underscored enough, either. She recommends watching Childhood 2.0 , a documentary about children and teens navigating the challenging digital age, and even sharing it with kids.

Park points to positive technology usage, such as downloading mental health and wellness apps like Skylight , which are designed to help teens relax, embrace self-care, and even tune into their spiritual side.

”Of 11,915 individuals who use digital wellness apps, more than half of Gen Z (55%) report finding these apps on their own. This means that they are actively seeking ways to address their wellbeing," Park adds.

As the social media landscape is ever-changing, we’re constantly learning about its impact on society and on our kids. At the very least, we as parents need to be compassionate about the types of struggles our kids face that we may not have.

Why Many Parents and Teens Think It's Harder Being a Teen Today . Pew Research Center . 2024.

Cyberbullying linked with suicidal thoughts and attempts in young adolescents . National Institutes of Health . 2022.

Teens and Cyberbullying 2022 . Pew Research Center . 2022.

Problematic social media use mediates the effect of cyberbullying victimisation on psychosomatic complaints in adolescents . Nature Scientific Reports . 2024.

Gen Z mental health: The impact of tech and social media . McKinsey Health Institute . 2023.

Related Articles

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Environ Res Public Health

Case Reports: Multifaceted Experiences Treating Youth with Severe Obesity

Karen e. schaller.

1 Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA; gro.snerdlihceirul@azirAA (A.J.A.); gro.snerdlihceirul@irdauQM (M.Q.); gro.snerdlihceirul@snniBH (H.J.B.)

2 Center on Obesity Management and Prevention, Mary Ann & J. Milburn Smith Child Health Research, Outreach, and Advocacy Center, Stanley Manne Children’s Research Institute, Chicago, IL 60611, USA; gro.snerdlihceirul@sremoSL

Linda J. Stephenson-Somers

3 Clinical Nutrition, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA

Adolfo J. Ariza

4 Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA

Maheen Quadri

Helen j. binns.

The management of youth with severe obesity is strongly impacted by social determinants of health and family dynamics. We present case studies of three patients seen in our tertiary care obesity treatment clinic as examples of the challenges faced by these patients and their families, as well as by the medical team. We discuss how these cases illustrate potential barriers to care, the role of child protective services, and we reflect upon lessons learned through the care of these patients. These cases highlight the need for comprehensive care in the management of youth with severe obesity, which can include: visits to multiple medical specialists, and mental and behavioral health providers; school accommodations; linkage to community resources; and, potentially, child protective services involvement. Through the care of these youth, our medical team gained more experience with using anti-obesity medications and meal replacements. The care of these youth also heightened our appreciation for the integral role of mental health services and community-based resources in the management of youth with severe obesity.

1. Introduction

Treatment of obese children involves family-wide change to increase physical activity and improve dietary habits; yet, such treatment has limited success [ 1 , 2 ]. There is an ever growing appreciation that social determinants of health (neighborhood and built environment, economic stability, education, social and community context, and health and healthcare) can greatly impact health [ 3 , 4 , 5 ], and can interfere with the improvement in health outcomes that are expected in response to the delivery of health care. For example, living in unsafe environments and lack of access to facilities may lower the ability of children to increase their activity levels [ 6 ]. Even when resources are available, individuals with low levels of education may be less likely to use a recommended strategy [ 7 ].

Factors such as living environment, family life experiences, levels of parental support, and peer relationships can impact a youth’s ability to make healthier dietary choices, reduce screen time and be physically active [ 7 , 8 ]. By modeling healthy dietary behaviors, having healthy foods in the home and having family meals, parents/caregivers can promote healthier choices for their children [ 6 , 8 , 9 , 10 ].

Patient excess adiposity causes structural and functional abnormalities [ 11 ] that can impair movement and lead to inability to perform routine activities of daily living. Excess adiposity can also impact brain response to visual and oral stimuli [ 12 , 13 ], making healthy dietary choices more difficult to achieve. In addition, patient mental health problems can be integrally related to excess weight gain, including issues such as disordered eating, eating in response to emotions or use of psychotropic medications; or mental health problems may develop as a consequence of obesity, sometimes related to bullying [ 14 , 15 , 16 ].

