The COVID-19 pandemic has generated substantial changes in the lives of the population, such as increased physical inactivity, which can lead to being overweight and, consequently, repercussions on ...glucose homeostasis. A cross-sectional study based on the adult population of Brazil was conducted by stratified, multistage probability cluster sampling (October and December 2020). Participants were classified as physically active or inactive during leisure time according to the recommendations of the World Health Organization. HbA1c levels were categorized as normal (≤6.4%) or with glycemic changes (≥6.5%). The mediating variable was being overweight (overweight and obese). Descriptive, univariate, and multivariate logistic regression analyses examined the association between physical inactivity and glycemic changes. Mediation was analyzed using the Karlson-Holm-Breen method to verify the influence of being overweight on the association. We interviewed 1685 individuals, mostly women (52.4%), 35-59 years old (45.8%), race/ethnicity brown (48.1%), and overweight (56.5%). The mean HbA1c was 5.68% (95% CI: 5.58-5.77). Mediation analysis verified that physically inactive participants during leisure time were 2.62 times more likely to have high levels of HbA1c (OR: 2.62, 95% CI: 1.29-5.33), and 26.87% of this effect was mediated by over-weight (OR: 1.30: 95% CI: 1.06-1.57). Physical inactivity at leisure increases the chances of high levels of HbA1c, and part of this association can be explained by being overweight.
Purpose:
To assess the association between a variety of sedentary activities and self-reported wellness outcomes to provide a comprehensive perspective for future development of sedentary guidelines ...for middle-aged and older adults.
Design:
Cross-sectional population study.
Setting:
Canadian Community Health Survey (Healthy Aging Cycle, 2008–2009).
Subjects:
Middle-aged (45–60 years; n = 8161) and older adults (60 years and older; n = 9128) were used for analysis.
Measures:
Self-reported perceived health, sense of belonging to community, mood disorder, and satisfaction with life were used as outcomes. Sedentary activities were playing bingo, computer use, doing crosswords/puzzles, handicrafts, listening to radio/music, playing a musical instrument, reading, visiting others, and watching TV.
Analysis:
Chi-squares, t-tests and multivariable logistic regressions.
Results:
Among respondents not diagnosed with a mood disorder, positive associations were noted for crosswords/puzzles in older adults (odds ratio OR: 1.39, confidence interval CI: 1.01–1.91) and listening to radio/music or playing an instrument in middle-aged adults (OR: 1.43, CI: 1.16–1.75; OR: 2.14, CI: 1.17–3.81). Satisfaction with life was positively associated with computer use in middle-aged (OR: 1.53, CI: 1.07–2.20) and older adults (OR: 1.42, CI: 1.09–1.84). Sense of belonging was consistently positively associated with sedentary activities.
Conclusion:
Several sedentary activities were found to be positively associated with self-reported measures of psychosocial wellness in middle-aged and older adults. These findings identify potential opportunities for sedentary time interventions and dual-task physical activity promotion.
The World Health Organization recommends adults to engage in muscle-strengthening activity (MSA) at least two times per week. The aim of this study was to determine the prevalence and correlates of ...MSA in Croatian adults. We analysed self-reported data collected among 4561 Croatians aged ≥18 years within the European Health Interview Survey (EHIS wave 2). We calculated the weighted prevalence of meeting the MSA guidelines, and odds ratios for different population groups, adjusted for a range of sociodemographic and lifestyle variables in a multivariable logistic regression analysis. The prevalence of meeting the MSA guidelines was 8.0% (95% CI: 7.2, 8.8) in the overall sample, 5.4% (95% CI: 4.5, 6.4) among females, and 10.9% (95% CI: 9.6, 12.3) among males. We found significantly lower odds of meeting the MSA guidelines for females, older age groups, inhabitants of sparsely populated areas, those with a low education level, obese individuals, and those who did not rate their health as “very good” (p < 0.05 for all). The vast majority of Croatian adults do not meet the MSA guidelines. Public health initiatives to promote MSA in Croatia should focus on females, seniors, sparsely populated areas, people with low education, obese individuals, and those with impaired health.
