Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU150032" target="_blank" >RIV/00216305:26230/23:PU150032 - isvavai.cz</a>
Result on the web
<a href="https://formative.jmir.org/2023/1/e47167/authors" target="_blank" >https://formative.jmir.org/2023/1/e47167/authors</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2196/47167" target="_blank" >10.2196/47167</a>
Alternative languages
Result language
angličtina
Original language name
Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study
Original language description
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
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
JMIR Formative Research
ISSN
2561-326X
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
2023
Country of publishing house
CA - CANADA
Number of pages
10
Pages from-to
1-10
UT code for WoS article
001107546900002
EID of the result in the Scopus database
2-s2.0-85177448861