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Obesity research: Moving from bench to bedside to population

* E-mail: [email protected]

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

• Ann Marie Schmidt

Published: December 4, 2023

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Acknowledgments

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

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• Review Article
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• Published: 18 July 2018

Epidemiology and Population Health

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

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

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

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• Epidemiology
• Risk factors

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

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Introduction.

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

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

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

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

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

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

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

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

Retail sales data

What are the data.

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

What has the data been used for?

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

What do they add over and above conventional data?

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

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

What are the limitations?

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

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

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

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

What have the data been used for?

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

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

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

Commercial weight management data

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

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

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

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

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

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

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

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

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

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

Social media

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

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

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

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

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

Smartphones and wearable technologies

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

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

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

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

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

Opportunities for big data research

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

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

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

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

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

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

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

ethical and legal questions around access and ownership of data

commercial sensitivities and potential costs

lack of control over data acquisition

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

finding appropriate comparators

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

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

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

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

Limitations of the review

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

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

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

Conclusions

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

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

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Acknowledgements

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

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

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Received : 08 October 2017

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Accepted : 25 February 2018

Published : 18 July 2018

Issue Date : December 2018

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

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

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

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

DOI: 10.5772/intechopen.98877

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

• bibliometric analysis

Author Information

Tauseef ahmad *.

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

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

1. Introduction

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

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

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

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

3.1 Study design

Bibliometric citation analysis study.

3.2 Searching strategy and database

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

3.3 Data extraction

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

3.4 Visualization network

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

4. Ethical approval

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

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

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

5.1 Most prolific authors

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

Top 100 most cited studies on obesity.

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

Authors with at least 4 studies.

5.2 Most active countries

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

Country with at least 3 studies.

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

5.3 Journals

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

Journals published at least 4 studies.

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

5.4 Commonly used keywords

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

The keywords used at least ten times.

5.5 Year of publication

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

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

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

5.6 Co-authorship countries network visualization

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

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

5.7 Co-occurrence all keywords network visualization

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

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

6. Discussion

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

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

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

7. Conclusion

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

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399 Obesity Essay Topics & Research Questions + Examples

