Introduction
The ENABLE London is a longitudinal study evaluating how active urban design influences the health and well-being of people moving into the former London 2012 Olympic and Paralympic Games Athletes’ Village, now known as ‘East Village’.61 East Village is a purpose-built, mixed-use, high-rise residential development, specifically designed to encourage healthy active living by improving walkability and access to public transport, which consists of a mix of social housing, intermediate (including affordable rent, shared ownership and shared equity) housing, and market-rent housing.61 This chapter draws on baseline data (prior to any potential move to East Village) to:
- examine predictors of physical activity and adiposity (measured objectively using accelerometry and bioelectrical impedance), including the housing tenure being sought and participants’ perceptions of their neighbourhood
- examine whether or not physical activity patterns over 7 days vary by housing sector
- examine whether or not adjustment for perceptions of the neighbourhood environment reduce housing sector differences in physical activity and adiposity.
Given the longitudinal design, the study also provides a unique opportunity to:
- examine change in objectively measured physical activity and adiposity after 2 years among those who moved to East Village compared with those who did not, which will add robust evidence to the debate about the effect of the built environment on health.
The study was also able to:
- objectively quantify the quality of the built environment in which people lived among those followed up at both time points using GIS-derived methods
- gauge the change in neighbourhood perceptions associated with moving to East Village.
Methods
Participants
Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) study participants were recruited from those seeking or who had applied for new accommodation in East Village. There were three types of housing tenure being sought based on level of income: social (provided by the local authority or housing association at subsidised rates), intermediate (a mixture of shared ownership, shared equity and affordable rent) and market-rent. The inclusion criteria was broad and included anyone interested/applying for single or multiple occupancy accommodation in East Village. There was no explicit exclusion criteria; adults of any age, gender, ethnic group, with or without handicap, were invited to participate. Current housing status was strongly linked to aspirational housing status; those seeking social accommodation were currently in social housing or on social housing waiting lists, and those seeking intermediate and market-rent accommodation were largely in privately rented housing. Aspirational housing tenure is integral to the design of ENABLE London, and we have shown that this provides a clear socioeconomic marker of study participants. For example, those seeking social housing in East Village are more likely to be unemployed, less educated and more likely to represent ethnic minorities (a classic marker of socioeconomic vulnerability), compared with those seeking affordable and market-rent accommodation (see Chapter 2).61 We have also shown key differences in mental health and well-being between housing sectors; those seeking social housing were more likely to be depressed, anxious and have poorer well-being than other housing sectors (see Chapter 6).64 Moreover, this is entirely consistent with earlier studies that found both current housing tenure and aspirational housing tenure are associated with a variety of health outcomes, including mental health and measures of general health.21,65
Assessments of participants were carried out in their place of residence before any potential move to East Village, and 2 years later (February 2015 to October 2017) for those who had moved to East Village, moved elsewhere or not moved.66
Measures
Physical activity
Participants wore a hip-mounted ActiGraph GT3X+ accelerometer during waking hours over a period of 7 days. Accelerometers provided daily measures of steps (primary outcome), time spent in moderate or vigorous physical activity and time spent sedentary, using established thresholds [sedentary < 100 counts per minute (CPM), moderate physical activity ≥ 1952 CPM].67 Periods of non-wear time were defined as ≥ 60 minutes of zero values, allowing for a 2-minute spike tolerance, to provide daily wear time. We excluded days of accelerometer data where the registered wear time was < 540 minutes. Participants with at least 1 day of data were included in the baseline analysis; those with at least 1 day of data at both baseline and follow-up were retained in the longitudinal analysis. We have previously used at least 1 day of 540 minutes (9 hours) of registered time to represent a satisfactory recording of daily activity in a randomised controlled trial of older but overlapping age groups.68 The at least 1 day of 540 minutes cut-off point was chosen to lessen attrition bias, and was used in this study to maximise inclusion of hard to reach groups, that is those from social housing, who notoriously comply less and recorded less physical activity data.
Adiposity
The protocol for body size and adiposity measurement was identical at baseline and at follow-up. Height was measured to the last complete millimetre using a portable stadiometer. A Tanita SC-240 Body Composition Analyser was used to measure weight to the nearest kilogram and leg-to-leg bioelectrical impedance, from which fat-free mass and fat mass were estimated. BMI was calculated as weight (kg)/height (m)2 and percentage fat mass was calculated as 100 × fat mass (kg)/weight (kg).
Covariates
A team of trained fieldworkers administered self-completion questionnaires using a laptop during home visits (see Table 1). Age, sex, self-defined ethnicity, work status and occupation, and whether or not the participant had a LLI or disability (lasting or expected to last at least 12 months) were collected via a questionnaire. Participants were defined as ‘white’, ‘Asian’, ‘black’, ‘mixed’ or ‘other’; the last two categories were combined in the analysis. Occupation-based socioeconomic status was coded using the NS-SEC to categorise participants into ‘higher managerial or professional occupations’, ‘intermediate occupations’ and ‘routine or manual occupations’.63 An additional ‘economically inactive’ category included those seeking employment, those unable to work owing to disability or illness, those who were retired, those looking after home and family, and students. We sought information on educational attainment; participants were categorised into ‘degree or equivalent/higher’, ‘intermediate qualifications’ (including A levels and General Certificates of Secondary Education), and ‘other/none’ (including work-based or foreign qualifications).
Built environment variables
Residential built environment characteristics were derived using GIS data at baseline and at follow-up for those households in the Greater London area. These included network distance from home to the closest park (using data from the London Development Database),69 public transport access,51 and measures of neighbourhood walkability, land use mix, residential density and street connectivity computed within a 1-km street network home-centred buffer (see Chapter 4).
Neighbourhood perceptions
Participants completed questionnaires assessing neighbourhood perceptions; the methods used are outlined further in Chapter 6.64 In brief, five items assessed perceived crime (e.g. ‘There is a lot of crime in my neighbourhood’; Cronbach’s α = 0.87) and six items assessed neighbourhood quality (e.g. ‘This area is a place I enjoy living in’; Cronbach’s α = 0.78). Responses on items were summed and scores ranged from –10 to +10 for perceived crime and –12 to +12 for perceived quality, such that positive scores indicate less perceived crime and better neighbourhood quality whereas negative scores indicate more perceived crime and poorer quality. The scales were derived following an exploratory factor analysis of 14 questions regarding the neighbourhood (see Report Supplementary Material 1). Two neighbourhood perception scores, measuring crime-free neighbourhood (i.e. vandalism, feeling unsafe to walk in neighbourhood, presence of threatening groups) and neighbourhood quality (i.e. accessible features, attractiveness and enjoyment of living in neighbourhood), were derived at baseline using exploratory factor analysis on 14 neighbourhood perception items in the questionnaire,64 and the same items were used to obtain scores at follow-up.
