Document Type

Article

Publication Date

6-27-2019

Identifier

DOI: 10.1371/journal.pone.0218595; PMCID: PMC6597058

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.

Journal Title

PLoS One

Volume

14

Issue

6

First Page

0218595

Last Page

0218595

MeSH Keywords

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

Keywords

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

Comments

Grant support

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publisher's Online Access: https://doi.org/10.1371/journal.pone.0218595

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