Introduction
This chapter provides a description of the methods used in the ENABLE London study, providing an overview of the research design, participant recruitment and data collection. Key exposure and outcome variables collected during the study are outlined, along with a summary of the quantitative data analysis methods used. Derived variables and detailed analytic approaches used for different aspects of the study are described in each chapter accordingly.
Overall research design
The main aim of the study was to investigate the effect of a major change in the residential built environment, triggered by moving to East Village, an area purposefully planned on active design principles and on the physical activity levels of residents, in particular the levels of walking and cycling. There was also a broader set of research questions that this study sought to examine; importantly, examining change in the objective measures of the built environment associated with moving to East Village, in addition to neighbourhood perceptions of the area, to confirm that the development had the desired effect. Without being able to demonstrate change in the built environment (i.e. the primary exposure), change in health-related outcomes attributable to the built environment could not be expected. Other outcomes included change in travel mode and other health behaviours, including mental health and well-being. The study was originally conceived as a longitudinal study of adults seeking to move into the three housing tenures in East Village, with assessments taking place prior to and after the move. Within the cohort would be a control population: those who were seeking to move but did not move to East Village. The quantitative methods implemented in this study were supported by qualitative methods on a small sample of the cohort to further understand drivers and perceptions of moving to East Village and the potential effects on travel behaviours.
The East Village development was scheduled for occupancy in the summer of 2013. The original research design envisaged baseline measures to be taken from late 2012 to early 2013, and follow-up to be taken 1 year later from late 2013 to early 2014. However, delays to the opening of East Village led to the postponement of advertising the accommodation for occupancy and staggered release of accommodation by housing type, with the first residents of East Village not moving in until 2014. The research design of the study was therefore adapted to accommodate the delays to the opening of East Village and the staggered release of accommodation. With the delays in finalising the development and staged release of accommodation, coupled with the scientific need for 2-year as opposed to 1-year follow-up,44 recruitment and baseline assessments of participants took place from 2013 to 2015, with a 2-year follow-up from 2015 to 2017.
Ethics considerations
Ethics approval for the study was provided by City Road and Hampstead Review Board (Research Ethics Committee reference number 12LO1031).
Study population
The inclusion criteria of the ENABLE London study were families residing in Greater London (largely from East London and the London Borough of Newham) who were seeking to move into social, intermediate and market-rent accommodation in East Village. Participants who moved to East Village at follow-up were directly exposed to the new social and built environment, and its active design features. Participants who were seeking to move to East Village but remained in their place of origin (largely from East London) or moved elsewhere formed the control group. Applicants registering an interest or applying to move into East Village were invited to take part in the study (including at least one adult and one child aged ≥ 8 years to allow accelerometer assessment); a maximum of four family members per household were included. Although the study was aimed at families (i.e. parents and children), working-aged adult-only households were also invited. Households with disabled family members were invited to the study when appropriate, although the primary focus of the study was able-bodied family members. Non-English-speaking families were also approached, using interpreters when applicable.
Socioeconomic position
The inclusion of occupants seeking social, intermediate and market-rent accommodation in East Village (largely based on level of income) allowed inclusion of people from widely different social and ethnic origins, representative of the diverse East London population. This allowed sociodemographic differences in the use of the local area and potential effects on health outcomes to be gauged,5,45 particularly among individuals and households of lower socioeconomic status, who potentially have the most to gain from improvements in the residential built environment.46
Recruitment
Participants were recruited through the East Thames Group Housing Association (responsible for social housing tenants), Triathlon Homes (intermediate housing: affordable market-rent/shared ownership/shared equity) and Get Living London (market-rent). Prospective tenants went through a rigorous financial process to determine eligibility for accommodation. Those applying to move into East Village social housing were provided with information about the study and invited to take part by East Thames Group representatives directly, whereas the ENABLE London team (in association with Triathlon Homes and Get Living London) invited those from intermediate and market-rent housing sectors.
Data collection
Baseline and 2-year follow-up of study participants was carried out at the participants’ home (or at a location convenient to the participant). Data items collected in the ENABLE London study at baseline and follow-up are listed in Box 1 and summarised below.
Physical activity and location data:
- ActiGraph GT3X+ accelerometer (ActiGraph LLC, Pensacola, FL, USA) worn over a consecutive 7-day period.
- QStarz BT-1000XT GPS travel recorder (QStarz International Co, Ltd, Taipei, Taiwan) worn for 1 week.
- Ordnance Survey AddressBase® Premium versions 2015 and 2017 (Ordnance Survey, Southampton, UK) mapping of place of residence at baseline and 2-year follow-up to provide measures of land use mix, street connectivity, residential density, walkability and connectivity indices (see www.ordnancesurvey.co.uk/business-government/products/addressbase-premium; accessed 16 September 2019).
Anthropometry:
- Height measured to the last complete millimetre (Leicester Stadiometer; Seca, Birmingham, UK).
- Weight measured to the last complete 0.1 kg using an electronic digital scale, and fat mass (kg), fat-free mass (kg) and muscle mass (kg) measured by leg-to-leg bioimpedance (Tanita SC-240 Body Composition Analyser; Tanita Inc, Tokyo, Japan).
- Body mass index calculated as weight/height squared in kg/m2.
Questionnaire data:
- Demographics, including date of birth, gender and ethnicity, of the participant.
- Number of people living in the household, relationships, type of accommodation, household features (including lifts, stairs, garden), type of tenure, duration at current property, vehicles owned and dog ownership.
- Qualifications, employment status, and job title of adult participants (based on Census 2011 questions).45
- Method of travel to work/place of study and daily commuting times.
- Household income as either weekly or monthly amounts (based on National Evaluation of Sure Start income questions).
- Perception of general health, self-report of health problems (based on Census 2011 questions)45 and effects on mobility.
- Health outcomes including assessments of mobility, self-care, usual activities, pain/discomfort, anxiety/depression and overall perception of health on a scale from 0 to 100 [using EuroQol-5 Dimensions (EQ-5D) questions].
- Satisfaction scores including perception of overall levels of satisfaction, feeling happy and anxious, on a scale from 1 to 10 (based on questions used in the Measuring National Well-being Programme), and further assessment of anxiety and depression based on the Hospital Anxiety and Depression Scale.
- Current and previous smoking status, and current alcohol consumption (using Health Survey for England questions47).
- Perceptions of the local area/neighbourhood, including transport, leisure activities, vandalism, litter, traffic, attractiveness and safety, as well as assessment of social participation, support, cohesion and trust.
- Type of activities carried out and frequency of carrying out vigorous, moderate, walking, sitting activities in the last 7 days (based on the short-form IPAQ).
- Cost of activities including membership fees, vouchers received and equipment bought to do physical activity.
- Attitudes to exercise.
- Television and computers/screen time assessment.
- Fruit and vegetable consumption, and usual sleeping times.
Physical activity level/pattern and location
Objectively measured physical activity (daily steps) was the primary outcome and was assessed over a consecutive 7-day period using hip-mounted ActiGraph GT3X+ accelerometers, combined with an assessment of physical activity location using Global Positioning System (GPS) travel recorders (BT-1000XT). Accelerometers provided daily measures of steps and different intensities of activity, including moderate to vigorous physical activity (MVPA) (both overall and in 10-minute bouts, in accordance with UK physical activity recommendations, although levels in bouts were too low to be usefully used in analyses).5 The approaches to analysing ActiGraph data are detailed in Chapter 3. Simultaneous use of ActiGraph accelerometers and GPS travel recorders allowed active travel components of physical activity (i.e. walking), as well as indoor and outdoor activities, to be identified using methods previously described by the investigators48,49 and the methods detailed in Chapter 5. In addition, GPS data allowed the geographical location at which different levels of physical activity occurred to be identified (from sedentary to vigorous, using established cut-off points in accelerometer data), both at baseline and at follow-up. Together, these measures allowed accelerometry data to be interpreted in depth, allowing the nature and location of recorded activities, particularly active forms of transport such as walking and cycling, to be identified. Moreover, it allowed the contribution of active transport local to place of residence to be quantified and compared between those living in East Village and control areas (see Chapter 5).
