Introduction
In this chapter we focus on exploring whether or not there are associations between objectively measured features of the built environment and physical activity at baseline and whether or not changes in the built environment are associated with changes in physical activity.
Physical activity is a protective factor for a wide range of physical and psychological disorders.1,6,101 The current population levels of physical activity are too low both in the UK and globally,102 and individuals from more disadvantaged socioeconomic groups are less physically active, in terms of both daily steps70 and MVPA.17,103 In recent years, epidemiological research has increasingly incorporated socioecological models that acknowledge the role of the built environment in determining physical activity behaviours.104 Researchers have hypothesised that a deprivation amplification effect exists,105 whereby disadvantaged individuals have greater exposure to less health-promoting neighbourhood environments.106–109 Policies aimed at modifying built environment factors have been promoted as potential levers for reducing socioeconomic inequalities in health behaviours, including physical activity. However, the extent to which differences in residential built environment factors contribute to individual socioeconomic inequalities in physical activity remains uncertain.
In the UK, environmental factors that have been found to be positively associated with higher physical activity levels include greater neighbourhood walkability,110 improved accessibility to green space32,111 and improved accessibility to public transport.32 However, these findings rely on a limited number of studies. This evidence is also mixed, with, for instance, both positive32,111 and null112 associations found between accessibility to green space and physical activity. More work is needed to establish whether or not specific features of the residential built environment shape physical activity behaviours in the UK, and whether or not differences in the accessibility of these features contribute to socioeconomic differences in physical activity.
Associations between residential physical activity facilities and physical activity have been shown to vary by time of the day and day of the week.113 Daily variations in the number of steps and minutes of MVPA have been reported, with more physical activity on weekdays than at weekends.70,114,115 Time use studies show that week and weekend days markedly differ in terms of human activity, with the former focused more on work and the latter more on leisure and recreational activities.116 Use of the residential built environment may, therefore, be expected to be different at weekdays and weekends. This suggests that we should take into account weekday and weekend variations in physical activity when seeking to better understand the contribution of the residential built environment to physical activity.117
Many of these studies rely on cross-sectional designs and are therefore prone to biases, such as residential self-selection.118 As environments can change in response to residents’ preferences and residents may choose to live in locations consistent with their preferred lifestyles, cross-sectional designs limit our ability to make causal inferences. Longitudinal studies that examine associations between time-varying features of the residential built environment and physical activity are needed to strengthen evidence for a causal effect. Natural experiments of changes to the built environment could help generate better ‘causal’ evidence that environmental factors are important for physical activity.119
Aims
In this chapter we assess whether or not:
- the residential built environment is associated with the number of daily steps walked and the total daily minutes of MVPA accumulated on weekdays and at the weekend
- associations between the residential built environment and physical activity levels are modified by socioeconomic status
- the distribution of residential built environment factors differs by socioeconomic status, and, if so, whether or not this contributes to socioeconomic differences in the number of daily steps and the time in MVPA on weekdays and at the weekend
- changes in exposure to features of the residential built environment can predict changes in physical activity levels among adult participants and whether or not these effects differ by socioeconomic status.
Methods
In this chapter we use baseline and follow-up measurements of adult physical activity and sociodemographic data before and after relocation into East Village.
Baseline data
Of the 1278 study participants, we excluded those who lived outside Greater London (n = 81) and those without sufficient accelerometer data (at least 9 hours for at least 1 day is sufficient) on either weekdays or weekends (n = 144) (some participants fit multiple exclusion categories). Those who were excluded from the analytical sample had similar characteristics to those included in the sample in terms of housing status (p = 0.17), age (p = 0.27), sex (p = 0.06) and ethnicity (p = 0.18) (see Report Supplementary Material 10). Of the 1064 participants retained for analyses, 442 were seeking relocation into social, 436 into intermediate and 186 into market-rent accommodation; 1053 participants had physical activity data on weekdays, 848 on weekends and 837 on both weekdays and weekends. To maximise power to detect associations and reduce selection bias, analyses were performed with the maximum number of participants available (i.e. 1053 individuals in analyses on physical activity on weekdays and 848 individuals in analyses on physical activity at weekends). Sensitivity analyses were performed on the subset of 837 participants who had data on both weekdays and weekends.
Follow-up data
Of the 877 participants followed up (69%), those who lived outside Greater London at either baseline or follow-up and those who did not have physical activity data at baseline or follow-up (n = 190) were excluded. Those excluded from the analytical sample had similar characteristics to those included with regard to sex (p = 0.36) and ethnicity (p = 0.76), but were younger (p = 0.009) and had more intermediate and less market-rent seekers compared with those included (p = 0.003) (see Report Supplementary Material 11). A total of 687 participants were retained for analyses; 680 participants had physical activity data on weekdays and 517 on weekend days, both at baseline and at follow-up. Of the 687 participants, 283 were seeking relocation into social housing, 301 into intermediate housing, and 103 into market-rent accommodation.
Primary outcomes
Accelerometer-derived physical activity
Mean daily steps and mean daily time (minutes) spent in MVPA were collected from accelerometer data among those who wore the device for at least 9 hours per day for at least 1 day. Daily steps were adjusted for day of the week, day order of recording and month of accelerometer data collection.70 Mean daily steps and mean daily time spent in MVPA specifically on weekdays and at weekends were derived.
Environmental exposures
Participants were geocoded to the centroid of the footprint of their building of residence, using OS AddressBase Premium (versions 2015 and 2017 for geocoding at baseline and at follow-up, respectively) to match participants’ declared residential addresses with XY coordinates. Residential locations were then used to derive a range of built environment factors that were hypothesised to be associated with physical activity, as follows.
Residential neighbourhood walkability
Walkability scores were calculated by summing the z-scores of the three following GIS-derived variables aggregated within a 1-km street network home-centred buffer: (1) street connectivity, as the number of junctions of three or more road branches per street-kilometre, (2) land use mix, as the evenness of distribution of square footage of residential, commercial, office, entertainment and institutional building footprints, based on existing literature52,110 and (3) net residential density, as the unique number of residential addresses per squared kilometre of building footprint devoted to residential use (i.e. residential building and attached gardens) (see Report Supplementary Material 12). Both baseline and follow-up z-score transformations were based on the mean and standard deviation of the baseline sample, as has been done previously.89 Rescaling scores at follow-up using the baseline population sample estimates ensured comparability of scores both across participants and across time for the same participant, hence providing a meaningful quantification of the change of walkability over time. Land use mix and net residential density variables were log-transformed to fit a comparable scale. The choice of 1-km street network home-centred buffers was motivated by two considerations. First, destinations that are within 1 km (a 15-minute walk) from home have been defined as reachable by foot in the literature.120 In the absence of more personalised measures of the residential neighbourhood, 1 km was, thus, judged as a reasonable aggregation unit to encompass the opportunities available in the residential area of each participant. Second, a 1-km buffer has been used in many studies that found significant associations between features of the built environment and physical activity outcomes.121,122
Proximity from home to parks
Using data from Greenspace Information for Greater London (GiGL),123 proximity to three types of park – ‘metropolitan’, ‘district’ and ‘local’ – was calculated on the basis of street network distance from the home address to the nearest entrance for each park type. Park type is derived from the Greater London Authority London Plan, March 2016,124 and is based on park size and the number and type of facilities a park provides. ‘Metropolitan’ parks are the largest and have the most amenities, and ‘local’ parks are the smallest and are the least well-equipped of these three types (see Report Supplementary Material 13). Where there were missing entrance points to parks in the GiGL database (n = 22, 2.9%), they were manually geocoded based on visual data drawn from Google Maps (Google Inc., Mountain View, CA, USA).
