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. 2023 Mar 24;81:103019. doi: 10.1016/j.healthplace.2023.103019

Canadian children's independent mobility during the COVID-19 pandemic: A national survey

Richard Larouche a,∗, Mathieu Bélanger b, Mariana Brussoni c,d,e,f, Guy Faulkner g, Katie Gunnell h, Mark S Tremblay i,j,k
PMCID: PMC10036309  PMID: 36996593

Abstract

Independent mobility (IM) is associated with children's physical activity and indicators of social, motor, and cognitive development. We surveyed Canadian parents of 7- to 12-year-olds (n = 2291) about social-ecological correlates of IM in the second wave of COVID-19 (December 2020). We used multi-variable linear regression models to identify correlates of children's IM. Our final model (R2 = 0.353) included four individual-, eight family-, two social environment- and two built environment-level variables. The correlates of boys' and girls' IM were similar. Our findings suggest that interventions to support children's IM in a pandemic context should target multiple levels of influence.

Keywords: Children's independent mobility, Correlates, Social-ecological model, Coronavirus, Parents

1. Introduction

Children's independent mobility (IM) represents their freedom to move around in public spaces without adult supervision (Hillman et al., 1990). Previous research shows that children with higher IM accumulate more physical activity (PA), spend more time outdoors, and are more likely to engage in active transportation (AT) to/from school (Gray et al., 2014; Page et al., 2009, 2010; Schoeppe et al., 2013; Stone et al., 2014). IM can also contribute to children's social, motor, and cognitive development (Riazi and Faulkner, 2018). Over the last century, large decreases in IM have been reported in many countries (Fyhri et al., 2011; Gaster, 1992; Hillman et al., 1990; Kyttä et al., 2015; Schoeppe et al., 2016; Shaw et al., 2013). In parallel, the proportion of children who walk or cycle to school has declined markedly (Gray et al., 2014; Larouche, 2018; McDonald et al., 2011).

Research on the correlates of IM has expanded remarkably over the last 20 years (Marzi and Reimers, 2018; Riazi et al., 2022). A recent systematic review identified several consistent correlates of IM, including child age, race/ethnicity, having siblings, home ownership, proximity to parks, access to local destinations, and parental attitudes about IM and concerns about traffic safety (Riazi et al., 2022). Interestingly, this review obtained inconsistent findings for parent-perceived neighbourhood safety and urban form variables such as neighbourhood walkability, type of home (e.g., single-vs. multi-family), and level of urbanization (Riazi et al., 2022). Although lower density neighbourhoods may be viewed as safer by parents, they may provide fewer opportunities to engage in AT, play outdoors, or use public transit (Kyttä, 2004). In Canada, most studies on IM have been conducted in larger cities, such as Toronto and Vancouver (Stone et al., 2014; Mitra et al., 2014; Vlaar et al., 2019; Riazi et al., 2021), underscoring a need for studies that include smaller cities and rural areas where opportunities for IM may differ substantially.

More recently, the COVID-19 pandemic had a profound impact on children's health behaviours with a scoping review including 110 empirical studies consistently indicating decreases in PA, increases in screen time, and shifts to later bed and wake times (Paterson et al., 2021). The closure of parks and other recreational facilities may have reduced children's IM, outdoor play, and PA (Moore et al., 2020; Riazi et al., 2021). Yet, very few studies have focussed specifically on IM during the pandemic. In a national survey, 56.6% of Canadian parents perceived no changes in their child's IM since the outbreak of COVID-19, but 32.8% reported a decrease and only 10.6% reported an increase (Larouche et al., 2022). In this context, a more in-depth investigation of the correlates of IM during COVID-19 is warranted to inform strategies to support IM, particularly during pandemics, which are likely to happen more frequently in the future (Myers and Frumkin, 2020; Patz et al., 2014).

Therefore, we investigated the correlates of IM during the second wave of the COVID-19 pandemic (December 2020) with a national sample of Canadian parents of 7- to 12-year-olds. Based on previous research, this age range corresponds to when IM is most variable and it appears to be a crucial period for the development of IM (Shaw et al., 2015). Our study was guided by the social-ecological model based on previous research on the correlates of IM (Marzi and Reimers, 2018; Mitra, 2013; Vlaar et al., 2019; Riazi et al., 2019; Riazi et al., 2022). Social-ecological models posit that behaviours are determined by multiple levels of influence, and they have been widely adopted in PA research (Bauman et al., 2012; Sallis et al., 2006). In our analyses, we considered individual (e.g., characteristics of the child such as age, gender, (dis)ability, etc.), family (e.g., characteristics of other members of the household such as parent respondent's gender and concerns about COVID-19, sociodemographic characteristics), social environment (e.g., social cohesion and perceived crime and traffic safety), and built environment (e.g., neighbourhood walkability and region of Canada) factors.