Statistical analysis
All analyses were carried out using Stata® Special Edition version 15 for Windows® (StataCorp LP, College Station, TX, USA).
To obtain average physical activity variables, we fitted a multilevel linear model for each outcome to allow for repeated measurements of daily physical activity, by fitting participant as a random effect and adjusting for day of the week, day order of recording and month as fixed effects. Raw level one residuals were obtained from the model and a within-person average value of each outcome variable was obtained by averaging these raw residuals. The average of these raw residuals for each participant was added to the sample mean for that particular physical variable to derive an unbiased average level of each physical activity variable at baseline and at follow-up for each person.
Baseline analyses
Physical activity and adiposity outcome variables were inspected for normality; BMI was log-transformed because of its skewed distribution. Multilevel linear regression models were fitted, mutually adjusted for housing sector and participant characteristics (sex, age group, ethnic group and LLI) as fixed effects, with a random effect to allow for household clustering. Age groups (16–24 years, 25–34 years, 35–49 years and ≥ 50 years) were chosen to give a wide spread of ages representative of young adults, young professionals, early middle age and later middle-age/older-age people. Residuals did not show departure from linearity, suggesting that the model assumptions were appropriate. Absolute differences or percentage differences for log-transformed outcomes (i.e. BMI) are presented by sex, age group, ethnic group, LLI and housing sector. Sensitivity analyses examined whether or not associations remained when the sample was restricted to 931 participants (84%) with > 4 days of ≥ 540 minutes per day of recording.
To assess differences in physical activity by day of the week as opposed to overall levels of physical activity, we took the following approach. Daily physical activity data were examined using multilevel models with random effects to allow for multiple days of recording within-person and household clustering. An interaction between housing sector and day of the week was fitted and models were adjusted for sex, age group, ethnic group, LLI, day order of recording and month of measurement as fixed effects.
The associations between neighbourhood perception scales and adiposity and physical activity outcomes at baseline were examined. Each of the neighbourhood quality and crime scores were included in the models as quintiles, to examine the differences in outcomes between the top and bottom quintile. The effect of adjustment for neighbourhood perception on differences in the adiposity and physical activity levels between housing sectors was examined. If associations between outcomes and neighbourhood perceptions appeared linear, models examining housing sector differences were additionally adjusted for neighbourhood perceptions as a continuous variable.
Follow-up analyses
Multilevel linear regression models were fitted to examine the effect of moving to East Village on levels of physical activity and adiposity compared with controls who did not live in East Village (in all cases the distributions of residuals from the models were checked for normality). The primary outcome was specified a priori to be daily steps; secondary outcomes included time spent in MVPA (both total and in ≥ 10 minutes bouts per week), the daily sedentary time, BMI and fat mass percentage.61 Average daily steps at follow-up was regressed on average daily steps at baseline, adjusting for East Village or control group as a fixed effect and baseline household as a random effect to allow for household clustering. Throughout, an alpha of less than 0.05 (p-value < 0.05) was used to determine the statistical significance of effects. Because of baseline differences in sociodemographic factors, further adjustment for participant characteristics, including sex, age group and ethnic group, were examined. Stratified models by housing tenure examined the effect in the different housing sectors (work status, occupation and child status were not adjusted for as these were strongly related to housing status). We also included an interaction term between East Village/control group and housing sector. Sensitivity analyses were carried out for daily steps, the primary outcome, by:
- restricting the sample to those with at least 4 days of ≥ 540 minutes’ recording at baseline and at follow-up
- repeating analyses for weekdays only and weekend days only (as this will modify exposure to the residential built environment)
- comparing East Village participants with controls who remained at their baseline address at follow-up and controls who had moved elsewhere
- examining the impact of missing accelerometry data at follow-up using imputation methods.
Results
At baseline
Overall participation rates and participation rates by housing sectors were outlined earlier (see Chapter 2). For the main outcomes of interest, complete data on adiposity were available for 1240 participants (97%); of these, a subset of 1107 participants (89%) met the inclusion criteria for analyses of objectively measured physical activity. Participant characteristics (age, sex, ethnicity) and levels of adiposity were similar among those who did and did not provide physical activity data; however, participants from black and Asian ethnic groups were less likely to provide physical activity data. Report Supplementary Material 2 shows characteristics of participants at baseline for the 1240 adults with measurements of adiposity at baseline, which are similar to those with physical activity data.
Adjusted mean levels of physical activity, and adiposity outcomes by housing sector and participant characteristics, are shown in Report Supplementary Material 3. Table 3 shows the association of housing sector and other participant characteristics with objectively measured physical activity (steps, time spent in MVPA, time spent in MVPA in ≥ 10-minute bouts) and BMI and percentage fat mass. Participants seeking social housing had markedly lower levels of steps, MVPA and MVPA in ≥ 10-minute bouts and markedly higher BMI and percentage fat mass than those seeking intermediate housing, although there were no differences between those seeking market-rent and those seeking intermediate accommodation.