Public transport accessibility
Each ENABLE London participant was assigned a public transport accessibility level (PTAL) score based on the closest location of public transport to their place of residence where a PTAL value was made available by TfL, the local government body responsible for the transport system in Greater London. PTAL is an averaged measure of the densities of the London public transport access points (trains, buses, underground, Docklands Light Railway, trams) and also accounts for the frequency of service. It is available for the centroid of each 100 m × 100 m cell of a grid covering the whole of Greater London and is expressed as a nine-category variable (lower scores reflecting poorer accessibility). The nine PTAL categories were collapsed into three categories (low: PTAL scores 0, 1a, 1b; intermediate: PTAL scores 2, 3, 4; high: PTAL scores 5, 6a, 6b) to increase the number of participants per category, especially for those residing in areas with the lowest accessibility scores.
Data sources and versions used for computing these environmental variables are detailed in Report Supplementary Material 14.
Covariates
Covariates included sex (female, male), age group (16–24, 25–34, 35–49 and ≥ 50 years), ethnicity (white, black, Asian, mixed/other) and aspirational housing tenure being sought (social, intermediate, market-rent). Seekers of intermediate accommodation were used as the reference group, as this group had the largest number of participants.
Household income, occupational status, presence of children in the household and education level were highly correlated to aspirational housing status, hence, these variables were not adjusted for in the final regression models to avoid overadjustment (see Report Supplementary Material 15). Other hypothesised covariates, including car use and attitude towards physical activity, were excluded after initial consideration, as they were not found to be associated with exposures and outcomes in bivariate analyses or their addition to fully adjusted models did not appreciably alter coefficients.
Statistical analyses
All analyses were carried out using Stata Special Edition version 15 for Windows.
Analyses of baseline data
First, aspirational housing tenure differences in sociodemographic characteristics, residential built environment factors (i.e. walkability, distance to parks and accessibility to public transport) and daily steps and MVPA (minutes) were examined. Chi-squared tests were used to assess the differences between housing tenure for sociodemographic characteristics and public transport availability (PTAL score). Analysis of variance was used to assess differences between housing tenure for the residential built environment and physical activity variables. Second, multilevel linear regression models, including a random effect to allow for clustering at baseline household level, were used to assess whether or not environmental variables were associated with the daily steps taken overall, on weekdays and at weekends separately. Environmental variables were examined both separately (i.e. entered in turn in the models) and concomitantly (to examine their independent association). All models were adjusted for clustering at baseline household level, sex, age group, ethnicity and aspirational housing tenures as fixed effects and household as a random effect to allow for clustering at baseline household level. We also examined whether or not effects of environmental variables on physical activity behaviour differed by aspirational housing tenure. MVPA in 10-minute bouts was not used, as most participants had none or low values and no transformation would have permitted easy interpretation of the regression coefficients. Third, where environmental factors showed significant associations with the number of daily steps, multilevel regression models further examined whether or not housing tenure differences in physical activity were attenuated after adjustment for environmental factors.
Analyses of follow-up data
First, changes in neighbourhood walkability, residential density, land use mix, street connectivity, distance to a park and accessibility to public transport were quantified in the whole sample and by housing sector, and differences between housing sectors were assessed using analysis of variance. Second, multilevel linear regression models were fitted to examine the effect of changes in exposure to residential built environment features on changes in total daily steps and total daily MVPA (minute) (one model per residential built environment exposure variable and per physical activity outcome). Average daily steps (daily MVPA) at follow-up were regressed on average daily steps (daily MVPA) at baseline, adjusting for a change in exposure as a fixed effect and baseline household as a random effect to allow for household clustering. Models with further adjustment for sex, age group and ethnic group were also fitted. In addition, the same outcomes were examined separately for both weekdays and weekend days. Finally, an interaction term between each change in environmental exposure (taken in turn) and housing sector was included.
Results
Associations between the built environment and physical activity at baseline
Descriptive analyses
Baseline descriptive statistics are shown in Table 9. Females constituted 58% of the analytical sample, which was largely white (48%). Women, older people and those belonging to ethnic minorities were more prevalent among social housing seekers than among intermediate and market-rent housing seekers (p < 0.001); the sociodemographic characteristics of intermediate and market-rent housing seekers were similar. Participants seeking relocation to social housing were less physically active, with 9.4% (social: 8618 steps, 95% CI 8247 to 8990 steps; intermediate: 9516 steps, 95% CI 9170 to 9862 steps) and 25.3% (social: 6909 steps, 95% CI 6390 to 7428 steps; intermediate: 9385 steps, 95% CI 8925 to 9846 steps) fewer steps on weekdays and at weekends, respectively, than participants seeking intermediate housing. Social housing seekers also resided in less walkable areas, according to walkability score, than intermediate housing seekers (social: –0.51, 95% CI –0.70 to –0.31; intermediate: 0.21, 95% CI –0.05 to 0.46); lived further away (median km) from metropolitan parks [social: 2.53 km, interquartile range (IQR) 1.43–3.71 km; intermediate: 1.90 km, IQR 0.95–2.99 km]; were closer (median km) to their nearest local park than intermediate housing seekers (social: 0.62 km, IQR 0.38–0.98 km; intermediate: 0.88 km, IQR 0.48–1.35 km); and had poorer accessibility to public transport (22.9% had high accessibility compared with 41.3% for intermediate housing seekers).
