Pedometer-facilitated interventions encourage physical activity via the accumulation of steps. Mixed evidence suggests that neighborhood walkability might influence the effectiveness of physical activity interventions, including pedometer-facilitated interventions. Our study investigated the moderating effect of neighborhood walkability on immediate (4-week) and short-term (12-week) changes in self-reported neighborhood-specific leisure and transportation walking, leisure-based moderate and vigorous-intensity physical activity, and leisure-based screen time during a pedometer-facilitated intervention (UWALK).
This quasi-experiment undertaken in Calgary (Canada) compared behavior changes during the 12-week intervention between two neighborhood groups classified as ‘walkable’ or ‘car dependent’ based on Walk Score®. Of the 573 volunteers (adults in the contemplation and preparation stages of physical activity behavior change), 466 participated in UWALK. Surveys captured sociodemographic characteristics, perceived neighborhood walkability, neighborhood preferences, motivation, physical activity and screen-based leisure. Covariate-adjusted linear mixed models estimated the differences in physical activity and leisure screen time between the neighborhood walkability groups at baseline, 4-weeks, and 12-weeks.
UWALK participants included mostly females (83%) and had an average age of 49.2 years. Weekly minutes of walking for transport inside the neighborhood was higher (p < .001) among participants from walkable versus car dependent neighborhoods at baseline (42.5 vs. 21.1), 4-weeks (81.2 vs. 48.2), and 12-weeks (87.2 vs. 48.0). Regardless of neighborhood walkability, all physical activity outcomes were higher and leisure screen time lower at 4-weeks and 12-weeks compared with baseline. We found no significant neighborhood group by time interactions.
Citation: McCormack GR, Spence JC, McHugh T-L, Mummery WK (2022) The effect of neighborhood walkability on changes in physical activity and sedentary behavior during a 12-week pedometer-facilitated intervention. PLoS ONE 17(12): e0278596. https://doi.org/10.1371/journal.pone.0278596
Editor: Esmat Mehrabi, Tabriz University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN
Received: July 13, 2022; Accepted: November 19, 2022; Published: December 1, 2022
Copyright: © 2022 McCormack et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because the original conditions under which ethical approval was granted does not allow participant data to be shared. Under the ethics approval provided for this study, data cannot be publicly shared nor stored in a data repository. For qualified researchers wishing to gain approval to access to these data, please contact the University of Calgary Conjoint Health Research Ethics Board (email@example.com; 1-403-220-2297).
Funding: This study was supported by funding from the Canadian Institutes of Health Research Project Scheme [MOP-142261; GMR, JCP, T-LM, WKM] and the Canadian Institutes of Health Research Foundations Scheme [FDN-154331; GRM]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Regular participation in physical activity can improve cardiovascular, metabolic, and mental health . Even small increases in physical activity, especially among inactive adults can provide health benefits . Interventions that increase physical activity are therefore important for health promotion. While different types of physical activity intervention exist [3,4], pedometer-facilitated interventions can be low cost and broad reaching and support increases in low-to-moderate intensity physical activity by encouraging adults to walk more . Pedometer-facilitated interventions are effective at increasing physical activity [3,5,6] and improving health (e.g., weight loss) .
Pedometer-facilitated interventions typically do not require that participants adhere to a prescribed physical activity routine or to use certain facilities or equipment although they might encourage participants to achieve a step goal (e.g., 10,000 steps/day) [8–10]. Thus, participants in pedometer-facilitated interventions accumulate their physical activity of their own accord meaning they can undertake their preferred physical activities how and where they choose. Because pedometer-facilitated interventions encourage participants to accumulate steps, walking is a common physical activity undertaking during this type of intervention, especially among inactive adults [11,12].
There are several commonly reported barriers to physical activity [13–15]. Notably, the neighborhood built environment can support or constrain physical activity. For instance, neighborhood built characteristics, such as walkability, are associated with physical activity including walking for different purposes [16–19]. Neighborhoods that offer higher pedestrian connectivity, a mix of land use or destinations, recreational opportunities such as parks, and higher levels of personal and traffic safety support physical activity [16,17,19,20]. Neighborhoods with higher walkability may also encourage residents to replace short-car trips with active transport modes such as walking and cycling [21,22].
Neighborhood walkability is often estimated using Walk Score® —a publically available measure that reflects the proximity to community amenities (e.g., grocery stores, coffee shops, parks, stores) . Walk Score® is positively associated with transportation and leisure walking and physical activity [21,24–30] including when Walk Score® is categorized [28,31]. For example, Hajna et al.  found a two-fold increase in utilitarian walking for at least one-hour per week among adults residing in neighborhoods in the highest versus lowest quartile of Walk Score® but observed no significant difference in accelerometer-measured steps. Using predefined Walk Score® classifications (i.e., very car dependent, car dependent, somewhat walkable, very walkable, and walker’s paradise), Twardzik et al.  found lower minutes of accelerometer-measured moderate-to-vigorous intensity physical activity among adults residing in less walkable neighborhoods versus those residing in neighborhoods classified as very walkable/walker’s paradise. In contrast, the researchers also found more minutes of total physical activity (light, moderate, and vigorous intensity) among adults residing in very car-dependent versus very walkable/walker’s paradise neighborhoods controlling for individual and neighborhood level socioeconomic status. Notably, these studies did not differentiate between the contexts in which physical activity was undertaken (e.g., inside versus outside the neighborhood) and only included a limited range of physical activity outcomes.