Environmental exposures
Geographical information systems (GIS) were used to extract objective data on features of the local environment (see Chapter 4). In combination with ActiGraph and GPS data from study participants, this allowed the location of different levels of physical activity (including both high and low levels of activity) to be accurately identified. This method has been used previously by the investigators to establish the important contribution of walking to school and location (including land use type) to MVPA levels in children.49,50 A number of data sources were used to identify environmental and activity-permissive features within East Village and control areas, including Ordnance Survey (OS) MasterMap Tomography Layer (versions December 2018, May 2014, June 2015) (Ordnance Survey, URL: www.ordnancesurvey.co.uk/business-government/products/mastermap-topography; accessed September 2019), Integrated Transport Network and Transport for London (TfL)51 sources, Olympic Delivery Authority and local authority data, as well as other online resources. In particular, OS data were used to derive indices, such as land use mix, street connectivity, residential density, walkability and connectivity indices, including walking distance to particular features of the built environment, including green space.52
Anthropometric measurements
Height was measured to the last complete millimetre with a portable stadiometer (Leicester Stadiometer; Seca, Birmingham, UK) at baseline and follow-up. Both weight and leg-to-leg bioimpedance were assessed using an electronic Tanita SC-240 body composition analyser (Tanita Inc., Tokyo, Japan) to provide measures of fat mass (kg) and fat-free mass (kg); body mass index (BMI) was calculated as weight/height2 (kg/m2). In total, eight Leicester Stadiometers and Tanita SC-240 body composition analysers were used to measure the participants. The Tanita devices were operated using factory default settings and were regularly checked in accordance with recommended review procedures.
Questionnaire data
Questionnaires were converted into electronic format using SNAP Surveys software (version 11, SNAP Surveys, London, UK), and completed by study participants using dedicated laptops. The questionnaires used established validated methodologies to collect detailed information on patterns and types of activity that were local to the place of residence. In particular, the ‘Neighbourhood Physical Activity Questionnaire’ provided data to examine walking within the neighbourhood,53 and the ‘Neighbourhood Environment Walking Scale’ provided data on perceptions of the neighbourhood environment.54,55 Information on self-defined ethnic origin (based on the 2011 Census45) and a range of social markers were recorded (including employment status, income, duration and location of work), together with home address and postcode of residence, allowing GIS-determined distance to local amenities to be measured. Questions about general health/health status,45 well-being, anxiety and depression, including both clinical and subclinical forms of assessment suitable for use in community settings, were also used (see Chapter 6).22,56–58 Physical activity was assessed using an adaptation of the short-form, self-reported International Physical Activity Questionnaire (IPAQ)59 to provide perceived levels of physical activity in addition to objective measures. Adults were asked about their attitudes to physical activities (including both sedentary, such as screen time, and physically active forms) and factors that influence their physical activity behaviour. Participants were asked about perceived personal, social and environmental influences on physical activity, their use of recreational space (particularly walkways and cycle paths) and facilities in their residential neighbourhood (including costs incurred). Participants were also asked about the availability, accessibility (method of travel and journey times) and usage of local amenities (walkways, cycle paths, parks, swimming pools, etc.); their perceptions of the safety of these amenities and the degree to which they permit their child independent or supervised use were also elicited. The questionnaire also included sections to ascertain levels of social participation, support, cohesion and trust.60 These items were particularly relevant to gauge how the use and perceptions of the local area by others had an impact on individual use and how this might differ from objectively measured features of their neighbourhood. The main questionnaire is included in Appendix 1. Questions were repeated at follow-up, but further questions were included to capture use and frequency of use of the Olympic Park and shopping facilities in the East Village area. Additionally, given the 2-year follow-up, a further question was included to establish whether or not participants had ever lived in East Village between baseline and follow-up interviews.