2. Materials and methods

We employed baseline data from a national longitudinal study. We hired Léger (leger360.com), a market survey firm that assembled an online panel including >450,000 Canadians who volunteer to participate in online studies, to collect baseline data in December 2020. The participants (2291 parents of 7- to 12-year-olds) were recruited to be demographically representative of the target population (Göritz, 2007) based on education and household income. Only parents who could complete the survey in English or French, had a child aged 7–12 years, and agreed to be invited again for 6-, 12-, and 18-month follow-ups were eligible to participate. If parents had multiple children in the target age range, they were asked to answer the survey for the child whose name comes first alphabetically. The survey was self-administered using a computer-aided web interviewing method. As per Léger's usual practice, parents were provided with a modest $3 (Canadian) compensation for each survey. The study protocol was approved by participating research ethics committees and parents consented electronically after reviewing an information letter.

During data collection, most schools in Canada were open with policies and practices to minimize contact between students from different classrooms (e.g., class bubbles, staggered breaks), and some schools offered online learning (Breton et al., 2022). Although education is a provincial responsibility, school policies were relatively consistent across Canada at the time of data collection (Breton et al., 2022). However, there were substantial regional differences in access to parks and recreational facilities that could have affected IM, especially during the first wave of the pandemic (de Lannoy et al., 2020, Paterson et al., 2021).

2.1. Measures

2.1.1. IM index

We used 6 questions assessing the “mobility licenses” proposed by Hillman et al. (1990) that reflect permissions for the child to do the following activities on their own: travel home from school, go to other places within walking distance, cross main roads, cycle on main roads, use local buses, and go out after dark. Responses were dichotomized for each item as ‘yes’ (1) or ‘no’ (0) and summed to compute an IM index ranging from 0 to 6, with higher values indicating greater IM. In a pilot study, this index showed good test-retest reliability based on both child and parent answers (intraclass correlation coefficient [ICC] = 0.76 and 0.77, respectively) and convergent validity between children and parent responses (ICC = 0.76) (Larouche et al., 2017).

2.1.2. COVID-19 related questions

Our survey (provided in Supplementary Material 1) was designed to assess movement behaviors in the week before the survey. We asked parents to report how their child attended school in the last week (in person, online, blended, and non applicable). The latter category would include homeschooled children and those who did not attend school in the week prior to the survey. We also included a 3-level item about parental concerns with COVID-19 (not concerned, somewhat concerned, very concerned).

2.1.3. Other potential correlates of IM

We collected data on household income and parental employment status using standard questions from Léger. Other sociodemographic and household characteristics assessed included: age and gender of the child and the parent, province/territory of residence, type of home (low rise apartment/condo, high rise apartment/condo, townhouse, semi-detached house, detached house, other), dog ownership, number of children and adults in the household, whether the child had a mobile phone or a disability/chronic condition (yes or no, with an option to specify), motor vehicle ownership (zero, one, two or more), and the number of years that the child has lived in Canada (2 years or less; 3–5 years; 6 years or more; born in Canada).

Because many parents drive their children to school on the way to work (Faulkner et al., 2010; McDonald and Aalborg, 2009), we assessed parents’ travel mode to work with the question “On a typical day, the MAIN part of your journey FROM home to work is made by” (walking; bicycle; bus, train, streetcar, subway, or boat/ferry; car, motorcycle, or moped; “other, please specify”; and “Not applicable; I work from home or do not work”). This question was adapted from the school travel mode item from the Canadian site of the Health Behavior in School-aged Children survey (Gropp et al., 2012).

We assessed parental tolerance to risk with an adapted version of the Tolerance of Risk in Play Scale (TRiPS), which has shown a person reliability index of 0.87 and good construct validity (Hill and Bundy, 2014). The TRiPS has been rigorously translated into French by Chabot et al. (2017). We made minor wording changes to facilitate comprehension by English- and French-Canadian parents (e.g., added the term “forest” to an item from the original Australian version about playing in the “bush” and clarified that playing “chase” is what Canadians know as playing “tag”). The TRiPS had excellent internal consistency in our study (α = 0.90). We used a validated 5-item subscale from Sampson and colleagues’ (1997) collective efficacy scale to measure the constructs of social cohesion. This subscale had satisfactory internal consistency in the present study (α = 0.77). We assessed parental concerns about traffic and crime with subscales from the Neighbourhood Environment Walkability Scale – Youth (Rosenberg et al., 2009). In the present study, the respective α values were 0.53 and 0.90 for these subscales. These subscales were translated into French in a previous study (Katzmarzyk et al., 2013).

