Paul Watts

A multilevel analysis of the association between social networks and support on leisure time physical activity: Evidence from 40 disadvantaged areas in London

There has been extensive conceptual and empirical work on the associations between social relations and health in recent years. However, the specific pathways through which social interactions impact on health have not been fully elucidated. The aim of this paper is to estimate associations between Leisure Time Physical Activity (LTPA) and social networks and support. Using data from a cross-sectional household survey in 40 disadvantaged lower super-output areas in London, we applied a multilevel model to investigate psychosocial and environmental determinants of physical activity in these populations. Our findings present a strong case for the influence of individual-level social networks on the level of LTPA, although the associations between the types of social support and LTPA were insignificant. We also found that crime rate was an important area-level correlates of LTPA.

Types of social capital and mental disorder in deprived urban areas: a multilevel study of 40 disadvantaged London neighbourhoods.

Objectives To examine the extent to which individual and ecological-level cognitive and structural social capital are associated with common mental disorder (CMD), the role played by physical characteristics of the neighbourhood in moderating this association, and the longitudinal change of the association between ecological level cognitive and structural social capital and CMD. Design Cross-sectional and longitudinal study of 40 disadvantaged London neighbourhoods. We used a contextual measure of the physical characteristics of each neighbourhood to examine how the neighbourhood moderates the association between types of social capital and mental disorder. We analysed the association between ecological-level measures of social capital and CMD longitudinally. Participants 4,214 adults aged 16-97 (44.4% men) were randomly selected from 40 disadvantaged London neighbourhoods. Main Outcome Measures General Health Questionnaire (GHQ-12). Results Structural rather than cognitive social capital was significantly associated with CMD after controlling for socio-demographic variables. However, the two measures of structural social capital used, social networks and civic participation, were negatively and positively associated with CMD respectively. ‘Social networks’ was negatively associated with CMD at both the individual and ecological levels. This result was maintained when contextual aspects of the physical environment (neighbourhood incivilities) were introduced into the model, suggesting that ‘social networks’ was independent from characteristics of the physical environment. When ecological-level longitudinal analysis was conducted, ‘social networks’ was not statistically significant after controlling for individual-level social capital at follow up. Conclusions If we conceptually distinguish between cognitive and structural components as the quality and quantity of social capital respectively, the conclusion of this study is that the quantity rather than quality of social capital is important in relation to CMD at both the individual and ecological levels in disadvantaged urban areas. Thus, policy should support interventions that create and sustain social networks. One of these is explored in this article. Trial Registration Controlled-Trials.com ISRCTN68175121 http://www.controlled-trials.com/ISRCTN68175121

The influence of environmental factors on the generalisability of public health research evidence: physical activity as a worked example

BackgroundIt is rare that decisions about investing in public health interventions in a city, town or other location can be informed by research generated in that specific place. It is therefore necessary to base decisions on evidence generated elsewhere and to make inferences about the extent to which this evidence is generalisable to the place of interest. In this paper we discuss the issues involved in making such inferences, using physical activity as an example. We discuss the ways in which elements of the structural, physical, social and/or cultural environment (environmental factors [EFs]) can shape physical activity (PA) and also how EFs may influence the effectiveness of interventions that aim to promote PA. We then highlight the ways in which EFs may impact on the generalisability of different types of evidence.DiscussionWe present a framework for thinking about the influence of EFs when assessing the generalisability of evidence from the location in which the evidence was generated (place A) to the location to which the evidence is to be applied (place B). The framework relates to similarities and differences between place A and place B with respect to: a) the distributions of EFs; b) the causal pathways through which EFs or interventions are thought to exert their effect on PA and c) the ways in which EFs interact with each other. We suggest, using examples, how this scheme can be used by public health professionals who are designing, executing, reporting and synthesising research on PA; or designing/implementing interventions.SummaryOur analysis and scheme, although developed for physical activity, may potentially be adapted and applied to other evidence and interventions which are likely to be sensitive to influence by elements of the structural, physical, social and/or cultural environment such as the epidemiology of obesity and healthy weight promotion.

Clustering of lifestyle risk behaviours among residents of forty deprived neighbourhoods in London: lessons for targeting public health interventions

BACKGROUND Clustering of lifestyle risk behaviours is very important in predicting premature mortality. Understanding the extent to which risk behaviours are clustered in deprived communities is vital to most effectively target public health interventions. METHODS We examined co-occurrence and associations between risk behaviours (smoking, alcohol consumption, poor diet, low physical activity and high sedentary time) reported by adults living in deprived London neighbourhoods. Associations between sociodemographic characteristics and clustered risk behaviours were examined. Latent class analysis was used to identify underlying clustering of behaviours. RESULTS Over 90% of respondents reported at least one risk behaviour. Reporting specific risk behaviours predicted reporting of further risk behaviours. Latent class analyses revealed four underlying classes. Membership of a maximal risk behaviour class was more likely for young, white males who were unable to work. CONCLUSIONS Compared with recent national level analysis, there was a weaker relationship between education and clustering of behaviours and a very high prevalence of clustering of risk behaviours in those unable to work. Young, white men who report difficulty managing on income were at high risk of reporting multiple risk behaviours. These groups may be an important target for interventions to reduce premature mortality caused by multiple risk behaviours.