Qualitative data
In addition to the rich quantitative data, focus groups among study participants in the social housing sector, who were the first to move into East Village, were carried out to identify issues of importance, particularly about perceptions and use of their local environment, and to confirm whether or not the ENABLE London study questionnaire was capturing a suitable and accurate insight of constructs that would identify environmental factors influencing health behaviour (see Chapter 7). GIS and GPS data were also combined with qualitative spatial narratives among study participants to explore differences in active travel associated with moving to East Village. These narratives used individual participant maps to assess how the built environment influences travel behaviour and physical activity: examining change in travel patterns, reasons for the change, purpose of travel and choice of route (see Chapter 8).
Sample recruited and examined
A total of 1819 households (749 social, 738 intermediate and 332 market-rent) expressed an interest in taking part in the study, of which 1006 (392 social, 421 intermediate and 193 market-rent) were examined at baseline from January 2013 to January 2016 (Figure 1). More than one person per household was invited to take part. In total, 1497 individuals took part: 1278 adults (520 social, 524 intermediate and 234 market-rent) and 219 children (209 social, eight intermediate and two market-rent). Figure 2 shows the geographic home locations of study participants at baseline, which highlights the Newham focus of those in social housing, and Greater London geographic diversity of participants seeking intermediate and market-rent accommodation. The small number of children recruited was not expected given that the accommodation in East Village was largely designed for family occupation. Most of the children recruited (95%) were from the social housing sector, and very few children were recruited from intermediate and market-rent groups, which were largely adult-only households. The overall number of children was too small to provide sufficient power to detect change in physical activity; therefore, only adults were considered from here on. The smaller number of adult participants recruited from the market-rent sector reflected the limitations placed on the extent and duration of access to applicants seeking this type of accommodation in East Village. The ENABLE London cohort was predicated on recruiting 1200 adults from 1200 households and succeeded in recruiting 1278 adults from 1006 households. Given the modest imbalance between movers and non-movers, the compliance and the follow-up rate observed, the study was powered to detect a 750-step change [0.3 standard deviation (SD)] at 90% power and with a probability of 0.01 among those who move to East Village.61
FIGURE 1 Flow diagram of households and participants examined at baseline and follow-up in the ENABLE London study. Reproduced from Ram et al.61 Published by the BMJ Publishing Group Limited. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. (Figure not reproduced here; see the original article.)
FIGURE 2 Baseline locations of social, intermediate and market-rent households participating in the ENABLE London study. Reproduced from Ram et al.61 Published by the BMJ Publishing Group Limited. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. (Figure not reproduced here; see the original article.)
The characteristics of the baseline sample by housing sector are summarised in Table 1. Participants seeking social housing in East Village were older, mainly female, and more likely to be from ethnic minority groups than the intermediate and market-rent sectors. The social housing sector was largely residing in households of four or more people (58% vs. intermediate 29% and market-rent 27%), which included children (82% vs. intermediate 18% and market-rent 10%). Only one-quarter (24%) of the social housing sector had a degree qualification compared with 82% of intermediate and 80% of market-rent participants. Half of the social housing sector were employed, compared with 94% of the intermediate and 87% of the market-rent participants. There were also social gradients in the type of employment, with 12% of the social housing sector participants in higher managerial or professional occupations compared with 72% of the intermediate sector participants and 66% of the market-rent sector participants. Fewer participants in the social housing sector reported very good or good overall health (76%) than did intermediate (88%) and market-rent (91%) participants.
