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Identifying Multicomponent Patterns and Correlates of Accelerometry-Assessed Physical Behaviors Among Postmenopausal
Kelly R Evenson1, Annie Green Howard2,3, Fang Wen1
1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina - Chapel Hill, Chapel Hill, North Carolina, United States.
Journal for the Measurement of Physical Behaviour
|August 18, 2025
Summary
Five distinct physical behavior patterns were identified in postmenopausal women using accelerometry. These patterns, ranging from high activity to high sedentary behavior, can guide targeted health interventions for this demographic.
Area of Science:
- Gerontology
- Public Health
- Biomedical Engineering
Background:
- Understanding physical activity and sedentary behavior patterns is crucial for developing effective health interventions.
- Postmenopausal women represent a diverse demographic with unique health considerations.
Purpose of the Study:
- To identify and describe multi-component patterns of physical activity and sedentary behavior using accelerometry.
- To explore correlates of these physical behavior patterns in a large cohort of diverse postmenopausal women.
Main Methods:
- Cross-sectional study combining two US-based cohorts (Women's Health Study and WHI Objective Physical Activity and Cardiovascular Health Study).
- Utilized accelerometry (ActiGraph GT3X+) data from 22,612 postmenopausal women (aged 62-97) over seven days.
- Employed latent class analysis to classify hour-by-hour physical behavior patterns (sedentary, light, moderate-to-vigorous activity).
Main Results:
- Identified five distinct physical behavior patterns, characterized by varying levels of physical activity and sedentary behavior.
- Higher physical activity and lower sedentary behavior were associated with younger age, higher education, better general health, normal weight, non-smoking status, and faster walking speed.
- Conditions like diabetes and cardiovascular disease were associated with lower physical activity/higher sedentary behavior patterns.
Conclusions:
- Latent class analysis successfully delineated distinct physical behavior profiles among postmenopausal women.
- These identified patterns provide a basis for developing targeted interventions to improve physical activity and reduce sedentary behavior in this population.
- Demographic and health factors are significantly associated with these physical behavior patterns, offering insights for personalized health strategies.
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