• - Google Chrome

Intended for healthcare professionals

  • Access provided by Google Indexer
  • 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

Home

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
  • Claire D Madigan , senior research associate 1 ,
  • Henrietta E Graham , doctoral candidate 1 ,
  • Elizabeth Sturgiss , NHMRC investigator 2 ,
  • Victoria E Kettle , research associate 1 ,
  • Kajal Gokal , senior research associate 1 ,
  • Greg Biddle , research associate 1 ,
  • Gemma M J Taylor , reader 3 ,
  • Amanda J Daley , professor of behavioural medicine 1
  • 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.

Fig 1

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.

Fig 2

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.

Fig 3

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).

Fig 4

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 ,

obesity disease management case study

  • Research article
  • Open access
  • Published: 02 May 2019

Enhancing knowledge and coordination in obesity treatment: a case study of an innovative educational program

  • Tonje C. Osmundsen   ORCID: orcid.org/0000-0002-5776-6694 1 ,
  • Unni Dahl 2 &
  • Bård Kulseng 3 , 4  

BMC Health Services Research volume  19 , Article number:  278 ( 2019 ) Cite this article

4249 Accesses

7 Citations

1 Altmetric

Metrics details

Currently, there is a lack of knowledge, organisation and structure in modern health care systems to counter the global trend of obesity, which has become a major risk factor for noncommunicable diseases. Obesity increases the risk of diabetes, cardiovascular diseases, musculoskeletal disorders and cancer. There is a need to strengthen integrated care between primary and secondary health care and to enhance care delivery suited for patients with complex, long-term problems such as obesity. This study aimed to explore how an educational program for General Practitioners (GPs) can contribute to increased knowledge and improved coordination between primary and secondary care in obesity treatment, and reports on these impacts as perceived by the informants.

In 2010, an educational program for the specialist training of GPs was launched at three hospitals in Central Norway opting for improved care delivery for patients with obesity. In contrast to the usual programs, this educational program was tailored to the needs of GPs by offering practice and training with a large number of patients with obesity and type 2 diabetes for an extended period of time. In order to investigate the outcomes of the program, a qualitative design was applied involving interviews with 13 GPs, head physicians and staff at the hospitals and in one municipality.

Through the program, participants strengthened care delivery by building knowledge and competence. They developed relations between primary and secondary care providers and established shared understanding and practices. The program also demonstrated improvement opportunities, especially concerning the involvement of municipalities.

Conclusions

The educational program promoted integrated care between primary and secondary care by establishing formal and informal relations, by improving service delivery through increased competence and by fostering shared understanding and practices between care levels. The educational program illustrates the combination of advanced high-quality training with the development of integrated care.

Peer Review reports

Obesity and diabetes are grave international health problems [ 1 , 2 ] and the economic burden for both patients and national economies is significant [ 3 ]. Obesity is a complex condition often involving several other chronic and serious diseases requiring lifelong follow-up [ 4 , 5 , 6 ]. It often affects whole families and intervention is needed at multiple levels, including social and psychological dimensions. Treatment is therefore complex and time-consuming and poses a challenge for the organisation of health care services [ 7 , 8 ]. Currently, there is a lack of well-established integrated approaches for prevention and treatment across health care levels.

Treatment offerings

To tackle these challenges, the coordination of services needs to be improved [ 7 , 9 , 10 ]. In Norway, the Coordination Reform was launched in 2010 [ 11 ]. Here, as in other countries [ 12 ], integrated care is accompanied by goals for moving patients from secondary health care to primary care, increasing focus on prevention and health-promoting activities and patient involvement. Obese and overweight patients with related diseases have traditionally been offered treatment in primary care, but in recent years the number of patients with severe obesity and complications has increased and consequently, so has the number of patients referred to secondary health care. To counter such a development, primary and secondary health care providers should jointly develop integrated care based on an understanding of the disease’s complexity. In addition, knowledge and capacity at the primary health care level must be improved and services targeted at prevention and cure must be made available.

Although health care workers are key players in the effort to stop the obesity trend in the population and to prevent the complications of obesity, international research suggests that obese patients do not receive adequate help for their health problems. Studies have shown that health care providers’ treatment and attitude towards overweight and obese patients are governed by poor knowledge and inconsistencies [ 13 , 14 ] and have a strong weight bias, indicating stigma [ 15 , 16 ]. A mapping in Central Norway showed a lack of knowledge and tools for how to treat and prevent overweight and obesity [ 17 ]. Less than half of the obese patients who sought medical help for their lifestyle problems were advised to lose weight by their GP [ 18 ] or given exercise counselling [ 19 ], and while research has shown that such advice and counselling may have an effect on weight loss, it is inadequate [ 20 ]. Limited treatment contact is thought to be the main reason why modest weight loss is achieved [ 20 ]. There are available treatment guidelines for primary care, but there are currently few sufficiently effective established treatment offerings available to children and adults struggling with overweight and obesity within primary health care [ 21 ]. More extensive treatment in primary care is often randomly organised, at times by local enthusiasts. Treatment is difficult to establish in primary care because of the complexity involved in treating obesity, and while GPs are competent to diagnose obesity, there is a general lack in knowledge about treating the disease [ 13 , 14 ] and available time [ 20 ]. Furthermore, considering coordination between primary and secondary health care providers, integrated care for patients with obesity is underdeveloped [ 4 , 14 , 22 , 23 , 24 ]. In Norway, obese patients are normally referred to secondary health care when they have a Body Mass Index (BMI) ≥ 40 or BMI ≥ 35 kg/m 2 with complications of obesity, while children should have an iso-BMI > 35 or iso-BMI > 30 kg/m2 with complications of obesity [ 25 ]. However, until a patient reaches this stage in the development of the disease, few treatments are available, and when a patient is referred to secondary health care, a lack of coordination and cooperation between primary and secondary care leads to treatments that may be limited in scope and time.

Education of GP specialists in the health care system in Norway

The health care system in Norway is primarily a public system organised in two levels. Secondary healthcare services are owned and financed by the Ministry of Health and Care Services and managed through four regional health authorities. The primary care level includes general practitioners (GPs), nursing homes, home care services, maternal and child health centres and out-of-hours services. Primary care is organised and financed by the local authorities (municipalities). Even though GPs are organised as a part of the primary care level, GPs are private contractors and not organised in a shared formal organisation that can instruct GPs or act as a partner on behalf of GPs [ 26 ].

The educational program for becoming a GP specialist in Norway includes 1 year of practice at a hospital. Currently, there are no positions targeted at GPs’ educational needs, so a GP seeking specialization must apply for a regular specialist training position at a hospital. In most of these positions, the doctor is enrolled in the rota system. This means that candidates will often spend time in emergency admissions on evening and night shifts that are compensated by time off. This leaves little time to attend in-house patients and perform routine follow-up of patients, both of which are relevant for their practice as GPs.

Theoretical basis

Mur-Veeman et al. [ 27 ] have shown that organizational and financial splits between health care providers, such as those present in Norway between primary and secondary health care, hinder integrated care development and delivery. Organizational divides are closely linked to contradictory interests, separate professional cultures, power relations and mistrust between health care providers. Martinussen [ 28 ] has shown that the interaction between GPs and hospital physicians has improvement potential, and weak collaboration between GPs and hospitals has been the focus of several studies [ 29 , 30 , 31 ]. Delayed or inaccurate communication can have substantial implications for the quality of care, which is especially apparent when patients need lifelong follow-up. Efforts to strengthen integrated care can counteract such inadequate treatment at the interstices between providers. In this paper, integrated care is defined as “[ ] a coherent set of methods and models on the funding, administrative, organizational, service delivery and clinical levels designed to create connectivity, alignment and collaboration within and between the cure and care sectors” [ 32 ].

There are many strategies available to foster integrated care. It is found that different commitments, goals and tasks can be major obstacles for collaboration between care levels [ 33 ]. Thus, defining roles and having a shared purpose is essential to achieve successful interorganizational collaboration [ 34 ]. Other approaches include training of medical staff, a focus on how they perform their responsibilities and tasks, and how they work together with colleagues and patients [ 32 ]. Face-to-face interaction is well known to foster trust and collaborative relations. This has also earlier been shown to apply to the relationships between GPs and hospital specialists [ 28 , 35 ]. Networking and collaboration both horizontally and vertically across health care providers promotes integrated care, as well as a “Shared understanding of patient needs, common professional language and criteria, the use of specific, agreed-upon practices and standards throughout the lifecycle of a particular disease or condition…” [ 32 ].

Fruitful integration between care levels is dependent on communication between primary and secondary health care providers [ 36 ], and this collaboration becomes even more important for patients with multiple complex conditions and needs [ 37 ]. Efforts to improve integration should aim to understand the perspectives of clinicians in each setting and implement strategies that engage both groups by way of shared communication through direct access to each other, interpersonal relations, shared electronic medical records and clearly defined accountability [ 31 ]. However, organizational and financial splits between these two parts of the health care system impede such collaboration. The lack of a common hierarchy and governance structure necessitates professionals to create combined responsibilities for shared accountability and decision making to deliver integrated care [ 38 ]. There is therefore a need for models and methods that may enhance care delivery suited for patients with complex, long term problems that cut across multiple care providers and settings. Such models should combine the clinical expertise of the specialist and the ability of GPs to bridge the gap between medical and social problems [ 39 ] to allow for continuity of care over time. The development of agreed care pathways has the potential to align clinical, management and service user interests across primary and secondary care [ 40 ] but has been shown to be most effective in contexts where patient care trajectories are predictable [ 41 ]. When pathways are more variable, this is a demanding intervention that requires comprehensive and prolonged efforts by health care professionals in the involved organizations [ 42 ].

We have witnessed many efforts to foster integrated care in the past decade, and this topic has received substantial political interest [ 11 , 27 ]. However, there are few reports on how educational programs for care providers can contribute. A noteworthy exception is Hirsh et al. [ 43 ], who studied how a clerkship model may provide undergraduate students with training relevant for the continuity of care. Concerning the specialist training of GPs, there are few examples of similar discussions. Surveying former research in the area revealed that specialist training is rarely debated, and when it is, the discussion concerns evaluation forms, attendance and curricula. We did find a few examples of case studies in which GPs visited local hospitals for knowledge exchange [ 44 , 45 ]. Such cases have been reported as beneficial for integrated care and mutual learning between GPs and hospital staff, but collaboration lasts a short time, does not involve GPs practising at the hospital and does not demand much involvement between GPs and hospital staff. In response to the challenges described above, the Centre for Obesity Research (ObeCe) at St. Olavs Hospital, Trondheim University Hospital wanted to develop an educational program fostering integrated care. Thus, in 2010, an educational program for the specialization of GPs was established at three regional hospitals in Norway to enhance the exchange of knowledge and strengthen coordination between primary and secondary healthcare providers.

The research question addressed in this paper is thus: what are the main outcomes of the educational program relevant for care delivery to obese patients, as experienced by the participants?

In this study, we investigated how an educational program for GPs in one region in Norway, including one university hospital and two general hospitals, could contribute to enhancing the continuation of care across health care providers. This educational program provides a case study of how educational measures may be designed to promote integrated care. To evaluate the program, understand its potential contribution to integrated care and reveal how it may be improved, a qualitative study [ 46 ] was undertaken. Central documents concerning the establishment and organization of the program were read and analysed, and 13 informants were interviewed. All participants in the program, as well as their closest collaborators at the hospitals and a representative from one municipality, were interviewed.

Research setting

The program was initiated in November 2010 by the management of the Centre for Obesity Research (ObeCe). ObeCe was established in 2005 in line with national and regional health policies [ 47 , 48 ] and is a research and development centre that has carried out several projects to promote collaboration between primary and secondary health care regarding overweight and diabetes. ObeCe does not have patient treatment as its main concern. This allows the unit the freedom and mandate to create new practices like the educational program for GPs [ 49 ]. By June 2014, eight GPs had participated or were currently participating in the project.

The program was designed by ObeCe to provide GPs with relevant training and education for their general practice and to strengthen the connection between primary and secondary care. It was also geared towards providing primary health care providers with competence concerning a grave public health problem, as it focused on subject areas relevant for the prevention and treatment of overweight and obesity. The GPs received extensive training with the same patient groups that they meet in their general practice and which they considered challenging. The costs of the educational program were limited to salary expenses for the involved GPs, in addition, they contributed financially to their respective departments by treating patients.

The GPs were employed in educational positions on temporary contracts limited to one-year full time. They had the possibility to work part time, leaving them the opportunity to continue with their own practice. During the program, the GPs participated both in clinical practice and theoretical studies. They were part of multidisciplinary teams at different departments at the hospital. The program was designed to provide the GPs with knowledge and training in four main areas: clinical practices, theoretical studies, research and the development of integrated care. However, the informants emphasized that the balance between these four areas could be adjusted to allow for individual interests and needs. A tutor supported them during the course of the program.

The GPs worked at three different departments to gain clinical practice: the Multidisciplinary Outpatient Clinic for Obesity, the Department of Endocrinology and ObeCE. The multidisciplinary Outpatient Clinic for Obesity is a clinic with health professionals such as surgeons, psychologists, nutritional experts, physiotherapists and endocrinologists that receives children, adults and families, and has a broad perspective on obesity-related issues similar to the perspective of a GP. The GPs did not participate in the rota system, so they had fixed days each week at the different departments: three days at the Department of Endocrinology, one day at the Multidisciplinary Outpatient Clinic for Obesity and one day at ObeCe. They were given their own list of patients to follow for an extended period.

They were involved in patient care at the clinics and in preparing obese patients for operations and for self-management. They also received training in treating patients with type 2 diabetes at the Department of Endocrinology. The GPs evaluated referrals to the hospital from other GPs, conducted physical examinations, reviewed medical histories, provided diagnoses and followed up with tests. These examinations provided the basis to determine the severity of the disease and to create a treatment plan for the patient. They followed patients long enough to observe the effects of the treatments and the patients’ experiences.

The educational program was devised to give the GPs access to theoretical studies and they participated in courses, both initiated by the hospital in general and available at each clinic. Internal courses at the hospital (two hours each week) are required for any specialist under training, but in this program, the GPs also attended lectures at each clinic for one hour each week. In addition, they contributed themselves by holding lectures for the staff at the clinics and at ObeCe. They were also given leeway and encouraged to take initiative in areas they felt they could improve and contribute to.

