Influence of Neighbourhood Characteristics on Sports Club Participation

Overview of Research Context

Researchers have investigated whether the participation of residents in sports clubs is affected by their neighbourhood characteristics, particularly the presence of green or blue spaces. Engaging in physical activity offers numerous benefits, and being part of a sports club has been shown to significantly enhance psychological and social well-being. However, societal changes have led to a decline in participation in traditional sports clubs, with individual activities like running, cycling, and gym workouts becoming more popular. This shift has resulted in increased use of informal venues, such as parks and outdoor areas, for sports activities.

Importance of Understanding Participation Factors

Gaining insight into the factors that influence sports participation is crucial for developing policies aimed at increasing involvement in sports. While earlier studies have concentrated on whether individuals participate in sports and how frequently, there is a scarcity of information regarding how neighbourhood characteristics impact sports participation. Although it may seem logical that residents near walking trails or sports facilities are more likely to engage in physical activities, empirical research is needed to validate these assumptions.

Research Study on Neighbourhood Characteristics

Survey Methodology

To address this gap, Deelen and colleagues conducted an online survey in September 2014 focusing on how neighbourhood characteristics—such as land use, availability of sports facilities, urban density, socio-economic status, and safety—affect residents’ sports participation and preferred locations for sporting activities. Their findings were published in a recent issue of BMC Public Health, encompassing data from 1,663 adults aged 18 to 80 across six municipalities in the Netherlands.

Categories of Sports Participation

Participants’ sports involvement was categorized into four groups:
1. No sports participation (less than once per month or no participation at all)
2. Sports participation in public open spaces
3. Membership in a sports club (e.g., soccer, hockey)
4. Participation in public or private sports facilities (e.g., gyms, swimming pools)

Land Use Classification

Land use was classified into several categories, including:
– Roads
– Facilities (such as churches, hospitals, shops, restaurants, and educational institutions)
– Green space (including parks, allotments, forests, and moorland)
– Blue space (e.g., rivers, lakes, and seas)

Socio-spatial data encompassed urban density, neighbourhood safety, and socio-economic status.

Findings on Sports Participation

Impact of Environmental Factors

From the 1,663 survey participants, 1,201 provided complete data for analysis. The researchers discovered that the presence of green space, blue space, and numerous sports facilities significantly positively influenced sports participation in public areas, sports clubs, or sports facilities, with blue space having the greatest impact. Conversely, urbanization negatively affected participation in public spaces, sports clubs, and facilities, while a higher socio-economic status was positively correlated with sports involvement across all types of locations.

Conclusions and Future Directions

These findings indicate that neighbourhood characteristics substantially influence sports participation, with a greater likelihood of engagement in physical activities in areas abundant in green space, blue space, and sports facilities. Although the results underscore the necessity for creating more exercise-friendly environments, further research is essential to fully understand the cause-and-effect relationship between neighbourhood characteristics and sports participation, which can inform policy development.

Reference

Deelen I, Jansen M, Dogterom NJ, Kamphuis CBM and Ettema D. Do objective neighbourhood characteristics relate to residents’ preferences for certain sports locations? A cross-sectional study using a discrete choice modelling approach. BMC Public Health (2017) 17:943 DOI 10.1186/s12889-017-4949-5