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Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues

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1994

Year

TLDR

The study used twin‑wrist actigraphy, collecting polysomnographic and wrist actigraph data from dominant and nondominant hands of 20 adults and 16 adolescents during a single laboratory night, to assess how placement and device sensitivity affect automatic sleep‑wake scoring and to identify breathing‑related motion artifacts. Despite activity differences between wrists during sleep and wake, the resulting sleep‑wake scoring algorithms were essentially identical and explained 64 % of variance; using the nondominant‑hand algorithm yielded 91–93 % agreement with polysomnography in both calibration and validation samples, with similar performance for the dominant wrist, higher agreement for sleep than wake, and robustness to moderate activity‑level changes.

Abstract

The effects of actigraph placement and device sensitivity on actigraphic automatic sleep-wake scoring were assessed using concomitant polysomnographic and wrist actigraphic data from dominant and nondominant hands of 20 adults and 16 adolescents during 1 laboratory night. Although activity levels differed between dominant and nondominant wrists during periods of sleep (F = 4.57; p < 0.05) and wake (F = 15.5; p < 0.0005), resulting sleep–wake scoring algorithms were essentially the same and were equally explanatory (R2 = 0.64; p < 0.0001). When the sleep-wake scoring algorithm derived from the nondominant hand was used to score the nondominant data for sleep-wake, overall agreement rates with polysomnography scoring ranged between 91 and 93% for the calibration and validation samples. Results obtained with the same algorithm for the dominant-wrist data were within the same range. Agreement for sleep scoring was consistently higher than for wake scoring. Statistical manipulation of activity levels before applying the scoring algorithm indicated that this algorithm is quite robust toward moderate changes in activity level. Use of "twin-wrist actigraphy" enables identification of artifacts that may result from breathing-related motions.

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