The effect of moving to East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village, on mode of travel (ENABLE London study, a natural experiment)

Background Interventions to encourage active modes of travel (walking, cycling) may improve physical activity levels, but longitudinal evidence is limited and major change in the built environment / travel infrastructure may be needed. East Village (the former London 2012 Olympic Games Athletes Village) has been repurposed on active design principles with improved walkability, open space and public transport and restrictions on residential car parking. We examined the effect of moving to East Village on adult travel patterns. Methods One thousand two hundred seventy-eight adults (16+ years) seeking to move into social, intermediate, and market-rent East Village accommodation were recruited in 2013–2015, and followed up after 2 years. Individual objective measures of physical activity using accelerometry (ActiGraph GT3X+) and geographic location using GPS travel recorders (QStarz) were time-matched and a validated algorithm assigned four travel modes (walking, cycling, motorised vehicle, train). We examined change in time spent in different travel modes, using multilevel linear regresssion models adjusting for sex, age group, ethnicity, housing group (fixed effects) and household (random effect), comparing those who had moved to East Village at follow-up with those who did not. Results Of 877 adults (69%) followed-up, 578 (66%) provided valid accelerometry and GPS data for at least 1 day (≥540 min) at both time points; half had moved to East Village. Despite no overall effects on physical activity levels, sizeable improvements in walkability and access to public transport in East Village resulted in decreased daily vehicle travel (8.3 mins, 95%CI 2.5,14.0), particularly in the intermediate housing group (9.6 mins, 95%CI 2.2,16.9), and increased underground travel (3.9 mins, 95%CI 1.2,6.5), more so in the market-rent group (11.5 mins, 95%CI 4.4,18.6). However, there were no effects on time spent walking or cycling

Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study.

BACKGROUND: We assessed whether the residential built environment was associated with physical activity (PA) differently on weekdays and weekends, and contributed to socio-economic differences in PA. METHODS: Measures of PA and walkability, park proximity and public transport accessibility were derived for baseline participants (n = 1,064) of the Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) Study. Multilevel-linear-regressions examined associations between weekend and weekday steps and Moderate to Vigorous PA (MVPA), residential built environment factors, and housing tenure status as a proxy for socio-economic position. RESULTS: A one-unit decrease in walkability was associated with 135 (95% CI [28; 242]) fewer steps and 1.2 (95% CI [0.3; 2.1]) fewer minutes of MVPA on weekend days, compared with little difference in steps and minutes of MVPA observed on weekdays. A 1km-increase in distance to the nearest local park was associated with 597 (95% CI [161; 1032]) more steps and 4.7 (95% CI [1.2; 8.2]) more minutes of MVPA on weekend days; 84 fewer steps (95% CI [-253;420]) and 0.3 fewer minutes of MVPA (95%CI [-2.3, 3.0]) on weekdays. Lower public transport accessibility was associated with increased steps on a weekday (767 steps, 95%CI [-13,1546]) compared with fewer steps on weekend days (608 fewer steps, 95% CI [-44, 1658]). None of the associations between built environment factors and PA on either weekend or weekdays were modified by socio-economic status. However, socio-economic differences in PA related moderately to socio-economic disparities in PA-promoting features of the residential neighbourhood. CONCLUSIONS: The residential built environment is associated with PA differently at weekends and on weekdays, and contributes moderately to socio-economic differences in PA

En rapport om politisk filosofi og sundhed

This report takes the matter of a state’s intervention towards its individuals into dis-cussion from the perspective of social health care, and the recent changes in law, concerning smoking, and taxes on fat. The government in Denmark has been accused of being paternalistic and of interacting too much in the citizen’s personal life. By including John Stuart Mill, recent investigations, the public discussion and a list of modern ethical magazine articles, we set up a discussion about, under which pre-mises, and why a state should be allowed to adjust the behaviour of its citizens. The answer to this question is not clear, at it fairly early in the process became clear that the arguments is based, not only on a matter of principal opinion, but also on, in which way the agitator views upon freedom. We, as I. Berlin, divide freedom into two categories: Positive and negative freedom. The project concludes that if arguing from a point of view that understands freedom in the positive (or total) sense, it is not possible to create valid arguments for interfering with an individual’s behaviour, as long as it does not affect the life of other individuals. However, understanding free-dom in the positive way will make it possible to interject, when people are living an unhealthy life. The report also concludes that living in a modern democracy, with an understanding of the freedom as an unbendable (and positive) size, is impossible

Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study.

