The use of accelerometers to characterize minute-by-minute intensity, cumulative physical activity counts, and daily activity patterns provides detailed data not gathered by traditional subjective methods, particularly at low levels of activity. The findings of a 1.3% decrease per year in activity from mid-to-late life, and the corresponding drop in afternoon and evening activity, provide new information that may be useful when targeting future interventions. Further, this methodology addresses essential gaps in understanding activity patterns and trends in more sedentary sectors of the population.

Purpose We examined total, light, and moderate-to-vigorous physical activity (MVPA) as predictors of mortality in a nationally representative sample of older adults. Then, we explored the theoretical consequences of replacing sedentary time with the same duration of light activity or MVPA. Methods Using accelerometer measured activity, the associations between total, light (100 to 2019 counts per minute), and moderate-to-vigorous (>2019 counts per minute) activity counts and mortality were examined in adults aged 50 to 79 in the National Health and Nutrition Examination Survey, 2003-2006 (n=3,029), with mortality follow-up through December 2011. Cox proportional hazards models were fitted to estimate mortality risks. An isotemporal substitution model was used to examine the theoretical consequences of replacing sedentary time with light activity or MVPA on mortality. Results After adjusting for potential confounders, including age, sex, race/ethnicity, education, body mass index, and the presence of comorbid conditions, those in the highest tertile of total activity counts had one fifth the risk of death of those in the lowest tertile (HR: 0.21, 95% CI: 0.12, 0.38), and those in the middle tertile had one third the risk of death (HR: 0.36, 95% CI: 0.30, 0.44). In addition, replacing thirty minutes of sedentary time with light activity was associated with significant reduction in mortality risk (After 5 years of follow-up: HR = 0.80, 95% CI: 0.75, 0.85). Replacing thirty minutes of sedentary time with MVPA was also associated with reduction in mortality risk (HR = 0.49, 95% CI: 0.25, 0.97). Conclusions Greater total activity is associated with lower all-cause mortality risk. Replacing sedentary time with light activity or MVPA may reduce mortality risk for older adults.

IMPORTANCE Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems. OBJECTIVES To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders.

Summary This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly six hundred subjects over five days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Thus, functional fixed effects are estimated while accounting for within-function and within-subject correlations, and major directions of variability within and between subjects are identified. Fixed effect coefficient functions and principal component basis functions are estimated using penalized splines; model parameters are estimated in a Bayesian framework using , a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a twenty-four hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects.