Determinants of Engagement in Leisure-Time Physical Activity and Club Sports among University Students: A Large-Scale Study.
Mohamad MotevalliClemens DrenowatzDerrick R TanousGerold WirnitzerWerner KirschnerGerhard RuedlKatharina C WirnitzerPublished in: Journal of functional morphology and kinesiology (2024)
Various socio-demographic, environmental, and lifestyle-related factors have been reported to be associated with physical activity (PA) habits. However, there exist insufficient data comparing different forms of engagement in PA, sports, and exercise. This study aimed to investigate potential factors associated with the engagement in leisure-time PA (LPA) and club sports (CSs) in a large sample of college/university students. This Austria-wide study followed a cross-sectional design with a final sample of 4508 participants (mean age: 24.9 years; 65.9% female) from 52 Austrian colleges/universities. A standardized survey was used to collect data on demographics, anthropometric characteristics, and a wide range of health-related lifestyle factors, including patterns of PA and underlying motivations for PA engagement as well as details of dietary habits, sleep routines, smoking, and alcohol consumption. Descriptive statistics, chi-squared, logistic regression, and MANCOVA were used for data analysis. Across the entire sample, 85.7% of participants reported regular participation in LPA, including 22.5% who were active members of sports clubs. Of the 36 potential motives listed in the questionnaire, "maintaining physical health", "feeling good", and "refreshing the mind" were the most commonly reported factors motivating students to engage in either LPA or CSs. Ten socio-demographic, dietary, or lifestyle factors were identified as predictors of CSs participation ( p < 0.01), whereas only two variables (specifically sleep-related factors) were identified as predictors of LPA participation ( p < 0.001). These findings emphasize the importance of considering the type of PA and sport participation and the associated determinants when designing tailored strategies to promote an active lifestyle.
Keyphrases
- physical activity
- data analysis
- sleep quality
- body mass index
- social media
- alcohol consumption
- metabolic syndrome
- cross sectional
- cardiovascular disease
- healthcare
- mental health
- body composition
- high school
- machine learning
- artificial intelligence
- electronic health record
- public health
- depressive symptoms
- human health
- high intensity
- deep learning