Journal of Physical Activity and Health, 2015, 12, 200 -207 http://dx.doi.org/10.1123/jpah.2013-0098 © 2015 Human Kinetics, Inc.
Official Journal of ISPAH www.JPAH-Journal.com ORIGINAL RESEARCH
How do Different Occupational Factors Influence Total, Occupational, and Leisure-Time Physical Activity? Corneel Vandelanotte, Camille Short, Matthew Rockloff, Lee Di Millia, Kevin Ronan, Brenda Happell, and Mitch J. Duncan 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. Keywords: full-time, part-time, blue collar, white collar, shift work, workplace Regular physical activity has the ability to prevent or reduce the development of many chronic diseases. Unfortunately,1,2 the majority of adults living in wealthy countries are not meeting the minimum physical activity guidelines to accrue these health benefıts.3 Therefore, more research is needed to better understand the correlates that influence physical activity behavior, so that better health behavior change interventions can be developed.4 A group of little studied correlates of physical activity are occupational indicators, such as working full- or part-time, working normal- or shift-work hours, working in a white- or blue-collar job or having a low or highly physically-demanding job.5 Technological advances and globalization have changed the face of the workforce.6 In the last 5 decades, on-the-job energy expenditure has decreased even in the most labor-intensive occupations.7 For example, contrary to the past, most blue-collar workers employed in the mining industry operate large machinery that requires little physical effort.8 Hence, our knowledge of how occupation impacts physical activity levels needs to be reevaluated. Vandelanotte ([email protected]
), Short, and Duncan are with the Institute for Health and Social Science, Centre for Physical Activity Studies, Central Queensland University, Rockhampton, Australia. Rockloff is with the Institute for Health and Social Science, School of Health and Human Services, Central Queensland University, Bundaberg, Australia. Di Millia is with the Institute for Health and Social Science, School of Management and Marketing, Central Queensland University, Rockhampton, Australia. Ronan is with the Institute for Health and Social Science, Centre for Longitudinal and Preventative Health Research, Central Queensland University, Rockhampton, Australia. Happell is with the Institute for Health and Social Science, Centre for Mental Health Nursing Innovation, Central Queensland University, Rockhampton, Australia. 200
Further, the relationships between occupational and leisuretime physical activity is unclear.9 It is commonly believed there are ‘compensation effects’, indicating that when people engage in a lot of occupational physical activity at work they will be less active in their leisure-time, and vice versa.10 While several studies have paid attention to this topic, the findings are inconsistent.11,12 Finally, while most studies have focused on a single occupational indicator and a single type of physical activity, research that evaluates a broad range of occupational indicators simultaneously is required. Given that most adults in Western nations spend most of their lives employed, it is important to clarify the influence of occupational indicators on different types of physical activity, as this information can be used to develop better public health interventions. Therefore, the aim of this study was to evaluate the associations between several occupational indicators and total, occupational, and leisure physical activity.
Methods Sample Cross-sectional self-report data were obtained from the Healthy Shift Worker Study conducted for the Institute for Health and Social Science Research by the Population Research Laboratory (PRL) at Central Queensland University in November of 2011. The computer-assisted telephone interview (CATI) surveys were conducted by trained interviewers. A two-staged sampling design was used to select households (random-digit-dialing of landline telephones) and individuals (one employed person, 18 years of age or older, within each household) in 3 distinct geographical areas in Queensland (Gladstone, Mackay, and Rockhampton Regional Council areas), Australia. To ensure an adequate subsample of shift
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Physical Activity and Occupational Indictors 201
workers, if there was a person employed in a shift-working position in the household, they were the preferred respondent. If the interviewers were unsuccessful in establishing contact on their first call, a minimum of 5 call-back attempts were made. Participation was voluntary, confidential, and remuneration was not paid. Informed consent was obtained from participants before conducting the survey and approval by the Human Research Ethics Committee of Central Queensland University Australia was obtained.
blue-collar (includes machine operators, laborers, production, transport) job;20 having worked in the same organization more or less than 5 years; being a shift worker (working outside of normal hours) or not; working night shifts or not; working more or less than 8 hours on a usual day; and having a job with high or low physical demand. Demographic variables included sex, age (above or below the age of 45), educational attainment (more or less than high school education), and family income (over or under A$100,000 per year).
