Dr Claire Griffiths

BACKGROUND: It is uncertain which simple measures of childhood obesity are best for predicting future obesity-related health problems and the persistence of obesity into adolescence and adulthood. OBJECTIVES: To investigate the ability of simple measures, such as body mass index (BMI), to predict the persistence of obesity from childhood into adulthood and to predict obesity-related adult morbidities. To investigate how accurately simple measures diagnose obesity in children, and how acceptable these measures are to children, carers and health professionals. DATA SOURCES: Multiple sources including MEDLINE, EMBASE and The Cochrane Library were searched from 2008 to 2013. METHODS: Systematic reviews and a meta-analysis were carried out of large cohort studies on the association between childhood obesity and adult obesity; the association between childhood obesity and obesity-related morbidities in adulthood; and the diagnostic accuracy of simple childhood obesity measures. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and a modified version of the Quality in Prognosis Studies (QUIPS) tool. A systematic review and an elicitation exercise were conducted on the acceptability of the simple measures. RESULTS: Thirty-seven studies (22 cohorts) were included in the review of prediction of adult morbidities. Twenty-three studies (16 cohorts) were included in the tracking review. All studies included BMI. There were very few studies of other measures. There was a strong positive association between high childhood BMI and adult obesity [odds ratio 5.21, 95% confidence interval (CI) 4.50 to 6.02]. A positive association was found between high childhood BMI and adult coronary heart disease, diabetes and a range of cancers, but not stroke or breast cancer. The predictive accuracy of childhood BMI to predict any adult morbidity was very low, with most morbidities occurring in adults who were of healthy weight in childhood. Predictive accuracy of childhood obesity was moderate for predicting adult obesity, with a sensitivity of 30% and a specificity of 98%. Persistence of obesity from adolescence to adulthood was high. Thirty-four studies were included in the diagnostic accuracy review. Most of the studies used the least reliable reference standard (dual-energy X-ray absorptiometry); only 24% of studies were of high quality. The sensitivity of BMI for diagnosing obesity and overweight varied considerably; specificity was less variable. Pooled sensitivity of BMI was 74% (95% CI 64.2% to 81.8%) and pooled specificity was 95% (95% CI 92.2% to 96.4%). The acceptability to children and their carers of BMI or other common simple measures was generally good. LIMITATIONS: Little evidence was available regarding childhood measures other than BMI. No individual-level analysis could be performed. CONCLUSIONS: Childhood BMI is not a good predictor of adult obesity or adult disease; the majority of obese adults were not obese as children and most obesity-related adult morbidity occurs in adults who had a healthy childhood weight. However, obesity (as measured using BMI) was found to persist from childhood to adulthood, with most obese adolescents also being obese in adulthood. BMI was found to be reasonably good for diagnosing obesity during childhood. There is no convincing evidence suggesting that any simple measure is better than BMI for diagnosing obesity in childhood or predicting adult obesity and morbidity. Further research on obesity measures other than BMI is needed to determine which is the best tool for diagnosing childhood obesity, and new cohort studies are needed to investigate the impact of contemporary childhood obesity on adult obesity and obesity-related morbidities. STUDY REGISTRATION: This study is registered as PROSPERO CRD42013005711. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Background: Researchers often use composite variables (e.g., BMI and change scores). By combining multiple variables (e.g., height and weight or follow-up weight and baseline weight) into a single variable it becomes challenging to untangle the causal roles of each component variable. Composite variable bias – an issue previously identified for exposure variables that may yield misleading causal inferences – is illustrated as a similar concern for composite outcomes. We explain how this occurs for composite weight outcomes: BMI, ‘weight change’, their combination ‘BMI change’, and variations involving relative change. Methods: Data from the National Child Development Study (NCDS) cohort surveys (n = 9223) were analysed to estimate the causal effect of ethnicity, sex, economic status, malaise score, and baseline height/weight at age 23 on weight-related outcomes at age 33. The analyses were informed by a directed acyclic graph (DAG) to demonstrate the extent of composite variable bias for various weight outcomes. Results: Estimated causal effects differed across different weight outcomes. The analyses of follow-up BMI, ‘weight change’, ‘BMI change’, or relative change in body size yielded results that could lead to potentially different inferences for an intervention. Conclusions: This is the first study to illustrate that causal estimates on composite weight outcomes vary and can lead to potentially misleading inferences. It is recommended that only follow-up weight be analysed while conditioning on baseline weight for meaningful estimates. How conditioning on baseline weight is implemented depends on whether baseline weight precedes or follows the exposure of interest. For the former, conditioning on baseline weight may be achieved by inclusion in the regression model or via a propensity score. For the latter, alternative strategies are necessary to model the joint effects of the exposure and baseline weight – the choice of strategy can be informed by a DAG.

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Background/objective: Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of ‘big data’ presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). ‘Additional computing power’ introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. Methods and results: Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle. Conclusions: The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

INTRODUCTION: To compare the ability of Body Mass Index (BMI), waist circumference (WC) and waist to height ratio (WHtR) to estimate cardiovascular disease (CVD) risk levels in adolescents. EVIDENCE ACQUISITION: A systematic review and meta-analysis was performed after a database search for relevant literature (Cochrane, Centre for Review and Dissemination, PubMed, British Nursing Index, CINAHL, BIOSIS citation index, ChildData, metaRegister). EVIDENCE SYNTHESIS: The study included 117 records representing 96 studies with 994,595 participants were included in the systematic review, 14 of which (13 studies, N.=14,610) were eligible for the meta-analysis. The results of the meta-analysis showed that BMI was a strong indicator of systolic blood pressure, diastolic blood pressure, triglycerides, high-density lipoprotein cholesterol and insulin; but not total cholesterol, low-density lipoprotein or glucose. Few studies were eligible for inclusion in the meta-analysis considering WC or WHtR (N.≤2). The narrative synthesis found measures of central adiposity to be consistently valid indicators of the same risk factors as BMI. CONCLUSIONS: BMI was an indicator of CVD risk. WC and WHtR were efficacious for indicating the same risk factors BMI performed strongly for, though there was insufficient evidence to judge the relative strength of each measure possibly due to heterogeneity in the methods for measuring and classifying WC.

INTRODUCTION: “Fatness” and “fitness” are recognised as important influences on cardiometabolic health in young people. Body mass index (BMI) is normally used to represent adiposity, whilst cardiorespiratory fitness (CRF) is usually presented as V̇O2MAX scaled to total body mass. The aim of the current thesis was to assess whether using different measures to represent “fatness” and “fitness” influences the relationships between these factors and cardiovascular disease (CVD) risk in young people.  STUDY ONE: A systematic review (n=994,595) and meta-analyses (n=18,526) compared the efficacy of using BMI, waist circumference (WC) and waist-to-height ratio (WHtR) for indicating CVD risk in 11-19 year olds. In meta-analyses BMI was a strong indicator of systolic blood pressure (mean difference with 95% confidence intervals, normal weight versus overweight/obese: -6.61mmHg, -8.71 to -4.90), diastolic blood pressure (-3.87mmHg, -4.90 to -2.84), triglycerides (-0.19mmol·L-1 , -0.24 to -0.14), high-density lipoprotein cholesterol (0.10mmol·L-1 , 0.06 to 0.14) and insulin (-30.17pmol·L-1 , -43.12 to -17.21); but not total cholesterol, low-density lipoprotein cholesterol or glucose. WC and WHtR were strong indicators of the same risk factors, though ≤2 studies were available for each comparison for these adiposity indices. Narrative synthesis results supported these outcomes. There was however insufficient evidence to judge which adiposity index was most efficacious for assessing CVD risk in young people. STUDY TWO: A prospective, cross-sectional study with 18-20 year olds (n=64). CRF, body composition (dual-energy X-ray absorptiometry) and CVD risk (blood pressure, blood lipids, glucose, insulin and inflammatory markers) were measured. Agreement was relatively poor between CRF scores when different scaling methods were used (k≤0.60 for most, “moderate” agreement). SBP and metabolic risk score were significantly associated with absolute CRF (r=0.379 and 0.461 respectively). Leptin was strongly associated with absolute CRF (r=-0.602) and CRF scaled to total body mass (r=-0.602). CRF was more strongly associated with most CVD risk factors when scaled to a method that included a measure of body fat. These relationships were removed when adiposity was controlled for. The method of scaling CRF had a large influence on the classification of CRF and the relationship of “fitness” with CVD risk in young people, so should be considered carefully.  STUDY THREE: A retrospective analysis of mostly obese 8-18 year olds (n=426). CRF, body composition (air-displacement plethysmography) and CVD risk were assessed. There was relatively poor agreement between CRF scores when different scaling methods were used (k≤0.60 for most, “moderate” agreement) and between adiposity measures (BMIsds v WCsds mean difference=0.27sds, 95% limits of agreement ±0.69sds). Adiposity was a strong, independent predictor of CVD risk; but there was little difference in the predictive ability of BMI, WC and WHtR. CRF was more strongly associated with CVD risk when scaled to methods influenced by body fatness, with relationships attenuated when adiposity was controlled for. No CRF measure added to the predictive ability of adiposity measures, thus there was no evidence “fitness” should be considered alongside “fatness” when assessing young people’s CVD risk.  CONCLUSIONS: There was no clear difference in the efficacy of different adiposity measures for indicating CVD risk, thus the findings do not suggest BMI should be replaced by WC or WHtR as a marker of “fatness” in young people. The observed relationships between “fitness” and CVD risk were due largely to an influence of body fatness on the CRF score as a result of inappropriate scaling. There was also no support for including any measure of CRF alongside adiposity indices for predicting CVD risk in young people. Previous research concluding an independent relationship between CRF and CVD risk without considering potential mathematical coupling with adiposity, due to the method of scaling, should therefore be reconsidered.

Background Intervention differential effects (IDE) occur when change in a health outcome following an intervention depends upon the baseline value of that outcome. Oldham’s method and multilevel modelling are methods used to detect IDEs. However, the conditions under which these methods are robust are not well documented. One condition that has not been explored is detection of IDEs in studies which recruit according to baseline health status (i.e. above a threshold). We hypothesised that recruiting/selecting above a threshold affects the reliability of existing methods to detect IDEs because of regression to the mean. We hypothesised that comparing these ‘truncated’ samples with a control group restores the robustness of these methods. Using weight-loss interventions as an example, we show how to overcome the challenges of regression to the mean in studies with threshold selection criteria.Methods We simulated two datasets comprising repeated measures of body mass index (BMI) data for 1000 males aged 25–34 (‘population’ datasets). One dataset was simulated to have an IDE, and the other (‘null’) dataset was simulated without. Half the population in each dataset were simulated to receive a weight-loss intervention. To emulate real-word weight-loss interventions, we truncated each population dataset to select intervention and control group samples with BMI scores above ≥30 kg/m2. Oldham’s method and multilevel modelling were used on the ‘population’ intervention groups and corresponding ‘truncated’ samples for each simulation. We repeated each analysis to contrast the intervention and control group datasets (using Fisher’s z-transformation and student’s t-test for Oldham’s method, and the likelihood ratio test for multilevel modelling). Simulations were repeated 10 000 times to generate Type I error rates and 95% credible intervals. Simulations were performed in R and MLwiN.Results Under the null of no IDE, Oldham’s method and the multilevel model yielded Type I error rates >90%, confirming that selecting above a threshold leads to bias due to regression to the mean. Type I error rates returned to 5% for the multilevel model when a control group was introduced and the likelihood ratio test employed, while Type I error rates improved but remained elevated when Fisher’s z-transformation and student’s t-test were used to contrast groups.

Background Because responses to interventions can be heterogeneous, interest may lie in the extent to which any given individual’s response to an intervention relates to their baseline status (an intervention differential effect; IDE). In interventions designed to prevent excess childhood weight gain, researchers may want to investigate whether children with higher or lower body weights at baseline respond differently to the intervention. However, when investigating the potential of an IDE, it is necessary to avoid the issue of mathematical coupling (MC), where change in weight is analysed with respect to initial weight using correlation or regression. The problem of MC, and methods used to overcome it (Oldham’s method and multilevel modelling) have been described previously for outcomes that are homoscedastic. However, the literature does not explore how these methods perform in identifying IDEs in outcomes that are inherently heteroscedastic (such as growth in childhood body weight). We hypothesised that methods for detecting IDEs in heteroscedastic outcomes are only robust when analyses in the intervention group are compared with analyses in control group data.Methods We explored the performance of Oldham’s method and multilevel modelling in overcoming MC within heteroscedastic data. We simulated longitudinal data derived from child weight growth statistics, designed to be heterogeneous to reflect real-world growth data. To emulate weight-management programmes, an intervention group was simulated with an IDE, and a control group was simulated without. Methods for detecting IDEs were evaluated: first in the intervention group only, then with analyses of the intervention group contrasted with the control group. Simulations were performed in R and MLwiN.Results We demonstrated that Oldham’s method and multilevel modelling were biased when used to estimate an IDE within inherently heteroscedastic data. However, we showed that introducing a control group comparison enabled both methods to robustly detect an IDE in heteroscedastic data, providing that parametric assumptions of growth were justified and modelled explicitly (e.g. as linear, quadratic, etc.).

© 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Background: Intervention differential effects (IDEs) occur where changes in an outcome depend upon the initial values of that outcome. Although methods to identify IDEs are well documented, there remains a lack of understanding about the circumstances under which these methods are robust. One context that has not been explored is the identification of intervention differential effect in studies where sample selection is based on the initial value of the outcome being evaluated. We hypothesise that, in such settings, established methods for detecting IDEs will struggle to discriminate these from regression to the mean. Methods: Using simulated datasets of weight-loss intervention programmes that recruit according to initial body mass index, we explore the reliability of Oldham's method and multilevel modelling (MLM) to detect IDEs. Results: In datasets simulated with no IDE, Oldham's method and MLM yield Type I error rates >90%, confirming that threshold selection/truncation leads to bias due to regression to the mean. Type I error rates return close to 5% for both methods when a control group is introduced. Conclusions: Oldham's method and MLM can robustly detect IDEs in this setting, but only if analyses incorporate a control group for comparison.

