Abstract
Objectives
To identify multidomain predictors of functional decline among nursing home residents with type 2 diabetes (T2D).Methods
A secondary analysis of longitudinal Minimum Data Set 3.0 data from Iowa nursing home residents with T2D was conducted. Three machine learning models were developed to predict functional decline, defined as worsening Activities of Daily Living Long-Form scores between first and last assessments. Predictor variables were guided by the Functional Consequences Theory and represented biological, psychological, and environmental factors.Results
The sample included 5,440 residents with T2D from 430 nursing homes (mean age 81; 63% female). The multi-class classification model demonstrated the best fit to predict functional decline (AUC = .93). Lower baseline function, poorer cognition, higher depressive symptoms, older age, and a greater number of assessments were the most influential predictors. Residents' belief in improving function, coexisting chronic diseases, and rurality were prominent predictors.Conclusions
Functional decline in residents with T2D reflects interrelated biological, psychological, and environmental vulnerabilities, several of which are modifiable.Clinical implications
Routine assessment of baseline function, cognition, and depressive symptoms may support early risk identification. Interdisciplinary interventions supporting mental health and residents' confidence in functional improvement may improve outcomes, particularly in rural and medically complex populations.