Abstract 4125691: A machine learning approach to examining the associations of minority stressors and physical activity among sexual and gender minority adults

Introduction: Physical activity (PA) is an important modifiable factor for cardiovascular disease (CVD). Sexual and gender minority (SGM; e.g., lesbian/gay, bisexual, transgender) adults face higher risks of inadequate PA and CVD due to increased exposure to minority stressors, such as experienced and anticipated discrimination, which may influence SGM adults’ willingness to engage in PA. To our knowledge, researchers have not investigated the impact of minority stressors on objectively-measured PA among SGM adults. Goal: Using k-means clustering, we sought to identify whether clusters characterized by greater exposure to minority stressors were associated with lower moderate and vigorous PA (MVPA) and higher sedentary behavior among SGM adults. Methods: This daily diary study included an online sample of SGM adults living in the United States. Participants completed daily surveys about personally experienced discrimination and anticipated discrimination (i.e., expectation of experiencing discrimination) and wore wrist accelerometers for 28 days to objectively measure PA. We used k-means clustering to identify clusters based on reports of experienced and anticipated discrimination. We first determined the optimal number of clusters using established partition criteria. Next, we ran linear regression models (adjusted for demographic factors) to examine the associations of minority stress clusters with MVPA and sedentary time per week. Results: Among 42 SGM adults (mean age 27.0±7.7 years) with 1133 person-days of accelerometry data (~3% missing data), we identified four minority stress clusters: low anticipated/low experienced (LALE; reference group); low anticipated/high experienced (LAHE); high anticipated/low experienced (HALE); and high anticipated/high experienced discrimination (HAHE). Participants in the HALE cluster (n=12) engaged in 202 fewer minutes of MVPA than those in the LALE cluster (n=7). Participants in the LAHE cluster (n=10) had 123 fewer minutes of vigorous PA than those in the LALE cluster. No differences were identified for sedentary time. Conclusions: This is the first study to examine the association of minority stressors with objective PA among SGM adults. Participants in the HALE and LAHE clusters engaged in significantly lower PA than those with low levels of both experienced and anticipated discrimination. Findings underscore the importance of assessing minority stressors and the need for interventions to improve PA among SGM adults.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2024-11-12
DOI
https://doi.org/10.1161/circ.150.suppl_1.4125691
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 4125691: A machine learning approach to examining the associations of minority stressors and physical activity among sexual and gender minority adults

David Lopez Veneros, Ipek Ensari, Billy A. Caceres, Yashika Sharma et al.
Circulation
Mobile Health and mHealth Applications
article

Abstract 4125691: A machine learning approach to examining the associations of minority stressors and physical activity among sexual and gender minority adults

David Lopez Veneros, Ipek Ensari, Billy A. Caceres, Yashika Sharma, Riya Bhilegaonkar
article en

Abstract

Introduction: Physical activity (PA) is an important modifiable factor for cardiovascular disease (CVD). Sexual and gender minority (SGM; e.g., lesbian/gay, bisexual, transgender) adults face higher risks of inadequate PA and CVD due to increased exposure to minority stressors, such as experienced and anticipated discrimination, which may influence SGM adults’ willingness to engage in PA. To our knowledge, researchers have not investigated the impact of minority stressors on objectively-measured PA among SGM adults. Goal: Using k-means clustering, we sought to identify whether clusters characterized by greater exposure to minority stressors were associated with lower moderate and vigorous PA (MVPA) and higher sedentary behavior among SGM adults. Methods: This daily diary study included an online sample of SGM adults living in the United States. Participants completed daily surveys about personally experienced discrimination and anticipated discrimination (i.e., expectation of experiencing discrimination) and wore wrist accelerometers for 28 days to objectively measure PA. We used k-means clustering to identify clusters based on reports of experienced and anticipated discrimination. We first determined the optimal number of clusters using established partition criteria. Next, we ran linear regression models (adjusted for demographic factors) to examine the associations of minority stress clusters with MVPA and sedentary time per week. Results: Among 42 SGM adults (mean age 27.0±7.7 years) with 1133 person-days of accelerometry data (~3% missing data), we identified four minority stress clusters: low anticipated/low experienced (LALE; reference group); low anticipated/high experienced (LAHE); high anticipated/low experienced (HALE); and high anticipated/high experienced discrimination (HAHE). Participants in the HALE cluster (n=12) engaged in 202 fewer minutes of MVPA than those in the LALE cluster (n=7). Participants in the LAHE cluster (n=10) had 123 fewer minutes of vigorous PA than those in the LALE cluster. No differences were identified for sedentary time. Conclusions: This is the first study to examine the association of minority stressors with objective PA among SGM adults. Participants in the HALE and LAHE clusters engaged in significantly lower PA than those with low levels of both experienced and anticipated discrimination. Findings underscore the importance of assessing minority stressors and the need for interventions to improve PA among SGM adults.

CirculationVol. 150(Suppl_1)
University of Connecticut (US), Columbia University (US), Icahn School of Medicine at Mount Sinai (US)
Gender equality
Openalex Percentile: Top 64%
Mobile Health and mHealth Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.