The GPs had 20% of their time dedicated to research and were encouraged to contribute to the professional development of the field of obesity and overweight. They were expected to update themselves on the latest research results, and as a requirement of the program, the GPs participated in on-going research projects within one month of their employment. The GPs who participated in the program received their formal qualifications as specialists and re-certification in line with the purpose of the program.

Participants

All those involved or related to the program at the time of the study and available for interviews were asked to participate, and all accepted. Those interviewed included five GPs who were enrolled in the program, two nurses, one research assistant, four head physicians and one manager from the local municipality, thereby covering those informants most involved and familiar with the project. Interviews were conducted in two rounds: in April 2012 and in September 2013. The methodological approach was deemed appropriate, as the study was exploratory and aimed to uncover respondents’ experiences and opinions. Each interview lasted from one to two hours. Participation was voluntary.

Data collection and analysis

As a basis for the interviews, a semi-structured guide was developed reflecting the research questions of the study and sent to each informant before the interview (see Additional file 1 , Interview guide). Informants were asked how they experienced the program, in particular about the effects of the program on their own knowledge, expertise and ability to provide quality of care, as well as the results for the various clinics at the hospital and the primary health care services. Informants were also asked whether they saw the program as useful for the particular patient group and society at large, and if so, in what ways. The guide was flexible and allowed the informants to include any new themes they found relevant to describe the program and their experience. All interviews were taped and later transcribed. The data was systematically categorised and coded. The analytical process focused on identifying and differentiating the concepts and topics the informants described, both those introduced by the interview guide and those provided by the informants themselves. Concrete examples of integrated care practices and other examples of results from the program were noted. Similarities and differences between each informant and the informant’s group were coded and compared. The analytical approach was inductive and exploratory, focusing on the concepts and categories as described by the informants.

In interviews, both GPs and their collaborators at the hospital emphasized three main areas they saw as important outcomes of the program: (1) the establishment of relations and networks which breached the organizational divide between primary and secondary care; (2) increased knowledge and competence both at the primary care level and at the hospital; and (3) the development of shared practices and the use of shared standards. In addition, the informants also identified shortcomings, mainly related to the weak integration the GPs experienced with their own municipalities.

Establishing relations and networks

Through the program, GPs and hospital staff became acquainted and formed collaborative relations, both during the time the GPs were at the hospital and after. Since the GPs worked in three different departments at the hospital, they were able to establish relations with several colleagues at the hospital, both doctors and nurses. Both GPs and hospital staff emphasised in interviews the value of the personal relations and networks they had been able to build through the program. One of the GPs stated:

“The most important thing is the increased competence and the network of contacts you get. It becomes so much simpler. And that is a part of the point, that it becomes seamless and that it should work like this” (GP3).

The GPs were encouraged to contribute to areas they felt they could improve and, as a result, they established the first formal arena for knowledge exchange between staff at the ObeCe and the Outpatient Clinic for Obesity so that employees from the two departments can meet at regular intervals for lectures and research updates. This was made possible because the GPs worked fixed days at both departments and were, unlike their colleagues at the hospital, not in the rota system. The rota system affords less individual predictability, as it is not known well in advance which person will occupy which shift. The GPs would know months in advance where they would work each day, so it was easier to plan ahead and take responsibility to schedule activities for knowledge exchange with their hospital colleagues.

The program aided in forming new relations and networks. The GPs emphasised in interviews how this was made possible because they were met by informed and positive colleagues at the hospital. Respect and trust characterised the relations that were established and laid the grounds for open discussions and mutual learning.

Increased knowledge and competence

The program offered several opportunities for training, both through formal courses initiated at the hospital and through the time specified for research. The interviewed GPs explained that they had increased their knowledge of overweight and obese patients, and related diseases such as diabetes, through participating in the program.

According to the informants, the professional environment of the hospital, the time set aside for research and the tutoring they received during the program gave room for investigations that the GPs rarely had time for in their general practice.

The program also provided knowledge and competence that extends beyond those of a formal qualification in overweight and obesity treatment. In an interview, one of the participating GPs explained how the program had changed her attitude towards the patient group and increased her understanding of the complexity of the field of overweight and obesity:

“It is easy to have prejudices concerning this patient group, and there is much stigmatisation. I notice that with colleagues and others who are not familiar with the field. It is easy to conclude, as with other lifestyle related diseases, that it is self-inflicted and weak individuals who are not capable of changing their own situation. It is of course not so simple” (GP1).

The GPs also stated they had an increased sense of confidence in treating obese and overweight patients with related diseases such as diabetes when returning to their medical practices. During their time at the hospital, they were able to see a large number of patients with type 2 diabetes, which increased their confidence in their ability to treat this patient group. Earlier, they would have referred type 2 diabetes patients to secondary health care because they did not feel confident enough to treat them themselves. As explained by GP2:

“My attitude changes while I am here [at the hospital]. I see possibilities for all that can be done in my general practice. Before, I would think that ‘now I have tried with this patient for half a year, and nothing happens, we won’t get any further.’ Now, with new knowledge, I believe there is more we can do” (GP2).

There was a mutual exchange of knowledge between the GPs and medical personnel at the hospital, especially concerning care delivery. A head physician at one of the clinics where the GPs worked said:

“Professionally, it has been very positive for our clinic that they have brought with them the GPs’ views into treatment for diabetes. We are able to discuss what is feasible to do in a general practice, and what we need to continue to do here” (Head Physician).

Shared practices and use of shared standards

It was stated in the objectives of the program that the GPs were to contribute to increased cooperation between the primary and secondary health care levels. The program had thus allocated funds for arranging conferences and meetings between the GPs and their respective municipalities.

The program resulted in the dissemination of knowledge not only to the participating GPs, but also to health personnel at the primary care level. Several of the GPs initiated training courses and lectures for fellow GPs in their municipalities that occurred at their own medical centres, in larger conferences and in permanent colloquium groups where a smaller number of GPs met regularly for research updates and discussions. One of the projects in which the GPs were involved investigated how an intermediate care service at the primary health care level could assume the postoperative follow-up of obese patients in collaboration with local GPs.

An internet course qualifying GPs for their specialisation was developed by one of the GPs in the program and launched nationwide. Also, general information leaflets were developed, providing information to GPs concerning the treatment of patients who have undergone gastric bypass operations. This included a document listing the short- and long-term side effects of gastric bypass. The involvement of the GPs at ObeCe also resulted in several international publications written by them and their colleagues at the hospital, thus disseminating knowledge to a broader international audience.

In the clinical domain, the program resulted in improved shared understanding and practices between the primary and secondary health care levels. This was achieved by the close involvement of the GPs at different departments in the hospital, and through the activities the GPs initiated at their own medical centres and in primary care in general. The GPs said they had realised that through their knowledge and experience of both primary and secondary health care, they could play an important role in creating shared practices across care providers.

Shared practices between primary and secondary care were developed and continuously improved because of the program. These measures allowed for the increased involvement of and information to GPs when their patients were at the hospital. One of the nurses summed up the new practices for the GPs’ strengthened involvement:

“[GP1] has done much for communications with the GPs. Patients receive a letter, and the GPs get notified that the patient is in a post-operative group. Patients receive a form they need to fill out. This didn’t exist before [GP1] and the other GPs were here” (Nurse 1).

Also, specific procedures for follow-up of patients who had undergone gastric bypass were amended, as they did not function properly. Patients had earlier failed to attend their appointments at the hospital and the patients’ GPs were not involved. Also, in the case of diabetes patients, the primary care level was not actively involved. Alternate consultations between the hospitals and respective GPs were therefore initiated as a result of the program. This was seen as a way to enable the primary health care level to be more involved in follow-up care. In addition, the program resulted in initiatives to improve horizontal integration across health services, social services and other care providers.

Both physicians and nurses said they gained much knowledge through the close contact with GPs. They emphasised having an increased understanding of the GPs’ opportunities and constraints when attending patients in their general practices. Hospital staff also claimed that they now recognised the need to collaborate more closely with GPs while their respective patients were receiving treatment at the hospital and after, and that they understood more of how to improve the collaboration between primary and secondary health services. Some also said they had changed their practices as a result of what they learned from the GPs.

The informants perceived that the educational program had immediate value for those involved in enabling and supporting the development of integrated care. It provided high quality training for GPs while meeting the national and local challenges of achieving integrated care. One of the GPs concluded:

“The project has been successful, because we have become a part of the work at ObeCe, meaning integrated care in practice, because we are GPs playing on the same field as the secondary health care services” (GP5).

Shortcomings

However, the program also had shortcomings. These concerned the relationship and collaboration between the GPs and their respective municipalities. One of the reasons given by the respondents was that the organisation of the municipalities’ services and the decision-making process is fragmented. The interviewed GPs were unsure of who they could present ideas to at the municipality to initiate projects they believed could improve health care services and did not know whether their ideas would be in line with current plans and budgets. One stated:

“Yes, I am sitting here and I want to make things happen in my municipality, but it is not so easy when there are no systems or frameworks for it. You have to make it happen yourself, and nobody expects anything” (GP2).

The GPs found it difficult to know whom to approach at the municipalities and how they could work to realise their ideas. The informants suggested that the educational program should include clear expectations for how the GPs could establish plans for their work in the municipalities while under training, because when they returned to their general practices, there would be less time to plan and think about new projects.

Outcomes of the program

The educational program incorporated several strategies earlier identified as beneficial for fostering integrated care in three important domains: organisational, service delivery and clinical practice [ 32 ]. Central to the development of integrated care is vertical integration between primary and secondary health care through formal and informal relations, networks and collaboration , which breaches the organizational divide between the two systems [ 27 ]. High quality service delivery hinges on the knowledge and competence of medical staff both at the primary and secondary health care levels, and is not only related to the specific disease(s), but also to care delivery [ 32 ]. In the clinical domain, a shared understanding of patient needs and use of shared practices and standards between providers is essential [ 37 ]. Interviews with personnel involved in the program indicate that the program showed results in these directions, even though there were also shortcomings. For example, the interviewed GPs did not know whom to approach in their respective municipalities to realize new ideas and changes in care delivery.

The educational program has been shown to be able to foster relations between hospital staff and GPs, which are lacking in the existent health care system. This is important for a patient group that will continuously be in need for care to avoid serious complications and that has the risk of becoming revolving-door patients due to a fragmented and poorly integrated health care system. As Tricco et al. found [ 50 ], multidisciplinary care is needed for chronic patients with complex conditions, and improving care for this group is effective at reducing readmissions. Care needs to be provided in a continuous interplay between primary and secondary care by health professionals who have defined roles and responsibilities and a shared purpose [ 31 , 33 , 34 ]. This corresponds to earlier recommendations for care delivery for patients with complex care needs [ 37 ], that shows that their need for care is best met by close interaction and collaboration between primary and secondary health care providers.

The educational program contributes to integrated care for obese patients by combining the expertise of specialists from the hospital with the broader and more holistic experience and competence of GPs [ 39 ]. Obesity is a condition that requires caregivers to bridge medical and social problems. With the increased prevalence of complex conditions, hospitals cannot simply discharge patients to primary health care without themselves offering to share their knowledge and expertise. Secondary health care has experienced a strong increase in referrals for patients suffering from obesity and related conditions. To reduce this burden, secondary health care providers need to engage with care givers in primary care to strengthen their ability and capacity to treat this patient group. Also, medical staff at secondary health care institutions need to gain an understanding of how obesity and subsequent treatment are intertwined with broader issues such as work, family life and social problems, as well as the framework conditions of the patients’ local communities. A Cochrane review [ 51 ] concluded that audit and feedback strategies can be important to improve professional practice, but this improvement depend on how the feedback is provided and by whom. Creating a learning environment, as in this educational program where health professionals openly discuss practices and alternative approaches, can thus be a potential strategy for enhancing quality of care.

Through collaboration and direct dialogue, the GPs and specialists involved in the educational program create and shape shared understanding and practices. Patient’s awareness of such dialogues between the GP and specialists from the hospital has earlier been suggested to strengthen patients’ sense of security [ 52 ]. Collaboration between caregivers from primary and secondary health care services is important both for the quality of care that is given to this patient group and to ensure continuity of care. Other strategies, such as developing agreed care pathways, could provide stronger alignment between primary and secondary health care providers, but it might also demand much efforts and prolonged engagement to implement [ 42 ], especially when patient pathways are variable [ 41 ], as in the case of obesity.

The educational model thus promises to compensate for some of the problems of the current organization of the health care system [ 27 ]. The artificial division between clinical specialists at hospitals and GPs in primary care has earlier been shown to lead to weak communication, which affects the continuity and quality of care [ 28 , 29 , 30 , 31 ]. A doctoral thesis concluded that integration depends on the collaborative partners’ ability to develop all-embracing objectives and view their services and work as a part of the total chain of care. Integration depends on sufficient communication and interorganisational teamwork, a learning environment, common perspectives and clarified roles [ 36 ]. Through developing relations, enhancing knowledge and competence and shared understanding and practices, the educational program studied here promises to breach obstacles to continuity of care for patients suffering from obesity. There are many different strategies that have been shown to be conducive to enhancing quality of care, both within and across primary and secondary care. However, as Grol and Grimshaw [ 53 ] argue, approaches should be fit for purpose and adapted to the barriers and facilitators to change in each situation. The approach chosen here answered a perceived need to strengthen knowledge in primary care. The program continues and is now in use at several departments at St. Olavs Hospital. It should be considered a step towards strengthening integrated care between primary and secondary care.

In Central Norway, since 2010, the educational program has gradually been instituted as a permanent program that is offered to a number of departments at St. Olavs Hospital. As of today, 35 GPs have been employed at seven different departments (Department of Ophthalmology, Department of Ear, Nose and Throat, Head and Neck Surgery, Child Department, Department of Endocrinology, Department of Neurology, Department of Clinical Pharmacology and Department of Gynaecology) at St. Olavs Hospital, Trondheim University Hospital, and in three different departments (Department of Geriatrics, Child Department and Department of Surgery) at Namsos Hospital. According to the Director of Integrated Healthcare at St. Olavs, the experiences from these departments are univocally positive (Personal communication, email to author BK, 6.1.2018). In sum, these experiences reflect this study’s findings. The departments found it useful to interact with GPs to learn more about the expertise in general practice and the GPs increased their knowledge, which in turn was transferred to their colleagues in primary care. Increased knowledge and competence in primary care resulted in fewer referrals to the hospital. Finally, the capacities of the different departments at the hospital increased with the aid of the GPs.