Finally, we asked participants to provide their 6-digit postal code to determine the walkability of their home neighbourhood based on the Canadian Active Living Environments (Can-ALE) database (Herrmann et al., 2019). The database includes z-scores of intersection density, dwelling density, and points of interest. From these z-scores, Herrmann et al. (2019) used k-medians cluster analyses to derive a 5-point variable categorizing neighbourhood walkability from the least (1) to the most (5) walkable (referred to as the Can-ALE class). Consistent with previous work by Colley et al. (2019a, 2019b), we used the latter measure, which is available at the dissemination area level across Canada. Dissemination areas represent small geographic units defined by Statistics Canada that typically include a population of 400–700 people. We used a bespoke Python™ script (Python Software Foundation, Beaverton, OR) to link postal codes with the corresponding dissemination area.

2.2. Data treatment

Consistent with previous studies on changes in PA associated with COVID-19, we restricted our sample to parents aged 20–64 years and parents and children who identified as boy or girl (Mitra et al., 2020; Moore et al., 2020). These restrictions minimized problems associated with small cell sizes and led to the removal of 33 participants. Based on the observed distribution, we recoded household income into three categories (CAD$39,999 or less; $40,000–99,999; $100,000 or more). We also recoded parent occupation (working full-time, homemaker, or others) and type of home (detached/semi-detached vs. others). We also classified participants’ province/territory of residence into five geographical regions, namely Pacific (British Columbia and Yukon), Prairies (Alberta, Saskatchewan, Manitoba, Northwest Territories), Ontario, Quebec, and Atlantic (Newfoundland and Labrador, New Brunswick, Prince Edward Island, Nova Scotia). For years living in Canada, we combined the categories 2 years or less and 3–5 years due to the small number of recent immigrants. Finally, we combined the Can-ALE classes 4 and 5 given the small number of participants living in the most walkable neighbourhoods, which based on Herrmann et al. (2019), appear uncommon outside of large cities.

2.3. Statistical analyses

We first computed descriptive statistics, including means and standard deviations for continuous variables and frequencies and percentages for categorical variables. Second, we used chi-square and t-tests to examine differences in descriptive statistics by gender and between participants included in the analytic sample (n = 1841) vs. excluded. Next, we ran bivariate linear regression models examining potential correlates of IM using the general linear model procedure in SPSS, version 28 (IBM, Armonk, NY). We retained variables associated with IM at a liberal threshold of p < 0.20 for potential inclusion in multivariable models (Gropp et al., 2012). Then, we used a backward selection process to achieve a more parsimonious multivariable model by removing non-significant variables (p > 0.05). We excluded missing data listwise. In the final model, we estimated the proportion of variance explained by each individual variable and by the whole model with the partial eta-squared (ηp 2) and R2 statistics, respectively. As per Cohen (1988, 1992), we interpreted the following values as representing small (η2 = 0.01; R2 = 0.02), medium (η2 = 0.06; R2 = 0.15), and large (η2 = 0.14; R2 = 0.35) effect sizes.

We found no extreme outliers and there was no evidence of multicollinearity in the final multivariable model (all tolerance values ≥ 0.601 and variance inflation factor values ≤ 1.665). However, the variance of the residuals indicated heteroskedasticity (Koenker test p < 0.0001), so we used a macro developed by Daryanto (2020) to calculate robust standard errors and confidence intervals.

3. Results

Table 1 provides descriptive statistics for the full sample and with stratification by gender. On average, children were 9.9 ± 1.7 years of age and had 1.9 ± 1.7 mobility licenses. 28.4% of children had zero licenses. Overall, 35.6% of parent respondents identified as men and 51.6% of included children identified as boys. In the week before the survey, 27.9% of children did not attend school in person and 24.8% of parents did not commute to work or school. IM did not differ significantly between boys and girls in bivariate models (1.9 vs. 1.8; p = 0.084). Parents of girls (vs. boys) had marginally higher crime safety concern scores (2.24 vs. 2.18; p = 0.048). A higher proportion of boys had a disability/chronic condition (13.2 vs 8.6%, p < 0.001) and a higher proportion of parent respondents who answered the survey for a boy identified as man (37.7 vs. 33.3%; p = 0.032). Conversely, a greater proportion of girls had a mobile phone (35.3 vs. 30.4%, p = 0.012) and more parents of girls had ≥2 vehicles (57.7 vs 50.0%, p < 0.001).

Table 1.

Descriptive characteristics of the sample, stratified by gender.

