# Examining Longitudinal Changes in Accelerometer-Measured Physical Activity in Preventing Cardiovascular Disease with Novel Function Data Analysis Approaches

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $485,003

## Abstract

Physical activity (PA) has been linked to health in many epidemiological studies. Physical
inactivity and sedentary behavior are known risk factors for cardiovascular disease (CVD),
which is the leading cause of death in the United States with more than half a million deaths
among older Americans annually. The relationship between PA and CVD has been
investigated with an increasing emphasis on objectively measured PA using accelerometer-
based activity trackers. Past analyses of accelerometer-measured PA rely heavily on cut-
point based summary metrics that aggregate minute-level PA records into day-level metrics.
However, this aggregate approach loses valuable information on the temporal dependence
of activity levels, and important diurnal patterns of PA are thus lost to subsequent analyses.
 In this project we propose to use a novel functional data analysis framework based
on Riemann manifold model to obtain deeper insights into the role of longitudinal changes in
PA in CVD prevention. Leveraging the highly diverse Women’s Health Initiative Strong &
Healthy (WHISH) trial data, which includes a unique longitudinal substudy on accelerometer-
measured PA, and our strong capacity in statistical methodology development, our overall
objectives in the proposed study are to: 1. elucidate effects of longitudinal changes in PA
diurnal patterns on CVD incidence using the novel Riemann manifold framework; 2. examine
the effectiveness of new metrics derived from our Riemann manifold model in characterizing
longitudinal changes in PA, and 3. further develop statistical and machine learning tools for
longitudinal accelerometer-measured PA. With the proposed study we will address the
deficiencies in current analysis of longitudinal accelerometer-measured PA and its
association with CVD and advance in both statistical methodology and analysis of the role of
longitudinal changes in PA in health. Knowledge of important PA patterns in the prevention
of CVD will also be immensely useful in designing effective interventions to promote healthy
PA habits in CVD prevention.

## Key facts

- **NIH application ID:** 10803641
- **Project number:** 1R01HL166802-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Jingjing Zou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $485,003
- **Award type:** 1
- **Project period:** 2024-01-01 → 2028-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803641

## Citation

> US National Institutes of Health, RePORTER application 10803641, Examining Longitudinal Changes in Accelerometer-Measured Physical Activity in Preventing Cardiovascular Disease with Novel Function Data Analysis Approaches (1R01HL166802-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10803641. Licensed CC0.

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