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This paper is in the following e-collection/theme issue:

mHealth for Telemedicine and Homecare (894) Use and User Demographics of mHealth (512) Use and Perceptions of Technology in Everyday Life and for Rehabilitation (95) User Needs and Competencies (600) Machine Learning (3313) Stroke (327) Supporting Home Care and Family Caregivers (309)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73903, first published .
Young Asian woman relaxing on a couch, looking at her smartphone.

Interpretable Machine Learning Models for Analyzing Determinants Affecting the Use of mHealth Apps Among Family Caregivers of Patients With Stroke in Chinese Communities: Cross-Sectional Survey Study

Interpretable Machine Learning Models for Analyzing Determinants Affecting the Use of mHealth Apps Among Family Caregivers of Patients With Stroke in Chinese Communities: Cross-Sectional Survey Study

Authors of this article:

Yun Du1 Author Orcid Image ;   Jun-Ying Fan1, 2 Author Orcid Image ;   Guang-Zhi Liu1 Author Orcid Image ;   Zi-Yue Yang1 Author Orcid Image ;   Yang Lei3 Author Orcid Image ;   Yu-Fang Guo1 Author Orcid Image
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Journals

  1. Shen Y, Chen M, Wang X, Qi Z, Qiu X. Technophobia Among Older Adults in the Context of Smart Healthcare: A Scoping Review. Clinical Gerontologist 2026:1 View
  2. Zheng Y, Ma S, Liu X, Yang Y, Gan R, Lai J, Qi Y, Li J, Wang L, Chen M. Clinical risk stratification of stroke caregiver distress: an interpretable nonlinear model of caregiving-load categories. Frontiers in Psychology 2026;17 View
  3. Gwiasda M, Martin D, Jenetzky E. Navigating childhood fever: A registry-based analysis of parental engagement patterns and information needs in mHealth. DIGITAL HEALTH 2026;12 View