Introduction

Given its utilitarian nature, promoting active transportation (non-motorized modes, such as walking, or cycling to and from destinations) through policy, systems, and environmental change is acknowledged as “one of the leading evidence-based strategies to increase physical activity regardless of age, income, racial/ethnic background, ability, or disability” (Young et al., 2020, pe167). The Canadian Public Health Association endorses this position while also highlighting the co-benefits of active transportation (AT) in improving health by reducing air pollution and reducing health inequities (Perrotta et al., 2021). Similarly, the United Nations (n.d.) recognizes the importance of AT in making communities more inclusive, resilient, and sustainable in the face of climate change pressures and increased demand for energy.

However, engagement in AT tends to be relatively low in most developed countries (Olsen et al., 2017; Whitfield et al., 2020). In Canada, the 2021 ParticipACTION Report Card on Physical Activity for Adults graded Active Travel as an F. This was based on data demonstrating that only 7% of adults living in Canada used AT in commuting to work (ParticipACTION, 2021), which remained relatively stable during the first wave of the COVID-19 pandemic (Statistics Canada, 2020). While less is known about AT to destinations other than work or school among Canadian adults, the data does imply a strategic intervention focus on adult AT may have significant population health impact. The launch of Canada’s first National Active Transportation Strategy (Infrastructure Canada, 2021) and an associated Active Transportation Fund of $400 million over five years for building and expanding networks of pathways, bike lanes, trails, and pedestrian bridges is a step in that direction.

Aligned with socioecological frameworks (Götschi, de Nazelle and Brand, 2017; Spence and Lee, 2003), supporting AT requires multi-level intervention addressing policy, environmental, social, and individual factors. An “upstream” focus on policy change is likely to facilitate AT equitably. For instance, Young et al. (2020) recommended three levels of AT policies: the macroscale of mixed and compact land use, the mesoscale of safe pedestrian and bicycle networks, infrastructure (e.g., Complete Streets policies), and the microscale of design interventions such as safety and traffic calming measures. Given the global commitments to sustainable transportation (United Nations, n.d.), governments at all levels have responsibility to establish and coordinate policies that promote and facilitate AT within their respective jurisdictions (Bull et al., 2004).

However, public resistance to policy may deter implementation and adherence, and ultimately result in its withdrawal from consideration (Li, Shryane and Elliot, 2017). Conversely, public support for different policy interventions may be a prerequisite for decision makers in developing and implementing those policies. Public approval will be influenced by the varying level of intrusiveness of any policy interventions (Diepeveen et al., 2013; Yun et al., 2019). The Nuffield Council on Bioethics (2007) proposes “an intervention ladder” as a useful way of conceptualizing levels of intrusiveness, whereby policy categories are positioned from most to least restrictive (i.e., top to bottom) in the following order: eliminate choices, restrict choices, use of disincentives, use of incentives, change the default policy, enable choice, and provide information. Policies nearer the top of the ladder require greater justification regarding their benefits to ensure that interference and financial costs are proportionate. The question is, to what extent do Canadians approve of various policy actions to facilitate AT?

Therefore, the objectives of this study were to examine public support for AT policy options, varying by intrusiveness, among a representative sample of Canadians, and to identify demographic and AT behavioural predictors of support for each policy approach.

Methods

Participants and recruitment

As part of the Canadian National Active Transportation Survey 2021 (Spence et al., 2022), a total of 2,868 Canadian adults aged 18 years and older were recruited from the Leger Opinion (LEO) panel. The panel includes over 400,000 Canadians and is designed to provide representative data of the Canadian population, using probability sampling methods. Recruitment was conducted by telephone through random digit dialing combining landlines and cell phones. Surveys were administered via an email link between November 26, 2021 and December 7, 2021, in English or French. In terms of reliability of the estimates, were this a probability sample, we could expect a margin of error of ±1.83% 19 times out of 20 (95% confidence). This study was reviewed and approved by the Human Research Ethics Board at the University of Alberta (ethics no. Pro00110489).

Measures

Full details regarding the variables collected are reported elsewhere (Spence et al., 2022).

Demographics

The demographic variables employed in this analysis were collapsed as follows: gender (man, woman, other), education (no post-secondary, post-secondary), income (<30,000, >30,000, don’t know), location of residence (city, other), and region (West [British Columbia, Alberta, Saskatchewan, Manitoba], Ontario, Quebec, Atlantic [Prince Edward Island, New Brunswick, Newfoundland and Labrador, Nova Scotia]).

Active transportation use

For walking/wheeling and cycling, participants were asked to think about a typical week in the past three months (i.e., August 2021 to October 2021) and to indicate the number of days (i.e., 0 through 7) they engaged in walking/wheeling or cycling to commute to work or school. They were instructed that this could include walking/wheeling or cycling to a train station or bus stop on the way to work or school. Individuals could respond “not applicable” (N/A) to indicate if the question was not relevant to them (e.g., retired, work from home). Based on the responses, applicable participants were then dichotomized into inactive commuters (0 days of walking/wheeling and cycling to work or school each week) and active commuters (>0 days walking/wheeling and cycling to work or school each week).

Support for active transportation-related policies

Participants were asked to rate their support for eight AT-related policy options (e.g., charging higher rates for parking so as to subsidize costs for AT infrastructure; see Table 2) on a seven-point Likert scale from “strongly oppose” to “strongly support” (response options: 1 = strongly oppose; 2 = moderately oppose; 3 = somewhat oppose; 4 = neutral; 5 = somewhat support; 6 = moderately support; 7 = strongly support). Items were developed based on previous studies examining support for obesity and physical activity (PA) related policies (Raine et al., 2014; Yun et al., 2018), with some adaptations and additions made to reflect AT. Most of these policies have demonstrated effectiveness in facilitating PA and/or AT (Gelius et al. 2020; Nieuwenhuijsen and Khreis, 2016; Rivers and Plumptre, 2018; Wilkie; Townshead, Thompson and Ling, 2018; Young et al., 2020). For ease of interpretation and analysis, the responses were collapsed into “oppose” (i.e., strongly, moderately, somewhat oppose), “neutral,” and “support” (i.e., strongly, moderately, somewhat support). Finally, the policy options were categorized according to the levels of the Nuffield Council on Bioethics (2007) intervention ladder (e.g., restrict choices, use of incentives, enable choice) to describe the extent of intrusiveness.