The educational program did not seek to alter the organizational divides between primary and secondary care, but focused on strengthening connectivity and collaboration across these divides by involving GPs in secondary care for a defined time period. According to the informants, this had positive impacts for both primary and secondary care, as discussed above. This was considered a necessary first step to demonstrate the usefulness and feasibility of the program, considering the large number (47) of municipalities in this region, all with highly diversified tasks and structures. An important lesson learnt from this program is that while obesity and diabetes are a growing concern in Norwegian municipalities, it is important to designate funding of assigned positions directed towards such illnesses as a cost-sharing scheme across several municipalities. The continuation of the program now (2018) shows that groups of municipalities are engaging with secondary health care providers to incorporate the increased knowledge and experience of the GPs in municipal structures.

Limitations and future research

The empirical basis for this article is limited, with 13 respondents and one case, although it was carried out in three hospitals. The results are indicative of how such an educational program may contribute to integrated care, but a more extensive program and more studies are needed to reach findings that can be considered representative. The strength of the study is its reporting of a novel model that may foster integrated care and strengthen the expertise of primary care while reducing the burden on the acute sector.

Revising educational programs in line with the model described here may be an affordable and feasible approach to dealing with some of the organizational splits between health care providers. The costs of the program were limited to salary expenses for the participating GPs, and their work at the hospital contributed financially to the respective departments. However, further research should also assess the costs of the intervention and compare these to other strategies for integration. Nevertheless, there is a need for more systematic knowledge of how educational programs may contribute to integrated care and how such programs may have long-term effects on the collaboration between primary and secondary health care providers. Further research should study the effects of such programs, and especially seek to assess how patients experience strengthened interactions and collaboration between GPs and hospital staff.

The educational program illustrates how one may combine high quality training with integrated care. It constitutes a promising path for both increased medical competence and improved integrated patient care because it involves health care personnel from both primary and secondary care who together develop practices that are implemented across care providers. The program is applicable to different professional domains, especially those where patients can benefit from coordinated health services and where health personnel can collaborate to develop practices that merge competences and approaches from both primary and secondary health care services.

Important challenges remain in engaging more municipalities to incorporate the increased knowledge of GPs into municipal structures and to disseminate the lessons learnt from this program to other regions.

Abbreviations

Body Mass Index

General practitioner

Centre for Obesity Research

Yach D, Stuckler D, Brownell KD. Epidemiologic and economic consequences of the global epidemics of obesity and diabetes. Nat Med. 2006;12(1):62–6.

Article   CAS   Google Scholar  

James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res. 2001;9(Suppl 4):228–33.

Article   Google Scholar  

Withrow D, Alter DA. The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obes Rev. 2011;12(2):131–41.

World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. 2000;WHO Tech Report 894. http://www.who.int/nutrition/publications/obesity/WHO_TRS_894/en/ . Accessed 10 Apr 2017.

World Health Organization. Global strategy on diet, nutrition, and the prevention of chronic diseases. 2003;WHO Tech Report 916. http://apps.who.int/iris/bitstream/10665/42665/1/WHO_TRS_916.pdf . Accessed 10 Apr 2017.

Rivenes AC, Harvey SB, Mykletun A. The relationship between abdominal fat, obesity, and common mental disorders: results from the HUNT study. J Psychosom Res. 2009;66(4):269–75.

Truswell AS, Hiddink GJ, Green LW, Roberts R, van Weel C. Practice-based evidence for weight management: alliance between primary care and public health. Fam Pract. 2012;29(Suppl 1):6–9.

Seidell JC, Halberstadt J, Noordam H, Niemer S. An integrated health care standard for the management and prevention of obesity in the Netherlands. Fam Pract. 2012;29(Suppl 1):153–6.

McDonald J, Jayasuriya R, Harris MF. The influence of power dynamics and trust on multidisciplinary collaboration: a qualitative case study of type 2 diabetes mellitus. BMC Health Serv Res. 2012. https://doi.org/10.1186/1472-6963-12-63 .

Berendsen AJ, Benneker WH, Jong BM, Klazinga NS, Schuling J. Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Serv Res. 2007. https://doi.org/10.1186/1472-6963-7-4 .

Norwegian Ministry of Health and Care Services. The Coordination Reform. Proper treatment - at the right place and right time. Oslo: Norwegian Ministry of Health and Care Services; Report No. 47 (2008–2009) to the Storting. [Summary in English, full version in Norwegian].

Mur-Veeman I, van Raak A, Paulus A. Comparing integrated care policy in Europe: does policy matter? Health Policy. 2008;85(2):172–83.

Vetter ML, Herring SJ, Sood M, Shah NR, Kalet AL. What do resident physicians know about nutrition? An evaluation of attitudes, self-perceived proficiency and knowledge. J Am Coll Nutr. 2008;27(2):287–98.

Kirk SFL, Price SL, Penney TL, Rehman L, Lyons RF, Piccinini-Vallis H, et al. Blame, shame, and lack of support: a multilevel study on obesity management. Qual Health Res. 2014;24(6):790–800.

Schwartz MB, Chambliss HO, Brownell KD, Blair SN, Billington C. Weight bias among health professionals specializing in obesity. Obes Res. 2003;11(9):1033–9.

McVey GL, Walker KS, Beyers J, Harrison HL, Simkins SW, Russell-Mayhew S. Integrating weight bias awareness and mental health promotion into obesity prevention delivery: a public health pilot study. Prev Chronic Dis. 2013;10:120185.

Kulseng B, Ødegård R, Følling I. En evalueringsrapport av prosjektet ‘Overvekt og Folkehelse’ – modell for samhandling mellom første og andrelinjetjenesten. [an evaluation report of the ‘obesity and public health project’ – a model for coordination between primary and secondary health care.]. Regionalt senter for sykelig overvekt, St. Olavs Hospital; 2012. [in Norwegian].

Foster GD, Wadden TA, Makris AP, Davidson D, Sanderson RS, Allison DB, Kessler A. Primary care physicians’ attitudes about obesity and its treatment. Obes Res. 2003;11(10):1168–77.

Lobelo F, Duperly J, Frank E. Physical activity habits of doctors and medical students influence their counselling practices. Br J Sports Med. 2009;43(2):89–92.

Wadden TA, Volger S, Sarwer DB, Vetter ML, Tsai AG, Berkowitz RI, et al. A two-year randomized trial of obesity treatment in primary care practice. N Engl J Med. 2011;365:1969–79.

Mazur A, Matusik P, Revert K, Nyankovskyy S, Socha P, Binkowska-Bury M, et al. Childhood obesity: knowledge, attitudes, and practices of European pediatric care providers. Pediatrics. 2013;132(1):100–8.

Eger K, Gleichweit S, Rieder A, Stein KV. Prioritising integrated care initiatives on a national level. Experiences from Austria. Int J Integr Care. 2009;9(3).

Nguyen N, Champion JK, Ponce J, Quebbemann B, Patterson E, Pham B, et al. A review of unmet needs in obesity management. Obes Surg. 2012;22(6):956–66.

Mühlbacher A, Bethge S. Preferences of overweight and obese patients for weight loss programs: a discrete-choice experiment. Int J Integr Care. 2013;13(3).

Helsedirektoratet. Nasjonal faglig retningslinje for forebygging, utredning og behandling av overvekt og fedme hos barn og unge. [National standard for prevention, examination and treatment of obesity and overweight in children and adolescents in Norwegian]. 2010. https://helsedirektoratet.no/retningslinjer/nasjonal-faglig-retningslinje-for-forebygging-utredning-og-behandling-av-overvekt-og-fedme-hos-barn-og-unge . Accessed 10 Apr 2017.

Romøren TI, Torjesen DO, Landmark B. Promoting coordination in Norwegian health care. Int J Integr Care. 2011;11.

Mur-Veeman I, Hardy B, Steenbergen M, Wistow G. Development of integrated care in England and the Netherlands: managing across public-private boundaries. Health Policy. 2003;65:227–41.

Martinussen PE. Hospital physicians’ assessments of their interaction with GPs: the role of physician and community characteristic. Health Policy. 2013;(1):14–21.

Garasen H, Johnsen R. The quality of communication about older patients between hospital physicians and general practitioners: a panel study assessment. BMC Health Serv Res. 2007;7:133.

Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297:831–41.

Jones CD, Vu MB, O’Donnell CM, Anderson ME, Patel S, Wald HL, et al. A failure to communicate: a qualitative exploration of care coordination between hospitalists and primary care providers around patient hospitalizations. J Gen Intern Med. 2015;30:417. https://doi.org/10.1007/s11606-014-3056-x .

Article   PubMed   Google Scholar  

Kodner DL, Spreeuwenberg C. Integrated care: meaning, logic, applications, and implications - a discussion paper. Int J Integr Care. 2002;2.

Johannessen A-K, Lurås H, Steihaug S. The role of an intermediate unit in a clinical pathway. Int J Integr Care 2013;13(1). http://www.ijic.org/articles/abstract/10.5334/ijic.859/ , accessed 19 June 2018.

Olson CA, Balmer JT. Factors contributing to successful Interorganizational collaboration: the case of CS2day. J Contin Educ Heal Prof. 2011;31(Suppl 1):S3–12.

Tjerbo T, Kjekshus LE. Coordinating health care: lessons from Norway. Int J Integr Care. 2005;5.

Dahl U. The impact of an Intermediate Care Hospital on the chain of care for hospitalized elderly people. PhD thesis. Norwegian University of Science and Technology, Faculty of Medicine, Dep. of Public Health and General Practice. 2015.

Coleman EA, Fox PD. One patient, many places: managing health care transitions part II: practitioner skills and patient and caregiver preparation. Ann Longterm Care. 2004;12:34–9.

Google Scholar  

Valentijn PP, Schepman SM, Opheij W, Bruijnzeels MA. Understanding integrated care: a comprehensive conceptual framework based on the integrative functions of primary care. International Journal of Integrated Care. 2013;13(1):None.

McWhinney I. A textbook of family medicine. New York: Oxford University Press; 1997.

Allen D. From boundary concept to boundary object: the practice and politics of care pathway development. Soc Sci Med. 2009;69(3):354–61.

Allen D. Systematic review of the effectiveness of integrated care pathways: what works, for whom, in which circumstances. Int J Evid Based Healthcare. 2009;7:61–74.

Røsstad T, Garåsen H, Steinsbekk A, et al. Implementing a care pathway for elderly patients, a comparative qualitative process evaluation in primary care. BMC Health Serv Res. 2015;15:86.

Hirsh DA, Ogur B, Thibault GE, Cox M. ‘Continuity’ as an organizing principle for clinical education reform. N Engl J Med. 2007;356(8):858–66.

Frydenberg K, Nylehn P. General practice consultants at all Norwegian hospitals. Tidsskrift for Norsk Laegeforening. 2003;123:2481 [in Norwegian].

Senanayake S, Bowden F, Ironside J, Robertson T. A teaching ward round in infectious diseases - a pilot module. Aust Fam Physician. 2006;35:357–8.

PubMed   Google Scholar  

Silverman D. Doing qualitative research. 3rd ed. London: SAGE Publications Ltd; 2009.

Helse- og omsorgsdepartementet. [Ministry of Health Care Services]. Styringsdokument 2004. [Governing document 2004]. https://www.regjeringen.no/globalassets/upload/hod/bestillerdokumnet/styringsdokument-helse-midt-norge.pdf . [in Norwegian]. Accessed 10 Apr 2017.

Helse Midt Norge RHF Styret. Styresak 59/08 HMN RHF. [Central Norway health authority board item 59/08 HMN RHF]. 2008. https://ekstranett.helse-midt.no/1001/Sakspapirer/sak%2059-08%20Behandlingstilbud%20til%20personer%20med%20sykelig%20overvekt%20vedl%201%20Utredn%20og%20beh%20av%20sykelig%20overvekt%20i%20speshelsetj%20-voksne.pdf . [in Norwegian]. Accessed 10 Apr 2017.

Mørk BE, Hoholm T, Maaninen-Olsson E, Aanestad M. Changing practice through boundary organizing: a case from medical R&D. Hum Relat. 2012;65(2):263–88.

Tricco AC, Antony J, Ivers NM, et al. Effectiveness of quality improvement strategies for coordination of care to reduce use of health care services: a systematic review and meta-analysis. CMAJ. 2014;186:568–78.

Ivers N, Jamtvedt G. Flottorp S, et al. Audit and feedback: effects on professional practice and healthcare outcomes. In The Cochrane library. John Wiley & Sons, Ltd.; 2012 http://cochranelibrary-wiley.com/doi/10.1002/14651858.CD000259.pub3/full , accessed June 20, 2018.

Osmundsen TC, Jaatun EA, Heggem GF, Kulseng B. Service innovation from the edges – enhanced by telemedicine decision support. PUC. 2015;19(3):699–708.

Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients’ care. Lancet. 2003;362(9391):1225–30.

Download references

Acknowledgements

The authors are grateful to Central Norway Regional Health Authority for supporting the research, and to the informants at St. Olavs Hospital, Trondheim University Hospital, Namsos Hospital, Ålesund Hospital and an anonymous municipality who willingly contributed with their opinions and experiences.

The ObeCe covered the costs of data collection unrestrictedly. The writing of this paper was partly financed through the SPIS project (Norwegian Research Council, grant number 220553).

Availability of data and materials

The data from the qualitative interviews is not made publicly available in order to fully protect the informants’ anonymity.

Author information

Authors and affiliations.

NTNU Social Research, Dragvoll Allé 38b, N-7491, Trondheim, Norway

Tonje C. Osmundsen

Norwegian Hospital Construction Agency, Klæbuveien 118, 7031, Trondheim, Norway

Centre for Obesity Research (ObeCe), Clinic of Surgery, St. Olavs University Hospital, 7006, Trondheim, Norway

Bård Kulseng

Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, N-7489, Trondheim, Norway

You can also search for this author in PubMed   Google Scholar

Contributions

Each author has read and approved the final version and all made important contributions. TCO: Conception and design, data collection, analysis and interpretation, writing the article, critical revision of the article, final approaval. UD: Analysis and interpretation, writing the article, critical revision of the article, final approval. BK: Initiation of the study, analysis and interpretation, writing the article, critical revision of the article, final approval.

Corresponding author

Correspondence to Tonje C. Osmundsen .

Ethics declarations

Ethics approval and consent to participate.

The project received approval from the Ombudsman for Research and Social Science Data Service in Norway, which serves as an ethics committee for Norwegian Research Institutes. We received informed consent for the interviews and for recording of the interviews by e-mail. This information was repeated verbally to the informants before the interviews started. All data has been treated and presented to preserve anonymity and confidentiality.

