Journal of Environmental and Public Health Table 2: Mean scores and standard deviations for BMI, cycling (minutes per day), social status neighbourhood score (SSNS), and crime rates. N 470 230 240 235 235 362 108 115 115 120 120 BMI (SD) 27.5 (4.1) 26.8 (3.3) 28.1 (4.8) .001 27.6 (4.3) 27.4 (4.0) .608 25.7 (2.5) 33.4 (2.9) <.001 27.1 (3.6) 26.5 (2.9) .162 27.9 (4.9) 28.3 (4.7) .532 Cycling (SD) 13.2 (23.7) 15.4 (28.5) 11.0 (17.7) .042 10.9 (20.5) 15.3 (26.2) .041 14.2 (25.4) 9.5 (16.3) .066 12.4 (23.9) 18.5 (32.3) .007 10.3 (19.0) 11.7 (16.4) .268 SSNS (SD) −.09 (.88) −.11 (.92) −.06 (.85) .492 −.71 (.80) .51 (.43) <.001 −.05 (.85) −.20 (1.00) .134 −.76 (.83) .53 (.41) <.001 −.63 (.77) .51 (.45) <.001 Crime (SD) 22.1 (27.0) 21.9 (26.4) 22.3 (27.7) .872 29.8 (30.8) 14.7 (20.3) <.001 21.0 (26.2) 25.7 (29.5) .116 28.4 (30.6) 14.9 (19.2) <.001 29.6 (31.1) 15.0 (21.6) <.001 4 Total Gender Men Women P value SNSS Low status High status P value Obesity Nonobese Obese P value Men Low SSNS High SSNS P value Women Low SSNS High SSNS P value Table 3: Unstandardized regression coeﬃcients, standard errors, and standardized regression coeﬃcients from the regression analyses, in men and women residing in low and high socio-economic status neighbourhoods. Men low status Men high status Women low status Women high status Unstand. SE Stand. Unstand. SE Stand. Unstand. SE Stand. Unstand. SE Stand. Cycling-BMI −.052 .016 −.290∗∗ −.017 .021 −.076 −.023 .011 −.184∗ −.015 .012 −.117 Crime-BMI .039 .011 .333∗∗∗ .014 .014 .094 .012 .015 .077 −.029 .021 −.138 Crime-cycling −.005 .002 −.242∗ −.005 .003 −.143 −.004 .002 −.193∗ −.004 .003 −.136 Note. All analyses were adjusted for age, personal educational level, and personal income level. Unstand.: unstandardized regression coeﬃcient; SE: standard error; Stand.: standardized regression coeﬃcient. ∗P < .05; ∗∗P < .01; ∗∗∗P < .001. neighbourhoods. In these men, less cycling was signiﬁ- cantly associated with higher crime rates and, importantly, signiﬁcantly higher crime rates were observed in low SES neighbourhoods. The cross-sectional nature of the data for the present study withheld us from conducting mediation analyses regarding the potential mediating role of cycling in the crime-BMI relationship, however. Future longitudinal or experimental studies could provide further insights in this respect. The results of our study should be viewed in light of several limitations. First, height and weight were used to determine BMI and obesity. Due to a decline in stature, use of height commonly overestimates BMI in the elderly and obesity in this age group is probably better reﬂected by waist circumference than by BMI [36, 37, 49]. Second, cycling behaviour was assessed based on self-reports. Although interviews are regarded as more valid than questionnaires [50], self-reported behaviour is known to be sensitive to over- and underreporting [51]. Our measure did not include the type of cycling behaviour (e.g., cycling for transport versus cycling for leisure), however, making it impossible to relate type of cycling behaviour to our explanatory vari- ables. Third, we isolated crime rates from other potentially important (environmental) determinants of cycling. Several studies have indicated that environmental factors such as availability of green areas and recreational spaces [52], aesthetics and proximity to facilities [53] are associated with physical activity in The Netherlands, as well as elsewhere [22, 54–56]. In addition, Ball and colleagues [34] showed that neighbourhood-level social environmental variables may be of greater importance than safety from crime for engaging in physical activity. Fourth, selection bias may have occurred since nonresponse has been selective for the older and less healthy subjects. Paradoxically, oversampling in the most vulnerable strata of the older population (i.e., speciﬁcally targeting the inclusion of suﬃcient older males and those in