Variable Whole sample (n = 2258)
 Girls (n = 1092)
 Boys (n = 1166)
Frequency (%) Mean (SD) Frequency (%) Mean (SD) Frequency (%) Mean (SD)
IM index (number of licenses) 1.9 (1.7) 1.8 (1.6) 1.9 (1.7)
Child age 9.9 (1.7) 9.9 (1.7) 9.9 (1.7)
Number of adults in household 2.0 (0.7) 2.0 (0.7) 2.0 (0.6)
Number of children in household 2.0 (1.0) 2.0 (1.0) 2.0 (0.9)
TRiPS scale 16.2 (6.5) 16.3 (6.5) 16.2 (6.5)
Social cohesion scale 3.5 (0.7) 3.5 (0.8) 3.5 (0.7)
Traffic safety scale 2.5 (0.6) 2.5 (0.7) 2.5 (0.6)
Crime safety scale* 2.2 (0.8) 2.2 (0.8) 2.2 (0.8)
Parent gender*
 Man 803 (35.6) 364 (33.3) 439 (37.7)
 Woman 1455 (64.4) 728 (66.7) 727 (62.3)
Household income (n = 2054)
 $39,999 or less 256 (12.5) 124 (12.4) 132 (12.5)
 $40,000 to $99,999 1018 (49.6) 486 (48.8) 532 (50.3)
 $100,000 or more 780 (38.0) 386 (38.8) 394 (37.2)
Region
 Pacific 261 (11.6) 123 (11.3) 138 (11.8)
 Prairies 435 (19.3) 218 (20.0) 217 (18.6)
 Ontario 878 (38.9) 432 (39.6) 446 (38.3)
 Quebec 513 (22.7) 249 (22.8) 264 (22.6)
 Atlantic 171 (7.6) 70 (6.4) 101 (8.7)
School delivery
 In person 1629 (72.1) 774 (70.9) 855 (73.3)
 Blended 118 (5.2) 57 (5.2) 61 (5.2)
 Online 380 (16.8) 198 (18.1) 182 (15.6)
 N/A (e.g., home-schooled) 131 (5.8) 63 (5.8) 68 (5.8)
Disability or chronic condition*
 No 2010 (89.0) 998 (91.4) 1012 (86.8)
 Yes 248 (11.0) 94 (8.6) 154 (13.2)
Child owns a mobile phone*
 No 1518 (67.2) 706 (64.7) 812 (69.6)
 Yes 740 (32.8) 386 (35.3) 354 (30.4)
 Type of home
 Other 597 (26.4) 270 (24.7) 327 (28.0)
 Detached or semi-detached 1661 (73.6) 822 (75.3) 839 (72.0)
Dog ownership
 Yes 869 (38.5) 418 (38.3) 451 (38.7)
 No 1389 (61.5) 674 (61.7) 715 (61.3)
Employment (n = 2246)
 Work full-time 1430 (63.7) 689 (63.4) 741 (63.9)
 Homemaker 244 (10.9) 128 (11.8) 116 (10.0)
 Other 572 (25.5) 269 (24.8) 303 (26.1)
Concerns about COVID-19
 Not concerned 311 (13.8) 155 (14.2) 156 (13.4)
 Somewhat concerned 1226 (54.3) 594 (54.4) 632 (54.2)
 Very concerned 721 (31.9) 343 (31.4) 378 (32.4)
Time since child lived in Canada
 5 years or less 107 (4.7) 54 (4.9) 53 (4.5)
 6 years or more 297 (13.2) 145 (13.3) 152 (13.0)
 Born in Canada 1854 (82.1) 893 (81.8) 961 (82.4)
Vehicle ownership*
 No 136 (6.0) 55 (5.0) 81 (6.9)
 One 909 (40.3) 407 (37.3) 502 (43.1)
 Two or more 1213 (53.7) 630 (57.7) 583 (50.0)
Parent travel mode to work
 Active 293 (13.0) 139 (12.7) 154 (13.2)
 Motorized 1404 (62.2) 685 (62.7) 719 (61.7)
 N/A 561 (24.8) 268 (24.5) 293 (25.1)
Walkability (Can-ALE class; n = 1971)
 1 567 (28.8) 274 (28.8) 293 (28.7)
 2 630 (32.0) 318 (33.5) 312 (30.6)
 3 512 (26.0) 250 (26.3) 262 (25.7)
 4-5 262 (13.3) 108 (11.4) 154 (15.1)
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Note: Descriptive statistics were performed with children and parents identifying as boy/man or girl/woman and parents aged 20–64 years (n = 2258). Can-ALE: Canadian Active Living Environments scale; IM: independent mobility; TRiPS: tolerance to risk in play scale. * denotes statistically significant differences between boys and girls (p < 0.05).