BACKGROUND: Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed. METHODS: Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out. RESULTS: A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research. CONCLUSIONS: Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.

© Author(s) (or their employer(s)) 2019. Background Internationally, the prevalence of adults with obesity is a major public health concern. Few studies investigate the explanatory pathways between fast-food outlets and body mass index (BMI). We use structural equation modelling to explore an alternative hypothesis to existing research using area-level deprivation as the predictor of BMI and fast-food outlets and diet quality as mediators. Methods Adults (n=7544) from wave II of the Yorkshire Health Study provided self-reported diet, height and weight (used to calculate BMI). Diet quality was based on sugary drinks, wholemeal (wholegrain) bread and portions of fruit and vegetables. Fast-food outlets were mapped using the Ordnance Survey Points of Interest within 2 km radial buffers around home postcode which were summed to indicate availability. Age (years), gender (female/male) and long-standing health conditions (yes/no) were included as covariates. Results There was little evidence linking fast-food outlets to diet or BMI. An independent association between fast-food outlet availability and BMI operated counterintuitively and was small in effect. There was also little evidence of mediation between fast-food outlet availability and BMI. However, there was more evidence that area-level deprivation was associated with increased BMI, both as an independent effect and through poorer diet quality. Conclusion This exploratory study offers a first step for considering complexity and pathways linking fast-food outlets, area-level deprivation, diet quality and BMI. Research should respond to and build on the hypothesised pathways and our simple framework presented within our study.

Background: There has been considerable interest in the role of access to unhealthy food options as a determinant of weight status. There is conflict across the literature as to the existence of such an association, partly due to the dominance of cross-sectional study designs and inconsistent definitions of the food environment. The aim of our study is to use longitudinal data to examine if features of the food environment are associated to measures of adolescent weight status. Methods: Data were collected from secondary schools in Leeds (UK) and included measurements at school years 7 (ages 11/12), 9 (13/14), and 11 (15/16). Outcome variables, for weight status, were standardised body mass index and standardised waist circumference. Explanatory variables included the number of fast food outlets, supermarkets and ‘other retail outlets’ located within a 1 km radius of an individual’s home or school, and estimated travel route between these locations (with a 500 m buffer). Multi-level models were fit to analyse the association (adjusted for confounders) between the explanatory and outcome variables. We also examined changes in our outcome variables between each time period. Results: We found few associations between the food environment and measures of adolescent weight status. Where significant associations were detected, they mainly demonstrated a positive association between the number of amenities and weight status (although effect sizes were small). Examining changes in weight status between time periods produced mainly non-significant or inconsistent associations. Conclusions: Our study found little consistent evidence of an association between features of the food environment and adolescent weight status. It suggests that policy efforts focusing on the food environment may have a limited effect at tackling the high prevalence of obesity if not supported by additional strategies.

Smoking is a leading cause of preventable morbidity and mortality globally. During the COVID-19 pandemic, Smoking Cessation (SC) services faced many challenges, including lockdown and social distancing restrictions. Consequently, SC services had to adapt to the challenges in different ways or halt delivery. This research evaluated the impact of COVID-19 on the delivery and outcomes of SC services. This was achieved by comparing service delivery and outcomes pre-COVID-19 and during the pandemic and drawing insights for the delivery of SC services post-pandemic. Secondary analysis was performed on the data of 11,533 participants who attended the One Life Suffolk (OLS) SC services pre- and during the COVID-19 pandemic. A total of 4923 and 6610 participants attended SC services pre-COVID-19 and during COVID-19 respectively. Fifty-four percent of participants achieved quit status at week-4 while attending the SC services during the COVID-19 pandemic, compared with 46% pre-COVID-19, (X2(1) = 38.2, p-value<0.001). Participants who attended the SC services during the COVID-19 period were 1.7 times more likely to achieve quit status at week-4 than pre-COVID-19. However, the proportion of participants lost-to-follow-up (LTF) was significantly higher during the COVID-19 period (11%) compared to pre-COVID-19 (7%), (X2(1) = 51.4, p-value <0.001). There was an increased participation and quit rate during the pandemic for modified, remotely delivered SC services indicating successful delivery of remote services during the pandemic. Although switching from face-to-face to online helped some smokers to access the service at a time of motivational readiness, despite the COVID-19 restrictions, some smokers could not access or use some aspects of the remote delivery due to a lack of internet access, poor digital literacy, no peer support and no commitment to a group during face-to-face sessions, contributing to an increased rate of LTF. Posing a major challenge to SC services delivery, COVID-19 compelled OLS SC services to adapt and be more innovative in their delivery. SC services need to continue to evolve and adapt by applying the lessons learnt during the pandemic in terms of flexibility and person-centered delivery given what did and did not work well for different demographics within the population.

Background: Food insecurity (lack of reliable access to affordable and nutritious food) is a major concern in high-income countries because it increases the risk of poor nutrition, obesity and associated adverse health outcomes. Healthier diets are often also more environmentally sustainable (hereafter; sustainable), an important factor in reducing climate change. Practice-based interventions are therefore urgently needed to support people living with food insecurity and obesity to access and afford healthier and sustainable foods. Supermarkets are a key area for intervention, as purchasing can be an antecedent to consumption. However, the retailers’ perspectives on the feasibility of implementing affordability interventions is often overlooked and under-researched. Therefore, this study explored the perspectives, views, and experiences of major UK supermarket senior nutritionists on the acceptability and feasibility of using affordability interventions for healthier and more sustainable food in the supermarket.Methods: We recruited seven UK senior supermarket nutritionists who represented 85% of the UK grocery market share. We used semi-structured interviews and analysed the data using a reflective thematic analysis approach.Results: Supermarket nutritionists perceived that their business did prioritise health and environmental sustainability for customers. However, there were several challenges encountered when trying to promote healthier and more sustainable food in the supermarket environment, including profitability concerns, unpredictability of intervention outcomes, control over own-brand products, perceived intention-behaviour gap, and a belief that they are already implementing affordability interventions. Differences in how supermarkets approach the evaluation of interventions also emerged, as well as a willingness to collaborate with academics and other retailers to optimise the evaluation of interventions. Lastly, supermarket nutritionists raised the need for an operationalised definition for sustainable food products.Conclusions: Affordability interventions to support customers to purchase healthier and more sustainable food require supermarkets to consider multiple challenges. Findings highlight the need for upstream intervention that mandates and facilitates multi-lever approaches to health and sustainability without compromising commercial viability, along with practice-based approaches to implementation and evaluation.

Good health is viewed as essential to enable citizens to live fulfilling lives, shape communities, and drive economic growth. However, health is socially patterned. Low socioeconomic status is associated with an increased risk of non-communicable diseases, where poor dietary patterns and diet-related obesity are likely contributors. Food purchasing can be influenced by many factors, including cost and income. Most food purchased to be consumed at home is acquired from supermarkets, and any increase in food prices disproportionately impacts low-income households, contributing to food insecurity. This study explored the factors that helped and hindered people living with obesity and food insecurity in purchasing healthy, environmentally sustainable food from supermarkets. Semi-structured interviews (n = 25) and focus groups (n = 7) were conducted between June and December 2023 with adults living in Scotland and England who self-identified as living with obesity and food insecurity. Using thematic analysis, six main themes were identified: (1) Supermarket deals: perceptions surrounding the good, the bad, and the ugly side of supermarket offers and promotions; (2) Skepticism about supermarkets and the wider food system: questioning supermarket pricing motives but recognizing the role of the wider food system in food pricing; (3) Other peoples’ role in enhancing or undermining healthy diet intentions: the impact of others in shaping food purchases; (4) Financial restrictions facing non-UK nationals: additional challenges faced by those with no recourse to public funds; (5) The overwhelming in-store supermarket experience: sensory overload and attempts to prevent unintended, impulse purchases; (6) Unconscious, environmentally sustainable shopping practices: cost saving practices that lead to environmentally sustainable purchasing patterns and behaviors as a unintentionally created outcome of budget maximizing strategies. However, such strategies, that is, limiting food waste and purchasing less meat, although beneficial for environmental sustainability, do not necessarily indicate that a healthier diet is being purchased or consumed. While views on some factors believed to help or hinder healthy, environmentally sustainable food purchases varied, there was general agreement amongst participants on the need for upstream changes, including having access to adequate benefits and wages.

Background: Food insecurity (lack of reliable access to affordable and nutritious food) is a major concern in high-income countries because it increases the risk of poor nutrition, obesity and associated adverse health outcomes. Healthier diets are often also more environmentally sustainable (hereafter; sustainable), an important factor in reducing climate change. Practice-based interventions are therefore urgently needed to support people living with food insecurity and obesity to access and afford healthier and sustainable foods. Supermarkets are a key area for intervention, as purchasing can be an antecedent to consumption. However, the retailers’ perspectives on the feasibility of implementing affordability interventions is often overlooked and under-researched. Therefore, this study explored the perspectives, views, and experiences of major UK supermarket senior nutritionists on the acceptability and feasibility of using affordability interventions for healthier and more sustainable food in the supermarket.Methods: We recruited seven UK senior supermarket nutritionists who represented 85% of the UK grocery market share. We used semi-structured interviews and analysed the data using a reflective thematic analysis approach.Results: Supermarket nutritionists perceived that their business did prioritise health and environmental sustainability for customers. However, there were several challenges encountered when trying to promote healthier and more sustainable food in the supermarket environment, including profitability concerns, unpredictability of intervention outcomes, control over own-brand products, perceived intention-behaviour gap, and a belief that they are already implementing affordability interventions. Differences in how supermarkets approach the evaluation of interventions also emerged, as well as a willingness to collaborate with academics and other retailers to optimise the evaluation of interventions. Lastly, supermarket nutritionists raised the need for an operationalised definition for sustainable food products.Conclusions: Affordability interventions to support customers to purchase healthier and more sustainable food require supermarkets to consider multiple challenges. Findings highlight the need for upstream intervention that mandates and facilitates multi-lever approaches to health and sustainability without compromising commercial viability, along with practice-based approaches to implementation and evaluation.

Individuals participate in research for numerous reasons, however, the global economic downturn may have driven some to participate solely for monetary recompense. While inauthentic participation is more widely recognised in quantitative survey studies, it is increasingly becoming an issue in qualitative research. Drawing on our experiences and supported by the wider literature, we highlight ways in which inauthentic participation can be identified and addressed. We argue it is pertinent researchers are aware of the risks and potential impact of inauthentic participants and recommend researchers consider this phenomenon from study planning stages onwards. We identify Universities and ethics committees as well placed to provide training and ensure, where necessary, mitigation plans are in place before granting study approvals. We suggest funders and publishers request inauthentic participation be considered and reported. These recommendations would establish awareness, prevent wasting valuable project resources, increase transparency of reporting and ensure data integrity is protected.

Public health is increasingly engaging with multi-faceted obesity prevention efforts. Although parks represent key community assets for broader public health, they may not be distributed equitably and associations with obesity are equivocal. We investigated park access and quality relative to deprivation and obesity with individual-level data from the Yorkshire Health Study. Compared to the least deprived areas, the moderately and most deprived areas had a greater park access and park quality in terms of features and amenities. However, parks in the moderately and most deprived areas also had the most safety concerns and incivilities. Although deprivation was associated with obesity, contrary to current policy guidance, both park access and quality appear less important for understanding variations in obesity within this study. Although sub-group analyses by deprivation tertile revealed that low quality park amenities in highly and moderately deprived areas may be important for understanding obesity prevalence, all other associations were non-significant.

Widespread allegations of doping in sport consistently make front page news. The findings of an independent commission for the WADA1 underscore the importance of moving beyond a focus on individual athletes to concurrently address individual, social and environmental factors in anti-doping policy and practice (socioecological perspective).

Little research has investigated associations between a combined measure of the food and physical activity (PA) environment, BMI (body-mass-index) and obesity. Cross-sectional data (n=22,889, age 18-86 years) from the Yorkshire Health Study were used [2010-2013]. BMI was calculated using self-reported height and weight; obesity=BMI≥30. Neighbourhood was defined as a 2km radial buffer. Food outlets and PA facilities were sourced from Ordnance Survey Points of Interest (PoI) and categorised into ‘fast-food’, ‘large supermarkets’, ‘convenience and other food retail outlets’ and ‘physical activity facilities’. Parks were sourced from Open Street Map. Latent class analysis was conducted on these five environmental variables and availability was defined by quartiles of exposure. Linear and logistic regression were then conducted for BMI and obesity respectively for different neighbourhood types. Models adjusted for age, gender, ethnicity, area-level deprivation, and rural/urban classification. A five-class solution demonstrated best fit and was interpretable. Neighbourhood typologies were defined as; ‘low availability’, ‘moderate availability’, ‘moderate PA, limited food’, ‘saturated’ and ‘moderate PA, ample food’. Compared to low availability, one typology demonstrated lower BMI (saturated, b= -0.50, [95% CI= -0.76,-0.23]), while three showed higher BMI (moderate availability, b= 0.49 [0.27,0.72]; moderate PA, limited food, b=0.30 [0.01,0.59]; moderate PA, ample food, b=0.32 [0.08,0.57]). Furthermore, compared to the low availability, saturated neighbourhoods showed lower odds of obesity (OR=0.86 [0.75,0.99]) while moderate availability showed greater odds of obesity (OR=1.18 [1.05,1.32]). This study supports population-level approaches to tackling obesity however neighbourhoods contained features that were health-promoting and -constraining.