| Characteristic | All housing sectors | Housing sector | p-valuea | ||
|---|---|---|---|---|---|
| Social | Intermediate | Market-rent | |||
| Total participants, n (%) | 1064 (100) | 442 (42) | 463 (44) | 186 (17) | |
| Sociodemographics, n (%) | |||||
| Sex: female | 621 (58) | 330 (75) | 210 (48) | 81 (44) | < 0.001 |
| Age group (years) | |||||
| 16–24 | 222 (21) | 95 (21) | 77 (18) | 50 (27) | < 0.001 |
| 25–34 | 464 (44) | 112 (25) | 254 (58) | 98 (53) | |
| 35–49 | 310 (29) | 198 (45) | 92 (21) | 20 (11) | |
| ≥ 50 | 68 (6) | 37 (8) | 13 (3) | 18 (10) | |
| Ethnicity | |||||
| White | 511 (48) | 83 (19) | 301 (69) | 127 (68) | < 0.001 |
| Black | 270 (25) | 210 (48) | 46 (11) | 14 (8) | |
| Asian | 172 (16) | 90 (20) | 61 (14) | 21 (11) | |
| Mixed/other | 111 (10) | 59 (13) | 28 (6) | 24 (13) | |
| Physical activity facilities in the residential area | |||||
| Walkability, mean (95% CI) | 0.04 (–0.12 to 0.20) | –0.51 (–0.70 to –0.31) | 0.21 (–0.05 to 0.46) | 0.94 (0.48 to 1.40) | < 0.001 |
| Street connectivity, mean (95% CI) | 8.7 (8.6 to 8.7) | 8.5 (8.4 to 8.6) | 8.7 (8.6 to 8.8) | 9.0 (8.8 to 9.2) | < 0.001 |
| Residential density (1000 habitants/km2), mean (95% CI) | 12.2 (11.9 to 12.6) | 10.3 (9.9 to 10.7) | 13.1 (12.5 to 13.8) | 14.8 (13.8 to 15.9) | < 0.001 |
| Land use mix, mean (95% CI) | 0.38 (0.37 to 0.39) | 0.34 (0.33 to 0.36) | 0.39 (0.37 to 0.41) | 0.46 (0.43 to 0.49) | < 0.001 |
| Distance to metropolitan parks (km), mean (IQR) | 2.2 (1.2–3.5) | 2.5 (1.4–3.5) | 1.9 (0.9–3.5) | 1.9 (1.0–3.5) | < 0.001 |
| Distance to district parks (km), mean (IQR) | 2.2 (1.4–3.1) | 2.4 (1.7–3.1) | 2.0 (1.1–3.1) | 2.0 (1.4–3.1) | < 0.001 |
| Distance to local parks (km), mean (IQR) | 0.7 (0.4–1.2) | 0.6 (0.4–1.2) | 0.9 (0.5–1.2) | 0.9 (0.5–1.2) | < 0.001 |
| Public transport accessibility (PTAL score), n (%) | |||||
| Low | 96 (9) | 47 (11) | 37 (8) | 12 (6) | |
| Intermediate | 606 (57) | 294 (67) | 219 (50) | 93 (50) | |
| High | 362 (34) | 101 (23) | 180 (41) | 81 (44) | |
| Physical activity,b mean (95% CI) | |||||
| Daily steps on weekdays | 9126 (8919 to 9333) | 8618 (8247 to 8990) | 9516 (9170 to 9862) | 9409 (8895 to 9923) | 0.005 |
| Daily steps on weekend days | 8448 (8170 to 8725) | 6909 (6390 to 7428) | 9385 (8925 to 9846) | 9540 (8874 to 10,206) | < 0.001 |
| Daily minutes of MVPA on weekdays | 61 (59 to 63) | 57 (54 to 60) | 63 (61 to 66) | 66 (62 to 70) | 0.001 |
| Daily minutes of MVPA on weekend days | 55 (53 to 58) | 46 (41 to 50) | 61 (57 to 65) | 64 (59 to 70) | < 0.001 |
Participants seeking relocation to market-rent housing had a similar level of physical activity to participants seeking intermediate housing in both daily steps and MVPA accumulated both on weekdays and at weekends. Participants seeking market-rent housing also resided in more walkable areas according to walkability score (market-rent: 0.94, 95% CI 0.48 to 1.40; intermediate: 0.21 95% CI –0.05 to 0.46), but both groups lived at a similar distance to their closest metropolitan, district and local parks, and had similar accessibility to public transport.
Associations between built environment and physical activity
The associations between built environment factors and the number of daily steps and amount of MVPA taken on weekdays and at weekends are presented in Table 10, and displayed graphically in Report Supplementary Material 16.
| Residential built environment factors | Model 1a β (95% CI) | Model 2b β (95% CI) | Model 3c β (95% CI) | Model 1a β (95% CI) | Model 2b β (95% CI) | Model 3c β (95% CI) |
|---|---|---|---|---|---|---|
| Daily steps on weekdays (n = 1053) | Daily steps on weekends (n = 848) | |||||
| Walkability | 52 (–30 to 134) | –5 (–86 to 76) | 38 (–57 to 132) | 255 (139 to 371)d | 135 (28 to 242)d | 144 (19 to 269)d |
| Street connectivity | 48 (–132 to 228) | –39 (–215 to 137) | – | 322 (63 to 582)d | 156 (–79 to 391) | – |
| Residential density (1000 habitants/km2) | 41 (5 to 76)d | 5 (–31 to 41) | – | 129 (78 to 179)d | 51 (3 to 99)d | – |
| Land use mix | 498 (–644 to 1641) | –291 (–1419 to 837) | – | 3520 (1884 to 5156)d | 1547 (34 to 3061)d | – |
| Distance to closest metropolitan park (km) | –294 (–446 to –143)d | –206 (–354 to –58)d | –264 (–422 to –107)d | –363 (–582 to –144)d | –125 (–323 to 74) | –144 (–354 to 66) |
| Distance to closest district park (km) | –93 (–265 to 79) | 7 (–160 to 174) | –64 (–240 to 113) | –370 (–617 to –124)d | –180 (–402 to 42) | –138 (–372 to 96) |
| Distance to closest local park (km) | 170 (–174 to 514) | –84 (–420 to 253) | –185 (–529 to 158) | 1147 (669 to 1624)d | 597 (161 to 1032)d | 598 (155 to 1040)d |
| Accessibility to public transport | ||||||
| Low | 442 (–362 to 1247) | 767 (–12 to 1546) | 1186 (296 to 2076)d | –1352 (–2516 to –187)d | –608 (–1657 to 442) | 60 (–1136 to 1256) |
| Intermediate | –481 (–948 to –15)d | –152 (–609 to 305) | 8 (–487 to 503) | –1128 (–1803 to –453)d | –364 (–981 to 252) | –8 (–670 to 654) |
| High | – | – | – | – | – | – |
| Daily MVPA (minutes) on weekdays (n = 1053) | Daily MVPA (minutes) on weekends (n = 848) | |||||
| Walkability | 0.7 (–0.0 to 1.3) | 0.1 (–0.6 to 0.6) | 0.4 (–0.3 to 1.2) | 2.2 (1.2 to 3.1)d | 1.2 (0.3 to 2.1)d | 1.4 (0.4 to 2.5)d |
| Street connectivity | 0.6 (–0.9 to 2.0) | –0.3 (–1.7 to 1.0) | – | 2.7 (0.6 to 4.8)d | 1.3 (–0.6 to 3.2) | – |
| Residential density (1000 habitant/km2) | 0.4 (0.1 to 0.7)d | 0.0 (–0.2 to 0.3) | – | 1.1 (0.7 to 1.5)d | 0.4 (0.0 to 0.8)d | – |
| Land use mix | 9.1 (0.0 to 18.2)d | 1.3 (–7.5 to 10.1) | – | 32.1 (18.9 to 45.2)d | 16.8 (4.6 to 29.0)d | – |
| Distance to closest metropolitan park (km) | –2.5 (–3.7 to –1.3)d | –1.8 (–2.9 to –0.5)d | –2.1 (–3.4 to –0.9)d | –2.9 (–4.7 to –1.2)d | –1.1 (–2.7 to 0.4) | –1.2 (–2.9 to 0.5) |
| Distance to closest district park (km) | –0.6 (–2.0 to 0.7) | 0.3 (–1.0 to 1.6) | –0.2 (–1.5 to 1.2) | –2.4 (–4.4 to –0.4)d | –0.9 (–2.7 to 0.9) | –0.4 (–2.3 to 1.4) |