Data Analysis

First, descriptive statistics (i.e., frequencies and means, as appropriate) were generated for demographic variables and support for each policy approach. Subsequently, multinomial regressions examined demographics and AT behaviours as predictors of support for each policy approach, yielding a total of eight models with adjusted ORs. Though ordinal models are often preferred when categories of a variable are ordered, multinomial regressions may be more appropriate in many cases (Liang et al., 2020; Leadbetter, 2020). Further, the assumptions of ordinal regression are much stricter (e.g., parallel lines); therefore, multinomial regressions provide an alternative means of analysis when these stringent assumptions are not met (Liang et al., 2020). To ensure that findings were representative of the Canadian population, the data were weighted to reflect the age, gender, and regional composition of the country. All analyses were completed in SPSS version 28. Given the large sample size and number of analyses conducted, an alpha level of p < .01 was adopted.

Results

As shown in Table 1, a little over half of respondents were women (50.8%), most were aged 45 years or older (56.6%), employed or in school (63.7%), had some post-secondary education (82.3%), reported a household income of greater than $60,000 (55.7%), and resided in a city (65.6%). The proportion of Canadian adults walking/wheeling and cycling for AT, at least once per week, were 37.3% and 13.8%, respectively.

Table 1

Demographics of the sample (N = 2,868).

Variable Categories Frequency %
Gender Woman 1458 50.8
Man 1380 48.1
Other 30 1.0
Age (yrs.) 18–24 years old 311 10.9
25–44 years old 934 32.6
45–64 years old 1015 35.4
65 years or older 607 21.2
Education No schooling 1 0
Elementary school 12 0.4
Some or completed high school 497 17.3
Some or completed community college or technical school 903 31.5
Some or completed University 1456 50.8
Household income Less than $30,000 408 14.2
$30,000 to $59,000 641 22.4
$60,000 to $79,999 397 13.9
$80,000 to $99,999 396 13.8
$100,000 to $120,000 323 11.3
More than $120,000 479 16.7
Don’t Know 224 7.8
Location of residence City 1881 65.6
Town 623 21.7
Village 139 4.9
Hamlet 115 4.0
Other 110 3.8
Region British Columbia 389 13.6
Alberta 322 11.2
Saskatchewan 86 3.0
Manitoba 101 3.5
Ontario 1101 38.4
Quebec 673 23.5
New Brunswick 63 2.2
Nova Scotia 78 2.7
Prince Edward Island 12 0.4
Newfoundland and Labrador 44 1.5

Support for active transportation-related policies

Most Canadians supported policy approaches that enable AT choices and change AT policy related to environmental features as well as fiscal measures that incentivize AT including: implementing transportation policies designed to promote PA through safe routes, cycle facilities, adequate lighting, etc. (69.7%); offering tax credits for public transit passes (64.6%); changing the design of neighbourhoods and communities to encourage informal PA in daily life (62.6%); spending government money on more dedicated bicycle paths in my community to make streets safer for cyclists, cars, and pedestrians (62.3%); and offering tax credits for bicycles and other equipment for active transportation (59%) (see Table 2). Well under half of Canadians supported policies aimed at restricting choice or disincentives including: banning all traffic in high use pedestrian areas during peak hours to support active (e.g., walking, cycling) or public transportation (40.3%); charging a tax on all motor vehicles to support investments in infrastructure for AT (29.1%); and charging higher rates for parking so as to subsidize costs for AT infrastructure (e.g., bike lanes, walking paths) (25.3%).

Table 2

Support for government investments, spending, and policy in relation to facilitating active transportation.

Initiative Oppose (%) Neutral (%) Support (%) Mean (SD)
Restrict choice
Banning all traffic in high-use pedestrian areas during peak hours to support active (e.g., walking, cycling) or public transportation 33.8% 25.9% 40.3% 4.10 (1.81)
Guide choice through disincentives
Charging a tax on all motor vehicles to support investments in infrastructure for active transportation 49.7% 21.3% 29.1% 3.38 (1.96)
Charging higher rates for parking to subsidize costs for active transportation infrastructure (e.g., bike lanes, walking paths) 52.8% 22.0% 25.3% 3.25 (1.91)
Guide choice through incentives
Offering tax credits for public transit passes 13.1% 22.3% 64.6% 5.06 (1.66)
Offering tax credits for bicycles and other equipment for active transportation 17.5% 23.4% 59.0% 4.80 (1.73)
Guide choices through changing the default policy
Implementing transportation policies designed to promote physical activity through safe routes, cycle facilities, adequate lighting, etc. 9.1% 21.3% 69.7% 5.31 (1.48)
Enable choice
Changing the design of neighbourhoods and communities to encourage informal physical activity in daily life 13.6% 23.8% 62.6% 4.94 (1.57)
Spending government money on more dedicated bicycle paths in my community to make streets safer for cyclists, cars, and pedestrians 17.8% 19.9% 62.3% 4.93 (1.76)

Multinomial regressions examining predictors of support for policy approaches

The full models with all predictors were significant when compared to the null (i.e., constant only) models for each behaviour. Thus, as a group, demographic and AT use variables predicted support for each policy approach (policies to promote PA via safe routes, cycle facilities, lighting, etc.: χ2 = 171.60, df = 32, p < 0.001; tax credits for public transit passes: χ2 = 146.43, df = 32, p < 0.001; changing community design to encourage informal PA: χ2 = 117.74, df = 32, p < 0.001; government spending on bike paths to make streets safer: χ2 = 141.62, df = 32, p < 0.001; tax credits for bikes and other equipment for AT: χ2 = 133.83, df = 32, p < 0.001); banning traffic in high use pedestrian areas in peak hours: χ2 = 193.56, df = 32, p < 0.001; charging a tax on vehicles to support AT infrastructure: χ2 = 215.45, df = 32, p < 0.001; increasing parking rates to subsidize AT infrastructure: χ2 = 203.31, df = 32, p < 0.001). However, a small amount of the variance was accounted for by the models (Nagelkerke’s R2 = 0.05 – 0.08) and overall classification rates ranged from 45% to 70%.

Demographics

Table 3 and Table 4 present the likelihood ratio tests and ORs for the demographic and behavioural predictors of each policy approach. Significant associations existed between gender and support for policies to promote PA via safe routes, cycle facilities, adequate lighting, etc., tax credits for public transit passes, changing the design of neighbourhoods and communities to encourage informal PA, government spending on bike paths in one’s community to make streets safer, tax credits for bikes and other equipment for AT, and increasing parking rates to subsidize AT infrastructure. Overall, compared to men, women were less likely to oppose or indicate a neutral stance towards policy approaches (ORs ranging from 0.57 to 0.77).

