The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns.

The purpose of this study is to characterize the impact of the timing and duration of missing actigraphy data on interdaily stability (IS) and intradaily variability (IV) calculation. The performance of three missing data imputation methods (linear interpolation, mean time of day (ToD), and median ToD imputation) for estimating IV and IS was also tested. Week-long actigraphy records with no non-wear or missing timeseries data were masked with zeros or 'Not a Number' (NaN) across a range of timings and durations for single and multiple missing data bouts. IV and IS were calculated for true, masked, and imputed (i.e., linear interpolation, mean ToD and, median ToD imputation) timeseries data and used to generate Bland-Alman plots for each condition. Heatmaps were used to analyze the impact of timings and durations of and between bouts. Simulated missing data produced deviations in IV and IS for longer durations, midday crossings, and during similar timing on consecutive days. Median ToD imputation produced the least deviation among the imputation methods. Median ToD imputation is recommended to recapitulate IV and IS under missing data conditions for less than 24 h.

Objective underpinnings of self-reported sleep quality in middle-aged and older adults: The importance of N2 and wakefulness.

STUDY OBJECTIVES: The measurable aspects of brain function (polysomnography, PSG) that are correlated with sleep satisfaction are poorly understood. Using recent developments in automated sleep scoring, which remove the within- and between-rater error associated with human scoring, we examine whether PSG measures are associated with sleep satisfaction. DESIGN AND SETTING: A single night of PSG data was compared to contemporaneously collected measures of sleep satisfaction with Random Forest regressions. Whole and partial night PSG data were scored using a novel machine learning algorithm. PARTICIPANTS: Community-dwelling adults (N = 3165) who participated in the Sleep Heart Health Study. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Models explained 30% of sleep depth and 27% of sleep restfulness, with a similar top four predictors: minutes of N2 sleep, sleep efficiency, age, and minutes of wake after sleep onset (WASO). With increasing self-reported sleep quality, there was a progressive increase in N2 and decrease in WASO of similar magnitude, without systematic changes in N1, N3 or REM sleep. In comparing those with the best and worst self-reported sleep satisfaction, there was a range of approximately 30 min more N2, 30 min less WASO, an improvement of sleep efficiency of 7-8%, and an age span of 3-5 years. Examination of sleep most proximal to morning awakening revealed no greater explanatory power than the whole-night data set. CONCLUSIONS: Higher N2 and concomitant lower wake is associated with improved sleep satisfaction. Interventions that specifically target these may be suitable for improving the self-reported sleep experience.