In the statistical analysis, we dichotomized personal data and used mean values of environmental perception scores as cut points for an easier interpretation of logistic regression estimates. The frequency distributions of all participant characteristics were tabulated for the analytic sample. First, we used the chi-squared test to compare the values and the frequencies of the participants’ baseline characteristics and mean ratings of the perceptions of neighborhood quality and social well-being by the physical activity level. Quantitative variables were reported as mean values and standard deviations. Statistical significance was set at p-value <0.05. We used Fisher’s exact tests to compare the qualitative characteristics between the groups. Second, we applied multivariable logistic regression models to estimate the relationship between the quality of the built environment and physical activity and hypertension, diastolic blood pressure, and stress level in 45–64-year-old participants. The relationship between the variables was estimated as odds ratios (OR) and their 95% confidence intervals (CI), controlling for possible confounding variables such as sex, education level, age, smoking status, and income. In the multivariable logistic regression models, we applied higher than 0.05 p-value thresholds (such as <0.2) for the inclusion of predictor variables from bivariate statistics in order to prevent the exclusion of relevant factors [54]. For this reason, we also retained the variables that changed the adjusted odds ratios (aOR) by 10% or more for inclusion in the multivariable logistic regression analysis. Statistical analyses were performed using SPSS version 25.0 package (IBM Corporation, New York, NY, USA).

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