Research Study Abstract

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


  • 1

    Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America

  • 2

    Department of Family Medicine & Public Health, University of California San Diego, La Jolla, California, United States of America

  • 3

    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America

  • 4

    Center for Children’s Healthy Lifestyles and Nutrition, Children's Mercy Hospital, Kansas City, Missouri, United States of America

  • 5

    Department of Parks, Recreation, and Tourism Management, North Carolina State University, Raleigh, North Carolina, United States of America

  • 6

    Perelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • 7

    Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America

  • 8

    Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America

  • 9

    Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America




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