Wearables & Cognitive Function: Electronic Framingham Heart Study

Sun, 01 Sep 2024 00:00:00 +0000

Overview

Wearable devices and smartphones now generate continuous, high-resolution behavioral and physiological data at a scale that was impossible to collect in traditional epidemiological studies. The Electronic Framingham Heart Study (eFHS) is one of the first large cohort studies to integrate this data at scale, capturing smartwatch-derived measures from thousands of participants over extended follow-up periods.

This PhD project analyzes that data to investigate whether and how digital biomarkers derived from wearables (activity patterns, heart rate variability, sleep signatures) associate with cognitive function in older adults.

Scale

The dataset spans more than 1 billion observations, requiring statistical methods and computational pipelines designed for large-scale longitudinal data, not just larger versions of standard analyses.

Methods

Longitudinal analysis: Repeated measures and mixed-effects models for continuous wearable data streams
Sampling-based models: Methods that handle data density and irregular observation timing
Digital biomarker derivation: Processing raw sensor data into interpretable summary features
Tools: R, SAS, and shell scripting for large-scale data pipelines on HPC environments

Selected Output

He, J., et al. Associations Between Smartwatch-Derived Measures and Cognitive Function: Findings from the Electronic Framingham Heart Study. In Review, 2025.
Zhang, Y., …, He, J., et al. Factors Associated with Longitudinal Digital Survey Engagement and Smartwatch Usage in the Electronic Framingham Heart Study. In Review, 2025.

Advisor

Prof. Chunyu Liu, Department of Biostatistics, Boston University School of Public Health