Physical Activity Levels and Predictors during COVID-19 Lockdown among Lebanese Adults: The Impacts of Sociodemographic Factors, Type of Physical Activity and Work Location

Abstract

Background: Although effective against COVID-19, national lockdowns have several deleterious behavioral and health effects, including physical inactivity. The objective of this study is to assess physical activity (PA) levels during lockdown and the predictors of PA among Lebanese adults, while comparing classical statistics to machine learning models. Methods: Data were collected using an online questionnaire, with PA being evaluated through the “International Physical Activity Questionnaire” (IPAQ)—long form. Machine learning models were applied to predict total PA ≥ 600 MET·min/week. Results: Among 795 participants, while 67.5% auto-declared a decrease in PA level during lockdown, 36.2% did not meet the minimum recommendations for PA. Multivariate analysis showed that participants who went to their workplace during lockdown had significantly higher total and job-related PA, higher walking and moderate PA, and lower sitting time. PA level and intensity increased with age, while sitting time decreased. Participants who practiced a combination of both outdoor and at-home workouts had higher total, housework and leisure-related PA, and higher moderate and vigorous-intensity PA. Machine learning models confirmed these findings as well as the importance of outdoor activity for total PA levels, with Random Forest being the highest-performing model. Conclusions: Bringing to light the levels of physical inactivity and sedentary behavior, this study highlighted the importance of outdoor activity in contributing to PA.

Keywords

• exercise
• sedentary behavior
• quarantine
• machine learning
• workplace