| n | Daily stepsa | Daily minutes spent in MVPAa | Daily minutes spent in MVPA in ≥ 10-minute boutsa | BMI (kg/m2)b | Fat mass (%) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Differencec | 95% CI | p-value | Differencec | 95% CI | p-value | Differencec | 95% CI | p-value | Differencec | 95% CI | p-value | Differencec | 95% CI | p-value | ||
| Sex | ||||||||||||||||
| Male (reference) | 522 | – | – | – | – | – | ||||||||||
| Female | 718 | –570 | –946 to –194 | 0.003 | –9.3 | –12.2 to –6.4 | < 0.0001 | –4.1 | –6.1 to –2.0 | < 0.001 | –1.2 | –3.2 to 0.9 | 0.26 | 11.1 | 10.3 to 12.0 | < 0.0001 |
| Age group (years) | ||||||||||||||||
| 16–24 (reference) | 269 | – | – | – | – | – | ||||||||||
| 25–34 | 531 | 502 | 11 to 992 | 0.04 | 4.0 | 0.2 to 7.9 | 0.04 | 1.0 | –1.9 to 3.8 | 0.51 | 6.3 | 3.5 to 9.1 | < 0.0001 | 3.2 | 2.1 to 4.3 | < 0.0001 |
| 35–49 | 358 | 699 | 173 to 1224 | 0.01 | 3.9 | –0.2 to 8.0 | 0.07 | –1.1 | –4.0 to 1.8 | 0.46 | 13.4 | 10.2 to 16.6 | < 0.0001 | 6.4 | 5.2 to 7.6 | < 0.0001 |
| ≥ 50 | 82 | –9 | –832 to 813 | 0.98 | –6.0 | –12.4 to 0.5 | 0.07 | –2.0 | –6.8 to 2.7 | 0.40 | 17.6 | 12.6 to 22.9 | < 0.0001 | 9.2 | 7.3 to 11.0 | < 0.0001 |
| Ethnic group | ||||||||||||||||
| White (reference) | 595 | – | – | – | – | – | ||||||||||
| Black | 314 | –1116 | –1657 to –575 | < 0.0001 | –7.4 | –11.7 to –3.2 | < 0.001 | –6.6 | –9.8 to –3.4 | < 0.0001 | 6.2 | 3.3 to 9.3 | < 0.0001 | 3.6 | 2.4 to 4.8 | < 0.0001 |
| Asian | 210 | –1409 | –1972 to –845 | < 0.0001 | –11.5 | –15.9 to –7.0 | < 0.0001 | –8.1 | –11.4 to –4.8 | < 0.0001 | –0.3 | –3.1 to 2.7 | 0.85 | 0.02 | –1.2 to 1.3 | 0.97 |
| Other/mixed | 121 | –430 | –1100 to 239 | 0.21 | –4.6 | –9.8 to 0.7 | 0.09 | –4.0 | –7.9 to –0.04 | 0.05 | 1.3 | –2.3 to 5.0 | 0.48 | 1.0 | –0.5 to 2.5 | 0.18 |
| LLI | ||||||||||||||||
| No (reference) | 1087 | – | – | – | – | – | ||||||||||
| Yes | 153 | –1081 | –1666 to –496 | < 0.001 | –5.7 | –10.3 to –1.1 | 0.01 | –2.8 | –6.1 to 0.5 | 0.10 | 4.3 | 1.1 to 7.5 | 0.01 | 1.6 | 0.3 to 2.9 | 0.01 |
| Housing sector | ||||||||||||||||
| Social | 512 | –1125 | –1629 to –620 | < 0.0001 | –7.5 | –11.5 to –3.6 | < 0.001 | –6.5 | –9.5 to –3.5 | < 0.0001 | 5.0 | 2.2 to 7.8 | < 0.001 | 2.7 | 1.5 to 3.8 | < 0.0001 |
| Intermediate (reference) | 503 | – | – | – | – | – | ||||||||||
| Market-rent | 225 | –104 | –633 to 424 | 0.70 | 2.3 | –1.9 to 6.4 | 0.29 | 2.8 | –0.3 to 6.0 | 0.08 | –0.8 | –3.6 to 2.0 | 0.57 | –0.2 | –1.4 to 1.0 | 0.70 |
All physical activity measures were lower among females. Steps and MVPA were slightly higher in 25- to 34-year-olds and steps were also higher among 35- to 49-year-olds than among 16- to 24-year-olds; however, there were no age group differences for MVPA in ≥ 10-minute bouts. Participants of black and Asian ethnicities had lower levels of steps, MVPA and MVPA in ≥ 10-minute bouts than people of white ethnicity. Participants who reported having a LLI had lower levels of steps and MVPA, but not MVPA in ≥ 10-minute bouts. Percentage fat mass was higher in females than males, although there was no difference in BMI (see Table 3). BMI and percentage fat mass were higher among all older age groups than among 16- to 24-year-olds. Participants of black ethnicity had higher levels of BMI and percentage fat mass than people of white ethnicity; there were no differences in BMI and percentage fat mass between Asian or other/mixed ethnic groups and people of white ethnicity. Those with a LLI had higher levels of both BMI and percentage fat mass. Educational attainment level was not associated with any of the outcomes once housing sector had been adjusted for, and adjustment for educational attainment did not materially alter housing sector differences in physical activity or other outcomes (data not presented).
Sensitivity analyses for physical activity outcomes were carried out in 931 participants who wore an ActiGraph for ≥ 4 days with ≥ 540 minutes of recording per day at baseline (see Report Supplementary Material 4). There were no differences between market-rent and intermediate groups (consistent with the main analysis presented in Table 3). Differences between social and intermediate groups were broadly similar with the results presented in Table 3 for the main analysis.
Baseline differences in physical activity variables between housing sectors were examined by day of the week to explore whether or not differences between sectors were consistent across the week (Figure 3). Levels of physical activity [steps (Figure 3a), MVPA (Figure 3b) and MVPA in ≥ 10-minute bouts (Figure 3c)] were generally consistent across weekdays (Monday to Friday) among all groups. In the intermediate group, steps were higher on Saturdays and lower on Sundays; MVPA and MVPA in ≥ 10-minute bouts were lower on Sundays, but there was no difference on Saturdays compared with weekday activity. In the market-rent sector, steps, MVPA and MVPA in ≥ 10-minute bouts were higher on Saturdays and similar to weekdays on Sundays. In the social group, steps, MVPA and MVPA in ≥ 10-minute bouts were on average lower on Saturdays and lower still on Sundays. Registered time of recorded physical activity (Figure 3d) was lowest on average in the social group during weekdays, decreasing on Saturdays and Sundays. The intermediate and market-rent groups had higher levels of registered time during weekdays than the social group, which decreased on average on Saturdays and Sundays (despite recording more steps and minutes in MVPA, suggesting a higher intensity of activity). Mean levels of steps, MVPA, and MVPA in ≥ 10-minute bouts on weekdays, and differences on Saturday and Sunday compared with weekdays, are shown by housing sector in Report Supplementary Material 5. The marked differences in activity between weekdays and weekend days in the social group were not explained by differences in registered time.
FIGURE 3 Daily physical activity by day of the week and housing sector: N = 6206 days from 1107 participants. Means and 95% CIs are adjusted for sex, age group, ethnic group, LLI, month of recording, day order of recording, day of week, housing sector, an interaction between housing sector and day of week and random effects to allow for multiple days of measurement and clustering of participants within households. Reproduced from Nightingale et al.70 © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. (Figure not reproduced here; see the original article.)