Consent for publication

The informants involved in this study gave consent for direct quotes from their interviews to be used in this manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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

Additional file

Additional file 1:.

Interview guide. (DOCX 24 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Osmundsen, T.C., Dahl, U. & Kulseng, B. Enhancing knowledge and coordination in obesity treatment: a case study of an innovative educational program. BMC Health Serv Res 19 , 278 (2019). https://doi.org/10.1186/s12913-019-4119-9

Download citation

Received : 30 May 2017

Accepted : 24 April 2019

Published : 02 May 2019

DOI : https://doi.org/10.1186/s12913-019-4119-9

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

  • Integrated care
  • General practitioners
  • Specialist training

BMC Health Services Research

ISSN: 1472-6963

obesity disease management case study

  • Open access
  • Published: 21 June 2021

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

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

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

5701 Accesses

9 Altmetric

Metrics details

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

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

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

Systematic review registration

PROSPERO CRD42020214560 .

Peer Review reports

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

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

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

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

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

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

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

Information sources and search strategy

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

Eligibility criteria

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

Screening and study selection process

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

Data extraction

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

Quality appraisal of included studies

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

Data synthesis

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

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

Assessment of confidence in the review findings

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

Reflexivity

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

Dissemination of findings

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

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

Availability of data and materials

Not applicable.

Abbreviations

Body mass index

Critical appraisal skills programme

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

Innovative Medicines Initiative

Medical Subject Headings

Population, phenomenon of interest, context, study type

Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy

Wharton S, Lau DCW, Vallis M, Sharma AM, Biertho L, Campbell-Scherer D, et al. Obesity in adults: a clinical practice guideline. Can Med Assoc J. 2020;192(31):E875–91. https://doi.org/10.1503/cmaj.191707 .

Article   Google Scholar  

World Health Organisation. Fact sheet: obesity and overweight. Geneva: World Health Organisation; 2020.

Google Scholar  

Mechanick J, Hurley D, Garvey W. Adiposity-based chronic disease as a new diagnostic term: the American Association of Clinical Endocrinologists and American College Of Endocrinology position statement. Endocrine Pract. 2017;23(3):372–8. https://doi.org/10.4158/EP161688.PS .

Garvey W, Mechanick J. Proposal for a scientifically correct and medically actionable disease classification system (ICD) for obesity. Obesity. 2020;28(3):484–92. https://doi.org/10.1002/oby.22727 .

Article   PubMed   Google Scholar  

Biener A, Cawley J, Meyerhoefer C. The high and rising costs of obesity to the US health care system. J Gen Intern Med. 2017;32(Suppl 1):6–8. https://doi.org/10.1007/s11606-016-3968-8 .

Article   PubMed   PubMed Central   Google Scholar  

Department of Health and Social Care. Healthy lives, healthy people: a call to action on obesity in England. London: Department of Health and Social Care; 2011.

Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, de Gonzalez AB, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776–86. https://doi.org/10.1016/S0140-6736(16)30175-1 .

Sharma AM. M, M, M & M: a mnemonic for assessing obesity. Obesity Reviews. 2010;11(11):808–9. https://doi.org/10.1111/j.1467-789X.2010.00766.x .

Article   PubMed   CAS   Google Scholar  

Asnawi A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract 2010;89:309-19. Diab Res Clin Pract. 2010;89:309–19.

Dagfinn A, Teresa N, Lars JV. Body mass index, abdominal fatness and the risk of gallbladder disease. 2015;30(9):1009.

Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA, et al. Adipose tissue dysfunction as determinant of obesity-associated metabolic complications. Int J Mol Sci. 2019;20(9).

Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years of life lost due to obesity. 2003;289(2):187-193.

Grover SA, Kaouache M, Rempel P, Joseph L, Dawes M, Lau DCW, et al. Years of life lost and healthy life-years lost from diabetes and cardiovascular disease in overweight and obese people: a modelling study. 2015;3(2):114-122.

Ackerman S, Blackburn O, Marchildon F, Cohen P. Insights into the link between obesity and cancer. Curr Obes Rep. 2017;6(2):195–203. https://doi.org/10.1007/s13679-017-0263-x .

Wolin K, Carson K, Colditz G. Obesity and cancer. Oncol. 2010;15(6):556–65. https://doi.org/10.1634/theoncologist.2009-0285 .

Resolution 420: Recognition of obesity as a disease [press release]. 05/16/13 2013.

Brownell KD. Personal responsibility and control over our bodies: when expectation exceeds reality. 1991;10(5):303-10.

Puhl RM, Latner JD, O'Brien K, Luedicke J, Danielsdottir S, Forhan M. A multinational examination of weight bias: predictors of anti-fat attitudes across four countries. 2015;39(7):1166-1173.

Browne NT. Weight bias, stigmatization, and bullying of obese youth. 2012;7(3):107-15.

Sutin AR, Stephan Y, Terracciano A. Weight discrimination and risk of mortality. 2015;26(11):1803-11.

Thomas SL, Hyde J, Karunaratne A, Herbert D, Komesaroff PA. Being “fat” in today’s world: a qualitative study of the lived experiences of people with obesity in Australia. 2008;11(4):321-30.

Ogden K, Barr J, Rossetto G, Mercer J. A “messy ball of wool”: a qualitative study of the dimensions of the lived experience of obesity. 2020;8(1):1-14.

Ueland V, Furnes B, Dysvik E, R¯rtveit K. Living with obesity-existential experiences. 2019;14(1):1-12.

Avenell A, Robertson C, Skea Z, Jacobsen E, Boyers D, Cooper D, et al. Bariatric surgery, lifestyle interventions and orlistat for severe obesity: the REBALANCE mixed-methods systematic review and economic evaluation. 2018;22(68).

The PLoS Medicine Editors. Qualitative research: understanding patients’ needs and experiences. Plos Med. 2007;4(8):1283–4.

Boulton M, Fitzpatrick R. Qualitative methods for assessing health care doi:10.1136/qshc.3.2.107. Qual Health Care. 1994;3:107–13.

Johnstone J, Herredsberg C, Lacy L, Bayles P, Dierking L, Houck A, et al. What I wish my doctor really knew: the voices of patients with obesity. Ann Fam Med. 2020;18(2):169–71. https://doi.org/10.1370/afm.2494 .

Brown I, Thompson J, Tod A, Jones G. Primary care support for tackling obesity: a qualitative study of the perceptions of obese patients. Br J Gen Pract. 2006;56(530):666–72.

PubMed   PubMed Central   Google Scholar  

Brown I, Gould J. Qualitative studies of obesity: a review of methodology. Health. 2013;5(8A3):69–80.

Garip G, Yardley L. A synthesis of qualitative research on overweight and obese people’s views and experiences of weight management. Clin Obes. 2011;1(2-3):10–126.

Coulman K, MacKichan F, Blazeby J, Owen-Smith A. Patient experiences of outcomes of bariatric surgery: a systematic review and qualitative synthesis. Obes Rev. 2017;18(5):547–59. https://doi.org/10.1111/obr.12518 .

European Patients’ Forum. “Patients’ Perceptions of Quality in Healthcare”: Report of a survey conducted by EPF in 2016 Brussels: European Patients’ Forum; 2017.

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1. https://doi.org/10.1186/2046-4053-4-1 .

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349(jan02 1):g7647. https://doi.org/10.1136/bmj.g7647 .

Toye F, et al. Meta-ethnography 25 years on: challenges and insights for synthesising a large number of qualitative studies. BMC Med Res Methodol. 2014;14(80).

Lockwood C, Porrit K, Munn Z, Rittenmeyer L, Salmond S, Bjerrum M, et al. Chapter 2: Systematic reviews of qualitative evidence. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis: JBI; 2020, doi: https://doi.org/10.46658/JBIMES-20-03 .

Methley AM, et al. PICO, PICOS and SPIDER: a comparison study of spcificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Res. 2014;14.

Covidence. Cochrane Community; 2020. Available from: https://www.covidence.org .

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8(1):45. https://doi.org/10.1186/1471-2288-8-45 .

Lewin S, Booth A, Glenton C, Munthe-Kaas H, Rashidian A, Wainwright M, et al. Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series. Implement Sci. 2018;13(1):2. https://doi.org/10.1186/s13012-017-0688-3 .

Download references

Acknowledgements

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

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

Author information

Authors and affiliations.

School of Education, University College Dublin, Belfield, Dublin 4, Ireland

Emma Farrell, Eva Hollmann & Deirdre McGillicuddy

University College Dublin Library, Dublin, Ireland

Marta Bustillo

Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland

Carel W. le Roux

Obesity Action Coalition, Tampa, USA

Joe Nadglowski

You can also search for this author in PubMed   Google Scholar

Contributions

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

Corresponding author

Correspondence to Emma Farrell .

Ethics declarations

Ethics approval and consent to participate, 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.

Supplementary Information

Additional file 1:..

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

Additional file 2: Table 1

. Search PubMed search string.

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/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 28 October 2020

Accepted : 14 May 2021

Published : 21 June 2021

DOI : https://doi.org/10.1186/s13643-021-01706-5

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

  • Lived experience
  • Patient experience
  • Obesity treatment
  • Qualitative

Systematic Reviews

ISSN: 2046-4053

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

obesity disease management case study

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
  • Partnerships
  • Patient Portal
  • Membership Application Form
  • Member Benefits
  • 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 Schools
  • Accreditation
  • SCOPE Fellows
  • Leadership Programme
  • SCOPE Pricing
  • SCOPE Accredited Events
  • Event Archive
  • International Congress on Obesity

Sign up for notifications

Provider Competencies for Management of Adult Obesity

Case Studies

Curricular case studies.

These case studies are intended to showcase real-world competency integration strategies that might inspire leaders in each profession to prioritize obesity education across the continuum of training.

We developed a series of profession-specific case studies to highlight programs and institutions that have demonstrated a commitment to equipping students, trainees, and/or practicing health professionals with the skills and knowledge needed to care competently and compassionately for persons with obesity. The case studies are intended to provide examples of how the Provider Competencies for the Prevention and Management of Obesity can be integrated into the formative training and continuing education of nurses, physicians, physician assistants, dietitians, physical therapists, occupational therapists, pharmacists, dentists, psychologists, exercise physiologists, public health practitioners, and social workers in the United States.

The case studies were authored by STOP Obesity Alliance staff using information obtained through material reviews and interviews. Case studies were selected to reflect diverse experiences across professions, geographies, institution types, interventional approaches, and care settings. Eligible participants:

  • were accredited health professional training programs OR other entities authorized to oversee and/or deliver provider education
  • addressed one or more of the Provider Competencies for the Prevention & Management of Obesity
  • promoted evidence-based practices consistent with national obesity care guidelines (e.g. USPSTF, TOS, ENDO, AHA, CMS)

Embedding Weight Sensitivity in the Nursing Care Practicum

Villanova University , M. Louise Fitzpatrick College of Nursing

FOODS-C: A 3-Year Integrated Obesity Curriculum for Medical Trainees

Touro University – California , College of Osteopathic Medicine

Enhanced Oral Health Training Curriculum to Address Obesity in Early Childhood and Adults

University of Washington , School of Dentistry

Pharmacist-Driven Disease Management: Delivering an On-Campus Weight Management Service

Auburn University , Harrison School of Pharmacy

mHealth Curriculum: Training in Use of Medical and Patient Mobile Apps for Weight Management

University of Texas Southwestern , School of Health Professions

Obesity-Focused Clinical Public Health Summit: Experiential Learning to Improve Community Health

George Washington University , School of Medicine & Health Sciences

Obesity and Health: An Interdisciplinary Undergraduate Minor for Future Health Professionals

Indiana University Bloomington , School of Public Health

Improving Obesity Education Through Policy: Mandated Continuing Education on Nutrition and Obesity

Council of the District of Columbia

Healthy Homes, Healthy Futures: A Home Visitation Curriculum for Pediatric Residents

Children’s National Health System , Department of General & Community Pediatrics

Lifestyle Redesign®: Preparing Trainees to Implement Occupational Therapy Interventions for Obesity

University of Southern California , Division of Occupational Therapy

The Role of Individualized Exercise Prescription in Obesity Management-Case Study

Affiliations.

  • 1 Department of Health Sciences and Sport Medicine, University of Physical Education, 1123 Budapest, Hungary.
  • 2 YourPowerMed Health Center, 1015 Budapest, Hungary.
  • 3 Institute of Physiotherapy and Sport Science, Faculty of Health Science, University of Pécs, 7621 Pécs, Hungary.
  • 4 Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.
  • PMID: 34831781
  • PMCID: PMC8621483
  • DOI: 10.3390/ijerph182212028

Introduction: Obesity, or adiposity-based chronic disease (ABCD), is one of the most common health risk factors nowadays. Regular exercise-part of complex lifestyle medicine program-is effective treatment for obesity but is still underestimated. Monitoring andindividualization by an exercise professional is needed to define the accurate dose effect.

Materials and methods: The 30-week lifestyle change program of a 65-year-old male patient (body mass index (BMI) 43.8 kg/m 2 ) was followed by a medical doctor, exercise physiologist, and nutritionist. Over regular controls and blood tests, each training activity was measured with a heart rate monitor watch, and a diet diary was written.

Results: Bodyweight decreased by 24.1 kg (18.4%) and BMI to 35.8 kg/m 2 . Decreased resting heart rate (from 72 bpm to 63 bpm), diastolic blood pressure (from 72 mmHg to 67 mmHg), and increased systolic blood pressure (from 126 mmHg to 135 mmHg) were reported, besides the reduction in antihypertensive and antidiabetic medicines. Blood test results and fitness level improved, and daily steps and time spent training increased.

Conclusions: Lifestyle medicine with professional support is an effective and long-term treatment for ABCD. Individualized exercise and nutritional therapy are essential, and wearable technology with telemedicine consultation also has an important role.

Keywords: adiposopathy; body mass index; exercise; lifestyle medicine; wearable technology.

Publication types

  • Case Reports
  • Research Support, Non-U.S. Gov't
  • Blood Pressure
  • Exercise Therapy
  • Obesity Management*
  • Prescriptions

Advertisement

Advertisement

A Systematic Review of the Evidence for Non-surgical Weight Management for Adults with Severe Obesity: What is Cost Effective and What are the Implications for the Design of Health Services?

