Purpose: The main purpose of this study was to investigate the relationships between neighbourhood built characteristics (i.e., neighbourhood walkability and greenness) and health-related fitness (i.e., grip strength and body fat percent). A secondary aim was to assess how movement behaviour (i.e., physical activity and sitting time) influences our main aim.

Methods: This study was a cross-sectional secondary analysis using a Canadian provincial (Alberta) longitudinal cohort study (Alberta’s Tomorrow Project; ATP). ATP participant data were linked via their 6-digit residential postal codes to built environment data, including the Canadian Active Living Index (Can-ALE; walkability) and Normalized Difference Vegetation Index (NDVI; greenness). ATP measured muscular strength via hand grip dynamometry and body composition was measured via bioelectrical impedance. Four sex-stratified covariate-adjusted linear regressions were undertaken estimating unstandardized beta coefficients (β) and 95% confidence intervals (CI). Model 1 adjusted for sociodemographic and select health variables; Model 2 adjusted for variables in Model 1 plus weekly physical activity; Model 3 adjusted for variables in Model 1 plus total weekly hours of sitting; and Model 4 adjusted for variables in Model 1 plus weekly MET-hours of physical activity and total weekly hours of sitting.

Results/findings: For males and females neighbourhood walkability was negatively associated with both grip strength (males β = -0.21; 95%CI: -0.31 to -0.11, females β = -0.06; 95%CI: -0.10 to -0.01) and body fat percent (males β = -0.08; 95%CI: -0.15 to -0.02, females β = -0.08; 95%CI: -0.14 to -0.02). Moreover, the inclusion of physical activity and sitting did not influence the results. For males and females neighbourhood greenness was positively associated with grip strength (males β = 6.99; 95%CI: 3.62 to 10.36, females β = 2.72; 95%CI: 1.22 to 4.22) and not associated with body fat percent (males β = -0.30; 95%CI: -2.61 to 2.00, females β = 0.30; 95%CI: -1.60 to 2.21). Moreover, the inclusion of physical activity and sitting did not influence the results.

Conclusions: Neighbourhood walkability and greenness may support body composition and muscular strength, respectively. Moreover, notably, such support may not be influenced by movement behaviour.

Purpose: Despite clear benefits of an active lifestyle, most fail to meet physical activity (PA) guidelines. Because of its safety and ease, walking is a population-level strategy to increase and maintain PA. Get WalkIN’, a statewide 12-week email-based walking program was developed to address inactivity. While Get WalkIN’ has demonstrated effectiveness, a further understanding of how location, such as urban vs. rural settings, impact outcomes to tailor the program if needed.

Methods: Four years of data were analyzed across three timepoints (baseline, post-program, and maintenance) (n=800 with complete data). Participant-reported County of residence was matched with USDA rural-urban continuum codes to categorize location as urban, suburban, or rural. Self-reported PA was assessed using the International Physical Activity Questionnaire Short-Form. Participant PA was categorized as high active (achieving at least 1500 MET minutes/week), moderately active (achieving 600-1499 MET minutes/week), or low active (achieving less than 600 MET minutes/week). To examine potential differences in program outcomes between locations, activity level across the three timepoints was compared using Friedman’s chi-square tests.

Results: Approximately half of participants (52.9; n=416) lived in urban areas; 24% in suburban areas, and 23% in rural areas. On average, participants were white (96%), middle age (53±17 years old) females (91%). At baseline there was no significant difference in activity status by location: 15% classified as low PA, 15% as moderate PA, and 70% as high PA. On average participants significantly increased their activity immediately after the program (85% high active) with no significant differences based on location. Significant differences in activity levels by location did not emerge until the maintenance period, 3 months after the program ended. Urban participants were significantly less likely to be low active (10.5% vs 26% of suburban and 24% of rural) and more likely to be high active (82% vs 68% of suburban and 67% of rural participants) at maintenance.