A recent systematic review of 20 experimental studies found mixed evidence of the objectively-measured neighborhood built environment affecting the accumulation of physical activity during individual-level interventions . Seventy percent of the studies reviewed found at least one neighborhood built environment variable that either positively or negatively impacted the effectiveness of a physical activity intervention . For instance, during a 12-month pedometer-facilitated intervention implemented in Scotland, objectively measured neighborhood built features related to dangerous and busy roads and traffic signals and pedestrian signage were negatively associated with steps while features related to commercial and residential land use were positively associated with steps . Further, Hino et al.  found that neighborhood bus stop density was positively associated with self-reported steps, while Hino et al.  found proximity to the nearest railway station and higher population density were positively associated with recorded steps among adults exposed to a city-wide pedometer-facilitate intervention in Japan.
In a Canadian sample, we previously estimated associations which included perceived and objectively measured walkability (i.e., Walk Score®), but found that only perceived walkability was positively associated with the accumulation of daily steps during a 12-week pedometer-facilitated intervention (UWALK) . In follow-up interviews, participants identified proximity to destinations and recreation facilities, aesthetics, nature, and pathways as physical activity enablers and unstimulating environments, garbage, homeless people, off-leash dogs, and the lack of destinations and green space as physical activity barriers during UWALK . UWALK participants were also more of aware of their patterns of sedentary and physical activity, and they felt encouraged to do more leisure and transportation walking in and around their neighborhoods .
Pedometers encourage and can capture the accumulation of physical activity, however, these devices do not collect information about the contexts in which physical activity is accumulated, nor do they provide information about the purpose for which physical activity was undertaken. In this study we extend our previous analysis of UWALK [36,37] by examining the moderating effect of neighborhood walkability (Walk Score®) on immediate (4-week) and short-term (12-week) changes in self-reported neighborhood-specific leisure and transportation walking, leisure-based moderate and vigorous-intensity physical activity, and leisure-based screen activity during the pedometer-facilitated intervention. We hypothesized that changes in self-reported physical activity at 4 weeks and 12 weeks during the intervention would be significantly higher for adults residing in neighborhoods that were more versus less walkable.
Materials and methods
The study design and recruitment has been described elsewhere . Briefly, between May 2016 and August 2017, adult volunteers were recruited to participate in an internet-based pedometer intervention via newsletters, websites, and social media (i.e., Facebook and Twitter) of 147 community associations, twitter tweets to members of the University of Calgary, City of Calgary, and Federation of Calgary Communities, and via an advertisement in a freely available local newspaper (Metro News). Interested participants contacted the research coordinator and were screened for eligibility. To qualify, participants were at least 18 years of age, in the “contemplation” or “preparation” stages of physical activity behavior change, not previously or currently registered in UWALK, reported no mobility constraints, and had internet access. Participants were identified as physical activity contemplators if they reported “true” to the following two statements: “I currently do not participate in recreational or transportation-related physical activity” and “I intend to participate in recreational or transportation-related physical activity in the next 3 months”. Participants were identified as physical activity preparers if they reported “false” to the following two statements: “I currently do not participate in recreational or transportation-related physical activity” and “I am currently participating in recreational or transportation-related physical activity ≥3 days/week”. Thus participants were considered to have low levels of physical activity . Only one adult per household was eligible to participate in the study.
This quasi-experiment compared behavior at baseline and during UWALK (4 weeks and 12 weeks) between two neighborhood groups classified as ‘walkable’ or ‘car dependent’ based on pre-determined Walk Score® cut-points. Studies examining the influence of the built environment on intervention-facilitated physical activity have typically focused on interventions implemented for 6-months or longer with few studies examining interventions of shorter length . Eligible participants (n = 573) completed a baseline telephone survey capturing sociodemographic characteristics, perceived neighborhood walkability, neighborhood preferences, physical activity motivation (i.e., self-efficacy and intention), physical activity and screen-based leisure. After completing the survey, participants were mailed instructions on how to use and wear the included pedometer, how to register and track physical activity on the UWALK website, a daily tracking sheet, and UWALK promotional material . The study involved a 12-week internet-delivered pedometer intervention during which participants recorded their steps daily or weekly into a website to track their own progress (www.uwalk.ca) and completed two follow-up online surveys (i.e., at 4-weeks and 12 weeks after the baseline survey). Physical activity and screen-based leisure were re-measured during each of the follow-up surveys. UWALK was informed by previous community-based, theory-driven, pedometer-based interventions [8–10]. It used established health promotion approaches based on social cognitive theory to encourage individuals to walk as a means of increasing their physical activity, including achieving 10,000 steps per day . Participants were encouraged to use activity trackers (e.g., pedometers) to self-monitor their physical activity. For our study, participants received a Piezo StepX pedometer . They were instructed to wear the pedometer on their hip at all times except while sleeping, swimming, bathing, or undertaking contact sports.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the University of Calgary Conjoint Health Research Ethics Board (REB15-2944) and the University of Alberta Human Research Ethics Board (Pro00110373) with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants provided oral consent prior to their involvement in the study.
Objectively measured neighborhood walkability.
