Abstract
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.
Plain language summary
Frailty occurs during human aging and is associated with a broad range of unfavourable outcomes. Frailty is measured using various scores but these often rely on subjective information, are labor intensive to measure, and are not assessed over time. This work presents objective measures indicative of frailty, based on wearable sensors that measure movement. This was tested in a group of people with an average age of 75. Application of a computational model using this data enabled long-term outcomes, including hospitalization and death, to be more accurately predicted than using existing frailty measures. This work demonstrates that 48 hours of data collection per patient is sufficient. This type of system could be used on a larger number of people, enabling those at risk of unfavourable outcomes to be targeted with medical interventions or support.
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Data availability
Requests for original data can be handled through (www.ciberfes.es/) via a review committee. The hospital reviews and determines the purposes for the data requests and what data can be released. Data requests can be sent to: Research and teaching unit, Virgen del Valle Hospital Ctra. Cobisa S/N, 45071 Toledo – Spain, info@estudiotoledo.com. All procedures were approved by the Clinical Research Ethics Committee of the Toledo Hospital and were conducted in accordance with the Declaration of Helsinki for human studies.
Supplemental data can be accessed either as the included.csv files or as.rda files available at the github repository in the results folder https://github.com/tripodlaboratories/actigraphy-frailty/tree/main/results52. The included supplementary data files 1, 2, 4 and 8 contain results (rho, p-values, and RMSE) for each individual model repeat of FTS components, risk factors & outcomes, FTS5 components, and the various frailty metrics respectively. Supplementary Data 3, 5, 6 and 7 contain the average model repeat prediction evaluations for risk factors & outcomes, FTS5 components, FTS components, and the various frailty metrics respectively. Actigraphy activity data and descriptions of clinical outcomes, risks, and frailty components are provided as.csv’s in the data folder of the associated github https://github.com/tripodlaboratories/actigraphy-frailty/tree/main/data52.
Code availability
Code necessary to reproduce results, figures, and models can be found at https://github.com/tripodlaboratories/actigraphy-frailty52. This code was run using R 4.1.2 and xgboost 1.5.2.1 on a macOS 12.3.1 system. There are no restrictions on its access or use.
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We acknowledge funding from the NIH R35GM138353 (to N.A.).
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A.C.—Conceptualisation, Methodology, Analysis Design, Analysis, Writing, Visualization, Data Curation. A.M.—Conceptualisation, methodology, Data Acquisition, Writing. K.S., F.J.G.-G., J.L.-R., L.M.A., and L.R.-M.—Writing, Data Interpretation. A.L.C., C.E., D.D.F., T.P., M.B., M.X., N.G.R., R.F., B.G., and M.A.—Review and Editing, Analysis Design. I.A., and N.A.—Conceptualisation, Review and Editing, Supervising, Project Administrating
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Culos, A., Manas, A., Shidara, K. et al. A machine learning model for frailty based on wearable device measurements. Commun Med (2026). https://doi.org/10.1038/s43856-026-01419-7
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DOI: https://doi.org/10.1038/s43856-026-01419-7