A total of 1841 parents (80.4% of the total sample) provided data for all variables included in the multi-variable model. The variables with the most missing data were Can-ALE class due to missing postal code (13.1%) and household income (9.2%). Supplementary Material 2 summarizes differences between included and excluded participants. Of all the variables examined, only child and parent gender and parental employment met Cohen's thresholds for small effect size (Cohen's d = 0.2 or Cramer's V = 0.1 for t-tests and chi-square tests, respectively). As explained in the data treatment section, participants not identifying as boy/man or girl/women were excluded due to small cell sizes. Excluded participants had lower IM and household income, and their parents had greater concerns about traffic. A higher proportion of excluded participants attended school in person in the week prior to the survey, lived in detached/semi-detached houses and in Ontario or the Prairies. Conversely, the proportion of parents identifying as man, working full-time, and living in Can-ALE class 2, 4 and 5 neighbourhoods was lower among excluded participants.

3.1. Multivariable model of the correlates of IM by levels of the social-ecological model

Table 2 summarizes our final multi-variable regression model, including the unstandardized regression coefficients (B, expressed in mobility licenses), 95% confidence intervals (CI), p-values, and partial η2. This model explained 35.3% of the variance in IM.

Table 2.

Multivariable model of the correlates of independent mobility.

Variable B 95% CI p ηp2
Child gender – boy (ref: girl) 0.15 0.03, 0.28 0.016 0.003
Child age – each additional year 0.30 0.26, 0.35 < 0.001 0.104
Child has a disability/chronic illness – yes (ref: no) −0.23 −0.43, -0.03 0.023 0.003
Child has a mobile phone – yes (ref: no) 0.64 0.48, 0.80 < 0.001 0.039
Parent gender – man (ref: woman) 0.24 0.10, 0.38 < 0.001 0.006
Parent employment – homemaker (ref: full-time work) −0.36 −0.57, -0.14 0.001 0.005
Parent employment – other (ref: full-time work) −0.14 −0.30, 0.03 0.101 0.001
Parent travel mode to work – active (ref: motorized) 0.58 0.35, 0.81 < 0.001 0.018
Parent travel mode to work – N/A (ref: motorized) −0.01 −0.15, 0.13 0.868 <0.001
Household income – ≤$39,999 (ref: ≥$100,000) 0.23 −0.02, 0.48 0.070 0.002
Household income – $40,000–99,999 (ref: ≥ $100,000) 0.23 0.08, 0.37 0.002 0.005
Household vehicle ownership – 0 (ref: ≥ 2 vehicles) 0.35 0.05, 0.65 0.021 0.003
Household vehicle ownership – 1 (ref: ≥ 2 vehicles) 0.30 0.15, 0.45 < 0.001 0.009
Household dog ownership – yes (ref: no) 0.34 0.20, 0.47 < 0.001 0.013
Parent concern with COVID-19 – not concerned (ref: very concerned) 0.30 0.07, 0.52 0.009 0.004
Parent concern with COVID-19 – somewhat concerned (ref: very concerned) 0.06 −0.09, 0.20 0.446 <0.001
Parent tolerance to risk in play scale – each unit increase 0.04 0.03, 0.05 < 0.001 0.031
Traffic safety scale – each unit increase in concerns −0.17 −0.27, -0.06 0.002 0.005
Crime safety scale – each unit increase in concerns −0.29 −0.40, -0.17 < 0.001 0.016
Region – Pacific (ref: Atlantic) 0.05 −0.24, 0.34 0.729 <0.001
Region – Prairies (ref: Atlantic) 0.34 0.08, 0.61 0.011 0.003
Region – Ontario (ref: Atlantic) 0.10 −0.15; 0.34 0.442 <0.001
Region – Quebec (ref: Atlantic) 0.52 0.26, 0.78 < 0.001 0.006
Can-ALE class 1 neighbourhood (ref: classes 4–5) 0.06 −0.17, 0.29 0.622 <0.001
Can-ALE class 2 neighbourhood (ref: classes 4–5) 0.30 0.08, 0.52 0.007 0.004
Can-ALE class 3 neighbourhood (ref: classes 4–5) 0.15 −0.07, 0.37 0.185 0.001
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Note: B represents unstandardized regression coefficients expressed in mobility licenses. ηp2 = partial eta squared. Boldface indicates statistical significance (p < 0.05). Can-ALE: Canadian Active Living Environment (classes 4–5 represent the most walkable neighbourhoods). Model R2: 0.353.

3.1.1. Individual level

At the individual level, boys had higher IM than girls (B = 0.15; 95% CI = 0.03, 0.28) and IM increased with each year of age (B = 0.30; CI = 0.26, 0.34). Children with a disability/chronic condition had lower IM (B = −0.23; CI = −0.43, −0.04), whereas children with a mobile phone had higher IM (B = 0.64; CI = 0.49, 0.79).

