TY - JOUR
T1 - Physical Activity Levels and Predictors during COVID-19 Lockdown among Lebanese Adults
T2 - The Impacts of Sociodemographic Factors, Type of Physical Activity and Work Location
AU - Harmouche-Karaki, Mireille
AU - Mahfouz, Maya
AU - Salameh, Pascale
AU - El Helou, Nour
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - exercise
KW - machine learning
KW - quarantine
KW - sedentary behavior
KW - workplace
UR - http://www.scopus.com/inward/record.url?scp=85166550423&partnerID=8YFLogxK
U2 - 10.3390/healthcare11142080
DO - 10.3390/healthcare11142080
M3 - Article
AN - SCOPUS:85166550423
SN - 2227-9032
VL - 11
JO - Healthcare (Switzerland)
JF - Healthcare (Switzerland)
IS - 14
M1 - 2080
ER -