Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women

PLoS One. 2019 Jun 27;14(6):e0218595. doi: 10.1371/journal.pone.0218595. eCollection 2019.

Abstract

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Accelerometry
  • Adult
  • Aged
  • Chronic Disease
  • Cohort Studies
  • Cross-Sectional Studies
  • Exercise / physiology*
  • Female
  • Fitness Trackers
  • Health Status
  • Humans
  • Middle Aged
  • Rest / physiology
  • Risk Factors
  • Sedentary Behavior*
  • Sleep / physiology*
  • Time Factors
  • Young Adult