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

Formative Evaluation of Digital Health Interventions (2226) Fitness Trackers and Smart Pedometers/Accelerometers (543) Artificial Intelligence (1216) Dementia and Cognitive Decline (429) Wearable Devices and Sensors (825) Machine Learning (1401)

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 Tweetations Metrics

Qidi Xu   1 , MSc ;   Yejin Kim   1 , PhD ;   Karen Chung   2 , BSc ;   Paul Schulz   2 , MD ;   Assaf Gottlieb   1 , PhD

1 McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States

2 McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States

Corresponding Author:

  • Assaf Gottlieb, PhD
  • McWilliams School of Biomedical Informatics
  • University of Texas Health Science Center at Houston
  • 7000 Fannin St
  • Houston, TX, 77030
  • United States
  • Phone: 1 7135003698
  • Email: assaf.gottlieb@uth.tmc.edu