  • Health Services and Programs (R Welbourn and C Borg, Section Editors)
  • Open access
  • Published: 21 November 2022
  • Volume 11 , pages 356–385, ( 2022 )

Cite this article

You have full access to this open access article

  • Elisabet Jacobsen   ORCID: orcid.org/0000-0002-3211-936X 1 ,
  • Dwayne Boyers   ORCID: orcid.org/0000-0002-9786-8118 1 ,
  • Paul Manson   ORCID: orcid.org/0000-0002-1405-1795 2 &
  • Alison Avenell   ORCID: orcid.org/0000-0003-4813-5628 2  

3478 Accesses

4 Citations

1 Altmetric

Explore all metrics

Purpose of Review

Severe obesity (BMI ≥ 35 kg/m 2 ) increases premature mortality and reduces quality-of-life. Obesity-related disease (ORD) places substantial burden on health systems. This review summarises the cost-effectiveness evidence for non-surgical weight management programmes (WMPs) for adults with severe obesity.

Recent Findings

Whilst evidence shows bariatric surgery is often cost-effective, there is no clear consensus on the cost-effectiveness of non-surgical WMPs.

Thirty-two studies were included. Most were short-term evaluations that did not capture the long-term costs and consequences of ORD. Decision models often included only a subset of relevant ORDs, and made varying assumptions about the rate of weight regain over time. A lack of sensitivity analyses limited interpretation of results. Heterogeneity in the definition of WMPs and usual care prevents formal evidence synthesis. We were unable to establish the most cost-effective WMPs. Addressing these limitations may help future studies provide more robust cost-effectiveness evidence for decision makers.

Similar content being viewed by others

obesity disease management case study

Cost-effectiveness of bariatric surgery and non-surgical weight management programmes for adults with severe obesity: a decision analysis model

D. Boyers, L. Retat, … and the REBALANCE team

obesity disease management case study

The Relevant Perspective of Economic Evaluations Informing Local Decision Makers: An Exploration in Weight Loss Services

Sebastian Hinde, Louise Horsfield, … Gerry Richardson

Behavioural Interventions for Severe Obesity Before and/or After Bariatric Surgery: a Systematic Review and Meta-analysis

Fiona Stewart & Alison Avenell

Avoid common mistakes on your manuscript.

Introduction

In England, 29% of adults have obesity (body mass index (BMI) ≥ 30 kg/m 2 ) [ 1 ], whilst at least 7% of men and 9% of women have severe obesity (which we define as BMI ≥ 35 kg/m 2 ) [ 2 ]. Obesity-related diseases (ORDs) such as type 2 diabetes mellitus (T2DM), cardiovascular diseases, stroke, and obesity-related cancers reduce life expectancy [ 3 ] and are detrimental to patient health and quality of life. The economic burden of obesity in England is projected to be approximately £16 billion per year [ 4 ]. In 2017/2018, 711,000 hospital admissions were associated with obesity, an increase of 15% from the previous year, demonstrating that obesity is a growing health concern [ 1 ].

Economic evaluations are comparative analyses of the costs and benefits of different health care interventions and provide information to help decision makers reach evidence-based decisions on the efficient allocation of scarce health care funding resources. International decision makers, such as the National Institute for Health and Care Excellence (NICE) in the UK and Canadian Agency for Drugs and Technologies in Health (CADTH) in Canada provide funding recommendations on the use of health technologies using economic evidence as an integral part of their decision-making processes. For example, in the UK, NICE published obesity guidance in 2014 [ 5 ] that recommended a weight management programme (WMP) for people with obesity, pharmacotherapy if WMPs had failed, a very low calorie diet (VLCD) for people that needed to lose weight quickly (such as for infertility treatment or joint replacement) and bariatric surgery for those with a BMI ≥ 40 kg/m 2 and BMI of 35–40 kg/m 2 for people with comorbidities.

Despite the substantial health, social and economic burden, there remains a lack of evidence synthesis that clarifies the most effective and cost-effective management strategies for people with severe obesity (and their comorbidities). The aim of this paper is twofold. First, we report the findings of existing cost-effectiveness studies evaluating non-surgical WMPs for people with severe obesity. Secondly, we identify common evaluation challenges, with a view to providing recommendations for the conduct of future obesity economic evaluations.

Search Strategy

We searched MEDLINE and EMBASE databases from 1980; NHS Economic Evaluation Database (NHS EED), Health Technology Assessment (HTA) database, Cost-effectiveness Analysis Registry, and Research Papers in Economics (RePEc) from inception. Original searches by us up to May 2017 were conducted as part of the REview of Behaviour And Lifestyle interventions for severe obesity: AN evidenCE synthesis (REBALANCE) study [ 6 ••]. Updated searches were conducted up until November 2020. Full details of search strategies are provided in our REBALANCE report [ 6 ••].

Inclusion and Exclusion Criteria

English language studies, reporting full economic evaluations, defined as a comparative assessment of two or more non-surgical WMPs (i.e. cost-utility analysis (CUA), cost-effectiveness analysis (CEA), cost–benefit analysis (CBA) or cost-minimisation analysis (CMA) frameworks) were deemed eligible for inclusion. Eligible populations were adults aged 18 and over, with severe obesity (BMI ≥ 35 kg/m 2 ) based on mean or median BMI in source clinical effectiveness studies (or a modelled cohort with (BMI ≥ 35 kg/m 2 )). Interventions were eligible for inclusion so long as they were a WMP, where the key target of the intervention was weight loss or weight loss maintenance. This also included VLCDs, defined here as ≤ 800 ± 10% kcal/day. Partial economic evaluations such as evaluations of costs alone or outcomes alone, cost-consequence analyses (costs and consequences not compared but reported separately) and methodological studies were all excluded. The only pharmacotherapy included was Orlistat because, at the time of writing, it was the only drug prescribed for weight loss in the UK.

Data Extraction

Abstract screening was conducted by one health economist. Full texts were evaluated against the inclusion and exclusion criteria and checked by a second health economist for consensus. All included studies were data extracted into a predefined online data extraction form. The data extraction form for our REBALANCE review was designed to include all economic data available within the studies, but in the updated review, a targeted data extraction form was used, extracting only data required for the current article [ 7 ]. The updated data extraction form is provided in the Supplementary Material Table 1 .

Narrative Evidence Synthesis

Findings from the systematic review were tabulated, and a narrative synthesis of the cost-effectiveness evidence provided. Data were not synthesised quantitatively due to substantial heterogeneity across included studies in terms of evaluation frameworks (CUA, CEA), evaluation approach (within trial evaluations or decision models), scope of evaluation (narrowly defined such as diabetes vs broadly defined multiple ORDs), differences across health care systems, definitions of interventions and comparators. Methodological limitations of the studies were identified and catalogued, with a view to providing guidance for future research.

Quality Assessment

Included studies (in our REBALANCE report [ 6 ••]) were quality assessed using standardised checklists, recommended by Cochrane: economic evaluations (EEs) alongside clinical trials and decision analysis models used Drummond and Jefferson [ 8 ] and Philips et al. [ 9 ] checklists, respectively. Quality assessment was done independently by two health economists for the individual review, the results of which can be found in the REBALANCE report [ 6 ••].

Studies identified in this updated review were assessed against the methodological issues identified in the REBALANCE review to identify whether the quality of studies has improved over time.

Identified Studies

The searches, combined for the original and updated reviews, identified 3478 potentially relevant titles and abstracts. N = 352 full texts were retrieved and assessed against the inclusion/exclusion criteria . N = 32 studies were finally included in the review (reported in 36 papers). Further details are provided in the PRISMA flow chart (Fig. 1 ).

figure 1

PRISMA flow chart for identification of studies from 1990 to 2020

Economic evaluations included evaluations of WMPs ( n = 29) and pharmacotherapies ( n = 5). Two studies evaluated both WMPs and pharmacotherapies [ 10 , 11 ]. These are listed in Table 1 and categorised in three groups: economic evaluations alongside randomised controlled trials (RCTs) ( n = 13), others (neither RCT-based nor model-based) ( n = 4) and decision models ( n = 15). The majority of studies were published within the past 10 years ( n = 29), and the remainder were published in 2005 ( n = 3). The WMPs are further categorised as lifestyle WMPs ( n = 25) [ 6 ••, 10 – 22 , 23 ••, 24 •, 25 , 26 •, 27 – 32 ,  40 ], VLCDs ( n = 4) [ 6 ••, 26 •, 27 , 29 ], meal replacements ( n = 2) [ 10 , 11 ], group intervention (vs intervention delivered on individual basis) ( n = 1) [ 33 ], and remote interventions ( n = 6) [ 12 – 14 , 34 – 36 ]. Five studies included Orlistat in their assessment ( n = 5) [ 10 , 11 , 37 – 39 ]. Some studies evaluated multiple interventions and therefore a study can have multiple WMP categories. The WMP categories are listed in Table 1 , the study characteristics table.

Cost-Effectiveness Results

The cost-effectiveness results are presented in Figs. 2 , 3 , 4 , 5 , 6 , and 7 . The control groups are described in detail in Table 1 and include a variety of minimal interventions such as do-nothing, self-help booklet and usual care. More detailed results are reported in the Supplementary Material Table 2 . A summary of results for each WMP category is provided below.

figure 2

Cost-effectiveness results–weight management programmes–decision models (cost per QALY (£))

figure 3

Cost-effectiveness results–weight management programmes–decision models (cost per QALY (US$))

figure 4

Cost-effectiveness results–pharmacotherapy–decision models (cost per QALY (EUR) and cost per DALY (AU$))

figure 5

Cost-effectiveness results–weight management programmes–within trial economic evaluations (cost per QALY (US$, £))

figure 6

Cost-effectiveness results–weight management programmes–within trial economic evaluations (cost per kg lost (US$))

figure 7

Cost-effectiveness results–weight management programmes–neither within trial economic evaluations nor decision models (cost per QALY (US$))

Weight Management Programmes (WMP)

Lifestyle WMPs (11 within trial, 11 decision models and 3 neither within trial nor decision models) included diet and physical activity advice [ 6 ••, 12 , 13 , 15 – 22 , 24 •, 25 , 30 , 31 , 40 ], low carbohydrate diets [ 14 , 21 ], commercial WMPs (Weight Watchers and Vtrim, Slimming World) [ 10 , 11 , 28 , 32 ], the Counterweight programme [ 19 ] and Look AHEAD [ 6 ••, 23 ••]. The comparators were either no active treatment (most often occurring in decision models) or usual care, with heterogeneous definition of usual care across the studies. Many studies include a “usual care” comparison arm that includes an active intervention/education that may not necessarily reflect usual care as delivered to the general population. The duration of follow-up varied from 12 weeks to 9.6 (median) years, with the majority of studies having a follow-up of 1–2 years. The longest follow-up intervention was Look AHEAD. The ICERs across studies ranged from: US$22 to US$1224 per kg lost for CEAs and from dominant (i.e. less costly and less effective vs different dietary advice) to US$335,952 (vs unclearly described usual care) per QALY for CUAs. The ICER for the WMP with the longest follow-up (Look AHEAD) was uncertain in the within trial analysis [ 23 ••] and borderline cost-effective (vs baseline population trends) or extendedly dominated (vs other non-surgical and surgical WMPs) [ 6 ••].

Four studies [ 6 ••, 26 •, 27 , 29 ] (all decision models) included a VLCD as an intervention [ 6 ••, 26 •, 27 , 29 ]. The VLCD interventions (LighterLife Total [ 27 ], Optifast [ 29 ], Cambridge Weight Plan UK [ 26 •] and different meta-analysed VLCD interventions [ 6 ••]) were followed by a WMP of varying intensity. Duration of follow-up varied from 1 to 4 years across the VLCD studies. The ICERs for the VLCD intervention ranged from US$6,475 (vs no intervention) per QALY [ 29 ] to dominated (i.e. more costly and less effective compared to other WMPs and bariatric surgery) [ 6 ••].

Two meal replacement studies [ 10 , 11 ] were included (neither of which were within trial nor decision model but extrapolated benefits using meta-analysed data). In both studies, the Jenny Craig meal replacement intervention included a prescribed calorie intake and counselling. Jenny Craig was compared to other WMPs, with ICERs ranging from to US$369,000 [ 10 ] to US$588,620 per QALY [ 11 ].

A group intervention (within trial) included counselling through a conference call, instead of individually (control group) [ 33 ]. The ICER was US$9249 (less costly, less effective). Follow-up was only 1 year.

The interventions that were delivered remotely (4 within trial, 1 decision model and 1 neither within trial nor decision model) were Internet or telephone-based. Other evaluations were for interventions delivered remotely rather than in-person [ 12 – 14 , 35 , 36 ]. Follow-up ranged from 6 months to 2 years. The ICER ranged from US$275 [ 12 ] to US$2204 [ 34 ] per kg lost for CEAs and £151,142 to £232,911 (vs usual primary care; the decision modelling study) per QALY [ 36 ] for CUAs.

Five studies (3 decision models and 2 neither within trial nor decision model) evaluated the cost-effectiveness of Orlistat and low-fat diet and showed mixed results [ 10 , 11 , 37 – 39 ]. When compared to placebo (plus a low-fat diet), Orlistat was cost-effective [ 38 , 39 ]. However, when compared to existing population trends or more intense interventions (that were defined as usual care), Orlistat was no longer cost-effective [ 10 , 11 , 37 ]. Orlistat was not cost-effective in the lifetime decision modelling study [ 37 ].

Some interventions were evaluated in multiple studies. Counterweight was deemed cost-effective when compared to no treatment [ 32 ]. However, Counterweight was not cost-effective compared to Weight Watchers [ 27 ]. Slimming World was cost-effective compared to being given information verbally or through written material [ 28 ]. However, in a different study, Slimming World was not found cost-effective compared to Counterweight, Weight Watchers and Lighterlife Total [ 27 ]. Look AHEAD was borderline cost-effective compared to baseline population trends [ 6 ••] but mixed results when compared to a lifestyle WMP including physical activity and dietary advice [ 6 ••, 23 ••].

