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Background <p>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.</p> Methods <p>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 -log<sub>10</sub>(P-value).</p> Results <p>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.</p> Conclusions <p>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.</p>

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A machine learning model for frailty based on wearable device measurements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

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  • Anthony Culos,
  • Asier Manas,
  • Kie Shidara,
  • Ramin Fallazadeh,
  • Francisco Jose Garcia-Garcia,
  • Jose Losa-Reyna,
  • Luis M. Alegre,
  • Leocadio Rodriguez-Manas,
  • Alan L. Chang,
  • Camilo Espinosa,
  • Davide De Francesco,
  • Thanaphong Phongpreecha,
  • Martin Becker,
  • Maria Xenochristou,
  • Neal G. Ravindra,
  • Brice Gaudilliere,
  • Martin S. Angst,
  • Ignacio Ara,
  • Nima Aghaeepour

摘要

Background

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.

Methods

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).

Results

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.

Conclusions

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.