The present study uses data collected in the RECORD (Residential Environment and COronary heart Disease) study, which investigated spatial disparities in health. From February 2007 to March 2008, individuals who came to one of the four sites of the IPC (Investigation Préventive et Clinique) Medical Center for a free medical examination offered by the French National Insurance System for working, unemployed, or retired employees and their families were invited to enter the RECORD study. Eligibility criteria were age 30-79 years and residence in ten given districts of Paris (out of 20) or in 111 other municipalities of the Ile-de-France region, as well as sufficient cognitive and linguistic abilities to comply with the guidelines. During the second wave of the study (between September 2013 and June 2015), former and newly recruited participants underwent a medical examination, after which they were invited to enter the RECORD MultiSensor ancillary study whenever sensors were available. In this study, they were asked to wear body-mounted sensors, including a pair of accelerometers and a GPS receiver. Participants in this ancillary study were instructed to wear the sensors for 7 consecutive days, as they carried out their usual activities in free-living conditions, and to keep a logbook with the places that they had visited, as well as the transport modes used in journeys between those places. Participants’ trips during the monitoring period were determined by combining processed GPS tracks with a mobility survey conducted through the phone (the method is described in detail elsewhere (35)). In addition, participants were asked to answer a questionnaire regarding their health and dietary habits, neighborhood, demographics, and SES. The study protocol was approved by the French Data Protection Authority (Decision No. DR-2013-568 on 2/12/2013), and a written informed consent was filled by all participants.
The present study only retained home-work trips in the data. We removed journeys that included segments outside of Ile-de-France (region of Paris). Journeys that included any stop not related to travel (for example stopping at shops or at friends’ house) were not included in the analysis. The final dataset included 692 journeys recorded for 121 participants.
Physical behaviors during travel
Direct measures of PA and SB were derived from the two tri-axial Vitamove Research-V1000® devices, worn at the right upper leg and on the chest during wake time (except for water-based activities). From the acceleration signal, the VitaScore software derives motion type and orientation of the body compared to the gravitational axis, which are combined to determine the subjects’ behavior. We grouped the behavioral categories provided by the software into three broader categories: SB (lying or sitting), standing (ST) still and light movements and PA (walking, running and bicycling).
The environmental attributes of the journeys were calculated along the shortest (walking) routes between subjects’ homes and workplaces, which was determined using GoogleMaps.
We used two methods to measure greenery level. First, we sampled a set of equidistant points (each 200 meters) along the shortest routes and calculated the mean of the shortest distances between the points and the network of green paths. This variable represents the opportunity cost, in terms of distance covered, of using the green network. Second, we calculated the proportion of green spaces for a buffer zone of 100m radius around the shortest routes, capturing the direct proximity to greenery during the journey, as illustrated in Figure 1. We calculated these measures using the 2008 open data of the Ile-de-France Institute for Urbanism (40).
Destination density was calculated as the number of destinations per km2, including public services, shops, entertainment facilities etc., in a buffer zone of 100m radius around the shortest routes (French National Institute for Statistics and Economic Studies INSEE, 2011 (41)).
Accessibility to and time cost of public transportation
Two methods were used to estimate the incentive to use public transports. First, for the residence and workplace, the Euclidian distance to the nearest bus, metro, tramway, or railway station (Île-de-France Mobilités, 2012 (42)) was calculated, and the larger distance of the two was defined as the accessibility variable. Second, the time cost variable was defined as the ratio of the travel time using public transports to the travel time using a car, as estimated by GoogleMaps.
To estimate the SES of the areas crossed by the participants, we used the buffers of 100m radius around the shortest route for the journey. In each buffer, the educational attainment of the population was defined as the share of residents aged 20 and over holding a university degree (INSEE 2010 census, geocoded at the street address level (43)).
Several potentially confounding factors known to influence PA were accounted for. At the journey level, the length (in km) of the shortest route was accounted for as it probably plays a role in the choice of the travel mode. At the individual level, we considered sex, age, being in couple, having children under 14 years at home, household income (by tertiles), and individual educational attainment (no higher education, undergraduate, graduate). To control for neighborhood selection bias, we used questionnaire data (17) where participants were asked to score, on a scale varying from ‘not at all’ to ‘very much’ (coded 0-3), how important the greenery level, presence of shopping facilities, SES, and accessibility of public transports were in the choice of neighborhood to which they moved.
The goal of the analysis was to estimate the effect of exposure to environmental route attributes on the time budget of physical behaviour during the journey. Thus, the physical behaviours were regarded as compositional data adding up to 1, where each of the three parts corresponds to the share of the time spent in sedentary postures, standing, and PA (25). Compositions add up to a constant sum and are hence interdependent and constrained between 0 and 1. To model compositions of physical behaviors ( ) as dependent variables explained by environmental measures, we applied the additive log ratio (alr) transformation to the data (44), taking sedentary time as reference:
With this linear transformation, the log-ratios can take any real value and be modelled using usual tools of statistical analysis, while preserving their relational nature.
To avoid infinite values when applying the alr transformation an epsilon (0.01) was added to all parts. The two log-ratios were modelled as linear functions of the environmental and social factors and co-variables, using mixed linear regression with participants as random intercepts. The predicted log-ratio vector was then back-transformed to predict a composition for any set of values of environmental features.
The environmental factors (greenery ratio, distance to greenery, destination density, time cost of public transport, distance to the nearest station, and average area education,) were included with the following co-variables: age, sex, being in couple, having children at home, individual educational level, income level, length of shortest route, squared length, and neighborhood selection factors. A squared term was added for route length as we assumed that it has a non-linear association with the probability to be active. In all models, average area education was included as a proxy for the SES of the areas crossed, as we suspected it to be a confounding variable causally influencing both the environmental explanatory variables and the outcome of interest. As we had no reason to assume that the relationships between environmental factors and physical behaviors were linear, we tested a squared term and retained it in the models only if relevant (p-value<0.05 for a likelihood ratio test).
All analyses were run using R (45), with libraries ‘rgeos’ (46) ‘sf’ and ‘sp’ (47) for spatial analysis and ‘lme4’ for mixed models (48).