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    • this content Each participant was equipped with two actigrap

    2018-11-15

    Each participant was equipped with two actigraphs (MTI® model 7164 accelerometer, ActiGraph, Pensacola, FL); one was worn on the non-dominant wrist, and the other was affixed to the hip. The actigraphs were initialized prior to recording using the same computer that was used for PSG recording so that the actigraphs were synchronized to the internal timing of the computer, thus allowing precise and accurate verification of the beginning and end of each recording period. Moreover, synchronization of the initiation of the PSG and actigraphic recordings also was performed each night with manually activated event markers on the computer and actigraph. At each participant׳s customary bedtime, which varied from 9pm to 1am, lights were turned out and participants were asked to attempt to sleep as desired, for up to a maximum of 8h. Recordings that included at least 6h of complete PSG as well as wrist and hip actigraphy data were included in the analysis. PSG data were initially scored using the automated Morpheus® software (Widemed ltd., Tel Aviv, Israel) in 30-s epochs. These data were then manually edited according to standardized criteria by a registered PSG technologist [21,23]. In order to compare PSG scoring with MTI sleep estimates, which are scored in 60-s epochs, PSG recordings from every other 30-sec this content were synchronized to actigraphy data and scored for the presence of wake or sleep, without consideration of sleep stage. Ancillary analyses based on sensitivity, specificity, and minute-by-minute agreement indicated that differences between this approach and alternative data matching strategies within a given one minute interval were negligible. These strategies and their results are addressed in greater detail in the discussion section. Actigraph epochs were scored for sleep using the Cole–Kripke algorithm [24], which was incorporated into the manufacturer׳s software. The MTI ActiLife5© software used for this purpose applied a combination of regression parameters as follows:where D<1 indicates being asleep, D≥1 denotes being awake, P is a scale factor for the entire equation, W0, W−4, W−3,W−2,W−1, W+1 and W+2 are weighting factors for present (0), previous (−), and subsequent minutes (+), and A0, A−4, A−3, A−2,A−1, A+1,A+2 are activity scores for the corresponding present (0), previous (−), and subsequent minutes (+). For instance, A−4 represents activity scores four time units before the present time and W−4 is the associated weighting factor for A−4. The resulting algorithm in ActiLife5© iswhere activity scores were constrained not to exceed a maximum of 300. This means, for example, that if an epoch had an activity score of 450 a score of 300 was used. We attempted to develop a novel method to improve actigraphic sleep estimates by applying a smoothing spline to actigraph activity data using PSG as the reference. The penalized smoothing spline fits a non-linear curve to discretely observed data while maintaining the pattern of the original data. The penalty parameter controls how curvilinear (or smooth) the curve can be. By modeling sleep actigraphy data as continuous rather than discrete time points, our intent was to estimate an actigraphic time series that more closely approximated the pattern of sleep–wakefulness recorded via PSG. This was performed in two stages. First, an overall adjustment to the original activity amplitude was implemented because the magnitude of wrist and hip actigraphy data can be low, which would generate false negative assignment of sleep using the algorithm denoted above. Second, the temporal pattern of the adjusted activity was estimated via a penalized cubic spline, where its smoothness was controlled by a penalty parameter. The inferred non-linear curve was then used to estimate activity levels that were processed using the Cole–Kripke algorithm [24] found in the manufacturer׳s software to score each epoch as awake or asleep. The overall adjusting magnitude and penalty parameter were selected such that their combination minimized the difference in sleep efficiency between the actigraphy and PSG data. Scoring for sleep–wake using the predicted activity values of wrist and hip actigraphy data was conducted in the same manner as the original actigraphy data using the Cole–Kripke algorithm included within the manufacturer׳s software (ActiLife 5).