Conclusions: Findings highlight the importance of understanding how location may influence PA maintenance. Future research will examine how reported barriers and motivators may differ by location. Tailoring community-based programs to address location-specific barriers and motivators may enable more participants to effectively change and maintain PA.

Purpose: This project aims to examine the implementation of the Our Voice method across diverse communities engaged in the State of California’s CalFresh Healthy Living (CFHL) programs under the University of California’s Cooperative Extension initiative (UCCE). Community members are equipped with citizen science tools, enabling them to: 1) document facilitators and barriers hindering adoption of healthy living habits (physical activity, healthy eating); 2) identify viable solutions to address barriers; and 3) advocate for meaningful local changes that contribute to overall enhancements in healthy living practices.
Methods: Using the multilingual Discovery Tool App, local community members recorded geotagged photos and comments that addressed the question, “What makes it easy or hard to walk around your community?” Upon reviewing the gathered data, community members actively participated in categorizing their photos into key themes, generating solutions and strategies, identifying local allies, and leveraging their findings to activate positive changes in support of a healthier community.
Results/findings: Across three California counties, five projects engaged diverse groups of high schoolers, parent groups, older adults, and adults with disabilities. Groups documented facilitators and barriers impacting walkability in their communities through 36-370 photos+comments per project and identified at least one idea for change they wanted to advocate for to improve the area's walkability (e.g., fixing pathways, adding shade trees). Citizen scientists from each group met with local decision-makers to discuss and propose specific improvements. Thus far, six+ environmental changes have been initiated by the community members, including adding speed controls & pedestrian-friendly street signage, and introducing a high school edible/native tree planting project.
Conclusion: Diverse UCCE citizen science teams prioritized individuals most profoundly affected by community factors that impact their ability to walk in their neighborhoods. Embracing inclusivity, the approach entails strategic planning for change and cultivating community members as leaders to advance healthy living through a health equity lens. Notably, partnering with such state- or region-wide organizations can provide an effective means for scale-up of this type of participatory action research method through sharing of insights and actions across this type of network.

Purpose: Transport interventions are key to promoting physical activity and reducing obesity. Despite road safety and health benefits, lowering speed limits from 50 to 30km/h remains a challenge. We examine how local decision-makers justify and implement 30 km/h policies, and whether or not health benefits were considered. We also studied urban-rural and neighbourhood socio-economic differences herein.

Methods: Two researchers independently searched legal traffic decision documents from the Official Gazette of the Kingdom of the Netherlands to obtain all annual speed limit implementations in Amsterdam and Rotterdam and six rural municipalities between 2016-2022. One researcher extracted data using a customized form, including justifications, and additional physical measures included, which was checked by the second researcher. Descriptive statistics were provided on justifications and implementation aspects for the whole sample and separately for urban and rural areas and (in planned analysis) neighbourhood socio-economic status. Chi-square tests will be conducted to test for urban-rural differences and neighbourhood socio-economic status differences.

Results/findings: We identified 105 traffic documents. The most common justifications for 30km/h limits were traffic safety (N=50), liveability (N=13), lower speed limits in nearby areas (N=13), and children and school environments (N=10). Only two documents explicitly described potential health benefits as justification for 30km/h limits. Regarding implementation, a minority of implementations included additional physical measures, with the most common ones being speed bumps (N=13), pedestrian crossings (N=7), and equal crossings (N=6). In rural municipalities, lower nearby speed limits (e.g., 15km/h) were more often mentioned as a justification to implement 30km/h than in urban municipalities (50% vs. 8%).

Conclusions: Traffic safety is proposed as main benefit of 30km/h, while wider health impacts are rarely put forward, even though based on scientific evidence this might be possible. Considering these wider health impacts can also increase acceptance among the general population for 30km/h limits and improve willingness among decision makers. Research therefore should further highlight these wider health impacts and inform future policymaking.