| Distance to closest local park (km) | 2.0 (–0.8 to 4.7) | –0.3 (–3.0 to 2.3) | –1.0 (–3.7 to 1.6) | 9.0 (5.2 to 12.9)d | 4.7 (1.2 to 8.2)d | 4.9 (1.3 to 8.5)d |
| Accessibility to public transport | ||||||
| Low | 2.7 (–3.7 to 9.1) | 5.8 (–0.3 to 11.9) | 9.7 (2.7 to 16.6)d | –9.5 (–18.9 to –0.1)d | –4.0 (–12.5 to 4.5) | 2.7 (–7.0 to 12.4) |
| Intermediate | –4.5 (–8.2 to –0.8)d | –1.7 (–5.3 to 1.8) | –0.2 (–4.1 to 3.6) | –8.2 (–13.7 to –2.7)d | –2.7 (–7.6 to 2.3) | 0.7 (–4.7 to 6.0) |
| High | – | – | – | – | – | – |
Weekdays
In models adjusted for sociodemographic characteristics, both daily steps taken and MVPA accumulated were negatively associated with the distance from home to the closest metropolitan park [mean difference in daily steps (–206 steps, 95% CI –354 to –58 steps); mean difference in daily minutes of MVPA (–1.8 minutes, 95% CI –2.9 to –0.5 minutes) per kilometre of distance to the closest metropolitan park], indicating that the more distant the metropolitan park, the smaller the overall number of steps taken and the shorter the time spent in MVPA (Figure 4), particularly among those from intermediate and market-rent tenured groups (see Figure 4). Associations between accessibility to public transport and daily steps and MVPA were borderline statistically significant in models adjusted for sociodemographic characteristics (mean differences in daily steps: 767 steps, 95% CI –12 to 1546 steps; mean difference in daily minutes of MVPA: 5.8 minutes, 95% CI –0.3 to 11.9 minutes for those with low accessibility compared with those with high accessibility), and reached statistical significance after further adjustment for the other residential built environmental factors (mean differences in daily steps: 1186 steps, 95% CI 296 to 2076 steps; mean difference in daily minutes of MVPA: 9.7 minutes, 95% CI 2.7 to 16.6 minutes). Adjustment for other residential built environmental factors, however, led to an inflation of the regression estimate and a widening of the CI compared with models adjusting only for sociodemographic characteristics, raising potential multicollinearity issues.
FIGURE 4 Daily steps and MVPA (mean and 95% CI) by quintile of selected residential built environmental factors, overall and by housing sector. (a) Daily steps at weekends and walkability; (b) daily steps at weekends and distance to closest local park; (c) daily steps on weekdays and distance to closest metropolitan park; (d) daily MVPA at weekends and walkability; (e) daily MVPA at weekends and distance to closest local park; and (f) daily MVPA on weekdays and distance to closest metropolitan park. Notes: mean daily steps (95% CI) at weekends or on weekdays are plotted against median value of quintile of built environment variable, for all housing sectors combined and by housing sector. Daily steps are adjusted for sex, age group, ethnic group as fixed effects and household as a random effect in a multilevel model. Daily steps for all housing sectors are additionally adjusted for housing sector as a fixed effect. Footnotes: mean daily MVPA (95% CI) at weekends or on weekdays is plotted against median value of quintile of built environment variable, for all housing sectors combined and by housing sector. Daily MVPA is adjusted for sex, age group, ethnic group as fixed effects and household as a random effect in a multilevel model. Daily MVPA for all housing sectors is additionally adjusted for housing sector as a fixed effect. (Figure not reproduced here; see the original article.)
On weekend days
In minimally adjusted models, both daily steps and MVPA accumulated were positively associated with residential walkability (mean difference in daily steps and daily minutes of MVPA per unit of walkability: 255 steps, 95% CI 139 to 371 steps, and 2.2 minutes, 95% CI 1.2 to 3.1 minutes, respectively), and with the distance from home to the closest local park (mean difference in daily steps and daily minutes of MVPA per kilometre of distance to the closest local park: 1147 steps, 95% CI 669 to 1624 steps, and 9.0 minutes, 95% CI 5.2 to 12.9 minutes, respectively). Conversely, steps taken and minutes of MVPA were negatively associated with the distance (km) to the nearest metropolitan park (–363 steps, 95% CI –582 to –144 steps, and –2.9 minutes, 95% CI –4.7 to –1.2 minutes, respectively), the distance (km) to the closest district park (–370 steps, 95% CI –617 to –124 steps, and –2.9 minutes, 95% CI –4.7 to –1.2 minutes, respectively) and accessibility to public transport (–1352 steps, 95% CI –2516, to –187 steps, and –9.5 minutes, 95% CI –18.9 to –0.7 minutes, respectively, for those with low compared with those with high accessibility). After adjustment for sociodemographic characteristics, associations of residential walkability with daily steps taken (135 steps, 95% CI 28 to 242 steps) and with minutes of MVPA (1.2 minutes, 95% CI 0.3 to 2.1 minutes), and of distance from home to the nearest local park with daily steps taken (597 steps, 95% CI 161 to 1032 steps) and with minutes of MVPA (4.7 minutes, 95% CI 1.2 to 8.2 minutes) remained statistically significant. This suggests that the more walkable the residential environment the higher the number of steps taken and the greater the time spent in MVPA, and that overall the further the closest local park, the higher the number of steps taken and the greater the time spent in MVPA on weekend days (Figure 4); this appeared more evident in the social and intermediate housing sectors (Figure 4). Associations remained consistent after further adjustment for the other residential built environmental factors (model 3).
Interaction analyses
An interaction between low accessibility to public transport and social versus intermediate housing seekers in relation to mean daily steps on weekdays was observed (p = 0.042). However, a Wald test for the interaction term between accessibility to public transport and housing suggested that the interaction term could be omitted from the model (p = 0.06).
