Analysis of Participation Patterns and Injury-Triggering Factors in Elderly Grassroots Sports Using Association Rule Mining

TL;DR

This study uses data mining to find patterns linking how elderly people participate in sports with their injury risks, aiming to improve safety guidelines.

Contribution

The study introduces association rule mining to uncover complex injury-triggering patterns in elderly sports participation.

Findings

Irregular late-night participation in non-sport-specific locations increases injury risk.

Excessive movement during prolonged morning sports participation is linked to joint injuries.

Injuries commonly affect upper and lower limb joints in older adults.

Abstract

This study aimed to identify structural relationships between sports participation patterns and injury risk factors among older adults using data mining techniques, addressing limitations of prior descriptive research. Data from the 2024 Sports Safety Accident Survey were analyzed, including 352 adults aged 65 years and older. Eight key variables related to participation and injury were examined using association rule analysis with the Apriori algorithm. High-risk injury patterns were associated with irregular participation in non-sport-specific locations during late-night hours and with excessive movement during prolonged, daily participation, particularly in the morning. Injuries most frequently affected major joints of the upper and lower limbs. Sports injuries in older adults arise from complex interactions among temporal, environmental, and behavioral factors. These findings support the development of targeted safety guidelines and injury prevention strategies tailored to participation patterns in the aging population.