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• The Causes and Effects of Obesity
• Childhood Obesity: The Parents’ Responsibility
• Causes and Consequences of Childhood Obesity
• Unhealthy Food Culture and Obesity
• Obesity as a Disease: Arguments For and Against
• Childhood Obesity: Causes and Solutions
• Humanistic Theory in Childhood Obesity Research
• Parents Are Not to Blame for Obesity in Children This paper discusses the issue of childhood and adolescent obesity and argues that parents should not be blamed for this problem.
• Obesity as a Global Health Issue The purpose of this research is to identify obesity as a global health issue, evaluate the methods and findings conducted on obesity, and find solutions to reduce obesity globally.
• Obesity: A Personal Problem and a Social Issue Obesity is a problem affecting many persons and society as a whole. According to World Health Organization, over 40% of the US population is either overweight or outright obese.
• Obesity Issue: Application of Nursing Theory This analysis will show that well-established theories are valuable to nursing problem-solving as frameworks for analyzing issues and planning solutions.
• Health Promotion for Obesity in Adults This is a health promotion proposal for preventing obesity among adults in the US. People get obesity when they acquire a given body mass index.
• Obesity Prevention: Social Media Campaign A variety of programs aimed at reducing the risk of obesity has been suggested by healthcare practitioners and scholars. Among them, diet interventions are highly popular.
• Obesity From Sociological Perspectives The social problem under focus is obesity originating from Latino food norms. The problem of obesity is the direct result of adherence to social norms.
• Childhood Obesity: Medical Complications and Social Problems The children have also suffered from the adverse effects that have been instilled into our society. Obesity has become a common problem in children of American and European countries.
• Obesity in Children and Adolescents: Quantitative Methods Obesity in children and adolescents has increasingly become prevalent in the recent past and is now a major problem in most developed countries.
• The Role of Social Workers in Addressing Teenage Obesity The social worker should be the bridge uniting obese individuals and society advertising social changes, and ending injustice and discrimination.
• Childhood Obesity: Prevention and Mitigation Over the past three decades, childhood obesity has developed into an epidemic and is considered as one of the major health issues in the world.
• Obesity Management and Intervention Many patients within the age brackets of 5-9 admitted in hospital with obesity cases have a secondary diagnosis of cardiovascular disease exceptionally high blood pressure.
• Link Between Watching Television and Obesity One of the primary causes of obesity is a sedentary lifestyle, which often includes excessive screen-watching periods.
• Prevention of Obesity in Teenagers This paper aims to create an education plan for teenage patients and their parents to effectively inform them and help them avoid obesity.
• Childhood Obesity: Methods and Data Collection The first instrument that will be used in data collection is body mass index (BMI). The BMI is measured by dividing a patient’s weight in kilograms by height in meters squared.
• Childhood Obesity Study and Health Belief Model A field experiment will be used in the research to identify the impact of a healthy lifestyle intervention on children diagnosed with obesity.
• Children Obesity Research Method and Sampling This paper presents a research method and sampling on the investigation of the issue of childhood obesity and the impact parents` education might have on reducing excess weight.
• Junk Food and Children’s Obesity Eating junk foods on a regular basis causes weight gain and for one in five Americans, obesity, is a major health concern though no one seems to be sounding the alarm.
• The Role of Family in Childhood Obesity Families and healthcare providers develop numerous interventions in order to provide their children with a chance to avoid obesity complications.
• Link Between Obesity and Genetics Obesity affects the lives through limitations implemented on the physical activity, associated disorders, and even emotional pressure.
• Health Promotion Proposal Obesity Prevention The purpose of this proposal is to inform and educate parents, children and adolescents of the importance of having a well balance diet and exercise in their daily lives to avoid obesity.
• Obesity From Sociological Imagination Viewpoint Most obese individuals understand that the modern market is not ready to accept them due to negative sociological imagination.
• Adolescent Obesity in the United States The article reflects the problem of overweight in the use, a consideration which the authors blame on influential factors such as age and body mass index.
• Obesity in the World: the Prevalence, Its Effects to Human Health, and Causes There are various causes of obesity ranging from the quantity of food ingested to the last of physical exercises that utilize the accumulated energy.
• Eating Fast Food and Obesity Correlation Analysis The proposed study will attempt to answer the question of what is the relationship between eating fast food and obesity, using correlation analysis.
• Technology as the Cause of Obesity Today, humanity witnesses the third industrial revolution, or the broad implementation of innovative solutions into various spheres of activity.
• Betty Neuman’s System Model for Adult Obesity Betty Neuman’s system model can beneficially influence a physical and emotional state of the person who is experiencing difficulties with being overweight.
• Technological Progress as the Cause of Obesity Obesity is the increase of the body’s weight over the natural limit because of accumulated fats. Technology is a cost to the lost creativity and control over the required healthy lifestyle.
• Health Promotion Strategies for Obesity The paper outlines and critically analyses the population based strategy as a method of managing and preventing obesity used in United Kingdom.
• Depression as It Relates to Obesity This paper will argue that there is a positive correlation between depression and obesity. The paper will make use of authoritative sources to reinforce this assertion.
• Adult Obesity: Treatment Program An effective treatment program for obese patients ought to have a significant impact on the utilization of medical resources and the costs of health care.
• Children’s Obesity in the Hispanic Population The purpose of this manuscript is to examine nurses’ knowledge concerning the major risk factors of obesity in school-age Hispanic population.
• Childhood Obesity: Problem Analysis The introduced project addresses childhood obesity problem and highlights the inconsistency between the existing programs and their implementation in real life.
• Obesity Problem in the United States Obesity is not just people going fat; it is a disease that causes maladies like type-2 diabetes, heart disease, cancer and strokes.
• Adolescent Obesity: Theories and Interventions This paper explores the issue of adolescent obesity and provides a cohesive action plan to propose how to remedy barriers to the success of implemented interventions.
• Childhood Obesity: Research Methodology Based on their body mass index measurement or diagnosis by a qualified physician, all children in the sample should be qualified as having obesity.
• Obesity Prevention and Weight Management Theory The issue of obesity prevention will be guided by a nursing theory. One of the theories applicable in the case of childhood overweight is a theory of weight management.
• Adult Obesity Causes & Consequences Through analyzing a family’s genetic history, the danger of becoming overweight was identified as one of the most probable health developments for the participant.
• Pediatric Obesity and Self-Care Nursing Theory The presence of excess body fat in children has to be given special consideration since healthy childhood is a prerequisite to normal physical and psychological maturation.
• Obesity Caused by Fast-Food as a Nursing Practice Issue The proposed intervention will emphasize the necessity to increase the intake of fruit and vegetables as a method of reducing the consumption of fast food.
• Childhood Obesity: Data Management The use of electronic health records (EHR) is regarded as one of the effective ways to treat obesity in the population.
• Childhood Obesity and Community Nursing Intervention In the recent decades, the issue of childhood obesity in the US has been increasingly coming to the forefront in the public view and in academia as a major health problem.
• Discussion of Freedman’s Article “How Junk Food Can End Obesity” David Freedman, in article “How Junk Food Can End Obesity”, talks about various misconceptions regarding healthy food that are common in society.
• Nature vs. Nurture: Child Obesity On the basis of the given assessment, it is evident that a child’s environment is a stronger influencer than his or her genetic makeup
• Obesity: Background and Preventative Measures Obesity is an epidemic. It tends to have more negative than positive effects on the economy and can greatly reduce one’s life expectancy.
• Obesity: Cause and Treatment The sphere of contemporary medicine faces the problem of obesity as a troublesome trend that proceeds to embrace the global citizens.
• Physical Exercises as Obesity Treatment Exercise cannot be considered an effective tool for weight loss, but it does help individuals to maintain their normal and healthy weight.
• Obesity Prevention in Community: Strategic Plan This paper is a plan of how to change the way the community should treat obesity and improve people’s health through the required number of interventions.
• Childhood Obesity Prevention: The Role of Nursing Education Nurse practitioners have to deal with childhood obesity challenges and identity healthy physical and environmental factors to help pediatric patients and their parents.
• Childhood Obesity and Public Policies in England The study identifies the preventive measures of the English government to deal with childhood obesity and compares the trends in England with the rest of the UK.
• Prevention of Obesity in Children The aim of the study is to find out whether the education of parent on a healthy lifestyle for the children compared with medication treatment, increase the outcome and prevention of obesity.
• Childhood Obesity and Socio-Ecological Model Childhood obesity can be significantly reduced through a public health intervention grounded in the socio-ecological model.
• Best Interventions for Obesity The best plan for preventing obesity involves the combination of healthy eating habits and regular physical exercises.
• The Childhood Obesity Problem Significance Childhood obesity is one of the most severe issues that affects children and teenagers. It involves various risks to their health.
• Parental Education to Overcome Childhood Obesity Parental education plays a crucial role in addressing childhood obesity by influencing children’s behaviors and habits. Encouraging healthy eating, and promoting physical activity.
• Obesity Management: Educational Behavioral Interventions The current project is devoted to the use of educational behavioral interventions in the management of obesity.
• Reducing Obesity Among Children Aged 5-19 From Low-Income Families According to Jebeile et al., since 1975, the number of obese children has increased by 4.9% among girls and 6.9% among boys.
• Obesity and Lack of Its Treatment Project The paper aims to treat obesity in a primary care setting, thus reducing the individual and social health burden that obesity poses.
• “Overweight and Obesity Statistics” by the USDHHS In the article “Overweight and Obesity Statistics” by the USDHHS, the dire situation concerning excessive weight in adults and children is discussed.
• Obesity: High Accumulation of Adipose Tissue It is important to point out that obesity is a complex and intricate disease that is associated with a host of different metabolic illnesses.
• Obesity and Iron Deficiency Among College Students The study seeks to establish the relationship between obesity and iron deficiency by analyzing the serum hepcidin concentration among individuals aged between 19 to 29 years.
• Childhood Obesity During the COVID-19 Pandemic While the COVID-19 pandemic elicited one of the worst prevalences of childhood obesity, determining its extent was a problem due to the lockdown.
• Overweight and Obesity Prevalence in the US Obesity is a significant public health problem recognized as one of the leading causes of mortality in the United States. Obesity and overweight are two common disorders.
• Obesity Screening Training Using the 5AS Framework The paper aims to decrease obesity levels at the community level. It provides the PCPs with the tools that would allow them to identify patients.