Neighborhood walkability was estimated using Walk Score®; a publicly available walkability index that reflects proximity to nearby walkable destinations (i.e., grocery stores, coffee shops, restaurants, bars, movie theatres, schools, parks, libraries, book stores, fitness centres, drug stores, hardware stores, clothing/music stores) [23,42]. Walk Score® values range from 0 to 100 with higher scores reflecting higher walkability. Complete household address information was not available for participants, thus each participant’s residential 6-digit postal code was assigned a Walk Score® (extracted from www.walkscore.com in 2017) which was used to categorize neighborhoods as “car dependent” (value <50) or “walkable” (value 50–100)” . In Calgary (Canada), postal code locations are considered an accurate surrogate measure of the geographical location of households, with households located within mean and median Euclidean distances of 146.2 (SD 369.3) meters and 69.1 meters, respectively, of the true address location .
Self-reported physical activity.
The Neighborhood Physical Activity Questionnaire (NPAQ)  captured usual weekly minutes of walking for transport (WT) and leisure (WL) inside and outside the neighborhood (within a 10–15 minute walk from home), as well as minutes of other recreational moderate-intensity (MPA) and vigorous-intensity (VPA). The NPAQ has acceptable test-retest reliability [44,45] and is associated with neighborhood built characteristics in the Canadian context [46,47]. Physical activity was captured at baseline, 4-week follow-up, and 12-week follow-up. Pedometer data were not available prior to participants registering in UWALK thus, we do not have a true pre-intervention baseline measure of steps and do not present these data.
Self-reported screen-based leisure time.
Participants were asked “On average how many hours per week do you spend watching television or using a computer, tablet, iPad, smartphone outside of work time? (e.g., video-games, computer games, DVD/movies, internet, email, texting, etc.)”. A similar item has been used previously to measure leisure-based screen time in Canadian adults and was found to be associated with the built environment [36,48]. The item used in our study was updated to capture contemporary sedentary behaviors (e.g., mobile technology) not included in the original item . Leisure-based screen time was captured at baseline, 4-week follow-up, and 12-week follow-up.
Self-efficacy reflects a person’s belief that they can successfully perform a behavior even when faced with barriers and obstacles that would otherwise inhibit or make performing the behavior difficult . To capture self-efficacy in relation to accumulating steps during UWALK, at baseline participants responded to the three items, each having 7-point response options: 1) “How much control do you have over whether you achieve 10,000 steps on most days during the next month (no control to complete control)”; 2) “For me, achieving 10,000 steps on most days during the next month will be (very difficult to very easy)”, and; 3) “I am confident that I can achieve 10,000 steps on most days during the next month (not at all confident to completely confident)”. Item responses were averaged, with higher scores representing higher self-efficacy (internal consistency: Cronbach’s α = 0.74).
Similarly, to capture baseline intentions in relation to accumulating steps during UWALK, participants responded to three statements (with 7-point response options): 1) “I intend to walk 10,000 steps on most days during the next month (definitely do not to definitely do); 2) “I will make an effort to walk 10,000 steps on most days during the next month (definitely false to definitely true)”, and; 3) I will try to walk 10,000 steps on most days during the next month (definitely will not to definitely will). Item responses were averaged with higher scores indicating stronger intentions (internal consistency: Cronbach’s α = 0.94).
Perceived neighborhood walkability.
At baseline, the Neighborhood Environment Walkability Scale (NEWS-A)  captured participant’s perceptions of built characteristics within a 15-minute walk from home (i.e., residential density, street connectivity, land use mix, park and transit proximity, aesthetics, traffic safety and safety from crime). Responses to 24-items were measured on a 4-point scale (i.e., strongly disagree to strongly agree). NEWS-A items have acceptable reliability and validity  and are associated with physical activity among Canadian adults . Similar to a previous study , we recoded item responses into disagree (0) and agree (1) and created an index that reflected the sum of supportive walkability characteristics in the neighborhood (internal consistency: Cronbach’s α = 0.80).
Perceived neighborhood preferences.
Preferences for particular neighborhood built characteristics may reflect lifestyle decisions, including physical activity, and residential choices . During the baseline telephone survey, on a 5-point bipolar response scale (i.e., strongly agree to strongly disagree), participants reported their preference to: 1) “Live within walking distance to shops, restaurants, and other amenities”; 2) “Live within walking distance to public transit”; 3) “Live within walking distance to parks”; 4) “Live within walking distance to recreational facilities”, and; 5) “Live in a neighborhood where there are sidewalks and walking paths”. Item responses were averaged, with lower index scores representing a preference to reside in a neighborhood that was supportive of walking (internal consistency: Cronbach’s α = 0.65).
Sociodemographic characteristics (covariates).
During the baseline survey participants reported their age, sex, self-rated health (scored 1 = poor to 5 = excellent), highest education achieved (high school or less, college/vocation/trade, or university), annual gross household income (≤$80000, $80,000–119,000, ≥$120,000, or unknown/refused to answer), number of dependents <6 years of age and dependent 6–18 years of age at home, dog ownership (owner, non-owner), marital status (married/common law or other) and availability of a motor vehicle for personal use (always/sometimes, never/do not drive).
