Our study showed that several contextual factors related to the socioeconomic environment, physical environment, and service environment were associated with recreational walking. Although these factors contributed to generate substantial disparities in recreational walking over the study territory, there were residual geographic variations in this walking behavior that remained unexplained.
Strengths and limitations
Strengths of the study include the multilevel-spatial modeling investigation performed, the numerous individual, environmental, and meteorological determinants considered, the determination of environmental variables in street network buffers using Python scripts that ensure replicability, and the two complementary variables analyzed to capture overall walking and walking in one’s residential neighborhood. Study limitations include its cross-sectional design and the declarative nature of walking data (a shortcoming that we address with GPS and accelerometers in the RECORD GPS and MultiSensor Studies [55, 56]). Concerns related to declarative walking data include the fact that the different population groups may have a distinct definition of recreational walking, a different recall rate of recreational walking episodes, a different perception of the social desirability of regular walking, and a different accuracy in walking times assessment. Overall, measurement error may either dilute the associations reported or systematically bias some of them, e.g., those between neighborhood education and walking. Another limitation is the lack of adjustment for neighborhood selection factors that likely confound the reported associations [10, 57–59]. For example, a high motivation for recreational walking leading certain participants to choose to reside, when moving, in a neighborhood with a lot of green spaces would confound the relationship between the spatial accessibility to green spaces and recreational walking.
Moreover, participants had to rely on a subjective definition of their neighborhood (i.e., no instructions were provided to the participants) to report (i) the perceptions of their neighborhood (that were used to determine the neighborhood ecometric variables) and (i) the location of walking. Regarding the first aspect, it seems relevant to adhere to the participants’ definition of neighborhood to assess their psychological relationship with it. Regarding the second aspect, however, using a subjective definition of neighborhood for the assessment of walking may have biased the analyses on the recreational walking time made in one’s residential neighborhood. The identified environmental determinants of a higher recreational walking time performed in one’s neighborhood may reflect perceived neighborhoods of larger sizes for the participants living in these environments (which would lead to a larger share of their walking activity in their perceived neighborhood). While we admit a possible bias, we do not believe these relatively small variations in the average size of perceived neighborhoods to be sufficient to drastically influence the reported time of recreational walking made in one’s residential neighborhood. What matters instead is whether there is or not a park around the residence that substantially increases recreational walking nearby the residence.
Finally, in addition to selection processes in the recruitment of the RECORD Cohort (convenience sample) , low educated and low income participants had higher odds to be excluded due to missing data in walking times (Additional file 1A). Adjustment of regression models on walking for education and income likely reduced selection biases. However, we could not examine whether participants and non-participants in RECORD, and whether participants excluded or not due to missing values in walking times differed in their true walking behavior.
Relationships between the environment and recreational walking
For the two outcomes investigated, two independent associations were reported with neighborhood socioeconomic factors (with neighborhood education), three with the physical environment (one of them with green/open spaces, two with air traffic), and one with the service environment (density of destinations) (see the classification of environmental factors reported in Table 1).
Previous literature did not systematically observe associations between the socioeconomic environment and recreational walking. For example, one study documented an association between residing in a high socioeconomic status neighborhood and the practice of recreational walking even after adjustment for perceived environmental factors . However, another study found that area socioeconomic status did not predict walking for recreation after adjustment for other environmental variables . In our study, neighborhood socioeconomic effects were captured by education rather than income, in line with most previous research in RECORD on clinical risk factors of cardiovascular diseases [37, 39, 40, 62, 63]. A potential explanation is that a high average education of local residents may foster a general climate of values favorable to healthy lifestyles.
In relation to the physical environment, the presence and high quality of green/open spaces were associated with a higher recreational walking time in one’s residential neighborhood. Additional file 1B demonstrates that the relationship between the presence/quality of green/open spaces and recreational walking in one’s residential neighborhood was not attributable to same-source bias (i.e., to the fact that the ecometric variable was based on the aggregation of information provided by the participants who themselves reported their walking time). This finding is coherent with a recent review  that indicated that a positive relationship between parks/open spaces and recreational walking was reported in 44% of the cases [61, 64, 65]. The fact that an association was documented with the presence/quality of green/open spaces (shared perception, as aggregated through the ecometric approach) rather than with the objective surface of green spaces suggests that the quality of green/open spaces is important in addition to their availability, a conclusion on the importance of the quality of recreational facilities that also emerged from previous literature .