The telephone survey was pilot-tested by trained interviewers with 71 randomly-selected households. Interviewer comments (eg, confusing wording, inadequate response categories, question order effect, and others), respondent feedback, average interview times, and pretest frequency distributions were reviewed before modifications were made to the questionnaire. The survey was approximately 40 minutes in duration and assessed sociodemographics, dietary habits, physical activity, sitting time, psychological well-being, and health. Only the details of the measures used in this study are presented below. Physical activity was measured using the last 7 days, long-form, telephone-administered, International Physical Activity Questionnaire (IPAQ). This 31-item questionnaire measures physical activity in 4 domains: at work, transportation, household, and leisure-time. In each domain participants were asked questions about the total time (days per week was multiplied by average minutes per day) they spend in walking, moderate intensity physical activity, and vigorous intensity physical activity. A total physical activity score, expressed in minutes of activity per week, was obtained when all questions across all domains were summed. The IPAQ has been shown to be a valid and reliable instrument to measure physical activity among adults at a population level.13 Standard IPAQ protocols were used to analyze the data.14 Participants were categorized as achieving high or low levels of total physical activity. While the IPAQ provides valuable physical activity information across several domains, it is known for over-reporting true activity levels,15–18 as such a high level of physical activity was defined as vigorous intensity activity achieved on at least 3 days of the last week resulting in a at least 1500 MET-minutes or any combination of walking, moderate, or vigorous intensity activities achieved on at least 7 days of the last week resulting in minimum 3000 MET-minutes.14 To calculate total occupational and leisure-time physical activity, only the items within these respective categories on the IPAQ questionnaire were summed, and a median split was used to categorize participants as achieving high or low levels of occupational and leisure-time physical activity. Sitting time was measured using the Adapted Workforce Sitting Questionnaire, which has demonstrated acceptable levels of validity.19 This 10-item questionnaire measures sitting time during work, transportation, while using a computer at home, while watching TV, and while doing other leisure-time activities for work and nonwork days during the last 7 days. Total sitting time was defined as the sum of sitting time in all domains for all days. Occupational indicators, which were selected based on their likelihood to have an impact on physical activity and which were most researched in previous studies,4 included working full- or parttime; having a white-collar (includes all types of clerical, service, and sales jobs), manager (includes administrators), professional (includes semiprofessionals and covers area’s such as arts, media, health, information technology, education, legal, business), technical (includes trade workers and covers areas such as construction, electricity, engineering, automotive, and telecommunication) or
Descriptive statistics (t tests and one-way ANOVAs) and multiple logistic regression were applied to identify differences and associations between a range of occupational indicators and physical activity. Collinearity was examined in all occupational indicators included in the multivariate regression models by producing a correlation matrix between all independent variables. When items significantly correlated higher than 0.5 only the occupational indicator with the strongest relationship to the dependent variable (as examined in the bivariate regression models) was retained.21 As such, 2 variables (how many hours do you work each week, and does your work involve rotational shifts) were not included in the analyses. Table 3 contains both bivariate and multiple logistic regression models; each multiple logistic regression model included all occupational indicators as well as the demographic variables described above to control for confounding effects. Alpha levels were set at .05 and SPSS v20.0 (IBM, Chicago, IL) was used to conduct the statistical tests.