• Prevalence and Control of Obesity in Texas Obesity has been a severe health issue in the United States and globally. A person is obese if their size is more significant than the average weight.
• Nutrition: Obesity Pandemic and Genetic Code The environment in which we access the food we consume has changed. Unhealthy foods are cheaper, and there is no motivation to eat healthily.
• Preventing Obesity Health Issues From Childhood The selected problem is childhood obesity, the rates of which increase nationwide yearly and require the attention of the government, society, and parents.
• Childhood Obesity: Causes and Effects Childhood obesity has many causes and effects, which denotes that parents and teachers should make children with obesity engage in regular physical exercise in school and at home.
• Describing the Problem of Childhood Obesity Childhood obesity is a problem that affects many children. If individuals experience a health issue in their childhood, it is going to lead to negative consequences.
• Researching of Obesity in Florida It is important to note that Florida does not elicit the only state with an obesity problem, as the nation’s obesity prevalence stood at 42.4% in 2018.
• Preventing Obesity Health Issues From the Childhood The paper is valuable for parents of children who are subject to gaining excess weight because the report offers how to solve the issue.
• Obesity and Health Outcomes in COVID-19 Patients The COVID-19 pandemic has posed many challenges over the last three years, and significant research has been done regarding its health effects and factors.
• Childhood Obesity in the US from Economic Perspective The economic explanation for the problem of childhood obesity refers to the inability of a part of the population to provide themselves and their children with healthy food.
• Addressing Teenage Obesity in America The paper states that adolescence is one of the most crucial developmental phases of human life during which the issue of obesity must be solved.
• Obesity in the United States of America The article discusses the causes of the obesity pandemic in the United States of America, which has been recognized as a pandemic due to its scope, and high prevalence.
• The Problem of Childhood Obesity Obesity in childhood is a great concern of current medicine as the habits of healthy eating and lifestyle are taught by parents at an early age.
• Oral Health and Obesity Among Adolescents This research paper developed the idea of using dental offices as the primary gateway to detect potential obesity among Texas adolescents.
• Obesity, Weight Loss Programs and Nutrition The article addresses issues that can help increase access to information related to the provision of weight loss programs and nutrition.
• Childhood Obesity in the US From an Economic Perspective Looking at the problem of childhood obesity from an economic point of view offers an understanding of a wider range of causes and the definition of government intervention.
• The Science Behind Obesity and Its Impact on Cancer The paper addresses the connection between cancer and physical activity, diet, and obesity in Latin America and the USA. The transitions in dietary practices may be observed.
• Should fast-food restaurants be liable for increasing obesity rates?
• Does public education on healthy eating reduce obesity prevalence?
• Is obesity a result of personal choices or socioeconomic circumstances?
• Should the government impose taxes on soda and junk food?
• Weight loss surgery for obesity: pros and cons.
• Should restaurants be required to display the caloric content of every menu item?
• Genetics and the environment: which is a more significant contributor to obesity?
• Should parents be held accountable for their children’s obesity?
• Does weight stigmatization affect obesity treatment outcomes?
• Does the fashion industry contribute to obesity among women?
• The Current Problem of Obesity in the United States The paper raises the current problem of obesity in the United States and informs people about the issue, as well as what effect obesity can have on health.
• Childhood and Adolescent Obesity and Its Reasons Various socio-economic, health-related, biological, and behavioral factors may cause childhood obesity. They include an unhealthy diet and insufficient physical activity and sleep.
• Pediatric Obesity and Its Treatment Pediatric obesity is often the result of unhealthy nutrition and the lack of control from parents but not of health issues or hormonal imbalance.
• Impact of Obesity on Healthcare System Patients suffering from obesity suffer immensely from stigma during the process of care due to avoidance which ultimately affects the quality of care.
• Trending Diets to Curb Obesity There are many trending diets that have significant effects on shedding pounds; however, the discourse will focus on the Mediterranean diet.
• Issues of Obesity and Food Addiction Obesity and food addiction have become widespread and significant problems in modern society, both health-related and social.
• Diet, Physical Activity, Obesity, and Related Cancer Risk One’s health is affected by their lifestyle, which should be well managed since childhood to set a basis for a healthier adulthood.
• Articles About Childhood Obesity The most straightforward technique to diagnose childhood obesity is to measure the child’s weight and height and compare them to conventional height and weight charts.
• Childhood Obesity and Overweight Issues The paper discusses childhood obesity. It has been shown to have a negative influence on both physical health and mental well-being.
• Obesity: Causes, Consequences, and Care Nowadays, an increasing number of people suffer from having excess weight. This paper analyzes the relationship between obesity and other diseases.
• Obesity Prevention Policy Making in Texas Obesity is a national health problem, especially in Texas; therefore, the state immediately needed to launch a policy to combat and prevent obesity in the population.
• Childhood Obesity: Quantitative Annotated Bibliography Childhood obesity is a problem that stands especially acute today, in the era of consumerism. Children now have immense access to the Internet.
• Obesity and How It Can Cause Chronic Diseases Obesity is associated with increased cardiovascular diseases, and cancer risks. The modifications in nutrition patterns and physical activity are effective methods to manage them.
• Ways of Obesity Interventions The paper discusses ways of obesity interventions. It includes diet and exercise, patient education, adherence to medication, and social justice.
• Aspects of Obesity Risk Factors Obesity is one of the most pressing concerns in recent years. Most studies attribute the rising cases of obesity to economic development.
• Obesity in Adolescence in the Hispanic Community The health risks linked to Hispanic community adolescent obesity range from diabetes, heart problems, sleep disorders, asthma, and joint pain.
• Obesity as a Wellness Concern in the Nursing Field A critical analysis of wellness can provide an understanding of why people make specific health-related choices.
• Physio- and Psychological Causes of Obesity The paper states that obesity is a complex problem in the formation of which many physiological and psychological factors are involved.
• How Junk Diets Can Reduce Obesity To control obesity there is a need to ensure that the junk foods produced are safe for consumption before being released into the foods market.
• The Problem of Obesity: Weight Management Obesity is now a significant public health issue around the world. The type 2 diabetes, cardiac conditions, stroke, and metabolism are the main risk factors.
• Behavioral Modifications for Patients With Obesity This paper aims to find out in obese patients, do lifestyle and behavioral changes, compared to weight loss surgery, improve patients’ health and reduce complications.
• Sleep Deprivation Effects on Adolescents Who Suffer From Obesity The academic literature on sleep deprivation argues that it has a number of adverse health effects on children and adolescents, with obesity being one of them.
• Hypertensive Patients Will Maintain Healthy Blood Pressure and Prevent Obesity Despite hypertension and obesity are being major life threats, there are safer lifeways that one can use to combat the problem.
• The Consequences of Obesity: An Annotated Bibliography To review the literature data, the authors searched for corresponding articles on the PubMed database using specific keywords.
• Obesity: Racial and Ethnicity Disparities in West Virginia Numerous social, economic, and environmental factors contribute to racial disparities in obesity. The rates of obesity vary depending on race and ethnicity in West Virginia.
• The Worldwide Health Problem: Obesity in Children The paper touch upon the main causes of obesity, its spread throughout the world, the major effects of the condition and ways of prevention.
• Obesity in Low-Income Community: Diet and Physical Activity The research evaluates the relationship between family earnings and physical activity and overweight rates of children in 8 different communities divided by race or ethnicity.
• Dealing with Obesity as a Societal Concern This essay shall discuss the health issue of obesity, a social health problem that is, unfortunately, growing at a rapid rate.
• Obesity Problem Solved by Proper Nutrition and Exercise Most people who suffer from obesity are often discouraged to pursue nutrition and exercise because their bodies cannot achieve a particular look.
• Hispanic Obesity in the Context of Cultural Empowerment This paper identifies negative factors directly causing obesity within the Hispanic people while distinguishing positive effects upon which potential interventions should be based.
• Health Psychology and Activists’ Views on Obesity This paper examines obesity from the psychological and activists’ perspectives while highlighting some of the steps to be taken in the prevention and curbing of the disease.
• Childhood Obesity Teaching Experience and Observations The proposed teaching plan aimed at introducing the importance of healthy eating habits to children between the ages of 6 and 11.
• Care Plan: Quincy Town, Massachusetts With Childhood Obesity This study will develop a community assessment program based on the city with the aim of creating a care plan for tackling the issue of child obesity in the town.
• Obesity, Diabetes and Self-Care The paper discusses being overweight or obese is a high-risk factor for diabetes mellitus and self-care among middle-aged diabetics is a function of education and income.
• Obesity in Hispanic American Citizens The issue of obesity anong Hispanic Americans occurs as a result of poor dieting choices caused by misinformed perceptions of proper eating.
• Multicausality: Reserpine, Breast Cancer, and Obesity All the factors are not significant in the context of the liability to breast cancer development, though their minor influence is undeniable.
• The Home Food Environment and Obesity-Promoting Eating Behaviours Campbell, Crawford, Salmon, Carver, Garnett, and Baur conducted a study to determine the associations between the home food environment and obesity.
• The Situation of Obesity in Children in the U.S. The paper will discuss the situation of obesity in Children in the U.S. while giving the associated outcomes and consequences.
• Childhood Obesity and Healthy Lifestyles The purpose of this paper is to discuss childhood obesity and the various ways of fostering good eating habits and healthy lifestyles.
• Screen Time and Pediatric Obesity Among School-Aged Children Increased screen time raises the likelihood of children becoming overweight/obese because of the deficiency of physical exercise and the consumption of high-calorie foods.
• The link between excess weight and chronic diseases.
• The role of genetics in obesity.
• The impact on income and education on obesity risks.
• The influence of food advertising on consumer choices.
• Debunking the myths related to weight loss.
• Obesity during pregnancy: risks and complications.
• Cultural influences on eating patterns and obesity prevalence.
• Community initiatives for obesity prevention.
• The healthcare and societal costs of obesity.
• The bidirectional relationship between sleep disorders and obesity.
• Policymaker Visit About the Childhood Obesity Problem The policy issue of childhood obesity continues to be burning in American society. It causes a variety of concurrent problems including mental disorders.
• Public Health Interventions and Economics: Obesity The purpose of this article is to consider the economic feasibility of public health interventions to prevent the emergence of the problem of obesity.
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StudyCorgi. (2021, September 9). 399 Obesity Essay Topics & Research Questions + Examples. https://studycorgi.com/ideas/obesity-essay-topics/

