Purpose: Living in urban environments is associated with higher levels of loneliness and inactivity, which are detrimental to mental and physical health. Therefore, it is necessary to identify factors that counteract these health decreasing characteristics. In this regard, there is convincing evidence that social participation, such as pursuing social activities, is related to better mental health. However, studies addressing factors that predict social activities in urban environments are lacking, especially as existing studies mostly focus on older age populations. Hence, this study aims to examine the simultaneous impact of residential environment features (REF) and physically active mobility (PAM) on social activities in mid-aged adults living in an urban area.

Methods: Cross-sectional data regarding PAM (walking to work, running errands by foot, bicycling to work, general bicycling, and strolling), social activities (i.e, frequencies or pursuing social activities, e.g., meeting friends/family, going to a bar/restaurant, etc.), REF (shortened NEWS-I, i.e., satisfaction with the living environment, e.g., available amenities, etc.), and Multidimensional Mood State Questionnaire (MDMQ) were collected from adults in several preselected residential areas in Stuttgart, Germany via an online questionnaire. JASP was used for a regression analysis to predict social activities from REF and PAM, and mental health from social activities.

Results: Data of 246 individuals (47% female, Mage = 46.19, SDage = 16.42) were included in the analysis. Satisfaction with REF [b = 3.28, t(209) = 3.11, p = .002] and PAM [b = 3.03, t(209) = 2.95, p = .003] both positively and significantly predicted social activities, (F(2,209) = 10.981, p < .001) accounting for 8.6% of the explained variability. In addition, social activities positively predicted mental health [b = 0.09, t(222) = 2.22 , p = .027 ], (F(1,222) = 4,942, p = .027, R² = .017).

Conclusion: Results indicate that pursuing social activities is associated with the perception of physical environmental features of the living area and the amount of walking / bicycling in the residential environment. Gaining knowledge about the dependencies of social activities and mental health help to create health-enhancing urban areas.

Purpose: People living in ‘walkable’ areas are more physically active, but current approaches to assessing and improving walkability focus on those who live in urban environments. Informed by systems approaches to understanding environmental influences on active living in rural communities this participatory research project asked citizen scientists to identify and explore the characteristics of their town that influence walkability and active living.

Methods: Adopting a citizen science approach, citizen scientists (i.e., local community members) in three rural towns conducted an active living audit of their town using modified Rural Active Living Assessment Tools (RALA) and taking photos of town features. The RALA tool includes; i) a town-wide assessment, ii) program and policy assessment and, iii) street segment assessments. Audit and photo data were collated before researchers facilitated workshops in each town to enable further sense-making with community members. Data from the RALA tools were scored and synthesised with data from photos and workshops before undergoing thematic analysis.  

Results: Three rural Australian towns (population 300; 850; 2,890 respectively) were involved in the study with a total of 10 citizen scientists completing audits and 11 community members attending workshops. The town-wide assessment scores that reflect amenities and physical features varied (26-74%) while programs and policies scores were consistently low (21-26%). The two smaller towns were bisected by a main highway creating safety concerns relating to traffic and only the larger town had regular physical activity programs available to the community.  In all towns the condition of and/or lack of footpaths, lack of road shoulders, traffic speed and inadequate signage were identified as barriers to walkability. However, improved connectivity between existing town features and trails was identified as the highest priority for enhancing walkability and as a mechanism for enhancing social cohesion.   

Conclusions: The audit tools were useful in providing an overview of the rural towns involved, but the workshops were critical for sense-making and enhanced understanding of priority concerns for each town. The citizen science approach supported data collection and exhibited the potential to empower citizens to use the data to support local advocacy efforts to improve connectivity.   

Purpose. Relationships between park access, park use, and wellbeing remain poorly understood, especially in Asian metropolitan areas. The objectives of this study were to investigate: (1) perceived and objective park access in relation to park use and physical activity in parks; and; (2) perceived and objective park access, park use and physical activity in parks and their associations with wellbeing.