Physical Activity in Deprived Communities in London: Examining Individual and Neighbourhood-Level Factors

Introduction The objectives of this study were to examine relationships between neighbourhood-level and individual-level characteristics and physical activity in deprived London neighbourhoods. Methods In 40 of the most deprived neighbourhoods in London (ranked in top 11% in London by Index of Multiple Deprivation) a cross-sectional survey (n = 4107 adults aged > = 16 years), neighbourhood audit tool, GIS measures and routine data measured neighbourhood and individual-level characteristics. The binary outcome was meeting the minimum recommended (CMO, UK) 5×30 mins moderate physical activity per week. Multilevel modelling was used to examine associations between physical activity and individual and neighbourhood-level characteristics. Results Respondents living more than 300 m away from accessible greenspace had lower odds of achieving recommended physical activity levels than those who lived within 300 m; from 301–600 m (OR = 0.7; 95% CI 0.5–0.9) and from 601–900 m (OR = 0.6; 95% CI 0.4–0.8). There was substantial residual between-neighbourhood variance in physical activity (median odds ratio = 1.7). Other objectively measured neighbourhood-level characteristics were not associated with physical activity levels. Conclusions Distance to nearest greenspace is associated with meeting recommended physical activity levels in deprived London neighbourhoods. Despite residual variance in physical activity levels between neighbourhoods, we found little evidence for the influence of other measured neighbourhood-level characteristics.

Well London: Results of a Community Engagement Approach to Improving Health Among Adolescents from Areas of Deprivation in London

ABSTRACT This article reports findings among adolescents of the impact of Well London, a program of wellbeing interventions delivered across 20 deprived neighbourhoods of London using a community engagement model. Twelve hundred and fifty-four adolescents were surveyed from matched intervention and control areas. There was no significant intervention effect on the main outcome measures: unhealthy eating, physical activity, and mental health. Factors influencing the results may include the possibility that the communities defined by the cluster randomized controlled trial (CRCT) were not an accurate reflection of the adolescent’s natural community, and interactions within the school environment in particular could have led to a dilution of effect.

Social, cognitive, behavioural and neighbourhood characteristics associated with sedentary time in men and women living in deprived neighbourhoods

Abstract Multiple individual and neighbourhood characteristics are theorised to influence adult sedentary behaviour. The aim of this study was to examine associations between individual and neighbourhood-level characteristics in 40 deprived neighbourhoods in London, UK. A cross-sectional design was utilised with baseline data from the Well London Cluster Randomised Controlled Trial in 40 deprived neighbourhoods in London. Multilevel linear regression was used to examine associations between individual characteristics (measured by household survey), neighbourhood characteristics (neighbourhood audit, GIS and routinely available datasets) and sedentary behaviour (sitting time). Individual-level positive mental well-being and health behaviours were associated with sedentary time. Individual-level social networks were associated with decreased sedentary time in men and increased sedentary time in women. Neighbourhood-level measures of social networks and perceived neighbourhood quality were associated with reduced sedentary time. Fifteen per cent of the variance in sedentary time was attributable to differences at the neighbourhood level (intra-class correlation coefficient = 0.15). These findings suggest that social networks at the individual and neighbourhood levels, collective perceptions of neighbourhood quality, individual-level positive mental well-being and other health behaviours may be important components of interventions developed to reduce sedentary time in deprived populations.

Determinants of physical activity in deprived communities in London: Examining the effects of individual and neighbourhood characteristics

Background The benefits of regular physical activity are well known. Few adults in the UK meet the recommended levels. There is growing evidence suggesting that neighbourhood characteristics interact with individual characteristics to determine physical activity levels. Objective Examine relationships between neighbourhood-level and individual-level characteristics and physical activity. Setting 40 of the most deprived neighbourhoods in London (census lower super output areas (LSOA) ranked in the top 11% of LSOAs in London by Index of Multiple Deprivation). Design Cross-sectional, interviewer-administered survey with households in the 40 neighbourhoods (n=4107 adults aged >=16 years); fieldworker-completed neighbourhood audit tool to measure area characteristics. Methods Physical activity was measured using the International Physical Activity Questionnaire in the household survey. The outcome measure was meeting the minimum recommended levels of physical activity (5 x 30 min moderate activity per week). Multilevel modelling in Stata 11 was used to examine the effects of individual and neighbourhood characteristics on achieving the recommended weekly physical activity levels. Multiple imputation was used to account for missing data. Results 70% of respondents reported physical activity equivalent to the recommended 5x30 min per week; the intra-neighbourhood correlation coefficient was 0.15. Preliminary results (adjusted for socio-demographic characteristics) indicate that women were less likely than men to meet the recommended physical activity levels (OR=0.6; 95% CI 0.4 to 0.7); individuals with positive mental health were more likely to meet the recommended levels (OR for 1 point increase in Hope Scale=1.2; 95% CI 1.0 to 1.3). After adjusting for individual characteristics, 9% of the residual variance in propensity to meet recommended physical activity was attributable to neighbourhood differences. Neighbourhood levels of incivilities, proximity and quality of parks/green space, walkability and cyclability were not associated with meeting recommended physical activity levels. Further results for other neighbourhood characteristics will be presented and social inequalities in the effects of neighbourhoods on health will be examined. Conclusion The level of physical activity in low income communities in London is higher than the national average measured in the Health Survey for England. In preliminary analyses we found no evidence of neighbourhood effects on physical activity levels in deprived London neighbourhoods. These observations may be due to: (i) limited clustering of physical activity levels across the most deprived neighbourhoods in London; (ii) limited variability in neighbourhood environments in these communities; (iii) individual level characteristics are stronger determinants of physical activity in these communities; (iv) complex and contingent causal pathways of physical activity, not captured in our models.