Results In total, 2323 households were contacted for this study and 1194 participated. The average BMI was 28.0 (± 5.6) and 66% were classed as overweight or obese (see Table 1), which is comparable to national Australian data.22 The average participant age was 45.3 (± 11.2) years, 53.9% of the sample was female, and the majority of participants (73.8%) had some education beyond high school. The majority of participants were employed full-time (77.9%) and had high physical activity levels (66.1%). About one-third of the sample was engaged in shift work (35.1%), a professional job (32.3%), or in a high physically demanding job (39.5%). Over half of the sample had worked in the same organization for more than 5 years (55.7%). On average, 53% of total physical activity was done at work, and 13.6% was done during leisure-time (household activities and active transport accounted for the remaining 33.4%). Similar proportions of walking (27.2% and 32.4%), moderate (28.5% and 19.2%), and vigorous (44.3% and 48.4%) intensity physical activity were conducted within the occupational and leisure-time domains of physical activity respectively. Table 2 provides a detailed overview of total, occupational, and leisure-time physical activity according to occupational indicators. As expected, differences between occupational indicators in total physical activity are predominantly driven by differences in occupational physical activity and not by differences in leisure-time physical activity. This is because differences between occupational categories in occupational physical activity were often large, whereas for leisure-time physical activity they were generally small (with the exception of working full- or part-time). For the unadjusted models, Table 3 shows that nearly all associations were significant for occupational physical activity; many were significant for total physical activity and few were significant for leisure-time physical activity. However, few outcomes were significant in the fully adjusted models across all types of physical
202 Vandelanotte et al
Table 1 Participant Characteristics
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Total Group N (%) Personal characteristics Sex Male Female Age < 45 years ≥ 45 years Education Up to high school More than high school Family income < A$100,000 ≥ A$100,000 Body mass index ≤ 25 > 25 Job characteristics Full-time job Part-time job White collar Manager Professional Technical Blue collar < 5 years in same organization ≥ 5 years in same organization Nonshift worker Shift worker No night shifts Does night shifts ≤ 8 h/day of work > 8 h/day of work Low physical demand High physical demand Health behavior Low physical activity High physical activity
Males N (%)
Females N (%)
551 (46.1) 643 (53.9)
561 (47.0) 633 (53.0)
260 (47.2) 291 (52.8)
301 (46.8) 342 (53.2)
292 (26.2) 823 (73.8)
130 (25.0) 390 (75.0)
162 (27.2) 433 (72.8)
333 (38.3) 536 (61.7)
147 (34.8) 276 (65.2)
186 (41.7) 260 (58.3)
351 (30.7) 792 (66.3)
118 (21.4) 422 (76.6)
233 (36.2) 370 (57.5)
926 (77.9) 263 (22.1) 180 (15.7) 284 (24.8) 370 (32.3) 130 (11.3) 183 (16) 217 (44.3) 650 (55.7) 775 (64.9) 419 (35.1) 925 (77.5) 269 (22.5) 643 (53.9) 551 (46.1) 721 (60.5) 471 (39.5)
506 (92.0) 44 (8.0) 25 (4.7) 136 (25.5) 127 (23.8) 120 (22.5) 125 (23.5) 225 (41.8) 313 (58.2) 293 (53.2) 258 (46.8) 369 (67.0) 182 (33.0) 165 (29.9) 386 (70.1) 318 (57.8) 232 (42.2)
420 (65.7) 219 (34.3) 155 (25.2) 148 (24.1) 243 (39.6) 10 (1.6) 58 (9.4) 292 (46.4) 337 (53.6) 482 (75.0) 161 (25.0) 556 (86.5) 87 (13.5) 478 (74.3) 165 (25.7) 403 (62.8) 239 (37.2)
405 (33.9) 789 (66.1)
394 (71.5) 157 (28.5)
395 (61.4) 248 (38.6)
activity. As expected, participants with high physically-demanding jobs were less likely to report low total physical activity when compared with those with a low physically-demanding job (OR = 0.25, 95% CI = 0.17 to 0.38) and they were also less likely to report low occupational physical activity levels (OR = 0.17, 95% CI = 0.12 to 0.25). 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. Participants employed part-time (OR = 1.74, 95% CI = 1.15 to 2.64) or engaged in shift work (OR = 1.86, 95% CI = 1.21 to 2.88) were more likely to report low leisure-time physical activity, compared with those employed full-time or in nonshift-work jobs, respectively.