The majority of studies were conducted in the USA ( n = 17). The WMPs considered cost-effective in the longer term (in terms of cost per QALY) in the USA were OPTIFAST (a VLCD) [ 29 ] (but with a 3-year time horizon) and a lifestyle intervention based on DPP [ 30 ] (but with a 5-year time horizon). The WMPs that were considered cost-effective in a UK setting ( n = 12) in the longer term were the WMP delivered in a football club [ 24 •, 25 ], Lighterlife Total [ 27 ], Slimming World (only when compared to usual care) [ 28 ], the Counterweight Programme (only when compared to no treatment) [ 32 ], Cambridge Weight Plan [ 26 •] and NHS Diabetes Prevention Programme [ 31 ]. The WMP considered in Sweden ( n = 1), Ireland ( n = 1) and Australia ( n = 1) was Orlistat, with ICERs ranging from €13,125 per QALY (vs placebo plus a low-fat diet) [ 38 ] to dominated (vs more intense interventions) [ 10 , 11 ].

Note that all the cost-effectiveness results here are compared against different thresholds, with differing health care systems and methodological quality. Therefore, in the following section, we will assess the methodological quality of the studies.

Trial-Based Economic Evaluations

About half of the economic evaluations were trial-based. The follow-up period for most studies ranged between 1 and 2 years. Studies with longer (than 2 years) follow-up periods were 3.5 years [ 24 •], 5 years [ 6 ••] and about 9 years (Look AHEAD). Within trial, economic evaluations do not capture the long-term costs and benefits, nor assumptions associated with a treatment for severe obesity due to the long-term impact on ORDs.

Decision Models

The following sections reflect the key methodological issues identified in the quality assessment of the included modelling studies. The most common model types were a Markov model and individual level simulation/microsimulation model. The most common framework for analysis was CUA, and the most common benefit measurement was the quality adjusted life year (QALY). The incremental cost effectiveness ratio (ICER) was therefore compared to a commonly used country-specific threshold.

Model Structure

Decision model time horizons ranged from 3 years to a lifetime horizon across the studies. 8/15 (53%) of decision models were built on a life-time horizon, which is likely required to capture all the costs and consequences of ORD such as stroke, cancer, diabetes and myocardial infarction. The varying time horizons further limit the comparability between the studies. Short-term decision models, such as those conducted over only 3 years are insufficient for decision making as they fail to capture the long-term benefits of weight loss interventions on ORD and may generate cost-effectiveness conclusions biased against WMPs. However, a counterargument is that longer term extrapolations require assumptions about the impact of transient weight loss on ORD, and assumptions about the long-term rate of weight regain over time (Weight Regain Assumptions). Longer term extrapolations, based on short-term data, add uncertainty to results, with a risk of drawing cost-effectiveness conclusions that are biased towards WMPs. To determine the most likely cost-effectiveness conclusions from a decision model, it is critical that models include a comprehensive range of sensitivity analyses to ascertain the impact of important assumptions such as transient effects and weight regain rates on results.

Furthermore, many of the obesity models did not include many of the relevant disease health states such as T2DM, stroke, cardiovascular disease, and obesity-related cancers. Some obesity models [ 6 ••, 24 •, 26 •, 31 ] (all UK studies) did include many of the ORD risks factors such as T2DM (all studies), obesity-related cancers [ 6 ••, 26 •, 31 ], stroke [ 6 ••, 24 •, 26 •], coronary heart disease [ 6 ••, 24 •], hypertension [ 6 ••, 24 •, 31 ], knee osteoarthritis [ 6 ••, 31 ] and congestive heart failure [ 31 ]. Obesity-related cancers included breast, colon, liver, kidney and pancreas cancers. The populations considered in the decision models were a mixture of the general population with obesity, with T2DM, at high risk of T2DM or with comorbidities. Two decision models only focused on T2DM [ 30 , 38 ]. Whilst this is suitable for studies only interested in T2DM as an outcome, the exclusion of other health states from studies modelling interventions for severe obesity may tend to underestimate the benefits of weight loss interventions in the long-term.

Weight Regain Assumptions

The modelling assumption on weight regain over time varied widely between the studies. This parameter is subject to uncertainty as we do not know what happens beyond the short trial time period, which was the case for studies on WMPs.

Studies assumed a variety of weight regain assumptions after the end of intervention delivery. 9/15 (60%) assumed a constant weight regain rate to baseline (often at 1-kg regain per year or a 5-year regain to baseline weight) or a linear projection of the BMI based on trial data. For the remainder of the studies, it was either unclear, not reported or done differently (i.e. assumed QALY gains from weight loss linearly reduced to zero or extrapolated a person’s measured glycated haemoglobin values instead of their BMI).

The weight regain rate has important implications for cost-effectiveness, particularly in models where the risk of ORD is directly linked to time-specific weight/BMI. Long-term follow-up data on WMPs is frequently lacking and therefore exploring the impact that the weight regain assumption has on results is crucially important. The longest follow-up for WMPs identified in the REBALANCE clinical effectiveness review [ 6 ••] was from the Look AHEAD study [ 41 ], with 9 years of data. This was an intensive longer term WMP which is dissimilar to the other WMPs identified in this review, which had much shorter follow-up. The Look AHEAD study was evaluated in two studies included in this review, one trial-based economic evaluation [ 23 ••] and in one decision model [ 6 ••]. However, for the majority of WMPs, there is an urgent need for longer term follow-up of RCT evidence to determine the most accurate assumptions for economic modelling.

Variation in Interventions and Comparators

The comparisons identified in this review varied widely. The interventions and comparators differed both between WMP categories and within categories. Lifestyle interventions varied widely and were compared to no active treatment (e.g. country-specific population BMI trajectory) or some form of usual care. VLCDs were compared to WMPs with varying intensity. The meal replacement (Jenny Craig) was compared to different WMPs. The group and remote interventions were compared to in-person lifestyle interventions. Because of the variation in the intervention and comparators, it is difficult to compare across the studies.

Sensitivity Analyses

Sensitivity analyses are key to unravelling the uncertainty in the cost-effectiveness results. Four studies varied the discount rate [ 6 ••, 26 •, 28 , 36 ], which generally had negligible impact on the cost-effectiveness results. Only a few studies looked at varying the time horizon, and not surprisingly, the longer the time horizon, the more cost-effective the intervention [ 6 ••, 29 ]. This is because costs are often incurred upfront but the benefits in terms of ORD avoided often occur far into the future.

The weight regain rate was varied in 4 studies [ 6 ••, 24 •, 26 •, 28 ]. In two of the studies where the weight regain rate was assumed to be more conservative (quicker weight regain to baseline weight) [ 24 •, 28 ], it did not change the cost-effectiveness conclusions. In one study, the intervention was more cost-effective when assuming a weight that was 1 kg below baseline weight beyond 5 years, rather than assuming that all weight was regained after 5 years. The intervention would remain cost-effective as long as the weight is kept off and is not all regained for at least 3 years [ 26 •]. Lastly, in our REBALANCE study [ 6 ••], the weight regain was assumed to follow a linear trajectory based on trial data instead of a 5-year weight regain. Look AHEAD went from being borderline cost-effective to cost-effective (vs baseline population trends) but for the other WMPs evaluated it both increased costs and reduced QALY gains (although remained cost-effective compared to baseline population trends) [ 6 ••].

In the younger age group (aged 20–34), a total diet replacement programme [ 26 •] (assuming a 5-year weight regain) was not cost-effective, and the cost per QALY was highest in the older age groups. However, this was not the case when assuming that 1-kg weight loss is maintained beyond 5 years (in this case the intervention was cost-effective for all age groups). This further highlights the importance of varying the weight regain assumption.

For the higher BMI groups, the cost per QALY was lower (still cost-effective in all age groups) [ 26 •] and more cost saving [ 29 ].

Only three studies [ 24 •, 25 , 36 ] conducted a value of information analysis (VOI). VOI is a framework for identifying where the greatest uncertainty lies to which future research should be directed. Considering the uncertain longer term weight loss, weight loss maintenance and associated clinical event management, VOI could help guide the direction of future research in the area of obesity.

We identified 32 studies (across 36 papers) evaluating the cost-effectiveness of non-surgical interventions for severe obesity (BMI ≥ 35 kg/m 2 ). The cost-effectiveness findings from the WMP and pharmacotherapy studies were mixed. Half of the WMP studies were economic evaluations alongside RCTs, not extrapolating costs and benefits over a longer time horizon, failing to capture the long-term impact of an intervention on obesity, a chronic disease. Furthermore, studies were subject to heterogeneity with regard to the chosen comparators, study populations, settings, decision model structure, costing methodology, weight regain assumptions and time horizons. To our knowledge, this (both our REBALANCE review and updated review) is the first systematic review of economic evaluations of different WMPs for severe obesity (BMI ≥ 35 kg/m 2 ).

Two reviews have recently been conducted on the cost-effectiveness of interventions for people with obesity [ 42 , 43 ]. However, unlike our review, they focused on bariatric surgery only their population of interest was people with obesity (BMI ≥ 30 kg/m 2 ) rather than severe obesity (BMI ≥ 35 kg/m 2 ), included partial economic evaluations (e.g. cost only, studies or effectiveness evaluations) in addition to full economic evaluations. As in the REBALANCE study, they also found surgery to be cost-effective. One of their included studies [ 44 ] applied a post-surgery complication risk over a 10-year period. This is a step in the right direction considering the evidence showing a longer term risk of complications following bariatric surgery [ 45 , 46 ]. More recent relevant data on longer term surgery complications would improve future obesity decision models.

The quality of the included studies varied. However, as we have learnt from the REBALANCE study, many of these quality assessment items were not captured in the quality assessment checklists. These additional items for the quality assessment checklists would improve the quality assessment of obesity models [ 7 ]. Firstly, weight regain assumptions in the decision models varied widely, were poorly justified and were rarely explored in sensitivity analyses (only in 4 studies). This is important especially for WMPs because the majority of WMPs were of short duration and therefore, the longer term weight regain rate is unknown. The assumed weight regain rate (BMI trajectory over time) is associated with an increased risk of developing ORDs. Therefore, an intervention assuming patients revert back to baseline in 5 years’ time is more likely to be cost-effective than assuming patients revert back to baseline BMI immediately. Secondly, many studies did not include all the relevant disease health states such as T2DM and stroke. Lastly, the trial results should be extrapolated over a longer time horizon. Including these items on the quality assessment checklist would be helpful to reviewers in assessing the quality of obesity models.

Two studies in the review (UK studies) evaluated multiple WMPs and bariatric surgery, however, one with only a 10-year time horizon for costs and outcomes [ 27 ] and the other with a lifetime horizon for costs and outcomes [ 6 ••]. The REBALANCE study [ 6 ••] included all the relevant comparators (both surgical and non-surgical options) that were identified through a systematic review of RCTs, and modelled over a lifetime horizon. From a UK NHS perspective, the generalisability of the results in the systematic review presented here to a UK setting is poor. A recent UK RCT was published evaluating a VLCD (DROPLET trial) offered in primary care, and was found to be cost-effective over a lifetime horizon [ 26 •]. However, the only comparator was nurse-led support. There is a need for a comparison of commonly available treatments in the UK NHS.

Strengths and Limitations

Key strengths of this study are the systematic approach to the literature review in identifying the cost-effectiveness evidence on interventions for severe obesity and the methodological quality assessment of the included studies. Furthermore, this review brings focus to the population with severe obesity, identifying value for money interventions for treating severe obesity.

Due to study heterogeneity, no quantitative synthesis of the study results by meta-analysis was attempted, a common issue with systematic reviews of economic evaluations. This is because studies were conducted in different countries with different health care systems, different definitions of comparator groups, model structures, costing methods and modelling assumptions. A detailed quality assessment was not conducted for all included studies, only for those identified through the REBALANCE review, but this informed our subsequent assessment of studies.

Conclusions

Most WMPs were cost-effective and pharmacotherapies showed mixed results. However, the cost-effectiveness evidence should be read with caution due to the varying methodological issues and study heterogeneity across the studies. About half of the WMPs were economic evaluations alongside RCTs, not accounting for the difference in long-term costs and outcomes between the considered interventions, crucial for a chronic disease such as obesity. WMPs tended to have short-term follow-up, rendering it even more important to make use of decision models. Decision models did not include most relevant health states and had varying assumptions around weight regain which was rarely explored in sensitivity analysis.

Although there exists a decision model assessing different types of interventions [ 6 ••], there is still a need for future economic evaluations to focus on effective interventions available on the UK NHS for people with severe obesity. Furthermore, there is room for improvement with regard to obesity models and their methodology. To improve decision models, there is a need for the inclusion of all the important health states, improved consistency in the assumed weight regain rate (which ideally should be based on best available evidence), and improved transparency in the description of the comparators (and interventions) to allow better comparison across studies.

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

NHS Digital. Statistics on obesity, physical activity and diet, England, 2019. 2019. https://digital.nhs.uk/data-and-information/publications/statistical/statistics-on-obesity-physical-activity-and-diet/statistics-on-obesity-physical-activity-and-diet-england-2019/final-page . Accessed 13 May 2020.

NHS Digital. Health survey for England - 2013. 2014. https://digital.nhs.uk/data-and-information/publications/statistical/health-survey-for-england/health-survey-for-england-2013 . Accessed 13 May 2020.

Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, de Gonzalez AB, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. The Lancet. 2016;388(10046):776–86.

Article   Google Scholar  

Department of Health (DoH). Healthy lives, healthy people: a call to action on obesity in England. 2011.  https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/216096/dh_127424.pdf .

National Institute for Health and Care Excellence, (NICE). Obesity: identification, assessment and management. Clinical Guideline CG189. NICE 2014.  https://www.nice.org.uk/guidance/cg189 . Accessed April 2018.

•• Avenell A, Robertson C, Skea Z, Jacobsen E, Boyers D, Cooper D, et al. Bariatric surgery, lifestyle interventions and orlistat for severe obesity: the REBALANCE mixed-methods systematic review and economic evaluation. Health Technol Assess. 2018. This is the only study evaluating multiple WMPs and bariatric surgery with a lifetime horizon for costs and outcomes. Bariatric surgery was found to be the most cost-effective intervention but if surgery was not an option, a low cost short-term WMP (called WMP1) would be the most cost-effective intervention.

Jacobsen E, Boyers D, Avenell A. Challenges of systematic reviews of economic evaluations: a review of recent reviews and an obesity case study. Pharmacoeconomics. 2020;38(3):259–67.

Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ. 1996;313(7052):275–83.

Article   CAS   Google Scholar  

Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Riemsma R, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess. 2004;8(36):iii–158.

Finkelstein EA, Verghese NR. Incremental cost-effectiveness of evidence-based non-surgical weight loss strategies. Clin Obes. 2019;9(2): e12294.

Finkelstein EA, Kruger E. Meta-and cost-effectiveness analysis of commercial weight loss strategies. Obesity. 2014;22(9):1942–51.