3.1.2. Family level

Children whose parents were not concerned (vs. very concerned) about COVID-19 had higher IM (B = 0.30; CI = 0.08, 0.51). Lower household income was associated with higher IM, though the difference was only significant when comparing children in families earning $40,000–99,999 vs. ≥$100,000/year (B = 0.23; CI = 0.08, 0.37). Children whose parent respondent identified as man (vs. woman) had higher IM (B = 0.24, 95% CI = 0.10, 0.38) whereas children whose parent identified as homemaker had lower IM (B = −0.36; CI = −0.59, −0.13). Household dog ownership was associated with higher IM (B = 0.34; CI = 0.20, 0.47). Children from households owning no (B = 0.35; CI = 0.06, 0.65) or only one motor vehicle (B = 0.30; CI = 0.16, 0.45) had higher IM than those from multi-vehicle households. Similarly, children whose parent respondent actively commuted to work had higher IM (B = 0.58; CI = 0.38, 0.78). Finally, each unit increase in risk tolerance based on the 30-item TRiPS scale was associated with higher IM (B = 0.04; CI = 0.03, 0.05).

3.1.3. Social environment level

Each unit increase in the crime (B = −0.29; CI = −0.39, −0.18) and traffic safety (B = −0.17, CI = −0.28, −0.06) concern scales were associated with lower IM.

3.1.4. Built environment level

Compared to children living in the most walkable neighbourhoods (Can-ALE classes 4 and 5), those living in Can-ALE class 2 neighbourhoods had higher IM (B = 0.30; CI = 0.09, 0.51). Children from Québec (B = 0.52; CI = 0.25, 0.78) and the Prairies (B = 0.34; CI = 0.07, 0.62) had higher IM than those living in the Atlantic region.

3.2. Gender-stratified models

Gender-stratified models are provided in Supplementary Material 3 and 4. In general, point estimates and the proportion of variance explained by the models were similar, but fewer variables were significantly associated with IM after stratification. The effect of having a disability/chronic condition, no vehicle (vs. ≥2) and living in the Prairies (vs. Atlantic region) were only significant in the pooled model. Boys, but not girls, had higher IM when the parent respondent identified as man or was not concerned (vs. very concerned) with COVID-19. Girls, but not boys, had higher IM if they lived in Can-ALE class 2 neighbourhoods vs. class 4–5 neighbourhoods.

4. Discussion

We examined the correlates of IM during the second wave of the COVID-19 pandemic among a national sample of Canadian parents. Overall, our final model explained a large proportion of the variance in IM based on Cohen's (1988, 1992) thresholds. We identified many modifiable characteristics that were independently associated with IM. Consistent with a previous systematic review (Riazi et al., 2022), we found that the correlates of IM correspond to multiple levels of influence of the social-ecological model, suggesting that multilevel interventions may be needed to halt or reverse the commonly reported decline in IM.

4.1. Individual level

Of all variables included in the final model, age had by far the largest effect size. Age is likely to be related to parents' confidence in their child's ability to travel safely in their neighbourhood, which is consistently associated with IM (Riazi et al., 2022). Although a child's age is not modifiable, the age at which children acquire mobility licenses has increased over time (Dodd et al., 2021; Hillman et al., 1990; Schoeppe et al., 2016; Shaw et al., 2013). Furthermore, a 16-country study found substantial differences between countries when examining mobility licenses by age (Shaw et al., 2015), suggesting that activities considered “age-appropriate” by parents vary with social norms, which can be challenged. For instance, the generational decline in IM (Gaster, 1991; Hillman et al., 1990; Shaw et al., 2013), suggests that the social norm has become less favourable for IM. Parents interviewed by Francis et al. (2017) reported that the fear of judgment from other parents led them to restrict their child's IM, whereas other respondents mentioned that observing other parents granting IM encouraged them to do the same. In the context of COVID-19, a qualitative study suggested that some parents restricted their child's IM based on the perception that they might not adhere to physical distancing guidelines (Riazi et al., 2021). In this regard, ensuring that children adhere to these guidelines may have been perceived as “good parenting” during the pandemic.

In our multivariable model only, boys had slightly higher IM than girls. In their systematic review, Riazi et al. (2022) found mixed associations between gender and IM, with some studies showing that boys had higher IM and others showing no difference. Of particular interest, our gender-stratified models suggested that the correlates of boys' and girls’ IM were generally similar.

We found that children who had a mobile phone had greater IM and phone ownership had the second largest effect size in our multivariable model. Phones may help reduce parental safety concerns (Riazi et al., 2019) as well as represent a way for parents to monitor their child (Malone, 2007). Yet, the use of mobile phones calls into question the nature of independence being conferred if a child's movements are being monitored by a parent. Future research could examine the balance required in mobile phones supporting IM by alleviating parental concerns while not acting counter to the developmental goals of IM and public health guidelines to limit screen time (Bull et al., 2020).