Associations between perceived neighbourhood quality and crime scales, and physical activity and adiposity outcomes are shown in Table 4, adjusted for the participant characteristics (see Table 3). Participants with the most positive perceptions of neighbourhood quality (highest quintile) had higher steps, recorded longer durations of MVPA and had a lower BMI than those who had the most negative perceptions of neighbourhood quality (lowest quintile). There were no significant associations between perceptions of neighbourhood crime and physical activity or adiposity.
| Outcome | Perceptions of neighbourhood | |||||
|---|---|---|---|---|---|---|
| Quality | Crime-free | |||||
| Differencea | 95% CI | p-value | Differencea | 95% CI | p-value | |
| Physical activity (N = 1107) | ||||||
| Daily steps | 677 | 108 to 1247 | 0.02 | –63 | –713 to 587 | 0.85 |
| Daily MVPA (minutes) | 4.5 | 0.02 to 9.0 | 0.05 | 1.1 | –4.0 to 6.2 | 0.68 |
| Daily MVPA in ≥ 10-minute bouts (minutes) | 2.7 | –0.6 to 6.0 | 0.11 | 2.4 | –1.4 to 6.1 | 0.22 |
| Adiposity (N = 1240) | ||||||
| Body mass index (kg/m2)b | –3.6 | –6.5 to –0.6 | 0.02 | –2.1 | –5.4 to 1.3 | 0.21 |
| Fat mass (%) | –1.2 | –2.5 to 0.06 | 0.06 | –0.8 | –2.2 to 0.7 | 0.30 |
The effect of adjustment for perceived neighbourhood quality on the differences in physical activity and adiposity between housing sector groups is presented in Table 5. All associations between perceived neighbourhood quality and crime-free neighbourhood and outcome variables were approximately linear and were therefore fitted as continuous variables in the model. In addition, associations between perceived neighbourhood quality and crime-free neighbourhood and outcome variables were similar across the three housing sectors (all p > 0.05). Adjustment for perceptions of neighbourhood quality reduced the differences in steps (by 10%), MVPA (by 10%) and MVPA in ≥ 10-minute bouts (by 7%), BMI (by 10%) and percentage fat mass (by 6%) between the social and intermediate groups. Differences between market-rent and intermediate groups in adiposity and physical activity variables were not statistically significant before or after adjustment. Larger proportions of the social–intermediate group differences in steps, MVPA and MVPA in ≥ 10-minute bouts on weekends were explained by adjustment for perceptions of neighbourhood quality (10%, 16% and 16%, respectively) than that for weekdays, which were reduced by 10%, 8% and 3% respectively (data not shown).
| Outcome | Housing sector | Model 1a | Model 2b | ||||
|---|---|---|---|---|---|---|---|
| Differencec | 95% CI | p-value | Differencec | 95% CI | p-value | ||
| Physical activity (N = 1107) | |||||||
| Daily steps | Social | –1125 | –1629 to –620 | < 0.0001 | –1016 | –1531 to –501 | < 0.001 |
| Intermediate | Reference group | ||||||
| Market-rent | –104 | –633 to 424 | 0.70 | –96 | –624 to 431 | 0.72 | |
| Daily MVPA (minutes) | Social | –7.5 | –11.5 to –3.6 | < 0.001 | –6.8 | –10.8 to –2.7 | 0.001 |
| Intermediate | Reference group | ||||||
| Market-rent | 2.3 | –1.9 to 6.4 | 0.29 | 2.3 | –1.8 to 6.5 | 0.27 | |
| Daily MVPA in ≥ 10-minute bouts (minutes) | Social | –6.5 | –9.5 to –3.5 | < 0.0001 | –6.0 | –9.1 to –3.0 | < 0.001 |
| Intermediate | Reference group | ||||||
| Market-rent | 2.8 | –0.3 to 6.0 | 0.08 | 2.8 | –0.3 to 6.0 | 0.08 | |
| Adiposity (N = 1240) | |||||||
| Body mass index (kg/m2)d | Social | 5.0 | 2.2 to 7.8 | < 0.001 | 4.5 | 1.7 to 7.3 | 0.002 |
| Intermediate | Reference group | ||||||
| Market-rent | –0.8 | –3.6 to 2.0 | 0.57 | –0.9 | –3.6 to 2.0 | 0.55 | |
| Fat mass (%) | Social | 2.7 | 1.5 to 3.8 | < 0.0001 | 2.5 | 1.4 to 3.6 | < 0.0001 |
| Intermediate | Reference group | ||||||
| Market-rent | –0.2 | –1.4 to 1.0 | 0.70 | –0.2 | –1.4 to 0.9 | 0.68 | |
At follow-up
In total, 877 adults (69%) from 710 households were examined after 2 years; 441 (50%) had moved and were living in East Village at follow-up. Data were available at baseline and follow-up for 762 participants for physical activity outcomes and 822 participants for adiposity variables. There were no differences in age, sex or ethnic group between those followed up and not followed up (see Report Supplementary Material 6), although those followed up had slightly higher socioeconomic status and recorded more sedentary time at baseline than those not followed up.
Baseline characteristics for the 877 adults who were seen at follow-up are shown in Table 6, by housing sector and overall. In the social housing sector, age, sex, socioeconomic status and children in the household were similar for those living in East Village and for controls, although the East Village group had a higher proportion of participants of black African/Caribbean ethnic origin and a lower proportion of participants of Asian ethnic origin than the control group. In the intermediate sector, the East Village group were younger, less likely to be female, and more likely to be of white ethnicity, be economically active and have no children in the household than controls. In the market-rent sector, age, sex, ethnicity, socioeconomic status and children in the household were similar in the East Village and control groups. For housing sectors combined, the proportion of females in the two groups was similar, but participants in the East Village group were younger, more likely to be of black African–Caribbean ethnicity, less likely to be of Asian ethnicity and less likely to be in higher managerial/professional occupations than the control group. Overall, baseline daily steps and daily levels of MVPA were greater in the control group than in the East Village group, although differences by housing sector were less apparent. Although there was no overall difference in baseline adiposity levels, controls in the intermediate group had higher levels of BMI and fat mass percentage than those living in intermediate East Village accommodation. Those who had moved to East Village reported a sizable increase in scores for the perceptions of crime-free neighbourhood and higher quality neighbourhood at follow-up (Table 7 and Report Supplementary Material 7). Compared with baseline data, participants living in East Village at follow-up lived closer to their nearest park, had better access to public transport and lived in a more walkable area (see Table 7 and Report Supplementary Material 7).
