PA accelerometer devices worn on either wrist provide valid estimates of TST, WASO, and SE when compared with PSG. Further studies are needed to investigate methods to improve assessment of sleep parameters by PA accelerometer devices to advance device integration and assessment 24-hour activity in populations.

Physical activity and time spent outdoors may be important non-pharmacological approaches to improve sleep quality and duration (or sleep patterns) but there is little empirical research evaluating the two simultaneously. The current study assesses the role of physical activity and time outdoors in predicting sleep health by using objective measurement of the three variables. A convenience sample of 360 adult women (mean age = 55.38 ±9.89 years; mean body mass index = 27.74 ±6.12) was recruited from different regions of the U.S. Participants wore a Global Positioning System device and ActiGraph GT3X+ accelerometers on the hip for 7 days and on the wrist for 7 days and 7 nights to assess total time and time of day spent outdoors, total minutes in moderate-to-vigorous physical activity per day, and 4 measures of sleep health, respectively. A generalized mixed-effects model was used to assess temporal associations between moderate-to-vigorous physical activity, outdoor time, and sleep at the daily level (days = 1931) within individuals. There was a significant interaction (p = 0.04) between moderate-to-vigorous physical activity and time spent outdoors in predicting total sleep time but not for predicting sleep efficiency. Increasing time outdoors in the afternoon (versus morning) predicted lower sleep efficiency, but had no effect on total sleep time. Time spent outdoors and the time of day spent outdoors may be important moderators in assessing the relation between physical activity and sleep. More research is needed in larger populations using experimental designs.

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Background Evidence suggests that short and long sleep durations are potential lifestyle factors associated with cardiovascular disease (CVD). Research on sleep duration and CVD risk is limited by use of self-report sleep measures, homogeneous populations, and studies on individual CVD risk factors. For women, risk of CVD and inadequate sleep duration increases with age. We hypothesized that accelerometer-measured sleep duration was associated with 10-year predicted probability of future CVD risk in a cohort of aging women. Methods This cross-sectional analysis included 3,367 older women (mean age 78.9 years; 53.3% White), from the Objective Physical Activity and Cardiovascular Health Study, ancillary study to the Women’s Health Initiative. Women wore ActiGraph GT3X+ accelerometers on the hip for 24 hours/7 days. A 10-year predicted probability of future CVD risk, the Reynolds Risk Score (RRS), was computed using age, systolic blood pressure, high-sensitivity C-reactive protein (CRP), total and HDL cholesterol, diabetes mellitus status, smoking status, and family history of CVD. Average nightly sleep duration was derived from accelerometer data. Adjusted linear regression models investigated the association between sleep duration and RRS. Results Results suggested a U-shaped relationship between sleep duration and RRS, with both short and long sleep associated with higher RRS (p < .001). The association remained significant after adjustments for race/ethnicity, education, lifestyle factors, and health status indicators. Conclusion In older women, actigraphy-ascertained sleep duration was associated with a 10-year predicted probability of future CVD risk. This study supports sleep duration as a modifiable risk factor for CVD in older women.

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.

Study Objectives Activities throughout the day, including sleep, sedentary behavior (SB), light-intensity physical activity (LIPA), and moderate to vigorous physical activity (MVPA) are independently associated with cardiometabolic health. Few studies have examined interrelationships between sleep and 24-hour activity and associations with cardiometabolic risk. The objective of this study is to understand how replacing time in SB, LIPA, or MVPA with sleep impacts cardiometabolic risk. Methods Women’s Health Initiative OPACH Study participants (N = 3329; mean age = 78.5 ± 6) wore ActiGraph GT3X+ accelerometers 24 hours/7 days. Adjusted linear regression estimated the relationship between sleep duration and cardiometabolic markers. Separately for shorter (&lt;8 hours) and longer (≥8 hours) sleepers, isotemporal substitution models estimated the cross-sectional associations with cardiometabolic markers with reallocating time in daytime activities to or from sleep. Results Longer sleep duration was associated with higher insulin, HOMA-IR, glucose, total cholesterol, and triglycerides (all p &lt; 0.05). The associations between sleep duration and C-reactive protein, waist circumference, and body mass index (BMI) were U-shaped (both p &lt; 0.05). For shorter sleepers, reallocating 33 minutes of MVPA to sleep was associated with higher values of insulin, HOMA-IR, glucose, triglycerides, waist circumference, and BMI (0.7%–11.5%). Replacing 91 minutes of SB time with sleep was associated with lower waist circumference and BMI (−1.3%, −1.8%). For long sleepers, shifting 91 minutes of sleep to SB was associated with higher waist circumference and BMI (1.3%, 1.4%). Conclusions This is one of the first isotemporal analyses to include objectively measured sleep duration. Results illuminate possible cardiometabolic risks and benefits of reallocating time to or from sleep.