ArticleHealthcare (Basel, Switzerland)2023
Physical Activity Levels and Predictors during COVID-19 Lockdown among Lebanese Adults: The Impacts of Sociodemographic Factors, Type of Physical Activity and Work Location.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Evolution of outdoor sports during the COVID-19 post-lockdown in Spain: a nationwide population-based study.BMC public health · 2026Article
- Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough 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.
methodsData 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.
resultsAmong 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.
conclusionsBringing to light the levels of physical inactivity and sedentary behavior, this study highlighted the importance of outdoor activity in contributing to PA.
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