Using Pearson’s chi-square and independent t tests, baseline sociodemographic and physical activity related characteristics were compared between those who registered (n = 466) and did not register (n = 107) for UWALK, between study completers (n = 321) and non-completers (n = 145), and between those residing in car dependent (n = 274) and walkable (n = 192) neighborhoods (Fig 1).
Linear mixed models (restricted maximum likelihood) were used to estimate the differences in physical activity and leisure screen activity between the car dependent and walkable neighborhood groups over three time periods (baseline, 4-week follow-up, and 12-week follow-up). A main effect for group (two levels: car dependent and walkable), a main effect for time (three levels: baseline, 4-weeks, and 12-weeks], an interaction term (group × time), and main effects for all covariates were included as fixed factors in the models. An unstructured covariance matrix was specified (random intercept and a random slope) to allow baseline outcomes and their slopes over time to vary between individuals. Within-group (car dependent vs. walkable) and between-group (by time) effects were analyzed using pairwise comparisons of the estimated marginal means derived from the models. Note, for the between group (time) effect, the 4-week and 12-week follow-ups were each compared with baseline (reference category). The models were adjusted for all baseline sociodemographic covariates (i.e., gender, age, education, income, marital status, children, dog ownership, motor vehicle access, self-efficacy, intention, neighborhood preferences, and perceived walkability) and 95% confidence intervals (CI) estimated. The average number of participants clustering per postal code was 1.07 (SD 0.30), thus we did not adjust for clustering in our analysis . Analysis was undertaken using Stata 15 (Stata Corp LLC, Texas, USA).
Study enrollment and participation
Among the participants who registered for UWALK, the majority were female (83%), had a university education (60.7%), were married or common-law (69.3%), did not own a dog (79%), and had access to a motor vehicle always or sometimes (91.2%) (Table 1). Almost one-third (31.5%) of participants reported a household income of <$80,000/year. On average participants were 49.2 years (SD = 14.4), had 0.3 (SD = 0.6) children under 6 years of age and 0.4 (0.9) children 6–18 years of age at home. Participants were also in good or better health (mean = 3.0; SD = 0.9], perceived their neighborhood to be highly walkable (mean = 18.7; SD = 3.4), preferred to live in neighborhoods with characteristics that supported walking (mean = 1.4; SD = 1.4), and had high intentions (mean = 6.0; SD = 1.3) and self-efficacy (mean = 5.0; SD = 1.3) in relation to UWALK (Table 1). Compared with eligible participants who did not register for UWALK, those who registered included a significantly (p < .05) higher proportion of participants who were married/common-law and had higher self-reported health. Baseline weekly MPA was also significantly (p < .05) higher among those who registered versus those who did not register for UWALK (mean = 37.9; SD = 77.7 vs. mean = 22.6; SD = 40.4 minutes/week, respectively). Apart from dog ownership (p < .05), participants who completed all three surveys (completers) were not significantly different to those who did not complete all three surveys (non-completers) on all baseline characteristics (including physical activity variables) (Table 1). Notably, walkability (i.e., Walk Score®) was not significantly different between those registered versus not registered for UWALK nor between completers versus non-completers.
Sociodemographic characteristics by neighborhood walkability
No significant differences existed in the proportion of participants from car dependent and walkable neighborhood groups who did not complete the 4-week (11% vs. 10%) and 12-week (29% vs. 34%) follow-up surveys (Table 1). However, compared with participants from car dependent neighborhoods, those from walkable neighborhoods had a significantly higher proportion of households incomes with less than $80,000/year (37.5% vs. 27.4%), fewer children 6–18 years of age (mean = 0.3 vs. 0.6 per household), and higher proportion reporting not driving or never having access to a motor vehicle for personal use (13.5% vs. 5.5%) (Table 2).
Differences in physical activity by neighborhood walkability during UWALK
Adjusting for all covariates, weekly minutes of WT inside the neighborhood was significantly (p < .001) higher among participants from walkable versus car dependent neighborhoods at baseline (42.5; CI = 35.9, 49.2 vs. 21.1; CI = 15.5, 26.6), 4-week follow-up (81.2; CI = 67.4, 95.1 vs. 48.2; CI = 36.6, 59.8), and 12-week follow-up (87.2; CI = 70.4, 104.1 vs. 48.0; CI = 34.3, 61.6) (Table 3). Despite no significant difference at baseline, compared with participants from car dependent neighborhoods, those from walkable neighborhoods also reported significantly (p < .05) more weekly minutes of WT outside the neighborhood (68.6, CI = 52.9; 84.4 vs. 47.1; CI = 33.9, 60.3) and more WL inside the neighborhood (161.0; CI = 135.8, 186.3 vs. 126.7; CI = 105.5, 147.9) at the 4-week follow-up. No other neighborhood group within-time differences were found.
The within group by time differences showed that all physical activity outcomes were higher at 4-weeks and 12-weeks compared with baseline among participants from car dependent and walkable neighborhoods (Table 3). Similarly, compared with baseline, leisure screen time was lower at the 4-week and 12-week follow-up for both groups. Notably, much of the observed change in physical activity and leisure screen time occurred within the 4-weeks after baseline. We found no significant group by time interactions suggesting that during the 12-week intervention, UWALK had similar effects on all physical activity outcomes and leisure-based screen time for participants from car dependent and walkable neighborhoods (Table 3).