We found that children with a disability or chronic condition had lower IM than their peers, but this association was only significant in the pooled multivariable model. In a review of the AT and IM literature, Ross and Buliung (2018) demonstrated that disability has been largely overlooked compared to other social determinants of health. The pandemic may have created additional barriers to IM for children with disabilities, which may be related to accessibility and/or greater vulnerability to COVID-19 (e.g., for children with asthma and other cardiorespiratory conditions). Clearly, future research needs to examine how to support IM among children with different types of disability/chronic conditions.

In the context of COVID-19, about a quarter of children and parents did not commute to work or school. Yet, we found no association between how children attended school in the week prior to the survey (e.g., in-person, online) and their IM. This may suggest that short-term changes in school delivery method may not affect IM, at least when it is operationalized as mobility licenses.

4.2. Family level

In our final multivariable model, eight family-level variables were independently associated with IM: household income, vehicle ownership, dog ownership, and the parent respondent's gender, employment, travel mode to work, concerns about COVID-19, and tolerance to risk. Notably, the majority of these variables are under the control of parents. Collectively, our findings are consistent with the view that parents are “gatekeepers” of their child's IM (Mitra, 2013), suggesting that IM interventions should pay particular attention to familial factors.

It is notable that greater concerns about COVID-19 were independently associated with less IM. Similarly, Larouche et al. (2022) observed that such concerns were associated with greater odds of parent-perceived declines in AT and IM since the pandemic outbreak. Qualitative findings by Riazi et al. (2021) suggested that parents restricted their child's IM due to concerns about their ability to follow COVID-19 guidelines. Taken together, these findings suggest a potential need to raise awareness about the benefits of IM, the potential for activities like AT and outdoor play to be done in compliance with public health recommendations, and the role of PA for minimizing the risk and severity of infections (Lee et al., 2022).

We found that lower household income was associated with higher IM. This is interesting from a social determinants of health perspective given that the mobility licenses correspond to relatively inexpensive forms of PA. Independent from income, children whose parents identified as homemakers had lower IM than those whose parents working full-time. Homemakers could have more time to escort their child to various places, potentially delaying the development of IM.

Our finding that children whose parents drove to work had lower IM appears logical given the role that perceived time savings play in parental decision-making related to school travel mode and accompaniment (Faulkner et al., 2010; McDonald and Aalborg, 2009). Furthermore, adults tend to underestimate the time needed to commute by car and overestimate the time that would be required if using other travel modes (Shannon et al., 2006). Such estimation bias could increase the odds that parents drive children to school on the way to work. Interestingly, we found that parents' travel mode to work and household vehicle ownership were independently associated with IM. Beyond commuting, owning more vehicles could increase opportunities to escort children to both organized and unorganized activities. Surveys in four European countries also highlighted the contribution of organized leisure activities for children to time pressure and increased car use, especially among families owning more cars (Fyhri et al., 2011; Hjorthol and Fyhri, 2009). Hence, greater organization of children's leisure time appears to come at the expense of IM and AT.

Consistent with a previous multi-site Canadian study (Riazi et al., 2019), we observed that dog ownership was positively associated with IM. The company of a dog could increase parental confidence in granting more IM to their child, while encouraging PA in children and adults alike (Christian et al., 2013). We also found that IM was higher when the parent respondent identified as man, especially for boys. Although the literature is not consistent in this regard (Riazi et al., 2022), our findings appear to concur with qualitative research suggesting that fathers are more comfortable with risk-taking than mothers (Brussoni et al., 2013).

We found that greater parental tolerance to risk was associated with higher IM with a small-to-medium effect size in our fully adjusted model. Until recently, this concept has been overlooked in the IM literature, but our findings concur with a British survey indicating that children whose parents had greater tolerance to risk were allowed to play outdoors unsupervised at an earlier age (Dodd et al., 2021). Recent and potentially scalable interventions have shown that parents' and teachers’ perceptions of risk can be reframed to support outdoor play and PA (Brussoni et al., 2021; Bundy et al., 2017).

4.3. Social environment

Consistent with a recent systematic review, we observed that parental concerns about traffic safety were associated with less IM (Riazi et al., 2022). The same review rated the evidence for associations between IM and parental perceptions of social cohesion and crime safety as “indeterminate”. In our study, greater parental concerns about crime was associated with lower IM, whereas greater social cohesion was associated with higher IM only in bivariate models (data not shown). To mitigate safety concerns, potential targets for intervention may include implementing traffic calming measures and lower speed limits (Riazi et al., 2022; Tranter, 2018), and addressing perceptions of risk (Riazi et al., 2022) that may partly be shaped by social norms and the media (Francis et al., 2017).

4.4. Built environment

We observed that children living in Québec and the Prairie provinces had more IM than those in the Atlantic provinces. When stratifying by gender, this association remained significant only for Québec vs. Atlantic provinces. Regional differences in COVID-19 measures and pre-existing differences could contribute to our findings. For instance, in a previous multisite study, children in the Québec site had higher IM than those from Ontario and British Columbia sites, even in multivariable models (Riazi et al., 2019).

