Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
30
05
2018
accepted:
05
06
2019
entrez:
28
6
2019
pubmed:
28
6
2019
medline:
14
2
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31247051
doi: 10.1371/journal.pone.0218595
pii: PONE-D-18-16216
pmc: PMC6597058
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0218595Subventions
Organisme : NCI NIH HHS
ID : U01 CA116850
Pays : United States
Organisme : NCI NIH HHS
ID : R00 CA201542
Pays : United States
Organisme : NHLBI NIH HHS
ID : K01 HL123612
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA155435
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA155850
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA155626
Pays : United States
Organisme : NCI NIH HHS
ID : F32 CA162847
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA155496
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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