Discussion While having a high physically-demanding job had a strong influence on physical activity levels, most other occupational indicators had little impact; even for those indicators that showed significant differences, the scale of the differences was small. The magnitude of the associations was lower than what would be expected based on stereotypical notions of differences between occupations, such as blue vs white collar workers and shift vs nonshift workers.4,9 This study therefore adds to the argument that far-reaching mechanization and new technologies continue to erase differences between different occupational classes in terms of energy expenditure and
Physical Activity and Occupational Indictors 203
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Table 2 Total, Occupational, and Leisure-Time Physical Activity According to Main Occupational Indicators, Mean (± SD)
Total group General Full-time job Part-time job White collar Manager Professional Technical Blue collar < 5 years in same organization ≥ 5 years in same organization Shift Nonshift worker Shift worker No night shifts Does night shifts Demand ≤ 8 h/day of work > 8 h/day of work Low physical demand High physical demand
Total Physical Activity (min/wk)a Mean (± SD) t test/ANOVA 369.1 (± 137.0) –
Occupational Physical Activity (min/wk) Mean (± SD) t test/ANOVA 113.4 (± 81.6) –
Leisure-Time Physical Activity (min/wk) Mean (± SD) t test/ANOVA 86.4 (± 80.6) –
375.1 (± 136.8) 346.9 (± 134.2) 344.2 (± 142.5) 351.6 (± 137.9) 362.6 (± 124.6) 433.1 (± 119.2) 378.6 (± 148.7) 379.3 (± 139.1) 361.2 (± 134.9)
116.6 (± 80.6) 102.5 (± 84.6) 97.5 (± 85.9) 99.3 (± 83.4) 105.5 (± 81.8) 159.8 (± 53.8) 130.4 (± 77.2) 119.4 (± 80.6) 108.5 (± 82.1)
91.3 (± 80.2) 68.6 (± 79.6) 78.7 (± 80.2) 84.2 (± 80.1) 95.4 (± 80.2) 93.7 (± 81.3) 74.2 (± 79.9) 88.5 (± 81.1) 85.3 (± 80.4)
90.4 (± 80.3) 78.9 (± 80.6) 87.9 (± 80.2) 81.2 (± 81.8)
83.0 (± 80.7) 90.3 (± 80.3) 89.4 (± 80.3) 81.7 (± 80.9)
359.5 (± 134.7) 386.6 (± 139.4) 365.3 (± 134.3) 382.1 (± 145.3)
358.6 (± 133.4) 381.3 (± 140.1) 333.6 (± 132.0) 422.5 (± 126.5)
104.9 (± 83.0) 129.0 (± 76.6) 109.5 (± 82.3) 127.0 (± 78.2)
103.9 (± 83.5) 124.5 (± 78.0) 87.5 (± 82.9) 153.1 (± 61.1)
a Total physical activity is the sum of occupational, transportation, household, and leisure-time physical activity; transportation and household physical activity are not reported.
*P < .05; **P < .01; ***P < .001.
physical activity.7 This study also shows that total physical activity is predominantly influenced by occupational physical activity, and that this influence is apparent regardless of different occupational indicators. Given the use of a measurement tool that assesses all opportunities to be active (including walking and light ambulation at work) across the different domains of physical activity, and given the fact that most adults spend half or more of their waking hours at work, it is not surprising that occupational physical activity dominates total physical activity. Furthermore, this finding is consistent with several other studies and emphasizes the importance of workplace interventions for people in jobs with low physical demand.23–26 Although some occupational indicators did influence leisuretime physical activity, the impact that occupational differences have on leisure-time physical activity was so small it barely affected total physical activity. For example, the results presented in Table 2 illustrate that while shift workers are less active in their leisure-time, this difference is offset by being more active on the job, resulting in higher total physical activity for shift workers. This is in line with a review examining the occupational correlates of leisure-time physical activity,4 in which it was concluded that although white-collar jobs are associated with more leisure-time physical activity, it does not compensate for their inactive occupation when considering total physical activity. However, this does not mean leisure-time physical activity should be ignored. Given the Australian socioeconomic situation, where most workers have a lot of leisure-time,27 the numbers illustrate that the participants spend most of their leisure-time being inactive and that there is plenty of room to increase activity levels. Therefore, this study points out that initiatives to increase leisure-