In our study, the relationship between neighbourhood walkability and IM appeared non-linear as children living in the most walkable neighbourhoods (Can-ALE classes 4 and 5) had less IM than those living in Can-ALE class 2 neighbourhoods. When stratifying by gender, this association was only found for girls. Using the Can-ALE database, Colley et al. (2019a) found that walkability was associated with higher PA in youth, adults, and older adults, but with lower parent-reported unorganized PA and accelerometry-measured PA in 5- to 11-year-olds. Although walkable neighbourhoods may provide access to a larger variety of nearby destinations for independent travel (Broberg et al., 2013; Kyttä, 2004), they may be associated with higher traffic volumes, which can deter IM (Giles-Corti et al., 2011). The need for accessible play space may have been exacerbated by the pandemic (Mitra et al., 2020), and lower density neighbourhoods may provide larger yards and more cul-de-sacs that can facilitate unsupervised outdoor play. Notwithstanding the benefits of walkability for youth and adults (Colley et al., 2019a, 2019b; Sallis et al., 2016), urban planners, transport engineers, and policymakers should ensure that neighbourhoods are also playable, child-friendly, and safe (Gleeson and Sipe, 2006).

In summary, we found that correlates of children's IM during the COVID-19 pandemic span the social-ecological model. Although some correlates are specific to the pandemic (e.g., parental concerns about COVID-19), many of our findings are consistent with a recent systematic review of the correlates of IM (Riazi et al., 2022), suggesting that they may be generalizable. Of particular interest, our findings underscore the importance of parents as “gatekeepers” of their child's IM. Hence, future interventions aiming to promote IM should involve parents and target multiple levels of influence.

4.5. Strengths and limitations

Strengths of the study include our large national sample, the use of a validated IM index as dependent variable, the consideration of a broad range of potential correlates of IM, and the completion of data collection during a short timeframe (15 days) that corresponded to the second wave of COVID-19. It is also notable that our sample included about twice the proportion of fathers compared to a systematic review of 667 observational studies on childhood obesity (35.3% vs. 17%) (Davison et al., 2016). Conversely, the cross-sectional design is an important limitation as we cannot establish temporality and causality. Given the large sample size and the number of potential correlates examined, the risk of type I error is considerable. Children's IM and potential correlates (except walkability) were assessed by parent-report which is subject to social desirability and recall bias. All data were collected in December, a month characterized by harsh weather and limited daylight, so we may have underestimated the average level of IM. Additionally, there was a substantial amount of missing data, especially for postal codes and household income. Finally, because COVID-19 restrictions varied between provinces and changed rapidly during data collection, we could not control for this.

5. Conclusion

We observed that the correlates of IM during the second wave of the COVID-19 pandemic span the social-ecological model. This overarching finding is consistent with pre-pandemic literature, suggesting that interventions aiming to support children's IM should target multiple levels while paying particular attention to the family level. Our findings suggest that encouraging dog ownership, supporting parental tolerance to risk, addressing parental concerns about traffic and crime safety, and promoting AT to work and school may be suitable targets for intervention to promote IM. Initiatives to promote IM in a pandemic context could also raise awareness about activities like walking, cycling, and outdoor play that can be done in compliance with public health recommendations. Finally, future studies should examine how to support IM among children with disabilities.

Funding

This study was funded by a grant-in-aid from the Heart and Stroke Foundation of Canada (grant # G-19-0026216). The funder had no role in study design, data collection, analysis, and interpretation of data, writing of the report, and the decision to submit the article for publication.

Declaration of competing interest

RL receives royalties from Elsevier for his book, Children's Active Transportation. Other authors have no interests to declare.

Acknowledgements

We thank Dr. Sarah Moore and Ms. Victoria Hecker for their input in the development of the questionnaire. We also thank Mr. Taylor Hecker who developed the Python script that we used to match participants' postal codes with the appropriate dissemination area to link walkability data from the Can-ALE database to our dataset. RL holds a Board of Governors Research Chair from the University of Lethbridge.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.healthplace.2023.103019.

Appendix A. Supplementary data

The following are the Supplementary data to this article.

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Data availability

The datasets generated for the current study are not publicly available because participants have not consented to this, but are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (35.2KB, docx)
Multimedia component 2
mmc2.docx (22.4KB, docx)
Multimedia component 3
mmc3.docx (15.8KB, docx)
Multimedia component 4
mmc4.docx (15.8KB, docx)

Data Availability Statement

The datasets generated for the current study are not publicly available because participants have not consented to this, but are available from the corresponding author on reasonable request.

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