Associations between occupational indicators and total, work-based and leisure-time sitting: a cross-sectional study

Abstract

BackgroundA better understanding of how occupational indicators (e.g. job type, doing shift-work, hours worked, physical demand) influence sitting time will aid in the design of more effective health behaviour interventions. The aim of the study was to examine the associations between several occupational indicators and total, occupational and leisure-time sitting.MethodsCross-sectional self-report data was collected in November 2011 from 1194 participants through a telephone interview in regional Queensland, Australia (response rate was 51.9%). The Workforce Sitting Questionnaire was used to measure sitting time. Multiple logistic regression was applied to examine associations between sitting time and occupational indicators.ResultsOf all participants 77.9% were employed full-time, 72.7% had white-collar jobs, 35.7% were engaged in shift-work, 39.5% had physically demanding jobs, and 53.2% had high total sitting time (>8 hours a day). Those in physically demanding and blue-collar occupations were less likely to report high total (physically demanding: OR = 0.41,95% CI = 0.29–0.58; blue-collar: OR = 0.55,95% CI = 0.37–0.82) and occupational (physically demanding: OR = 0.26,95% CI = 0.14–0.24; blue-collar: OR = 0.32,95% CI = 0.21–0.49) sitting time compared to those in physically undemanding and white-collar occupations respectively. Working more than 8 hours per day was inversely associated with high leisure-time sitting (OR = 0.44,95% CI = 0.29–0.68). No evidence for ‘compensation’ effects, where lower occupational sitting is compensated with higher leisure-time sitting, was found.ConclusionsBehaviour change interventions are needed to reduce sitting time as a means to prevent chronic disease. Workplace initiatives to reduce sitting time may be particularly important among individuals employed in white-collar and physical undemanding occupations, although other intervention strategies targeting leisure-time sitting are also required.

Total and domain-specific sitting time among employees in desk-based work settings in Australia

“…22,26 By contrast, sitting time was 1.5 times the amount of sitting time that has been reported in population samples of young and midaged Australian women 28 and more than two times higher than has been reported in the Australian general population. 16 This difference might be partially accounted for by the use of different questionnaires.…”

“…16 This difference might be partially accounted for by the use of different questionnaires. For example, Bauman et al 16 used a singleitem total sitting time measure from the International Physical Activity Questionnaireshort form (IPAQ-short) to assess the sitting levels of Australian adults, whereas the present study and other studies 22,26 have used a domain-specific questionnaire. Moreover, given that the present study recruited employed adults who mostly sat for their working tasks, it is likely that the higher sitting volumes than those previously reported reflect the restriction of our sample to adults working in desk-based settings.…”

Individual Versus Team-Based Financial Incentives to Increase Physical Activity: A Randomized, Controlled Trial

“…[55][56][57] In this study, participants in the control arm had mean daily step counts below the national average, supporting our hypothesis that this study population may be more sedentary. 36,37 Among participants in this study, 70 % had a BMI > 25 (overweight) and 39 % had a BMI > 30 (obese); further indication that these participants may be relatively sedentary. 58 Nevertheless, participants had a high engagement rate: 96.4 % of them completed the 26-week study despite no financial incentive of any kind during the follow-up period.…”

“…Many in this population had roles in which they were sitting most of the day and therefore may have been more sedentary than employees with more physically active roles. 36,37 Participants were excluded if they were already participating in another physical activity study, not able or willing to carry an iPhone or Android smartphone, currently pregnant or lactating, intending to become pregnant within the next 6 months, or stated any other reason that they did not expect to be able to complete the study.…”

Prediction of objectively measured physical activity and sedentariness among blue-collar work using survey questionnaires

“…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.…”