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

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• Effects of weight loss...

Effects of weight loss interventions for adults who are obese on mortality, cardiovascular disease, and cancer: systematic review and meta-analysis

• Related content
• Peer review
• Chenhan Ma , foundation year 1 doctor 1 ,
• Alison Avenell , professor 1 ,
• Mark Bolland , associate professor 2 ,
• Jemma Hudson , statistician 1 ,
• Fiona Stewart , research fellow 1 ,
• Clare Robertson , research fellow 1 ,
• Pawana Sharma , research fellow 1 ,
• Cynthia Fraser , information officer 1 ,
• Graeme MacLennan , professor 3
• 1 Health Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen AB25 2ZD, Scotland, UK
• 2 Bone and Joint Research Group, Department of Medicine, University of Auckland, Private Bag 92 019, Auckland 1142, New Zealand
• 3 Centre for Healthcare Randomised Trials, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen AB25 2ZD, Scotland, UK
• Correspondence to: A Avenell a.avenell{at}abdn.ac.uk
• Accepted 4 October 2017

Objective  To assess whether weight loss interventions for adults with obesity affect all cause, cardiovascular, and cancer mortality, cardiovascular disease, cancer, and body weight.

Design  Systematic review and meta-analysis of randomised controlled trials (RCTs) using random effects, estimating risk ratios, and mean differences. Heterogeneity investigated using Cochran’s Q and I 2 statistics. Quality of evidence assessed by GRADE criteria.

Data sources  Medline, Embase, the Cochrane Central Register of Controlled Trials, and full texts in our trials’ registry for data not evident in databases. Authors were contacted for unpublished data.

Eligibility criteria for selecting studies  RCTs of dietary interventions targeting weight loss, with or without exercise advice or programmes, for adults with obesity and follow-up ≥1 year.

Results  54 RCTs with 30 206 participants were identified. All but one trial evaluated low fat, weight reducing diets. For the primary outcome, high quality evidence showed that weight loss interventions decrease all cause mortality (34 trials, 685 events; risk ratio 0.82, 95% confidence interval 0.71 to 0.95), with six fewer deaths per 1000 participants (95% confidence interval two to 10). For other primary outcomes moderate quality evidence showed an effect on cardiovascular mortality (eight trials, 134 events; risk ratio 0.93, 95% confidence interval 0.67 to 1.31), and very low quality evidence showed an effect on cancer mortality (eight trials, 34 events; risk ratio 0.58, 95% confidence interval 0.30 to 1.11). Twenty four trials (15 176 participants) reported high quality evidence on participants developing new cardiovascular events (1043 events; risk ratio 0.93, 95% confidence interval 0.83 to 1.04). Nineteen trials (6330 participants) provided very low quality evidence on participants developing new cancers (103 events; risk ratio 0.92, 95% confidence interval 0.63 to 1.36).

Conclusions  Weight reducing diets, usually low in fat and saturated fat, with or without exercise advice or programmes, may reduce premature all cause mortality in adults with obesity.

Systematic review registration  PROSPERO CRD42016033217.

Introduction

Adults with obesity have an increased risk of premature mortality, cardiovascular disease, some cancers, type 2 diabetes, and many other diseases. 1 2 These associations inform the need for programmes to prevent obesity, but, apart from prevention of type 2 diabetes, 3 4 limited evidence from randomised controlled trials (RCTs) shows that weight loss interventions can prevent serious harm for people with obesity. Evidence from cohort studies has led to debate that deliberate weight loss for people who are overweight or obese, with body mass index (BMI) ≤35 kg/m 2 , might actually be harmful. 5 Studies show that older people, 6 and those with cardiovascular disease 7 who are less markedly obese, might experience adverse consequences from deliberate weight loss. Recent analyses by the Global BMI Mortality Collaboration, however, tried to limit confounding and corrected for reverse causality, finding that the risk of premature mortality was lowest at BMIs of 20-25. 8

Association studies cannot tell us if deliberate weight loss in adults with obesity can reduce their risk of premature mortality, cardiovascular disease, or cancer. Only one systematic review and meta-analysis of RCTs of intentional weight loss in adults with obesity has examined this question. 9 That review included 15 trials, reporting a 15% relative reduction in premature mortality (risk ratio 0.85, 95% confidence interval 0.73 to 1.00), but did not evaluate causes of death or cardiovascular and cancer outcomes. 9 We knew of many other weight loss RCTs with mortality data, as well as cancer and cardiovascular outcomes, from our database of long term RCTs of weight loss interventions for adult obesity, which was developed for health technology assessments 10 11 and is continually updated. We systematically reviewed long term (≥1 year) RCTs of weight loss interventions for adults with obesity to examine the effects of any type of weight loss diet on all cause, cardiovascular, and cancer mortality, cardiovascular disease, cancer, and body weight.

We adhered to the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines for systematic reviews of interventions. 12 We used a prespecified protocol, registered with PROSPERO (CRD42016033217). 13

Search strategy and selection criteria

We included RCTs with adults (mean or median age ≥18 years) and a minimum follow-up of one year. Participants had a mean BMI ≥30 at baseline. Included trials had to be focused clearly on weight loss with a weight reducing diet, with or without advice for increasing physical activity and/or provision of a physical activity programme to attend, compared with a control intervention. We didn’t include trials in pregnant or postpartum women.

