Health Reports
Occupational and sex differences in active commuting among Canadian workers from 2006 to 2016

Abstract

Background

Active commuting (AC) to and from work is associated with numerous health benefits, through increased physical activity. This study examined whether occupation types and part-time work, by sex, were associated with AC in a population-based sample of Canadian workers.

Data and methods

Cross-sectional public use microdata files from the 2006 (n=363,048), 2011 (n=370,672), and 2016 (n=362,310) Census of Population were examined. Multinomial logistic regression models were used to estimate the odds of cycling, walking, and using public transit, relative to using a private motorized vehicle, by occupation and sex. Time trends in mode share were also analyzed.

Results

In 2016, commuting by private motorized vehicle and cycling were more common among males, while public transit and walking were more common among females. Occupations in art, culture, recreation, and sport were associated with the greatest odds of cycling (odds ratio [OR]=3.02, 99% confidence interval [CI]: 2.65 to 3.39), while those in trades, transportation, natural resources, and manufacturing had the lowest odds of cycling (OR=0.47, 99% CI: 0.44 to 0.51) and walking (OR=0.36, 99% CI: 0.33 to 0.38). Since 2006, relative declines of 1% and 8% in the proportion of workers commuting by driving and walking, respectively, were observed (absolute change of -1% each). Relative increases of 14% and 12% were observed for cycling and public transit, respectively (absolute changes of less than 1% and 1.5%, respectively).

Interpretation

This study found that sex and occupation are important correlates of AC among Canadian workers. Further research aimed at understanding occupational barriers and facilitators may inform future AC interventions.

Keywords

Physical activity, active transportation, occupational health, workplace, epidemiology

Authors

Gabriella Christopher and Aviroop Biswas  (ABiswas@iwh.on.ca) are with the Institute for Work and Health and the Dalla Lana School of Public Health, University of Toronto. Justin J. Lang and Stephanie A. Prince (stephanie.prince.ware@phac-aspc.gc.ca) are with the Centre for Surveillance and Applied Research at the Public Health Agency of Canada and the School of Epidemiology and Public Health at the University of Ottawa. Justin J. Lang is also with the Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia.

 

What is already known on this subject?

• Most Canadian workers do not meet the physical activity recommendation of 150 minutes of moderate-to-vigorous intensity physical activity per week.
• Active commuting via cycling and walking to and from work is associated with health, economic, and environmental benefits.
• Occupational characteristics may facilitate or impede active commuting. Studies that have examined occupational differences in active commuting have used small non-representative samples and have not generally separated results by the type of active commuting, and most have not focused on describing gender and sex differences.

What does this study add?

• Workers in trades, transportation, natural resources, and manufacturing occupations were least likely to commute actively, compared with using a private motorized vehicle, and workers in art, culture, recreation, and sport were most likely to commute actively.
• Associations between occupation and active commuting were different between males and females.
• Commuting by private motorized vehicle and walking declined slightly from 2006 to 2016 (relative changes of -1% and -8%, respectively, each representing absolute changes of -1%), while cycling and public transit use increased (relative changes of +14% and +12%, respectively, and absolute changes of less than 1% and 1.5%, respectively).
• Future research should further investigate the characteristics of occupations that facilitate or impede active commuting to inform effective intervention design.

Introduction

Physical activity (PA) plays an important role in preventing chronic diseases and premature mortality.^Note 1 Adults are recommended to accumulate 150 minutes of moderate-to-vigorous intensity PA per week.^Note 2 However, most Canadian adults do not meet this recommendation.^Note 3 Active transportation refers to using human-powered means to get around, such as walking and cycling.^Note 4 Although public transit use is not generally considered a mode of active transportation, research has shown that individuals who use transit often cycle or walk to access transit stops, helping transit users accumulate PA.^Note 5, ^Note 6 Active transportation is also linked to physical and mental health benefits, as well as environmental health benefits because of its low carbon footprint.^Note 7, ^Note 8