Genetic inheritance, prenatal exposures and early life nutrition can also impact a child’s weight trajectory and adult weight [ 17 , 18 , 19 , 20 , 21 ]. Prenatal exposures can cause intellectual and behavioral impairments that pose additional challenges when managing children with obesity [ 22 , 23 ].

Obesity treatment efforts focusing narrowly on behavior change of diet and physical activity patterns [ 24 ] may accomplish little, if the myriad problems of the child and family are not also identified and addressed. Oftentimes to address these many factors (e.g., neighborhood safety, housing, living environment, school, family cohesion, parental/caregiver health and behaviors, finances, and mental health issues) a comprehensive team is needed. Tertiary care pediatric obesity treatment providers have sometimes reached out to child protective services (CPS) to gain support for the health behavior change process [ 25 ]. CPS has the ability to identify and address issues that are beyond the reach of providers in the tertiary care setting. The aim of this paper is to present three case studies that convey the complexity of the circumstances of youth with severe obesity and the multifaceted aspects of their care. To highlight connections between the medical care systems and community resources, we describe youth who had CPS involvement during the course of care delivery.

2. Materials and Methods

At our pediatric obesity treatment clinic, we provide patient- and family-centric care with a team of medical providers, nutritionists, and social workers. Approximately two-thirds of the patients we see have severe obesity [ 24 ]. At visits, we assess patient diet and physical activity and identify and address family, community, and psychosocial issues that may be barriers to treatment. We use motivational interviewing techniques to help patients and families set goals to overcome barriers to increasing physical activity and making improved dietary choices. At follow-up visits, we review progress and identify and address barriers to change. Most patients have return visits every 3 months, but visit frequency may vary. We partner with specialists to assess and manage the serious medical and psychiatric conditions that often accompany excess adiposity. While our treatment has a modicum of success, on par with other tertiary care pediatric obesity programs [ 26 ], for our most complex patients we struggle to provide care that leads to improved weight status and to address the many issues identified. Clinical management challenges may be related to poverty, poor housing, learning difficulties, low education, transportation, unsafe neighborhoods, and school issues. We sometimes reach outside our clinical setting to obtain the resources needed to overcome these challenges.

The youth in this report were selected from among those we have seen with CPS involvement, because they portray the difficult challenges of care and need for care coordination. We selected cases with varying CPS management strategies and varying outcomes. The Lurie Children’s Hospital Institutional Review Board determined that this project (#2019-2623) does not meet the definition of human subject research. To preserve anonymity, information on weight status changes over time is conveyed by presenting percent of the 95th BMI percentile for age and sex (BMIp95) [ 24 ]. This is the preferred measure for tracking weight-related change in youth with obesity [ 27 ]. For Case 2, we also present percent body fat (%BF) measurements [ 28 ]. Percent body fat measurements were not available for the other youth.

When she first presented to our clinic, AA, a teenage girl with severe obesity (BMIp95 302%), was living with her mother and siblings. The family had limited resources. She was attending school regularly. Her medical history included elevated liver enzymes, polycystic ovarian syndrome (PCOS), and obstructive sleep apnea. She had a sleep device system for at home use but was noncompliant, and she had already failed multiple appointments for an endocrinology evaluation. At the visit, she was noted to have hypertension and anxiety. We also identified interaction issues with her mother and other adults, including possible past physical abuse. Therapy and psychiatric services were recommended.