We hypothesized that changes in self-reported physical activity at 4 weeks and 12 weeks during the intervention would be significantly higher for adults residing in neighborhoods that were more versus less walkable. Adding to the mixed evidence  and contrary to our hypothesis, we found that level of neighborhood walkability did not affect changes in physical activity and sedentary behavior resulting from participation in an individual-level physical activity intervention. Congruent with previous studies [3,5,6] we did, however, find increases in physical activity as a result of participating in a short-term internet-delivered pedometer-facilitated intervention, independent of neighborhood walkability. Our findings extend this previous evidence by demonstrating that pedometer-facilitated interventions can support changes in different types and intensities of physical activity and physical activity undertaken for different purposes (i.e., leisure vs. transportation) regardless of levels of neighborhood walkability. Our finding that participation in a 12-week pedometer intervention is associated with a reduction in leisure-based screen activity also aligns with previous findings related to sedentary behavior .
In our study, we did not find any statistically significant group (car dependent vs. walkable) by time (baseline, 4-week, vs. 12-week) interactions, suggesting that UWALK had a similar effect on changes in time spent on physical activity and leisure-based screen activity regardless of neighborhood walkability. At the conclusion of the 12-week intervention, participants from car dependent and walkable neighborhoods similarly had approximately doubled or in some cases tripled their weekly minutes of transport and leisure walking inside and outside their neighborhood, and leisure-based moderate and vigorous intensity physical activity. The greatest relative improvements in physical activity were observed within the first 4-weeks of the intervention, which tended to be maintained until week 12. These findings are encouraging as they suggest internet-delivered pedometer-facilitated interventions can influence physical activities in addition to walking, and such improvements in physical activity could provide health benefits .
Contrary to our findings, other studies have found that the neighborhood built environment can affect the physical activity response to individual-level interventions , including those that use pedometers [33–35]. Our findings are based on a sample of adults who were in the contemplation and preparation stages of behavior change, and included mostly of women and those highly educated. Moreover, our sample on average had relatively high self-efficacy and intention, and positive perceptions of neighborhood walkability. In particular, high levels of self-efficacy may have allowed participants to persevere when they encountered obstacles, such as built environment features unsupportive of physical activity, during the UWALK intervention . In our previous study, which included perceived overall walkability and Walk Score®, only the former was positively associated with the accumulation of steps during the same 12-week intervention . Compared with objectively measured walkability, perceptions of walkability might be more strongly linked with changes in physical activity associated with pedometer-facilitated interventions. Individuals who perceive their neighborhoods to be less walkable may require additional support (e.g., receiving detailed neighborhood maps showing convenient, safe, and attractive walking routes and amenities) [57,58] to encourage them to accrue the full benefits from participating in pedometer interventions. Our findings overall show that the UWALK intervention appears to be effective for encouraging immediate and short-term improvements in walking, and moderate-intensity and vigorous-intensity physical activity regardless of neighborhood walkability suggesting that the intervention could be implemented more broadly in different neighborhood contexts.
We found that weekly minutes of neighborhood transportation walking at baseline was higher among participants from walkable versus car dependent neighborhoods. The difference between the walkable and car dependent neighborhoods remained at the 4-week and 12-week follow-up, despite participants in both groups increasing their neighborhood transport walking minutes. The positive association of neighborhood walkability with transport walking is consistent with previous findings [18,29,59]. Improving neighborhood walkability is important for supporting physical activity, in particular transport walking. However, our findings also suggest that pedometer-facilitated interventions might encourage other types of physical activity regardless of neighborhood walkability. Our findings may not extend to other types of physical activity interventions (e.g., motivational interviewing, mass media campaigns). For instance, in an Australian study, objectively-measured neighborhood walkability did not modify the effects of a mass media campaign (Find Thirty every day®) on physical activity (i.e., transport walking, overall walking, and total physical activity) , while in a US study, the effects of a media campaign (Wheeling Walks) on changes in walking were moderated only by the positive perceptions related to neighborhood aesthetics and facilities .
A notable finding of our study was the positive effect of UWALK on leisure-based screen activity. In their meta-analysis, Qiu et al.  found that pedometer-facilitated interventions (8 to 48 weeks in duration) reduced non-specific sedentary time by approximately 23 minutes per day (or 2.7 hours/week). Our study did not include total sedentary time, nevertheless we found that participation in UWALK was associated with a reduction in leisure-based screen time of about 3–4 hours per week at 4-weeks and 4 to 5 hours per week at 12 weeks. These findings suggest that pedometer-facilitated interventions encourage physical activity while also reducing sedentary time; both of which are independent risk factors for chronic disease [1,62,63]. Importantly, our findings extend this knowledge by demonstrating that neighborhood walkability may not moderate the effect of pedometer-facilitated interventions on leisure-based screen time.