Promoting PA in workers is an important target for public health interventions since around two-thirds of Canadian adults participate in the labour force and can spend one-third of their day at work.^Note 9 The characteristics of workers’ occupation may impact their ability to engage in PA, including active transportation. For instance, work that involves long hours, high physical exertion, and high psychological demands has been associated with reduced leisure-time PA, which may be the result of fatigue, reduced motivation, or perceived lack of time.^Note 10, ^Note 11, ^Note 12, ^Note 13 While some occupations incur PA as part of the job, some research suggests that occupational PA may not always be health enhancing.^Note 14, ^Note 15 Previous research has also found that workers in occupations involving low PA (technical, paraprofessional, management, and professional occupations) reported the highest transportation-related PA.^Note 16 Furthermore, workplace facilities and policies can also support greater levels of PA, including active transportation, such as the availability of showers or change rooms and flexible work schedules.^Note 17, ^Note 18

Active commuting (AC), using active transportation to commute to and from work, presents a practical way for workers to engage in daily PA. While AC is only one form of PA for workers, using active transportation is associated with higher levels of PA.^Note 4 In Canada, transportation-related PA has been positively associated with device-measured moderate-to-vigorous intensity PA levels.^Note 19 However, limited research has focused on understanding associations between different occupations and AC. Work by Foth et al. suggests public transit use may vary by occupation; however, there is little research in this area.^Note 20 Other studies investigating specific occupational classifications and AC are often limited by small non-representative samples, have combined cycling and walking as modes of AC, or do not describe gender or sex differences.^Note 18, ^Note 21 In Canada, the distribution of occupations,^Note 22 unpaid labour,^Note 23, ^Note 24 and rates of active transportation differ between men and women.^Note 25 At the same time, barriers and facilitators of AC may differ between men and women.^Note 26

This study examined the associations between occupational classifications, part-time work, and AC (i.e., walking, cycling) and public transit use, in a nationally representative sample of Canadian adults, while controlling for other relevant sociodemographic characteristics (e.g., education, income, urbanity)This study also explored how associations between occupational classifications and AC differed by sex and how AC rates may have changed over time. 

Methods

Data source

This study used public use microdata file (PUMF) data from the three most recent and available years of the Census of Population (2006, 2011, 2016). The census is a cross-sectional, nationally representative survey of the Canadian population and is completed around early May every five years. Completing the census is mandatory by law.^Note 27 In 2006 and 2016, 20% and 25% of households, respectively, were asked to complete the long-form census questionnaire, which, in addition to the short-form questionnaire’s demographic questions, asks social, economic, and employment-related questions. In 2011, the long-form census questionnaire was replaced with the voluntary National Household Survey (NHS); however, both measures and completion rates remained similar. Henceforth, the long-form census questionnaire and NHS are referred to as the census. For each census, the PUMF contains a representative subsample (2.7%) of Canadian households, providing access to non-aggregated data that maintain the confidentiality of participants.^Note 28, ^Note 29, ^Note 30

Population

This study includes data from adults aged 18 years and over who self-reported participating in the workforce (for pay or in self-employment) during the previous year. Participants were excluded if they reported working from home or did not have a fixed workplace address. Although data completeness was high for all variables, individuals with missing data on any exposure variable or covariate were excluded (2006: n=18,776, 2011: n=31,339, 2016: n=44,687).

Exposure variables: Occupation and part-time working status

Census occupation refers to the individual’s job held in the week before census completion (early May each census year) or the job that was held the longest since the January before if the respondent did not work that week or worked more than one job. Broad occupational categories were organized according to the National Occupation Classification (NOC). These categories were assigned by Statistics Canada based on two self-reported census questions outlining job descriptions and job tasks. Specifically, occupations were classified according to the NOC 2006 for the 2006 and 2011 Census cycles. The updated NOC 2016 was used to classify occupations for the 2016 Census cycle. Despite changes in occupational classifications between 2006 and 2016, the 10 broad occupational classifications remained the same, and a crosswalk was used to ensure comparability over time.^Note 31 These occupational categories were (1) management; (2) business, finance, and administration; (3) natural and applied sciences and related; (4) health; (5) education, law and social, community, and government services; (6) art, culture, recreation, and sport; (7) sales and service; (8) trades, transport, and equipment operators, and related; (9) natural resources, agriculture, and related production; and (10) manufacturing and utilities. However, three categories were collapsed into a single occupational group for men and women because of small cell sizes for AC among women: trades, transport, and equipment operators and related; natural resources, agriculture, and related production; and manufacturing and utilities. They are referred to as trades, transportation, natural resources, and manufacturing. These three occupational groups consisted of similar skill profiles. More information on occupations within broad NOC categories is available elsewhere.^Note 32

Part-time work was self-reported and defined as working less than 30 hours per week compared with full-time work (30 hours or more per week). If participants worked a combination of full-time and part-time work, they reported information on the job for which they worked the most.