Abstract Experimental models of physical inactivity associated with a sedentary lifestyle or extreme forms of inactivity with bed rest or spaceflight affect the balance between parasympathetic and ...sympathetic nervous system regulation of the cardiovascular system. Deconditioning effects are rapidly seen in the regulation of heart rate to compensate for physical modifications in blood volume and cardiac function. Reflex regulation of cardiovascular control during exercise by metaboreflex and baroreflex is altered by bed rest and spaceflight. These models of extreme inactivity provide a reference to guide physical activity requirements for optimal cardiovascular health.
(1) Background: Physical inactivity is a major public health problem that affects a significant number of adolescents throughout the world. Attaining regular physical activity is a major challenge ...for adolescents who study full-time. This study aimed to examine the associations between barriers to physical activity, physical activity levels, and time exposed to sedentary behavior during the coronavirus 2019 (COVID-19) pandemic among adolescent full-time students at an institute of professional and technical education. (2) Methods: We employed mixed methods in this case study, which enrolled 119 adolescent students (52.9% of whom were female). More specifically, we applied the Barriers to Physical Activity Questionnaire and the International Physical Activity Questionnaire and conducted semi-structured interviews. Our statistical analysis was based on Poisson regression with robust variance, and our qualitative analysis was based on content analysis. (3) Results: The adolescents frequently presented with a “low” level of physical activity (44.4%, n = 52). The following barriers were associated with a low level of physical activity: lack of time (prevalence ratio (PR) 1.546; confidence interval (CI) 1.111–2.151), lack of motivation (PR 1.573; CI 1.102–2.245), preference for other activities (PR 1.521; CI 1.073–2.155), lack of facilities close to home (PR 1.576; CI 1.077–2.307), laziness (PR 1.463; CI 1.031–2.076), and no way of getting to a facility (PR 1.619; CI 1.005–2.606). (4) Conclusions: We observed that physical activity barriers in the psychological, cognitive, emotional, and environmental dimensions were associated with low levels of physical activity. The students reported that full-time study was a barrier to physical activity that contributed negatively to their physical activity levels and time exposed to sedentary behaviors.
Sin good consumption entails health damage, which is in general not fully perceived by individuals, what results in its overconsumption. One way to tackle this problem is to tax these unhealthy ...goods. However, not all the individual choices that affect health status can be easily observed and effectively taxed by the government. This paper considers a setting where individuals can consume two types of sin goods that differ in their observability (taxability) by the government. As a benchmark, the first‐best taxes for the observable and non‐observable sin good are derived, considering homogeneous individuals. In the second‐best setting, where observability on sin good consumption is limited, the rule for the taxable sin good is shown to depend on the degree of complementarity or substitutability with the unobservable sin good. Finally, redistributional considerations are incorporated by extending the analysis to a setting where individuals differ in their wealth and in their degree of misperception of the health damage caused by sin good consumption. Policy implications are illustrated considering physical inactivity and illicit drugs as examples of non‐taxable sin goods, while alcohol, tobacco, fat and sugar account for the taxable sin goods
Shift work and physical inactivity Cheng, Wan-Ju; Härmä, Mikko; Ropponen, Annina ...
Scandinavian journal of work, environment & health,
05/2020, Letnik:
46, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Objectives Shift work is a risk factor for chronic diseases, and physical inactivity can have an influence on this association. We examined whether intra-individual changes in working time ...characteristics were associated with changes in physical inactivity and examined the risk factors for physical inactivity among shift workers in a 17-year longitudinal study cohort. Methods Study participants were 95 177 employees from the Finnish public sector. Work schedule information was based on questionnaire responses and additional register-based working time characteristics for 26 042 employees. The associations between working time characteristics and physical inactivity were examined using a fixed-effects logistic model. To investigate the risk factors for physical inactivity among shift workers, the odds ratios (OR) of worktime control and having small children were calculated. Results Compared with day work, shift work without night shifts was associated with physical inactivity among men OR 1.38, 95% confidence interval (CI) 1.09-1.74, whereas shift work with night shifts was negatively associated with physical inactivity among women (OR 0.85, 95% CI 0.76-0.96). Register-based working time data confirmed that workers with a higher percentage of night shifts had a lower risk of physical inactivity. Having small children was associated with physical inactivity among shift workers (OR 1.47, 95% CI 1.32-1.65). Conclusions Both survey and objective working hour data revealed that workers having work schedules with night shifts were more likely to be physically active. Having small children was a risk factor for physical inactivity among shift workers.