As also related to the physical environment, living in the air traffic area around the two international Paris Region airports (as objectively assessed) was inversely associated with recreational walking in two models. No previous study investigated the association between air traffic exposure and walking. A plausible hypothesis is that noise from air traffic around international airports discourages recreational walking, a situation of environmental injustice. While exposure to air traffic was associated with lower odds of recreational walking, other environmental nuisances such as the presence of a highway, road traffic pollution, or waste treatment facilities nearby the residence were not.
A recent review of previous literature identified recreational walking to be less dependent on destinations than utilitarian walking . Apart from the density of destinations, previous studies have reported exercise/recreational walking to increase with housing , residential , and population  density and with composite walkability indices . In our French context, in relation to the service environment, a high density of destinations and services nearby the residence was associated with a higher recreational walking time in one’s neighborhood (effects of density on recreational walking were better captured by service density than by densities of population, buildings, intersections, and public transportation stations). A potential interpretation is that there is no perfect separation between recreational and utilitarian walking and that hybrid walking episodes exist.
Although weather condition was associated with recreational walking, none of the weather variables interacted with the identified environmental effects. It suggests that these characteristics of the socioeconomic, physical, and service environment are supportive of recreational walking regardless of the weather.
It is, however, important to keep in mind that a large number of environmental factors that were tested were not associated with recreational walking in the final models. Overall, of the 50 associations with environmental factors that were tested (25 factors × 2 outcomes), 33 associations were documented when each environmental factor was introduced separately in the model without adjustment for other environmental factors (but with adjustment for individual and weather variables); but only 6 associations were documented when environmental predictors were mutually adjusted for. In addition to the factors not associated with walking discussed above, it should be noted that no association was documented with the social-interactional environment or with the symbolic environment. All the environmental factors that were associated were either already identified in previous literature (neighborhood socioeconomic status, density of destinations, green/open spaces); or identified in the different models, thus relatively consistent (neighborhood socioeconomic status, air traffic exposure); or related to plausible hypotheses (all factors, including air traffic exposure).
The associations between environmental factors and walking were of modest magnitude and had relatively wide confidence intervals. In the final models, none of the associations identified showed an odds ratio above 1.5 (or below 0.67). It should be noted, however, that the overall influence of the environments identified in the initial models was shared between the different environmental factors when they were considered simultaneously (i.e., the strength of the relationship of each environmental factor with walking was reduced when the different factors were adjusted for each other). An indicator of the overall disparities that were predicted by the different environmental factors suggests that the odds of reporting recreational walking and the odds of a higher neighborhood recreational walking time were, respectively, 1.59 times and 1.81 times higher in the most vs. the least supportive environments (lowest and highest quartiles of cumulated effects of the different environmental factors). Such an overall quantification of environmental influences on walking may be useful to convince policymakers that, in addition to information and health promotion campaigns, interventions that address the environmental determinants of walking are relevant.
More environmental predictors and stronger associations were documented for the recreational walking time in the neighborhood than for overall recreational walking, which was expected because in the first case both the exposure and the outcome refer to the residential neighborhood. Finally, it was found that environmental factors explained to a greater extent spatially structured than spatially unstructured variations between neighborhoods. This finding empirically confirms the usefulness of spatial-multilevel regression models that are able to isolate, in the unexplained between-neighborhood variation, a part of it that is spatially structured, which is useful to generate hypotheses. Such spatially structured neighborhood variations in walking were not completely eliminated by the environmental factors retained in the models. Our future work will have to build on the map of residual spatial variations in walking reported in the bottom part of Figure 2 to generate additional explanatory environmental hypotheses.