Outcome: Mode of commute

Census participants were asked to identify the mode of transportation used most frequently to travel between home and work, selecting from (1) walk; (2) bicycle; (3) public transit; (4) car, truck, or van as driver; (5) car, truck, or van as passenger; (6) motorcycle, scooter, or moped; or (7) other modes. In 2006, an additional category of “taxicab” was included, but it was not provided in 2011 or 2016. This category was grouped with  “other modes” and ultimately excluded from analyses. Individuals who used more than one travel mode were asked to report the mode used for most of their travel distance. For analytical purposes, the following responses were grouped into a single category of “private motorized vehicle”: car, truck, or van as driver; car, truck, or van as passenger; and motorcycle, scooter, or moped. The resulting four outcome categories of walking, cycling, public transit, and private motorized vehicle were analyzed.

Covariates

The models were stratified by sex (male or female). Models controlled for the following covariates: age group (18 to 24, 25 to 39, 40 to 54, 55 and over), straight-line distance to work (less than 5 km [walkable distance], 5 km to 14 km [bikeable distance], 15 km or more^Note 33), education level (less than secondary school diploma, secondary school diploma or more), income (approximate quintiles: less than $35,000, $35,000 to $54,999, $55,000 to $74,999, $75,000 to $99,999, $100,000 or more), marital status (married or in common-law relationships, not married or in common-law relationships), number of children under the age of 15 years (none, one or more), immigrant status (immigrant, non-immigrant), identification as a member of a racialized group (racialized group, not a racialized group), and urbanity of residence (living in a census-defined census metropolitan area [CMA],^Note 29 not living in a census-defined CMA). Because of small cell sizes for certain population groups, only a binary variable for racialized group could be included in models. However, larger cell sizes allowed for descriptive statistics of several racialized and cultural background groups. Additionally, given that distance to work was estimated by Statistics Canada based on representative points (block, collection unit, or census subdivision) of home and work addresses, distances may not reflect the actual distances travelled. Individuals who reported walking distances of 30 km or more were excluded because of implausibility (less than 1% per cycle).

Statistical analysis

Proportions were used for descriptive statistics with 95% confidence intervals (CIs). Unless otherwise stated, values reported are from the 2016 Census, with values from 2006 and 2011 showing similar results. Covariate-adjusted multinomial logistic regression was used to calculate the odds ratio (OR) and 99% CI of commuting by walking, cycling, and public transit relative to private motorized vehicle (referent) for different occupational characteristics stratified by sex. First, the association of working part-time compared with full-time hours with AC was investigated using multinomial logistic regression that additionally controlled for occupational category. Second, eight multinomial logistic regression models were fitted to examine the association of working in each broad occupational category compared with all other occupations by mode of commute. Each model investigated the association of a different occupational category by AC group with an additional adjustment for part-time work. To reduce the likelihood of making a type 1 error given the repeated models, 99% CIs were calculated for all multinomial logistic regression models.

CIs for each estimate were calculated according to the variance estimation procedure described in the 2006, 2011, and 2016 PUMF user guides using replicate weights.^Note 28, ^Note 29, ^Note 30 For a given estimate, the sample was divided into n representative replicates, estimates were calculated within each replicate, and variance was estimated based on differences across replicates. In instances where replicates were empty for certain covariate and outcome combinations, an estimate of 0 was assigned as outlined by the PUMF user guide. All analyses incorporated PUMF survey weights and were repeated for 2006, 2011, and 2016 Census years. All analyses were conducted using SAS 9.4. Data visualizations were conducted using R statistical software and the ggplot2 package. 