Food insecurity (FI), is defined as unreliable access to healthy and nutritious food, and is a major health concern in higher-income countries, primarily due to its association with an increased risk of obesity. Adherence to healthy eating recommendations promotes both a healthier and more environmentally sustainable diet. Supermarket-based interventions may influence population-level food purchasing behaviour, an antecedent to consumption, however, it is unclear whether there are specific characteristics that supermarket-based interventions should employ to resonate with vulnerable groups. This scoping review aimed to explore the characteristics of supermarket-based interventions that sought to support healthier and/or more environmentally sustainable food purchasing for people living with obesity and overweight (PLWO/Ow) and/or FI. A systematic literature search identified 35 studies, representing 43 interventions, eligible for inclusion. Most interventions focused on supporting the purchase of healthy food items, with three aimed at increasing the purchase of plant-based foods. No study applied a validated measure of FI. Area-level demographic data were used to identify FI related characteristics (i.e., area of low income, low socio-economic status) and in some cases, those living with obesity. Interventions utilised the behaviour change levers of price (n=8), promotion (n=2), placement (n=7), nudges (n=4) and education (n=2), or a combination of these (n=20). High heterogeneity in the way behavioural change levers were operationalised and combined, alongside the use of proxy measures to identify FI and PLWO/Ow, presents a challenge for determining intervention characteristics which best support changes in purchasing patterns in favour of heathy, sustainable food items in this population.

© 2019 Elsevier Ltd International research linking food outlets and body mass index (BMI) is largely cross-sectional, yielding inconsistent findings. However, addressing the exposure of food outlets is increasingly considered as an important adult obesity prevention strategy. Our study investigates associations between baseline food environment types and change in BMI over time. Survey data were used from the Yorkshire Health Study (n=8,864; wave one: 2010-2012, wave two: 2013-2015) for adults aged 18-86. BMI was calculated using self-reported height (cm) and weight (kg). Restaurants, cafés, fast-food, speciality, convenience and large supermarkets were identified from the Ordnance Survey Point of Interest database within 1600m radial buffer of home postcodes. K-means cluster analysis developed food environment typologies based on food outlets and population density. Large supermarkets, restaurants, cafés, fast-food, speciality and convenience food outlets all clustered together to some extent. Three neighbourhood typologies were identified. However, multilevel models revealed that relative to cluster one all were unrelated to change in BMI (cluster 2, b= -0.146 [-0.274, 0.566]; cluster 3, b= 0.065 [-0.224, 0.356]). There was also little evidence of gender-based differences in these associations when examined in a three-way interaction. Policymakers may need to begin to consider multiple types of food outlet clusters, while further research is needed to confirm how these relate to changed BMI.

To investigate associations between the food and physical activity (PA) environments and obesity. Cross-sectional data (n = 22,889) from the Yorkshire Health Study were used. Body mass index (BMI) was calculated using self-reported height and weight; obesity was defined as BMI ≥ 30. Waist circumference (WC) was also self-reported; ‘at risk’ was defined as ≥94 cm and ≥80 cm for males and females respectively. Food outlets (FO) and PA facility locations were mapped using the Ordnance Survey Points of Interest database. Park locations were obtained separately from Open Street Map. Home neighbourhoods were defined using 2 km buffers radiating from each participant’s home postcode. FO or PA opportunities within home neighbourhoods were then summed to indicate availability. FO were categorised as ‘takeaway’, ‘supermarket’ and ‘other food retail’. PA facilities and parks were considered as two separate categories. Multi-level logistic models were used to estimate associations between the food and PA environment and obesity in separate models for each environmental variable. To account for the skewed environment data we modelled availability in quartiles (Q1 least exposed, Q4 most exposed). Age, gender, ethnicity, deprivation and rural/urban classification were included as covariates in all models. For the food environment, 89.1% had immediate access to at least one takeaway and 69.9% to a supermarket. For the PA environment, 97.6% and 77.7% of individuals had one PA facility and park available within their home neighbourhood. Availability varied little by deprivation. Separate multi-level models showed no evidence of an association neither between (i) the number of FO, nor (ii) most PA factors and obesity in those most exposed (Q4) compared to those least exposed (Q1). However, there was evidence of a negative association between PA facilities and obesity in the most exposed quartile (Q4 OR = 0.88 [95% CI 0.78–0.98]) compared to those least exposed (Q1). Findings were substantively the same for BMI and WC. There is little evidence to suggest that availability within the food or PA environment is associated with obesity. The evidence presented here provides little support for policy interventions aiming to modify the local food or PA environment.

Inconsistencies in methodologies continue to inhibit understanding of the impact of the environment on body mass index (BMI). To estimate the effect of these differences, we assessed the impact of using different definitions of neighbourhood and data sets on associations between food outlet availability within the environment and BMI. Previous research has not extended this to show any differences in the strength of associations between food outlet availability and BMI across both different definitions of neighbourhood and data sets. Descriptive statistics showed differences in the number of food outlets, particularly other food retail outlets between different data sets and definitions of neighbourhood. Despite these differences, our key finding was that across both different definitions of neighbourhood and data sets, there was very little difference in size of associations between food outlets and BMI. Researchers should consider and transparently report the impact of methodological choices such as the definition of neighbourhood and acknowledge any differences in associations between the food environment and BMI.

Background: ‘Big data’ has great potential to help address the global health challenge of obesity. However, lack of clarity with regard to the definition of big data and frameworks for effectively using big data in the context of obesity research may be hindering progress. The aim of this study was to establish agreed approaches for the use of big data in obesity-related research. Methods: A Delphi method of consensus development was used, comprising three survey rounds. In Round 1, participants were asked to rate agreement/disagreement with 77 statements across seven domains relating to definitions of and approaches to using big data in the context of obesity research. Participants were also asked to contribute further ideas in relation to these topics, which were incorporated as new statements (n = 8) in Round 2. In Rounds 2 and 3 participants re-appraised their ratings in view of the group consensus. Results: Ninety-six experts active in obesity-related research were invited to participate. Of these, 36/96 completed Round 1 (37.5% response rate), 29/36 completed Round 2 (80.6% response rate) and 26/29 completed Round 3 (89.7% response rate). Consensus (defined as >70% agreement) was achieved for 90.6% (n=77) of statements, with 100% consensus achieved for the Definition of Big Data, Data Governance, and Quality and Inference domains. Conclusions: Experts agreed that big data was more nuanced than the oft-cited definition of ‘volume, variety and velocity’, and includes quantitative, qualitative, observational or intervention data from a range of sources that have been collected for research or other purposes. Experts repeatedly called for third party action, for example to develop frameworks for reporting and ethics, to clarify data governance requirements, to support training and skill development and to facilitate sharing of big data. Further advocacy will be required to encourage organisations to adopt these roles.

© 2018 This study investigated if the relationship between residential fast-food outlet availability and obesity varied due to methodological diversity or by age. Cross-sectional data (n = 22,889) from the Yorkshire Health Study, England were used. Obesity was defined using self-reported height and weight (BMI ≥ 30). Food outlets (“fast-food” “large supermarkets” and “convenience or other food retail outlets”) were mapped using Ordnance Survey Points of Interest (PoI) database. Logistic regression was used for all analyses. Methodological diversity included adjustment for other food outlets as covariates and continuous count vs. quartile. The association between residential fast-food outlets and obesity was inconsistent and effects remained substantively the same when considering methodological diversity. This study contributes to evidence by proposing the use of a more comprehensive conceptual model adjusting for wider markers of the food environment. This study offers tentative evidence that the association between fast-food outlets and obesity varies by age.

Aims: This study investigates associations between the combined physical activity environment and obesity and explores any sub-group effects by individual-level socioeconomic status. Methods: In a large cross-sectional cohort (n = 22,889) from the Yorkshire Health Study, body mass index was calculated using self-reported height and weight and obesity was defined as a body mass index ≥ 30. The physical activity environment was split into ‘unfavourable physical activity’, ‘moderately favourable physical activity’ and ‘favourable physical activity’ environments. This was based on the count of parks and physical activity facilities within a 2 km radial buffer centred on home addresses. A favourable physical activity environment was defined as having ≥1 physical activity facility and ≥1 park, unfavourable as having no physical activity facility and park and any other combinations defined as moderately favourable. Logistic regression (odds ratios) identified associations with obesity. Results: Relative to ‘unfavourable physical activity environments’, individuals within favourable physical activity environments were less likely to be obese (odds ratio = 0.90; 95% confidence interval = 0.82–0.97), and there was no effect for moderately favourable environment. Furthermore, once stratified by education level, this relationship was only present for those of higher education. Conclusion: Our findings provide novel UK evidence and is one of the first papers internationally that highlights the importance of considering the interplay of individual-level socioeconomic factors when investigating associations between the physical activity environment and obesity.

Understanding the determinants of attendance at public health interventions is critical for effective policy development. Most research focuses on individual-level determinants of attendance, while less is known about environmental-level determinants. Data were obtained from the Leeds Let's Get Active public health intervention in Leeds, England. Longitudinal data (April 2015–March 2016) on attendance were obtained for n = 25,745 individuals (n = 185,245 total visits) with baseline data on sociodemographic determinants and lifestyle practices obtained for n = 3621 individuals. This resulted in a total of n = 744,468 days of attendance and non-attendance. Random forests were used to explore the relative importance of the determinants on attendance, while generalised linear models were applied to examine specific associations (n = 3621). The probability that a person will attend more than once, the number of return visits, and the probability that a person will attend on a particular day were investigated. When considering if a person returned to the same leisure centre after one visit, the most influential determinant was the distance from their home. When considering number of return visits overall however, age group was the most influential. While distance to a leisure centre was less important for predicting the number of return visits, the difference between estimates for 300 m and 15,000 m was 7–10 visits per year. Finally, calendar month was the most important determinant of daily attendance. This longitudinal study highlights the importance of both individual and environmental determinants in predicting various aspects of attendance. It has implications for strategies aiming to increase attendance at public health interventions.

Background Modifying the environment is considered an effective population-level approach for increasing healthy behaviours, but associations remain ambiguous. This exploratory study aims to compare researcher-defined buffers and self-drawn neighbourhoods (SDN) to objectively measured availability of physical activity (PA) facilities and greenspaces in adolescents. Methods Seven consecutive days of GPS data were collected in an adolescent sample of 14–18 year olds (n = 69). Using Points of Interest and greenspace data, availability of PA opportunities within activity spaces were determined. We compared 30 different definitions of researcher-defined neighbourhoods and SDNs to objectively measured availability. Results :Findings showed low agreement for all researcher-defined buffers in measuring the availability of PA facilities in activity spaces. However, results were less clear for greenspace. SDNs also demonstrate low agreement for capturing availability to the PA environment. Conclusion This exploratory study highlights the inadequacy of researcher-defined buffers and SDNs to define availability to environmental features.

Background There is a persistent lack of understanding on the influence of the environment on behaviour and health. While the environment is considered an important modifiable determinant of health behaviour, past research assessing environments often relies on static, researcher-defined buffers of arbitrary distance. This likely leads to misrepresentation of true environmental exposures. This exploratory study aims to compare researcher-defined and self-drawn buffers in reflecting the spaces and time adolescents engage in physical activity (PA) and sedentary behaviour. It also investigates if adolescent’s access the PA facility and greenspace nearest their home or school for PA, as well as examine how much time adolescents spent in PA at any PA facilities and greenspaces. Methods Adolescents (aged 14–18 years; n = 34) were recruited from schools in West Yorkshire, England. Seven consecutive days of global positioning system (GPS) and accelerometer data were collected at 15 second intervals. Using ArcGIS, we compared 30 different researcher-defined buffers including: radial, network and ellipse buffers at 400m, 800m, 1000m, 1600m and 3000m and participant-defined self-drawn neighbourhoods to objectively measured PA and sedentary space and PA time. Location of PA was also compared to Points of Interest data to determine if adolescents use the nearest PA facility or greenspace to their home or school and to examine how much PA was undertaken within these locations. Results Our exploratory findings show the inadequacy of researcher-defined buffer size in assessing MVPA space or sedentary space. Furthermore, less than 35% of adolescents used the greenspaces or PA facilities nearest to their home or school. Approximately 50% of time spent in PA did not occur within the home, school, PA facility, or greenspace environments. Conclusion Our exploratory findings help to begin to quantify the inadequacy of researcher-defined, and self-drawn buffers in capturing adolescent MVPA and sedentary space, as well as time spent in PA. Adolescents often do not use PA facilities and greenspaces nearest their home and school and a large proportion of PA is achieved outside PA facilities and greenspaces. Further research with larger samples are needed to confirm the findings of this exploratory study.

Geographic Information Systems (GIS) are widely used to measure retail food environments. However the methods used are hetrogeneous, limiting collation and interpretation of evidence. This problem is amplified by unclear and incomplete reporting of methods. This discussion (i) identifies common dimensions of methodological diversity across GIS-based food environment research (data sources, data extraction methods, food outlet construct definitions, geocoding methods, and access metrics), (ii) reviews the impact of different methodological choices, and (iii) highlights areas where reporting is insufficient. On the basis of this discussion, the Geo-FERN reporting checklist is proposed to support methodological reporting and interpretation.