time physical activity should be targeted at all workers, regardless of their occupational background. This study found no evidence to support the existence of compensation effects, whereby people with low physically-demanding jobs are more active in their leisure-time or vice versa. While participants who indicated that they had a physically-demanding job had significantly higher total and occupational physical activity compared with those with less physically-demanding jobs, no differences were observed for leisure-time physical activity. This is in contrast with several studies who did find compensation effects,11,28 but in line with other studies who could also not find any associations between occupational activity levels and leisure-time physical activity.10,12,29 There are also studies that find that higher occupational physical activity leads to higher leisure-time physical activity,4,9 and suggest that people who are active in their jobs may have a higher overall level of self-efficacy to be active, and therefore also engage in more leisure-time physical activity,4 although the current study does not support that contention. In unadjusted models, participants with a technical or blue-collar job were significantly less likely to have low total and occupational physical activity when compared with participants with a white-collar job. This is in line with many other studies,26,30,31 however these effects were strongly reduced in the fully-adjusted models, with only technical and trade occupations showing a significant lower likelihood to have low total physical activity. This is comparable to a study by Duncan et al,32 which reported higher physical activity in blue-collar workers when compared with professional and white-collar workers, but these outcomes were also not significant
204 1 1.46 (0.93–2.31) 1 0.76 (0.44–1.33) 0.70 (0.41–1.19) 0.44 (0.20–0.97) 0.64 (0.34–1.20) 1 1.03 (0.71–1.47) 1 0.61 (0.36–1.02) 1 1.53 (0.85–2.77) 1 1.08 (0.72–1.60) 1 0.25 (0.17–0.38)
1 1.21 (0.91–1.61) 1 0.74 (0.51–1.08) 0.76 (0.53–1.09) 0.19 (0.11–0.35) 0.45 (0.28–0.69) 1 1.27 (1.00–1.63) 1 0.67 (0.51–0.86) 1 0.76 (0.57–1.03) 1 0.74 (0.58–0.94) 1 0.19 (0.15–0.26)
Low Total Physical Activity (Multiple)a
1 0.59 (0.47–0.74) 1 0.13 (0.09–0.17)
1 0.51 (0.39–0.65) 1 0.57 (0.43–0.76)
1 1.31 (0.99–1.72) 1 1.12 (0.77–1.64) 1.21 (0.84–1.73) 0.18 (0.10–0.31) 0.45 (0.29–0.69) 1 1.44 (1.14–1.81)
Low Occupational Physical Activity (Bivariate)
1 0.79 (0.53–1.18) 1 0.17 (0.12–0.25)
1 0.67 (0.40–1.12) 1 1.23 (0.69–2.19)
1 1.37 (0.86–2.21) 1 1.26 (0.71–2.25) 1.53 (0.87–2.67) 0.52 (0.24–1.12) 0.85 (0.45–1.60) 1 1.17 (0.81–1.70)
Low Occupational Physical Activity (Multiple)b
1 0.85 (0.69–1.09) 1 1.21 (0.95–1.52)
1 1.41 (1.11–1.79) 1 1.27 (0.96–1.67)
1 1.70 (1.29–2.25) 1 0.97 (0.66–1.40) 0.70 (0.50–1.02) 0.74 (0.47–1.17) 1.26 (0.83–1.91) 1 1.07 (0.85–1.35)
Low Leisure-Time Physical Activity (Bivariate)
logistic regression model adjusted for sex, age, education, income, body mass index, sitting time, and all work variables. logistic model adjusted for sex, age, education, income, body mass index, leisure time physical activity, sitting time, and all work variables. c Multiple logistic model adjusted for sex, age, education, income, body mass index, occupational physical activity, sitting time, and all work variables.