Results

The sample derivation is outlined in Figure 1. Data were analyzed for 362,310 individuals (51.4% female) from the 2016 Census, 370,672 individuals (51.3% female) from the 2011 Census, and 363,048 individuals (50.7% female) from the 2006 Census. The total sample size for the time trends analysis was 1,096,030. The most common mode of transportation to work in 2016 was private motorized vehicle (79.1%; 95% CI: 78.9% to 79.2%), followed by public transit (13.6%; 95% CI: 13.5% to 13.7%), walking (5.8%; 95% CI: 5.7% to 5.9%), and cycling (1.5%; 95% CI: 1.5% to 1.6%). In 2016, a greater proportion of females commuted by walking (female [F]: 6.4%, 95% CI: 6.3% to 6.5% versus male [M]: 5.2%, 95% CI: 5.1% to 5.3%) and public transit (F: 15.3%, 95% CI: 15.2% to 15.5% versus M: 11.8%, 95% CI: 11.6% to 12.0%), while a greater proportion of males commuted by cycling (F: 1.1%, 95% CI: 1.0% to 1.1% versus M: 2.0%, 95% CI: 2.0% to 2.1%) and private motorized vehicle (F: 77.2%, 95% CI: 77.1% to 77.4% versus M: 81.0%, 95% CI: 80.8% to 81.3%). Walking and cycling to work were more prevalent among younger compared with older workers, among those not married or in common-law relationships compared with those married or in common-law relationships, and among those with no children compared with those with children. Additionally, walking to work was more prevalent among those with a lower household income. A greater proportion of workers walked or cycled to work if they lived less than 5 km from their workplace compared with living farther away (5 km or more). A considerable number (12.7% of females, 10.3% of males) of people who lived less than 5 km from work also used public transit. Table 1 and 2 provide the distribution of modes of transportation by occupational and sociodemographic characteristics.

Description of Figure 1



The figure outlines the exclusion criteria and the size of the analyzed population for each census year. A flow diagram begins with the number of records in the public use microdata file (PUMF) for the 2006 Census long-form questionnaire, the 2011 National Household Survey, and the 2016 Census long-form questionnaire. For each census year, the number of records excluded for each exclusion criterion is displayed. These exclusions are displayed as a box to the right of the PUMF size connected via a blunt-end arrow. The result of the PUMF size less all exclusions is titled the eligible population. For each census year, the size of the eligible population is displayed below the PUMF size connected by a vertical arrow. Another box to the right of the eligible population size displays the number of records excluded because the mode of commute was listed as “other” or as “walking 30 km or more.” The eligible population size less those excluded because of their outcome category is displayed as the size of the analyzed sample for each census year. This box is below the eligible population size connected via a vertical arrow. Below the size, the proportion of females for each census year’s analyzed sample is also listed.

From the 2006 Census long-form questionnaire, the 2006 PUMF included 844,476 observations. Of these observations, 187,070 were excluded because they were under the age of 18 years; 178,298 were excluded because they were not employed in the previous year; 91,378 were excluded because they reported that they had no fixed workplace address or that they worked from home; and 18,776 were excluded because of missing information on an exposure or covariate included in the model. From the 2006 Census PUMF, the eligible population size was 387,730. Of this population, 3,850 observations were excluded because they reported a mode of commute other than a private motorized vehicle, walking, cycling, or public transit; 2,056 were excluded because they reported their mode of commute as walking but had a recorded distance between work and home of 30 km or more, which the authors deemed to be implausible. The resulting size of the analyzed population from the 2006 Census long-form questionnaire was 363,048, consisting of 50.7% females.

From the 2011 National Household Survey, the 2011 PUMF included 887,012 observations. Of these observations, 191,702 were excluded because they were under the age of 18 years; 191,827 were excluded because they were not employed in the previous year; 94,185 were excluded because they reported that they had no fixed workplace address or that they worked from home; and 31,339 were excluded because of missing information on an exposure or covariate included in the model. From the 2011 PUMF, the eligible population size was 409,298. Of this population, 5,475 observations were excluded because they reported a mode of commute other than a private motorized vehicle, walking, cycling, or public transit; 1,812 observations were excluded because they reported their mode of commute as walking but had a recorded distance between work and home of 30 km or more. The resulting size of the analyzed population from the 2011 National Household Survey was 370,672, consisting of 51.3% females.

