TY - JOUR
T1 - How do different occupational factors influence total, occupational, and leisure-time physical activity?
AU - Vandelanotte, Corneel
AU - Short, Camille
AU - Rockloff, Matthew
AU - Di Millia, Lee
AU - Kevin, Ronan
AU - HAPPELL, Brenda
AU - Mitch, Duncan
PY - 2015/2/1
Y1 - 2015/2/1
N2 - Background: A better understanding of how occupational indicators influence physical activity levels will aid the design of workplace interventions. Methods: Cross-sectional data were collected from 1194 participants through a telephone interview in Queensland, Australia. The IPAQ-long was used to measure physical activity. Multiple logistic regression was applied to examine associations. Results: Of participants, 77.9% were employed full-time, 32.3% had professional jobs, 35.7% were engaged in shift work, 39.5% had physically-demanding jobs, and 66.1% had high physical activity levels. Participants with a physicallydemanding job were less likely to have low total (OR = 0.25, 95% CI = 0.17 to 0.38) and occupational (OR = 0.17, 95% CI = 0.12 to 0.25) physical activity. Technical and trade workers were less likely to report low total physical activity (OR = 0.44, 95% CI = 0.20 to 0.97) compared with white-collar workers. Part-time (OR = 1.74, 95% CI = 1.15 to 2.64) and shift workers (OR = 1.86, 95% CI = 1.21 to 2.88) were more likely to report low leisure-time activity. Conclusions: Overall, the impact of different occupational indicators on physical activity was not strong. As expected, the greatest proportion of total physical activity was derived from occupational physical activity. No evidence was found for compensation effects whereby physically-demanding occupations lead to less leisure-time physical activity or vice versa. This study demonstrates that workplaces are important settings to intervene, and that there is scope to increase leisure-time physical activity irrespective of occupational background.
AB - Background: A better understanding of how occupational indicators influence physical activity levels will aid the design of workplace interventions. Methods: Cross-sectional data were collected from 1194 participants through a telephone interview in Queensland, Australia. The IPAQ-long was used to measure physical activity. Multiple logistic regression was applied to examine associations. Results: Of participants, 77.9% were employed full-time, 32.3% had professional jobs, 35.7% were engaged in shift work, 39.5% had physically-demanding jobs, and 66.1% had high physical activity levels. Participants with a physicallydemanding job were less likely to have low total (OR = 0.25, 95% CI = 0.17 to 0.38) and occupational (OR = 0.17, 95% CI = 0.12 to 0.25) physical activity. Technical and trade workers were less likely to report low total physical activity (OR = 0.44, 95% CI = 0.20 to 0.97) compared with white-collar workers. Part-time (OR = 1.74, 95% CI = 1.15 to 2.64) and shift workers (OR = 1.86, 95% CI = 1.21 to 2.88) were more likely to report low leisure-time activity. Conclusions: Overall, the impact of different occupational indicators on physical activity was not strong. As expected, the greatest proportion of total physical activity was derived from occupational physical activity. No evidence was found for compensation effects whereby physically-demanding occupations lead to less leisure-time physical activity or vice versa. This study demonstrates that workplaces are important settings to intervene, and that there is scope to increase leisure-time physical activity irrespective of occupational background.
KW - Blue collar
KW - Full-time
KW - Part-time
KW - Shift work
KW - White collar
KW - Workplace
UR - http://www.scopus.com/inward/record.url?scp=84926334454&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/different-occupational-factors-influence-total-occupational-leisuretime-physical-activity
U2 - 10.1123/jpah.2013-0098
DO - 10.1123/jpah.2013-0098
M3 - Article
SN - 1543-3080
VL - 12
SP - 200
EP - 207
JO - Journal of Physical Activity and Health
JF - Journal of Physical Activity and Health
IS - 2
ER -