Background: Current UK policy in relation to the influence of the ‘food environment’ on childhood obesity appears to be driven largely on assumptions or speculations because empirical evidence is lacking and findings from studies are inconsistent. The aim of this study was to investigate the number of food outlets and the proximity of food outlets in the same sample of children, without solely focusing on fast food. Methods: Cross sectional study over 3 years (n = 13,291 data aggregated). Body mass index (BMI) was calculated for each participant, overweight and obesity were defined as having a BMI >85th (sBMI 1.04) and 95th (sBMI 1.64) percentiles respectively (UK90 growth charts). Home and school neighbourhoods were defined as circular buffers with a 2 km Euclidean radius, centred on these locations. Commuting routes were calculated using the shortest straight line distance, with a 2 km buffer to capture varying routes. Data on food outlet locations was sourced from Leeds City Council covering the study area and mapped against postcode. Food outlets were categorised into three groups, supermarkets, takeaway and retail. Proximity to the nearest food outlet in the home and school environmental domain was also investigated. Age, gender, ethnicity and deprivation (IDACI) were included as covariates in all models. Results: There is no evidence of an association between the number of food outlets and childhood obesity in any of these environments; Home Q4 vs. Q1 OR = 1.11 (95% CI = 0.95-1.30); School Q4 vs. Q1 OR = 1.00 (95% CI 0.87 – 1.16); commute Q4 vs. Q1 OR = 0.1.00 (95% CI 0.83 – 1.20). Similarly there is no evidence of an association between the proximity to the nearest food outlet and childhood obesity in the home (OR = 0.77 [95% CI = 0.61 – 0.98]) or the school (OR = 1.01 [95% CI 0.84 – 1.23]) environment. Conclusions: This study provides little support for the notion that exposure to food outlets in the home, school and commuting neighbourhoods increase the risk of obesity in children. It seems that the evidence is not well placed to support Governmental interventions/recommendations currently being proposed and that policy makers should approach policies designed to limit food outlets with caution.

Despite considerable research, evidence supporting associations between the ‘Retail Food Environment’ (RFE) and obesity remains mixed. Differences in the methods used to measure the RFE may explain this heterogeneity. Using data on a large (n = 10,111) sample of adults from the Yorkshire Health Study (UK), we modelled cross-sectional associations between the RFE and weight status using (i) multiple definitions of ‘Fast Food’, ‘Convenience’ and ‘Supermarkets’ and (ii) multiple RFE metrics, identified in a prior systematic review to be common in the literature. Both the choice of outlet definition and the choice of RFE metric substantively impacted observed associations with weight status. Findings differed in relation to statistical significance, effect sizes, and directions of association. This study provides novel evidence that the diversity of RFE measurement methods is contributing to heterogeneous study findings and conflicting policy messages. Greater attention is needed when selecting and communicating RFE measures in research.

Background: Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI) and food hygiene data from the Food Standards Agency (FSA), against street audits in England and appraises the utility of these data. Methods: Audits were conducted across 52 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit-identified outlets were matched to outlets in the POI and FSA data to identify true positives (TP: outlets in both the audits and the POI/FSA data), false positives (FP: outlets in the POI/FSA data only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP+FP)) and sensitivities (TP/(TP+FN)). Variations in sensitivities and PPVs across environment and outlet types were assessed using multi-level logistic regression. Proprietary classifications within the POI data were additionally used to classify outlets, and agreement between audit-derived and POI-derived classifications was assessed. Results: Street audits identified 1172 outlets, compared to 1100 and 1082 for POI and FSA respectively. PPVs were statistically significantly higher for FSA (0.91, CI: 0.89-0.93) than for POI (0.86, CI: 0.84-0.88). However, sensitivity values were not different between the two datasets. Sensitivity and PPVs varied across outlet types for both datasets. Without accounting for this, POI had statistically significantly better PPVs in rural and affluent areas. After accounting for variability across outlet types, FSA had statistically significantly better sensitivity in rural areas and worse sensitivity in rural middle affluence areas (relative to deprived). Audit-derived and POI-derived classifications exhibited substantial agreement (p < 0.001; Kappa = 0.66, CI: 0.63 - 0.70). Conclusions: POI and FSA data have good agreement with street audits; although both datasets had geographic biases which may need to be accounted for in analyses. Use of POI proprietary classifications is an accurate method for classifying outlets, providing time savings compared to manual classification of outlets.

Abstract

Background The United Kingdom faces complex economic and structural challenges that have disrupted food pricing, contributing to widespread food insecurity. These fluctuations diminish the affordability and accessibility of healthy, nutrient-dense foods among vulnerable groups. In high-income countries, food insecurity is associated with higher levels of obesity, and in the UK specifically, the cost of living crisis, where the cost of food has increased quicker than wages, is likely to have exacerbated existing dietary inequalities. This qualitative paper explores insights of people living with obesity and food insecurity, in an ethnically diverse community, to develop further understanding on their food shopping experiences. Methods A secondary analysis of qualitative data from four focus groups (8–11 participants per group; 92% female) was undertaken with participants who self-reported as living with obesity and food insecurity (n = 39) and were attempting to reduce their weight. Results Three themes and eight subthemes were generated using deductive and reflexive thematic analysis: (1) the Conscious Consumer, reflects the preparation and planning participants undertook by participants to maximise their limited resources. Subthemes include advanced meal planning, and price-comparison shopping. Despite these efforts, participants frequently encountered barriers to being able to purchase nutritionally balanced foods. (2) the Restricted Consumer highlights how structural and systemic limitations, including time pressures due to work or caregiving responsibilities, further constrained participants’ food purchasing choices. and (3) Mitigating the rising cost of food, describes the actions required to manage the challenges in purchasing foods with rising costs. Subthemes include substituting affordable, less-healthy products for costlier fresh produce and bulk buying of staple items. Conclusions Findings challenge societal beliefs that people living on low incomes need to budget more carefully to afford a healthy diet. People living with obesity and food insecurity often report experiencing cognitive dissonance. In this context, participants faced difficult and emotive trade-offs, as they recognised the suboptimal nutritional value of their food purchases but felt compelled by necessity to buy unhealthier food that matched their budget. Findings provide further insights to support healthy, sustainable food purchasing, as part of transforming the UK food system.

Introduction: Food insecurity (lack of reliable access to affordable and nutritious food) is a major concern in high-income countries because it increases the risk of poor nutrition, obesity and associated adverse health outcomes. Healthier diets are often also more environmentally sustainable (hereafter; sustainable), an important factor in reducing climate change. Practice-based interventions are therefore urgently needed to support people living with food insecurity and obesity to access and afford healthier and sustainable foods. Supermarkets are a key area for intervention, as purchasing can be an antecedent to consumption. However, the retailers’ perspectives on the feasibility of implementing affordability interventions is often overlooked and under-researched. Therefore, this study explored the perspectives, views, and experiences of major UK supermarket senior nutritionists on the acceptability and feasibility of using affordability interventions for healthier and sustainable food in the supermarket. Methods: We recruited seven UK senior supermarket nutritionists who represented 85% of the UK grocery market share. We used semi-structured interviews and analysed the data using a reflective thematic analysis approach. Results: Supermarket nutritionists perceived that their business did prioritise health and environmental sustainability for customers. However, there were several challenges encountered when trying to promote healthier and more sustainable food in the supermarket environment, including profitability concerns, unpredictability of intervention outcomes, control over own-brand products, perceived intention-behaviour gap, and a belief that they are already implementing affordability interventions. Differences in how supermarkets approach the evaluation of interventions also emerged, as well as a willingness to collaborate with academics and other retailers to optimise the evaluation of interventions. Lastly, supermarket nutritionists raised the need for an operationalised definition for sustainable food products. Discussion: Affordability interventions to support customers to purchase healthier and more sustainable food require supermarkets to consider multiple challenges. Findings highlight the need for upstream intervention that mandates and facilitates multi-lever approaches to health and sustainability without compromising commercial viability, along with practice-based approaches to implementation and evaluation.

Individuals participate in research for numerous reasons, however, the global economic downturn may have driven some to participate solely for monetary recompense. While inauthentic participation is more widely recognised in quantitative survey studies, it is increasingly becoming an issue in qualitative research. Drawing on our experiences and supported by the wider literature, we highlight ways in which inauthentic participation can be identified and addressed. We argue that pertinent researchers are aware of the risks and potential impact of inauthentic participants and recommend researchers consider this phenomenon from study planning stages onwards. We identify institutions, including Universities, as well placed to provide training and ensure, where necessary, mitigation plans are in place. We suggest ethics committees, funders and publishers request inauthentic participation be considered and reported. These recommendations would establish awareness of this phenomenon, prevent wasting valuable project resources, increase transparency of reporting and ensure data integrity is protected.

Introduction: Engagement denotes the extent to which, and how, individuals and families participate in a weight management (WM) programme. Data suggest that between 8-83% of families do not complete the paediatric WM programme which they initiated, but the underlying mechanisms which drive participant engagement are poorly understood. This thesis aims to 1) establish if participant-and programme-characteristics are associated with various engagement trajectories in paediatric WM programmes, and 2) explain the factors associated with initial-and continued-programme engagement. The Model of Retention – translated from Higher Education – underpins this thesis.  Study 1: Methods – Secondary data of 2948 MoreLife participants (age: 10.44±2.80 years, BMI SDS: 2.48±0.87 units, white ethnicity: 70.52 %) were used. Multivariable linear regression and multivariable logistic regression examined the predictors of attendance and engagement groups (e.g. early dropout, late dropout, completion…) respectively. Results – Six variables were associated with engagement (Programme: group size, delivery period, & programme year; Participant: BMI SDS, Ethnicity, & IMD score). Programme characteristics were stronger predictors of engagement than participant characteristics, and the predictors varied between engagement groups. A small proportion of the variance in engagement was explained by the final predictors.  Study 2: Methods – Qualitative data were collected from 31 families (parents and children) across three paediatric WM programmes (MoreLife, SHINE, and Weigh To Go) at the early-and late-programme stages. The Model of Retention guided the lines of inquiry and data analysis framework. Results – Six factors were central to engagement: 1) having support; 2) self-efficacy in one’s ability to attend; 3) coping with the demands of programme engagement; 4) controlling engagement decisions; 5) experiencing benefits from engagement; and, 6) having an engagement promoting programme design. The importance and dominance of these factors varied between the early-and lateprogramme stages, and moreover, between parents and children.  Conclusions: Participant engagement was demonstrated to be a complex phenomenon; one that is challenging to predict and currently better explained through qualitative investigation. This thesis, and the collective discussion within, illuminate methods of improving WM programme engagement through design and delivery modification. The Model of Retention offers a means of comprehensively explaining engagement. Finally, this thesis provides a conceptual framework for discussing programme engagement.

The physical environment is considered a contributing factor to elevated body mass index (BMI) and odds of obesity in adulthood. However, associations within current literature are inconsistent in scale and direction. This thesis used individual-level data from The Yorkshire Health Study including self-reported BMI (wave one: 2010-12, n=27,806 and wave two: 2013-15, n=11,164). The physical environment was characterised using measures of the food (fast-food, large supermarkets, convenience and other food outlets) and physical activity (PA) environment (PA facilities, parks, green, and blue space) corresponding with the baseline individual-level data. Home addresses were geocoded to postcode zone centroids. A 2km radial buffer defined neighbourhood. Analyses used multi-level models, latent class analysis (LCA) and subgroup analyses by socioeconomic status. In most cross sectional (n=22,889, age 18-86 years), and especially longitudinal findings (n=8,864), the physical environment, BMI, and obesity were inconsistently related. While PA facilities were associated with reduced BMI in longitudinal and cross-sectional findings, effects were very small. An original measure of neighbourhood typologies, was associated with BMI and obesity in cross-sectional findings, yet was unrelated in longitudinal evidence. Acknowledging the risk of residual confounding, this thesis advances evidence by suggesting that the physical environment may be relevant for BMI and obesity, but only among certain population sub-groups, for instance, low socioeconomic status individuals. Compared to current evidence, this research provides an original and rigorous longitudinal perspective that utilises advanced analytical approaches in a large sample. An extensive measurement of the physical environment from several different data sources, allowed a unique synthesis of evidence. However, even given these strengths, associations between the physical environment, BMI, and obesity are convincingly shown to be small in scale, and inconsistent in direction across this thesis. This provides preliminary evidence for an opportunity to reconsider the current trajectory of both research and policy in this field.

Background: There continues to be a lack of understanding as to the geographical area at which the environment exerts influence on behaviour and health. This exploratory study compares different potential methods of both researcher- and participant-defined definitions of neighbourhood reflect an adolescent's activity space. Methods: Seven consecutive days of global positioning system (GPS) tracking data were collected at 15 s intervals using a small exploratory adolescent sample of 14–18 year olds (n = 69) in West Yorkshire, England. A total of 304,581 GPS tracking points were collected and compared 30 different definitions of researcher-defined neighbourhoods including radial, network and ellipse buffers at 400 m, 800 m, 1000 m, 1600 m and 3000 m, as well as participant-defined self-drawn neighbourhoods. Results: This exploratory study supports emerging evidence cautioning against the use of static neighbourhood definitions for defining exposure. Traditional buffers (network and radial) capture at most 67% of activity space (home radial), and at worst they captured only 3.5% (school network) and range from capturing between 3 and 88% of total time. Similarly, self-drawn neighbourhoods captured only 10% of actual daily movement. Interestingly, 40% of an adolescent's self-drawn neighbourhood was not used. We also demonstrate that buffers capture a range of space (22–95%) where adolescents do not go, thus misclassifying the exposure. Conclusion: Our exploratory findings demonstrate that neither researcher- nor participant-defined definition of neighbourhood adequately captures adolescent activity space. Further research with larger samples are needed to confirm the findings of this exploratory study.

OBJECTIVE: It has been suggested that childhood obesity is inversely associated with deprivation, such that the prevalence is higher in more deprived groups. However, comparatively few studies actually use an area-level measure of deprivation, limiting the scope to assess trends in the association with obesity for this indicator. Furthermore, most assume a linear relationship. Therefore, the aim of this study was to investigate associations between area-level deprivation and three measures of adiposity in children: body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR). DESIGN: This is a cross-sectional study in which data were collected on three occasions a year apart (2005-2007). SUBJECTS: Data were available for 13,333 children, typically aged 11-12 years, from 37 schools and 542 lower super-output areas (LSOAs). MEASURES: Stature, mass and WC. Obesity was defined as a BMI and WC exceeding the 95th centile according to British reference data. WHtR exceeding 0.5 defined obesity. The Index of Multiple Deprivation affecting children (IDACI) was used to determine area-level deprivation. RESULTS: Considerable differences in the prevalence of obesity exist between the three different measures. However, for all measures of adiposity the highest probability of being classified as obese is in the middle of the IDACI range. This relationship is more marked in girls, such that the probability of being obese for girls living in areas at the two extremes of deprivation is around half that at the peak, occurring in the middle. CONCLUSION: These data confirm the high prevalence of obesity in children and suggest that the relationship between obesity and residential area-level deprivation is not linear. This is contrary to the 'deprivation theory' and questions the current understanding and interpretation of the relationship between obesity and deprivation in children. These results could help make informed decisions at the local level.