| Characteristic | All housing sectors, n (%) | Housing sector,a n (%) | ||
|---|---|---|---|---|
| Social housing | Intermediate | Market-rent | ||
| Cohort | ||||
| Participants | ||||
| Households | 1006 (100) | 392 (52) | 421 (57) | 193 (58) |
| Adults | 1278 (100) | 520 (41) | 524 (41) | 234 (18) |
| Individual characteristics | ||||
| Age, median (IQR) | ||||
| Age | 31 (26–40) | 37 (27–44) | 30 (26–35) | 28 (24–33) |
| Sex | ||||
| Male | 547 (43) | 141 (27) | 275 (52) | 131 (56) |
| Female | 731 (57) | 379 (73) | 249 (48) | 103 (44) |
| Ethnicity | ||||
| White | 617 (48) | 96 (18) | 358 (68) | 163 (70) |
| Asian | 214 (17) | 108 (21) | 77 (15) | 29 (12) |
| Black | 323 (25) | 251 (48) | 55 (11) | 17 (7) |
| Mixed/other | 124 (10) | 65 (13) | 34 (6) | 25 (11) |
| Household-level characteristics | ||||
| Household composition | ||||
| 1 person | 97 (8) | 28 (5) | 38 (7) | 31 (13) |
| 2 people | 385 (30) | 88 (17) | 217 (41) | 80 (34) |
| 3 people | 278 (22) | 101 (19) | 116 (22) | 61 (26) |
| ≥ 4 people | 518 (41) | 303 (58) | 153 (29) | 62 (27) |
| Living with partner | ||||
| Yes | 556 (44) | 202 (39) | 261 (50) | 93 (40) |
| No | 615 (48) | 244 (47) | 245 (47) | 126 (54) |
| Unknown | 107 (8) | 74 (14) | 18 (3) | 15 (6) |
| Living with children | ||||
| Yes | 542 (42) | 425 (82) | 93 (18) | 24 (10) |
| No | 736 (58) | 95 (18) | 431 (82) | 210 (90) |
| Socioeconomic characteristics | ||||
| Educationb | ||||
| Degree or equivalent/higher | 736 (58) | 122 (24) | 428 (82) | 186 (80) |
| Intermediate qualification | 380 (30) | 280 (54) | 70 (13) | 30 (13) |
| Other/none | 159 (12) | 117 (23) | 25 (5) | 17 (7) |
| Employment statusc | ||||
| Employed | 948 (74) | 252 (49) | 492 (94) | 204 (87) |
| Unemployed | 91 (7) | 73 (12) | 7 (1) | 11 (5) |
| Economically inactive | 236 (18) | 192 (37) | 25 (5) | 19 (8) |
| NS-SECd | ||||
| Higher managerial/professional | 591 (46) | 61 (12) | 375 (72) | 155 (66) |
| Intermediate occupations | 179 (14) | 62 (12) | 79 (15) | 38 (16) |
| Routine/manual | 170 (13) | 125 (24) | 34 (6) | 11 (5) |
| Health characteristics | ||||
| General health status | ||||
| Very good | 365 (29) | 140 (27) | 153 (29) | 72 (31) |
| Good | 703 (55) | 253 (49) | 310 (59) | 140 (60) |
| Fair | 179 (14) | 103 (20) | 58 (11) | 18 (8) |
| Bad | 25 (2) | 19 (4) | 2 (0.4) | 4 (2) |
| Very bad | 6 (0.5) | 5 (1) | 1 (0.2) | 0 (–) |
| Limiting longstanding illness | ||||
| Yes | 162 (13) | 107 (21) | 41 (8) | 14 (6) |
| Physical activitye | ||||
| Daily steps, mean (SD) | 8950 (3190) | 7803 (3303) | 9684 (2924) | 9337 (2990) |
| MVPA (minutes/day), mean (SD) | 60 (26) | 50 (26) | 65 (23) | 65 (25) |
| MVPA in 10-minute boutsf | 15 (6–30) | 7 (1–15) | 21 (10–34) | 21 (21–36) |
| Anthropometry | ||||
| Height (m), mean (SD) | 1.69 (0.10) | 1.65 (0.09) | 1.71 (0.10) | 1.72 (0.10) |
| Weight (kg)f | 71 (62–82) | 71 (63–84) | 71 (62–81) | 73 (61–80) |
| BMI (kg/m2)f | 25 (22–28) | 26.3 (23–31) | 24 (22–27) | 24 (22–26) |
| Fat mass (kg)f,g | 18 (13–26) | 23 (16–31) | 15 (11–21) | 15 (11–21) |
| Mental health | ||||
| Depressionh | 173 (14) | 100 (21) | 52 (10) | 21 (9) |
| Anxietyh | 392 (31) | 169 (33) | 146 (28) | 77 (33) |
| Well-being | ||||
| Low levels of life satisfactioni | 330 (26) | 169 (33) | 117 (22) | 44 (19) |
| Low levels of feeling that life is worthwhilei | 265 (21) | 123 (24) | 95 (18) | 47 (20) |
| Low levels of feeling happyi | 358 (28) | 157 (30) | 130 (25) | 71 (30) |
| Neighbourhood perceptionsj | ||||
| Crime-free, mean (SD) | 2.03 (4.40) | 0.69 (4.59) | 2.94 (3.96) | 2.95 (4.18) |
| Quality, mean (SD) | 3.52 (4.53) | 2.41 (4.58) | 4.36 (4.41) | 4.13 (4.20) |
Follow-up participation rates