Despite the null findings, to our knowledge this is the first study to estimate the effect of an interaction between objectively-measured walkability and a physical activity intervention on context (i.e., neighborhood) specific physical activity . Our study has several positive attributes such as measuring change in a range of context-specific physical activities over three time points, statistical adjustment for baseline motivational characteristics (intention, self-efficacy), perceived walkability, preference for residing in a walkable neighborhood, self-rated health, and sociodemographic characteristics, using an established walkability index (Walk Score®), and recruitment of adults who were in the contemplation and preparation stages of physical activity change. Our study also has several limitations. Despite using equivalent questions, the baseline survey involved a telephone-interview while the 4-week and 12-week surveys were self-administered using an online questionnaire. We cannot dismiss the effect of survey modality on the observed behavior changes between baseline and follow-up , although this effect is likely to be small . We also cannot rule-out systemic external factors that may have contributed to behavior change; however, the magnitude of changes observed during the 12-week intervention together with supporting qualitative findings  suggest that UWALK likely influenced physical activity and sedentary behavior. Nevertheless, the inclusion of a control group along with randomization would have strengthened the study’s internal validity. Self-reported physical activity and sedentary behavior are susceptible to recall and memory bias [66,67] and despite having pedometer data, we did not capture pre-intervention steps. While Walk Score® is correlated with other objective walkability indices [24–26], built features that are not captured by Walk Score® could have impacted individual responses to UWALK. Individual responses to UWALK might also differ when more extreme Walk Score® classifications are compared (e.g., very car-dependent vs. walker’s paradise) [28,29] however, our sample size and the distribution of walkability scores did not provide sufficient statistical power to explore more refined group differences. Finally, participants self-selected into the study and the potential volunteer bias limits the external validity of our findings.
Pedometer-facilitated interventions have the potential to provide immediate positive impacts on different physical activities and sedentary behavior. Importantly, individuals residing in low walkable or car-dependent neighborhoods participating in pedometer-facilitated interventions similarly benefit in terms of improved physical activity and sedentary behavior compared with those residing in walkable neighborhoods. However, more research is needed to explore the effect of objectively-measured and perceived neighborhood built features on the physical activity and sedentary behavior impacts of different types of interventions.
The authors thank Rosemary Perry (study coordinator), Rhianne Fiolka (recruitment and data collection), Anita Blackstaffe and Anna Consoli (data collection and management), and Cally Jennings (UWALK coordination) for their contributions.
- 1. Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Curr Opin Cardiol. 2017;32(5):541–556. pmid:28708630
- 2. Warburton DE, Bredin SS. Reflections on physical activity and health: what should we recommend? Can J Cardiol. 2016;32(4):495–504. pmid:26995692
- 3. Kahn EB, Ramsey LT, Brownson RC, et al. The effectiveness of interventions to increase physical activity. A systematic review. Am J Prev Med. 2002;22(4 Suppl):73–107. pmid:11985936
- 4. Murray JM, Brennan SF, French DP, Patterson CC, Kee F, Hunter RF. Effectiveness of physical activity interventions in achieving behaviour change maintenance in young and middle aged adults: A systematic review and meta-analysis. Soc Sci Med. 2017;192:125–133. pmid:28965003
- 5. Bravata DM, Smith-Spangler C, Sundaram V, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007;298(19):2296–2304. pmid:18029834
- 6. Ogilvie D, Foster CE, Rothnie H, et al. Interventions to promote walking: systematic review. BMJ. 2007;334(7605):1204. pmid:17540909
- 7. Richardson CR, Newton TL, Abraham JJ, Sen A, Jimbo M, Swartz AM. A meta-analysis of pedometer-based walking interventions and weight loss. Ann Fam Med. 2008;6(1):69–77. pmid:18195317
- 8. Brown WJ, Mummery K, Eakin E, Schofield G. 10,000 Steps Rockhampton: evaluation of a whole community approach to improving population levels of physical activity. J Phys Act Health. 2006;3(1):1–14.
- 9. Mummery W, Schofield G, Hinchliffe A, Joyner K, Brown W. Dissemination of a community-based physical activity project: The case of 10,000 steps. J Sci Med Sport. 2006;9(5):424–430. pmid:16890489
- 10. Davies C, Corry K, Van Itallie A, Vandelanotte C, Caperchione C, Mummery WK. Prospective associations between intervention components and website engagement in a publicly available physical activity website: the case of 10,000 Steps Australia. J Med Internet Res. 2012;14(1):e4. pmid:22260810
- 11. Hillsdon M, Thorogood M, White I, Foster C. Advising people to take more exercise is ineffective: a randomized controlled trial of physical activity promotion in primary care. Int J Epidemiol. 2002;31(4):808–815. pmid:12177026
- 12. Tudor-Locke C, Lutes L. Why do pedometers work?: a reflection upon the factors related to successfully increasing physical activity. Sports Med. 2009;39(12):981–993. pmid:19902981
- 13. Dishman RK, Sallis JF, Orenstein DR. The determinants of physical activity and exercise. Public Health Rep. 1985;100(2):158–171. pmid:3920714
- 14. Borodulin K, Sipilä N, Rahkonen O, et al. Socio-demographic and behavioral variation in barriers to leisure-time physical activity. Scand J Public Health. 2015;44(1):62–69. pmid:26392420
- 15. Cerin E, Leslie E, Sugiyama T, Owen N. Perceived barriers to leisure-time physical activity in adults: An ecological perspective. J Phys Act Health. 2010;7(4):451. pmid:20683086
- 16. Salvo G, Lashewicz BM, Doyle-Baker PK, McCormack GR. Neighbourhood built environment influences on physical activity among adults: a systematized review of qualitative evidence. Int J Environ Res Public Health. 2018;15(5). pmid:29724048
- 17. Ferdinand AO, Sen B, Rahurkar S, Engler S, Menachemi N. The relationship between built environments and physical activity: a systematic review. Am J Public Health. 2012;102(10):e7–e13. pmid:22897546
- 18. McCormack GR, Shiell A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. International Journal of Behavioral Nutrition and Physical Activity. 2011;8:125. pmid:22077952
- 19. Wendel-Vos W, Droomers M, Kremers S, Brug J, Van Lenthe F. Potential environmental determinants of physical activity in adults: a systematic review. Obes Rev. 2007;8(5):425–440. pmid:17716300
- 20. Kaczynski AT, Henderson KA. Environmental correlates of physical activity: a review of evidence about parks and recreation. Leis Sci. 2007;29(4):315–354.
