Abstract
Two validation studies were conducted to optimize the sleep-detection algorithm of the Actillume. The first study used home recordings of postmenopausal women (age range: 51 to 77 years), which were analyzed to derive the optimal algorithm for detecting sleep and wakefulness from wrist activity data, both for nocturnal in-bed recordings and considering the entire 24 h. The second study explored the optimal algorithm to score in-bed recordings of healthy young adults (age range: 19 to 34 years) monitored in the laboratory. In Study I, the algorithm for in-bed recordings (
n=39) showed a minute-by-minute agreement of 85% between Actillume and polysomnography (PSG), a correlation of .98, and a mean measurement error (ME) of 21 min for estimates of sleep duration. Using the same algorithm to score 24-h recordings with Webster's rules, an agreement of 89%, a correlation of .90, and 1 min ME were observed. A different algorithm proved optimal to score in-bed recordings (
n=31) of young adults, yielding an agreement of 91%, a correlation of .92, and an ME of 5 min. The strong correlations and agreements between sleep estimates from Actillume and PSG in both studies suggest that the Actillume can reliably monitor sleep and wakefulness both in community-residing elderly and healthy young adults in the laboratory. However, different algorithms are optimal for individuals with different characteristics.