We sought summary data for three primary outcomes: all cause mortality, cardiovascular mortality, and cancer mortality. Secondary outcomes were participants with a new cardiovascular event, participants with a new cancer, and weight change. In our main analysis we used cardiovascular mortality and events as defined by the investigators but did not include the development of hypertension. We undertook post hoc analyses of cardiovascular mortality and cardiovascular events as defined in the American College of Cardiology/American Heart Association (ACC/AHA) guidelines. 14

We identified RCTs by searching the full texts of trial reports in our database of all long term (≥1 year) RCTs of weight loss interventions for adults with obesity used in our previous systematic reviews and health technology assessments. Our database is derived from previous search strategies compiled from Medline, Embase, and the Cochrane Central Register of Controlled Trials, from 1966 to December 2015. 10 11 We performed an updated search from August 2015 to December 2016. We didn’t apply any language exclusions. In 2016-17 we contacted the authors of 48 RCTs to clarify data or request unpublished outcome data, where trial reports implied that relevant data might be available; for example, when the trial reported hospital admissions or adverse events without giving further details.

Data analysis

AA and CM independently confirmed study eligibility. CM, FS, CR, and PS extracted data, which were then checked by a second author (AA, CM). Cancer outcome and cardiovascular outcome data (including coding outcomes defined by the ACC/AHA guideline 14 ) were further adjudicated by MB, with differences resolved by Andrew Grey (associate professor in the Department of Medicine, University of Auckland). Two authors (AA, CM, FS, CR, PS) independently assessed quality using the Cochrane risk of bias tool. 15 All differences were resolved by discussion.

We used random effects meta-analysis to analyse pooled outcome data. For binary outcomes, we estimated risk ratios and 95% confidence intervals, using all participants randomised for the denominators. We estimated weighted mean differences and 95% confidence intervals for continuous outcomes, giving preference to intention to treat data and data taking account of dropouts (preferentially baseline observation carried forward) if these were provided. We included outcome data from two cluster RCTs 16 17 using the correction method described in the Cochrane Handbook 18 and the intraclass correlation coefficients reported in the original trial publications. We assessed heterogeneity between studies using Cochran’s Q statistic and the I 2 test. We originally planned meta-regression to investigate heterogeneity in disease outcomes, but I 2 tests for disease outcomes were 0%, so it was not appropriate. We carried out a sensitivity analysis with a random effects bayesian logistic regression model (with non-informative priors) using WinBUGS 1.4.3 19 because some trials reported few events, which may cause sparse data bias. We performed all other analyses using Stata Release 14 20 and used funnel plots to examine small study bias.

For all outcomes we performed prespecified subgroup analyses for sex, age (<60 v ≥60), BMI (<40 v ≥40, later changed to <35 v ≥35 as we found no trial with BMI ≥40), glycaemic control (normal v impaired glucose tolerance or impaired fasting glucose v type 2 diabetes), ethnicity (defined if ≥80% of participants belonged to an ethnic group, otherwise defined as mixed), physical activity interventions (none v advice only v exercise programme provided).

In post hoc additional analyses we added trials in any Asian population group if the mean BMI was ≥25, as diseases associated with obesity are known to occur at lower BMI in Asian populations than other ethnic groups. 21 No single BMI cut-off has been agreed to define obesity in Asian populations. Although the World Health Organization recommends 27.5 as a BMI threshold for a high risk of comorbidities, 21 it also suggests that Asian countries develop their own specific BMI cut-offs for obesity. India and Japan have set ≥25 as the threshold for obesity, 22 23 and in China the risk of comorbidities has been found to increase for BMI over 28. 24

For all outcomes we performed two prespecified sensitivity analyses for allocation concealment (low risk of bias vs other risk of bias) and follow-up (<80% vs ≥80%).

We used GRADE ( grading of recommendations, assessment, development, and evaluations) to judge the quality of the evidence for mortality, cardiovascular, and cancer outcomes. 25

Role of the funding source

The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing this report. CM and AA had full access to all study data and had final responsibility for the decision to submit for publication.

Patient involvement

No patients or members of the public were involved in the development of research questions, the design of the study, or the development of outcome measures. No patients were asked to advise on interpretation or writing up of results. There are plans to disseminate the results of the research to the relevant patient community.

Trial characteristics

We screened 1174 full text trial reports and 5982 titles and abstracts (fig 1 ⇓ ) and identified 54 RCTs for inclusion 3 4 16 17 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 in the final review.

Fig 1  Study selection

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Table 1 ⇓ provides details of the included studies, involving 30 206 adults with obesity. Nine trials (16.7%) included women only, 26 44 45 50 51 52 77 88 94 and two (3.7%) men only. 58 72 Twelve trials (22.2%) recruited participants with no reported existing medical conditions or no reported increased risk of developing comorbidities related to obesity. Other trials recruited participants with increased risk of type 2 diabetes or hypertension or included participants that already had at least one of the following conditions: hypertension, type 2 diabetes, hyperlipidaemia, breast cancer, colorectal adenoma, psychiatric illnesses, cognitive impairment, osteoarthritis of the knee, coronary heart disease, or urinary incontinence.

Characteristics of randomised controlled trials

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Five trials (9.3%) were undertaken in Asian populations, 16 17 59 75 80 but only one with BMI ≥30, 2 16 one trial (1.9%) was in a population of black people in the USA, 50 31 (57.4%) in populations of white people, and 17 (31.5%) in mixed population groups. Thirty one (57.4%) trials took place in North America, 16 (29.6%) in Europe, two (3.7%) in Australia, and one (1.9%) in Brazil. The four trials in Asian populations outside the UK had mean BMIs between 25 and 30. 17 59 75 80 Thirty six (66.7%) trials had participants with a mean or median BMI <35, and 14 (25.9%) had BMIs ≥35 (table 1 ⇑ ).

Most trials recruited predominantly middle aged adults. Fourteen (25.9%) had a mean or median age at baseline of 60 years or more, none had a mean or median age of under 40 years. Thirty one (57.4%) trials followed participants for two years or longer, and seven (13.0%) trials (9,937 participants) followed participants for five years or longer. In 39 trials (72.2%) the drop-out rate was <20% at trial completion.

Detailed descriptions of the weight loss diets were not always clearly provided in the trials. All but one of the trials described at least one of their interventions as being a low fat weight reduction diet (usually ≤30% of energy as fat, although this was not always specified) or had sufficient information to establish that a reduction in fat intake was prescribed. Most trials also described the prescription of a reduction in saturated fat. One trial described using a balanced Mediterranean diet. 79 One trial included the option to undertake a diet with ≤50 g/day of carbohydrate. 96 Two weight loss trials specifically described diets to reduce low glycaemic index as part of their intervention, 26 30 whereas other trials generally described diets that would be compatible with lowering glycaemic indices by increasing intake of complex carbohydrates and dietary fibre. Four trials (7.4%) were based on the DASH (dietary approaches to stop hypertension) diet. 31 39 40 54 Eight (14.8%) trials based their diets on those of the US Diabetes Prevention Program, 4 26 52 60 67 74 93 94 and four trials (7.4%) described basing their content in part on different editions of the Dietary Guidelines for Americans. 64 69 72 76

Only three trials (5.6%) did not report providing exercise advice or an exercise programme. 45 55 68 Twenty two trials (40.7%) provided an exercise programme for participants to attend, and 29 trials (53.7%) described providing advice to increase exercise only, without an exercise programme.

