Environmental, Demographic, and Temporal Determinants of Physical Activity Incidence in Urban Parks in Cape Town, South Africa: A Spatial Econometric Approach.
1
Health through Physical Activity, Lifestyle, and Sport (HPALS) Research Centre, Faculty of Health Sciences, University of Cape Town, University of Cape Town, Cape Town, South Africa
2
Health Economics Unit, School of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
3
School of Health and Medical Sciences, Faculty of Health, Engineering & Sciences, University of Southern Queensland, Toowomba, Australia
4
Programmes, Events & Partnerships, Recreation and Parks Department, Community Services and Health, Cape Town, South Africa
5
Research and Knowledge Management, Recreation and Parks, Community Services and Health, Cape Town, South Africa
Popul. Med. 2026;8(Supplement Supplement 1):A1400
ABSTRACT
BACKGROUND:
Creating safe spaces for vulnerable populations is essential for physical and mental well-being. Outdoor gyms (OGs) are frequently integrated into the built environment of parks in the City of Cape Town (CCT). The design of OGs provides critical opportunities for everyone to engage in physical activity (PA). This observational study determined the extent and pattern of use for recreational facilities and OGs by children across both low- and high-income areas.
METHODS:
A cross-sectional study was performed across 17 parks and recreational facilities in the CCT. Data collection utilized an adaptation of the System of Observation for Play and Recreation in Communities (SOPARC) tool and GIS mapping to record user numbers, age groups, and PA intensity. Spatial econometric models (SAR, SEM) were utilized to control for neighbourhood effects and determine the influence of environmental, temporal, and demographic factors on PA incidence. Diagnostic tests, including Moran’s I, were used to assess spatial autocorrelation.
RESULTS:
Ordinary Least Squares regression (R2 = 0.84) identified areas specifically designated for moderate-to-vigorous PA (Tarea_MPA) as the primary significant predictor of PA incidence (p < 0.01). Spatial modelling (SAR and SEM) provided a refined fit (Pseudo R2 = 0.85), revealing that being an OG user (p < 0.05) and weekend usage (p < 0.05) also significantly influenced activity levels. Increased Tarea_MPA and weekend usage correlated with higher PA incidence, whereas OG user status showed a negative association. Moran’s I tests (p > 0.30) confirmed the absence of significant spatial autocorrelation, validating the robustness of these predictors.
CONCLUSIONS:
The study demonstrates that PA incidence is driven more by localized site characteristics and timing than by neighbouring geographic influence. Urban health initiatives should prioritize the expansion of areas designed to encourage MPA and tailor initiatives for different user experience levels to effectively boost community engagement to stay healthy.
Creating safe spaces for vulnerable populations is essential for physical and mental well-being. Outdoor gyms (OGs) are frequently integrated into the built environment of parks in the City of Cape Town (CCT). The design of OGs provides critical opportunities for everyone to engage in physical activity (PA). This observational study determined the extent and pattern of use for recreational facilities and OGs by children across both low- and high-income areas.
METHODS:
A cross-sectional study was performed across 17 parks and recreational facilities in the CCT. Data collection utilized an adaptation of the System of Observation for Play and Recreation in Communities (SOPARC) tool and GIS mapping to record user numbers, age groups, and PA intensity. Spatial econometric models (SAR, SEM) were utilized to control for neighbourhood effects and determine the influence of environmental, temporal, and demographic factors on PA incidence. Diagnostic tests, including Moran’s I, were used to assess spatial autocorrelation.
RESULTS:
Ordinary Least Squares regression (R2 = 0.84) identified areas specifically designated for moderate-to-vigorous PA (Tarea_MPA) as the primary significant predictor of PA incidence (p < 0.01). Spatial modelling (SAR and SEM) provided a refined fit (Pseudo R2 = 0.85), revealing that being an OG user (p < 0.05) and weekend usage (p < 0.05) also significantly influenced activity levels. Increased Tarea_MPA and weekend usage correlated with higher PA incidence, whereas OG user status showed a negative association. Moran’s I tests (p > 0.30) confirmed the absence of significant spatial autocorrelation, validating the robustness of these predictors.
CONCLUSIONS:
The study demonstrates that PA incidence is driven more by localized site characteristics and timing than by neighbouring geographic influence. Urban health initiatives should prioritize the expansion of areas designed to encourage MPA and tailor initiatives for different user experience levels to effectively boost community engagement to stay healthy.
| eISSN: | 2654-1459 |
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