TY  - JOUR
AU  - Xu, Qidi
AU  - Kim, Yejin
AU  - Chung, Karen
AU  - Schulz, Paul
AU  - Gottlieb, Assaf
PY  - 2024
DA  - 2024/7/18
TI  - Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers
JO  - JMIR Form Res
SP  - e55575
VL  - 8
KW  - mild cognitive impairment
KW  - Fitbits
KW  - fitness trackers
KW  - sleep
KW  - physical activity
AB  - Background: Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). Objective: This study aimed to use data collected from fitness trackers to predict MCI status. Methods: In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. Results: Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. Conclusions: Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e55575
UR  - https://doi.org/10.2196/55575
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39024003
DO  - 10.2196/55575
ID  - info:doi/10.2196/55575
ER  -