Daumit GL, Janssen EM, Jerome GJ, Dalcin AT, Charleston J, Clark JM, et al. Cost of behavioral weight loss programs implemented in clinical practice: the POWER trial at Johns Hopkins. Transl Behav Med. 2020;10(1):103–13.

Delahanty LM, Levy DE, Chang Y, Porneala BC, Goldman V, McCarthy J, et al. Effectiveness of lifestyle intervention for type 2 diabetes in primary care: the real HEALTH-Diabetes randomized clinical trial. J Gen Intern Med. 2020;35(9):2637–46.

Little P, Stuart B, Hobbs FR, Kelly J, Smith ER, Bradbury KJ, et al. Randomised controlled trial and economic analysis of an internet-based weight management programme: POWeR (Positive Online Weight Reduction). Health Technol Assess. 2017;21(4):1–62.

McKnight T, Demuth JR, Wilson N, Leider JP, Knudson A. Assessing effectiveness and cost-benefit of the Trinity Hospital Twin City Fit for Life program for weight loss and diabetes prevention in a rural Midwestern town. Prev Chronic Dis. 2018;15:E98.

McRobbie H, Hajek P, Peerbux S, Kahan BC, Eldridge S, Trépel D, et al. Tackling obesity in areas of high social deprivation: clinical effectiveness and cost-effectiveness of a task-based weight management group programme-a randomised controlled trial and economic evaluation. Health Technol Assess. 2016;20(79):1–149.

Meenan RT, Stumbo SP, Yarborough MT, Leo MC, Yarborough BJH, Green CA. An economic evaluation of a weight loss intervention program for people with serious mental illnesses taking antipsychotic medications. Adm Policy Ment Health Ment Health Serv Res. 2016;43(4):604–15.

Patel N, Beeken RJ, Leurent B, Omar RZ, Nazareth I, Morris S. Cost-effectiveness of habit-based advice for weight control versus usual care in general practice in the Ten Top Tips (10TT) trial: economic evaluation based on a randomised controlled trial. BMJ Open. 2018;8(8): e017511.

Perri MG, Limacher MC, von Castel-Roberts K, Daniels MJ, Durning PE, Janicke DM, et al. Comparative effectiveness of three doses of weight-loss counseling: two-year findings from the rural LITE trial. Obesity. 2014;22(11):2293–300.

Rhodes EC, Chandrasekar EK, Patel SA, Narayan KV, Joshua TV, Williams LB, et al. Cost-effectiveness of a faith-based lifestyle intervention for diabetes prevention among African Americans: a within-trial analysis. Diabetes Res Clin Pract. 2018;146:85–92.

Tsai AG, Glick HA, Shera D, Stern L, Samaha FF. Cost-effectiveness of a low-carbohydrate diet and a standard diet in severe obesity. Obes Res. 2005;13(10):1834–40.

Tsai AG, Wadden TA, Volger S, Sarwer DB, Vetter M, Kumanyika S, et al. Cost-effectiveness of a primary care intervention to treat obesity. Int J Obes. 2013;37(1):S31–7.

•• Zhang P, Atkinson KM, Bray GA, Chen H, Clark JM, Coday M, et al. Within-trial cost-effectiveness of a structured lifestyle intervention in adults with overweight/obesity and type 2 diabetes: results from the Action for Health in Diabetes (Look AHEAD) Study. Diabetes Care. 2021;44(1):67–74. A large randomised controlled trial of a lifestyle WMP (called Look AHEAD) with 9-year follow-up, the longest trial duration that currently exists for WMPs with an economic evaluation. The comparator was standard diabetes support and education. A within-trial economic evaluation was conducted and the cost-effectiveness of the Look AHEAD intervention vs comparator was unclear.

• Gray CM, Wyke S, Zhang R, Anderson AS, Barry S, Brennan G, et al. Long-term weight loss following a randomised controlled trial of a weight management programme for men delivered through professional football clubs: the Football Fans in Training follow-up study. Public Health Res. 2018;6(9):1–114. This study is a 3.5-year follow-up of an older UK randomised controlled trial published in one of the economic evaluations included in the review (Wyke et al. 2015). This is also one of the most recent model-based economic evaluations with a lifetime horizon for costs and outcomes. The updated cost-effectiveness results showed that the WMP delivered in a football club for men remained cost-effective compared to being given a booklet on losing weight.

Wyke S, Hunt K, Gray C, Fenwick E, Bunn C, Donnan P, et al. Football fans in training (FFIT): a randomised controlled trial of a gender-sensitised weight loss and healthy living programme for men. Public Health Res. 2015;3(2):1–129.

• Kent S, Aveyard P, Astbury N, Mihaylova B, Jebb SA. Is doctor referral to a low‐energy total diet replacement program cost‐effective for the routine treatment of obesity? Obesity. 2019;27(3):391–8. This is one of the most recent model-based economic evaluations with a lifetime horizon for costs and outcomes. The UK randomised controlled trial evaluating a VLCD (DROPLET trial) offered in primary care was found to be cost-effective compared to nurse-led support.

Lewis L, Taylor M, Broom J, Johnston KL. The cost-effectiveness of the LighterLife weight management programme as an intervention for obesity in England. Clin Obes. 2014;4(3):180–8.

Meads DM, Hulme CT, Hall P, Hill AJ. The cost-effectiveness of primary care referral to a UK commercial weight loss programme. Clin Obes. 2014;4(6):324–32.

CAS   Google Scholar  

Nuijten M, Marczewska A, Araujo Torres K, Rasouli B, Perugini M. A health economic model to assess the cost-effectiveness of OPTIFAST for the treatment of obesity in the United States. J Med Econ. 2018;21(9):835–44.

Radcliff TA, Côté MJ, Whittington MD, Daniels MJ, Bobroff LB, Janicke DM, et al. Cost-effectiveness of three doses of a behavioral intervention to prevent or delay type 2 diabetes in rural areas. J Acad Nutr Diet. 2020;120(7):1163–71.

Thomas C, Sadler S, Breeze P, Squires H, Gillett M, Brennan A. Assessing the potential return on investment of the proposed UK NHS diabetes prevention programme in different population subgroups: an economic evaluation. BMJ Open. 2017;7(8): e014953.

Counterweight Project Team, Trueman P, Haynes SM, Felicity Lyons G, Louise McCombie E, McQuigg M, et al. Long-term cost-effectiveness of weight management in primary care. Int J Clin Pract. 2010;64(6):775–83.

Hollenbeak CS, Weinstock RS, Cibula D, Delahanty LM, Trief PM. Cost-effectiveness of SHINE: a telephone translation of the Diabetes Prevention Program. Health Serv Insights. 2016;9:HSI-S39084.

Ritzwoller DP, Glasgow RE, Sukhanova AY, Bennett GG, Warner ET, Greaney ML, et al. Economic analyses of the Be Fit Be Well program: a weight loss program for community health centers. J Gen Intern Med. 2013;28(12):1581–8.

Krukowski RA, Tilford JM, Harvey-Berino J, West DS. Comparing behavioral weight loss modalities: incremental cost-effectiveness of an internet-based versus an in-person condition. Obesity. 2011;19(8):1629–35.

Miners A, Harris J, Felix L, Murray E, Michie S, Edwards P. An economic evaluation of adaptive e-learning devices to promote weight loss via dietary change for people with obesity. BMC Health Serv Res. 2012;12(1):190.

Veerman JL, Barendregt JJ, Forster M, Vos T. Cost-effectiveness of pharmacotherapy to reduce obesity. PLoS One. 2011;6(10):e26051.

Hertzman P. The cost effectiveness of orlistat in a 1-year weight-management programme for treating overweight and obese patients in Sweden. Pharmacoeconomics. 2005;23(10):1007–20.

Lacey LA, Wolf A, O’shea D, Erny S, Ruof J. Cost-effectiveness of orlistat for the treatment of overweight and obese patients in Ireland. Int J Obes. 2005;29(8):975–82.

Wilson KJ, Brown HS III, Bastida E. Cost-effectiveness of a community-based weight control intervention targeting a low-socioeconomic-status Mexican-origin population. Health Promot Pract. 2015;16(1):101–8.

AHEAD Research Group, Wing RR, Bolin P, Brancati FL, Bray GA, Clark JM. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013;369(2):145–54.

Xia Q, Campbell JA, Ahmad H, Si L, de Graaff B, Palmer AJ. Bariatric surgery is a cost-saving treatment for obesity–a comprehensive meta-analysis and updated systematic review of health economic evaluations of bariatric surgery. Obes Rev. 2020;21(1): e12932.

Campbell JA, Venn A, Neil A, Hensher M, Sharman M, Palmer AJ. Diverse approaches to the health economic evaluation of bariatric surgery: a comprehensive systematic review. Obes Rev. 2016;17(9):850–94.

Alsumali A, Eguale T, Bairdain S, Samnaliev M. Cost-effectiveness analysis of bariatric surgery for morbid obesity. Obesity Surg. 2018;28(8):2203–14.

Reges O, Greenland P, Dicker D, Leibowitz M, Hoshen M, Gofer I, et al. Association of bariatric surgery using laparoscopic banding, Roux-en-Y gastric bypass, or laparoscopic sleeve gastrectomy vs usual care obesity management with all-cause mortality. JAMA. 2018;319(3):279–90.

Jakobsen GS, Småstuen MC, Sandbu R, Nordstrand N, Hofsø D, Lindberg M, et al. Association of bariatric surgery vs medical obesity treatment with long-term medical complications and obesity-related comorbidities. JAMA. 2018;319(3):291–301.

Download references

Acknowledgements

The REBALANCE team: REBALANCE Project management team were Elisabet Jacobsen (Health Economics Research Unit, University of Aberdeen, Aberdeen, UK), Dwayne Boyers (Health Economics Research Unit, University of Aberdeen, Aberdeen, UK), David Cooper (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Lise Retat (UK Health Forum +), Paul Aveyard (Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK), Fiona Stewart (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Graeme MacLennan (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Laura Webber (UK Health Forum +) Emily Corbould (UK Health Forum), Benshuai Xu (UK Health Forum), Abbygail Jaccard (UK Health Forum), Bonnie Boyle (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Eilidh Duncan (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Michal Shimonovich (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Cynthia Fraser (Health Services Research Unit, University of Aberdeen, Aberdeen, UK) and Lara Kemp (Health Services Research Unit, University of Aberdeen, Aberdeen, UK), Zoe Skea (Health Services Research Unit, University of Aberdeen, UK), Clare Robertson (Health Services Research Unit, University of Aberdeen, UK), Magaly Aceves-Martins (Health Services Research Unit, University of Aberdeen, UK), Alison Avenell (Health Services Research Unit, University of Aberdeen, UK), Marijn de Bruin (Radboud University Medical Center, IQ Healthcare, Radboud Institute for Health Sciences, Nijmegen, The Netherlands). We thank the REBALANCE Advisory Group for all their advice and support during this project: Margaret Watson, Lorna Van Lierop, Richard Clarke, Jennifer Logue, Laura Stewart, Richard Welbourn, Jamie Blackshaw and Su Sethi. +Current address HealthLumen, London.

This is a substantial update to the systematic review of economic evaluations that was conducted as part of our REBALANCE project. The REBALANCE project was funded by the NIHR Health Technology Assessment Programme (Project number: 15/09/04). See the HTA Programme website for further project information. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Department of Health, or the funders that provide institutional support for the authors of that report. The Health Services Research Unit and Health Economics Research Unit are core funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorate.

Author information

Authors and affiliations.

Health Economics Research Unit, University of Aberdeen, Polwarth Building, Foresterhill, Aberdeen, AB25 2ZD, UK

Elisabet Jacobsen & Dwayne Boyers

Health Services Research Unit, University of Aberdeen, Aberdeen, UK

Paul Manson & Alison Avenell

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Elisabet Jacobsen or Dwayne Boyers .

Ethics declarations

Conflict of interest.

AA, DB and EJ were authors on Avenell 2018 (reference 6, the REBALANCE report) described here as one of the included studies. We have no other conflicts of interest to declare.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's note.

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

This article is part of the Topical Collection on Health Services and Programs

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 25 KB)

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

Jacobsen, E., Boyers, D., Manson, P. et al. A Systematic Review of the Evidence for Non-surgical Weight Management for Adults with Severe Obesity: What is Cost Effective and What are the Implications for the Design of Health Services?. Curr Obes Rep 11 , 356–385 (2022). https://doi.org/10.1007/s13679-022-00483-z

Download citation

Accepted : 05 April 2022

Published : 21 November 2022

Issue Date : December 2022

DOI : https://doi.org/10.1007/s13679-022-00483-z

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

  • Severe obesity
  • Weight management programmes
  • Systematic review
  • Cost-effectiveness
  • Find a journal
  • Publish with us
  • Track your research

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

935 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

Rent or buy this article

Prices vary by article type

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

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

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

  • 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

obesity disease management case study

U.S. flag

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

Logo of ijerph

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.

  • Open access
  • Published: 12 February 2024

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

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

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

395 Accesses

Metrics details

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

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

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

Conclusions

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

Peer Review reports

Introduction

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

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

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

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

Study design and eligibility criteria

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

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

Database searching and study selection

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

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

Data extraction and risk of bias assessment

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

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

Statistical analysis

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

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

figure 1

Study selection process

Study characteristics

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

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

Quality assessment and publication bias

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

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

Overall meta-analysis results

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

figure 2

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

Subgroup analysis and meta-regression

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

figure 3

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

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

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

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

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

Strengths and limitations

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

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

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

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

Data availability

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

Abbreviations

Cardiovascular disease

High-density lipoprotein

  • Myocardial infarction

Excess body weight

Body mass index

Fasting blood sugar

Triglyceride

Low-density lipoprotein

Newcastle Ottawa scale

Hazard ratio

Confidence interval

Adult treatment panel III

International diabetes federation

Li X, Zhai Y, Zhao J, He H, Li Y, Liu Y et al. Impact of Metabolic Syndrome and It’s Components on Prognosis in Patients With Cardiovascular Diseases: A Meta-Analysis. 2021. p. 704145-.

Sarrafzadegan N, Gharipour M, Sadeghi M, Nezafati P, Talaie M, Oveisgharan S, et al. Metabolic syndrome and the risk of ischemic stroke. J Stroke Cerebrovasc Dis. 2017;26(2):286–94.