BACKGROUND: We assessed whether the residential built environment was associated with physical activity (PA) differently on weekdays and weekends, and contributed to socio-economic differences in PA. METHODS: Measures of PA and walkability, park proximity and public transport accessibility were derived for baseline participants (n = 1,064) of the Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) Study. Multilevel-linear-regressions examined associations between weekend and weekday steps and Moderate to Vigorous PA (MVPA), residential built environment factors, and housing tenure status as a proxy for socio-economic position. RESULTS: A one-unit decrease in walkability was associated with 135 (95% CI [28; 242]) fewer steps and 1.2 (95% CI [0.3; 2.1]) fewer minutes of MVPA on weekend days, compared with little difference in steps and minutes of MVPA observed on weekdays. A 1km-increase in distance to the nearest local park was associated with 597 (95% CI [161; 1032]) more steps and 4.7 (95% CI [1.2; 8.2]) more minutes of MVPA on weekend days; 84 fewer steps (95% CI [-253;420]) and 0.3 fewer minutes of MVPA (95%CI [-2.3, 3.0]) on weekdays. Lower public transport accessibility was associated with increased steps on a weekday (767 steps, 95%CI [-13,1546]) compared with fewer steps on weekend days (608 fewer steps, 95% CI [-44, 1658]). None of the associations between built environment factors and PA on either weekend or weekdays were modified by socio-economic status. However, socio-economic differences in PA related moderately to socio-economic disparities in PA-promoting features of the residential neighbourhood. CONCLUSIONS: The residential built environment is associated with PA differently at weekends and on weekdays, and contributes moderately to socio-economic differences in PA

Cohort profile: Examining Neighbourhood Activities in Built Living Environments in London: the ENABLE London-Olympic Park cohort.

PURPOSE: The Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) project is a natural experiment which aims to establish whether physical activity and other health behaviours show sustained changes among individuals and families relocating to East Village (formerly the London 2012 Olympics Athletes' Village), when compared with a control population living outside East Village throughout. PARTICIPANTS: Between January 2013 and December 2015, 1497 individuals from 1006 households were recruited and assessed (at baseline) (including 392 households seeking social housing, 421 seeking intermediate and 193 seeking market rent homes). The 2-year follow-up rate is 62% of households to date, of which 57% have moved to East Village. FINDINGS TO DATE: Assessments of physical activity (measured objectively using accelerometers) combined with Global Positioning System technology and Geographic Information System mapping of the local area are being used to characterise physical activity patterns and location among study participants and assess the attributes of the environments to which they are exposed. Assessments of body composition, based on weight, height and bioelectrical impedance, have been made and detailed participant questionnaires provide information on socioeconomic position, general health/health status, well-being, anxiety, depression, attitudes to leisure time activities and other personal, social and environmental influences on physical activity, including the use of recreational space and facilities in their residential neighbourhood. FUTURE PLANS: The main analyses will examine the changes in physical activity, health and well-being observed in the East Village group compared with controls and the influence of specific elements of the built environment on observed changes. The ENABLE London project exploits a unique opportunity to evaluate a 'natural experiment', provided by the building and rapid occupation of East Village. Findings from the study will be generalisable to other urban residential housing developments, and will help inform future evidence-based urban planning

Housing, neighbourhood and sociodemographic associations with adult levels of physical activity and adiposity: baseline findings from the ENABLE London study.

Objectives The neighbourhood environment is increasingly shown to be an important correlate of health. We assessed associations between housing tenure, neighbourhood perceptions, sociodemographic factors and levels of physical activity (PA) and adiposity among adults seeking housing in East Village (formerly London 2012 Olympic/Paralympic Games Athletes’ Village). Setting Cross-sectional analysis of adults seeking social, intermediate and market-rent housing in East Village. Participants 1278 participants took part in the study (58% female). Complete data on adiposity (body mass index (BMI) and fat mass %) were available for 1240 participants (97%); of these, a subset of 1107 participants (89%) met the inclusion criteria for analyses of accelerometer-based measurements of PA. We examined associations between housing sector sought, neighbourhood perceptions (covariates) and PA and adiposity (dependent variables) adjusted for household clustering, sex, age group, ethnic group and limiting long-standing illness. Results Participants seeking social housing had the fewest daily steps (8304, 95% CI 7959 to 8648) and highest BMI (26.0 kg/m2, 95% CI 25.5kg/m2 to 26.5 kg/m2) compared with those seeking intermediate (daily steps 9417, 95% CI 9106 to 9731; BMI 24.8 kg/m2, 95% CI 24.4 kg/m2 to 25.2 kg/m2) or market-rent housing (daily steps 9313, 95% CI 8858 to 9768; BMI 24.6 kg/m2, 95% CI 24.0 kg/m2 to 25.2 kg/m2). Those seeking social housing had lower levels of PA (by 19%–42%) at weekends versus weekdays, compared with other housing groups. Positive perceptions of neighbourhood quality were associated with higher steps and lower BMI, with differences between social and intermediate groups reduced by ~10% following adjustment, equivalent to a reduction of 111 for steps and 0.5 kg/m2 for BMI. Conclusions The social housing group undertook less PA than other housing sectors, with weekend PA offering the greatest scope for increasing PA and tackling adiposity in this group. Perceptions of neighbourhood quality were associated with PA and adiposity and reduced differences in steps and BMI between housing sectors. Interventions to encourage PA at weekends and improve neighbourhood quality, especially among the most disadvantaged, may provide scope to reduce inequalities in health behaviour