Effect of adjustment for built environmental factors and sensitivity analyses
The effect of adjustment for built environment factors on the differences between aspirational housing tenures in both daily steps and daily minutes of MVPA is presented in Table 11. Adjustment for walkability decreased differences in daily steps and MVPA on weekend days between social and intermediate housing seekers by 1.5% and 2.6%, respectively (i.e. a decrease of 36 steps and a decrease of 0.4 minutes in the gap between social and intermediate housing seekers). Adjustment for distance to the closest local park decreased differences in daily steps and MVPA on weekend days between social and intermediate housing seekers by 4.7% and 5.9%, respectively (i.e. a decrease of 113 steps and a decrease of 0.9 minutes). Adjustment for distance to the nearest metropolitan park reduced differences in steps taken on weekdays between social and intermediate housing seekers by 9.9% and 11.9%, respectively (i.e. a decrease of 81 steps and a decrease of 0.7 minutes). Sensitivity analyses on the subsample of 837 participants with physical activity data on both weekdays and weekends showed consistency with the main findings presented in the weekday and weekend day sensitivity analyses in the previous sections (see Report Supplementary Materials 17–19). The base model adjusts for sex, age group, ethnic group as fixed effects and household as a random effect to allow for clustering at the household level.
| Outcome | Base model, difference (95% CI) | Base model with further adjustments, difference (95% CI) | Land use mix, difference (95% CI) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Walkability | Distance to local parks | Distance to district parks | Distance to metropolitan parks | Accessibility to public transport | Walkability, distance to local, district and metropolitan parks, accessibility to public transport | Street connectivity | Residential density | |||
| Weekday daily steps (N = 1053) (steps) | ||||||||||
| Social | –898 (–1448 to –348) | –899 (–1451 to –348) | –914 (–1468 to –360) | –899 (–1452 to –347) | –817 (–1369 to –266) | –884 (–1438 to –330) | –814 (–1373 to –255) | –901 (–1452 to –350) | –888 (–1443 to –333) | –906 (–1458 to –354) |
| Intermediate | – | – | – | – | – | – | – | – | – | – |
| Market-rent | –107 (–701 to 487) | –103 (–701 to 495) | –107 (–701 to 488) | –107 (–702 to 487) | –95 (–687 to 498) | –86 (–680 to 508) | –87 (–682 to 508) | –95 (–692 to 502) | –115 (–713 to 482) | –88 (–687 to 510) |
| Weekday daily MVPA (N =1053) (minutes) | ||||||||||
| Social | –6.6 (–10.9 to –2.3) | –6.6 (–10.9 to –2.3) | –6.6 (–11.0 to –2.3) | –6.7 (–11.0 to –2.4) | –5.9 (–10.2 to –1.6) | –6.4 (–10.7 to –2.1) | –5.8 (–10.2 to –1.4) | –6.6 (–10.9 to –2.3) | –6.5 (–10.8 to –2.2) | –6.5 (–10.8 to –2.2) |
| Intermediate | – | – | – | – | – | – | – | – | – | – |
| Market-rent | 2.3 (–2.4 to 6.9) | 2.2 (–2.4 to 6.9) | 2.3 (–2.4 to 6.9) | 2.3 (–2.4 to 6.9) | 2.4 (–2.2 to 7.0) | 2.4 (–2.2 to 7.1) | 2.3 (–2.3 to 7.0) | 2.4 (–2.3 to 7.0) | 2.2 (–2.5 to 6.9) | 2.2 (–2.5 to 6.9) |
| Weekend daily steps (N = 848) (steps) | ||||||||||
| Social | –2477 (–3232 to –1721) | –2441 (–3194 to –1689) | –2364 (–3122 to –1607) | –2427 (–3184 to –1671) | –2436 (–3194 to –1678) | –2409 (–3172 to –1646) | –2242 (–3007 to –1477) | –2469 (–3224 to –1714) | –2385 (–3142 to –1627) | –2442 (–3197 to –1688) |
| Intermediate | – | – | – | – | – | – | – | – | – | – |
| Market-rent | 155 (–624 to 934) | 33 (–748 to 815) | 149 (–628 to 925) | 168 (–609 to 946) | 161 (–617 to 940) | 136 (–644 to 916) | 38 (–742 to 818) | 101 (–682 to 883) | 66 (–715 to 847) | 39 (–747 to 824) |
| Weekend daily MVPA (N = 848) (minutes) | ||||||||||
| Social | –15.3 (–21.4 to –9.1) | –14.9 (–21.0 to –8.8) | –14.4 (–20.5 to –8.3) | –15.0 (–21.1 to –8.9) | –14.9 (–21.0 to –8.8) | –14.8 (–20.9 to –8.6) | –13.6 (–19.8 to –7.4) | –15.2 (–21.3 to –9.1) | –14.4 (–20.5 to –8.3) | –14.9 (–0.9 to –8.8) |
| Intermediate | – | – | – | – | – | – | – | – | – | – |
| Market-rent | 3.3 (–3.0 to 9.6) | 2.2 (–4.1 to 8.5) | 3.3 (–3.0 to 9.6) | 3.4 (–2.9 to 9.7) | 3.4 (–2.9 to 9.7) | 3.2 (–3.1 to 9.5) | 2.2 (–4.1 to 8.5) | 2.9 (–3.4 to 9.2) | 2.6 (–3.7 to 8.9) | 2.1 (–4.2 to 8.4) |
Change in built environment and change in physical activity
Descriptive analyses
Baseline descriptive statistics for the followed-up participants are shown in Table 12. Women, those who were middle-aged (35–49 years) and those belonging to ethnic minorities were more prevalent among social than among intermediate housing seekers (p < 0.0001). Compared with participants seeking intermediate accommodation, participants seeking social housing took fewer daily steps and accumulated fewer daily minutes in MVPA overall (p < 0.0001), on weekdays (p < 0.01) and on weekend days (p < 0.0001) at baseline. Sociodemographic characteristics and levels of physical activity at baseline of the intermediate and market-rent housing seekers were largely similar. At baseline, social housing seekers also resided in less walkable areas (walkability score social: –0.4, 95% CI –0.6 to –0.1; intermediate: 0.2, 95% CI –0.1 to 0.5; market-rent 0.4, 95% CI –0.2 to 1.1; p = 0.004), and had poorer accessibility to public transport (PTAL score social: 4.3, 95% CI 4.1 to 4.5; intermediate: 4.8, 95% CI 4.6 to 5.0; market-rent 5.0, 95% CI 4.6 to 5.3; p < 0.001).