Research suggests that there has been a leveling off in obesity prevalence occurring in the child population. However, a concern with the evidence base is that all of the studies have relied upon the use of BMI. The purpose of this study was to compare waist circumference (WC), BMI, and waist-to-height ratio (WHtR) data in three different sample of children (total number: 14,697) typically aged 11–12 years. Obesity prevalence defined by BMI did not change significantly between measurement years (2005 boys 20.6%, girls 18.0%; 2006 boys 19.3%, girls 17.3%; 2007 boys 19.8%, girls 16.4%). Obesity prevalence defined by WC was considerably higher especially, in girls (2005 boys 26.3%, girls 35.6%; 2006 boys 20.3%, girls 28.2%; 2007 boys 22.1%, girls 30.1%). The prevalence of children defined as “at risk” according to WHtR (2005 boys 23.3%, girls 21.1%; 2006 boys 16.7%, girls 15.6%; 2007 boys 17.6%, girls 17.2%) was found to be between obesity prevalence, estimated using BMI and WC. This data are the most up to date collection that includes BMI and WC in three large samples of children and clearly demonstrates inconsistencies between different measurements based on current classification systems. There is a need to understand the relationship between BMI and WC, with growth and health risk to establish a consistent public health message that is easily understood by the public

Objective: The purpose of this study was to examine the prevalence of obesity over time in the same individuals comparing body mass index (BMI), waist circumference (WC) and waist to height ratio (WHtR). Study design: Five year longitudinal repeated measures study (2005–2010). Children were aged 11–12 (Y7) years at baseline and measurements were repeated at age 13–14 (Y9) years and 15–16 (Y11) years. Methods: WC and BMI measurements were carried out by the same person over the five years and raw values were expressed as standard deviation scores (sBMI and sWC) against the growth reference used for British children. Results: Mean sWC measurements were higher than mean sBMI measurements for both sexes and at all assessment occasions and sWC measurements were consistently high in girls compared to boys. Y7 sWC = 0.792 [95% confidence interval (CI) 0.675–0.908], Y9 sWC = 0.818 (95%CI 0.709–0.928), Y11 sWC = 0.943 (95%CI 0.827–1.06) for boys; Y7 sWC = 0.843 (0.697–0.989), Y9 sWC = 1.52 (95%CI 1.38–0.67), Y11 sWC = 1.89 (95%CI 1.79–2.04) for girls. Y7 sBMI = 0.445 (95%CI 0.315–0.575), Y9 sBMI = 0.314 (95%CI 0.189–0.438), Y11 sBMI = 0.196 (95%CI 0.054–0.337) for boys; Y7 sBMI = 0.353 (0.227–0.479), Y9 sBMI = 0.343 (95%CI 0.208–0.478), Y11 sBMI = 0.256 (95%CI 0.102–0.409) for girls. The estimated prevalence of obesity defined by BMI decreased in boys (18%, 12% and 10% in Y 7, 9 and 11 respectively) and girls (14%, 15% and 11% in Y 7, 9 and 11). In contrast, the prevalence estimated by WC increased sharply (boys; 13%, 19% and 23%; girls, 20%, 46% and 60%). Conclusion: Central adiposity, measured by WC is increasing alongside a stabilization in BMI. Children appear to be getting fatter and the additional adiposity is being stored centrally which is not detected by BMI. These substantial increases in WC are a serious concern, especially in girls.

Purpose: The criteria for participant completion of a weight management programme (WMP) is arbitrary. Programme commissioners (WMP purchasers) will frequently establish the percentage of attendance that classifies programme completion (e.g. 70% attendance). Differential criteria for WMP completion make it impossible for researchers, practitioners and policy makers to conclude what constitutes an effective programme and what factors predict WMP completion. This study exemplifies the impact of variable completion status on 1) BMI reduction, 2) volume of completers and 3) predictors of completion. Methods: Secondary data was obtained from MoreLife – a UK-based, community WMP for children (aged 4-17 years). 2948 children attended between 2009-2014 (Age 10.4±2.8 years, BMI 26.0±5.7kg/m2, Standardised BMI (BMI SDS) 2.48±0.87 units, White 70.3%). Separate analyses were conducted for research aims 1-2, and aim 3. Programme completion was adjusted incrementally by 10% (i.e. 10%, 20% attendance etc…) for research aims 1-2. The volume of programme completers and change in BMI SDS was calculated at each increment of the completion criteria (0-100%). For aim 3, programme completion was defined using five classifications from previous WMP studies (e.g. 50% sessions attended). Multivariable logistic regression determined participant and programme variables predictive of programme completion. Percentage difference between the odds ratio of the original model (completion = 70% attendance) and the four subsequent models was calculated. Results: The volume of participants completing the programme decreased in a linear manner (r = -0.99, p = 0.00) when completion classification became more stringent (i.e. 70-100% attendance). Conversely, the change in BMI SDS became incrementally greater (r = 0.98, p = 0.00). Predictors of completion varied by up to 24.2% in certain variables (e.g. Programme Intake Period) when using five different completion classifications. Statistical significance of the predictor variables were reliant on completion classification (e.g. WMP Group Size was significant in two of five models). Conclusions: The volume of completers and change in BMI SDS were strongly associated with programme completion classification. Poor programme outcomes (e.g. minimal change in BMI SDS) can be masked by (un)demanding completion criteria. Moreover, completion criteria mediates participant and programme characteristics predictive of programme completion. Standardised completion criteria are called for.

BACKGROUND: Approximately 50% of participants complete a paediatric weight management programme, yet the predictors of attendance and dropout are inconsistent. This study investigates subject and intervention-design characteristics associated with attendance at a group based, family weight management programme. SETTING AND SUBJECTS: Secondary data analysis of 2948 subjects (Age 10.4±2.8 years, BMI 26.0±5.7kg/m2, Standardised BMI (BMI SDS) 2.48±0.87, White 70.3%) from 244 MoreLife (UK) programmes. Subjects attend weekly for 10-12 weeks, sessions last 2-3 hours. Sessions include lifestyle guidance and physical activity. METHOD: Subject characteristics (demographics, psychological (body satisfaction & self-esteem) and sedentary behaviour) were gathered at first contact and BMI SDS was noted weekly. Intervention-design characteristics were recorded (year, length (weeks), group size, age segregation and day of session). Attendance was calculated as total number of sessions attended (%). Multivariate linear regression examined predictors of attendance and multiple imputation countered missing data. RESULTS: Average attendance was 59.4%±29.3%. Baseline subject characteristics were ‘poor’ predictors of attendance. Intervention year, group size and day of session significantly predicted attendance (Tables 1 & 2). Yet, the most predictive marker of attendance was a change in BMI SDS during the programme (B = -0.38, 95% CI = -0.43 - -0.33). CONCLUSION: A reduction in BMI was seen to predict greater attendance. However, baseline subject characteristics were weakly associated with attendance, refuting past findings. Dominant intervention characteristics (large groups, weekend sessions and recent delivery) predicted lower attendance. Future programmes may be better informed.

BACKGROUND: Approximately 50% of paediatric weight management (WM) programme attendees do not complete their respective programmes. High attrition rates compromise both programme effectiveness and cost-efficiency. Past research has examined pre-intervention participant characteristics associated with programme (non-)completion, however study samples are often small and not representative of multiple demographics. Moreover, the association between programme characteristics and participant engagement is not well known. This study examined participant and programme characteristics associated with engagement in a large, government funded, paediatric WM programme. Engagement was defined as the family's level of participation in the WM programme. METHODS: Secondary data analysis of 2948 participants (Age: 10.44 ± 2.80 years, BMI: 25.99 ± 5.79 kg/m(2), Standardised BMI [BMI SDS]: 2.48 ± 0.87 units, White Ethnicity: 70.52%) was undertaken. Participants attended a MoreLife programme (nationwide WM provider) between 2009 and 2014. Participants were classified into one of five engagement groups: Initiators, Late Dropouts, Low- or High- Sporadic Attenders, or Completers. Five binary multivariable logistic regression models were performed to identify participant (n = 11) and programmatic (n = 6) characteristics associated with an engagement group. Programme completion was classified as ≥70% attendance. RESULTS: Programme characteristics were stronger predictors of programme engagement than participant characteristics; particularly small group size, winter/autumn delivery periods and earlier programme years (proxy for scalability). Conversely, participant characteristics were weak predictors of programme engagement. Predictors varied between engagement groups (e.g. Completers, Initiators, Sporadic Attenders). 47.1% of participants completed the MoreLife programme (mean attendance: 59.4 ± 26.7%, mean BMI SDS change: -0.15 ± 0.22 units), and 21% of those who signed onto the programme did not attend a session. CONCLUSIONS: As WM services scale up, the efficacy and fidelity of programmes may be reduced due to increased demand and lower financial resource. Further, limiting WM programme groups to no more than 20 participants could result in greater engagement. Baseline participant characteristics are poor and inconsistent predictors of programme engagement. Thus, future research should evaluate participant motives, expectations, and barriers to attending a WM programme to enhance our understanding of participant WM engagement. Finally, we suggest that session-by-session attendance is recorded as a minimum requirement to improve reporting transparency and enhance external validity of study findings.

Objectives: Current research in the field of childhood weight management (WM) effectiveness is hampered by inconsistent terminology and criterion for WM programme completion, alongside other engagement-related concepts (e.g. adherence, dropout, and attrition). Evidence reviews are not able to determine conclusive intervention effectiveness because of this issue. This study aims to quantify how various completion criterion impact upon on: 1) the percentage of WM completers; 2) the standardised Body Mass Index (BMI SDS) reduction; and 3) the predictors of WM completion. Study Design: A methodological, sensitivity analysis to examine how differential completion criteria affect programme outcomes and predictors. Methods: Secondary data of 2948 children were used. All children attended a MoreLife WM programme between 2009 and 2014. The completion criterion was incrementally adjusted by 10% (i.e. completer attends 10%, 20%, 30%... of sessions) for research aims 1-2, with the percentage of completers and change in BMI SDS calculated at each increment. For aim 3, the stability (strength, direction, and significance) of the predictors were examined when using the completion criterion of four alternative studies against our previous study (completion ≥70% attendance). Results: The volume of programme completers decreased in a linear manner as the completion criterion became more stringent (i.e. 70-100% attendance). The change in BMI SDS conversely became incrementally greater. The strength, direction, and significance of the predictors was highly dependent on the completion criterion; the odds ratio varied by 24.2% across a single predictor variable (delivery period). The degree of change is evidenced in the paper. Conclusions: Inconsistent completion criterion greatly limits the synthesis of programme effectiveness, and explains some of the inconsistency in the predictors of engagement. Standardised criterion for engagement-related terminology are called for.

ObjectiveTo assess whether calorific screening thresholds improved the agreement between objective consumer purchase data, from supermarket transaction records, and self-reported dietary intake, from a Food Frequency questionnaire (FFQ), for people living with (PLWOw/Obwith) and without (PLWOw/Obwithout) overweight/obesity.DesignParticipants were recruited across a one-year period (1st June 2020 – 31st May 2021). Six screening thresholds were employed, using the estimated number of calories purchased for the individual, to filter participant data. Bland-Altman analyses were compared between PLWOw/Obwith and PLWOw/Obwithout for energy, sugar, total fat, saturated fat, protein and sodium.SettingPartnered with a large UK retailer.ParticipantsParticipants (N=1788) were recruited via the retailer’s loyalty card customer database. Participants with completed FFQs, shared transaction records, height, weight and household composition data were included for analysis (N=642).ResultsAgreement was found between objective purchase data and self-reported dietary intake at ≥1000 Kcal/day (energy, sugar, total fat and saturated fat) and ≥1500 Kcal/day (protein and sodium). PLWOw/Obwith consumed greater energy (19%), sugar (36%), total fat (22%) and saturated fat (25%) than they were estimated to have purchased at the retailer. PLWOw/Obwithout only consumed greater sugar (19%). ConclusionsThe application of screening thresholds based on estimated individual calories purchased may provide a valuable preprocessing step within the analysis of consumer purchase data, allowing agreement to be found for absolute nutrient values. Differences in bias between PLWOw/Obwith and PLWOw/Obwithout show that insights into purchase and consumption patterns can be identified using consumer purchase data.

The Impact of Obesity when Validating Supermarket Transaction Records In Dietary Evaluation: FIO-STRIDE 1,2Thomas Sawczuk, 1Hannah C Greatwood, 1Mark S Gilthorpe, 3,4Michelle A Morris, 3,4Victoria Jenneson, 1Claire Griffiths, on behalf of the FIO-Food Team 1Obesity Institute, Leeds Beckett University, Leeds, LS6 3QT, UK; 2Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, LS6 3QT, UK; 3Leeds Institute for Data Analytics, Level 11 Worsley Building, Clarendon Way, University of Leeds, Leeds, LS2 9JT, UK; 4School of Food Science and Nutrition, University of Leeds, Willow Terrace Road, Leeds, LS2 9JT, UK. Background: Supermarket transaction data reflect dietary purchasing behaviours. FIO-STRIDE compares dietary purchase patterns (supermarket transactions) with consumption (Food Frequency Questionnaire (FFQ)) for people living with and without overweight/obesity. Methods: Participants (n=683) in England were recruited via a UK retailer’s loyalty card database within the Supermarket Transaction Records In Dietary Evaluation (STRIDE) study, where survey data were collected including: height, weight, and FFQ (Jenneson et al. 2023). Bland-Altman plots assessed agreement for absolute measures of: Kcal, sugar, fat, saturated fat, protein, and sodium among people living with and without overweight/obesity. Participant purchase data were filtered by estimated individual calorific consumption:≥1000 Kcal/day/person and ≥1500 Kcal/day/person. Results: People living with overweight/obesity consumed greater kcal, sugar, fat, and saturated fat than they purchased, whereas those living without consumed greater sugar. Agreements were observed among participants for their consumption of kcal, sugars, fat, and saturated fat after filtering for ≥1000 Kcal/day consumption, and for their consumption of protein and sodium after filtering for ≥1500 Kcal/day consumption. Conclusions: Agreement between food purchase and consumption records differed by weight status. The number of calories purchased should be considered as a filter to exclude inconsistent shoppers when using supermarket purchase data as a proxy for consumption.