| Characteristic | All housing sectors (N = 877) | Housing sector | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Social (N = 344) | Intermediate (N = 377) | Market-rent (N = 156) | ||||||||||
| Control (N = 436) | East Village (N = 441) | p-valuea | Control (N = 124) | East Village (N = 220) | p-valuea | Control (N = 203) | East Village (N = 174) | p-valuea | Control (N = 109) | East Village (N = 47) | p-valuea | |
| Age (years), n (%) | ||||||||||||
| 16–24 | 75 (17) | 104 (24) | 0.01 | 18 (15) | 47 (21) | 0.27 | 30 (15) | 38 (22) | < 0.001 | 27 (25) | 19 (40) | 0.07 |
| 25–34 | 185 (42) | 194 (44) | 32 (26) | 61 (28) | 100 (49) | 113 (65) | 53 (49) | 20 (43) | ||||
| 35–49 | 138 (32) | 123 (28) | 66 (53) | 95 (43) | 61 (30) | 22 (13) | 11 (10) | 6 (13) | ||||
| ≥ 50 | 38 (9) | 20 (5) | 8 (6) | 17 (8) | 12 (6) | 1 (1) | 18 (17) | 2 (4) | ||||
| Sex, n (%) | ||||||||||||
| Female | 248 (57) | 247 (56) | 0.79 | 91 (73) | 158 (72) | 0.75 | 107 (53) | 70 (40) | 0.02 | 50 (46) | 19 (40) | 0.53 |
| Ethnic group, n (%) | ||||||||||||
| White | 225 (52) | 212 (48) | < 0.001 | 25 (20) | 38 (17) | < 0.001 | 122 (60) | 139 (80) | < 0.001 | 78 (72) | 35 (74) | 0.42 |
| Black | 81 (19) | 131 (30) | 40 (32) | 120 (55) | 32 (16) | 9 (5) | 9 (8) | 2 (4) | ||||
| Asian | 91 (21) | 56 (13) | 47 (38) | 31 (14) | 35 (17) | 18 (10) | 9 (8) | 7 (15) | ||||
| Other | 39 (9) | 42 (10) | 12 (10) | 31 (14) | 14 (7) | 8 (5) | 13 (12) | 3 (6) | ||||
| NS-SEC, n (%) | ||||||||||||
| Higher managerial/professional | 246 (57) | 179 (41) | < 0.001 | 24 (20) | 23 (11) | 0.12 | 146 (73) | 124 (72) | 0.01 | 76 (70) | 32 (68) | 0.53 |
| Intermediate | 54 (12) | 69 (16) | 16 (13) | 27 (12) | 22 (11) | 34 (20) | 16 (15) | 8 (17) | ||||
| Routine/manual | 44 (10) | 56 (13) | 26 (21) | 46 (21) | 13 (6) | 10 (6) | 5 (5) | 0 (0) | ||||
| Economically inactive | 89 (21) | 133 (30) | 57 (46) | 121 (56) | 20 (10) | 5 (3) | 12 (11) | 7 (15) | ||||
| Children in household, n (%) | ||||||||||||
| Yes | 172 (39) | 203 (46) | 0.05 | 109 (88) | 178 (81) | 0.09 | 52 (26) | 21 (12) | < 0.001 | 11 (10) | 4 (9) | 1.00 |
| Physical activity,b mean (SD) | ||||||||||||
| Total, n | 405 | 403 | 112 | 199 | 189 | 164 | 104 | 40 | ||||
| Daily steps | 9192 (3284) | 8644 (3104) | 0.01 | 7707 (3069) | 7730 (3345) | 0.95 | 9639 (3224) | 9592 (2584) | 0.88 | 9980 (3128) | 9304 (2468) | 0.22 |
| Daily MVPA (minutes) | 61 (26) | 58 (25) | 0.06 | 50 (25) | 50 (25) | 0.97 | 63 (24) | 66 (22) | 0.31 | 70 (26) | 65 (21) | 0.32 |
| Daily MVPA in ≥ 10-minute bouts (minutes) | 22 (20) | 19 (18) | 0.02 | 12 (14) | 12 (13) | 0.85 | 23 (19) | 25 (19) | 0.45 | 30 (21) | 28 (21) | 0.70 |
| Daily sedentary time (minutes) | 588 (81) | 583 (84) | 0.36 | 544 (84) | 545 (83) | 0.91 | 598 (75) | 617 (68) | 0.01 | 616 (67) | 627 (74) | 0.42 |
| Adiposity, mean (SD) | ||||||||||||
| Total, n | 428 | 435 | 124 | 218 | 199 | 172 | 105 | 45 | ||||
| BMI (kg/m2)c | 26 (5) | 26 (6) | 0.23 | 27 (5) | 28 (7) | 0.10 | 26 (5) | 24 (4) | 0.01 | 24 (4) | 23 (3) | 0.07 |
| Fat mass per centd | 27 (10) | 27 (11) | 0.56 | 31 (10) | 32 (11) | 0.28 | 26 (9) | 22 (8) | < 0.001 | 23 (9) | 21 (8) | 0.20 |
| Perceptions/characteristics | All housing sectors (N = 436) | Housing sector | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Social (N = 124) | Intermediate (N = 203) | Market-rent (N = 109) | ||||||||||
| Mean | 95% CI | p-value | Mean | 95% CI | p-value | Mean | 95% CI | p-value | Mean | 95% CI | p-value | |
| Neighbourhood perception scoresa | ||||||||||||
| Control group | ||||||||||||
| Participants, n | 436 | 124 | 203 | 109 | ||||||||
| Crime score | 0.6 | 0.2 to 1.0 | 0.004 | 1.2 | 0.4 to 2.1 | 0.004 | 0.1 | –0.5 to 0.7 | 0.81 | 0.9 | 0.2 to 1.7 | 0.02 |
| Quality score | 0.7 | 0.3 to 1.2 | < 0.001 | 1.3 | 0.5 to 2.0 | 0.001 | 0.5 | –0.2 to 1.1 | 0.14 | 0.6 | –0.2 to 1.4 | 0.15 |
| East Village group | ||||||||||||