Of the 1278 adult participants from 1006 households examined at baseline, 877 (69%) adults from 710 (71%) households were examined at follow-up (see Figure 1). A total of 401 adult participants seen at baseline were not followed up.
Social housing sector
Social housing sector participants were followed up between February 2015 and September 2016. Of 520 adult participants from 392 households examined at baseline in the social housing sector, 66% (n = 344) of adult participants from 265 (68%) households were examined at follow-up. Overall, the social housing sector represented 39% (n = 344/877) of the adults seen at follow-up and 37% (n = 265/710) of households.
Intermediate housing sector
Follow-up examinations of the intermediate housing sector took place between July 2015 and September 2017. Among the 524 adults from 421 households examined at baseline, 72% (n = 377) from 312 (74%) households were examined at follow-up. The intermediate sector represented 43% (n = 377/877) of adults and 44% (n = 312/710) of households.
Market-rent housing sector
Market-rent participants were followed up between January 2016 and October 2017. Of 234 adults from 193 households examined at baseline, 67% (n = 156/234) of adults from 133 (69%) households were examined at follow-up. The market-rent sector represented 18% (n = 156/877) of the adults, and 19% (n = 133/710) of households seen at follow-up.
Follow-up rates by East Village and control groups
Half of the adult participants (50%, n = 441/877) had moved to East Village at follow-up and half had not; the participants who had not moved formed the control group (see Figure 1). Of the 441 adult participants who moved to East Village at follow-up, 50% were from the social housing sector, 39% were seeking intermediate accommodation and 11% were seeking market-rent accommodation. Within the control group (n = 436 adults), 28% were social housing participants, 47% were intermediate participants and 25% were market-rent participants. Among the social housing sector, participants in the East Village group had a similar sociodemographic profile to those in the control group. No differences were observed in the following characteristics: age, sex, household composition, living with partner, living with children, education, employment status, socioeconomic status [(National Statistics Socioeconomic Classification (NS-SEC)] and limiting longstanding illness (LLI) (Table 2). However, there were marked differences in ethnicity between East Village and control groups (p < 0.001), with a higher proportion of black ethnic participants in the East Village group than in the control group (55% and 32%, respectively), and a lower proportion of Asian participants in the East Village group than in the control groups (14% and 38%, respectively) in the social housing sector. For intermediate participants, the East Village and control groups were similar in household composition, living with partner, qualifications and NS-SEC, but there were differences in age between East Village and control groups (p < 0.001), sex (p = 0.02), ethnicity (p < 0.001), living with children (p = 0.004), employment status (p = 0.02), and LLI (p = 0.004). The intermediate participants in the East Village group were younger, less likely to be female and living with children, and reported a LLI, and were more likely to be of white ethnicity, employed and in intermediate occupations than the control group (see Table 2). Market-rent participants in the East Village and control groups were similar in their baseline sociodemographic profile, with little difference observed. Differences in baseline physical activity levels, anthropometry, neighbourhood perceptions, mental health and well-being outcomes, and the analytic approach used for respective outcomes, are outlined in Chapters 3–6.