| Characteristic | All housing sectors | Housing sector | p-valuea | ||
|---|---|---|---|---|---|
| Social | Intermediate | Market-rent | |||
| Total participants, n (%) | 687 (100) | 283 (41) | 301 (44) | 103 (15) | |
| Sociodemographics, n (%) | |||||
| Sex: female | 401 (58) | 206 (73) | 151 (50) | 45 (44) | < 0.001 |
| Age group (years) | |||||
| 16–24 | 130 (19) | 53 (19) | 50 (17) | 27 (26) | < 0.001 |
| 25–34 | 289 (42) | 72 (26) | 172 (57) | 45 (44) | |
| 35–49 | 223 (33) | 137 (48) | 70 (23) | 16 (16) | |
| ≥ 50 | 45 (7) | 21 (7) | 9 (3) | 15 (15) | |
| Ethnicity | |||||
| White | 334 (49) | 52 (18) | 209 (69) | 73 (71) | < 0.001 |
| Black | 172 (25) | 135 (48) | 30 (10) | 7 (7) | |
| Asian | 110 (16) | 59 (21) | 43 (14) | 8 (8) | |
| Mixed/other | 71 (10) | 37 (13) | 19 (6) | 15 (15) | |
| Physical activity facilities in the residential area | |||||
| Walkability, mean (95% CI) | 0.0 (–0.2 to 0.2) | –0.4 (–0.6 to –0.1) | 0.2 (–0.1 to 0.5) | 0.4 (–0.2 to 1.1) | 0.004 |
| Street connectivity, mean (95% CI) | 8.6 (8.6 to 8.7) | 8.5 (8.4 to 8.7) | 8.7 (8.6 to 8.9) | 8.8 (8.5 to 9.1) | 0.06 |
| Residential density (1000 habitants/km2), mean (95% CI) | 11.9 (11.5 to 12.3) | 10.3 (9.8 to 10.8) | 12.7 (12.0 to 13.4) | 13.7 (12.4 to 15.1) | < 0.001 |
| Land use mix, mean (95% CI) | 0.37 (0.36 to 0.39) | 0.35 (0.33 to 0.37) | 0.38 (0.36 to 0.40) | 0.42 (0.38 to 0.46) | 0.01 |
| Distance to closest park (m), mean (95% CI) | 663 (633 to 692) | 609 (570 to 648) | 703 (656 to 749) | 694 (602 to 787) | 0.06 |
| Public transport accessibility (PTAL score), mean (95% CI) | 4.6 (4.5 to 4.8) | 4.3 (4.1 to 4.5) | 4.8 (4.6 to 5.0) | 5.0 (4.6 to 5.3) | < 0.001 |
| Physical activity,b mean (95% CI) | |||||
| Daily steps, mean | 8947 (8713 to 9182) | 8162 (7742 to 8582) | 9458 (9077 to 9840) | 9611 (8984 to 10,238) | |
| Daily steps on weekdays | 9173 (8915 to 9431) | 8525 (8062 to 8987) | 9581 (9162 to 10,000) | 9754 (9065 to 10,443) | |
| Daily steps on weekend days | 9173 (8915 to 9431) | 8525 (8062 to 8987) | 9581 (9162 to 10,000) | 9754 (9065 to 10,443) | |
| Daily minutes of MVPA | 60 (58 to 61) | 54 (50 to 57) | 63 (60 to 66) | 67 (62 to 72) | |
| Daily minutes of MVPA on weekdays | 61 (59 to 63) | 56 (52 to 59) | 64 (61 to 67) | 69 (63 to 74) | |
| Daily minutes of MVPA on weekend days | 55 (53 to 58) | 45 (40 to 50) | 61 (57 to 65) | 64 (57 to 71) | |
Longitudinal changes in built environment exposures
Within-person changes in exposure to built environment factors over the 2-year period between baseline and follow-up are shown in Table 13. Followed-up participants experienced a positive change in neighbourhood walkability of 1.4 units (95% CI 1.2 to 1.6 units). Social housing seekers had the greatest improvement in neighbourhood walkability (1.7 units, 95% CI 1.4 to 2.0 units) compared with intermediate (1.3 units, 95% CI 0.9 to 1.6 units) and market-rent (1.0 units, 95% CI 0.3 to 1.7 units) housing participants. Improvement in walkability scores was mostly driven by increases in the residential density (7779 residential units per km2, 95% CI 6910 to 8648 residential units per km2) and land use mix (0.21 units, 95% CI 0.19 to 0.23 units). Participants experienced a mean decrease in distance to the nearest park of 270 m (95% CI 232 to 307 m), with no significant differences across housing sector. They also had a positive change of 0.7 units (95% CI 0.6 to 0.9 units) in accessibility to public transport. Social housing participants experienced the greatest amount of change (1.5 units, 95% CI 1.2 to 1.8 units) compared with intermediate (0.2 units, 95% CI 0.0 to 0.5 units) and market-rent (0.1 units, 95% CI –0.3 to 0.5 units) housing participants.
| Change | All housing sectors (N = 687) | Housing sector | p-value | ||
|---|---|---|---|---|---|
| Social (N = 283) | Intermediate (N = 301) | Market-rent (N = 103) | |||
| Physical activity facilities in the residential area, mean (95% CI) | |||||
| Walkability (score) | 1.4 (1.2 to 1.6) | 1.7 (1.4 to 2.0) | 1.3 (0.9 to 1.6) | 1.0 (0.3 to 1.7) | 0.03 |
| Street connectivity (intersections per km of road) | –0.5 (–0.6 to –0.4) | –0.6 (–0.7 to –0.4) | –0.5 (–0.7 to –0.4) | –0.4 (–0.7 to –0.1) | 0.63 |
| Residential density (1000 habitants/km2) | 7.8 (6.9 to 8.7) | 8.9 (7.6 to 10.2) | 7.2 (5.9 to 8.5) | 6.5 (4.0 to 8.9) | 0.09 |
| Land use mix (score) | 0.21 (0.19 to 0.23) | 0.25 (0.21 to 0.27) | 0.20 (0.17 to 0.23) | 0.12 (0.07 to 0.17) | < 0.001 |
| Distance to the closest park (m) | –270 (–307 to –232) | –303 (–349 to –256) | –262 (–322 to –203) | –201 (–325 to –76) | 0.19 |
| Accessibility to public transport (PTAL score) | 0.7 (0.6 to 0.9) | 1.5 (1.2 to 1.8) | 0.2 (0.0 to 0.5) | 0.1 (–0.3 to 0.5) | < 0.001 |
Overall, positive changes in the built environment exposures were mostly observed in those who relocated into East Village (n = 357). On average, they experienced a 2.4-unit (95% CI 2.1 to 2.7 units) increase in neighbourhood walkability, a 531-m (95% CI 488 to 574 m) decrease in the distance to their nearest park, and a 1.6-point (95% CI 1.3 to 1.9 points) increase in accessibility to public transport (see Report Supplementary Material 20). Comparatively small changes in exposures were observed for those who did not move to East Village (n = 330) (see Report Supplementary Material 20).
