Detail publikace

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study

SUKEI, E. ROMERO-MEDRANO, L. DE LEON MARTINEZ, S. HERRERA, J. CAMPANA-MONTES, J. OLMOS, M. BACA-GARCIA, E. ARTES, A.

Originální název

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning-based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage errors of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning-based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions-an important aspect in clinical practice.

Klíčová slova

WHODAS; functional limitations; mobile sensing; passive ecological momentary assessment; predictive modeling; interpretable machine learning; machine learning; disability; clinical outcome 

Autoři

SUKEI, E.; ROMERO-MEDRANO, L.; DE LEON MARTINEZ, S.; HERRERA, J.; CAMPANA-MONTES, J.; OLMOS, M.; BACA-GARCIA, E.; ARTES, A.

Vydáno

30. 10. 2023

ISSN

2561-326X

Periodikum

JMIR Formative Research

Ročník

7

Číslo

2023

Stát

Kanada

Strany od

1

Strany do

10

Strany počet

10

URL

https://formative.jmir.org/2023/1/e47167/authors

BibTex

@article{BUT186905,
  author="SUKEI, E. and ROMERO-MEDRANO, L. and DE LEON MARTINEZ, S. and HERRERA, J. and CAMPANA-MONTES, J. and OLMOS, M. and BACA-GARCIA, E. and ARTES, A.",
  title="Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study",
  journal="JMIR Formative Research",
  year="2023",
  volume="7",
  number="2023",
  pages="1--10",
  doi="10.2196/47167",
  issn="2561-326X",
  url="https://formative.jmir.org/2023/1/e47167/authors"
}