Background: Persons experiencing food insecurity (FI) compared to lower levels are more likely to live with obesity and purchase foods of lower dietary quality. Communications within the retail environment have potential to influence behaviours, but little is known on how the messaging is perceived by this target audience. This qualitative paper explores the insights of people living with obesity (PLWO) and FI on two national campaigns targeted at: i) supporting customers with increased food prices; and ii) promoting the consumption of healthy sustainable meals. Methods: PLWO and FI (n=39) expressed their perceptions of purchasing healthier and more environmentally sustainable foods through four focus groups. Reflexive thematic analysis was used to generate themes. Results: Five themes and 12 subthemes were generated: (i) Do I have the resource? i.e. financial and/or time (ii) Do I know what it means? e.g. clarity of images (iii) Do I trust it? e.g. authentic images (iv) Do I want it? e.g. lack of appeal (v) Recommendations for future promotional communications. Conclusions: Findings provide insights for retailers on the need for upstream changes within the wider food system and the importance of tailored communications and messaging that supports PLWO and FI purchase healthier and more sustainable foods.

The environment may lead to lower body mass index (BMI) and obesity risk by providing opportunities to be physically active. However, while intuitively appealing, associations are often inconsistent in direction and small scale. This longitudinal study examined if change in BMI and obesity was associated with the availability of physical activity (PA) facilities and parks and explored if these associations differed by age. Longitudinal data (n = 8,864, aged 18-86 years) were provided at baseline (wave I: 2010-2012) and follow up (wave II: 2013-2015) of the Yorkshire Health Study. BMI was calculated using self-reported height (cm) and weight (kg) (obesity = BMI≥30.00). To define availability, home addresses were geocoded based on postcode zone centroids and neighbourhood was defined as a 2 km radial buffer. PA facilities were sourced from Ordnance Survey Points of Interest (PoI) and parks were sourced from OpenStreetMap. Environmental data temporally matched individual-level baseline data collection. PA facilities (b = -0.006 [-0.015, 0.003]) and parks (b = -0.001 [-0.015, 0.013]) at baseline were not associated with change in BMI. Change in obesity was unrelated to parks (OR = 0.994 [0.975, 1.015]) and while PA facilities were related (OR = 0.979 [0.965, 0.993]), effects were small. A combined measure of the recreational PA environment including parks and PA facilities was unrelated to change in BMI and obesity. Despite this, statistically significant interactions were found for both PA facilities, parks, and change in obesity by age. Based on the premise that an individual's mobility varies with age, and although effects were small, this offers tentative evidence which suggests it may be useful for policymakers in Public Health and Planning to consider the impact of environmental interventions across the life course.

Background Obesity remains one of the most challenging public health issues of our modern time. Despite the face validity of claims for influence, studies on the causes of obesity have reported the influence of the food environment to be inconsistent. This inconsistency has been attributed to the variability of measures used by researchers to represent the food environments—Researcher-Defined Food Environments (RDFE) like circular, street-network buffers, and others. This study (i.) determined an individual’s Activity Space (AS) (ii.) explored the accuracy of the RDFE in representing the AS, (iii.) investigated the accuracy of the RDFE in representing actual exposure, and (iv.) explored whether exposure to food outlet reflects the use of food outlets. Methods Data were collected between June and December 2018. A total of 65 participants collected Global Positioning System (GPS) data, kept receipt of all their food purchases, completed a questionnaire about their personal information and had their weight and height measured. A buffer was created around the GPS points and merged to form an AS (GPS-based AS). Results Statistical and geospatial analyses found that the AS size of participants working away from home was positively related to the Euclidean distance from home to workplace; the orientation (shape) of AS was also influenced by the direction of workplace from home and individual characteristics were not predictive of the size of AS. Consistent with some previous studies, all types and sizes of RDFE variably misrepresented individual exposure in the food environments. Importantly, the accuracy of the RDFE was significantly improved by including both the home and workplace domains. The study also found no correlation between exposure and use of food outlets. Conclusions Home and workplace are key activity nodes in modelling AS or food environments and the relationship between exposure and use is more complex than is currently suggested in both empirical and policy literature.

Background: Dietary behaviours of adolescence are concerning, and this may impact long-term well-being. Aim: This study examined the socio-ecological determinants of dietary behaviours in a national prospective cohort study of English adolescents. Methods: Latent class analysis was used to identify the typologies of eight dietary behaviours: fruit, vegetable, breakfast, sugar-sweetened beverages, artificial-sweetened beverages, fast-food, bread, and milk from 7402 adolescents aged 13-15 years (mean 13.8 ± 0.45 years) (50.3% female and 71.3% white ethnicity) participating in the U.K. Millennium Cohort Study (sixth survey). Multinomial logistic regression and path analysis predicted associations between personal characteristics, individual, influential others, social environment and physical environment determinants and three distinct diet typologies: (1) healthy, (2) less-healthy and (3) mixed, (reference category = mixed). Results: Within Path analysis, the magnitudes of coefficients were small to moderate suggesting a relatively weak relationship between the variables. Model 1 reported adolescents within the less-healthy compared to mixed typology had lower levels of physical activity (β = 0.074, 95% CI = -0.115, -0.033), and have siblings (β = 0.246, 95% CI = 0.105, 0.387). Model 2 reported adolescents within the healthy compared to mixed typology had lower screen time (β = 0.104, 95% CI = 0.067, 0.141), and lower social media usage (β = 0.035, 95% CI = 0.024, 0.046). Conclusion: This study highlights the importance of considering multiple dietary determinants. These findings are likely to be useful in supporting the development of multi-faceted interventions. They emphasise the need to move away from investigating silo behaviours on individual diet components and a step towards more systems thinking to improve adolescent eating behaviours.

Objective The purpose of this study was to evaluate the healthy weight services in one local authority in England, where obesity levels have been above the national average since 2006. Design We conducted process and outcome evaluation using a qualitative methodology. Data were generated in focus group discussions and semi-structured interviews with clients, practitioners, healthcare professionals and volunteers. Results Ninety-one individuals from six services participated in the evaluation. Staff competencies and empowerment outcomes were identified as areas of strength. However, despite examples of excellent practice and enthusiastic recommendations from clients, access and referral processes were areas of weakness. Conclusion In England, local authorities have an important role to play in tackling obesity. It is crucial that they are provided with the tools to be able to implement healthy weight interventions effectively. A whole-systems approach presents a real opportunity for staff in local authorities and public health to work collaboratively and innovatively towards the same goal of continuous improvement in obesity management.

This article explores the potential of using Global Positioning Systems (GPS) to capture valuable data in measuring food environments. Such data, when triangulated with more conventional methods of collection, for example daily 24-hour dietary recalls and food purchase receipts, allow researchers to gain a fuller picture of individual activity in dynamic food environments. This is vital to understanding both individual and environmental factors that influence individuals’ decision and behaviour patterns within food environments. However, the practicalities of triangulating data collection methods are challenging to both researchers and participants, and so a pilot study was undertaken to test different methods of measuring the food environment. Recruitment for the pilot study took place between August and September 2017 and of the 16 participants initially recruited, 13 took part and completed all data collection methods and provided valuable feedback about the experience. The participants’ perspectives on the process of triangulating methods, along with the findings, are discussed in the paper.

Purpose: Approximately 50% of families who initiate a weight management programme (WMP) will not complete. It is fundamental to understand why participants initiate and complete a programme, and to ensure that programmes are effectively designed and delivered. This study examined the reasoning for family (young person and parent) engagement in three different and diverse WMPs. Methods: A multiple instrumental case study approach was employed. Three community-based WMPs participated: MoreLife, SHINE, and Weigh to Go. Clear design and implementation differences existed between WMPs. Multiple WMPs were recruited to examine the generalisability of research findings, and extract key features associated with participant engagement. Thirty families took part (~10 per programme). Data were collected early in the programme (0-2 weeks) and immediately after completion or dropout (within two weeks). Young people took part in a Participatory Action Research (PAR) session (interactive activities to generate meaningful information), and parents completed semi-structured interviews. A deductive line of inquiry was used; questions were based upon participant characteristics, environmental interactions, psychological processes and programme interactions. Interview data was transcribed verbatim and analysed alongside the PAR data using content and thematic analysis (themes presented in italics). Results: Preliminary findings indicate that families often engage in a WMP for non-weight related reasons. Such reasons include: management of mental health, to improve self -esteem, and to create friendships. Families remain in a WMP when: the programme suits their needs, they fit in amongst other participants, strong relationships are fostered with staff, and have strong support networks. Numerous families completing programmes prioritised WMP attendance above other leisure activities, and had plans in place to ensure they could attend each session. Low engagement was due to situational factors (e.g. logistic barriers [transport, timing…]) rather than programme dissatisfaction. Conclusions: Families attend community-based WMPs for reasons beyond weight management. Additionally, the families identified unique WMP features (e.g. maintenance programmes and non-clinical staff) which encourage programme attendance. Such features can be replicated in multiple, diverse settings. Understanding participant engagement is critical to designing and implementing efficacious WMPs.

Background: Childhood weight management programmes (WMP) are used within the UK to stem the rising prevalence of pediatric obesity. These WMPs often provide children and young people (CYP) and their family’s with methods of stabilising and reducing the severity of the weight issue. That said, low engagement in WMPs is often encountered but the reasoning is not well known. Misaligned and unrealistic outcome expectations have been hypothesised as a reason for low engagement. This paper explores 1) the parent and CYP outcome expectations of a WMP, and 2) the qualitative level of agreement between parent and CYP expectations. Methods: 30 families were recruited from three, UK-based WMPs (10 families per programme). Qualitative research methods were used to examine both the parent and CYP outcome expectations. Participatory research methods were used with CYP and semi-structured interviews with parents. Data were collected from parents and CYP independently, and notably, were collected from participants within two weeks of starting a WMP. Data were analysed using thematic analysis. In separate analyses, the alignment between parent and CYP responses were examined. Results: Preliminary findings indicate that parents reported 24 different outcome expectations (varying from ‘anger management’ to ‘weight management [not loss]’ to ‘understanding consequences of obesity’), whilst CYP reported 25 expectations (ranging from ‘aesthetic improvement’ to ‘physical activity opportunities’ to ‘not wanting to attend’). Weight loss was the most cited outcome expectation amongst parents and CYP, however friendship, CYP confidence, dietary education, and the reinforcement of parent messages were also strongly cited. Of note, weight loss was not always cited as the primary outcome expectation. The qualitative level of agreement between CYP and parents shall be reported. Conclusions: Families do not always initiate a WMP for the sole purpose of weight loss and management. Practitioners would benefit from understanding what families hope to achieve during their attendance, and subsequently tailor the programme, comments and feedback to reflect this. By tailoring messages and feedback directly to the family expectations, families may see a greater benefit in WMP attendance and therefore be encouraged to persist in treatment. Attendance and weight-related outcomes are strongly correlated.

Background: The United Kingdom faces complex economic and structural challenges that have disrupted food pricing, contributing to widespread food insecurity. These fluctuations diminish the affordability and accessibility of healthy, nutrient-dense foods among vulnerable groups. In high-income countries, food insecurity is associated with higher levels of obesity, and in the UK specifically, the cost of living crisis, where the cost of food has increased quicker than wages, is likely to have exacerbated existing dietary inequalities. This qualitative paper explores insights of people living with obesity and food insecurity, in an ethnically diverse community, to develop further understanding on their food shopping experiences.Methods: A secondary analysis of qualitative data from four focus groups (8–11 participants per group; 92% female) was undertaken with participants who self-reported as living with obesity and food insecurity (n=39) and were attempting to reduce their weight. Results: Three themes and eight subthemes were generated using deductive and reflexive thematic analysis: (1) the Conscious Consumer, reflects the preparation and planning participants undertook by participants to maximise their limited resources. Subthemes include advanced meal planning, and price-comparison shopping. Despite these efforts, participants frequently encountered barriers to being able to purchase nutritionally balanced foods. (2) the Restricted Consumer highlights how structural and systemic limitations, including time pressures due to work or caregiving responsibilities, further constrained participants’ food purchasing choices. and (3) Mitigating the rising cost of food, describes the actions required to manage the challenges in purchasing foods with rising costs. Subthemes include substituting affordable, less-healthy products for costlier fresh produce and bulk buying of staple items. Conclusions: Findings challenge societal beliefs that people living on low incomes need to budget more carefully to afford a healthy diet. People living with obesity and food insecurity often report experiencing cognitive dissonance. In this context, participants faced difficult and emotive trade-offs, as they recognised the suboptimal nutritional value of their food purchases but felt compelled by necessity to buy unhealthier food that matched their budget. Findings provide further insights to support healthy, sustainable food purchasing, as part of transforming the UK food system.