| Characteristic | All housing sectors (N = 877) | Housing sector | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Social (N = 344) | Intermediate (N = 377) | Market rent (N = 156) | ||||||||||
| East Village (N = 441), n (%) | Control (N = 436), n (%) | p-value | East Village (N = 220), n (%) | Control (N = 124), n (%) | p-value | East Village (N = 174), n (%) | Control (N = 203), n (%) | p-value | East Village (N = 47), n (%) | Control (N = 109), n (%) | p-value | |
| Individual characteristics | ||||||||||||
| Age group (years) | ||||||||||||
| 16–24 | 104 (24) | 75 (17) | 47 (21) | 18 (15) | 38 (22) | 30 (15) | 19 (40) | 27 (25) | ||||
| 25–34 | 194 (44) | 185 (42) | 61 (28) | 32 (26) | 113 (65) | 100 (49) | 20 (43) | 53 (49) | ||||
| 35–49 | 123 (28) | 138 (32) | 95 (43) | 66 (53) | 22 (13) | 61 (30) | 6 (13) | 11 (10) | ||||
| ≥ 50 | 20 (5) | 38 (9) | 0.01a | 17 (8) | 8 (6) | 0.27a | 1 (1) | 12 (6) | < 0.001a | 2 (4) | 18 (17) | 0.07a |
| Sex | ||||||||||||
| Female | 247 (56) | 248 (57) | 0.80a | 158 (72) | 91 (73) | 0.76a | 70 (40) | 107 (53) | 0.02a | 19 (40) | 50 (40) | 0.53a |
| Ethnicity | ||||||||||||
| White | 212 (48) | 225 (51) | 38 (17) | 25 (20) | 139 (80) | 122 (60) | 35 (74) | 78 (72) | ||||
| Asian | 56 (13) | 91 (21) | 31 (14) | 47 (38) | 18 (10) | 35 (17) | 7 (15) | 9 (8) | ||||
| Black | 131 (30) | 81 (19) | 120 (55) | 40 (32) | 9 (5) | 32 (16) | 2 (4) | 9 (8) | ||||
| Mixed/other | 42 (10) | 39 (9) | < 0.001a | 31 (14) | 12 (10) | < 0.001a | 8 (5) | 14 (7) | < 0.001a | 3 (6) | 13 (12) | 0.42b |
| Household characteristics | ||||||||||||
| Household composition | ||||||||||||
| 1 person | 30 (7) | 54 (12) | 14 (6) | 25 (4) | 9 (5) | 17 (8) | 7 (15) | 12 (11) | ||||
| 2 people | 127 (29) | 174 (40) | 33 (15) | 47 (16) | 76 (44) | 82 (40) | 18 (38) | 45 (41) | ||||
| 3 people | 94 (21) | 121 (28) | 45 (20) | 40 (13) | 38 (22) | 52 (26) | 11 (23) | 29 (27) | ||||
| ≥ 4 people | 190 (43) | 87 (20) | 0.20a | 128 (58) | 12 (67) | 0.22a | 51 (29) | 52 (26) | 0.45a | 11 (23) | 23 (21) | 0.88a |
| Living with partner | ||||||||||||
| Yes | 186 (42) | 210 (48) | 84 (38) | 60 (55) | 82 (47) | 101 (50) | 20 (43) | 49 (45) | ||||
| No | 215 (49) | 205 (47) | 103 (47) | 50 (46) | 88 (51) | 96 (47) | 24 (51) | 59 (54) | ||||
| Unknown | 40 (9) | 21 (5) | 0.02a | 33 (15) | 14 (13) | 0.17a | 4 (2) | 6 (3) | 0.79b | 3 (6) | 1 (1) | 0.17b |
| Living with children | ||||||||||||
| Yes | 206 (47) | 172 (39) | 0.03a | 177 (20) | 109 (12) | 0.08a | 24 (14) | 52 (26) | 0.004a | 5 (11) | 11 (10) | 0.92a |