From the 2016 Census long-form questionnaire, the PUMF included 930,421 observations. Of these, 197,379 were excluded because they were under the age of 18 years; 212,875 were excluded because they were not employed in the previous year; 105,997 were excluded because they reported that they had no fixed workplace address or that they worked from home; and 44,687 were excluded because of missing information on an exposure or covariate included in the model. From the 2016 PUMF, the eligible population size was 414,187. Of this population, 5,783 observations were excluded because they had a mode of commute other than a private motorized vehicle, walking, cycling, or public transit; 1,390 were excluded because they reported their mode of commute as walking but had a recorded distance between work and home of 30 km or more. The resulting size of the analyzed population from the 2016 Census long-form questionnaire was 362,310, consisting of 51.4% females.

 

Differences in mode of commute by occupation, part-time work, and sex

Figure 2 depicts the odds of walking, cycling, and using public transit compared with using a private motorized vehicle across (a) occupational groups and (b) part-time work by sex. For both sexes, AC and public transit use were highest for those in art, culture, recreation, and sport occupations. Working in these occupations was associated with a three times greater likelihood of cycling among males (OR=3.03, 99% CI: 2.36 to 3.71) and females (OR=3.12, 99% CI: 2.82 to 3.41). This occupational group was also significantly associated with greater odds of walking and using public transit. Natural and applied sciences and related occupations were also associated with increased odds of walking, cycling, and using public transit compared with driving. However, the magnitude of the association differed between males and females. Females working in natural and applied sciences and related occupations had increased odds of cycling relative to private motorized vehicle use compared with males (F: OR=3.35, 99% CI: 2.73 to 3.97 versus M: OR=1.91, 99% CI: 1.61 to 2.20). Conversely, males working in these occupations had increased odds of public transit use compared to females (F: OR=1.42, 99% CI: 1.24 to 1.61 versus M: OR=1.80, 99% CI: 1.69 to 1.91). Unadjusted ORs are available in Appendix Table A, and adjusted ORs are available in Appendix Table B.

Description of Figure 2



This figure includes two forest plots showing the odds ratio (OR) of commuting by (from left to right) walking, cycling, or public transit relative to commuting by private motorized vehicle for different occupational factors and stratified by sex. The top plot illustrates the OR of each mode of commute for eight different occupational groups, each one relative to all other occupations. The bottom plot illustrates the OR of commuting by each mode for those who work part-time hours (less than 30 hours per week) compared with full-time hours.

The X-axis is titled “odds ratio.” The plot is divided vertically into three sections, the first titled “Walking,” the second titled “Cycling,” and the third titled “Public transit.” The X-axis ranges from 0 to 4 in each of the three sections, representing the range of odds ratios, relative to private motorized vehicles. 

The Y-axis is titled “occupational group” in the top plot and “work hours” in the bottom plot. “Occupational group” has footnote 1 and “work hours” has footnote 2. Within each plot, ticks on the Y-axis represent the occupational categories. From top to bottom, the occupational groups are “Art, culture, recreation, and sport;” “Natural and applied sciences and related;” “Education, law and social, community, and government services;” “Business, finance, and administration;” “Sales and service;” “Management;” “Health;” and “Trades, transportation, natural resources, and manufacturing.” Within the bottom plot, there is one category listed as “Part-time hours (less than 30 hours per week).”

The OR for each group is displayed as a blue triangle for males and a red dot for females. Behind each data point, a black horizontal line represents the 99% confidence interval. There is a grey vertical line where the OR is equal to 1 in each plot, representing the line of no effect. Dots and triangles to the left of the grey line represent a negative association between the occupational factor and the mode of commute, while dots and triangles to the right represent positive associations.  

Occupations in trades, transportation, natural resources, and manufacturing were associated with the lowest likelihood of AC relative to private motorized vehicle use. The OR of walking for females in these occupations compared with all others was 0.46 (99% CI: 0.39 to 0.52) and for males in these occupations was 0.34 (99% CI: 0.31 to 0.36). In addition, occupations in health, management, and sales and service were, in general, associated with the next lowest likelihood of AC and public transit use. 

