Full, Kelsie M.; Moran, Kevin; Carlson, Jordan; Godbole, Suneeta; Natarajan, Loki; Hipp, Aaron; Glanz, Karen; Mitchell, Jonathan; Laden, Francine; James, Peter; Kerr, Jacqueline (2018). Data from: Latent profile analysis of objectively measured sleep, physical activity, and sedentary time and associations with health in adult women. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0HM56N6

Objectives: To identify latent rest-activity behavior profiles of objectively measured total sleep time, sleep efficiency, physical activity, and sedentary time in a diverse sample of U.S. women.
Methods: 372 women (mean age 55.4 + 10.2) were recruited from studies conducted across four US universities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous physical activity (MVPA) and percentage of wear-time spent sedentary was estimated from the hip device. Total sleep time and sleep efficiency were estimated from the wrist device. Latent profile analyses were performed with these four rest-activity variables, and adjusted ANOVAs were conducted to compare behaviors, demographics, and health conditions across resulting behavior profiles.
Results: Participants achieved an average of 21.1 (SD=18.9) minutes of daily MVPA, and 62.1 (SD=8.7) percent of their daily time was spent sedentary. Mean nightly sleep time was 408.9 (SD=55.7) minutes, with an average sleep efficiency of 85.6 (7.5). These rest-activity variables clustered to form five behavior profiles including: 1) fairly active poor sleepers, 2) inactive average sleepers, 3) fairly active average sleepers, 4) active average sleepers, and 5) very active average sleepers & low sitters. Sleep was comparable across four of five behavior profiles (n=345, 92.5%). The behavior profile with the poorest sleep had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). The largest profile (n=151, 40.6%) engaged in 7 min of MVPA per day. BMI and physical functioning varied across behavior profiles. Other health variables did not vary statistically, but trended in hypothesized directions.
Conclusions: Daily rest-activity behaviors do cluster in women to form distinct behavior profiles. These identified behavior profiles can inform population targeting or intervention goals of multiple health behavior interventions.

• 2012-10-10 to 2014-01-13

• 2018

• Carlson, Jordan
• Full, Kelsie M.
• James, Peter
• Mitchell, Jonathan
• Natarajan, Loki

• Glanz, Karen
• Hipp, Aaron
• Kerr, Jacqueline
• Laden, Francine

• Moran, Kevin

• Godbole, Suneeta

This work was supported by: National Cancer Institute (http://www.cancer.gov/) Centers for Transdisciplinary Research on Energetics and Cancer grants U01 CA116850, U54 CA155496, U54 CA155626, U54 CA155435, U54 CA155850; National Institutes of Health (https://www.nih.gov/) grants UM1 CA176726 and R01 ES017017; Harvard National Heart Lung and Blood Institute (http://www.nhlbi.nih.gov/) Cardiovascular Epidemiology Training Grant T32 HL 098048 (PJ); National Cancer Institute (http://www.cancer.gov/) grant F32CA162847 (JM) and K99CA201542 (PJ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

• 24-hour activity
• Accelerometer
• Cluster analysis
• Health behavior

• data

Doi: https://doi.org/10.6075/J0HM56N6

Primary associated publication

• Full KM, Moran K, Carlson J, Godbole S, Natarajan L, Hipp A, et al. (2019) Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women. PLoS ONE 14(6): e0218595. https://doi.org/10.1371/journal.pone.0218595

Creative Commons Attribution 4.0 International Public License

• UC Regents

Under copyright (US)

Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.

Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.

Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)

2022-11-28