Accelerometry-assessed physical activity and sedentary behavior patterns using single- and multi-component latent class analysis among postmenopausal women
| Autor: | Kelly R Evenson, Fang Wen, Chongzhi Di, Michael Kebede, Michael J LaMonte, I-Min Lee, Lesley Fels Tinker, Andrea Z LaCroix, Annie Green Howard |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Women's Health, Vol 20 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1745-5065 17455057 |
| DOI: | 10.1177/17455057241257361 |
| Popis: | Background: Patterns of physical activity and sedentary behavior among postmenopausal women are not well characterized. Objectives: To describe the patterns of accelerometer-assessed physical activity and sedentary behavior among postmenopausal women. Design: Cross-sectional study. Methods: Women 63–97 years (n = 6126) wore an ActiGraph GT3X + accelerometer on their hip for 1 week. Latent class analysis was used to classify women by patterns of percent of wake time in physical activity and sedentary behavior over the week. Results: On average, participants spent two-thirds of their day in sedentary behavior (62.3%), 21.1% in light low, 11.0% in light high, and 5.6% in moderate-to-vigorous physical activity. Five classes emerged for each single-component model for sedentary behavior and light low, light high, and moderate-to-vigorous physical activity. Six classes emerged for the multi-component model that simultaneously considered the four behaviors together. Conclusion: Unique profiles were identified in both single- and multi-component models that can provide new insights into habitual patterns of physical activity and sedentary behavior among postmenopausal women. Implications: The multi-component approach can contribute to refining public health guidelines that integrate recommendations for both enhancing age-appropriate physical activity levels and reducing time spent in sedentary behavior. |
| Databáze: | Directory of Open Access Journals |
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