Utilizing Fitness Trackers for Early Detection of Mild Cognitive Impairment: A Pilot Study on Non-Invasive Digital Biomarkers

Poster Number: P123
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Mobile Health, Machine Learning, Disease Models, Biomarkers
Primary Track: Applications

Background: Early signs of Alzheimer's 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 in predicting conversion from normal to mild cognitive impairment (MCI).
Objective: Use data collected from fitness trackers to predict MCI status.
Methods: In this pilot study, fitness trackers were worn by 20 participants: twelve MCI and eight age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to a 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 (AUC=1.0). The top predictive features from the model include 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 control and MCI patients 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.

Speaker(s):
Qidi Xu, PhD student
University of Texas Health Science Center at Houston