Methods. An interviewer-administered survey collected data on perceived time to walk to parks, park use time, park physical activity time and wellbeing (using the Stanford WELL for Life Scale, containing nine domains) amongst adult participants of the Singapore Multi-Ethnic Cohort. Geospatial maps of parks and the “walkable” street networks were created for the city-state of Singapore to objectively determine distances to accessible points on park boundaries. Multiple linear regression models estimated the importance of park access to park use and associations of park access and park use with wellbeing, adjusting for potential confounders.

Results. Participants’ (n=3 435) average age was 48.8 years (SD, 12.8), 44.8% were male and 72.6% were of Chinese ethnicity. Better perceived but not true park access was significantly associated with greater park use. Park access (perceived or true) was not associated with physical activity time in parks. As participants’ park time and physical activity time in parks increased there was a statistically significant increase in wellbeing scores (p<0.001). The differences in wellbeing scores between the reference groups, who spent negligible time in parks, and the highest quartiles of time in parks (10.8 hours/month) and physical activity in parks (8.3 hours/month) were 3.2 (95% CI 2.1-4.4) and 4.2 (95% CI 4.1-6.3) points out of 100 respectively. These associations were similar for most domains of wellbeing, with clear dose-response relationships.

Conclusions. While perceived park access was strongly associated with park use and well-being, true park access was not, and neither park access measure was associated with park physical activity. The consistent associations of park use and particularly physical activity in parks with wellbeing suggest that promoting park use, and especially physical activity in parks, is a promising strategy for improving wellbeing in urban settings.

Purpose: Studies using individual-level data have shown that physically active travel (walking, cycling) is associated with lower overweight/obesity risk, while sedentary travel (car use) is associated with higher risk. Area-level evidence of how prevalence of active/sedentary travel is related to obesity rates is also needed to inform transport and planning policies. However, there is little area-level evidence on travel behaviours and overweight/obesity. We examined the relationships by linking travel and health surveys.

Methods: Travel behaviour data were obtained from 41,544 adult participants of travel surveys conducted in Australia in 2009–10. Obesity data were derived from the 2014–15 Australian National Health Survey. Aggregated data from these surveys were linked at the level of Population Health Area (PHA). The exposure measures were the proportion of participants engaged in active (≥30 min/d in active modes and 0 min/d in cars), mixed (>0 min/d in active modes and in cars), and sedentary travel (0 min in active modes and >0 min/d in cars) in each PHA. The outcome measure was the age-standardised rate of higher waist circumference (>94cm for men; >80cm for women, objectively measured). Bayesian conditional autoregressive models examined associations of the outcome with the proportions of active, mixed and sedentary travel participants.

Results: On average, 63% of health survey participants in the study areas (160 PHAs) had higher waist circumferences. The mean proportion of travel survey participants engaged in active, mixed, and sedentary travel was 6%, 14%, and 75%, respectively. After adjusting for area-level covariates, one standard deviation increment in the proportion of active, mixed, and sedentary travel was associated with 1.4% lower (95%CI: -3.6, -0.4), 0.9% lower (-2.7, -0.3), 1.9% higher (1.1, 3.8) rates of higher waist circumference, respectively.

Conclusions: PHAs with more active and mixed travel participants had lower prevalence of overweight/obesity, while those with higher proportions of sedentary travel participants had higher prevalence. Our study suggests that walking/cycling can be protective against obesity even in the presence of car use. Environmental and policy initiatives to promote active travel are recommended in areas where sedentary travel is predominant.

Purpose: Environmental interventions can enhance cycling for transport but are expensive and hard to change once executed. Therefore, this study investigated if the “future environment” is perceived as more safe and more appealing by visualising it in a 3D model and comparing this to perceptions of the current environment. Secondly, there is explored whether age, gender, socio-economic status, participants’ perceived ability to interpret a 2D plan, cycling behaviour/experience and active transport habits moderate these associations.