Table 3

Likelihood ratio tests based on multinomial logistic regression analyses demonstrating overall relationships between demographic and behavioural predictors and support for each policy approach.

Policy Approach Gender Age Education Income Location Region (province) Walk to work/school Cycle to work/school
Policies to promote PA (e.g., safe routes) χ2 = 28.96
df = 4
p < 0.001		 χ2 = 24.26,
df = 6
p < 0.001		 χ2 = 22.84,
df = 2
p < 0.001		 χ2 = 26.81,
df = 4
p < 0.001		 χ2 = 0.11,
df = 2
p = 0.99		 χ2 = 40.54,
df = 6
p < 0.001		 χ2 = 9.33,
df = 4
p = 0.05		 χ2 = 10.53,
df = 4
p = 0.03
Tax credits for transit passes χ2 = 30.49,
df = 4
p < 0.001		 χ2 = 4.71,
df = 6
p = 0.58		 χ2 = 17.06,
df = 2
p < 0.001		 χ2 = 8.15,
df = 4
p = 0.09		 χ2 = 14.78,
df = 2
p = < 0.001		 χ2 = 28.81,
df = 6
p < 0.001		 χ2 = 19.62,
df = 4
p < 0.001		 χ2 = 10.52,
df = 4
p = 0.03
Changing community design χ2 = 22.0,
df = 4
p < 0.001		 χ2 = 24.36,
df = 6
p < 0.001		 χ2 = 15.50,
df = 2
p < 0.001		 χ2 = 10.85,
df = 4
p = 0.03		 χ2 = 1.64,
df = 2
p = 0.44		 χ2 = 22.02,
df = 6
p = 0.001		 χ2 = 14.39,
df = 4
p = 0.006		 χ2 = 4.13,
df = 4
p = 0.39
Government spending on bike paths χ2 = 20.12,
df = 4
p = < 0.001		 χ2 = 15.89,
df = 6
p = 0.014		 χ2 = 22.42,
df = 2
p = < 0.001		 χ2 = 10.94,
df = 4
p = 0.03		 χ2 = 5.02,
df = 2
p = 0.08		 χ2 = 37.38,
df = 6
p = < 0.001		 χ2 = 11.92,
df = 4
p = 0.02		 χ2 = 13.67,
df = 4
p = 0.008
Tax credits for equipment for AT χ2 = 19.13,
df = 4
p < 0.001		 χ2 = 27.91,
df = 6
p < 0.001		 χ2 = 17.21,
df = 2
p < 0.001		 χ2 = 11.01,
df = 4
p = 0.03		 χ2 = 0.05,
df = 2
p = 0.98		 χ2 = 23.72,
df = 6
p < 0.001		 χ2 = 12.04,
df = 4
p = 0.02		 χ2 = 3.81,
df = 4
p = 0.43
Ban traffic in high-use pedestrian areas χ2 = 5.45,
df = 4
p = 0.24		 χ2 = 18.41,
df = 6,
p = 0.005		 χ2 = 20.03,
df = 2,
p < 0.001		 χ2 = 18.65,
df = 4,
p < 0.001		 χ2 = 2.18,
df = 2,
p = 0.34		 χ2 = 54.51,
df = 6,
p < 0.001		 χ2 = 22.29,
df = 4,
p = < 0.001		 χ2 = 19.06,
df = 4,
p <0.001
Tax vehicles to support AT χ2 = 8.00,
df = 4
p = 0.09		 χ2 = 24.72,
df = 6
p = < 0.001		 χ2 = 15.54,
df = 2
p = < 0.001		 χ2 = 14.78,
df = 4
p = 0.005		 χ2 = 8.07,
df = 2
p = 0.02		 χ2 = 32.73,
df = 6
p = < 0.001		 χ2 = 36.73,
df = 4
p = < 0.001		 χ2 = 23.47,
df = 4
p = < 0.001
Increase parking rates χ2 = 20.64,
df = 4
p < 0.001		 χ2 = 21.30,
df = 6
p = 0.002		 χ2 = 8.89,
df = 2
p = 0.012		 χ2 = 13.35,
df = 4
p = 0.01		 χ2 = 6.97,
df = 2
p = 0.03		 χ2 = 33.11,
df = 6
p =< 0.001		 χ2 = 36.13,
df = 4
p < 0.001		 χ2 = 15.12,
df = 4
p = 0.004
Table 4

Adjusted odds ratios for logistic regression analyses examining differences in support for policy approaches (i.e., “oppose” and “neutral” vs. “support”) based on demographic and behavioural predictors.