Longitudinal associations between changes in exposure and changes in physical activity
Associations between changes in the built environment factors and changes in total daily steps and total daily MVPA (overall, weekdays and weekend days) are presented in Table 14. In fully adjusted models, a 1 SD increase in neighbourhood walkability was associated with an overall increase of 302 steps per day (95% CI 110 to 494 steps per day) and 360 steps per weekday (95% CI 149 to 567 steps per weekday). For residential density, a 1 SD increase was associated with an overall increase of 313 steps per day (95% CI 123 to 504 steps per day) and 452 steps per weekday (95% CI 246 to 658 steps per weekday). For land use mix, a 1 SD increase was associated with an overall increase of 201 steps per day (95% CI 5 to 398 steps per day) and 257 steps per weekday (95% CI 43 to 471 steps per weekday). Associations with physical activity on weekend days were all in the same direction as weekdays, but none reached statistical significance.
| Change in exposure (baseline to follow-up) | Overall (n = 687) | Weekdays (n = 680) | Weekends (n = 517) | |||
|---|---|---|---|---|---|---|
| Model 1a | Model 2b | Model 1a | Model 2b | Model 1a | Model 2b | |
| Outcome: daily steps, standardised effectsc (95% CI); p-value | ||||||
| Walkability | 313 (121 to 504); 0.001 | 302 (110 to 494); 0.002 | 360 (152 to 567); 0.001 | 358 (149 to 567); 0.001 | 273 (–82 to 629); 0.132 | 266 (–86 to 619); 0.139 |
| Residential density | 331 (141 to 521); 0.001 | 313 (123 to 504); 0.001 | 460 (255 to 666); < 0.001 | 452 (246 to 658); < 0.001 | 231 (–118 to 580); 0.194 | 199 (–146 to 545); 0.258 |
| Land use mix | 217 (25 to 409); 0.027 | 201 (5 to 398); 0.044 | 251 (42 to 560); 0.018 | 257 (43 to 471); 0.019 | 180 (–171 to 531); 0.315 | 162 (–190 to 515); 0.366 |
| Street connectivity | 135 (–59 to 324); 0.176 | 133 (–58 to 325); 0.173 | 146 (–63 to 355); 0.170 | 135 (–75 to 344); 0.208 | 104 (–249 to 458); 0.563 | 122 (–230 to 474); 0.497 |
| Distance to park | 33 (–159 to 224); 0.739 | 55 (–136 to 247); 0.571 | 3 (–207 to 211); 0.980 | 18 (–192 to 228); 0.863 | 26 (–327 to 380); 0.884 | 55 (–296 to 406); 0.760 |
| Accessibility to public transportation | –5 (–198 to 189); 0.963 | –7 (–205 to 191)d; 0.943 | –54 (–264 to 156); 0.612 | –44 (–259 to 172)e; 0.690 | –67 (–424 to 289); 0.711 | 12 (–348 to 373); 0.946 |
| Outcome: MVPA (minutes), standardised effectsc (95% CI); p-value | ||||||
| Walkability | 1.8 (0.3 to 3.3); 0.018 | 1.7 (0.2 to 3.2); 0.026 | 1.7 (0.0 to 3.4); 0.047 | 1.6 (–0.0 to 3.3); 0.056 | 2.2 (–0.6 to 5.0); 0.123 | 2.2 (–0.6 to 5.0); 0.121 |
| Residential density | 1.8 (0.3 to 3.3); 0.017 | 1.7 (0.2 to 3.2); 0.031 | 2.3 (0.6 to 3.9); 0.008 | 2.1 (0.5 to 3.8); 0.012 | 2.2 (–0.5 to 5.0); 0.110 | 2.0 (–0.7 to 4.7); 0.143 |
| Land use mix | 0.9 (–0.6 to 2.4); 0.237 | 0.8 (–0.8 to 2.3); 0.336 | 0.6 (–1.1 to 2.3); 0.479 | 0.6 (–1.1 to 2.3); 0.498 | 1.7 (–1.1 to 4.4); 0.238 | 1.5 (–1.3 to 4.3); 0.289 |
| Street connectivity | 1.1 (–0.4 to 2.6); 0.134 | 1.1 (–0.4 to 2.6); 0.137 | 1.2 (–0.5 to 2.8); 0.170 | 1.0 (–0.6 to 2.7); 0.221 | 0.6 (–2.2 to 3.4); 0.681 | 0.9 (–1.9 to 3.6); 0.540 |
| Distance to park | 0.4 (–1.1 to 1.9); 0.636 | 0.6 (–0.9 to 2.1); 0.440 | 0.3 (–1.3 to 2.0); 0.687 | 0.5 (–1.1 to 2.2); 0.540 | –0.5 (–3.3 to 2.3); 0.718 | –0.2 (–3.0 to 2.6); 0.884 |
| Accessibility to public transportation | –0.4 (–1.9 to 1.2); 0.642 | –0.2 (–1.8 to 1.3); 0.755 | –1.0 (–2.7 to 0.7); 0.246 | –0.7 (–2.5 to 1.0)f; 0.400 | –0.6 (–3.4 to 2.2); 0.684 | 0.1 (–2.7 to 2.9); 0.942 |
Interactions
Interactions between accessibility to public transport and housing sector in relation to mean daily steps, mean daily steps on weekdays and mean MVPA on weekdays were observed (results not shown). Effect sizes by housing sector for these associations are shown in the footnotes of Table 14. A 1 SD increase in accessibility to public transport was significantly associated with 395 fewer daily steps (95% CI 70 to 720 daily steps) and 2.9 fewer minutes of MVPA (95% CI 0.3 to 5.5 minutes) on weekdays for social housing seekers, but, conversely, 657 more daily steps (95% CI 4 to 1309 steps) and 5.7 more minutes of MVPA (95% CI 0.4 to 10.9 minutes) on weekdays for market-rent housing participants.
Discussion
It has been suggested that, the more walkable an urban environment is, the more attractive it is for active modes of travel such as walking.126,127 In this study, we found that, at baseline, participants took more steps and undertook more MVPA at weekends when living in more walkable neighbourhoods. Our findings suggest that individuals may be more influenced by residential built environment factors at weekends than on weekdays, which could be explained by the greater use of their local neighbourhood during the weekend. At follow-up, there were considerable improvements in built environment exposures hypothesised to support physical activity. There were improvements in walkability driven by increases in residential density and land use mix, which were both strongly associated with physical activity. Distance to their nearest park reduced and there was an increased accessibility to public transport. These changes were mostly driven by those who moved to East Village, reflecting its location on the edge of the Queen Elizabeth II Park and the nature of the high-density neighbourhood development built on active design principles. Non-movers to East Village experienced, on average, small changes in exposure. This could be because the environment did not change in any meaningful way for those who remained at the same address, or because the residential locations that individuals moved to were similar to that at baseline. Our multivariable regression findings indicate that positive changes in neighbourhood walkability were associated with increases in daily steps and total amount of MVPA overall and on weekdays, but less so at weekends (although power to examine weekend and weekday associations was reduced).
Baseline findings
Participants took a greater number of steps at the weekend when living further away from a local park. This may arise because physical activity associated with the use of local parks relies on the journey to the park (assuming a physically active commute), rather than on the activities undertaken within them. Conversely, living further away from the closest metropolitan park was associated with fewer steps taken on weekdays. Metropolitan parks are sparser and therefore further away on average than local parks, and this may reflect decreased interest in reaching this type of park with increasing distance. Overall, our findings highlight the importance of distinguishing between different park types. This may help to explain inconsistent findings in the literature on the access to green space and physical activity, which reports both null112,122 and positive32,111,113,128–130 associations. Exploring individuals’ mobility behaviours, for instance through the combined use of GPS and accelerometer data,131 would help unpick the use made of these different parks and refine the mechanisms by which they could be associated with weekday and weekend physical activity.