Article   PubMed   Google Scholar  

Popa S, Moţa M, Popa A, Moţa E, Serafinceanu C, Guja C, et al. Prevalence of overweight/obesity, abdominal obesity and metabolic syndrome and atypical cardiometabolic phenotypes in the adult Romanian population: PREDATORR study. J Endocrinol Investig. 2016;39(9):1045–53.

Article   CAS   Google Scholar  

He F, Rodriguez-Colon S, Fernandez-Mendoza J, Vgontzas AN, Bixler EO, Berg A, et al. Abdominal obesity and metabolic syndrome burden in adolescents-penn state children cohort study. J Clin Densitometry. 2015;18(1):30–6.

Article   Google Scholar  

Lovic MB, Djordjevic DB, Tasic IS, Nedeljkovic IP. Impact of metabolic syndrome on clinical severity and long-term prognosis in patients with myocardial infarction with ST-segment elevation. Hellenic J Cardiol. 2018;59(4):226–31.

Hirokawa W, Nakamura K, Sakurai M, Morikawa Y, Miura K, Ishizaki M, et al. Mild metabolic abnormalities, abdominal obesity and the risk of cardiovascular diseases in middle-aged Japanese men. J Atheroscler Thromb. 2010;17(9):934–43.

Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Disease. 2016;5:2048004016633371.

Cheong KC, Lim KH, Ghazali SM, Teh CH, Cheah YK, Baharudin A et al. Association of metabolic syndrome with risk of cardiovascular disease mortality cause mortality among Malaysian adults: a retrospective cohort study. 2021:1–9.

Nejadghaderi SA, Grieger JA, Karamzad N, Kolahi A-A, Sullman MJM, Safiri S, et al. Burden of diseases attributable to excess body weight in the Middle East and North Africa region, 1990–2019. Sci Rep. 2023;13(1):20338.

Article   PubMed   PubMed Central   Google Scholar  

Caleyachetty R, Thomas GN, Toulis KA, Mohammed N, Gokhale KM, Balachandran K, et al. Metabolically healthy obese and Incident Cardiovascular Disease events among 3.5 million men and women. J Am Coll Cardiol. 2017;70(12):1429–37.

Yeh T-l, Chen H-h, Tsai S-y, Lin C-y, Liu S-j. Chien K-l. The relationship between Metabolically Healthy Obesity and the risk of Cardiovascular Disease: a systematic review and Meta-analysis. 2019(Cvd):1–15.

Hinnouho GM, Czernichow S, Dugravot A, Nabi H, Brunner EJ, Kivimaki M, et al. Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: the Whitehall II cohort study. Eur Heart J. 2015;36(9):551–9.

Hosseinpanah F, Tasdighi E, Barzin M, Mahdavi M, Ghanbarian A, Valizadeh M et al. The association between transition from metabolically healthy obesity to metabolic syndrome, and incidence of cardiovascular disease: Tehran lipid and glucose study. PLoS ONE. 2020;15(9 September).

Lavie CJ, Milani RV, Ventura HO. Disparate effects of metabolically healthy obesity in coronary heart disease and heart failure. Elsevier USA; 2014. pp. 1079–81.

Oh CM, Park JH, Chung HS, Yu JM, Chung W, Kang JG, et al. Effect of body shape on the development of cardiovascular disease in individuals with metabolically healthy obesity. Medicine. 2020;99(38):e22036–e.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Salari N, Doulatyari PK, Daneshkhah A, Vaisi-Raygani A, Jalali R, Jamshidi Pk, et al. The prevalence of metabolic syndrome in cardiovascular patients in Iran: a systematic review and meta-analysis. Diabetol Metab Syndr. 2020;12(1):96.

Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.

Matthew JP, Joanne EM, Patrick MB, Isabelle B, Tammy CH, Cynthia DM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Google Scholar  

Sistrom CL, Mergo PJ. A simple method for obtaining Original data from published graphs and plots. Am J Roentgenol. 2000;174(5):1241–4.

Wells G, Shea B, O’Connell D, Peterson j, Welch V, Losos M et al. The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis. ᅟ. 2000;&#4447.

Jonathan ACS, Alex JS, John PAI, Norma T, David RJ, Joseph L, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002.

Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101.

Article   CAS   PubMed   Google Scholar  

Xu Y, Li H, Wang A, Su Z, Yang G, Luo Y, et al. Association between the metabolically healthy obese phenotype and the risk of myocardial infarction: results from the Kailuan study. Eur J Endocrinol. 2018;179(6):343–52.

Simons LA, Simons J, Friedlander Y, McCallum J. Is prediction of cardiovascular disease and all-cause mortality genuinely driven by the metabolic syndrome, and independently from its component variables? The Dubbo study. Heart Lung Circ. 2011;20(4):214–9.

Ogorodnikova AD, Kim M, McGinn AP, Muntner P, Khan U, Wildman RP. Incident cardiovascular disease events in metabolically benign obese individuals. Obes (Silver Spring). 2012;20(3):651–9.

Thomsen M, Nordestgaard BG. Myocardial infarction and ischemic heart disease in overweight and obesity with and without metabolic syndrome. JAMA Intern Med. 2014;174(1):15–22.

Lee SH, Jeong MH, Kim JH, Kim MC, Sim DS, Hong YJ, et al. Influence of obesity and metabolic syndrome on clinical outcomes of ST-segment elevation myocardial infarction in men undergoing primary percutaneous coronary intervention. J Cardiol. 2018;72(4):328–34.

Sánchez-Iñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez JA. Risk of incident ischemic stroke according to the metabolic health and obesity states in the vascular-metabolic CUN cohort. Int J Stroke. 2017;12(2):187–91.

Ding J, Chen X, Shi Z, Bai K, Shi S. Association of Metabolically Healthy Obesity and risk of Cardiovascular Disease among adults in China: a retrospective cohort study. Diabetes Metab Syndr Obes. 2023;16:151–9.

Opio J, Wynne K, Attia J, Hancock S, Oldmeadow C, Kelly B, et al. Overweight or obesity increases the risk of cardiovascular disease among older Australian adults, even in the absence of cardiometabolic risk factors: a bayesian survival analysis from the Hunter Community Study. Int J Obes (Lond). 2023;47(2):117–25.

Lavie CJ, Milani RV, Ventura HO, Obesity, Disease C. Risk factor, Paradox, and impact of weight loss. J Am Coll Cardiol. 2009;53(21):1925–32.

Liu J, Chen Y, Cai K, Gong Y. Association of metabolic syndrome with cardiovascular outcomes in hypertensive patients: a systematic review and meta-analysis. J Endocrinol Investig. 2021;44(11):2333–40.

Sanguankeo A, Upala S. Metabolic syndrome increases mortality risk in Dialysis patients: a systematic review and Meta-analysis. Int J Endocrinol Metab. 2018;16(2):e61201.

PubMed   PubMed Central   Google Scholar  

Gallus S, Lugo A, Murisic B, Bosetti C, Boffetta P, La Vecchia C. Overweight and obesity in 16 European countries. Eur J Nutr. 2015;54(5):679–89.

Sanyaolu A, Okorie C, Qi X, Locke J, Rehman S. Childhood and adolescent obesity in the United States: a Public Health concern. Glob Pediatr Health. 2019;6:2333794x19891305.

Ansarimoghaddam A, Adineh HA, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: Meta-analysis of cross-sectional studies. Diabetes Metab Syndr. 2018;12(2):195–201.

Download references

Acknowledgements

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

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

Author information

Authors and affiliations.

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

Zahra Sedaghat

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

Soheila Khodakarim

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

Seyed Aria Nejadghaderi

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

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

Siamak Sabour

You can also search for this author in PubMed   Google Scholar

Contributions

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

Corresponding author

Correspondence to Siamak Sabour .

Ethics declarations

Ethics approval and consent to participate.

The study protocol was evaluated and approved by the ethics committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (ethics code: IR.SBMU.PHNS.REC.1400.149).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

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/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 17 October 2023

Accepted : 09 January 2024

Published : 12 February 2024

DOI : https://doi.org/10.1186/s12889-024-17707-7

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

  • Metabolic syndrome
  • Systematic review
  • Meta-analysis

BMC Public Health

ISSN: 1471-2458

obesity disease management case study

IMAGES

  1. (PDF) Obesity case study. Eye series--14

    obesity disease management case study

  2. Nursing Case Study On Obesity : Obesity, BMI, and Health

    obesity disease management case study

  3. Obesity Case Study

    obesity disease management case study

  4. Case study on obesity

    obesity disease management case study

  5. Obesity Case Study

    obesity disease management case study

  6. New, Accredited Case Study Modules for Obesity Management

    obesity disease management case study

COMMENTS

  1. Obesity: Risk factors, complications, and strategies for sustainable long‐term weight management

    Introduction. Obesity is an increasing, global public health issue. Patients with obesity are at major risk for developing a range of comorbid conditions, including cardiovascular disease (CVD), gastrointestinal disorders, type 2 diabetes (T2D), joint and muscular disorders, respiratory problems, and psychological issues, which may significantly affect their daily lives as well as increasing ...

  2. Dietary Management of Obesity: A Review of the Evidence

    Obesity is a multi-factorial disease and its prevention and management require knowledge of the complex interactions underlying it and adopting a whole system approach that addresses obesogenic environments within country specific contexts.

  3. Building Successful Models in Primary Care to Improve the Management of

    Although several obesity clinical practice guidelines are available and relevant for primary care, a practical and effective medical model for treating obesity is necessary. The aim of this study was to develop and implement a holistic population health-based framework with components to support primary care-based obesity management in US health care organizations. The Obesity Care Model ...

  4. Obesity in adults: a clinical practice guideline

    Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health, increases the risk of long-term medical complications and reduces lifespan. 1 Epidemiologic studies define obesity using the body mass index (BMI; weight/height 2 ), which can stratify obesity-related health risks at the population level.

  5. Effectiveness of weight management interventions for adults delivered

    Abstract 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.

  6. PDF Case Studies in the Medical Management of Obesity

    naltrexone-bupropion. Class: Obesity. Action: naltrexone, an opioid antagonist, and bupropion, an antidepressant - might help with cravings. Dosing: 8/90mg, 1 tab po qam titrating to max of 2 tabs po q am and 1 tab po q pm. Pregnancy: X. Monitoring: Cr at baseline, BP, HR, depression/suicide.

  7. Clinical Assessment and Management of Adult Obesity

    Overweight and obesity are the most common medical problems seen in primary care practice, affecting >68% of adults and 33.0% of children and adolescents in the United States. 1, 2 Obesity is a risk factor for several of the leading causes of preventable death, including cardiovascular disease, diabetes mellitus, and many types of cancer.

  8. Enhancing knowledge and coordination in obesity treatment: a case study

    Enhancing knowledge and coordination in obesity treatment: a case study of an innovative educational program Tonje C. Osmundsen, Unni Dahl & Bård Kulseng BMC Health Services Research 19, Article number: 278 ( 2019 ) Cite this article 4223 Accesses 7 Citations 1 Altmetric Metrics Abstract Background

  9. The lived experience of people with obesity: study protocol for a

    Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health and quality of life, increases the risk of long-term medical complications and reduces lifespan [].Operationally defined in epidemiological and population studies as a body mass index (BMI) greater than or equal to 30, obesity affects more than 650 million adults worldwide [].

  10. PDF Improving Obesity Management in Primary Care and Community Health

    4 Primary Care and Community Health Centers There is an alarming trend toward overweight and obesity in America. Nearly two-thirds of the population is overweight, with 34 percent considered medically obese.1 At the front lines of this epidemic are primary care providers and CHCs that see a great number of patients who

  11. The implications of defining obesity as a disease: a report from the

    This report presents the discussion on the potential impact of defining obesity as a disease on the patient, the healthcare system, the economy, and the wider society. ... a case-control study of 105 patients. Obes Surg. 2022; 32: 837 ... National and local strategies in The Netherlands for obesity prevention and management in children and ...

  12. Data and case studies

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

  13. The Role of Individualized Exercise Prescription in Obesity Management

    2.1. Patient Introduction and Initial Conditions. In the present case study, a 65-year-old male patient with body mass index (BMI) 43.8 kg/m 2 (obesity class III), hypertonia, prediabetes, hyperlipidemia, and knee arthrosis started a supervised and complex lifestyle medicine program. The patient was an elite football player in his twenties and, after that, exercised sparsely or not at all.

  14. Case Studies

    The case studies were authored by STOP Obesity Alliance staff using information obtained through material reviews and interviews. Case studies were selected to reflect diverse experiences across professions, geographies, institution types, interventional approaches, and care settings. ... Pharmacist-Driven Disease Management: Delivering an On ...

  15. The Role of Individualized Exercise Prescription in Obesity Management

    Abstract Introduction: Regular exercise-part of complex lifestyle medicine program-is effective treatment for obesity but is still underestimated. Monitoring andindividualization by an exercise professional is needed to define the accurate dose effect.

  16. The Role of Individualized Exercise Prescription in Obesity Management

    In the present case study, a 65-year-old male patient with body mass index (BMI) 43.8 kg/m 2 (obesity class III), hypertonia, prediabetes, hyperlipidemia, and knee arthrosis started a supervised and complex lifestyle medicine program. The patient was an elite football player in his twenties and, after that, exercised sparsely or not at all.

  17. A Systematic Review of the Evidence for Non-surgical Weight Management

    Purpose of Review Severe obesity (BMI ≥ 35 kg/m2) increases premature mortality and reduces quality-of-life. Obesity-related disease (ORD) places substantial burden on health systems. This review summarises the cost-effectiveness evidence for non-surgical weight management programmes (WMPs) for adults with severe obesity. Recent Findings Whilst evidence shows bariatric surgery is often cost ...

  18. Evidence from big data in obesity research: international case studies

    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...

  19. Case Reports: Multifaceted Experiences Treating Youth with Severe Obesity

    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.

  20. Once-Weekly Semaglutide in Adults with Overweight or Obesity

    Weight loss of 10 to 15% (or more) is recommended in people with many complications of overweight and obesity (e.g., prediabetes, hypertension, and obstructive sleep apnea). 1,20,21,27 In the ...

  21. Childhood Obesity: An Evidence-Based Approach to Family-Centered Advice

    Currently, there are 13.7 (around 17% of US population) million children and adolescents with obesity. Children with obesity face a lifetime of physical and psychological complications, yet this condition is often ignored and under addressed at most office visits. 1,2 Many reasons have been proposed for this gap in care services, including lack of effectiveness of any currently available ...

  22. Association between metabolic syndrome and ...

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