Policies to promote PA (e.g., safe routes) Tax credits for transit passes Changing community design Government spending on bike paths Tax credits for equipment for AT Ban traffic in high-use pedestrian areas Tax vehicles to support AT Increase parking rates
Predictors OR
(99% CI)		 OR
(99% CI)		 OR
(99% CI)		 OR
(99% CI)		 OR
(99% CI)		 OR
(99% CI)		 OR
(99% CI)		 OR
(99% CI)
Opposea
Gender (other) 1.01
(0.24, 4.24)		 0.51
(0.11, 2.45)		 1.77
(0.57, 5.44)		 0.41
(0.09, 1.91)		 0.75
(0.21, 2.70)		 0.63
(0.21, 1.92)		 0.33
(0.11, 1.05)		 0.63
(0.22, 1.84)
Gender (woman) 0.61
(0.43, 0.87)*		 0.57
(0.42, 0.77)*		 0.71
(0.53, 0.96)*		 0.69
(0.53, 0.90)*		 0.71
(0.54, 0.93)*		 0.91
(0.72, 1.15)		 1.05
(0.83, 1.33)		 1.41
(1.10, 1.79)*
Age (18–24 years) 0.91
(0.47, 1.74)		 0.82
(0.44, 1.51)		 0.80
(0.45, 1.42)		 1.00
(0.60, 1.68)		 0.45
(0.25, 0.79)*		 1.20
(0.76, 1.90)		 0.75
(0.48, 1.17)		 0.86
(0.55, 1.35)
Age (25–44 years) 0.84
(0.52, 1.37)		 1.01
(0.66, 1.56)		 0.77
(0.51, 1.15)		 0.90
(0.62, 1.32)		 0.60
(0.41, 0.87)*		 1.05
(0.75, 1.46)		 0.94
(0.68, 1.31)		 0.95
(0.67, 1.34)
Age (45–64 years) 0.78
(0.48, 1.25)		 1.12
(0.75, 1.69)		 0.78
(0.52, 1.15)		 1.00
(0.70, 1.43)		 0.74
(0.52, 1.05)		 1.02
(0.74, 1.40)		 1.18
(0.85, 1.62)		 1.21
(0.87, 1.69)
Education (no post-secondary) 1.09
(0.67, 1.78)		 1.63
(1.12, 2.38)*		 1.47
(1.01, 2.15)*		 1.39
(0.98, 1.98)		 1.47
(1.04, 2.09)*		 1.10
(0.80, 1.52)		 1.39
(1.00, 1.93)		 1.23
(0.87, 1.73)
Income (don’t know) 0.65
(0.29, 1.47)		 0.97
(0.53, 1.76)		 1.29
(0.75, 2.21)		 1.35
(0.83, 2.19)		 0.98
(0.58, 1.68)		 0.95
(0.60, 1.50)		 1.23
(0.77, 1.99)		 1.53
(0.91, 2.57)
Income (<30,000) 0.75
(0.42, 1.34)		 0.92
(0.58, 1.45)		 0.80
(0.51, 1.28)		 0.76
(0.50, 1.16)		 0.90
(0.59, 1.35)		 0.88
(0.62, 1.27)		 0.85
(0.61, 1.20)		 0.92
(0.64, 1.32)
Location (city) 0.97
(0.66, 1.41)		 0.63
(0.46, 0.86)*		 0.89
(0.65, 1.21)		 1.27
(0.95, 1.71)		 1.00
(0.75, 1.33)		 0.87
(0.68, 1.12)		 0.77
(0.59, 0.99)*		 0.76
(0.59. 1.00)
Region (Ontario) 0.50
(0.34, 0.75)*		 0.73
(0.51, 1.03)		 0.72
(0.51, 1.02)		 0.57
(0.42, 0.78)*		 0.81
(0.59, 1.10)		 0.66
(0.50, 0.87)*		 0.73
(0.55, 0.97)*		 0.67
(0.50, 0.90)*
Region (Quebec) 0.44
(0.27, 0.71)*		 0.63
(0.42, 0.94)*		 0.77
(0.52, 1.13)		 0.65
(0.46, 0.93)*		 0.83
(0.58, 1.19)		 0.46
(0.34, 0.64)*		 0.53
(0.39, 0.73)*		 0.57
(0.41, 0.79)*
Region (Atlantic) 0.40
(0.17, 0.92)*		 0.48
(0.25, 0.94)*		 0.46
(0.22, 0.95)*		 0.37
(0.19, 0.70)*		 0.37
(0.19, 0.73)*		 0.38
(0.23, 0.64)*		 0.77
(0.46, 1.27)		 0.59
(0.35, 0.99)*
Walking (N/A) 1.03
(0.63, 1.66)		 1.32
(0.88, 2.01)		 1.10
(0.74, 1.65)		 1.24
(0.86, 1.78)		 1.22
(0.84, 1.76)		 1.34
(0.97, 1.83)		 1.55
(1.14, 2.12)*		 1.68
(1.22, 2.32)*
Walking (inactive) 1.34
(0.79, 2.25)		 1.54
(0.97, 2.44)		 1.54
(0.98, 2.41)		 1.69
(1.12, 2.55)*		 1.36
(0.90, 2.06)		 1.74
(1.23, 2.48)*		 2.04
(1.42, 2.93)*		 2.01
(1.38, 2.94)*
Cycling (N/A) 0.70
(0.40, 1.24)		 0.87
(0.52, 1.46)		 0.94
(0.57, 1.56)		 1.02
(0.64, 1.62)		 1.17
(0.72, 1.88)		 1.65
(1.09, 2.52)*		 1.71
(1.14, 2.55)*		 1.66
(1.10, 2.47)*
Cycling (inactive) 0.69
(0.39, 1.21)		 0.78
(0.46, 1.32)		 0.78
(0.46, 1.31)		 0.78
(0.49, 1.26)		 1.05
(0.64, 1.70)		 1.95
(1.28, 2.97)*		 1.73
(1.16, 2.59)*		 1.76
(1.18, 2.64)*
Neutrala
Gender (other) 0.20
(0.03, 1.39)		 0.20
(0.03, 1.36)		 0.33
(0.07, 1.63)		 0.39
(0.09, 1.66)		 0.20
(0.03, 1.37)		 0.34
(0.08, 1.43)		 0.42
(0.11, 1.54)		 0.36
(0.08, 1.59)
Gender (woman) 0.70
(0.55, 0.91)*		 0.93
(0.73, 1.19)		 0.75
(0.59, 0.96)*		 0.77
(0.59, 0.99)*		 1.01
(0.80, 1.29)		 0.96
(0.75, 1.24)		 0.98
(0.74, 1.30)		 1.06
(0.79, 1.41)
Age (18–24 years) 1.56
(0.97, 2.53)		 1.19
(0.75, 1.88)		 2.01
(1.28, 3.15)*		 1.72
(1.07, 2.77)*		 1.36
(0.87, 2.12)		 2.05
(1.28, 3.28)*		 1.64
(0.98, 2.76)		 1.46
(0.85, 2.50)
Age (25–44 years) 1.87
(1.30, 2.70)*		 1.23
(0.80, 1.60)		 1.48
(1.04, 2.11)*		 1.50
(1.03, 2.18)*		 1.02
(0.72, 1.45)		 1.45
(1.01, 2.08)*		 1.44
(0.95, 2.20)		 1.60
(1.04, 2.44)*
Age (45–64 years) 1.42
(1.00, 2.03)		 1.03
(0.74, 1.43)		 1.40
(0.99, 1.96)		 1.61
(0.81, 1.67)		 1.01
(0.72, 1.42)		 1.21
(0.85, 1.72)		 1.48
(0.98, 2.22)		 1.53
(1.01, 2.32)*
Education (no post-secondary) 1.78
(1.31, 2.41)*		 1.48
(1.08, 2.02)*		 1.52
(1.12, 2.07)*		 1.78
(1.29, 2.46)*		 1.57
(1.15, 2.13)*		 1.71
(1.24, 2.35)*		 1.77
(1.21, 2.59)*		 1.56
(1.06, 2.30)*
Income (don’t know) 1.91
(1.27, 2.89)*		 1.54
(1.01, 2.34)*		 1.52
(1.00, 2.32)		 1.57
(1.00, 2.45)		 1.59
(1.05, 2.41)*		 1.59
(1.02, 2.48)*		 1.99
(1.19, 3.35)*		 2.06
(1.17, 3.64)*
Income (<30,000) 1.44
(1.02, 2.03)*		 1.16
(0.82, 1.63)		 1.22
(0.87, 1.71)		 0.98
(0.68, 1.41)		 1.23
(0.87, 1.72)		 1.42
(1.01, 2.02)*		 1.03
(0.69, 1.54)		 1.16
(0.77, 1.75)
Location (city) 0.97
(0.74, 1.27)		 0.84
(0.65, 1.10)		 1.06
(0.82, 1.37)		 1.13
(0.86, 1.50)		 0.98
(0.75, 1.27)		 0.90
(0.68, 1.18)		 0.92
(0.67, 1.25)		 0.89
(0.64, 1.22)
Region (Ontario) 0.92
(0.69, 1.23)		 1.03
(0.78, 1.37)		 0.87
(0.66, 1.15)		 0.90
(0.67, 1.22)		 0.99
(0.75, 1.31)		 0.85
(0.63, 1.14)		 0.92
(0.66, 1.29)		 0.92
(0.65, 1.30)
Region (Quebec) 0.63
(0.44, 0.90)*		 0.65
(0.46, 0.92)*		 0.64
(0.46, 0.90)*		 0.71
(0.49, 1.01)		 0.75
(0.53, 1.06)		 0.58
(0.41, 0.82)*		 0.56
(0.38, 0.83)*		 0.59
(0.39, 0.88)*
Region (Atlantic) 0.82
(0.49, 1.37)		 0.63
(0.37, 1.08)		 0.84
(0.52, 1.38)		 0.62
(0.36, 1.09)		 0.66
(0.39, 1.11)		 0.72
(0.44, 1.20)		 0.93
(0.51, 1.70)		 0.93
(0.51, 1.68)
Walking (N/A) 1.34
(0.96, 1.87)		 1.59
(1.14, 2.12)*		 1.41
(1.02, 1.95)*		 1.10
(0.79, 1.55)		 1.39
(1.01, 1.93)*		 1.37
(0.98, 1.90)		 1.51
(1.04, 2.20)*		 1.47
(1.00, 2.15)
Walking (inactive) 1.42
(0.95, 2.11)		 1.46
(0.98, 2.19)		 1.40
(0.96, 2.04)		 1.28
(0.85, 1.93)		 1.44
(0.98, 2.11)		 1.18
(0.79, 1.78)		 1.27
(0.80. 2.02)		 1.24
(0.78, 1.98)
Cycling (N/A) 1.25
(0.81, 1.94)		 0.88
(0.58, 1.34)		 1.09
(0.72, 1.65)		 1.12
(0.73, 1.72)		 1.19
(0.78, 1.82)		 1.10
(0.72, 1.66)		 0.94
(0.60, 1.47)		 1.30
(0.82, 2.05)
Cycling (inactive) 0.86
(0.55, 1.36)		 0.61
(0.39, 0.95)*		 0.86
(0.56, 1.32)		 0.67
(0.43, 1.06)		 0.92
(0.60, 1.43)		 1.08
(0.70, 1.65)		 0.84
(0.53, 1.33)		 1.27
(0.80, 2.03)