Over the next 9 months, she was seen monthly at our obesity treatment program. We initiated metformin to treat PCOS. During this time the state Medicaid structure changed to a managed care system and she lost access to her primary care provider. She continued to gain weight (BMIp95 347%). She was later diagnosed with depression and anxiety and stopped attending school. One year after starting services at our clinic she continued to gain weight rapidly (BMIp95 362%). She failed appointments for sleep apnea treatment and stopped taking prescribed medications. This prompted our first call to CPS. The CPS evaluation revealed a need for additional therapy for depression. The CPS case worker tried to arrange an admission to an outside psychiatric hospital, which was rejected due to AA’s weight and medical issues. We were able to obtain urgent outpatient psychiatric services (therapy and medications) through an emergency department evaluation. A few days later, obesity clinic providers arranged a medical admission for treatment of sleep apnea and hypertension. She was discharged home on medications and a new sleep device. At a clinic visit 3 weeks post-discharge, CPS was called again due to substantial weight gain. We tried to identify structured residential programs which could provide an environment conducive to weight loss. We also started biweekly visits. Additionally, CPS conducted biweekly home visits. AA responded well to close monitoring; her weight dropped, she was more active, using her sleep device and attending follow-up visits with specialists. She transitioned to a community-based facility close to her house for weekly psychiatric care. The CPS case worker identified additional resources to help the family with housing issues.

A few months later, the CPS case worker stopped communicating with our team. AA lost psychiatric services again due to facility closure and was re-connected to psychiatric care at our medical system. Her PCOS and pre-diabetes care transitioned to the endocrinology team. Due to metformin nonadherence, and a rising hemoglobin A1c level, she was hospitalized by endocrinology for diabetes education (this was 1.5 years after her first obesity treatment clinic visit; BMIp95 358%). Metformin was not restarted due to marked rise in her liver enzymes. Monthly visits were restarted in our obesity treatment program and she continued receiving psychiatric services. Despite being off metformin and never initiating home insulin use, her weight remained stable. Our social worker (SW) met with the family at every obesity treatment follow-up visit to identify and address needed resources. A liver biopsy led to diagnosis of autoimmune hepatitis.

AA met daily with her school-based counselor and established care at a community-based facility where she met with a therapist weekly and had care oversight provided by a psychiatrist. Following consultation with our team, the psychiatrist initiated additional medication to lower appetite (BMI95 353%).

There was increasing conflict between mother and AA and we considered options for alternative living environments. We investigated summer camps and boarding schools for teen patients with obesity; all facilities denied services due to her weight and health conditions. At about 2 years into our care, AA began meeting with the bariatric surgery team monthly, and she was seen every 2 weeks at our clinic. She continued frequent meetings with her counseling services. She was taking her medications and her weight remained stable.

Several months later, her weight was up again. The bariatric surgery team suggested initiating a liquid meal replacement plan which required weekly monitoring visits. AA began liquid meal replacement enthusiastically. Liquid meal replacement instructions were to mix 1 pouch in water and drink one in place of breakfast and one in place of lunch with a well-balanced, low calorie dinner. As treatment continued, mother expressed difficulty complying with the frequency of visits. The SW assisted with transportation, and we lessened visit frequency. Her school counselor and outside therapist both contacted us to relay AA’s concerns about the liquid meal replacement, her high levels of stress, and anxiety surrounding bariatric surgery. She was increasingly uncooperative and belligerent with medical providers. After a few weeks on the liquid meal replacement, she stopped use and stopped care with the bariatric surgery team, but continued visits to the obesity treatment clinic. She reverted back to unhealthy eating behaviors, including binge eating, excessive portions, and selection of calorie dense, low nutrient foods.

Conflict with her mother increased and AA started missing school. At this point, AA had essentially given up on making changes. In addition, AA stopped attending her counseling sessions and so was discharged from weekly counseling. At her last visit to our obesity treatment clinic (BMIp95 371%), she was upset and tearful. She did not want to know her weight. Her mother expressed that she did not have any control over her daughter. We reported to the family that we were planning to place another call to CPS (our third call in 2.5 years). Mother and AA did not return for any more visits to our program. We don’t know the outcome of our CPS call.