There were large sex differences within some occupational groups. For example, business, finance, and administration occupations were associated with a greater likelihood of walking, cycling, and using public transit, relative to using a private motorized vehicle, among males compared with females  (e.g., F: OR walking=0.97, 99% CI: 0.91 to 1.03 versus M: OR walking=1.24, 99% CI: 1.14 to 1.34). Greater likelihoods of AC among males than females were also seen for occupations in health; sales and service; and education, law and social, community, and government services. Refer to Figure 2 for sex differences in the associations between occupation and mode of commute.

Working part-time hours, compared with full-time hours, was associated with an increased likelihood of walking, among both sexes, and using public transit, among males, relative to using private motorized vehicles.

Time trends in mode of commute

Figure 3 depicts the proportion of workers using each mode of commute over time by sex. From 2006 to 2016, the share of private motorized vehicle users decreased from 77.6% (95% CI: 77.5% to 77.8%) to 77.3% (95% CI: 77.1% to 77.4%) among females (relative decrease of 0.5%) and 82.9% (95% CI: 82.8% to 83.1%) to 81.0% (95% CI: 80.8% to 81.3%) among males (relative decrease of 2.3%). Over the same time, public transit use increased among females from 14.3% (95% CI: 14.2% to 14.4%) to 15.3% (95% CI: 15.2% to 15.5%), a relative increase of 7.0%, and among males from 9.8% (95% CI: 9.7% to 9.9%) to 11.8% (11.6% to 12.0%), a relative increase of 20.0%. Among females, walking decreased from 7.2% (95% CI: 7.1% to 7.3%) to 6.4% (95% CI: 6.3% to 6.5%), a relative decrease of 8.1%. Among males, the percentage change in walking was a decrease of 3.4%, though non-significant (2016: 5.4%, 95% CI: 5.2% to 5.5%; 2006: 5.2%, 95% CI: 5.1% to 5.3%). Among females, the proportion of workers cycling increased from 0.8% (95% CI: 0.8% to 0.9%) to 1.1% (95% CI: 1.0% to 1.1%) a relative increase of 27.8%, while, among males cycling increased from 1.9% (95% CI: 1.8% to 1.9%) to 2.0% (95% CI: 2.0% to 2.1%), a relative increase of 7.9%.

Description of Figure 3



The figure illustrates the proportion of workers who commuted by walking, cycling, public transit, or private motorized vehicle over time from 2006 to 2016, stratified by sex. This is depicted as two line graphs, one for females on the left and one for males on the right.

The Y-axis reads “percentage of workers.”

The X-axis reads “year.”

The proportion of females who reported using each mode of commute is graphed on the left with solid lines. The proportion of males who used each mode of commute is graphed on the right with dashed lines.

The Y-axis ranges from 0 to 100, with tick marks every five percentage points. The X-axis has tick marks for the years 2006, 2011, and 2016.

There is a data series for each mode of commute: private motorized vehicle in red with square data points, public transit in blue with triangle data points, walking in green with diamond data points, and cycling in yellow with round data points.

Among females, the data series for commuting by private motorized vehicle shows a slightly downward sloping line, beginning at around 78 in 2006 and decreasing to around 77 in 2016. The data series for commuting by public transit shows a slightly upward sloping line from around 14 in 2006 to around 15 in 2011 but stabilizes from 2011 to 2016.

Among males, the data series for commuting by private motorized vehicle shows a downward sloping line beginning around 83 in 2006 and decreasing to around 81 in 2016. The data series for commuting by public transit shows an upward sloping line from around 10 in 2006 to around 12 in 2016.

Figure 4 depicts the proportion of workers using each mode of commute from 2006 to 2016 by occupation. Natural and applied sciences and related occupations, followed by art, culture, recreation, and sport occupations, saw the largest relative declines in private motorized vehicle use (-5.3% and -4.1%, respectively) and both saw large relative increases in public transit use and cycling. Trades, transportation, natural resources, and manufacturing occupations experienced a slight increase in private motorized vehicle use (+1.3%) and declines in cycling and walking. Management occupations and natural and applied sciences and related occupations experienced the largest relative increases in cycling (+58.7% and +34.6%, respectively) and were the only occupations with relative increases in walking (+5.2% and +6.0%, respectively). Sales and service occupations experienced the largest relative increase in public transit use (+24.2%).