  • a For the criterion variable, the reference group is support. For predictor variables, the reference groups are as follows: man for gender, > 65 years for age, post-secondary for education, greater than $30,000 for income, “other” (i.e., town, village, hamlet, other) for location of residence, Western provinces for region, “active” (i.e., commutes by walking > 0 days/week) for walking, “active” (i.e., commutes by cycling > 0 days/week) for cycling.

    * p < 0.01.

Region was significantly associated with all policy options: policies to promote PA via safe routes, cycle facilities, lighting, etc., tax credits for public transit passes, changing community design to encourage informal PA, government spending on bike paths to make streets safer, tax credits for bikes and other equipment for AT, banning traffic in high use pedestrian areas in peak hours, charging a tax on vehicles to support AT infrastructure, increasing parking rates to subsidize AT infrastructure. Compared with Atlantic provinces, Western provinces were less favourable towards all policy initiatives, except for charging a tax on vehicles to support AT infrastructure (ORs ranging from 0.37 to 0.59). Compared with Quebec and/or Ontario, Western provinces indicated less support across initiatives, apart from tax credits for bikes and other equipment to support AT (ORs ranging from 0.44 to 0.73).

Age was significantly associated with policies to promote PA via safe routes, cycle facilities, adequate lighting, etc., changing the design of neighbourhoods and communities to encourage informal PA, tax credits for bikes and other equipment for AT, banning traffic in high use pedestrian areas in peak hours, charging a tax on motor vehicles to support AT, and increased parking rates to subsidize AT. Compared with those 65 years and older, 18 to 24 and 25 to 44-years-olds were less likely to oppose and more likely to indicate a neutral stance towards tax credits for bikes and other equipment for AT, changing the design of neighbourhoods and communities to encourage informal PA, government spending on bike paths in one’s community to make streets safer, and banning traffic in high use pedestrian areas in peak hours (ORs ranging from 0.45 to 0.60 for oppose and 1.45 to 2.05 for neutral). Compared to those aged 65 and older, 25 to 44-year-olds were also more likely to indicate a neutral stance for policies to promote PA via safe routes, cycle facilities, adequate lighting, etc. and 25 to 44-year-olds and 45 to 64-year-olds were more likely to indicate neutrality toward increased parking rates to subsidize AT (ORs ranging from 1.46 to 1.87). When compared with 18 to 24-year-olds, 45 to 64-year-olds were more likely to oppose charging a tax on motor vehicles to support AT (OR = 1.58).

Significant associations were found between education and nearly all policies including, policies to promote PA via safe routes, cycle facilities, lighting, etc., tax credits for public transit passes, changing community design to encourage informal PA, government spending on bike paths to make streets safer, tax credits for bikes and other equipment for, banning traffic in high use pedestrian areas in peak hours, charging a tax on vehicles to support AT infrastructure. Individuals with no post-secondary education were more likely to oppose policies compared to those without (ORs ranging from 1.47 to 1.63).