Supplementary figure 1 provides our risk of bias assessments for individual studies. Only 15 trials (27.8%) reported methods of randomisation and allocation concealment judged to be at low risk of bias. Blinding of participants and study personnel was rarely possible, but we judged that lack of blinding of outcome assessment would rarely have been a source of bias except for weight outcomes. Only 10 (18.5%) trials were judged to be at low risk for attrition bias, and 12 (22.2%) at low risk for reporting bias. Seven (13.0%) trials were judged to be at high risk of bias as a result of premature trial termination, 52 65 75 change in the primary outcome, 16 influence of a drug placebo in the control group, 4 or trial investigators reporting that they were sponsored by grants from a commercial weight loss programme 71 or that they were co-owners of a company developing products related to the research. 72

Meta-analyses

Details of our adjudication processes for cardiovascular and cancer outcomes are provided in supplementary tables 1-3. Supplementary table 1 compares all cause mortality, cardiovascular mortality, and cancer mortality across all trials, showing that we were not always able to obtain causes of death from authors.

Based on the GRADE approach for judging quality of the evidence (supplementary table 4) we found high quality evidence from 34 trials (21 699 participants) providing data on all cause mortality (fig 2 ⇓ ), which showed a decrease in premature mortality with weight loss interventions (n=34 trials, 685 events; risk ratio 0.82, 95% confidence interval 0.71 to 0.95; I 2 =0%). The Look AHEAD trial had 54.6% of the weighting in the meta-analysis. 65 66 Without this trial weight loss interventions were still associated with decreased all cause mortality (n=33 trials, 309 events; risk ratio 0.78, 95% confidence interval 0.63 to 0.96; I 2 =0%). The funnel plot showed no evidence of small study bias (Egger’s test P=0.269, supplementary figure 2).

Fig 2  Random effects meta-analysis of the effects of weight loss interventions on all cause mortality. ADAPT=arthritis, diet, and activity promotion trial; CLIP=community level interventions for pre-eclampsia; DPP=diabetes prevention program; DPS=diabetes prevention study; FFIT=football fans in training; Look AHEAD=look action for health in diabetes; PRIDE=program to reduce incontinence by diet and exercise; TAIM=trial of antihypertensive interventions and management; TOHP=trials of hypertension prevention; TONE=trial of nonpharmacologic intervention in the elderly.

Fewer trials reported data for cardiovascular mortality and cancer mortality, resulting in considerable uncertainty in the estimates of effects of weight loss interventions on these outcomes. We found moderate quality evidence for an effect on cardiovascular mortality (n=8 trials, 134 events; risk ratio 0.93, 95% confidence interval 0.67 to 1.31; I 2 =0%) and very low quality evidence for an effect on cancer mortality (n=8 trials, 34 events; risk ratio 0.58, 95% confidence interval 0.30 to 1.11; I 2 =0%) (figs 3 and 4 ⇓ ). Limiting cardiovascular mortality to ACC/AHA defined events did not influence this result, as the data were identical (n=8 trials, 134 events; risk ratio 0.93, 95% confidence interval 0.67 to 1.31; I 2 =0%).

Fig 3  Random effects meta-analysis of the effects of weight loss interventions on cardiovascular mortality. DPP=diabetes prevention program; DPS=diabetes prevention study.

Fig 4  Random effects meta-analysis of the effects of weight loss interventions on cancer mortality. DPS=diabetes prevention study.

Twenty four trials (15 176 participants) reported high quality evidence on participants developing new cardiovascular events (n=24, 1043 events; risk ratio 0.93, 95% confidence interval 0.83 to 1.04; I 2 =0%). Using events classified according to ACC/AHA definitions, results were very similar (fig 5 ⇓ , supplementary figure 3). Nineteen trials (6330 participants) provided very low quality evidence on participants developing new cancers (n=19, 103 events; risk ratio 0.92, 95% confidence interval 0.63 to 1.36; I 2 =0%) (fig 6 ⇓ ). Bayesian meta-analyses for all of the above outcomes provided similar results (supplementary table 5).

Fig 5  Random effects meta-analysis of the effects of weight loss interventions on participants with a cardiovascular event. CLIP=community level interventions for pre-eclampsia; DPP=diabetes prevention program; FFIT=football fans in training.

Fig 6  Random effects meta-analysis of the effects of weight loss interventions on participants developing cancer. DPS=diabetes prevention study.

Interventions had a beneficial effect on weight change after one year (n=44, mean difference −3.42 kg; 95% confidence interval −4.09 to −2.75 kg; I 2 =92%), after two years (n=20, mean difference −2.51 kg; 95% confidence interval −3.42 to −1.60 kg; I 2 =89%) and after three or more years (n=8, mean difference −2.56 kg; 95% confidence interval −3.50 to −1.62 kg; I 2 =87%) (supplementary figures 4 to 6). Heterogeneity for each of these meta-analyses was very high (I 2 =87% to 92%), reflecting the wide diversity of weight loss interventions and their effects on weight.

Sensitivity analyses

Sensitivity analyses for allocation concealment (low risk of bias versus other risk of bias) and completion of follow-up (<80% v ≥80% of participants completed) did not show any statistically significant heterogeneity for mortality, cardiovascular outcomes, or cancer outcomes (supplementary table 6).

Weight change at final follow-up was lower in trials with low risk of bias for allocation concealment (n=17, mean difference −2.33 kg; 95% confidence interval −2.87 to −1.79 kg) than for trials with high or unclear risk of bias for allocation concealment (n=31, mean difference −3.24 kg; 95% confidence interval −4.00 to −2.49 kg).

Weight change at final follow-up was lower in trials with completed follow-up of less than 80% (n=15, MD −2.09 kg; 95% CI: −2.80 to −1.37 kg) than for trials with follow-up of 80% or more (n=33, MD −3.13 kg; 95% CI: −3.71 to −2.55 kg).

Subgroup analyses

We undertook many subgroup analyses, including post hoc analyses with the addition of trials in Asian populations with BMI ≥25 (supplementary table 6, supplementary figures 7-9). Tests for subgroup differences for mortality, cardiovascular outcomes, and cancer outcomes provided weak evidence that participants without type 2 diabetes might be at lower risk of a new cardiovascular event than participants with type 2 diabetes or those with impaired glucose tolerance or impaired fasting glycaemia. Similarly, we found weak evidence that groups of white participants may be at lower risk of a new cardiovascular event than black, mixed, or Asian population groups when following weight loss interventions.

Subgroup analyses for weight change at final follow-up provided weak evidence that participants aged 60 or over lost more weight than younger participants and that participants in trials in Asian populations lost less weight than those in trials with other population groups. Similarly, we found weak evidence of better long term weight loss with trials that provided a physical activity programme, compared with trials that gave only physical activity advice or did not report providing physical activity advice.

We found high quality evidence that weight reducing diets for adults with obesity, usually low in fat and low in saturated fat, were associated with a 18% relative reduction in premature mortality over a median trial duration of two years, corresponding to six fewer deaths per 1000 participants (95% confidence interval two to 10). This evidence provides a further reason for weight reducing diets to be offered alongside their already proven benefits, such as type 2 diabetes prevention. We were unable to show effects on cardiovascular and cancer mortality, or participants developing cardiovascular events or new cancers, although fewer trials reported events for these outcomes, resulting in much uncertainty around their effect estimates.

