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

Aug 5, 2026

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Early Alzheimer’s disease signs are difficult to detect, delaying diagnosis until substantial brain damage has already occurred while treatments have limited impact on slowing progression. Detecting cognitive decline early is crucial so patients can change lifestyle habits and consider new therapies. This pilot study uses longitudinal fitness-tracker data to predict mild cognitive impairment (MCI) status noninvasively and cost-effectively. Results show perfect separation between MCI and controls (AUC=1.0) using heart-rate zones, resting heart rate, deep sleep duration, and activity time.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Background

Objective

Methods

Results

Conclusions

Introduction

Rationale for early detection of Alzheimer disease

Need for noninvasive biomarkers

Sleep and heart metrics as candidate biomarkers

JMIR FORMATIVE RESEARCH Xu et al Original Paper Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers | Qidi Xu1, MSc; Yejin Kim 1, PhD; Karen Chung2, BSc; Paul Schulz2, MD; Assaf Gottlieb1, PhD | | --- | | 1McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States 2McGovern 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](assaf.gottlieb@uth.tmc.edu)
Abstract | | 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.
(JMIR Form Res 2024;8:e55575) doi: 10.2196/55575 | KEYWORDS mild cognitive impairment; Fitbits; fitness trackers; sleep; physical activity Introduction Alzheimer disease (AD) is the sixth leading cause of death in the United States and incurs a heavy economic burden of US $257 billion in direct costs [1] . The numbers are staggering—11.3% of Americans aged 65 years and older have Alzheimer dementia, and more than twice as many are anticipated to have Alzheimer dementia by 2050 [1] . Moreover, estimates suggest that 46.7 million Americans are already in a preclinical AD stage [2]. AD diagnoses are often significantly delayed to time points when brain damage has already occurred, owing to difficulty in detecting early signs of AD due to cost and effort, which makes it critical to identify efficient methods for early detection of AD signs. One pillar of AD programs, like the National Alzheimer's Project Act, focuses on early diagnosis of AD, as it allows patients to make lifestyle changes and consider new treatment options. Early diagnosis and identification of the trajectory of cognitive decline would also [https://formative.jmir.org/2024/1/e55575](https://formative.jmir.org/2024/1/e55575) XSL• FO RenderX JMIR Form Res 2024 | vol. 8 | e55575 | p. 1 (page number not for citation purposes) allow pharmaceutical companies to develop better therapeuticsto delay or halt the progression to AD and support enrollment in experimental trials. A growing body of evidence indicates that cognitive, sensory, and motor changes may precede clinical manifestations of AD by seve

FAQs

What is the goal of this pilot study?

To use data collected from fitness trackers to predict mild cognitive impairment (MCI) status.

How were the fitness-tracker data collected in this study?

Twenty participants wore fitness trackers for up to one month, providing physical activity, heart rate, and sleep data.

Which features contributed most to the machine learning predictions?

Peak, cardio, and fat burn heart rate zones, resting heart rate, average deep sleep time, and total light activity time.

Aug 5, 2026

Upload by Maeve