| Participants, n | 441 | 220 | 174 | 47 | ||||||||
| Crime score | 4.6 | 4.1 to 5.1 | < 0.001 | 5.6 | 4.9 to 6.3 | < 0.001 | 3.8 | 3.2 to 4.4 | < 0.001 | 2.7 | 1.2 to 4.3 | < 0.001 |
| Quality score | 6.8 | 6.4 to 7.3 | < 0.001 | 7.1 | 6.4 to 7.8 | < 0.001 | 6.5 | 5.8 to 7.2 | < 0.001 | 6.8 | 5.8 to 7.8 | < 0.001 |
| Built environment characteristics | ||||||||||||
| Control group | ||||||||||||
| Participants, n | 376 | 120 | 178 | 78 | ||||||||
| Distance to closest park (m)b | 6 | –37 to 49 | 0.79 | –16 | –72 to 39 | 0.56 | 10 | –58 to 78 | 0.77 | 31 | –83 to 144 | 0.59 |
| Access to public transport (PTAL)c | –0.2 | –0.3 to 0.0 | 0.07 | –0.2 | –0.5 to 0.0 | 0.07 | –0.1 | –0.4 to 0.2 | 0.411 | –0.2 | –0.6 to 0.2 | 0.45 |
| Walkabilityd | 0.3 | 0.1 to 0.5 | 0.01 | –0.2 | –0.5 to 0.2 | 0.34 | 0.6 | 0.2 to 1.0 | 0.005 | 0.4 | –0.1 to 0.9 | 0.09 |
| Land use mixe | 0.02 | 0.00 to 0.04 | 0.05 | –0.03 | –0.05 to 0.00 | 0.04 | 0.04 | 0.01 to 0.07 | 0.005 | 0.03 | –0.01 to 0.06 | 0.13 |
| Residential densityf | 1.9 | 1.2 to 2.6 | < 0.001 | 1.4 | 0.7 to 2.1 | < 0.001 | 2.4 | 1.2 to 3.5 | < 0.001 | 1.6 | 0.05 to 3.2 | 0.04 |
| Street connectivityg | 0.0 | –0.1 to 0.1 | 0.89 | –0.1 | –0.3 to 0.1 | 0.17 | 0.1 | –0.1 to 0.2 | 0.42 | 0.0 | –0.3 to 0.2 | 0.89 |
| East Village group | ||||||||||||
| Participants, n | 414 | 216 | 160 | 38 | ||||||||
| Distance to closest park (m)b | –525 | –565 to –485 | < 0.001 | –477 | –527 to –427 | < 0.001 | –570 | –633 to –506 | < 0.001 | –614 | –812 to –416 | < 0.001 |
| Access to public transport (PTAL)c | 1.6 | 1.4 to 1.9 | < 0.001 | 2.5 | 2.1 to 2.8 | < 0.001 | 0.8 | 0.4 to 1.3 | < 0.001 | 0.2 | –0.7 to 1.0 | 0.66 |
| Walkabilityd | 2.5 | 2.2 to 2.7 | < 0.001 | 2.8 | 2.5 to 3.0 | < 0.001 | 2.2 | 1.7 to 2.6 | < 0.001 | 2.1 | 0.6 to 3.7 | 0.01 |
| Land use mixe | 0.38 | 0.36 to 0.40 | < 0.001 | 0.39 | 0.37 to 0.41 | < 0.001 | 0.38 | 0.35 to 0.41 | < 0.001 | 0.30 | 0.20 to 0.39 | < 0.001 |
| Residential densityf | 13.2 | 12.0 to 14.4 | < 0.001 | 12.9 | 11.4 to 14.4 | < 0.001 | 12.6 | 10.6 to 14.6 | < 0.001 | 17.4 | 12.1 to 22.8 | < 0.001 |
| Street connectivityg | –0.9 | –1.1 to –0.8 | < 0.001 | –0.8 | –0.9 to –0.6 | < 0.001 | –1.1 | –1.3 to –0.9 | < 0.001 | –1.1 | –1.7 to –0.5 | < 0.001 |
The effect of moving to East Village on physical activity (daily steps, daily minutes of MVPA, daily sedentary time) and adiposity (BMI and percentage fat mass) is shown in Table 8, both overall and stratified by housing sector. Overall, there was weak evidence that moving to East Village was associated with a small increase in daily steps of 154 [95% confidence interval (CI) –231 to 539] after adjusting for sex, age group, ethnic group and housing sector. This appeared more pronounced in the intermediate sector (433 steps, 95% CI –175 to 1042 steps). There was also weak evidence of an increase in daily minutes of MVPA overall, both in total MVPA and in ≥ 10-minute bouts, in participants in the intermediate and market-rent sectors, and a decrease in daily minutes in participants in the social sector, although none of the effects were statistically significant. There was no evidence of a change in daily sedentary time, BMI or percentage fat mass, both overall and by housing sector (see Table 8). Inclusion of an interaction term between the East Village/control group and housing sector to allow for potential differential effects was not statistically significant (p > 0.1). Restricting analyses to 652 (86%) participants who recorded at least 4 days of ≥ 540 minutes accelerometry wear at baseline and at follow-up gave a larger effect for moving to East Village on daily steps (324 steps, 95% CI –93 to 741 steps; see Report Supplementary Material 8), but CIs still spanned the null value. There was weak evidence that the changes were larger at weekends (428 steps, 95% CI –288 to 1144 steps) than on weekdays (199 steps, 95% CI –223 to 620 steps), but CIs included the null value.