We identified 34 trials reporting mortality data compared with 15 in the previous systematic review by Kritchevsky and colleagues, 9 which included weight loss interventions irrespective of baseline BMI, and we made very considerable efforts to clarify data and retrieve unpublished data from 48 trialists. We used a comprehensive search strategy with full text searching of trials in our obesity database. The trials we included were not necessarily designed to collect data on mortality, cardiovascular, and cancer outcomes, although larger trials generally were. 65 66 81 82 83 84 85 86 87 We might have failed to identify all trials with outcome data, if trialists did not present these outcomes or presented them as unspecified adverse events. This may have biased results, although we could not see obvious funnel plot asymmetry for all cause mortality. Trials generally excluded participants with a recent diagnosis of cancer, but this was not always clear, so some participants may have had a recurrence of cancer, rather than a new event. Many of the trials had quite intensive control group interventions, and the unblinded nature of the interventions could have led to more medical treatment in control groups, tending to reduce differences between groups. 65 Using GRADE to assess the quality of the evidence aids interpretation of the limitations of the evidence. We undertook sensitivity and subgroup analyses, including post hoc analyses, which should be regarded with caution. Individual patient data meta-analyses are required for further exploration of these subgroup findings.

In systematic reviews of controlled cohort studies, bariatric surgery has been associated with significant reductions in mortality, cardiovascular events, myocardial infarction, stroke, and risk of cancer. 97 98 A systematic review and meta-analysis of population prospective cohort studies by Flegal and colleagues found that BMIs of 30 to <35 were not associated with higher mortality, compared with BMIs of 18.5 to <25. 5 By contrast, the Global BMI Mortality Collaboration found that obesity (BMI 30 to <35) was associated with higher mortality; the investigators reduced reverse causality by examining data in non-smokers and excluding the first five years of follow-up. 8 Their findings were consistent for men and women, up to 89 years, and in the four continents examined. Similar findings were seen for deaths due to coronary heart disease, stroke, cancer, and respiratory disease. Our findings for BMI from RCT evidence are consistent with data from the Global BMI Mortality Collaboration. 8 Epidemiological studies can demonstrate the risks of higher BMIs and, therefore, the necessity for preventing obesity, but epidemiological associations between changes in body weight and changes in disease and mortality are often limited by the lack of information on the intentionality of that weight loss. Furthermore, treatment effects found in RCTs might differ from those expected in epidemiological studies, whereby epidemiological studies might overestimate benefits. 99

Evidence from systematic reviews indicates that physical activity as an adjunct to weight reducing diets might be more effective than diets alone, in terms of weight loss and improvements in blood lipids and blood pressure. 100 We were unable to show differences for mortality, cardiovascular disease, and cancer between weight reducing diets alone, diets plus advice on exercise, and diets plus an exercise programme for people to attend, for which we had limited statistical power. The majority of RCTs of weight loss interventions for obesity in adults have used low fat, weight reducing diets. But a recent systematic review by Tobias and colleagues 101 found that low carbohydrate weight reducing diets were more effective for weight loss than low fat, weight reducing diets, but found no difference between low fat, weight reducing diets (defined as <30% fat) and higher fat, weight reducing diets on weight loss. Recent US guidelines 102 have been criticised for the lack of evidence from RCTs to support guidance. 103 Thus, we must consider whether the type of weight loss diet, particularly low fat, weight reducing diets, usually with <10% of energy as saturated fat, affects important health outcomes beyond cardiovascular risk factors or weight. 100 That all except one of the interventions included here used a low fat, weight reducing diet provides important evidence on all cause mortality for weight reduction with this type of diet. We do not have the evidence to establish whether other forms of weight reducing diets have this effect, and we cannot dissociate the effects of weight loss from the use of low fat diets in our results.

We encourage investigators studying weight reducing diets to adhere to CONSORT guidance on reporting harms by always reporting clinically important outcomes and adverse events, irrespective of whether they think these events are related to the interventions. 104 Collecting and reporting major disease outcomes in weight reducing trials for obesity is important, particularly cardiovascular disease and cancer. We did not have sufficient data to examine whether other types of diet or physical activity influence outcomes or whether certain groups in the population are more or less likely to benefit.

In conclusion, weight reducing diets, usually low in fat and low in saturated fat, with or without an exercise component, may reduce premature all cause mortality in adults who are obese. By implication, our data support public health measures to prevent weight gain and facilitate weight loss using these types of diet.

What is already known on this subject

Whether recommendations to follow weight reducing diets can reduce premature mortality, cardiovascular disease, and cancer for adults who are obese is unclear

What this study adds

Weight reducing diets, usually low in fat and saturated fat, with or without exercise advice or programmes, may reduce premature all cause mortality in adults who are obese

Our data provide supporting evidence for public health measures to prevent weight gain and facilitate weight loss using diets low in fat and saturated fat

We thank Andrew Grey for helping to resolve discrepancies in data extraction and interpretation for cardiovascular events and cancer events. We thank trialists from 16 studies for clarifying or providing additional information for this review (Andrews 2011, Aveyard 2016, Bennett 2012, de Vos 2014, Finnish Diabetes Prevention Study 2009, Goodwin 2014, Green 2015, Horie 2016, Hunt (FFIT) 2014, Katula 2013, Li (Da Qing) 2014, Logue 2005, Ma 2013, O’Neil 2016, Rejeski (CLIP) 2011, Uusitupa 1993) and others who provided information, but their trials were later found not to fulfil our inclusion criteria.

Contributors and sources: AA, CM, MJB, CF, and GM designed this study. CM, AA, and CF searched the literature. CM, AA, FS, CR, PS, and MJB extracted data. CM, AA, JH, MJB, and GM analysed data. CM and AA wrote the first draft of the manuscript. All authors contributed to revisions of the manuscript. AA is the guarantor.

Funding: The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorate.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorate. No author has financial relationships with any organisations that might have an interest in the submitted work in the previous three years.

Data sharing: All data are included in the paper or supplementary appendix. No additional data are available.