- 21. Hall CM, Ram Y. Walk Score® and its potential contribution to the study of active transport and walkability: A critical and systematic review. Transp Res D Trans Environ. 2018;61:310–324.
- 22. Smith M, Hosking J, Woodward A, et al. Systematic literature review of built environment effects on physical activity and active transport–an update and new findings on health equity. Int J Behav Nutr Phys Act. 2017;14(1):158. pmid:29145884
- 23. Walk Score Methodology. https://www.walkscore.com/methodology (accessed April 19 2021). 2017.
- 24. Duncan DT, Aldstadt J, Whalen J, Melly SJ, Gortmaker SL. Validation of walk score for estimating neighborhood walkability: an analysis of four US metropolitan areas. Int J Environ Res Public Health. 2011;8(11):4160–4179. pmid:22163200
- 25. Cole R, Dunn P, Hunter I, Owen N, Sugiyama T. Walk Score and Australian adults’ home-based walking for transport. Health and Place. 2015;35:60–65. pmid:26209804
- 26. Winters M, Barnes R, Venners S, et al. Older adults outdoor walking and the built environment: does income matter? BMC Public Health. 2015;15:n/a. pmid:26359159
- 27. Cole R, Dunn P, Hunter I, Owen N, Sugiyama T. Walk Score and Australian adults’ home-based walking for transport. Health & Place. 2015;35:60–65. pmid:26209804
- 28. Twardzik E, Judd S, Bennett A, et al. Walk Score and objectively measured physical activity within a national cohort. J Epidemiol Community Health. 2019;73(6):549–556. pmid:30944171
- 29. Hajna S, Ross NA, Joseph L, Harper S, Dasgupta K. Neighbourhood walkability, daily steps and utilitarian walking in Canadian adults. BMJ Open. 2015;5(11):e008964. pmid:26603246
- 30. Thielman J, Rosella L, Copes R, Lebenbaum M, Manson H. Neighborhood walkability: differential associations with self-reported transport walking and leisure-time physical activity in Canadian towns and cities of all sizes. Prev Med. 2015;77:174–180. pmid:26007297
- 31. Hirsch JA, Moore KA, Evenson KR, Rodriguez DA, Diez Roux AV. Walk Score® and Transit Score® and walking in the multi-ethnic study of atherosclerosis. Am J Prev Med. 2013;45(2):158–166.
- 32. McCormack G, Patterson M, Frehlich L, Lorenzetti D. The association between the built environment and intervention-facilitated physical activity. A narrative systematic review. International Journal of Behavioral Nutrition & Physical Activity. In press.
- 33. Robertson LB, Ward Thompson C, Aspinall P, Millington C, McAdam C, Mutrie N. The influence of the local neighbourhood environment on walking levels during the Walking for Wellbeing in the West pedometer-based community intervention. J Environ Public Health. 2012;2012:974786. pmid:22899944
- 34. Hino K, Taniguchi A, Hanazato M, Takagi D. Modal shift from cars and promotion of walking by providing pedometers in Yokohama City, Japan. International Journal of Environmental Research & Public Health. 2019;16(12):17.
- 35. Hino K, Lee JS, Asami Y. Interaction effect of neighborhood walkability and season on adults’ step count. Journal of Transport & Health. 2021;20:101027.
- 36. McCormack GR, Mardinger C. Neighbourhood urban form and individual-level correlates of leisure-based screen time in Canadian adults. BMJ Open. 2015;5(11):e009418. pmid:26608640
- 37. McCormack GR, McFadden K, McHugh T-LF, Spence JC, Mummery K. Barriers and facilitators impacting the experiences of adults participating in an internet-facilitated pedometer intervention. Psychol Sport Exerc. 2019;45:101549.
- 38. Consoli A, Nettel-Aguirre A, Spence JC, McHugh T-L, Mummery K, McCormack GR. Associations between objectively-measured and self-reported neighbourhood walkability on adherence and steps during an internet-delivered pedometer intervention. PLoS ONE. 2020;15(12):e0242999. pmid:33270692
- 39. Prochaska JO MB. The transtheoretical model: Application to exercise. Advances in exercise adherence 1994:161–180.
- 40. Jennings CA, Berry TR, Carson V, et al. UWALK: the development of a multi-strategy, community-wide physical activity program. Transl Behav Med. 2017;7(1). pmid:27282432
- 41. Colley RC, Barnes JD, Leblanc AG, Borghese M, Boyer C, Tremblay MS. Validity of the SC-StepMX pedometer during treadmill walking and running. Appl Physiol Nutr Metab. 2013;38(5):520–524. pmid:23668759
- 42. Carr LJ, Dunsiger SI, Marcus BH. Walk Score™ as a global estimate of neighborhood walkability. Am J Prev Med. 2010;39(5):460–463.
