A novel methodology for identifying environmental exposures using GPS data

Aim: While studies using global positioning systems (GPS) have the potential to refine measures of exposure to the neighbourhood environment in health research, one limitation is that they do not typically identify time spent undertaking journeys in motorised vehicles when contact with the environment is reduced. This paper presents and tests a novel methodology to explore the impact of this concern. Methods: Using a case study of exposure assessment to food environments, an unsupervised computational algorithm is employed in order to infer two travel modes: motorised and non-motorised, on the basis of which trips were extracted. Additional criteria are imposed in order to improve robustness of the algorithm. Results: After removing noise in the GPS data and motorised vehicle journeys, 82.43% of the initial GPS points remained. In addition, after comparing a sub-sample of trips classified visually of motorised, non-motorised and mixed mode trips with the algorithm classifications, it was found that there was an agreement of 88%. The measures of exposure to the food environment calculated before and after algorithm classification were strongly correlated. Conclusion: Identifying non-motorised exposures to the food environment makes little difference to exposure estimates in urban children but might be important for adults or rural populations who spend more time in motorised vehicles.APJ was partially supported by the Centre for Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, National Institute for Health Research and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged

Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map.

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

Is adolescent body mass index and waist circumference associated with the food environments surrounding schools and homes? A longitudinal analysis

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

Examining the validity and utility of two secondary sources of food environment data against street audits in England

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

Proactive Assessment of Obesity Risk during Infancy (ProAsk): A qualitative study of parents' and professionals' perspectives on an mHealth intervention

Background: Prevention of childhood obesity is a public health priority. Interventions that establish healthy growth trajectories early in life promise lifelong benefits to health and wellbeing. Proactive Assessment of Obesity Risk during Infancy (ProAsk) is a novel mHealth intervention designed to enable health professionals to assess an infant’s risk of future overweight and motivate parental behaviour change to prevent childhood overweight and obesity. The aim of this study was to explore parents’ and health professionals’ experiences of the overweight risk communication and behaviour change aspects of this mHealth intervention. Methods: The study was conducted in four economically deprived localities in the UK. Parents (N=66) were recruited to the ProAsk feasibility study when their infant was 6-8 weeks old. Twenty two health visitors (HVs) used a hand-held tablet device to deliver ProAsk to parents when their infants were 3 months old. Parents (N=12) and HVs (N=15) were interviewed when infants in the study were 6 months old. Interview data were transcribed and analysed thematically using an inductive, interpretative approach. Results: Four key themes were identified across both parent and health visitor data: engaging and empowering with digital technology; unfamiliar technology presents challenges and opportunity; trust in the risk score; resistance to targeting. Most participants found the interactivity and visual presentation of information on ProAsk engaging. Health visitors who were unfamiliar with mobile technology drew support from parents who were more confident using tablet devices. There was evidence of resistance to targeting infants at greatest risk of future overweight and obesity, and both parents and health visitors drew on a number of reasons why a higher than average overweight risk score might not apply to a particular infant. Conclusions: An mHealth intervention actively engaged parents, enabling them to take ownership of the process of seeking strategies to reduce infant risk of overweight. However, cognitive and motivational biases that prevent effective overweight risk communication are barriers to targeting an intervention at those infants most at risk

Macro-environmental factors and physical activity in 28 European Union countries

Data from the representative 2013 Eurobarometer survey were combined with macro-environmental data to assess relationships with different domains of physical activity (PA) in 28 European Union countries. Higher mean annual temperatures were the only macro-environmental factor found to be associated with levels of physical activity; an increase in the mean annual temperature by 1°C was associated with−0.94 fewer minutes of vigorous-intensity activity per week (95% CI: −1.66 to −0.23). This highlights the importance of modifiable influences (e.g. opportunities for active travel) on PA and underscores the potential of public health interventions to raise levels of physical activity

Greater access to fast-food outlets is associated with poorer bone health in young children

SUMMARY: A healthy diet positively influences childhood bone health, but how the food environment relates to bone development is unknown. Greater neighbourhood access to fast-food outlets was associated with lower bone mass among infants, while greater access to healthy speciality stores was associated with higher bone mass at 4 years.INTRODUCTION: Identifying factors that contribute to optimal childhood bone development could help pinpoint strategies to improve long-term bone health. A healthy diet positively influences bone health from before birth and during childhood. This study addressed a gap in the literature by examining the relationship between residential neighbourhood food environment and bone mass in infants and children.METHODS: One thousand one hundred and seven children participating in the Southampton Women's Survey, UK, underwent measurement of bone mineral density (BMD) and bone mineral content (BMC) at birth and 4 and/or 6 years by dual-energy X-ray absorptiometry (DXA). Cross-sectional observational data describing food outlets within the boundary of each participant's neighbourhood were used to derive three measures of the food environment: the counts of fast-food outlets, healthy speciality stores and supermarkets.RESULTS: Neighbourhood exposure to fast-food outlets was associated with lower BMD in infancy (β = -0.23 (z-score): 95% CI -0.38, -0.08) and lower BMC after adjustment for bone area and confounding variables (β = -0.17 (z-score): 95% CI -0.32, -0.02). Increasing neighbourhood exposure to healthy speciality stores was associated with higher BMD at 4 and 6 years (β = 0.16(z-score): 95% CI 0.00, 0.32 and β = 0.13(z-score): 95% CI -0.01, 0.26 respectively). The relationship with BMC after adjustment for bone area and confounding variables was statistically significant at 4 years, but not at 6 years.CONCLUSIONS: The neighbourhood food environment that pregnant mothers and young children are exposed may affect bone development during early childhood. If confirmed in future studies, action to reduce access to fast-food outlets could have benefits for childhood development and long-term bone health