Few significant associations were observed between income and policy support, with likelihood ratio tests demonstrating links with support for policies to promote PA via safe routes, cycle facilities, lighting, etc., banning traffic in high use pedestrian areas in peak hours, and charging a tax on vehicles to support AT infrastructure. Individuals from the lowest income households (<$30,000) were less likely to support policies to promote PA via safe routes, cycle facilities, adequate lighting, etc. and banning traffic in high use pedestrian areas in peak hours than those from higher income households (ORs ranging from 1.42 to 1.44 for neutral compared with support group).

Location of residence was associated only with support for tax credits for public transit passes. Those living in cities were more favourable towards tax credits for public transit passes and taxing motor vehicles to support AT infrastructure than residents of less populated areas (i.e., towns, villages, hamlets, other) (ORs ranging from 0.63 to 0.77 for oppose group compared with support group).

Active transportation use

Significant associations were demonstrated between walking/wheeling to work or school and support for tax credits for public transit passes, changing the design of neighbourhoods and communities to encourage informal PA, banning traffic in high use pedestrian areas in peak hours to support AT, charging a tax on vehicles to support AT infrastructure, and increasing parking rates to subsidize AT infrastructure. Individuals who do not actively commute by walking to school or work were more likely to oppose government spending on bike paths to make streets safer, banning traffic in high use pedestrian areas in peak hours to support AT, charging a tax on vehicles to support AT infrastructure, and increasing parking rates to subsidize AT infrastructure when compared with those who actively commute to work or school (ORs ranging from 1.69 to 2.04).

Cycling to work or school was a significant predictor of support for government spending on bike paths to make streets safer, banning traffic in high use pedestrian areas in peak hours to support AT, charging a tax on vehicles to support AT infrastructure, and increasing parking rates to subsidize AT infrastructure. Individuals who do not actively commute by cycling to work or school were more likely to oppose banning traffic in high use pedestrian areas in peak hours to support AT, charging a tax on vehicles to support AT infrastructure, and increasing parking rates to subsidize AT infrastructure (ORs ranging from 1.73 to 1.95).

Discussion

Most policy actions to promote AT were supported by Canadians although the level of support varied by the type of policy actions and by demographics and AT behaviour. The less intrusive policy approaches that targeted the enablement of choice, creation of safe transportation infrastructure, and the use of incentives were the most favoured. Conversely, a minority of Canadians supported policies that facilitate AT using disincentives and restriction of choices. For instance, those respondents who do not actively commute were more likely to oppose the more intrusive policies such as charging taxes on motor vehicles and increasing parking rates to subsidize AT. These findings are consistent with previous research showing that public support for PA policy interventions generally tends to decrease as their level of intrusiveness increases (McGetrick et al., 2019; Molner et al., 2023; Yun et al., 2018). Similarly, decision makers are less likely to endorse obesity prevention policies that are deemed to be more intrusive or restrictive (Raine et al., 2014).

In terms of demographics, level of support for AT policies was higher among women and those with post-secondary education. It was also generally stronger among younger respondents. This may reflect generational differences in attitudes about climate change and the need for action (Liu, Shryane and Elliot, 2022; Smith, Kim and Son, 2017). Speculatively, public support for AT policies may grow with age cohort progression (Hamilton, Hartter and Bell, 2019). Given that AT is identified as an integral part of any solution for addressing climate change (Alessio et al., 2021; Brand, 2021; United Nations, n.d.), one strategy for promotion could include focused social marketing campaigns with an emphasis on climate change benefits targeting younger adults and an emphasis on the health benefits of AT for older adults (Plotnikoff, Wright and Karunamuni, 2004).

Where Canadians lived had some bearing on their support for AT policies. The strongest associations were noted by region, with those in Western Canada being the least supportive and those in central and eastern Canada being more supportive. This may reflect some combination of political orientation and size of communities. For instance, much of western Canada is currently represented by more conservative leaning governments at both provincial and federal levels. Previous research shows that political orientation is associated with support for healthy public policy, with the more conservative orientations being less supportive of such policies (Ashley et al., 2001; McGeterick et al., 2019; Molner et al., 2023; Montez et al., 2022; Yun et al., 2019). As for location of residence, those living in cities were more favourable towards tax credits for public transit passes.

Assessing public support for AT policies may inform municipal, provincial, and national policy and planning efforts (Cradock et al., 2018). Policies can influence behavior by facilitating interventions that are mediated by individuals’ perceived capability, opportunity, and motivation (Michie, van Stralen and West, 2011). In the case of AT, environmental and social planning policies may work via education and environmental restructuring to then influence knowledge (i.e., Capability) and social norms (i.e., Opportunity). Thus, an evaluation of Canada’s National Active Transportation Strategy (Infrastructure Canada, 2021) should include tracking of public support for various initiatives to stimulate engagement of AT. The current data could serve as a baseline in that regard. One example would be how older adults perceive AT promotion and whether they are as receptive to the roll out of the national strategy. Furthermore, the multi-sectoral collaboration requited to foster AT promotion and delivery of services likely requires diffusion of policies that could be informed by these findings. For instance, separate strategies currently exist in Canada for promoting AT (Infrastructure Canada, 2021), PA (Public Health Agency of Canada (2018), sport (Sport Canada, 2012), and recreation (Canadian Parks and Recreation Association, 2015). The extent to which common objectives and actions can be identified across these frameworks for getting the population moving will likely determine success.

Strengths of this study include it being among the first to examine policy support for AT in a representative sample of Canadian adults. However, it is not without limitations that should be acknowledged. First, self-reports are susceptible to bias. This is even more the case when asking individuals about hypothetical scenarios (Burgess, Spence and Wild, 2010). Thus, respondents may have been overly positive about support for initiatives with which they may have no experience (e.g., redevelopment of neighbourhoods, the provision of bike lanes in their community). Second, data collection occurred during the latter part of the COVID-19 pandemic and some respondents may have still been limited or restricted in their travel choices. Though the overall PA of Canadian adults may not have been impacted by the pandemic (Colley and Watt, 2022), many either chose to or were required to work from home, which could have influenced the type of responses garnered on our survey. Furthermore, there was much public demand for various policy initiatives in Canadian municipalities, such as the construction of temporary bike lanes, to facilitate commuting while adhering to social distancing recommendations (Buckner, 2020; Pike, 2020). Thus, the appetite for policy interventions for AT as reflected in our findings may have been affected by this temporary interruption to daily routines and concerns about avoiding close contact with others. Third, due to the sampling techniques employed, no Canadians from the territories were included in this sample. We recognize this as a limitation because those individuals residing in remote areas of northern Canada may have different perspectives on AT policy initiatives. Finally, though the reliability of survey panels may be questioned, much effort is made to ensure they deliver representative samples and similar types of crowd sourcing have demonstrated good reliability (Peer et al., 2017).