PURPOSE
The COVID-19 pandemic and the public health measures related to COVID-19 may have had an impact on lifestyle behaviours. Greater physical activity (PA) is favorable for lower screen time and the prevention of obesity. This study aims to document the evolution of PA between 2015 and 2021 in Quebec and in Canada and to examine associations between PA, screen time and body mass index (BMI) during the COVID-19 pandemic period.

METHODS
This analysis is based on adults (≥18 years) from the Canadian Community Health Survey. Screen time and global physical activity (recreation, transportation, household or occupation) were self-reported. Being physically active was defined as moderate to vigorous physical activity (MVPA) ≥150 minutes weekly. We compare PA levels from 4 cross-sectional samples collected during 2015-2016 (n=98,041), 2017-2018 (n=102,071), 2020 (n=39,594) and 2021 (n=44,688). Effect of COVID-19 pandemic on self-reported BMI, PA, and screen time were tested using logistic regression analyses.

RESULTS
In 2021, the proportion of physically active adults (≥150 min/week) was 54.9% in Quebec and in Canada. In Quebec, there was a significant decrease of active adults between 2015 and 2020 (57.9 vs. 53.8%, p=0.01 ). This trend was observed at the Canadian level (57.8 vs. 53.2%, p=0.03). An increase in the proportion of light PA level (<150 minutes, but more than 0-minute weekly of MVPA) was observed for the same period in Quebec (32.3 vs. 35.4%, p=0.001) as in Canada (30.7 vs. 34.0%, p=0.001). During the COVID-19 pandemic period, in 2021, the proportion of Quebec adults spending ≤2 hours daily using screen media was higher among physical active than inactive adults (50.3 vs. 11.5%, p<0.0001). Moreover, the proportion of active adults was higher among those with a BMI within normal than in adults with obesity (58.6 vs. 44.2%, p<0.001).

CONCLUSIONS
This study shows that the COVID-19 pandemic had an impact on the PA levels of Quebec adults in 2020, with a trend of 2021. The same observations were found in the relationship between PA levels and BMI, but screen time seems to be higher among people reaching recommendations. Public health actors should explore strategy for reducing sedentary behaviours, not only increasing PA.

Purpose: Previous studies indicate higher levels of physical activity (PA) are correlated with longer sleep duration, with inconsistency between girls and boys. There are fewer studies investigating the relationship between PA and sleep among children in after-school programs. We aimed to (1) determine the proportion of school children meeting the PA (60 minutes of moderate-to-vigorous daily) and sleep duration (9-12 hours) guidelines, and (2) compare if PA and sleep duration are different by gender (male and female), age, as well as school-level income.

Methods: This cross-sectional study included 153 children (40% participation rate) aged 8-12 years, in grades 3-6, who attended after-school programs at 14 elementary schools in a school district outside of Phoenix, Arizona, U.S.A. The children wore ActiGraph GT3X+ accelerometers for a complete week to measure PA and sleep duration. Levels of moderate-to-vigorous PA were averaged across all daily waking hours using Chandler 2016 wrist-worn cutpoints processed with ActiLife. Sleep duration was averaged across all days measured from the evening through the subsequent morning with start/end times determined by hand and processed through ActiLife. Age was categorized to <10 and ≥10 years and percent eligible for free or reduced-priced meals was used to categorize school income-level. Bivariate analysis using Fisher's exact test was conducted using SPSS software.

Results: A total of 79 children met the wear time criteria of three weekdays and one weekend day of at least 10 hours. Of those, 98% of the children met the PA guideline (55% low-income, 51% male, 72% <10 years), while only 25% met the sleep guideline (55% high-income, 58% female, 67% <10 years). Only 26% of children met both PA and sleep guidelines. All associations were statistically not significant at a p-value of < 0.05.

Conclusion: This preliminary study reveals a larger proportion of children in after-school programs fail to meet the sleep guidelines, despite meeting the PA guidelines. Future studies with sufficient power should examine the role of these variables as moderators and/or mediators of the relationships between PA and sleep in children.