Identification of Predictors of Adaptability in Older Adults Based on the Roy Adaptation Model Using Machine Learning

Background: The Callista Roy Adaptation Model posits that adaptation in later life emerges from the interaction among physical, psychological, and social dimensions. However, empirical evidence integrating these domains through predictive approaches remains limited. The aim of this study was to identify the main predictors of adaptive classification in older adult women using functional and subjective well-being measures. Methods: A predictive study was conducted in older adult women enrolled in community-based exercise programs. Assessments included the Senior Fitness Test and the SF-12 and WHO-5 questionnaires. Multiclass classification models were trained, with Random Forest selected due to superior performance. Model evaluation incorporated oversampling strategies and robustness analyses without oversampling, using metrics resilient to class imbalance (macro-F1 and balanced accuracy). Model interpretability was examined through variable importance analysis, partial dependence, and ICE plots. Results: Under the oversampling framework, the Random Forest model achieved an overall accuracy of 74% and a macro-F1 score of 0.73, with reduced performance observed in robustness analyses, particularly for the minority "High" class. The most influential predictors were the physical component of the SF-12, the 2 min step test, the mental component of the SF-12, and the chair sit-and-reach test. Conclusions: The findings highlight the joint contribution of physical and psychosocial factors to adaptive processes, in alignment with the Roy Adaptation Model. This study provides exploratory evidence supporting the integrated use of the SFT, SF-12, and WHO-5; however, external validation and longitudinal evaluation are required prior to clinical implementation.