ACTIGRAPHY FOR ADOLESCENT SLEEP INTRODUCTION ADOLESCENCE IS A TIME OF RAPID CHANGES IN SLEEP PATTERNS AND OF RISING PREVALENCE OF SLEEPI- NESS, WITH ADOLESCENTS HAVING BEEN IDENTIFIED as being at high risk for problem sleepiness.1 Sleep deprivation

ADOLESCENCE IS A TIME OF RAPID CHANGES IN SLEEP PATTERNS AND OF RISING PREVALENCE OF SLEEPINESS, WITH ADOLESCENTS HAVING BEEN IDENTIFIED as being at high risk for problem sleepiness.1 Sleep deprivation among adolescents is a rising concern among parents, health professionals, and educators.2,3,4,5,6 In light of the concerns and growing awareness of the problems associated with adolescent sleep, the importance of measuring sleep duration in adolescents is increasingly recognized. Wrist actigraphy is a noninvasive and relatively inexpensive means of estimating sleep based on recording and analyzing motion data. Although actigraphy has been used to estimate sleep patterns for over 25 years, the methodology by which actigraphy data should be accumulated, processed, and scored in order to optimize sleep/wake inference continue to be explored.7 Previous research has shown that sleep differs in adolescence as compared to earlier periods of childhood and to adulthood.8,9 Because the relationship between sleep and movement may vary over the age spectrum, researchers have identified the need to independently test the validity of actigraphy-based sleep-wake inference in an infant population.10 However, little has been done to validate current methods for actigraphy data processing in an adolescent population. In healthy adults, actigraphy-based estimates of sleep correlate well with sleep data from EEG recordings obtained from polysomnography (PSG). However, estimates of sleep from actigraphy have been found to be less reliable as sleep becomes more disturbed.7,11,12,13 Others have shown that actigraphy appears to be limited in the accuracy of its estimation of sleep in the presence of the sleep fragmentation associated with sleep disordered breathing (SDB).12 Whether and to what extent these findings are generalizable to an adolescent population has yet to be shown. This study compared sleep/wake inference from actigraphy data to that obtained from overnight PSG in 181 adolescents, comparing subgroups by gender and SDB, in order to identify the mode of data processing that best correlates with PSG-determined sleep estimates. This study (1) sought to determine the optimal data mode for processing actigraphy data in adolescents, and (2) investigated the following hypotheses: i) total sleep time (TST) from actigraphy has high agreement with TST from PSG in adolescents, and ii) the agreement between actigraphy and PSG Sleep Estimation Using Wrist Actigraphy in Adolescents With and Without Sleep Disordered Breathing: A Comparison of Three Data Modes