BB was a preteen at his first obesity treatment visit (BMIp95 173%, 52.2% body fat). He was placed in foster care as an infant following a term birth to a cocaine-abusing mother. BB had cognitive impairment, severe behavioral challenges, and limited self-care skills. His difficult behavior was managed with an antipsychotic drug that can promote weight gain; his educational plan included a one-on-one aide when in school. He had attention deficit hyperactivity disorder, but stimulant medication had not been approved by his insurance. He was also receiving asthma maintenance therapy and had constipation with encopresis and nocturnal enuresis.

The foster parents had been concerned about his weight for the past 3 years and had started making changes just prior to the visit. They reported offering fruits for snacks, but he would choose the chips. They had started locking the refrigerator, as he was sneaking food and eating at night. He was reported to be a very picky eater, with a diet including almost no vegetables. They had also just started to address the constipation/encopresis management per primary care provider recommendations, and we referred him to gastroenterology for further management. Our first visit recommendations included household-wide changes to promote healthy foods for the entire family.

At a second visit, 2 months later, he had gained weight (BMI95p 175%, 53.5% body fat). BB was now taking a stimulant medication, but recommendations from the prior visit had not been implemented. His foster parents had health challenges of their own, which impacted their ability to make the suggested changes.

Upon return to our obesity treatment program, 5 months after his initial visit, BB had gained weight (BMI95p 178%, 57.5% body fat) and was noted to have bowed legs. He was receiving the same medications and was also taking an oral steroid burst for asthma control. The family reported that due to busy schedules they were cooking less at home. He still was not eating vegetables. BB was sent for radiographic evaluation: hip films were normal, but his right knee had findings consistent with Blount’s disease. The clinic provider called the primary care pediatrician and referred BB to a pediatric orthopedic surgeon.

At the next visit, 4 months later, we learned that for the past 2 months he had been living in a different foster home. Our team was not involved with this change. His weight improved (BMIp95 161%, 49.9% body fat). His knee was in a brace; responding well to treatment. The CPS case worker accompanied him to the visit and reported dietary improvements (eating more fruits and vegetables) along with continuing concern about his volatile moods. His new foster parents had implemented structured outdoor play and were providing healthy foods. His encopresis had stopped and he had started bathing himself.

At the next visit, 1.5 years after his first visit, he continued to do well (BMIp95 148%, 44% body fat). We learned that he had experienced a psychiatric crisis with erratic and aggressive mood swings, threatening harm to self and foster parents. He required hospitalization, was stabilized, and returned to the second foster family. His behavior was being managed with additional medications.

We next saw BB 10 months later, his weight status continued to improve (BMIp95 130%, 28.7% body fat). Due to additional psychiatric problems and threatening behaviors he had recently been moved into a group home setting. He reported reduced opportunities for activity, but was doing push-ups 5 days/week.

Our last visit with BB was 5 months later (BMIp95 129%, 28.6% body fat), almost 3 years since his initial visit. He was still living in the group home. He was usually getting just 1 fruit and 1 vegetable serving daily. He no longer needed a leg brace. He was physically active, primarily through school gym, but also reported doing exercises in his room. The CPS case worker who accompanied him to the visit was not familiar with his dietary or physical activity history.

CC was born extremely premature; he required nasogastric tube feedings for his first 6 months due to aspiration. He first presented for primary care services to our medical system in his preschool years (BMIp95 170%). The primary care provider identified speech and developmental delays. He also had asthma and snoring. He was using a bottle and had advanced untreated caries. The nutritionist identified parental feeding strategies (i.e., bottle use, chocolate flavoring of milk) aimed at keeping CC from crying. Over the course of several primary care visits he continued to gain weight and was referred to speech, nutrition, dental, ophthalmology, and otolaryngology. A sleep study showed significant oxygen desaturations during sleep, necessitating direct admission to the intensive care unit (ICU). An urgent tonsillectomy and adenoidectomy were performed. He was followed for 5 more months (still gaining weight), then the family transferred care outside of our medical system.