Description of Figure 4



The figure illustrates the proportion of workers who commuted by walking, cycling, public transit, or private motorized vehicle over time from 2006 to 2016, stratified by occupation. This is depicted as eight line graphs, one for each occupational group.

The eight line graphs are depicted in two rows of four. The graphs in the top row (from left to right) are for art, culture, recreation, and sport occupations;” “natural and applied sciences and related occupations;” “education, law and social, community, and government services occupations;” and “business, finance, and administration occupations.” The graphs in the bottom row (from left to right) are “sales and service occupations;” “management occupations;” “health occupations;” and “trades, transportation, natural resources, and manufacturing occupations.”

The Y-axis reads “percentage of workers.”

The X-axis reads “year.”

The Y-axis ranges from 0 to 100, with tick marks every 10 percentage points. The X-axis has tick marks for the years 2006, 2011, and 2016.

For each occupation, there is a data series for each mode of commute: private motorized vehicle in red with square data points, public transit in blue with triangle data points, walking in green with diamond data points, and cycling in yellow with round data points.

Among the four left-most occupations, the data series for commuting by private motorized vehicle shows a downward sloping line. Among “sales and service occupations,” this slope is steepest from 2011 to 2016, while among “art, culture, recreation, and sport occupations,” this slope is steepest from 2006 to 2011. By contrast, the data series are relatively flat or show slightly upward sloping lines for private motorized vehicles among “education, law and social, community, and government services occupations;” “health occupations;” and “trades, transportation, natural resources, and manufacturing occupations.”

The data series for public transit shows an upward sloping line from 2006 to 2011 for “art, culture, recreation, and sport occupations;” “natural and applied sciences and related occupations;” “education, law and social, community, and government services occupations;” “management occupations;” and “health occupations.” They show an upward sloping line from 2011 to 2016 for “natural and applied sciences and related occupations” and “sales and service occupations.”

Discussion

Summary of findings and comparisons with previous literature

This study fills an important research gap by identifying overall and sex-specific associations between occupational classifications and AC among Canadian working adults. It builds on previous research describing occupational differences in domain-specific PA, including transport-related PA.^Note 16 This study found that working part-time and having art, culture, recreation, and sport occupations or natural and applied sciences and related occupations were associated with an increased likelihood of AC compared with private motorized vehicle use. Occupations in trades, transportation, natural resources, and manufacturing were associated with a reduced likelihood of AC. Additionally, certain occupations were associated with a greater likelihood of walking, cycling, or using public transit, relative to using private motorized vehicles, among males compared with females: health; business, finance, and administration; and education, law and social, community, and government services. Results also suggest that, among Canadians, commuting by private motorized vehicle and walking has declined slightly over time, while public transit use and cycling have increased. Increases in public transit were more striking among males while increases in cycling were greater among females. Additionally, time trends in modes of commute differed by occupation; private motorized vehicle use declined the most for natural and applied sciences and related occupations while increasing for trades, transportation, natural resources, and manufacturing occupations.

Occupations in trades, transportation, natural resources, and manufacturing are often physically demanding.^Note 32 Previous work has linked physically demanding occupations with lower leisure-time PA,^Note 16, ^Note 34 and high occupational PA may contribute to lower AC for workers in these occupations. This aligns with previous findings that high occupational PA, relative to low, was associated with reduced odds of AC.^Note 35 Given the also relatively high occupational PA of health and sales and service occupations,^Note 36 results suggest a gradient of decreasing likelihood of AC and public transit use, relative to motorized vehicle use, as occupational physical demands increase. This trend is most striking for cycling, followed by public transit and walking.