- 43. Bow C, Waters N, Faris P, et al. Accuracy of city postal code coordinates as a proxy for location of residence. International journal of health geographics. 2004;3:5. pmid:15028120
- 44. Giles-Corti B, Timperio A, Cutt H, et al. Development of a reliable measure of walking within and outside the local neighborhood: RESIDE’s Neighborhood Physical Activity Questionnaire. Prev Med. 2006;42(6):455–459. pmid:16574208
- 45. McCormack G, Shiell A, Doyle-Baker P, Friedenreich C, Sandalack B, Giles-Corti B. Testing the reliability of neighborhood-specific measures of physical activity among Canadian adults. J Phys Act Health. 2009;6:367–373. pmid:19564667
- 46. McCormack GR, Koohsari MJ, Oka K, et al. Differences in transportation and leisure physical activity by neighborhood design controlling for residential choice. J Sport Health Sci. 2019. pmid:31720064
- 47. McCormack GR, Friedenreich C, Sandalack BA, Giles-Corti B, Doyle-Baker PK, Shiell A. The relationship between cluster-analysis derived walkability and local recreational and transportation walking among Canadian adults. Health & Place. 2012;18(5):1079–1087. pmid:22652511
- 48. Koohsari MJ, Oka K, Nakaya T, et al. Environmental attributes and sedentary behaviours among Canadian adults. Environ Res Commun. 2020;2(5):051002.
- 49. Bandura A, Freeman WH, Lightsey R. Self-Efficacy: The Exercise of Control. J Cogn Psychother. (2):158–166.
- 50. Saelens BE, Sallis JF, Black JB, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation. Am J Public Health. 2003;93(9):1552–1558. pmid:12948979
- 51. Cerin E, Saelens BE, Sallis JF, Frank LD. Neighborhood Environment Walkability Scale: validity and development of a short form. Med Sci Sports Exerc. 2006;38(9):1682–1691. pmid:16960531
- 52. Jack E, McCormack GR. The associations between objectively-determined and self-reported urban form characteristics and neighborhood-based walking in adults. Int J Behav Nutr Phys Act. 2014;11:71. pmid:24893719
- 53. Sallis JF, Bowles HR, Bauman A, et al. Neighborhood environments and physical activity among adults in 11 countries. Am J Prev Med. 2009;36(6):484–490. pmid:19460656
- 54. Lamb KE, Thornton LE, King TL, et al. Methods for accounting for neighbourhood self-selection in physical activity and dietary behaviour research: a systematic review. Int J Behav Nutr Phys Act. 2020;17(1):45. pmid:32238147
- 55. Clarke P. When can group level clustering be ignored? Multilevel models versus single-level models with sparse data. J Epidemiol Community Health. 2008;62(8):752–758. pmid:18621963
- 56. Qiu S, Cai X, Ju C, et al. Step counter use and sedentary time in adults: a meta-analysis. Medicine (Baltimore). 2015;94(35):e1412–e1412. pmid:26334900
- 57. McNeill LH, Emmons K. GIS walking maps to promote physical activity in low-income public housing communities: a qualitative examination. Prev Chronic Dis. 2012;9:E17–E17. pmid:22172184
- 58. Rosenberg D, Kerr J, Sallis JF, Patrick K, Moore DJ, King A. Feasibility and outcomes of a multilevel place-based walking intervention for seniors: a pilot study. Health & Place. 2009;15(1):173–179. pmid:18502164
- 59. Mayne SL, Auchincloss AH, Michael YL. Impact of policy and built environment changes on obesity-related outcomes: a systematic review of naturally occurring experiments. Obes Rev. 2015;16(5):362–375. pmid:25753170
- 60. Barnes R, Giles-Corti B, Bauman A, Rosenberg M, Bull FC, Leavy JE. Does neighbourhood walkability moderate the effects of mass media communication strategies to promote regular physical activity? Ann Behav Med. 2013;45 Suppl 1:S86–94. pmid:23334760
- 61. Gebel K, Reger-Nash B, Leyden K, Bauman A. Does the environment moderate the impact of a mass media campaign to promote walking? Am J Health Promot. 2011;26:45–49. pmid:21879942
- 62. González K, Fuentes J, Márquez JL. Physical inactivity, sedentary behavior and chronic diseases. Korean J Fam Med. 2017;38(3):111–115. pmid:28572885
- 63. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary behavior, exercise, and cardiovascular health. Circ Res. 2019;124(5):799–815. pmid:30817262
- 64. Bowling A. Mode of questionnaire administration can have serious effects on data quality. J Public Health. 2005;27(3):281–291. pmid:15870099
- 65. Vuillemin A, Oppert JM, Guillemin F, et al. Self-administered questionnaire compared with interview to assess past-year physical activity. Med Sci Sports Exerc. 2000;32(6):1119–1124. pmid:10862539
- 66. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport. 2000;71(sup2):1–14.
- 67. Ainsworth BE, Caspersen CJ, Matthews CE, Mâsse LC, Baranowski T, Zhu W. Recommendations to improve the accuracy of estimates of physical activity derived from self report. J Phys Act Health. 2012;9(s1):S76–S84. pmid:22287451