Abstract
BACKGROUND: Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data. METHODS: We used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets. RESULTS: Area under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression. CONCLUSIONS: Machine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.