Prevalence of Health-Risk Behaviors and Mental Well-Being of ASEAN University Students in COVID-19 Pandemic

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Abstract

The prevalence of epidemiological health-risk behaviors and mental well-being in the COVID-19 pandemic, stratified by sociodemographic factors in Association of South East Asian Nations (ASEAN) university students, were examined in the research. Data were collected in March–June 2021 via an online survey from 15,366 university students from 17 universities in seven ASEAN countries. Analyzed data comprised results on physical activity, health-related behaviors, mental well-being, and sociodemographic information. A large proportion of university students consumed sugar-sweetened beverages (82.0%; 95%CI: 81.4, 82.6) and snacks/fast food daily (65.2%; 95%CI: 64.4, 66.0). About half (52.2%; 95%CI: 51.4, 53.0) consumed less than the recommended daily amounts of fruit/vegetable and had high salt intake (54%; 95%CI: 53.3, 54.8). Physical inactivity was estimated at 39.7% (95%CI: 38.9, 40.5). A minority (16.7%; 95%CI: 16.1, 17.3) had low mental well-being, smoked (8.9%; 95%CI: 8.4, 9.3), and drank alcohol (13.4%; 95%CI: 12.8, 13.9). Country and body mass index had a significant correlation with many health-risk behaviors and mental well-being. The research provided important baseline data for guidance and for the monitoring of health outcomes among ASEAN university students and concludes that healthy diet, physical activity, and mental well-being should be key priority health areas for promotion among university students.

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Regina Guthold, Gretchen Stevens, Leanne Riley … (2018)

Insufficient physical activity is a leading risk factor for non-communicable diseases, and has a negative effect on mental health and quality of life. We describe levels of insufficient physical activity across countries, and estimate global and regional trends.

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2011 Compendium of Physical Activities: a second update of codes and MET values.

BARBARA E. AINSWORTH, WILLIAM L. HASKELL, STEPHEN HERRMANN … (2011)

The Compendium of Physical Activities was developed to enhance the comparability of results across studies using self-report physical activity (PA) and is used to quantify the energy cost of a wide variety of PA. We provide the second update of the Compendium, called the 2011 Compendium. The 2011 Compendium retains the previous coding scheme to identify the major category headings and specific PA by their rate of energy expenditure in MET. Modifications in the 2011 Compendium include cataloging measured MET values and their source references, when available; addition of new codes and specific activities; an update of the Compendium tracking guide that links information in the 1993, 2000, and 2011 compendia versions; and the creation of a Web site to facilitate easy access and downloading of Compendium documents. Measured MET values were obtained from a systematic search of databases using defined key words. The 2011 Compendium contains 821 codes for specific activities. Two hundred seventeen new codes were added, 68% (561/821) of which have measured MET values. Approximately half (317/604) of the codes from the 2000 Compendium were modified to improve the definitions and/or to consolidate specific activities and to update estimated MET values where measured values did not exist. Updated MET values accounted for 73% of all code changes. The Compendium is used globally to quantify the energy cost of PA in adults for surveillance activities, research studies, and, in clinical settings, to write PA recommendations and to assess energy expenditure in individuals. The 2011 Compendium is an update of a system for quantifying the energy cost of adult human PA and is a living document that is moving in the direction of being 100% evidence based.

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Multiple imputation by chained equations: what is it and how does it work?

Melissa Azur, Elizabeth A. Stuart, Constantine Frangakis … (2011)

Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.