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

Big Data Analytics for Elder Care (170) Machine Learning (3154) Disabilities (185) Frailty Detection, Assessment and Prediction (231) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (157)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66723, first published .
Medical professionals review disability prediction model on screen

Development of a Longitudinal Model for Disability Prediction in Older Adults in China: Analysis of CHARLS Data (2015-2020)

Development of a Longitudinal Model for Disability Prediction in Older Adults in China: Analysis of CHARLS Data (2015-2020)

Authors of this article:

Jingjing Chu1 Author Orcid Image ;   Ying Li1 Author Orcid Image ;   Xinyi Wang1 Author Orcid Image ;   Qun Xu2 Author Orcid Image ;   Zherong Xu1 Author Orcid Image
Article Authors Cited by (10) Tweetations Metrics

Journals

  1. Jin T, Halili A. Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data. Frontiers in Artificial Intelligence 2025;8 View
  2. Chen J, Lin Y, Hu R, Hu C. Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS. BMC Psychiatry 2025;25(1) View
  3. Li Z. Digital Pathways to Reducing Depression Among Aging Populations Through the “Broadband China” Pilot Program: Quasi-Natural Experiment. JMIR Aging 2025;8:e79260 View
  4. Mao Y, Guo Z, Gao W, Zhang Y, Jin J. Lagged effects of risk factors on the disability of older adults: A distributed lag non-linear model approach. International Journal of Nursing Sciences 2026;13(1):53 View
  5. Xu J, Ma Y. Identifying Older Adults at Risk for Future Poor Sleep Quality: A Multidimensional Predictive Framework. Clinical Gerontologist 2026:1 View
  6. Xiao G, Yang Q, Bai F, Jia Y, Nie T. Digital inclusion and depressive symptoms among Chinese older adults: The moderating role of cognitive function. DIGITAL HEALTH 2026;12 View
  7. Li X, Guo X, Fu X. Machine Learning-Based Multidimensional Health Decline Prediction Framework: Data-Driven Modeling for the Middle-Aged and Elderly Population. Healthcare 2026;14(14):2223 View
  8. Chen X, Wang Q, Zhao Z, Cheng Z, Jiang Q. Edentulism and incident hip fracture among middle-aged and older adults in China: evidence from the CHARLS. BMC Public Health 2026;26(1) View
  9. Xu J, Ma Y. Prospective multidimensional predictors of poor sleep quality in older adults: A cross‐national study. Applied Psychology: Health and Well-Being 2026;18(4) View
  10. Bao Y, Jiang Z, Wang F, Fang M, Wu Z, Chen Z. Latent profiles and network dynamics of psychological frailty in older adults: a computational simulation study. BMC Geriatrics 2026;26(1) View