Exploring outdoor activity limitation (OAL) factors among older adults using interpretable machine learning.

backgroundThe occurrence of outdoor activity limitation (OAL) among older adults is influenced by multidimensional and confounding factors associated with aging.

aimThe aim of this study was to apply interpretable machine learning (ML) to develop models for multidimensional aging constraints on OAL and identify the most predictive constraints and dimensions across multidimensional aging data.

methodsThis study involved 6794 community-dwelling participants older than 65 from the National Health and Aging Trends Study (NHATS). Predictors included related to six dimensions: sociodemographics, health condition, physical capacity, neurological manifestation, daily living habits and abilities, and environmental conditions. Multidimensional interpretable machine learning models were assembled for model construction and analysis.

resultsThe multidimensional model demonstrated the best predictive performance (AUC: 0.918) compared to the six sub-dimensional models. Among the six dimensions, physical capacity had the most remarkable prediction (AUC: physical capacity: 0.895, daily habits and abilities: 0.828, physical health: 0.826, neurological performance: 0.789, sociodemographic: 0.773, and environment condition: 0.623). The top-ranked predictors were SPPB score, lifting ability, leg strength, free kneeling, laundry mode, self-rated health, age, attitude toward outdoor recreation, standing time on one foot with eyes open, and fear of falling. DISCUSSION: Reversible and variable factors, which are higher in the set of high-contribution constraints, should be prioritized as the main contributing group in terms of interventions.

conclusionThe integration of potentially reversible factors, such as neurological performance in addition to physical function into ML models, yields a more accurate assessment of OAL risk, which provides insights for targeted, sequential interventions for older adults with OAL.