Art, culture, recreation, and sport occupations and natural and applied sciences and related occupations may be supportive of AC. It is possible that workers in some of these occupations experience greater schedule flexibility,^Note 37 which may in turn provide them with more time for AC.^Note 6, ^Note 17 Other employer supports of AC may involve an encouraging workplace culture or access to change rooms or bike storage facilities.^Note 17, ^Note 38 Additionally, interpersonal factors such as the activities of co-workers or normative beliefs of co-workers may support AC in occupations where AC is already high.^Note 18 On top of the employment-related factors discussed, the location of the workplace relative to the residence, the degree of telework (especially following the COVID-19 pandemic), the infrastructure supporting active transportation in the city of residence, and the neighbourhood built environment around workplaces may differ, perhaps contributing to differences in AC.^Note 39

Sex differences in associations between certain occupations and AC may have various explanations related to one’s occupation.^Note 26 For instance, factors of the built and social environment, including street lighting, interaction with traffic, and perceived safety, can influence women’s mobility choices.^Note 40, ^Note 41 Gender biases in perceptions of professionalism associated with AC or its effects on appearance may also negatively impact AC use more greatly among women, particularly in office settings (i.e., in management and in business, finance, and administration).^Note 42, ^Note 43 Job tasks and specific occupations within broad categories may also differ by gender, and this may contribute to differences in experiences of work and workplace culture and, in turn, differences in AC.

Implications for future research and practice

Reductions in private motorized vehicle use and shifts towards AC are encouraging for public health, considering the environmental implications of driving on climate change and the health benefits of PA. Canada’s first National Active Transportation Strategy, launched in July 2021, aims to increase the proportion of Canadians who use active transportation and recognizes the importance of AC for health, well-being, economic, and environmental benefits.^Note 44 AC behaviours are complex, with multiple interrelated determinants, which may include occupational factors.^Note 18

Given the emphasis placed by many organizations on encouraging health promotion and wellness, the study findings provide a first step towards understanding ways in which workplace policies and environments can support workers’ decision making around AC. Workplaces can be ideal settings to promote PA and behaviour change since working-aged adults can spend one-third of their day at work. Further exploration of vanguard workplaces representative of occupations associated with AC can inform how different institutional factors (e.g., greater flexibility in working hours and an organizational culture that supports health-promoting behaviours), interpersonal factors (e.g., social culture around AC), and the physical environment (e.g., perceived safety and access to workplace showers and change rooms and bicycle parking) enable or impede AC. Additionally, future research would benefit from exploring determinants of AC across different occupations, especially in those outside traditional office settings. This can inform targeted interventions that can improve the capacity of workplaces wanting to implement strategies to promote healthy behaviours and AC among their workforce and generate information that more clearly explains the drivers of promoting AC in the workforce.

Strengths and limitations

This study has several strengths. Firstly, by using census data with PUMF replicate weights, this study analyzes a population-representative sample of Canadian workers. This study also separates walking and cycling outcomes since they are unique modes of AC and stratifies the analyses by sex, providing initial insights into potential sex and gender differences. Lastly, the use of three census years enables an exploration of trends in AC. The study also has limitations. The first is the potential for heterogeneity within each broad occupational category. The broad occupational categories are based on the type, sector, and required training and experience of work performed within an occupation. However, there is potential for each broad occupational category to have different physical and psychological demands, possibly increasing within-group variability and reducing between-group variability. Without more detailed occupation data, this study was unable to directly explore associations between occupational task differences (e.g., occupational PA, psychological work demands) and AC. Additional limitations include the inability to study variations across cities with different policies or control for detailed environmental factors such as neighbourhood density, traffic speeds and volumes, cycling and pedestrian infrastructure, and public transit availability, which are associated with AC and public transit use.^Note 33, ^Note 39, ^Note 45 Additionally, because of the absence of gender-related variables in the census years explored, gender differences could not be directly measured. However, the observed sex differences may relate to differences in socially constructed roles and identities of gender in PA and occupations. The 2021 Census includes a measure of gender, which will help to further explore gender-related factors. A final limitation of this study is the inability to capture multimodal transit, which includes the use of more than one mode of travel to get to the workplace as either a single mode (e.g., walk some days and drive others) or as part of the same trip (intermodality; e.g., walking to and from light trail transit to get to the workplace).

Conclusion

Overall, these findings suggest that there are occupational and sex differences in modes of commute, and these may have important implications for public health (e.g., targeted interventions for certain occupational groups and genders), workplace policies (e.g., flexibility in work hours; creation of change room, shower, and bicycle storage facilities), and transportation planning (e.g., increased creation of infrastructure to support AC). Further research aimed at understanding occupational barriers and facilitators of AC may inform interventions to support AC.