Five years later, he was seen twice by a cardiologist at our medical system for a cardiac evaluation prior to starting an exercise program (BMIp95 274%); he was cleared for exercise. His next contact with our institution was 2 years later as a young teen (BMIp95 301%) when he was admitted to our ICU for an asthma exacerbation; he was not using his prescribed home sleep device. During the admission, he had hypertension and was diagnosed with diabetes. He received diabetes education, was started on medications, including metformin, and reinitiated use of his sleep device. The ICU team called CPS to initiate home monitoring and compliance with specialist care.

He was seen for a first visit at our obesity treatment clinic 3 months after hospital discharge (BMIp95 300%) and reportedly he was using his home sleep device and taking his medicines, but had not been attending school. He failed follow-up appointments with various specialties over the next 1.5 years. We are unsure of CPS involvement over this period.

Three years after the ICU admission, CC presented to an outside hospital emergency department for shortness of breath, abdominal pain, swelling of one leg with inability to ambulate. He was transferred to our ICU; his weight had increased substantially (BMIp95 415%). He had stopped using his sleep device for the prior 9 months. He was hospitalized for 5 weeks to treat a presumed lower extremity blood clot (imaging studies were inconclusive due to his body habitus), and managed for pulmonary hypertension and right ventricular heart failure. The father disclosed parental health concerns which may have limited parent’s ability to appropriately care for CC. The ICU team again called CPS to implement a plan to transition to a short term rehab facility and identify a foster home placement. Attempts were made to find placement for CC, but no facility would accept a patient with such severe medical conditions. The parents wanted CC to remain under their care. CC was discharged (BMIp95 306%) to parents’ care with close supervision by CPS. CPS arranged for transportation to his multiple outpatient visits.

He was seen shortly after hospital discharge in our obesity treatment clinic. He was receiving psychiatric services and had follow-up visits with various specialists. He attended school 1 day weekly and completed online schooling the other days. The CPS case was closed after 6 months of supervision. Transportation to appointments became a significant problem. He was seen for a third obesity treatment clinic visit 8 months post-hospital discharge. His weight had improved (BMIp95 268%), but he was using his sleep device intermittently. He was discharged from psychiatric care due to missed appointments, and didn’t show for visits to other specialists. The SW provided information on obtaining reduction in transit fares, but the family was unable to follow through with recommendations.

At a follow-up visit with cardiology (20 months post-hospitalization) his weight was up (BMIp95 277%); the cardiologist threatened calling CPS due to missed appointments, non-compliance with his home sleep device, and drinking 4–5 cups of juice per day. In his next visit to our clinic, one month later, CC’s weight had improved (BMIp95 271%). At 2 subsequent bi-monthly clinic visits we continued to reinforce goals, such as how to choose drinks that had no sugar. We re-referred him to sleep medicine service, but they failed several appointments so his mother was advised to transfer care. His weight remains relatively stable, he continues to have troubles with sleep device use, and recently cancelled visits due to loss of health insurance.

6. Discussion

Each of these cases illustrates the complexity of providing medical care for youth with severe obesity. These cases demonstrate the many barriers patients and families face when trying to implement recommended care strategies. These youth required comprehensive care, including visits to multiple medical providers, psychosocial interventions, special schooling services, community resources, and CPS involvement.

The traditional treatment of obesity that focuses on increasing physical activity and making healthier dietary choices has not been successful for some of our patients, due to factors related to social determinants of health, such as family health issues, limited education and/or intellectual abilities and inadequate resources. As youth with severe obesity age, parents may lose control or “lose heart” while attempting to sustain healthy changes for everyone in the household. The physiology of excess adipose tissue drives persistence of and continued excessive weight gain [ 29 , 30 ]. Youth with severe obesity experience multiple medical comorbidities [ 15 , 16 ]. For example, increasing body habitus leads to physical limitations. Activities like self-care, climbing stairs, and participating in gym can become difficult. All of the subjects in our cases had difficulties with schooling, including learning delays, poor school compliance and the need for alternate school learning environments (online schooling, therapeutic school). Two of our subjects had sleep apnea and noncompliance with home sleep device use, which was likely contributing to poor school attendance, mental health issues, and weight gain [ 31 , 32 ]. Consistent psychiatric care had a positive impact on all 3 youth. Two cases reported loss of psychiatric services or therapy from issues related to insurance, clinic closure, or patient discharge due to failure to attend scheduled visits.

