Step up! Promoting physical activity in aging populations: a machine learning analysis of park characteristics

Mobility is a key aspect of healthy aging, and local parks – as nature-based solutions – can play a vital role in promoting physical activity among older adults. This study examines the park features that encourage physical activity among older adults in Baghushkhaneh Park, Isfahan. A questionnaire survey was conducted with 135 older adults, collecting data on their perceptions and preferences regarding park features and their influence on park usage. The Support Vector Machine algorithm was used to analyze questionnaire results and prioritize the factors influencing mobility. The analysis indicates that natural, physical, social, and psychological aspects are key contributors to mobility in older adults. Features such as greenery, accessibility and inclusivity, space quality, social interaction, maintenance, and safety are the most influential. Among these, greenery is identified as the most important park feature. This study highlights that machine learning techniques offer a distinct advantage over traditional statistical methods, enabling faster and more efficient analysis of complex relationships. The findings emphasize the importance of considering contextual and cultural factors, as well as the characteristics of local parks, in facilitating environments conducive to healthy aging. Beyond methodological innovation, this study demonstrates that machine learning enables faster and more accurate identification of priority features.