Sociodemographic Determinants of Physical Inactivity of People Aged 60 Years and Older: A Cross-Sectional Study in Poland

Biernat, Elżbieta; Piątkowska, Monika

doi:10.1155/2020/7469021

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…It is well documented that lower education is associated with PI in general. Our findings reinforce data from the literature in which low education was reported as a factor that increases the likelihood of PI [ 36 ]. In a study by Kari et al [ 33 ], the authors stated that a higher level of education is related to making healthier lifestyle decisions, including PA.…”

Section: Discussionsupporting

confidence: 91%

COVID-Inconfidentes: how did COVID-19 and work from home influence the prevalence of leisure-time physical inactivity? An analysis of before and during the pandemic

et al. 2022

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Background The COVID-19 pandemic has brought drastic changes to the lives of the global population. The restrictions imposed by government agencies impacted the daily lives of citizens, influencing several health behaviors, such as physical activity (PA). Thus, the present study aimed to assess the prevalence of physical inactivity (PI) and its associated factors before and during the COVID-19 pandemic. Methods A population-based household seroepidemiological survey was conducted in two Brazilian municipalities located in the state of Minas Gerais, in which 1750 volunteers were interviewed between October and December 2020. Face-to-face interviews were conducted using a structured questionnaire in an electronic format. The moments considered for the PI analysis were M0 (before the pandemic), M1 (from March to July 2020), and M2 (from October to December 2020). Descriptive statistics and univariate and multivariate logistic regression were used to examine the factors associated with PI before (M0) and during the pandemic (M1 and M2). Results The prevalence of PI was higher in the first months of the pandemic (M1) (67.3%; 95% confidence interval (CI): 62.4–71.9) than in the months from October to December 2020 (M2) (58.7%; 95% CI: 52.8–64.3); however, at both times (M1 and M2), PI was more prevalent than in the period before the pandemic started (M0) (39.7%; 95% CI: 35.6–43.8). Individuals who were overweight, obese, and had low educational levels were more likely to be physically inactive. At both M1 and M2, individuals who worked at a work from home were less likely to have PI. Conclusions The results suggest that the COVID-19 pandemic negatively influenced PA, substantially increasing the prevalence of PI. The determinants associated with PI were education, body mass index, and work from home.

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Section: Discussionsupporting

confidence: 91%

COVID-Inconfidentes: how did COVID-19 and work from home influence the prevalence of leisure-time physical inactivity? An analysis of before and during the pandemic

et al. 2022

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“…It is well documented that lower education is associated with PI in general. Our ndings reinforce data from the literature in which low education was reported as a factor that increases the likelihood of PI [28].…”

Section: Discussionsupporting

confidence: 89%

COVID-Inconfidentes: How did COVID-19 and home office influence the prevalence of leisure-time physical inactivity? An analysis of before and during the pandemic

Moura¹,

Silva²,

Menezes-Júnior1³

et al. 2021

Preprint

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Background The COVID-19 pandemic has brought drastic changes to the lives of the global population. The restrictions imposed by government agencies impacted the daily lives of citizens, influencing several health behaviors, such as physical activity (PA). Thus, the present study aimed to assess the prevalence of physical inactivity (PI) and its associated factors before and during the COVID-19 pandemic. Methods A population-based household seroepidemiological survey was conducted in two Brazilian municipalities located in the state of Minas Gerais, in which 1750 volunteers were interviewed between October and December 2020. Face-to-face interviews were conducted using a structured questionnaire in an electronic format. The moments considered for the PI analysis were M0 (before the pandemic), M1 (from March to July 2020), and M2 (from October to December 2020). Descriptive statistics and univariate and multivariate logistic regression were used to examine the factors associated with PI before (M0) and during the pandemic (M1 and M2). Results The prevalence of PI was higher in the first months of the pandemic (M1) (67.3%; 95% confidence interval (CI): 62.4-71.9) than in the months from October to December 2020 (M2) (58.7%; 95% CI: 52.8-64.3); however, at both times (M1 and M2), PI was more prevalent than in the period before the pandemic started (M0) (39.7%; 95% CI: 35.6-43.8). Individuals who were overweight, obese, and had low educational levels were more likely to be physically inactive. At both M1 and M2, individuals who worked at a home office were less likely to have PI. Conclusions The results suggest that the COVID-19 pandemic negatively influenced PA, substantially increasing the prevalence of PI. The determinants associated with PI were education, body mass index, and home office work.

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“…Compared with women with no education, women with 1–9 years of education were less likely to engage in physical inactivity in this study. This finding is consistent with a study in a high-income country (Poland), 16 but it is the opposite to previous studies in India 4 and China. 15 While, among men, we did not find a significant association between education and physical inactivity, which is similar to a study in Japan.…”

Section: Discussionsupporting

confidence: 85%

“…Factors that increase the odds of physical inactivity may include sociodemographic, health status and health risk behaviours. Sociodemographic risk factors for physical inactivity include increasing age11 and female sex,4 8 12–14 those with higher education in India4 and China,15 lower education in Poland,16 and insignificant in terms of educational level in Japan 17. Health status factors associated with physical inactivity include poor self-rated health status,7 12 13 poor cognitive functioning,18 functional disability,7 19 chronic back pain, bodily pain, visual impairment, hearing problems, low body mass index (BMI)7 and obesity 20.…”

Section: Introductionmentioning

confidence: 99%

Prevalence and associated factors of physical inactivity among middle-aged and older adults in India: results of a national cross-sectional community survey

Pengpid

Peltzer

2022

BMJ Open

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ObjectiveThis study aimed to determine the prevalence and associated factors of physical inactivity in middle-aged and older adults in India.DesignPopulation-based cross-sectional study.SettingNationally representative sample of general community-dwelling middle-aged and older adult population in India.ParticipantsThe sample included 72 262 adults (45 years and older, mean age 58.8 years, SD=11.8), from the longitudinal ageing study in India wave 1 in 2017–2018.Primary and secondary outcome measuresSelf-reported physical activity, along with physical measurements, health status and health behaviour, and sociodemographic covariates. Multivariable logistic regression calculated OR with 95% CI for physical inactivity.ResultsOverall, 36.7% were physically inactive, 42.6% among men, and 32.4% among women (p<0.001). In the adjusted logistic regression analysis, among both men and women, older age (70 years and older), being Sikh, impaired vision and depressive symptoms were positively and cognitive functioning, current tobacco use and social participation were negatively associated with physical inactivity. In addition, among men, higher socioeconomic status, urban residence, functional disability and heart disease or stroke were positively associated with physical inactivity, and among women being married and higher education were negatively, and insomnia symptoms and poor or fair self-rated health status were positively associated with physical inactivity.ConclusionsAlmost 4 in 10 middle-aged and older adults in India had inadequate physical activity. Overall and gender specific risk factors for physical inactivity were identified. Interventions may operate at multiple levels and consider gender-related physical inactivity patterns.

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Sociodemographic Determinants of Physical Inactivity of People Aged 60 Years and Older: A Cross-Sectional Study in Poland

Cited by 5 publications

References 33 publications

COVID-Inconfidentes: how did COVID-19 and work from home influence the prevalence of leisure-time physical inactivity? An analysis of before and during the pandemic

COVID-Inconfidentes: how did COVID-19 and work from home influence the prevalence of leisure-time physical inactivity? An analysis of before and during the pandemic

COVID-Inconfidentes: How did COVID-19 and home office influence the prevalence of leisure-time physical inactivity? An analysis of before and during the pandemic

Prevalence and associated factors of physical inactivity among middle-aged and older adults in India: results of a national cross-sectional community survey

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