Physical activity (PA) is influenced by the built environment. However, little is known about the types of built environment where adults spend their time, and at what levels of PA they engage in those environments. Understanding the effect of the built environment on PA requires insight into PA behavior at different types of locations (e.g., home, work, shopping centers, and sports facilities). Therefore, this study describes where adults aged 45–65 years were active with moderate-to-vigorous intensity (MVPA), and examines associations of socio-demographic factors and neighborhood with MVPA at these locations. Participants’ (N = 308) PA was measured for seven days using accelerometers and GPS-devices. Adults spent most minutes of MVPA at home and work. Highest MVPA-ratios of total time spent at a location were achieved in sports facilities and during transport. Neighborhood characteristics and socio-demographic factors such as work status, health status and household structure, had significant effects on MVPA at various locations and on total MVPA. Understanding PA behavior at various locations may provide insights that allow professionals in different domains (e.g., health, landscaping, urban planning) to develop strategies to stimulate PA.

BackgroundTo improve our understanding of the neighborhood environment – physical activity (PA) relationship, it is of importance to assess associations between neighborhood environmental characteristics and neighborhood-based PA.MethodsParticipants’ (N = 308; 45–65 years) light PA (LPA) and moderate-vigorous PA (MVPA) within a 400, 800, and 1600 m buffer around adults’ homes was measured using accelerometers and GPS-devices. Land use data in ArcGIS provided neighborhood characteristics for the same buffers. Multilevel linear regression models, adjusted for socio-demographic variables and attitude towards PA, were used to assess associations of objective neighborhood characteristics with neighborhood-based LPA and MVPA.ResultsLPA was positively associated with the proportions of roads (within a 400 m buffer), and negatively associated with the proportions of recreational areas (within an 800 m buffer), and the proportion of green space (within the 800 m and 1600 m buffers). Multiple characteristics of 400 m buffers were positively associated with MVPA, i.e. proportions of green space, blue space, residences, shops and foodservice industry, sports terrain, and public social-cultural facilities. Also, characteristics of larger buffers were positively associated with MVPA, i.e. the proportions of shops and foodservice industry, sports terrain, and blue space (within an 800 m buffer), and the proportion of public social-cultural facilities (within the 800 m and 1600 m buffers).ConclusionsObjective neighborhood characteristics of smaller as well as larger sized buffers were associated with neighborhood-based LPA and MVPA. Green and blue spaces seem to be of particular importance for PA in the smallest buffer, i.e. in the direct surrounding of adults’ homes.

BackgroundThe number of sports facilities, sports clubs, or city parks in a residential neighbourhood may affect the likelihood that people participate in sports and their preferences for a certain sports location. This study aimed to assess whether objective physical and socio-spatial neighbourhood characteristics relate to sports participation and preferences for sports locations.MethodsData from Dutch adults (N = 1201) on sports participation, their most-used sports location, and socio-demographic characteristics were collected using an online survey. Objective land-use data and the number of sports facilities were gathered for each participant using a 2000-m buffer around their home locations, whereas socio-spatial neighbourhood characteristics (i.e., density, socio-economic status, and safety) were determined at the neighbourhood level. A discrete choice-modelling framework (multinomial probit model) was used to model the associations between neighbourhood characteristics and sports participation and location.ResultsHigher proportions of green space, blue space, and the number of sports facilities were positively associated with sports participation in public space, at sports clubs, and at other sports facilities. Higher degrees of urbanization were negatively associated with sports participation at public spaces, sports clubs, and other sports facilities.ConclusionsThose with more green space, blue space or sports facilities in their residential neighbourhood were more likely to participate in sports, but these factors did not affect their preference for a certain sports location. Longitudinal study designs are necessary to assess causality: do active people choose to live in sports-facilitating neighbourhoods, or do neighbourhood characteristics affect sports participation?

Background Having a physically active lifestyle after cancer diagnosis is beneficial for health, and this needs to be continued into survivorship to optimize long-term benefits. We found that patients, who participated in an 18-week exercise intervention, reported significant higher physical activity (PA) levels 4 years after participation in a randomized controlled trial of supervised exercise delivered during chemotherapy (PACT study). This study aimed to identify social-ecological correlates of PA levels in breast and colon cancer survivors 4 years after participation in the PACT study. Methods Self-reported PA levels and potential correlates (e.g. physical fitness, fatigue, exercise history, and built environment) were assessed in 127 breast and colon cancer survivors shortly after diagnosis (baseline), post-intervention and 4 years later. Multivariable linear regression analyses were performed to identify social-ecological correlates of PA 4 years postbaseline. Results The final model revealed that lower baseline physical fatigue (β =-0.25, 95% CI-0.26;-0.24) and higher baseline total PA (0.06, 95% CI, 0.03; 0.10) were correlated with higher total PA levels 4 years post-baseline. Higher baseline leisure and sport PA (0.02, 95% CI 0.01; 0.03), more recreational facilities within a buffer of 1 km (4.05, 95% CI = 1.28; 6.83), lower physical fatigue at 4-year follow-up (-8.07, 95% CI-14.00;-2.13), and having a