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<p>Green space activity exposure (GSAE) is increasingly recognized as an important environmental determinant of health, particularly among older adults. However, the complex, multidimensional health effects of GSAE remain insufficiently explored using advanced quantitative approaches. Understanding the dose-response relationships between GSAE parameters and health outcomes is essential for informing public health strategies and age-friendly urban planning. A cross-sectional survey was administered to 744 older adults across five urban parks in Fuzhou, China, collecting data on weekly GSAE frequency, duration, intensity (measured as metabolic equivalents, METs), and total activity volume. Multidimensional health was assessed using validated self-reported instruments encompassing psychological, physiological, social, and overall health domains. Generalized additive models (GAMs) were used to identify both linear and non-linear association patterns, potential threshold effects, and diminishing returns. The GAM analysis revealed non-linear association patterns between GSAE parameters and all four health dimensions. Most associations exhibited threshold effects, with associations appearing more pronounced in this sample around 7–12 sessions per week, 15–22 h of activity, moderate intensity (2.5–4.1 METs), and 40–90 MET-hours in total. Notably, social health also demonstrated a consistent positive linear association pattern with increasing levels of GSAE. Beyond these ranges, the associations tended to plateau or weaken. This study provides exploratory evidence that GSAE is associated with variations in multidimensional health among older adults. The findings underscore the value of integrating exposure-based indicators into environmental health research and urban policy. GAMs offer a flexible analytical tool to explore and visualize potential exposure ranges, which may help generate hypotheses for future research and planning considerations.</p>

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Green space activity exposure and linear/non-linear associations with multidimensional health among older adults: evidence from a generalized additive model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Yuanjing Wu,
  • Xinhui Fei,
  • Hanqing Zheng,
  • Yijing Cai,
  • Wei Zhou,
  • Huimin Zhang,
  • Qunyue Liu,
  • Siren Lan

摘要

Green space activity exposure (GSAE) is increasingly recognized as an important environmental determinant of health, particularly among older adults. However, the complex, multidimensional health effects of GSAE remain insufficiently explored using advanced quantitative approaches. Understanding the dose-response relationships between GSAE parameters and health outcomes is essential for informing public health strategies and age-friendly urban planning. A cross-sectional survey was administered to 744 older adults across five urban parks in Fuzhou, China, collecting data on weekly GSAE frequency, duration, intensity (measured as metabolic equivalents, METs), and total activity volume. Multidimensional health was assessed using validated self-reported instruments encompassing psychological, physiological, social, and overall health domains. Generalized additive models (GAMs) were used to identify both linear and non-linear association patterns, potential threshold effects, and diminishing returns. The GAM analysis revealed non-linear association patterns between GSAE parameters and all four health dimensions. Most associations exhibited threshold effects, with associations appearing more pronounced in this sample around 7–12 sessions per week, 15–22 h of activity, moderate intensity (2.5–4.1 METs), and 40–90 MET-hours in total. Notably, social health also demonstrated a consistent positive linear association pattern with increasing levels of GSAE. Beyond these ranges, the associations tended to plateau or weaken. This study provides exploratory evidence that GSAE is associated with variations in multidimensional health among older adults. The findings underscore the value of integrating exposure-based indicators into environmental health research and urban policy. GAMs offer a flexible analytical tool to explore and visualize potential exposure ranges, which may help generate hypotheses for future research and planning considerations.