| Socioeconomic characteristics | ||||||||||||
| Educationc | ||||||||||||
| Degree or equivalent/higher | 249 (57) | 287 (66) | 55 (25) | 34 (27) | 154 (89) | 166 (82) | 40 (85) | 87 (80) | ||||
| Intermediate qualification | 137 (31) | 102 (23) | 118 (54) | 59 (48) | 15 (9) | 26 (13) | 4 (9) | 17 (16) | ||||
| Other/none | 55 (13) | 45 (10) | 0.01a | 47 (21) | 30 (24) | 0.60a | 5 (3) | 11 (5) | 0.18a | 3 (6) | 4 (4) | 0.39b |
| Employment statusd | ||||||||||||
| Employed | 307 (70) | 347 (80) | 98 (45) | 67 (54) | 169 (97) | 183 (90) | 40 (85) | 97 (89) | ||||
| Unemployed | 36 (8) | 22 (5) | 33 (15) | 12 (10) | 1 (1) | 4 (2) | 2 (4) | 6 (6) | ||||
| Economically inactive | 97 (22) | 67 (15) | 0.004a | 88 (40) | 45 (36) | 0.18a | 4 (2) | 16 (8) | 0.02b | 5 (11) | 6 (6) | 0.51b |
| NS-SECe | ||||||||||||
| Higher managerial/professional | 179 (41) | 246 (56) | 23 (24) | 24 (36) | 124 (74) | 146 (81) | 32 (80) | 76 (78) | ||||
| Intermediate occupations | 69 (16) | 54 (12) | 27 (28) | 16 (24) | 34 (20) | 22 (12) | 8 (20) | 16 (16) | ||||
| Routine/manual | 56 (13) | 44 (10) | 0.003a | 46 (48) | 26 (39) | 0.23a | 10 (10) | 13 (7) | 0.12a | 0 (0) | 5 (5) | 0.43b |
| Health characteristics | ||||||||||||
| General health status | ||||||||||||
| Very good | 134 (30) | 112 (26) | 65 (30) | 27 (22) | 52 (30) | 52 (26) | 17 (36) | 33 (30) | ||||
| Good | 234 (53) | 253 (58) | 101 (46) | 61 (49) | 107 (61) | 125 (62) | 26 (55) | 67 (61) | ||||
| Fair | 63 (14) | 59 (14) | 45 (20) | 28 (23) | 15 (9) | 24 (12) | 3 (6) | 7 (6) | ||||
| Bad | 8 (2) | 12 (3) | 7 (3) | 8 (6) | 0 (–) | 2 (1) | 1 (2) | 2 (2) | ||||
| Very bad | 2 (1) | 0 (–) | 0.23a | 2 (1) | 0 (–) | 0.27a | 0 (–) | 0 (–) | 0.35 | 0 (–) | 0 (–) | 0.90 |
| LLI | ||||||||||||
| Yes | 58 (13) | 59 (14) | 0.87a | 49 (22) | 28 (23) | 0.95a | 6 (3) | 23 (11) | 0.004a | 3 (6) | 8 (7) | 1.00b |
Conclusions
The ENABLE London cohort provides a unique opportunity to examine the impact of a major and focused change in the urban built environment, associated with the rapid repurposed East Village development after the London 2012 Olympic and Paralympic Games, on the physical activity patterns and other health-related behaviours of the local population, particularly those from less privileged backgrounds. It provides an important test of principle that the built environment can alter health behaviours and health outcomes. If this is shown to be the case, the results will inform evidence-based urban planning in the future, and the way in which environmental changes have an impact on health inequalities.