© 2015 The Royal Society for Public Health. Objectives: The accurate mass assessment of physical activity is essential for effective Public Health policy and practice. Combined with a desire to minimize participant burden, the self-reported single-item physical activity screening measure has become increasingly attractive and widespread. To help reduce any potential misclassification, refining this instrumentation in line with any changes in prescribed activity levels is essential to optimize accuracy. Study design: This study compares the levels of agreement, sensitivity and specificity for the single-item measure versus International Physical Activity Questionnaire (IPAQ) using current physical activity recommendations. Methods: Agreement was assessed in a non-probability sample of 7650 adults. The κ statistic, sensitivity and specificity were used to assess agreement between the tools for classifying participants as sufficiently active for health (≥150 min of physical activity per week) or not, and being classified as inactive (<30 of minutes of physical activity per week) or not. Results: The single-item measure showed weak agreement with the IPAQ for identifying participants who met the current physical activity guidelines (κ = 0.13, 95% CI 0.12 to 0.14), sensitivity was 18.7% and specificity was 97.2%. For the classification of inactive participants it showed a moderate agreement with IPAQ (κ = 0.45, 95% CI 0.43 to 0.47), sensitivity was 74.2% and specificity was 79.7%. Conclusions: The single-item measure had a low diagnostic capacity compared to IPAQ. Further research is needed if it is to be used in large scale surveys and interventions where screening for sufficiently active or inactive individuals is the goal.

Background: People experiencing food insecurity (FI) are more likely to live with obesity and purchase foods of lower dietary quality. Retail campaigns have the potential to influence food purchasing behaviours. Still, little is known about how the retailers’ messaging is perceived by people living with obesity (PLWO) and FI. This qualitative paper explores the insights of PLWO and FI on two national online and in-store campaigns targeted at i) supporting customers with increased food prices, and ii) promoting the consumption of healthier and more environmentally sustainable meals. Methods: Participants who self-reported as living with obesity and FI (n=39) expressed their perceptions of campaign images, from one retailer, through four in-person focus groups. Findings from the focus groups were then presented to the retail partner in an online participatory workshop. Themes were generated using reflexive thematic analysis.Results: Five themes and 12 subthemes were generated from the focus groups: (i) ‘Do I have the resources needed?’ Finances and, or time influenced participants’ food purchasing. (ii) ‘Do I know what it means?’ Participants did not always understand the images presented. (iii) ‘Do I trust it?’ Participants questioned whether the prices or images in the campaigns were authentic. (iv) ‘Do I want it?’ Participants questioned whether the food presented in the images appealed to them. (v) ‘Recommendations for future promotional communications’. Participants outlined how they wanted messaging to apply to them by using ethnically diverse food images that are suitable for a range of health conditions. From the retail partner participatory workshop we identified one theme and three subthemes. (i) ‘It is a conundrum’, the diverse needs of subgroups for national campaigns make it challenging for retailers to communicate healthy sustainable food promotions.Conclusions: These findings provide insights for retailers on the need for tailored communications, that reflect the requirements of different customers, to support PLWO and FI to purchase healthier and more sustainable foods. Acknowledging and addressing the inherent complexity of promoting healthier and more environmentally sustainable food is vital to making meaningful improvements to the food environment.

Aims: The current study aimed to evaluate implementation fidelity of an Integrated Healthy Lifestyle Service (IHLS). Methods: A pragmatic sample of 28 individual interviews and 11 focus groups were conducted. This resulted in a total of 81 (22 male) individuals comprising key stakeholders (n = 18), as well as intervention staff across senior management (n = 4), team lead (n = 14) and practitioner (n = 11) roles, and intervention clients (n = 34). Results: A mixed degree of implementation fidelity was demonstrated throughout the five a priori fidelity domains of study design, provider training, intervention delivery, intervention receipt, and enactment. Stakeholders, staff and clients alike noted a high degree of intervention receipt across all services offered. Contrastingly, practitioners noted that they received minimal formal operational, data systems, clinical, and curriculum training as well as a lack of personal development opportunities. Consequently, practitioners reported low confidence in delivering sessions and collecting and analysing any data. A top-down approach to information dissemination within the service was also noted among practitioners which affected motivation and overall team morale. Conclusion: Results can be used to conceptualise best practices as a process to further strengthen the design, delivery and recruitment strategies of the IHLS.

The aggregation of marginal gains in our knowledge of physical activity measurement is central to the development of effective public health policy and programming. Use of the single-item physical activity measure has become increasingly widespread. However, it was originally developed and validated against the 2004 UK physical activity recommendations, which were updated in 2011. Whilst the changes are modest, they may alter an individual’s activity categorisation compared to the preceding guidelines. PURPOSE: This study assesses the levels of agreement, sensitivity and specificity for the single-item measure against the IPAQ using contemporary physical activity recommendations. METHODS: Validation was undertaken using a quota sampling system of 7650 adults. The sensitivity and specificity, and  statistic were used to assess agreement between the tools for classifying participants as sufficiently active for health (≥150 minutes of physical activity per week) of not, and being inactive (<30 of minutes of physical activity per week) or not. Spearman’s rank correlation coefficients assessed concurrent validity of the single-item measure against IPAQ. RESULTS: Overall agreement between the single-item measure and IPAQ at categorising sufficiently active participants was weak, sensitivity was 18.7% (k= 0.13, 95% CI 0.12 to 0.14). For the classification of inactive participants agreement between the two measures was moderate, sensitivity was 74.2% (k= 0.45, 95% CI 0.43 to 0.47). The single-item measure’s concurrent validity was fair when classifying sufficiently active participants (rs = 0.24). However, a stronger, moderate correlation was found for the assessment of inactive participants (rs = 0.47). CONCLUSION: Compared to IPAQ, the single-item measures capacity to detect participants achieving current physical activity guidelines was weak. However, in this cohort, its ability to identify inactive participants was more encouraging showing acceptable levels of agreement and sensitivity. Therefore, it could be legitimately included in large scale surveys and interventions for adults, where identifying inactive individuals is the goal, or is a requirement for entry to an intervention.

Background: UK levels of overweight and obesity in children are high and continue to increase (Health Survey of England 2004). Recently alternative methods to determine prevalence of overweight and obesity have gained recognition and been made available. Therefore this study was undertaken using three accepted methods for determining prevalence of overweight and obesity in a large sample of 11 year old children. Methods: 4711 children were assessed in 33 schools in Leeds, UK. All children were assessed for stature, body mass, waist circumference, % body fat and selected sports performance tests. Overweight and obesity was recognised using each of the three anthropometric measures (BMI, Waist circumference and % body fat) above the 85th percentile for age and gender. Results: Overweight and obesity prevalence was high with BMI, Waist circumference and % body fat mean prevalence being 34.4%, 47.9% and 25.2% respectively. Of concern was the high variability between schools, with the variation being 22.8% – 42.3% (BMI), 22.5% – 72.8% (waist circumference) and 16.4% - 36.9%% (Body fat). Conclusion: These data show that levels of childhood obesity are high in comparison to the 27.7% reported in the Health Survey of England (2004) and that there is high variation between schools in a large UK city. These findings provide strong evidence and information for targeted action in the form of both prevention and treatment for this population.

Objective: The aim of the present study was to compare percentage body fat (PF) estimates using bioelectrical impedance analysis (BIA) with that determined by air displacement plethysmography in overweight and obese children. Methods: All participants had standardised BMI values >85th centile according to National Centile Charts. 302 males: age 13.9±1.7 y, BMI 32.8±6.6 kg.m-2, PF ADP 40.8±9.6 %, and 354 females: age 14.5±1.8 y, BMI 33.8±6.2 kg.m-2, PF ADP 44.2±7.2 %, were assessed. BIA PF estimates were obtained at 50 kHz using a foot-plate system device (Tanita TBF-310) and the inbuilt manufacturer prediction equations. ADP PF measurements were obtained using the child-specific thoracic gas volume prediction equations of Fields (2004) and the age and gender-specific body density conversion equations of Lohman (1989). Results: BIA estimates of PF were significantly correlated with those of ADP (males r=0.80, females r=0.68; both P<0.001). BIA significantly underestimated mean PF compared to ADP (males -3.6 %, females -1.7 %; both P<0.001). According to the methods of Bland and Altman, the ±95 % limits of agreement were slightly higher in males (±12.0 %) than females (±10.4 %). Further, correlation on the Bland and Altman plots revealed a significant bias as a function of increasing PF in females (r = - 0.45; P<0.001). Conclusion: On an individual basis there may be large discrepancies between BIA and ADP PF estimates. Results from devices are therefore not interchangeable in overweight and obese children.

Background: In 2004 the UK Government, within the Choosing Health White Paper, outlined a target ‘To halt, by 2010, the year on year increase in obesity among children under 11 in the context of a broader strategy to tackle obesity in the population as a whole’. In order to justify action there is a need to highlight the scale of the problem. In addition, further analysis comparing differences between schools is important to identify where appropriate resources/support should be provided. Methods: During 2005 BMI measurements were obtained in 2425 boys and 2267 girls, aged 11.6±0.3y, from 33 out of 40 schools in Leeds, UK. Standardized values were calculated using National Centile Charts and overweight and obesity prevalence defined at the 85th and 95th centiles, respectively. Results: Overall UK BMI centile charts identified 33.5% of children overweight and obese compared to the 27.7% prevalence reported in the Health Survey of England (2003) for children aged 9–11y. When the data were analysed by school the prevalence of overweight and obesity varied from 23.0% to 42.9%, with 3 schools less than 30% and 4 schools higher than 40%. Conclusion: These data should be of major concern given the increasing trend in an age group extremely close to that the government has prioritised. In addition, it is clear that there was large variability in the prevalence of overweight and obesity according to school. The upper prevalence values are particularly high and demonstrate the scale of the problem in children attending specific schools.

This systematic review quantifies methods used to measure the ‘retail food environment’ (RFE), appraises the quality of methodological reporting, and examines associations with obesity, accounting for differences in methods. Only spatial measures of the RFE, such as food outlet proximity were included. Across the 113 included studies, methods for measuring the RFE were extremely diverse, yet reporting of methods was poor (average reporting quality score: 58.6%). Null associations dominated across all measurement methods, comprising 76.0% of 1937 associations in total. Outcomes varied across measurement methods (e.g. narrow definitions of ‘supermarket’: 20.7% negative associations vs 1.7% positive; broad definitions of ‘supermarket’: 9.0% negative associations vs 10.4% positive). Researchers should report methods more clearly, and should articulate findings in the context of the measurement methods employed.

Member led symposium at UK Congress on Obesity 2022

Research in nutrition and health has primarily targeted the treatment of chronic diseases. However, research suggests food intake can also affect mood (Geary, 2001: The food and mood handbook. London: Thornsons). Food intake may impact important self-perceptions, however little is known of the role played by nutrition on self perception in children. This gave the purpose to the current study. With institutional ethical approval, 146 year 7 pupils (females [n=92]; mean age=11.60, s=0.32) completed a modified version of the Harter Self-Perception Profile for Children (SPPC; Harter, 1985: Manual of the self-perception profile for children. Denver, CO: University of Denver). The SPPC provides a multi-dimensional overview of an individual’s ratings of competence on five elements sports competence, appearance, social acceptance, scholastic competence and behavioural conduct, combining in a single score for Global Self Worth. Two questions were drawn from the original 6 per subscale. Participants also completed a Food Frequency Questionnaire (FFQ), comprising 63 food categories, with six levels for frequency of consumption (Margetts et al., 1989: International Journal of Epidemiology 18, 868–873). Separate standard portion sizes for boys and girls were used in the analysis (Macdiarmid & Blundell, 1997: European Journal of Clinical Nutrition, 51, 199–200). Data were collected during PE lessons at the beginning of the academic year. Based on responses participants were categorized as either high/low self-perceptions around the mean subscale split. Pearsons Product Moment correlations found no significant relationship between Global Self Worthand energy intake. However, there was a significantlynegative correlation between total energy intake and (i) Appearance (r(146)=-0.166, P=0.021 and (ii) Behaviour (r(146)=-0.210, P=0.036) subscales, suggesting that as total energy intake increased perceptions of appearance and behaviour decreased The results provide preliminary findings to support a potential relationship between energy intake and self-perceptions in children. Findings highlight that children who report higher energy intakes have reduced levels of self-perception in certain subscales. It suggests that while energy intake was not negatively related to Global Self-worth, it is negatively linked to perceptions at the lower domain levels of appearance and behaviour. This confirms a potential detrimental effect of increased energy intake with how children view the way they look and the way they behave. Findings suggest that schools should be aware of the risk that higher energy intakes may have on self-perceptions and seek to develop strategies to enhance their beneficial elements.

