Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America
Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women
- Published on June 27, 2019
Independently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership.
A convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles.
Rest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78–91%, p < .001) and college graduates (28% vs 68–90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles.
In this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions.
- Kelsie M. Full 1,2
- Kevin Moran 3
- Jordan Carlson 4
- Suneeta Godbole 2
- Loki Natarajan 2
- Aaron Hipp 5
- Karen Glanz 6
- Jonathan Mitchell 7
- Francine Laden 8,9
- Peter James 8,9
- Jacqueline Kerr 2
Department of Family Medicine & Public Health, University of California San Diego, La Jolla, California, United States of America
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Center for Children’s Healthy Lifestyles and Nutrition, Children's Mercy Hospital, Kansas City, Missouri, United States of America
Department of Parks, Recreation, and Tourism Management, North Carolina State University, Raleigh, North Carolina, United States of America
Perelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America