A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients

Study Objectives: Current actigraphic algorithms are relatively less accurate in detecting sleep and wake in sleep apnea patients than in people without sleep apnea. In the current study, we attempted to validate a novel automatic algorithm, which was developed for actigraphic studies in normal subjects and patients with obstructive sleep apnea by comparing it on an epoch-by-epoch basis to standard polysomnography. Design: Prospective cohort study. Setting: Multicenter, university hospital, sleep laboratories. Participants: A total of 228 subjects from 3 different sleep centers (Skara, Boston, Haifa) participated. Intervention and Measurements: Simultaneous recording of polysomnography and Watch_PAT100, an ambulatory device that contains a built-in actigraph. The automatic sleep/wake algorithm is based on both the quantification of motion (magnitude and duration) and the various periodic movement patterns, such as those occurring in patients with moderate to severe obstructive sleep apnea. Results: The overall sensitivity and specificity to identify sleep was 89% and 69%, respectively. The agreement ranged from 86% in the normal subjects to 86%, 84%, and 80% in the patients with mild, moderate, and severe obstructive sleep apnea, respectively. There was a tight agreement between actigraphy and polysomnography in determining sleep efficiency (78.4 +/- 9.9 vs 78.8 +/- 13.4%), total sleep time (690 +/- 152 vs 690 +/- 154 epochs), and sleep latency (56.8 +/- 31.4 vs 43.3 +/- 45.4 epochs). While for most individuals the difference between the polysomnography and actigraphy was relatively small, for some there was a substantial disagreement. Conclusions: We conclude that this actigraphy algorithm provides a reasonably accurate estimation of sleep and wakefulness in normal subjects and patients with obstructive sleep apnea on an epoch-by-epoch basis. This simple method for assessment of total sleep time may provide a useful tool for the accurate quantification of obstructive sleep apnea in the home environment.