Conclusions

In the most comprehensive survey conducted on the topic to date, most policy actions to promote AT were supported by Canadians although the level of support varied by the type of policy actions and by demographics and self-reported AT behaviour. The challenge remains how to implement efficacious strategies that require a combination of public support and political will (Gelius et al., 2020; Raine et al., 2012), especially those that are considered more intrusive. An emphasis on issues that appeal to younger adults (e.g., climate change mitigation) along with demonstrated government leadership (Craddock et al., 2018) could be an effective approach for fostering further support for AT initiatives.

Funding Information

This paper draws on research supported by the Social Sciences and Humanities Research Council of Canada.

Competing Interests

The authors have no competing interests to declare.

Authors Contributions

JCS, GF, CC, & CCB conceptualized and drafted the proposal that funded the project and provided the data reported herein. JCS led the data collection and advised on all aspects of analysis. AM performed the statistical analyses. JCS, GF, and AM wrote the manuscript. All of the authors provided important intellectual content and feedback on the manuscript and gave approval for the version to be published.

References

1 Alessio, H. M., Bassett, D. R., Bopp, M. J., Parr, B. B., Patch, G. S., Rankin, J. W., Rojas-Rueda, D., Roti, M. W. and Wojcik, J. R. (2021). Climate change, air pollution, and physical inactivity: Is active transportation part of the solution? Medicine and Science in Sports and Exercise, 53(6), 1170–1178. DOI:  http://doi.org/10.1249/MSS.0000000000002569

2 Ashley, M. J., Cohen, J. E., Northrup, D. A. and Ferrence, R. G. (2001). How Canadian legislators view health promotion: Does party affiliation matter? Canadian Journal of Public Health, 92(1), 16–18. DOI:  http://doi.org/10.1007/BF03404836

3 Brand, C. (2021). Active travel’s contribution to climate change mitigation: Research summary and outlook. Active Travel Studies, 1(1), 1–13. DOI:  http://doi.org/10.16997/ats.1036

4 Bull, F. C., Bellew, B., Schӧppe, S. and Bauman, A. E. (2004). Developments in national physical activity policy: An international review and recommendations towards better practice. Journal of Science and Medicine in Sport, 7(1 Suppl), 93–104. DOI:  http://doi.org/10.1016/S1440-2440(04)80283-4

5 Burgess, J. A., Spence, J. C. and Wild, T. C. (2010). Reducing overestimated intentions and expectations for physical activity: The effect of a corrective entreaty. Psychology and Health, 25, 383–400. DOI:  http://doi.org/10.1080/08870440802491629

6 Buckner, D. (2020). Bike lanes installed on urgent basis across Canada during COVID-19 pandemic. CBC News, 7 June. https://www.cbc.ca/news/business/bike-lanes-covid-pandemic-canada-1.5598164.

7 Canadian Parks and Recreation Association/Interprovincial Sport and Recreation Council.(2015). A Framework for Recreation in Canada – 2015 – Pathways to Wellbeing. Ottawa, ON: Canadian Parks and Recreation Association. https://cpra.ca/wp-content/uploads/2021/04/FrameworkForRecreationInCanada_2016wcitation.pdf.

8 Colley, R. C. and Watt, J. E. (2022). The unequal impact of the COVID-19 pandemic on the physical activity habits of Canadians. Health Reports, 33(5), 22–33. DOI:  http://doi.org/10.25318/82-003-x202200500003-eng

9 Cradock, A. L., Barrett, J. L., Chriqui, J. F., Evenson, K. R., Valentine Goins, K., Gustat, J., Heinrich, K. M., Perry, C. K., Scanze, M., Schmid, T. L., Tabak, R. G., Umstattd Meyer, M. R. and Valko, C. (2018). Driven to support: Individual- and county-level factors associated with public support for active transportation policies. American Journal of Health Promotion, 32(3), 657–666. DOI:  http://doi.org/10.1177/0890117117738758

10 Diepeveen, S., Ling, T., Suhrcke, M., Roland, M. and Marteau, T. M. (2013). Public acceptability of government intervention to change health-related behaviours: A systematic review and narrative synthesis. BMC Public Health, 13(1), Article 756. DOI:  http://doi.org/10.1186/1471-2458-13-756

11 Gelius, P., Messing, S., Goodwin, L., Schow, D. and Abu-Omar, K. (2020). What are effective policies for promoting physical activity? A systematic review of reviews. Preventive Medicine Reports, 18, Article 101095. DOI:  http://doi.org/10.1016/j.pmedr.2020.101095

12 Götschi, T., de Nazelle, A., Brand, C. and on behalf of the PASTA Consortium.(2017). Towards a comprehensive conceptual framework of active travel behavior: A review and synthesis of published frameworks. Current Environmental Health Reports, 4, 286–295. DOI:  http://doi.org/10.1007/s40572-017-0149-9

13 Hamilton, L. C., Hartter, J. and Bell, E. (2019). Generation gaps in US public opinion on renewable energy and climate change. PLoS One, 14(7), Article e0217608. DOI:  http://doi.org/10.1371/journal.pone.0217608

14 Infrastructure Canada.(2021). National Active Transportation Strategy 2021–2026. https://www.infrastructure.gc.ca/alt-format/pdf/nats-snta/nats-strat-snta-en.pdf.

15 Liu, T., Shryane, N. and Elliot, M. (2022). Attitudes to climate change risk: Classification of and transitions in the UK population between 2012 and 2020. Humanities and Social Sciences Communications, 9, Article 279. DOI:  http://doi.org/10.1057/s41599-022-01287-1

16 McGetrick, J. A., Kongats, K., Raine, K. D., Voyer, C. and Nykiforuk, C. I. J. (2019). Healthy public policy options to promote physical activity for chronic disease prevention: Understanding Canadian policy influencer and general public preferences. Journal of Physical Activity & Health, 16(7), 565–574. DOI:  http://doi.org/10.1123/jpah.2018-0020

17 Michie, S., van Stralen, M. M. and West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6(1), Article 42. DOI:  http://doi.org/10.1186/1748-5908-6-4

18 Molner, B., Curtin, K. D., Kongats, K., Brown, J. A., Thomson, M., Atkey, K. and Nykiforuk, C. I. J. (2023). Policy influencer support for active transportation policy in two Canadian provinces: Implications for advocacy. Journal of Transport & Health, 28, Article 101537. DOI:  http://doi.org/10.1016/j.jth.2022.101537

