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

Formative Evaluation of Digital Health Interventions (3279) Fitness Trackers and Smart Pedometers/Accelerometers (674) Artificial Intelligence (2168) Dementia and Cognitive Decline (705) Wearable Devices and Sensors (1151) Machine Learning (2100)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55575, first published .
Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Authors of this article:

Qidi Xu1 Author Orcid Image ;   Yejin Kim1 Author Orcid Image ;   Karen Chung2 Author Orcid Image ;   Paul Schulz2 Author Orcid Image ;   Assaf Gottlieb1 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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  3. Cook D, Schmitter-Edgecombe M. Deep learning for early detection of mild cognitive impairment using smart home ambient sensor data. The Clinical Neuropsychologist 2025:1 View

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