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.