In line with other studies, we found that with poorer accessibility to pubic transport, fewer steps were taken and time spent in daily MVPA was lowest at the weekends.32,113 This suggests that on weekends people’s interest in the use of public transport may decrease with decreasing accessibility; however, there was weak evidence for an association after adjustment for sociodemographic factors. Conversely, on weekdays participants with a low accessibility to public transport were taking more daily steps than those with high accessibility. One explanation could be that participants rely on public transport for weekday activities such as commuting to work. As a result, those with poor accessibility to public transport walk more to reach transit points. Overall, differing patterning of associations on weekend days and weekdays suggest that the use of public transport depends on utility and travel function: a commute to work on weekdays and leisure and recreation at the weekends. Further exploration of the driving forces for the use of public transport on weekdays versus at weekends would be valuable to better understand the overall contribution of public transport accessibility to physical activity level.
We explored two plausible pathways through which the built environment may partly contribute to the observed aspirational housing tenure differences in physical activity: differences in the way the three groups interact with the physical activity-supportive features of their residential environment and differences in the availability of these features. We found little evidence for the former explanation, with weak evidence for an interaction. As for the latter explanation, we found that social housing seekers lived in less walkable environments, closer to local parks and further away from metropolitan parks than intermediate and market-rent housing seekers, which, in turn, were all associated with a decreased number of daily steps and daily minutes of MVPA. However, disparities in these three environmental features accounted for little of the variability in daily steps (1.5%, 4.7% and 9.9%, respectively) and in daily minutes of MVPA (2.6%, 5.9% and 11.9%, respectively) between social and intermediate participants. Our findings support previous work that suggests that housing tenure might relate to health behaviours by sorting disadvantaged people into neighbourhoods provided with fewer health-promoting amenities.132,133
Longitudinal findings
The longitudinal findings strengthen previous cross-sectional evidence that more walkable environments are associated with higher levels of physical activity.110,121,134–136 The significant associations observed were mostly driven by the two components of walkability, residential density and land use mix, which were both strongly and positively associated with increased physical activity levels, consistent with other longitudinal studies.89,95,137–140 Greater land use mix is posited to support walking by offering accessibility to a wider range of services and employment.141 As for higher residential density, it is assumed to provide more of a critical mass of walkers seen by other people, who may, in turn, be encouraged to walk by safety in numbers142 and/or by a desire to comply with the social norm.143 Traffic congestion associated with higher residential density may also promote active travel.144 Unlike others,145,146 we did not find that a change in street connectivity was associated with a change in the number of steps taken or the amount of MVPA accumulated. This may be partly because our street connectivity metric relied on street network data and therefore fell short in capturing the permeability of newly built active design environments, like East Village, composed of large pedestrianised areas and informal footpaths. Hence, the limitation of our street connectivity measure to reflect the permeability of the residential neighbourhood as experienced by East Village movers may have underestimated the strength of the effect between walkability and physical activity.
Although the relationships between walkability (and its component variables) and physical activity on weekend days were all in the same direction, none reached statistical significance. This may be because the built environment is more likely to be associated with transportation walking than other types of physical activity, including recreational walking.147 Alternatively, the limited number of participants who had physical activity data available at weekends compared with weekdays may have resulted in underpowered analyses for models fitting physical activity at weekends as an outcome.
We found evidence that increasing accessibility to public transport is associated with a statistically significant decrease in physical activity on weekdays among social housing participants and, conversely, an increase in weekdays physical activity among market-rent housing participants. Among possible explanations, different patterns in the distance between home and workplace and/or in the availability of individual modes of transport across socioeconomic groups may trigger different responses to increased accessibility to public transport. Overall, socioeconomic differences in the relationship between accessibility to public transport and physical activity may explain the mixed findings outlined by some literature reviews.147
Unlike others, we did not find evidence that living closer to a park at follow-up than at baseline was associated with a change in physical activity level.89 Previous studies have highlighted the importance of distinguishing between different types of park, as size and attractiveness are associated with physical activity.128 However, since half of the ENABLE London participants were relocated to the edge of the Olympic park, a large metropolitan park with many sport facilities, such a classification became meaningless. It was difficult to disentangle the different relationship that individuals may have with different types of parks, which may partly explain the null findings.
Those who have lived in their current home for < 2 years have been found to take longer to orientate themselves within their neighbourhood and have a greater mismatch between objectively measured and perceived neighbourhood features.148 This may partly explain the absence of findings between changes in exposure to some of the built environment features (i.e. distance to parks) and changes in physical activity behaviours. It also suggests that the associations found may strengthen over time.
Strengths
The strengths of this study include the use of validated objective measures of physical activity,149 considering weekday/weekend variations in physical activity undertaken,113 and exploring the contribution of the residential built environment in explaining socioeconomic differences in physical activity levels, which has been little assessed. Sensitivity analyses further strengthened the findings by showing consistent results in both the inclusive analytical sample and the more restricted sample of participants with physical activity data on both weekdays and weekends. This study is also one of very few longitudinal studies to have enabled examination of how change in GIS-derived residential built environment features is associated with change in accelerometer-based physical activity levels. Its design provided a great variability in changes of exposures to various features of the environment, facilitating the detection of associations. Moreover, a relatively large number of participants were enrolled compared with other longitudinal studies (e.g. Wells and Yang145 and McCormack et al.150).
It is of note that these analyses looking at physical activity in relation to change in the built environment showed statistically significant increases in physical activity, whereas the increases in physical activity were not statistically significant when comparing the East Village group with those who did not move into East Village. There are a number of possible explanations that we are exploring further. In particular, ‘change in walkability’, for example, is a continuous measure and therefore has more power to detect changes in physical activity than using the dichotomous variable East Village or control.
Limitations
Limitations also warrant consideration. The cross-sectional study design of the baseline data does not rule out the selection of more active people into neighbourhoods supportive of physical activity, and therefore restricts interpretations about the direction of effects. Moreover, not considering physical activity facilities available in routinely visited settings other than place of residence (e.g. workplace) may have led to an overestimation of the association between the residential environment and health behaviours.151 Because the sample was not randomly selected from the population at large, that is the study was targeted at those seeking to move to East Village, findings may not be more broadly generalisable, which may have implications for external validity.
Conclusions
The findings indicated that the residential built environment is associated with physical activity behaviours differently at weekends and on weekdays and that changes in the residential built environment are associated with changes in physical activity, and both of these differ by weekday and weekend. There was limited evidence that socioeconomic disparities in residential built environment factors contribute to socioeconomic differences in physical activity. These findings provide strong support for considering more walkable urban designs, composed of more mixed land use and higher residential density, as levers to increase physical activity. In addition, this work also suggests that policy-makers should be sensitive to the possibility that environmental interventions might have differential impacts on physical activity at weekdays and weekends.