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data.

BACKGROUND: Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. METHODS: The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. RESULTS: Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals' travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%). CONCLUSION: We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers

Evaluating the effect of change in the built environment on mental health and subjective well-being: a natural experiment

Background Neighbourhood characteristics may affect mental health and well-being, but longitudinal evidence is limited. We examined the effect of relocating to East Village (the former London 2012 Olympic Athletes’ Village), repurposed to encourage healthy active living, on mental health and well-being. Methods 1278 adults seeking different housing tenures in East village were recruited and examined during 2013–2015. 877 (69%) were followed-up after 2 years; 50% had moved to East Village. Analysis examined change in objective measures of the built environment, neighbourhood perceptions (scored from low to high; quality −12 to 12, safety −10 to 10 units), self-reported mental health (depression and anxiety) and well-being (life satisfaction, life being worthwhile and happiness) among East Village participants compared with controls who did not move to East Village. Follow-up measures were regressed on baseline for each outcome with group status as a binary variable, adjusted for age, sex, ethnicity, housing tenure and household clustering (random effect). Results Participants who moved to East Village lived closer to their nearest park (528 m, 95% CI 482 to 575 m), in more walkable areas, and had better access to public transport, compared with controls. Living in East Village was associated with marked improvements in neighbourhood perceptions (quality 5.0, 95% CI 4.5 to 5.4 units; safety 3.4, 95% CI 2.9 to 3.9 units), but there was no overall effect on mental health and well-being outcomes. Conclusion Despite large improvements in the built environment, there was no evidence that moving to East Village improved mental health and well-being. Changes in the built environment alone are insufficient to improve mental health and well-being

Cardiometabolic Risk Markers in Indian Children: Comparison with UK Indian and White European Children

Objective: UK Indian adults have higher risks of coronary heart disease and type 2 diabetes than Indian and UK European adults. With growing evidence that these diseases originate in early life, we compared cardiometabolic risk markers in Indian, UK Indian and white European children.Methods: Comparisons were based on the Mysore Parthenon Birth Cohort Study (MPBCS), India and the Child Heart Health Study in England (CHASE), which studied 9–10 year-old children (538 Indian, 483 UK Indian, 1375 white European) using similar methods. Analyses adjusted for study differences in age and sex.Results: Compared with Mysore Indians, UK Indians had markedly higher BMI (% difference 21%, 95%CI 18 to 24%), skinfold thickness (% difference 34%, 95%CI 26 to 42%), LDL-cholesterol (mean difference 0.48, 95%CI 0.38 to 0.57 mmol/L), systolic BP (mean difference 10.3, 95% CI 8.9 to 11.8 mmHg) and fasting insulin (% difference 145%, 95%CI 124 to 168%). These differences (similar in both sexes and little affected by adiposity adjustment) were larger than those between UK Indians and white Europeans. Compared with white Europeans, UK Indians had higher skinfold thickness (% difference 6.0%, 95%CI 1.5 to 10.7%), fasting insulin (% difference 31%, 95%CI 22 to 40%), triglyceride (% difference 13%, 95%CI 8 to 18%) and LDL-cholesterol (mean difference 0.12 mmol/L, 95%CI 0.04 to 0.19 mmol/L).Conclusions: UK Indian children have an adverse cardiometabolic risk profile, especially compared to Indian children. These differences, not simply reflecting greater adiposity, emphasize the need for prevention strategies starting in childhood or earlier