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

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.