19 Montez, J. K., Mehri, N., Monnat, S. M., Beckfield, J., Chapman, D., Grumbach, J. M., Zajacova, A. (2022). U.S. state policy contexts and mortality of working-age adults. PLoS ONE, 17(10), Article e0275466. DOI:  http://doi.org/10.1371/journal.pone.0275466

20 Nieuwenhuijsen, M. J. and Khreis, H. (2016). Car free cities: Pathway to healthy urban living. Environment International, 94, 251–262. DOI:  http://doi.org/10.1016/j.envint.2016.05.032

21 Olsen, J. R., Mitchell, R., Mutrie, N., Foley, L. and Ogilvie, D. (2017). Population levels of, and inequalities in, active travel: A national, cross-sectional study of adults in Scotland. Preventive Medicine Reports, 8, 129–134. DOI:  http://doi.org/10.1016/j.pmedr.2017.09.008

22 Nuffield Council on Bioethics.(2007). Policy process and practice. London. https://www.nuffieldbioethics.org/publications/public-health/guide-to-the-report/policy-process-and-practice.

23 ParticipACTION.(2021). ParticipACTION Report Card on Physical Activity for Adults: Moving toward a better normal. www.participaction.com/wp-content/uploads/2022/09/2021-ParticipACTION-Report-Card-on-Physical-Activity-for-Adults.pdf.

24 Peer, E., Brandimarte, L., Samat, S. and Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163. DOI:  http://doi.org/10.1016/j.jesp.2017.01.006

25 Perrotta, K., Macfarlane, R., Daniel, K., Mee, C. and Doyle, H. (2021). Invest in active travel to create healthy, green & just communities(Active Travel Backgrounder). Canadian Health Association for Sustainability and Equity, Canadian Public Health Association, and the Ontario Public Health Association. https://www.cpha.ca/sites/default/files/uploads/resources/climateaction/hgjr-active-travel-backgrounder-e.pdf.

26 Pike, H. (2020). Calgary bike advocates urge city to invest in infrastructure. CBC News, 6 June. https://www.cbc.ca/news/canada/calgary/calgary-biking-naheed-nenshi-gary-millard-kimberley-nelson-1.5601423.

27 Plotnikoff, R. C., Wright, M. F. and Karunamuni, N. (2004). Knowledge, attitudes and behaviours related to climate change in Alberta, Canada: Implications for public health policy and practice. International Journal of Environmental Health Research, 14(3), 223–229. DOI:  http://doi.org/10.1080/0960312042000218633

28 Public Health Agency of Canada.(2018). A Common Vision for Increasing Physical Activity and Reducing Sedentary Living in Canada: Let’s Get Moving. Government of Canada. https://www.canada.ca/en/public-health/services/publications/healthy-living/lets-get-moving.html#sp.

29 Raine, K. D., Muhajarine, N., Spence, J. C., Neary, N. E. and Nykiforuk, C. I. J. (2012). Coming to consensus on policy to create supportive built environments and community design. Canadian Journal of Public Health, 103(Suppl. 3), S5–S8. DOI:  http://doi.org/10.1007/BF03403828

30 Raine, K. D., Nykiforuk, C. I., Vu-Nguyen, K., Nieuwendyk, L. M., VanSpronsen, E., Reed, S. and Wild, T. C. (2014). Understanding key influencers’ attitudes and beliefs about healthy public policy change for obesity prevention. Obesity, 22, 2426–2433. DOI:  http://doi.org/10.1002/oby.20860

31 Rivers, N. and Plumptre, B. (2018). The effectiveness of public transit tax credits on commuting behaviour and the environment: Evidence from Canada. Case Studies on Transport Policy, 6(4), 651–662. DOI:  http://doi.org/10.1016/j.cstp.2018.08.004

32 Smith, T. W., Kim, J. and Son, J. (2017). Public attitudes toward climate change and other environmental issues across countries. International Journal of Sociology, 47(1), 62–80. DOI:  http://doi.org/10.1080/00207659.2017.1264837

33 Sport Canada.(2012). Canadian Sport Policy 2012. https://sirc.ca/wp-content/uploads/files/content/docs/Document/csp2012_en.pdf.

34 Statistics Canada.(2020). Commuting to work during covid-19. The Daily(Canadian Perspective Survey Series). https://www150.statcan.gc.ca/n1/pub/45-28-0001/2020001/article/00069-eng.htm.

35 United Nations.(n.d.). Make the SDGs a reality: Sustainable transport. Department of Economic and Social Affairs. https://sdgs.un.org/topics/sustainable-transport.

36 Whitfield, G. P., McKenzie, B., Graff, K. A., and Carlson, S. A. (2020). Monitoring state-level changes in walking, biking, and taking public transit to work – American Community Survey, 2006 and 2017. Preventing Chronic Disease, 17, Article E115. DOI:  http://doi.org/10.5888/pcd17.200097

37 Wilkie, S., Townshend, T., Thompson, E. and Ling, J. (2018). Restructuring the built environment to change adult health behaviors: A scoping review integrated with behavior change frameworks. Cities & Health, 2(2), 198–211. DOI:  http://doi.org/10.1080/23748834.2019.1574954

38 Young, D. R., Cradock, A. L., Eyler, A. A., Fenton, M., Pedroso, M., Sallis, J. F., Whitsel, L. P. and on behalf of the American Heart Association Advocacy Coordinating Committee.(2020). Creating built environments that expand active transportation and active living across the United States: A policy statement from the American Heart Association. Circulation, 142(11), e167–e183. DOI:  http://doi.org/10.1161/CIR.0000000000000878

39 Yun, L., Vanderloo, L., Berry, T., Latimer-Cheung, A., O’Reilly, N., Rhodes, R., Spence, J. C., Tremblay, M. and Faulkner, G. (2018). Assessing the social climate of physical(in)activity in Canada. BMC Public Health, 18, Article 1301. DOI:  http://doi.org/10.1186/s12889-018-6166-2

40 Yun, L., Vanderloo, L., Berry, T., Latimer-Cheung, A., O’Reilly, N., Rhodes, R., Spence, J. C., Tremblay, M. and Faulkner, G. (2019). Political orientation and public attributions for the causes and solutions of physical inactivity in Canada: Implications for policy support. Frontiers Public Health, 7, Article 153. DOI:  http://doi.org/10.3389/fpubh.2019.00153