2 with overweight prevalence rates [20, 21]. A large body of evidence links speciﬁc environmental factors within these neighbourhoods with levels of physical activity [21–28]. One such environmental factor is neighbourhood safety, but research on the association between safety from crime and physical activity among older adults revealed mixed results [29]. Some studies supported the relationship between crime-related safety and reduced physical activity of older adults [30–33], but others found no association [34–36]. Nonetheless, Foster and Giles-Corti [29] concluded that the current evidence suggests that, particularly for women and older adults, crime-related safety may constrain physical activity. Studies that speciﬁcally examine the relationship between objectively assessed safety from crime and cycling among older adults are lacking, however. The aim of the present study was twofold. First, we investigated diﬀerences in obesity prevalence rates and crime rates between low and high SES neighbourhoods. Second, we explored the associations between crime rates with cycling and BMI in a sample of Dutch elderly. 2. Subjects and Methods 2.1. Study Sample. Data for the present study were avail- able from the fourth data collection of the Longitudinal Aging Study Amsterdam (LASA) in 2001/2002. LASA is an ongoing multidisciplinary longitudinal study of predictors and consequences of changes in autonomy and well-being in the aging population. The design of LASA has been described in full detail elsewhere [37–39]. Brieﬂy, a nationally representative random sample of older adults stratiﬁed by age and sex was drawn from population registers of 11 municipalities in three culturally distinct geographical areas in The Netherlands. Subjects who completed the fourth data collection cycle in 2001/2002 were included in the present study (N = 1691). As the association between BMI and adiposity changes with aging [40, 41], subjects older than 70 years of age were excluded from the present analyses. The study was approved by the Ethics Review Board of the VU University Medical Center, and informed consent was obtained from all respondents. 2.2. Neighbourhood and Individual Socioeconomic Status. Neighbourhood SES was assessed using data from Statistics Netherlands on (1) average income per household, (2) percentage of households with low income, (3) percentage of unemployed people, and (4) percentage of households with low educational level. Factor loadings for these variables were all >.70. From these factors, a single score was computed into a social status neighbourhood score (SSNS), in which a higher score corresponded with a higher SES of the neighbourhood. These data were linked to participants using their four-digit postal code. A median split was used to categorize respondents in either low (n = 235) or high (n = 235) SES neighbourhoods. Educational level and personal income were used as indi- vidual indicators of SES using closed questions. Respondents were asked to report on their highest attained education, Journal of Environmental and Public Health ranging from (1) elementary school not completed to (9) university education. Monthly income in guilders was asked using categories ranging from approximately (1) (C450– C550) to (12) > C2.250, and then quartiles were used to categorize respondents. For educational level, responses were recoded into (1) no education (n = 34), (2) elementary and lower education (n = 207), (3) secondary and intermediate education (n = 147), and (4) higher education (n = 82). 2.3. Crime Rates. Data on crime rates were available from “misdaadmeter” [42], in which the frequency of six crimes (raid, theft/burglary/housebreaking, theft from car, murder and threat/robbery) is assessed annually at the municipal level using data from police reports. For each municipality, the frequency of these crime rates was divided by population of that municipality, and this resulting score was divided by 1000. These scores were then standardised and summed and ranged from 0 to 100, with a higher score indicating a higher crime rate. Respondents’ postal code was used to match crime rates with the participants, based on the municipal code by Statistics Netherlands [43]. 2.4. Obesity. Body height was measured to the nearest 0.001 m using a stadiometer, while body weight was mea- sured to the nearest 0.1 kg using a calibrated scale. When respondents wore a corset or clothes during the measure- ment, 1 or 2 kg, respectively, were subtracted from the measured body weight. BMI was computed as body weight (kg)/body height (m)2, with BMI ≥ 30 kg/m2 indicating obesity. 2.5. Cycling. In a face-to-face structured interview based on a validated questionnaire, named LAPAQ (LASA Physical Activity Questionnaire) [44], respondents were asked to report the frequency and duration of physical activities during the two weeks preceding the interview. The answers to the three questions regarding cycling were analyzed for the purpose of the present study. These questions were: Did you ride your bike in the past two weeks? On how many days of the past two weeks did you ride a bike? and How long, on average, did you ride a bike on these occasions? Multiplying frequency and duration and dividing that score by 14 computed an average time in minutes per day. 2.6. Statistical Analyses. Data was analysed with SPSS 18.0. First, logistic regression analysis was used to examine the relationship between gender and social status neighbour- hood score (SSNS) with obesity. Both unadjusted (crude) and adjusted (for age, personal educational level, and personal income level) odds ratios were computed. Since neighbourhood deprivation has been reported to be more strongly related with overweight in females than in males [16, 21], gender-stratiﬁed analyses were also conducted. Finally, within low and high SES neighbourhoods, gender-stratiﬁed linear regression analyses were conducted to explore asso- ciations between crime rates with cycling and BMI, while adjusting for age, personal educational level, and personal income level.