Keeping track of and attending the many appointments to several specialties was overwhelming for our youth and their families. Issues with transportation to visits were a recurring theme for 2 of the cases. Visits may require a parent to take the day off work (often without pay), making it especially difficult for families with inadequate financial resources. While medical visits are essential, we need to find ways to ease stress and the burden of multiple medical visits. We must find a balance between the necessity of patient monitoring and family/patient needs and limited resources. Many of the medical visits may not fully address the barriers to treatment that some families face. The resources in the community that may need to be involved include school, psychology services, food pantries, and housing alternatives. Extending our care beyond the medical setting requires careful coordination with community resources to ensure our patients have access.

To improve care of these youth, CPS was involved to advocate for the children to receive the services they needed. Similar to what others have found, the responsiveness of CPS varied [ 25 ]. When most responsive, CPS provided home visits, identified household needs, arranged transportation to medical visits, and placed a youth in a more structured environment (Case 2). There is another case report supporting the benefit of out of home placement for a child with severe obesity [ 33 ]. We worked with CPS to identify options for residential living outside of the family home for Cases 1 and 3, but were unable to identify any.

Caring for these youth and their families have taught us important lessons. Our failures to achieve optimal weight outcomes help reinforce our need to continue to improve our treatment strategies, including how to use meal replacements and medications to counteract the abnormal adipose physiology that is driving the maintenance of weight and the promotion of weight regain [ 34 , 35 ].

The review of these cases has heightened our awareness of the need to enhance motivation and work toward improved delivery of mental health services within our obesity treatment clinic. We also better understand the level of detailed information needed about the family and community to plan appropriate, individualized care strategies. We have learned that family and patient adherence to treatment strategies is not simply the result of being educated on healthy lifestyle habits, but is intricately intertwined with our patient’s psychosocial conditions which may need to be addressed before or in conjunction with medical treatments. These youth may have greatly benefited from bariatric surgery, but their mental health and medical issues precluded us from pursuing that option [ 36 , 37 ].

7. Conclusions

These case histories highlight the complexities of caring for youth with severe obesity. Care for these youth extends beyond targeting modifications of diet and physical activity behaviors. Low resource households and families, and social and mental health issues require multifaceted, coordinated care for these youth. Patients with serious obesity-related comorbidities require multiple medical visits with an array of providers. Oftentimes families do not have the resources to comply with provider expectations. The process of facilitating access to services outside of the medical setting, as was sought from CPS, can help identify and address household- and community-related barriers to successful treatment outcomes. However, our partnerships with CPS staff members were only sometimes successful. The review of these cases has helped us better understand the benefits and limitations of CPS involvement with our patients. It is our hope and our intent to use and share this information to guide our future approaches to meet the multifaceted needs of patients with severe obesity.

Acknowledgments

We want to thank the many individuals in care of these complex patients. We particularly acknowledge the outstanding care and effort of the social workers and psychiatry team. We thank Liliana Bolanos for help with manuscript preparation.

Author Contributions

Conceptualization, K.E.S., L.J.S.-S. and H.J.B.; Data Curation, K.E.S., L.J.S.-S., A.J.A. and H.J.B. Writing—Original Draft Preparation, K.E.S., H.J.B., A.J.A. and M.Q.; Writing—Review & Editing, K.E.S., L.J.S.-S., H.J.B., A.J.A. and M.Q.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.