| All housing sectors | Housing sector | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Social | Intermediate | Market-rent | ||||||||||
| Difference | 95% CI | p-value | Difference | 95% CI | p-value | Difference | 95% CI | p-value | Difference | 95% CI | p-value | |
| Physical activity outcomes | (n = 762) | (n = 290) | (n = 335) | (n = 137) | ||||||||
| Daily steps | ||||||||||||
| Model 1a | 192 | –173 to 557 | 0.30 | –129 | –728 to 469 | 0.67 | 500 | –63 to 1063 | 0.08 | 160 | –784 to 1105 | 0.74 |
| Model 2b | 235 | –136 to 605 | 0.21 | –187 | –803 to 429 | 0.55 | 433 | –175 to 1042 | 0.16 | 225 | –730 to 1181 | 0.64 |
| Model 3c | 154 | –231 to 539 | 0.43 | |||||||||
| Total daily MVPA (minutes) | ||||||||||||
| Model 1a | 0.5 | –2.4 to 3.4 | 0.73 | –1.7 | –6.2 to 2.8 | 0.45 | 2.9 | –1.4 to 7.3 | 0.19 | 1.1 | –6.9 to 9.2 | 0.78 |
| Model 2b | 0.6 | –2.3 to 3.5 | 0.67 | –2.8 | –7.4 to 1.9 | 0.24 | 1.7 | –3.0 to 6.3 | 0.49 | 1.9 | –6.3 to 10.0 | 0.65 |
| Model 3c | 0.2 | –2.9 to 3.2 | 0.91 | |||||||||
| Daily MVPA in ≥ 10-minute bouts (minutes) | ||||||||||||
| Model 1a | 0.5 | –1.8 to 2.7 | 0.67 | –0.9 | –3.8 to 2.0 | 0.55 | 3.1 | –0.5 to 6.8 | 0.09 | 2.5 | –4.0 to 9.0 | 0.45 |
| Model 2b | 0.6 | –1.7 to 2.8 | 0.62 | –1.2 | –4.2 to 1.9 | 0.45 | 1.6 | –2.3 to 5.4 | 0.43 | 3.1 | –3.5 to 9.6 | 0.36 |
| Model 3c | 0.8 | –1.5 to 3.1 | 0.48 | |||||||||
| Daily sedentary time (minutes) | ||||||||||||
| Model 1a | –8 | –18 to 2 | 0.12 | –8 | –28 to 11 | 0.39 | –2 | –17 to 12 | 0.77 | 3 | –19 to 25 | 0.78 |
| Model 2b | –8 | –18 to 2 | 0.12 | –13 | –33 to 7 | 0.20 | –4 | –19 to 12 | 0.64 | 7 | –15 to 29 | 0.54 |
| Model 3c | –4 | –15 to 7 | 0.45 | |||||||||
| Adiposity outcomes | (n = 822) | (n = 327) | (n = 358) | (n = 137) | ||||||||
| BMI (kg/m2) | ||||||||||||
| Model 1a | 0.3 | 0.0 to 0.5 | 0.06 | 0.4 | –0.2 to 1.0 | 0.16 | 0.1 | –0.2 to 0.5 | 0.54 | 0.2 | –0.4 to 0.8 | 0.52 |
| Model 2b | 0.2 | –0.1 to 0.5 | 0.14 | 0.2 | –0.4 to 0.8 | 0.49 | 0.1 | –0.3 to 0.5 | 0.66 | 0.2 | –0.4 to 0.8 | 0.52 |
| Model 3c | 0.2 | –0.1 to 0.5 | 0.25 | |||||||||
| Percentage fat massd | ||||||||||||
| Model 1a | 0.1 | –0.4 to 0.7 | 0.62 | –0.1 | –1.1 to 0.8 | 0.78 | 0.1 | –0.6 to 0.8 | 0.81 | 0.3 | –1.0 to 1.7 | 0.62 |
| Model 2b | 0.1 | –0.4 to 0.7 | 0.58 | –0.3 | –1.3 to 0.7 | 0.60 | 0.2 | –0.6 to 0.9 | 0.65 | 0.4 | –1.0 to 1.8 | 0.58 |
| Model 3c | 0.1 | –0.5 to 0.6 | 0.80 | |||||||||
Separate analyses comparing change in daily steps in East Village participants with (1) controls who remained at their baseline address and (2) controls who moved elsewhere, allowed the effect of any move to be examined (see Report Supplementary Material 8). Although none of the differences were statistically significant, there was a suggestion of larger differences in daily steps when those in East Village were compared with those who had moved elsewhere [particularly in the intermediate housing sector where a difference of 677 steps (95% CI 15 to 1339 steps) was observed], with a smaller difference when compared with those who did not move. It is noteworthy that daily steps were marginally lower among those in the social sector who moved to East Village than those who moved elsewhere, but effect sizes were not statistically significant. There was no evidence of a difference after removing 21 women who reported being pregnant at baseline or follow-up. The missing data imputation analyses (adding a further 46 individuals at follow-up) gave similar results to the complete case analysis (see Report Supplementary Material 9).
Discussion
Baseline findings
The baseline findings from this study showed that, in the social housing sector, levels of physical activity were particularly low at weekends compared with weekdays, possibly reflecting higher occupational physical activity and lower leisure-time physical activity; weekday–weekend differences in physical activity were less marked among those seeking intermediate and market-rent housing. However, the lower registered time at weekends but higher MVPA and steps suggests more intense activity at weekends in the intermediate and market-rent housing sectors. Participants seeking social housing in East Village also had lower levels of physical activity and higher levels of BMI and percentage fat mass than those seeking intermediate and market-rent housing, even when adjusted for demographic factors.70 Positive associations between perceived neighbourhood quality and physical activity, BMI and percentage fat mass at baseline were also shown. Adjustment for differences in perceived neighbourhood quality reduced the differences in physical activity and BMI by approximately 10% between social and intermediate housing sectors, equivalent to a reduction of 111 daily steps, 0.5 minutes of MVPA and 0.5 kg/m2 BMI. However, a larger proportion of the difference in physical activity was apparent at weekends; equivalent to a reduction of 222 daily steps and 2.2 minutes of MVPA. These baseline findings provide further insight into the physical activity patterns of the ENABLE London cohort, and how moving to East Village might affect physical activity and adiposity levels in different socioeconomic groups.
Relation of baseline findings to previous studies
Previous studies have shown that lower socioeconomic status is associated with lower levels of physical activity,71,72 and that those from more socially deprived backgrounds have the most barriers to being physically active.8 Previous research examining the role of housing tenure is limited. Findings from this study showed marked differences in physical activity and adiposity between those seeking social, intermediate and market-rent housing. In particular, lower physical activity and higher adiposity were observed in participants seeking social housing, a group that comprises a high proportion of people from more socioeconomically disadvantaged backgrounds.61 The higher levels of BMI and percentage fat mass in those seeking social housing than in those seeking intermediate or market-rent housing is consistent with systematic reviews that have found an association between lower socioeconomic status and higher levels of adiposity, particularly in higher income countries and among women.73 Although socioeconomic status is a strong determinant of housing status, to our knowledge this is the first study to explicitly examine housing sector differences in objectively measured physical activity and markers of adiposity levels (i.e. BMI and percentage fat mass). However, it is important to appreciate more broadly what these aspirational housing sector differences might represent before considering what change in the residential built environment might do. Related studies have shown that those in social housing are less likely to use active travel than owner occupiers,19 and that those in social housing and home owners with a mortgage are more likely to be obese and have higher levels of illness and disability than outright home owners, even after adjustment for other socioeconomic status markers.74 These findings suggest that the effect of home ownership may be more complex and cannot be simply explained by socioeconomic status. Neighbourhood quality may offer a partial explanation for these findings.75 Baseline findings from this study showed that perceptions of better neighbourhood quality were associated with physical activity whereas perceptions of crime were not. In contrast, a large UK-based study found that perceptions of feeling safe in the neighbourhood had the largest effect on levels of physical activity compared with perceptions of leisure facilities, a sense of belonging or access to public transport or amenities.76 Another study in the USA found that low perceived safety from crime was associated with lower levels of MVPA.77 However, a recent review concluded that higher-quality evidence is needed, including from prospective studies and natural experiments in areas of wide crime variability, to further understand the effect of crime on physical and mental health.28 Moreover, previous work has suggested that objective and perceived measures of the built environment correlate differently with physical activity levels, suggesting that these measures are assessing different dimensions of the built environment, which relate differently to health behaviour.78
The baseline findings showed that physical activity levels were particularly low at the weekend among those seeking social housing, which is consistent with findings from a systematic review which found that leisure-time physical activity (which may be more likely to take place at weekends) was lower among those from lower socioeconomic groups.9 This suggests that low-cost strategies to increase weekend physical activity may be particularly beneficial to more disadvantaged households. A free community-based programme in Bogotá, Colombia, temporarily closed streets on Sundays to encourage physical activity among more disadvantaged local residents.79 A similar programme has been trialled in the USA;80 however, the effectiveness, longevity and generalisability of these programmes to other socioeconomically deprived areas is yet to be established.