Methods: This cross-sectional study used a web-based survey, completed by 360 participants (data collection ongoing). The environment where environmental changes are planned was visualized by means of three different methods: (1) a video from the current environment, (2) a 2D plan visualizing the planned environmental changes and, (3) an interactive 3D model from the future environment in which participants could virtually cycle through on their computer. Overall perceived safety and -appeal as well as perceived safety and -appeal of specific environmental factors (e.g. greenery) were rated on a Likert scale. Furthermore, possible moderators were assessed. Repeated Measures Manova and linear regression analyses were used for the statistical analysis.

Results: Overall safety- and appeal perceptions were higher in the future environment, compared to the current environment (p<0.001). According to possible moderators, female (p=0.031) and younger participants (p=0.035) perceived the current environment less safe than male and older participants, while there were no differences in safety perceptions according to gender or age in the future environment. Additionally, participants who found it difficult or could not interpret a 2D plan assessed the 2D plan less safe and -appealing in comparison to participants who found it easy or were able to interpret the 2D plan, while there were no differences in safety- and appeal perceptions in the future environment (p<0.001). More comprehensive and qualitative results will be presented at the conference.

Conclusions: This innovative methodology is an inexpensive and effective way to assess if the future environment is an improvement compared to the current one. Additional, subgroup differences and participant’s concerns about the environmental adaptation could be a valuable source of information for different stakeholders involved in urban planning.

Purpose: Adolescents and their parents perceive different barriers for walking versus cycling to school and parental perceptions also vary by home-to-school distance. This study examined whether adolescents’ perceptions of walking and cycling differ by home-to-school distance.

Methods: Adolescents (n=1,401; age: 15.1±1.4 years; 55.1% females) completed an online survey about their school travel and perceptions of walking and cycling to school in Dunedin, New Zealand. Based on home-to-school distance, adolescents were categorised into three groups: ‘walkable’ (≤2.25 km; n=455), ‘cyclable’ (>2.25-≤4.0 km; n=286) and ‘beyond cyclable’ distance (>4.0 km; n=660). 

Results: Although rates of active transport to school decreased with increasing distance (‘walkable’/’cyclable’/’beyond cyclable’: 60.1%/16.4%/1.2%; p<0.001), most adolescents perceived walking and cycling to be healthy irrespective of distance to school. The proportion of adolescents intending to walk to school decreased with increasing distance (68.6%/30.1%/7.9%; p<0.001) whereas the proportion of those intending to cycle to school was low across all distance groups (2.9%/4.9%/4.2%; p=0.308). As distance to school increased, adolescents more frequently reported personal barriers (e.g., after-school schedule, for walking: 24.4%/44.4%/60.9%; for cycling: 42.0%/54.9%/73.0%), environmental barriers (e.g., distance perceived to be too far: for walking: 10.5%/52.4%/87.7%; for cycling: 11.4%/41.6%/81.1%) and safety-related barriers (for walking: 7.5%/14.3%/44.8%; for cycling: 35.4%/43.7%/63.9%); and decreased peer support (for walking: 61.3%/27.3%/13.3%; for cycling: 19.6%/18.2%/12.0%) and parental support (for walking: 78.5%/47.9%/16.4%; for cycling: 24.0%/22.4%/14.5%) (all p<0.001). Perceived absence of footpaths increased with distance 5.7%/14.0%/44.1%; p<0.001), while perceived absence of cycle lanes (61.5%/65.7%/65.8%, p=0.243) did not differ across distance categories. As distance increased, adolescents expressed greater concerns for too much traffic (27.5%/48.3%/63.9%), dangerous intersections (26.4%/42.0%/58.5%) and hills (23.1%/48.3%/62.7%) along the school route and increased trip chaining convenience (41.3%/72.0%/84.2%) (all p<0.001).

Conclusions: Adolescents’ perceptions of walking and cycling to school differed by distance to school. With increasing distance to school, social support for both modes decreased while personal, environmental and safety barriers became more pronounced. Absence of cycle lanes and low interest in cycling to school were consistent barriers across all distance categories. Thus, distance to school needs to be accounted for in active transport to school initiatives and walking- and cycling-specific barriers tackled.