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

ObjectivesIndependently, 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.MethodsA 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.ResultsRest-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.ConclusionsIn 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

Implementation-effectiveness trial of an ecological intervention for physical activity in ethnically diverse low income senior centers.

BackgroundAs the US population ages, there is an increasing need for evidence based, peer-led physical activity programs, particularly in ethnically diverse, low income senior centers where access is limited.Methods/designThe Peer Empowerment Program 4 Physical Activity' (PEP4PA) is a hybrid Type II implementation-effectiveness trial that is a peer-led physical activity (PA) intervention based on the ecological model of behavior change. The initial phase is a cluster randomized control trial randomized to either a peer-led PA intervention or usual center programming. After 18 months, the intervention sites are further randomized to continued support or no support for another 6 months. This study will be conducted at twelve senior centers in San Diego County in low income, diverse communities. In the intervention sites, 24 peer health coaches and 408 adults, aged 50 years and older, are invited to participate. Peer health coaches receive training and support and utilize a tablet computer for delivery and tracking. There are several levels of intervention. Individual components include pedometers, step goals, counseling, and feedback charts. Interpersonal components include group walks, group sharing and health tips, and monthly celebrations. Community components include review of PA resources, walkability audit, sustainability plan, and streetscape improvements. The primary outcome of interest is intensity and location of PA minutes per day, measured every 6 months by wrist and hip accelerometers and GPS devices. Secondary outcomes include blood pressure, physical, cognitive, and emotional functioning. Implementation measures include appropriateness & acceptability (perceived and actual fit), adoption & penetration (reach), fidelity (quantity & quality of intervention delivered), acceptability (satisfaction), costs, and sustainability.DiscussionUsing a peer led implementation strategy to deliver a multi-level community based PA program can enhance program adoption, implementation, and sustainment.Trial registrationClinicalTrials.gov, USA ( NCT02405325 ). Date of registration, March 20, 2015. This website also contains all items from the World Health Organization Trial Registration Data Set

Parameterizing and Validating Existing Algorithms for Identifying Out-of-Bed Time Using Hip-Worn Accelerometer Data from Older Women

Objective: To parameterize and validate two existing algorithms for identifying out-of-bed time using 24-hour hip-worn accelerometer data from older women. Approach: Overall, 628 women (80±6 years old) wore ActiGraph GT3X+ accelerometers 24 hours/day for up to 7 days and concurrently completed sleep-logs. Trained staff used a validated visual analysis protocol to measure in-bed periods on accelerometer tracings (criterion). The Tracy and McVeigh algorithms were adapted for optimal use in older adults. A training set of 314 women was used to choose two key thresholds by maximizing the sum of sensitivity and specificity for each algorithm and data (vertical axis, VA, and vector magnitude, VM) combination. Data from the remaining 314 women were then used to test agreement in waking wear time (i.e., out-of-bed time while wearing the accelerometer) by computing sensitivity, specificity, and kappa comparing the algorithm output with the criterion. Waking wear time-adjusted means of sedentary time, light-intensity physical activity (light PA) and moderate-to-vigorous-intensity physical activity (MVPA) were then estimated and compared. Main results: Waking wear time agreement with the criterion was high for Tracy_VA, Tracy_VM, McVeigh_VA, and highest for McVeigh_VM. Compared to the criterion, McVeigh_VM had mean sensitivity=0.92, specificity=0.87, kappa=0.80, and overall mean difference (±SD) of -0.04±2.5 hours/day. Minutes of sedentary time, light PA, and MVPA adjusted for waking wear time using the criterion measure and McVeigh_VM were not statistically different (p \u3e0.43 | all). Significance: The McVeigh algorithm with optimal parameters using VM performed best compared to criterion sleep-log assisted visual analysis and is suitable for automated identification of waking wear time in older women when visual analysis is not feasible

Assessing psychometric properties of the PROMIS Sleep Disturbance Scale in older adults in independent-living and continuing care retirement communities

PurposeSleep disturbances are associated with poor health outcomes in older adults. The Patient-Reported Outcomes Information System (PROMIS) Sleep Disturbance Scale was designed to assess self-reported general sleep and sleep disturbance. The objective of this study was to validate the short-form PROMIS Sleep Disturbance Scale for use among older adults living in independent-living and continuing care retirement communities.MethodsOlder adults (N = 307) were recruited from retirement communities in San Diego, CA, to participate in a physical activity intervention. Study participants were on average 83.6 years (SD 6.4) and predominately female (72.3%). Self-reported health outcomes included sleep disturbance, depressive symptoms, quality of life, stress, and pain. Internal consistency of the PROMIS Sleep Disturbance Scale was determined using Cronbach α, individual item means, and interitem correlations. Construct validity was examined using exploratory factor analysis techniques. Adjusted linear regression models assessed the predictive validity of the Sleep Disturbance Scale and associations with health outcomes.ResultsThe PROMIS Sleep Disturbance Scale had a Cronbach α = .856 and an interitem correlation of .504. All items loaded on 1 sole factor. Additionally, the sleep scale was significantly predictive of depressive symptoms, stress, and quality of life at 12 months.ConclusionsThe PROMIS 6-item Sleep Disturbance Scale had acceptable internal consistency and strong construct validity among a sample of elderly older adults in an independent-living community setting. These findings suggest that the PROMIS scale may provide an accurate assessment of sleep disturbance in older adults. Additional validation testing using objective measures of sleep is needed to confirm these findings further