A machine learning model for frailty based on wearable device measurements

Frailty is an important factor in human aging associated with a broad range of adverse outcomes. Frailty metrics are time intensive to collect making them difficult for larger scale application.

We apply machine learning to predict these frailty metrics, associated risk factors, and adverse outcomes from activity data. We use activity data collected using Actigraphy wearable accelerometer sensors, which are devices that measure acceleration along three axes of movement. Models were evaluated using Area Under the receiver operator Curve (AUC), Area Under Precision Recall Curve (AUPRC), Spearman rank test, Mann-Whitney U test, or Kruskal-Wallis test on repeated subsampling of train and test sets. All statistical tests are reported using -log₁₀(P-value).

Machine learning models show strong predictive performance even with small amounts of accelerometry data available. They are also able to better determine adverse outcomes such as hospitalization and mortality than frailty metrics themselves in our geriatric population.

This approach of wearable activity data-based prediction of frailty offers a surrogate (proxy or estimate) for determining frailty metrics in a scalable manner. It can also be used to determine adverse outcomes such as hospitalizations and mortality, allowing frailty to be used as a metric in other studies or medical practices.