Abstract 11546: Functional Co-Clustering of Physical Activity Patterns Among Sexual and Gender Minority Adults

Abstract

Introduction: Physical activity (PA) is a important modifiable risk factor for cardiovascular disease (CVD) and CVD mortality. There is growing evidence that sexual and gender minority (SGM; lesbian/gay, bisexual, transgender) adults are at higher risk of insufficient PA and CVD. Accelerometers allow for collection of granular PA data that can identify individual- and temporal heterogeneity in daily patterns. To date, no studies have investigated accelerometry-based PA patterns among SGM adults. Goal: To characterize daily PA trajectories among SGM adults and identify distinct phenotypes using a time series-based clustering technique. Methods: We recruited an online sample of healthy SGM adults in the United States who completed continuous wrist-worn accelerometry for 30 days to collect step counts. For clustering, we used a functional latent block models (FLBMs), where each 24-hour period was a Fourier-smoothed data curve. FLBMs allow hierarchical time-series data clustering by simultaneously clustering person- and day-level data. The optimal number of clusters was selected using the integrated completed likelihood (ICL) criterion. Results: Forty-two SGM adults with a mean age of 27.0 years (+/- 7.7) provided 1207 person-level days of accelerometry (range=6-31 days/person). A final model with 4 blocks (2 x 2 clusters) provided the best fit (ICL= -70086.4). Two person-level clusters were identified, which were characterized by differences in the amount and distribution of steps throughout the day were identified. Cluster 1 (n=26) had higher overall steps counts with significant morning and evening increases in step counts. Cluster 2 (n=16) had fewer steps that were within a narrower time period. We further identified 2 day-level clusters. Cluster 2 (n=14) had a wider temporal distribution of step counts and a higher variance on weekend days vs. weekdays relative to Cluster 1 (n=17). Conclusions: This is the first study to elucidate daily PA trajectories in SGM people. Using FLBM, we accounted for individual heterogeneity and relations among days. Findings can help identify individuals at increased risk of physical inactivity and subsequent negative health outcomes, providing important knowledge to inform behavioral interventions.