General Full-time job Part-time job White collar Manager Professional Technical Blue collar < 5 years in same organization ≥ 5 years in same organization Shift Nonshift worker Shift worker No night shifts Does night shifts Demand ≤ 8 h/day of work > 8 h/day of work Low physical demand High physical demand
Low Total Physical Activity (Bivariate)
1 0.89 (0.63–1.27) 1 0.92 (0.64–1.31)
1 1.86 (1.21–2.88) 1 0.76 (0.46–1.24)
1 1.74 (1.15–2.64) 1 1.51 (0.90–2.54) 1.28 (0.76–2.15) 1.09 (0.57–2.07) 1.75 (0.99–3.09) 1 0.87 (0.63–1.21)
Low Leisure-Time Physical Activity (Multiple)c
Table 3 Odds Off Having Low Levels of Total, Occupational, and Leisure-Time Physical Activity According to Occupational Indicators for Bivariate and Multiple Logistic Regression Models
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in the fully adjusted models. Most studies report fewer or different occupational classification categories, making comparisons with this study difficult. Nevertheless, studies that did provide physical activity outcomes according to a range of different occupations are in line with the current study, indicating that people with blue-collar and technical jobs are more active at work compared with those in other jobs.33–35 With regard to leisure-time physical activity, most studies find opposite patterns where white-collar and professional workers are more active in leisure-time when compared with blue-collar and technical workers,4,19 however this was not confirmed in the current study. White- and blue-collar participants reported similar amounts of physical activity, which were somewhat lower than those reported by professionals and technical workers. The differences were small and not significant in the bivariate or multivariate regression models. The evidence in relation to shift work is inconsistent. This is probably due to the many different types of shift work that people are engaged in (eg, shifts that include/do not include night work, shifts that include/do not include very long working days, shifts that are/ are not rotational). For both total and leisure-time physical activity there are studies that find associations36,37 and other studies that do not.38,39 This makes it difficult to compare the results from this study, as we did see an association for leisure-time physical activity, but not for total physical activity. The literature is more consistent in terms of occupational physical activity, where shift work has been associated with more occupational physical activity,24,31 however our study could not confirm this finding. This study found that those who work part-time are significantly less likely to engage in leisure-time physical activity, though no effects were found on total or occupational physical activity. Likewise, no associations were found between physical activity and a high or low number of hours worked per day or per week. Most of the literature seems to indicate that working more hours is associated with lower total and leisure-time physical activity,40–42 although some studies could not find associations.30,43
Limitations and Strengths The main limitation in this study is the use of a cross-sectional design, which does not allow interferences about causality; longitudinal data are needed to further study this area of interest. Secondly, all data were obtained through self-report questionnaires. Previously it has been shown that the measure used to assess physical activity (IPAQ) is prone to over-reporting.15–18 However, the numbers reported in this study are comparable to those reported by Bauman et al14 in the International Prevalence Study on Physical Activity (which also used the IPAQ long form), where the data were obtained through well-trained CATI interviewers and has been shown to be reliable.44 Further, a ‘wash out’ of associations originally observed in bivariate regression models is not uncommon in multivariate models that adjust for a range of variables.45 As such, another limitation of this study is the possibility that the remaining effects in the multivariate models might have been further reduced if the models also controlled for variables such as job satisfaction, job stress, and working from home, which might also influence activity levels but were not measured in this study. A strength of the study was the sampling design applied to select a random population to participate in this study. This study is also one of few that evaluated a broad range of occupational indicators simultaneously in terms of how they influence different types of physical activity. Most studies focus on a single occupational indicator and a single type of physical activity, making it more difficult to get an overview and see patterns.
Conclusions This study demonstrates that the variance for different types of physical activity (total, occupational, leisure) explained by specific occupational indicators was small, and that other factors (eg, demographic, socioeconomic, psycho-social) play a more influential role. The study nevertheless demonstrates that occupational physical activity had a much greater impact on the calculation of total physical activity compared with leisure-time physical activity. Leisure-time activity generally did not compensate for inactive occupations when summing to total physical activity. Moreover, no evidence was found that physically-demanding occupations led to less leisure-time physical activity or vice versa. As such, this study demonstrates that workplaces are an important consideration when trying to increase physical activity, but also that there is plenty of scope to increase leisure-time physical activity regardless of the occupational conditions of the target population. Finally, due to the reliance of self-reported measures, the findings of this study should be interpreted with caution, and future research should aim to apply objective monitoring of physical activity to establish associations with occupational indicators. Acknowledgments The Population Research Laboratory (PRL), managed by Ms. Christine Hanley, was responsible for participant recruitment and data collection. This study was funded through the Institute of Health and Social Science Research, Central Queensland University. The lead author was supported by a National Health and Medical Research Council of Australia (#519778) and National Heart Foundation of Australia (#PH 07B 3303) post-doctoral research fellowship.
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