A Study to Assess Gender Differences in Lifestyle Factors: A Logistic Regression Analysis

Background: Lifestyle factors such as sleep patterns, physical activity, caffeine intake, and screen time play a significant role in determining physical and mental health outcomes. Gender-based differences in these behaviors have been reported; however, evidence from contemporary Indian populations remains inconsistent. Objectives: To assess gender differences in selected lifestyle factors among adults using logistic regression analysis. Methods: A cross-sectional analytical study was conducted using secondary data from 150 adults. Gender (male/female) was considered the primary independent variable, while lifestyle factors—including sleep duration, physical activity, employment status, caffeine intake, and screen time—were analyzed as outcome variables where appropriate. Binary logistic regression analysis was performed using IBM SPSS Statistics version 26 to examine gender-related differences. Adjusted odds ratios (AORs) with 95% confidence intervals were calculated, and statistical significance was set at p < 0.05. Results: The study included 71 males (47.3%) and 79 females (52.7%). Logistic regression analysis revealed no statistically significant gender differences across the selected lifestyle factors. None of the examined behaviors demonstrated a significant association with gender after adjustment for relevant covariates. The overall models showed limited explanatory power. Conclusion: The findings indicate no significant gender-based differences in the selected lifestyle factors among the study population. These results suggest convergence in lifestyle behaviors across genders and highlight the need for larger, longitudinal studies incorporating objective measures to further explore gender-specific lifestyle patterns.