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

Information and Patient Education on Healthy Aging (52) Diagnostic Tools in Mental Health (326) Development and Evaluation of Research Methods, Instruments and Tools (744) Machine Learning (2076) Dementia and Cognitive Decline (696) Diagnostic Tools in Mental Health (326) Methods and New Tools in Mental Health Research (558) Development and Evaluation of Research Methods, Instruments and Tools (744) Methods and New Tools in Mental Health Research (558) Machine Learning (2076) Dementia and Cognitive Decline (696)

Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53240, first published .
Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Authors of this article:

Collin Sakal1 Author Orcid Image ;   Tingyou Li1 Author Orcid Image ;   Juan Li2 Author Orcid Image ;   Xinyue Li1 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

  1. Herrera C, Gimenes F, Herrera J, Cavalli R. Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning–Based Design Thinking Study. JMIR Research Protocols 2024;13:e55466 View
  2. Sakal C, Li T, Li J, Li X. Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study. npj Aging 2024;10(1) View
  3. Tew V, Barathan M, Nordin F, Law J, Ng M. Emerging Role of Mesenchymal Stromal Cell and Exosome Therapies in Treating Cognitive Impairment. Pharmaceutics 2025;17(3):284 View
  4. Sakal C, Chen T, Xu W, Zhang W, Yang Y, Li X. Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4–8 hours before sleep onset. SLEEP 2025;48(8) View
  5. Zhai X, Wang R, Liu R, Jiang D, Yu X. IADL for identifying cognitive impairment in Chinese older adults: insights from cross-lagged panel network analysis. BMC Geriatrics 2025;25(1) View
  6. Liu D, Tian Y, Liu M, Yang S. Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability. BMC Public Health 2025;25(1) View
  7. Sha T, Zhang Y, Wei J, Li C, Zeng C, Lei G, Wang Y. Sarcopenia and Risk of Cognitive Impairment: Cohort Study and Mendelian Randomization Analysis. JMIR Aging 2025;8:e66031 View
  8. Wang Y, Wang N, Zhao Y, Wang X, Nie Y, Ding L. Construction of a predictive model for cognitive impairment among older adults in Northwest China. Frontiers in Aging Neuroscience 2025;17 View