Previous studies have utilized regression models to investigate the impact of environmental factors on physical activity. However, such approaches are inadequate for data-driven analysis seeking to ...identify robust associations from the intricate and multi-variable interactions between physical activity and environmental factors. With the emergence of the concept of the exposome, which encompasses the totality of exposures, this paper explores machine learning models for predicting the percentage of physical inactivity in U.S. counties, while considering 28 social-, economic-, and physical-environmental factors. The aim of this study is to address the research gap and gain insight into the complex associations between environmental exposures and physical activity. Five machine learning models were tested, and the performances were compared to select the best classifier for further investigation. This study used data from the Behavioral Risk Factor Surveillance System (BRFSS) of the Centers for Disease Control and Prevention. The mean population of all counties was 102,841, and the mean percentage of population below 18 years was 22.3%. The partial dependence plot analysis indicated that only one feature - bachelor's degree - exhibited a close-to-linear relationship with physical inactivity. Motor-vehicle crash death rate and mean temperature showed nonlinear and non-monotonic relationships with the predicted percentage of physical inactivity.
Investigations of the independent associations of physical inactivity with cancer endpoints have been mounting in the epidemiological literature, in part due to the high prevalence of physical ...inactivity among cancer patients and to evidence that inactivity associates with carcinogenesis via pathways independent of obesity. Yet, physical inactivity is not currently recognized as a well-established risk or prognostic factor for lung cancer. As such, we examined the associations of lifetime physical inactivity with lung cancer risk and mortality in a hospital-based, case-control study.
The analyses included data from 660 lung cancer patients and 1335 matched cancer-free controls. Multivariable logistic regression analyses were utilized to assess the association between lifetime physical inactivity and lung cancer risk, and Cox proportional hazards models were utilized to estimate the association between lifetime physical inactivity and mortality among lung cancer cases.
We observed a significant positive association between lifetime physical inactivity and lung cancer risk: Odds ratio (OR)=2.23, 95% confidence interval (CI): 1.77–2.81; the association remained significant among never smokers (OR=3.00, 95% CI:1.33-6.78) and non-smokers (OR=2.33, 95% CI: 1.79–3.02). We also observed a significant positive association between lifetime physical inactivity and lung cancer mortality Hazard ratio (HR)=1.40, 95% CI: 1.14–1.71; the association remained significant in non-smokers (HR=1.51, 95% CI: 1.16–1.96).
These data add to the body of evidence suggesting that physical inactivity is an independent risk and prognostic factor for cancer. Additional research utilizing prospectively collected data is needed to substantiate the current findings.
Background
Studies have revealed that the COVID-19 pandemic has increased sedentary behavior and reduced the number of physical activities in public. The present study attempted to assess the changes ...in physical activity patterns among the residents of a south Indian city at different stages after the COVID-19 outbreak.
The present cross-sectional prospective study was conducted on 372 participants between November 2020 and March 2021. The physical activity patterns before, during, and after the lockdown phase were collected using a custom-built questionnaire, and the current level of physical activity was recorded using the international physical activity questionnaire–short form (IPAQ-SF).
Results
Higher number of respondents reported limiting the intensity of physical activities during and after lockdown (228/372; 61.29%) and (216/372; 58.06%), respectively. Additionally, respondents reporting lower physical activity intensity mean total metabolic equivalents of task (MET)/week: 1182.80 compared with (99/372; 26.61%), and (63/372; 16.93%) numbers of participants who engaged in moderate (mean total MET/week-3005.86) and high levels (mean total MET/week-4188.67) of physical activities respectively.
Conclusions
The results of the study reported immediate and long-term impacts on self-reported physical activity patterns among the study sample.
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