An increasing number of studies in children are being published using accelerometry to determine the levels of moderate-to-vigorous physical activity (MVPA). Although accelerometry is regarded as an objective measure, a review of the literature reveals a large disparity in the proportions of children meeting current MVPA recommendations. Therefore, the aim of the present study was to evaluate the variability of MVPA estimates in children using published accelerometry threshold values. With institutional ethical approval, 46 (27 males) children, aged 11–12 years, participated in the study. Weekday physical activity was measured using an Actigraph GT1M accelerometer, set at 10-s epochs. Time spent in MVPA was calculated using five adjusted intensity thresholds based on previously published counts per minute (cpm) values: (i) 1130 (Freedson et al., 1997: Medicine and Science in Sports and Exercise, 29, S45), (ii) 2000 (Ekelund et al., 2004: American Journal of Clinical Nutrition, 80, 584–590), (iii) 3000 (Treuth et al., 2004: Medicine and Science in Sports and Exercise, 36, 1259–1266), (iv) 3200 (Puyau et al., 2002: Obesity Research, 10, 150–157) and (v) 3600 (Riddoch et al., 2007: Archives of Disease in Childhood, 92, 963–969). Additionally, using each threshold the percentage of children acquiring an average of 60 min MVPA per day was calculated. Using the five intensity thresholds, participants spent (in ascending order) (i) mean 100, s=32 min, (ii) mean 59, s=23 min, (iii) mean 32, s=15 min, (iv) mean 28, s=13 min and (v) mean 21, s=11 min per weekday in MVPA, respectively. All differences were statistically significant (P<0.001). Within each threshold, the percentage of children acquiring an average of 60 min MVPA was 93, 41, 9, 2 and 0%, respectively. Although based on only a small sample, these findings illustrate the variability of defining MVPA using different thresholds. Although accelerometry overcomes many of the reliability and validity problems associated with self-report, pedometer and heart-rate assessment, careful consideration is warranted when interpreting accelerometry data. Indeed, even though acceleroAn increasing number of studies in children are being published using accelerometry to determine the levels of moderate-to-vigorous physical activity (MVPA). Although accelerometry is regarded as an objective measure, a review of the literature reveals a large disparity in the proportions of children meeting current MVPA recommendations. Therefore, the aim of the present study was to evaluate the variability of MVPA estimates in children using published accelerometry threshold values. With institutional ethical approval, 46 (27 males) children, aged 11–12 years, participated in the study. Weekday physical activity was measured using an Actigraph GT1M accelerometer, set at 10-s epochs. Time spent in MVPA was calculated using five adjusted intensity thresholds based on previously published counts per minute (cpm) values: (i) 1130 (Freedson et al., 1997: Medicine and Science in Sports and Exercise, 29, S45), (ii) 2000 (Ekelund et al., 2004: American Journal of Clinical Nutrition, 80, 584–590), (iii) 3000 (Treuth et al., 2004: Medicine and Science in Sports and Exercise, 36, 1259–1266), (iv) 3200 (Puyau et al., 2002: Obesity Research, 10, 150–157) and (v) 3600 (Riddoch et al., 2007: Archives of Disease in Childhood, 92, 963–969). Additionally, using each threshold the percentage of children acquiring an average of 60 min MVPA per day was calculated. Using the five intensity thresholds, participants spent (in ascending order) (i) mean 100, s=32 min, (ii) mean 59, s=23 min, (iii) mean 32, s=15 min, (iv) mean 28, s=13 min and (v) mean 21, s=11 min per weekday in MVPA, respectively. All differences were statistically significant (P<0.001). Within each threshold, the percentage of children acquiring an average of 60 min MVPA was 93, 41, 9, 2 and 0%, respectively. Although based on only a small sample, these findings illustrate the variability of defining MVPA using different thresholds. Although accelerometry overcomes many of the reliability and validity problems associated with self-report, pedometer and heart-rate assessment, careful consideration is warranted when interpreting accelerometry data. Indeed, even though accelerometry counts may be regarded as an objective assessment of physical activity, analysis of the data still requires a subjective choice of MVPA threshold. Moreover, until a quantifiable evidencebased definition of MVPA is developed in association with appropriate accelerometry thresholds, any conclusion regarding the proportions of 11–12 year old school children meeting current government guidelines must be interpreted with caution.

The negative health consequences of physical inactivity are independent from those of sedentary behaviour. However,injurious health prognoses occur when these behaviours coalesce. Whilst physical inactivity and sedentary behaviour are irreducible components of modern lifestyles, the evidence base connecting the two behaviours is limited.Aggregating our knowledge of how these behaviours cluster and who they cluster with may facilitate the development of more effective policy and intervention. PURPOSE: This study investigates how physical activity and sedentary behaviour cluster. It further examines how individuals cluster through shared behaviours and characteristics. METHODS: A non-probability sample of 22,836 participant’s self-reported demographics and completed the International Physical Activity Questionnaire (IPAQ). Using an observational between-subjects design, a 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences. RESULTS: A three cluster solution was identified. There were 27.7% (n=6,254) of participants assigned to cluster 1 (Ambulatory& Active), 44.4% (n=10,028) of participants within cluster 2 (Moderation) and 27.9% (6,286) of participants allocated to cluster 3 (Sedentary& Low Active). The ‘Ambulatory& Active’ (n=6,254) cluster sat for 2.5 to 5 hours daily and were highly active. In comparison, the ‘Sedentary& Low Active’ cluster (n=6,286) achieved <=60 MET.min.wk-1 of physical activity and sat for >=8 hours daily. CONCLUSIONS:This study adopted an original approach to understanding how people can be classified according to similarities in physical activity and sedentary behaviour. Data indicated that high levels of sedentary behaviour, determined by sitting time, clustered with low levels of physical activity. Importantly, the clusters can be distinguished conceptually and are likely to respond differently to varying approaches and/or interventions; therefore they are amenable to Public Health campaigns. Given the associated health implications, policy or intervention that is responsive to ‘Sedentary & Low Active’ group’s needs is not only a major Public Health challenge, but a best buy.

Background: Increasingly the health impacts of physical inactivity are being distinguished from those of sedentary behaviour. Nevertheless, deleterious health prognoses occur when these behaviours combine, making it a Public Health priority to establish the numbers and salient identifying factors of people who live with this injurious combination. Method: Using an observational between-subjects design, a non-probability sample of 22,836 participants provided data on total daily activity. A 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences. Results: High levels of sitting clustered with low physical activity. The ‘Ambulatory & Active’ cluster (n=6,254) sat for 2.5 to 5 h d-1 and were highly active. They were significantly younger, included a greater proportion of males and reported low Indices of Multiple Deprivation compared to other clusters. Conversely, the ‘Sedentary & Low Active’ cluster (n=6,286) achieved ≤60 MET.min.wk-1 of physical activity and sat for ≥8 h d-1. They were the oldest cluster, housed the largest proportion of females and reported moderate Indices of Multiple Deprivation. Conclusions: Public Health systems may benefit from developing policy and interventions that do more to limit sedentary behaviour and encourage light intensity activity in its place.

The negative health consequences of physical inactivity are independent from those of sedentary behaviour. However, injurious health prognoses occur when these behaviours coalesce. Whilst physical inactivity and sedentary behaviour are irreducible components of modern lifestyles, the evidence base connecting the two behaviours is limited. Aggregating our knowledge of how these behaviours cluster and who they cluster with may facilitate the development of more effective policy and intervention. PURPOSE: This study investigates how physical activity and sedentary behaviour cluster. It further examines how individuals cluster through shared behaviours and characteristics. METHODS: A non-probability sample of 22,836 participant’s self-reported demographics and completed the International Physical Activity Questionnaire (IPAQ). Using an observational between-subjects design, a 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences. RESULTS: A three cluster solution was identified. There were 27.7% (n=6,254) of participants assigned to cluster 1 (Ambulatory & Active), 44.4% (n=10,028) of participants within cluster 2 (Moderation) and 27.9% (6,286) of participants allocated to cluster 3 (Sedentary & Low Active). The ‘Ambulatory & Active’ (n=6,254) cluster sat for 2.5 to 5 hours daily and were highly active. In comparison, the ‘Sedentary & Low Active’ cluster (n=6,286) achieved ≤60 MET.min.wk-1 of physical activity and sat for ≥8 hours daily. CONCLUSION: This study adopted an original approach to understanding how people can be classified according to similarities in physical activity and sedentary behaviour. Data indicated that high levels of sedentary behaviour, determined by sitting time, clustered with low levels of physical activity. Importantly, the clusters can be distinguished conceptually and are likely to respond differently to varying approaches and/or interventions; therefore they are amenable to Public Health campaigns. Given the associated health implications, policy or intervention that is responsive to ‘Sedentary & Low Active’ group’s needs is not only a major Public Health challenge, but a best buy.

The negative health consequences of physical inactivity are independent from those of sedentary behaviour. However, injurious health prognoses occur when these behaviours coalesce. Whilst physical inactivity and sedentary behaviour are irreducible components of modern lifestyles, the evidence base connecting the two behaviours is limited. Aggregating our knowledge of how these behaviours cluster and who they cluster with may facilitate the development of more effective policy and intervention. PURPOSE: This study investigates how physical activity and sedentary behaviour cluster. It further examines how individuals cluster through shared behaviours and characteristics. METHODS: A non-probability sample of 22,836 participant’s self-reported demographics and completed the International Physical Activity Questionnaire (IPAQ). Using an observational between-subjects design, a 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences. RESULTS: A three cluster solution was identified. There were 27.7% (n=6,254) of participants assigned to cluster 1 (Ambulatory & Active), 44.4% (n=10,028) of participants within cluster 2 (Moderation) and 27.9% (6,286) of participants allocated to cluster 3 (Sedentary & Low Active). The ‘Ambulatory & Active’ (n=6,254) cluster sat for 2.5 to 5 hours daily and were highly active. In comparison, the ‘Sedentary & Low Active’ cluster (n=6,286) achieved ≤60 MET.min.wk-1 of physical activity and sat for ≥8 hours daily. CONCLUSION: This study adopted an original approach to understanding how people can be classified according to similarities in physical activity and sedentary behaviour. Data indicated that high levels of sedentary behaviour, determined by sitting time, clustered with low levels of physical activity. Importantly, the clusters can be distinguished conceptually and are likely to respond differently to varying approaches and/or interventions; therefore they are amenable to Public Health campaigns. Given the associated health implications, policy or intervention that is responsive to ‘Sedentary & Low Active’ group’s needs is not only a major Public Health challenge, but a best buy.

Obesity is a major public health challenge which continues to increase and disproportionally affects vulnerable population groups, resulting in widening health inequalities. There is consequently an urgent need for innovative approaches to identify and implement evidence-based policy and practice to prevent and treat obesity which has been accelerated by the COVID-19 pandemic. The population levels of obesity are driven by numerous interacting political, economic, environmental, social, cultural, digital, behavioural, and biological determinants. However, causal links between determinants and how they vary between different groups of individuals are not well defined. The identification, implementation, and evaluation of effective responses to the prevention and treatment of obesity require a set of approaches that work within this complexity. The limited efforts to date reflect a misunderstanding of the nature of the chronic and complex nature of obesity, and importantly a limited understanding of how the multifaceted nature of the problem should influence how research, policy, and practice approach it. To date, the evidence underpinning the current approach does not reflect the complexity of the condition: Evidence is largely generated by tools and methods developed to answer questions about the effectiveness of isolated interventions, commonly grounded in linear models of cause and effect. This is the pathway between a cause, for example, exposure to fast food restaurants, and the outcome, obesity, is assumed to be linear, when it is far more complex than this. There is a focus on individual behaviour, yet social and structural determinants of health have a far greater influence on obesity and contribute more to health inequalities. It is acknowledged that we live in an obesogenic environment, yet most approaches to addressing obesity are focused on behaviour change to support individuals adopt healthy weight behaviours, with little (or no) consideration of the environment in which they live. Outcomes are largely measured in the short term and the effects of efforts to reduce population obesity will take many years to be realised. Effectiveness is primarily determined by a narrow focus on weight change, which fails to capture the underlying complexity. Instead of investigating whether a single intervention is (cost-)effective in terms of fixing the problem (i.e. obesity), we need to understand how actions drive positive changes within the system. A systems approach captures and responds to complexity through a dynamic way of working: bringing together academic, policy, practice, and community representatives to develop a ‘shared understanding of the challenge’ and to integrate action to bring about sustainable, long-term systems change. The benefit of a systems approach to addressing population levels of obesity has been outlined: in 2013, the EPODE logic model retrospectively provided insight into the system dynamics of the programme; the ‘Improving the Health of the Public by 2040’ report acknowledged that responses to major public health challenges require a wider set of approaches; in 2017, Rutter et al. called for ‘a complex systems model of evidence for public health’, which was echoed in 2019, as part of The Lancet commission on obesity. More recently, the logic model underpinning the Amsterdam Healthy Weight Approach (AHWA) was published. There are also examples of projects that have embraced system approaches in an applied setting, as well as toolkits, guidance documents, and operational frameworks. These resources demonstrate that the concept of a systems approach to obesity is not new, and importantly that systems methods do not have to replace traditional methods, but instead incorporate and enhance them. Despite this activity and rhetoric, systems approaches are rarely operationalised in ways that generate relevant evidence or effective policies.

Purpose of Review: This article discusses what person-centred care is; why it is critically important in providing effective care of a chronic, complex disease like obesity; and what can be learnt from international best practice to inform global implementation. Recent Findings: There are four key principles to providing person-centred obesity care: providing care that is coordinated, personalised, enabling and delivered with dignity, compassion and respect. The Canadian 5AsT framework provides a co-developed person-centred obesity care approach that addresses complexity and is being tested internationally. Summary: Embedding person-centred obesity care across the globe will require a complex system approach to provide a framework for healthcare system redesign, advances in people-driven discovery and advocacy for policy change. Additional training, tools and resources are required to support local implementation, delivery and evaluation. Delivering high-quality, effective person-centred care across the globe will be critical in addressing the current obesity epidemic.

At both UK and global level, dietary consumption patterns need to change to address environmental, health and inequality challenges. Despite considerable policy interventions, the prevalence of overweight and obesity in the United Kingdom has continued to rise with obesity now a leading cause of mortality and morbidity. Obesity prevalence is greater among those on lower incomes and the current UK food system, including government policy, does not effectively address this. Current behavioural approaches, without the support of structural changes in the system, may even widen the inequalities gap. Hence, using behavioural insights from those living with obesity and food insecurity, the project will explore potential avenues that can be applied in the food system to promote healthier choices in the food retail environment. The National Food Strategy report recommends that the UK food system should ensure "safe, healthy, affordable food; regardless of where people live or how much they earn". However, the association between food insecurity and the development of obesity is not well understood in relation to purchasing behaviours in the UK retail food environment, nor is the potential effectiveness of interventions that seek to prevent and reduce the impact of diet-induced health harms. The FIO Food (Food insecurity in people living with obesity - improving sustainable and healthier food choices in the retail food environment) project provides a novel and multi-disciplinary collaborative approach with co-development at the heart to address these challenges. Using four interlinked work packages, the FIO Food project will combine our knowledge of large-scale population data with an understanding of lived experiences of food shopping for people living with obesity and food insecurity, to develop solutions to support more sustainable and healthier food choices in the UK retail food environment.