The baseline findings from the ENABLE London cohort suggest that perceived neighbourhood quality is associated with meaningful differences in physical activity and markers of adiposity. Differences in steps (680 steps) and BMI (3.6 kg/m2) between the lowest and highest quintiles of perceived neighbourhood quality should be considered in the context of an average of 10,000 steps per day, where a 5% increase (500 steps) would be a worthwhile population-level increase and a 5 kg/m2 increase in BMI would be associated with a 31% increase in all-cause mortality.81 Hence, improvements in neighbourhood quality could be associated with health benefits that are of public health importance. There were also substantial differences in physical activity, BMI and percentage fat mass between the three housing sectors studied. In particular, the very low levels of physical activity in the social housing sector during the weekend could provide a target for intervention to increase levels of physical activity; again, these differences should be considered in relation to 500 steps per day, which can be considered as an increase of population importance. Perceptions of neighbourhood quality reduced baseline differences in physical activity and adiposity between housing sector groups, and the measurement of more objective markers of neighbourhood quality has the potential to explain more.78
Follow-up findings
Compared with pre-move residential areas, East Village was assessed as a more walkable place with greater access to public transport and closer proximity to parks, and was perceived as having marked improvements in neighbourhood quality. Despite this, at 2-year follow-up there was weak evidence that moving to East Village was associated with an overall increase in daily steps compared with not moving. This increase was the largest in those who moved into intermediate housing, whereas those in social housing showed a small decline in their physical activity. Overall increases in time spent in MVPA daily and decreases in daily sedentary behaviour were also observed, but again effects were small. There were no changes in markers of adiposity associated with moving to East Village, either overall or by housing tenure.
Change in the built environment
A key issue is whether or not the change in the built environment, in this case East Village, ‘the intervention’, is sufficient and rapid enough to observe changes in health behaviour,33,82 and whether or not the correct components of the built environment to maximise physical activity were present.41,83 East Village includes a multitude of features that could plausibly have a beneficial effect on the physical activity patterns of occupants.61 These include more equitable access to improved public transport and active travel opportunities.42,43 East Village is also situated close to leisure-time physical activity-permissive features, including parkland, street furniture and pedestrianised areas, as well as major sporting facilities. It is also situated within walking distance of retail outlets.42,43 Hence, collectively, East Village has a multitude of activity-permissive features, which could plausibly increase physical activity levels by encouraging time spent outdoors.84,85 Differences in the built environment of East Village were evident by the substantial changes in objective GIS measures of the built environment (including increased walkability, access to public transport and parks) and the sizeable improvements in neighbourhood perceptions observed among residents, especially those from the social housing sector. This raises the question of why larger improvements in physical activity were not observed.
Relation of follow-up findings to previous studies
Previous research has shown cross-sectional associations between attributes of the built environment and physical activity,32,86,87 whereas prospective studies from fewer housing relocation or neighbourhood change studies have shown more modest effects.33 To our knowledge, the ENABLE London study is the first longitudinal study to use objective measures of physical activity in a cohort relocating to a new neighbourhood designed for healthy active living, rather than relying solely on less reliable self-report.88 Steps were chosen a priori as the primary outcome given the focus of examining the effect of changing to active design principles, and hence walkability of the built environment.61 We are not aware of any other longitudinal studies with a directly comparable outcome. Despite this, some broad comparisons with other longitudinal studies are possible. Although increases in active modes of travel have been observed in these studies (particularly in reported cycling and use of public transport),89–91 change in overall self-reported walking and physical activity levels have been less apparent.82,92,93 However, change in physical activity may be more nuanced. The RESIDential Environment (RESIDE) study recruited 1800 people who moved into new homes in Perth, Australia, and, although there were no overall differences in self-reported physical activity, increases in recreational walking and opposing decreases in transport-related walking were evident.94 These differences were attributed to an increase in access to recreational facilities (e.g. parks, sports fields and beaches) and a decrease in access to transport (within a 15-minute walk), respectively.95
Use of the residential built environment may differ throughout the week. We observed appreciable differences in weekday versus weekend physical activity across housing sectors within this cohort at baseline.70 Although prospective findings from this study also indicated increased steps at weekends compared with weekdays was associated with moving to East Village (i.e. 428 vs. 199 steps), these differences were both consistent with no effect (i.e. CIs included the null value of zero steps).
Conclusions
Teasing out the causal relationship between the built environment and health behaviours is complex,96 and the reasons for failing to observe clear effects in this longitudinal study when cross-sectional findings are less equivocal remain unclear.33 Some systematic reviews have avoided this issue by simply excluding longitudinal or experimental studies, which raise concerns that such reviews lead to greater certainty about biased findings,97–100 especially when those living in better neighbourhoods, with built environments more conducive to physical activity, may be fundamentally different from those who do not. However, it is notable that longitudinal studies may also be prone to selection bias, whereby those who choose to move to better neighbourhoods have different health behaviours from those who choose to remain. The ENABLE London study sought to minimise these effects by recruiting a cohort that was seeking to move before any move to East Village, where half of whom moved to East Village and the other half of whom did not. Further consideration of the strengths and limitations of the ENABLE London study and the potential implications of the findings are considered in Chapter 9.