Abstract:Objective: To describe the total and domain-specific daily sitting time among a sample of Australian office-based employees.
Methods:In April 2010, paper-based surveys were provided to desk-based employees (n=801) in Victoria, Australia. Total daily and domain-specific (work, leisure-time and transport-related) sitting time (minutes/day) were assessed by validated questionnaires. Differences in sitting time were examined across socio-demographic (age, sex, occupational status) and lifestyle characteristics (ph… Show more
“…In our full model, age was observed to be negatively associated with sedentary time at work, which corroborates many previous studies (26,30,32). BMI also tended to be negatively associated with time spent sedentary, and this contradicts previous studies (26,29).…”
Section: Gupta Et Alsupporting
confidence: 76%
“…The performance of the full model based on age, job group, BMI, and self-reported OST and OPA was similar to the best performances of previous questionnaires on occupational sedentary and physical activity (45)(46)(47). The performance of our model was even better than previously developed models using a customized set of variables to predict self-reported OST or OPA (26,29,30). Also, these previous models produce estimates of self-reported OST or OPA, and thus do not adjust for the bias present in these self-reports, relative to objectively measured data.…”
Section: Gupta Et Alsupporting
confidence: 73%
“…The predictors used for modelling in this study were Gupta et al selected a priori from the questionnaire based on (i) whether they would likely predict time spent sedentary or in physical activity according to previous studies (26,29,30,32,(36)(37)(38), (ii) whether they are commonly available in large epidemiological studies and surveys, and (iii) whether they showed a large relative dispersion between workers in our material. Based on these criteria, we arrived at including self-reported information on age, gender, body mass index (BMI), job type, OST, and OPA.…”
Section: Predictorsmentioning
confidence: 99%
“…Explicit prediction models have been proposed before to predict time spent sedentary and in physical activity (23)(24)(25), but these studies have not developed models for exposures at work, which may show associations with self-reported predictors other than leisure time exposures. A few previous studies have, indeed, developed prediction models for time spent sedentary and in physical activity specifically at work (26)(27)(28)(29)(30). However, they have mainly focused on predicting answers to some self-reported variables by another type of self-reported information.…”
“…In our full model, age was observed to be negatively associated with sedentary time at work, which corroborates many previous studies (26,30,32). BMI also tended to be negatively associated with time spent sedentary, and this contradicts previous studies (26,29).…”
Section: Gupta Et Alsupporting
confidence: 76%
“…The performance of the full model based on age, job group, BMI, and self-reported OST and OPA was similar to the best performances of previous questionnaires on occupational sedentary and physical activity (45)(46)(47). The performance of our model was even better than previously developed models using a customized set of variables to predict self-reported OST or OPA (26,29,30). Also, these previous models produce estimates of self-reported OST or OPA, and thus do not adjust for the bias present in these self-reports, relative to objectively measured data.…”
Section: Gupta Et Alsupporting
confidence: 73%
“…The predictors used for modelling in this study were Gupta et al selected a priori from the questionnaire based on (i) whether they would likely predict time spent sedentary or in physical activity according to previous studies (26,29,30,32,(36)(37)(38), (ii) whether they are commonly available in large epidemiological studies and surveys, and (iii) whether they showed a large relative dispersion between workers in our material. Based on these criteria, we arrived at including self-reported information on age, gender, body mass index (BMI), job type, OST, and OPA.…”
Section: Predictorsmentioning
confidence: 99%
“…Explicit prediction models have been proposed before to predict time spent sedentary and in physical activity (23)(24)(25), but these studies have not developed models for exposures at work, which may show associations with self-reported predictors other than leisure time exposures. A few previous studies have, indeed, developed prediction models for time spent sedentary and in physical activity specifically at work (26)(27)(28)(29)(30). However, they have mainly focused on predicting answers to some self-reported variables by another type of self-reported information.…”
“…Hence, physical inactivity represents sedentary behavior that does not involve muscle contraction, which is most prevalent during sitting and lying. Recent studies revealed that accelerometermeasured mean daily sedentary time was 8.2 hrs/day among New York City adults [13], whereas Australian desk workers reported an average of 9.0 hrs/day of sitting time [14]. Physical inactivity is not restricted to the general population; it can be observed in (half-)marathon runners as they have reported sitting 10.75 hrs/day on workdays and 8 hrs/day on non-workdays [15].…”
Section: Physical Inactivity and Sitting Behaviormentioning
Purpose of review:Habitual physical activity can reduce the risk of future cardiovascular morbidity and mortality. This review evaluates recent publications that have assessed the impact of the dose of physical (in)activity on cardiovascular outcomes.Recent findings: Sedentary behavior, characterized by prolonged sitting, is increasingly prevalent across the globe and increases the risk for cardiovascular events in a dose-dependent fashion. Similarly, the number of individuals performing endurance exercise events has tripled over the last 2 decades, and some studies suggest that the high volumes of exercise training and competition may attenuate the health benefits of a physically active lifestyle.Summary: Breaking-up sitting time or replacing sitting by (light) physical activity are effective strategies to attenuate its detrimental health effects. Low doses of physical activity, preferably at a high-intensity, significantly reduce the risk for cardiovascular and all-cause mortality. Larger doses of exercise yield larger health benefits. Extreme doses of exercise neither increase nor decrease the risk for adverse outcomes.Athletes demonstrate a transient cardiac dysfunction and biomarker release directly post-exercise. Chronic exercise training may increase the risk for atrial fibrillation, but is also associated with a superior life expectancy compared to the general population.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.