Transparency: AA and CM affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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• ↵ Penn L, White M, Oldroyd J, Walker M, Alberti KG, Mathers JC. Prevention of type 2 diabetes in adults with impaired glucose tolerance: the European Diabetes Prevention RCT in Newcastle upon Tyne, UK. BMC Public Health 2009 ; 359 : 342 . doi:10.1186/1471-2458-9-342   pmid:19758428 . OpenUrl
• ↵ Perri MG, Limacher MC, von Castel-Roberts K, et al. Comparative effectiveness of three doses of weight-loss counseling: two-year findings from the rural LITE trial. Obesity (Silver Spring) 2014 ; 359 : 2293 - 300 . doi:10.1002/oby.20832   pmid:25376396 . OpenUrl
• ↵ Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V. Indian Diabetes Prevention Programme (IDPP). The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 2006 ; 359 : 289 - 97 . doi:10.1007/s00125-005-0097-z   pmid:16391903 . OpenUrl
• ↵ Rejeski WJ, Brubaker PH, Goff DC Jr, et al. Translating weight loss and physical activity programs into the community to preserve mobility in older, obese adults in poor cardiovascular health. Arch Intern Med 2011 ; 359 : 880 - 6 . doi:10.1001/archinternmed.2010.522   pmid:21263080 . OpenUrl
• ↵ Rock CL, Flatt SW, Byers TE, et al. Results of the Exercise and Nutrition to Enhance Recovery and Good health for You (ENERGY) Trial: a behavioral weight loss intervention in overweight or obese breast cancer survivors. J Clin Oncol 2015 ; 359 : 3169 - 76 . doi:10.1200/JCO.2015.61.1095   pmid:26282657 . OpenUrl
• ↵ Sedjo RL, Flatt SW, Byers T, et al. Impact of a behavioral weight loss intervention on comorbidities in overweight and obese breast cancer survivors. Support Care Cancer 2016 ; 359 : 3285 - 93 . doi:10.1007/s00520-016-3141-2   pmid:26945570 . OpenUrl
• ↵ Ross R, Lam M, Blair SN, et al. Trial of prevention and reduction of obesity through active living in clinical settings: a randomized controlled trial. Arch Intern Med 2012 ; 359 : 414 - 24 . doi:10.1001/archinternmed.2011.1972   pmid:22371872 . OpenUrl
• ↵ Saito T, Watanabe M, Nishida J, et al. Zensharen Study for Prevention of Lifestyle Diseases Group. Lifestyle modification and prevention of type 2 diabetes in overweight Japanese with impaired fasting glucose levels: a randomized controlled trial. Arch Intern Med 2011 ; 359 : 1352 - 60 . doi:10.1001/archinternmed.2011.275   pmid:21824948 . OpenUrl
• ↵ Shea MK, Houston DK, Nicklas BJ, et al. The effect of randomization to weight loss on total mortality in older overweight and obese adults: the ADAPT Study. J Gerontol A Biol Sci Med Sci 2010 ; 359 : 519 - 25 . doi:10.1093/gerona/glp217   pmid:20080875 . OpenUrl
• ↵ Messier SP, Loeser RF, Miller GD, et al. Exercise and dietary weight loss in overweight and obese older adults with knee osteoarthritis: the Arthritis, Diet, and Activity Promotion Trial. Arthritis Rheum 2004 ; 359 : 1501 - 10 . doi:10.1002/art.20256   pmid:15146420 . OpenUrl
• ↵ Shea MK, Nicklas BJ, Houston DK, et al. The effect of intentional weight loss on all-cause mortality in older adults: results of a randomized controlled weight-loss trial. Am J Clin Nutr 2011 ; 359 : 839 - 46 . doi:10.3945/ajcn.110.006379   pmid:21775558 . OpenUrl
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Obesity Research

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

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

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

NHLBI research that really made a difference

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

Current research funded by the NHLBI

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

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

Current research on obesity and health disparities

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

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

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

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

Current research on obesity in pregnancy and childhood

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

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

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

Current research on obesity and sleep

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

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

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

Obesity research labs at the NHLBI

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

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

Related obesity programs and guidelines

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

Explore more NHLBI research on overweight and obesity

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

To find more studies:

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

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

• 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

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

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

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

Systematic review registration

PROSPERO CRD42020214560 .

Peer Review reports

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

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

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

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

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

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

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

Information sources and search strategy

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

Eligibility criteria

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

Screening and study selection process

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

Data extraction

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

Quality appraisal of included studies

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

Data synthesis

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

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

Assessment of confidence in the review findings

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

Reflexivity

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

Dissemination of findings

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

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

Availability of data and materials

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Abbreviations

Body mass index

Critical appraisal skills programme

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

Innovative Medicines Initiative

Medical Subject Headings

Population, phenomenon of interest, context, study type

Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy

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Acknowledgements

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

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

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

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

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

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403 Obesity Essay Topics & Paper Examples

Looking for obesity essay topics? Being a serious problem, obesity is definitely worth writing about.

🔝 Top-10 Obesity Essay Topics

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In your obesity essay, you might want to discuss its causes and effects or explore the issue of childhood obesity. If you’re planning to work on a quantitative nursing project, a good option is to make a case study. Here we’ve gathered top obesity topics for presentations & research papers. Obesity essay examples as an inspiring bonus!

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• The Obesity and Overweight Speech as an Artifact Reflecting on a speech about current obesity issues helped me to achieve my goals of understanding the seriousness of this issue and clearing up some misconceptions about the topic.
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• Childhood Obesity and Nutrition in the United States In this article, the author analyzes how people in the Northeastern United States discussed and valued the concept of ‘option’ in the context of reducing childhood obesity.
• Preventing Obesity Among the Hispanic Population The first factor within the dimension of relationships and expectations is associated with the perception of health-related values, beliefs, and attitudes that create a basis for an individual to engage in healthy behaviors.
• Cultural Context of American Obesity As Wexler argues, “the inventions of the industrial revolution such as cars, automation, and a variety of laborsaving devices sharply reduced levels of physical activity”, while the average intake of food remained the same.
• Interprofessional Collaboration in Treating Obesity This paper aims to discuss the topic of adult obesity in the US and the potential of interprofessional cooperation in its treatment. Treatment of obesity is a complex process, and teams of health professionals may […]
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• The Obesity: You Are Not Alone Social Advertising The main idea of the commercial is that the problem of obesity is global and overcoming it is a difficult and sometimes futile path.
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• Addressing Obesity in Adolescents The source’s purpose is to examine the physiological benefits of apple juice intake, green tea, yoga, and Prophet Dawood’s fasting method in terms of addressing obesity in adolescent patients.

How Does Obesity Affect Society?

Obesity severely impacts national productivity, defense, and the economy. The extra weight often translates into additional expenses for people suffering from this condition. They need more funds to treat obesity-related illnesses, are less productive, live shorter, and have a higher chance of becoming disabled.

Why Is Childhood Obesity a Problem?

This is an issue because it damages a child’s current and future mental and physical well-being. Such individuals are more prone to getting type 2 diabetes, high cholesterol, liver disease, and high blood pressure. They can also struggle in social situations and experience ridicule from their peers, causing emotional distress.

How Can We Prevent Obesity?

There are many ways people can keep their weight in check. They can explore exercise and diet options that suit their body type and metabolism. Some schools and workplaces also run programs to reduce weight among students and employees and help them live healthier and more productive lives.

Does Obesity Affect Self-Esteem?

Being obese can take a significant toll on people. Some of them suffer from social discrimination and poor self-image. Putting on extra weight can make them feel guilty or embarrassed about their appearance. Others suffer from low self-esteem, isolation, low productivity, and stress disorders. Additionally, it damages their productivity and physical health.

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

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• Health Promot Chronic Dis Prev Can
• v.40(11-12); 2020 Dec

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

Neeru gupta.

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

Andrea Bombak

Ismael foroughi, natalie riediger.

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

Introduction:

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

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

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

Conclusion:

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

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

Introduction

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

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

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

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

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

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

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

Study design

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

Discrimination in health care

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

Weight category

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

Statistical analysis

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

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

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

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

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

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

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

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

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

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

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

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

Strengths and limitations

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

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

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

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

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

Acknowledgements

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

Conflicts of interest

The authors declare they have no competing interests.

Authors’ contributions and statement

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

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

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