Wake detection capacity of actigraphy during sleep

Abstract

Study objectives: To evaluate the ability of actigraphy compared to polysomnography (PSG) to detect wakefulness in subjects submitted to 3 sleep conditions with different amounts of wakefulness: a nocturnal sleep episode and 2 daytime recovery sleep episodes, one with placebo and one with caffeine. A second objective was to compare the ability of 4 different scoring algorithms (2 threshold algorithms and 2 regression analysis algorithms) to detect wake in the 3 sleep conditions.

Design: Three nights of simultaneous actigraphy (Actiwatch-L, Mini-Mitter/Respironics) and PSG recordings in a within-subject design.

Setting: Chronobiology laboratory.

Participants: Fifteen healthy subjects aged between 20 and 60 years (7M, 8F).

Interventions: 200 mg of caffeine and daytime recovery sleep.

Results: An epoch-by-epoch comparison between actigraphy and PSG showed a significant decrease in actigraphy accuracy with increased wakefulness in sleep conditions due to the low sleep specificity of actigraphy (generally <50%). Actigraphy overestimated total sleep time and sleep efficiency more strongly in conditions involving more wakefulness. Compared to the 2 regression algorithms, the 2 threshold algorithms were less able to detect wake when the sleep episode involved more wakefulness, and they tended to alternate more between wake and sleep in the scoring of long periods of wakefulness resulting in an overestimation of the number of awakenings.

Conclusion: The very low ability of actigraphy to detect wakefulness casts doubt on its validity to measure sleep quality in clinical populations with fragmented sleep or in situations where the sleep-wake cycle is challenged, such as jet lag and shift work.

Figure 1

Mean (± SEM) sleep latency (A), total sleep time (B), sleep efficiency (C), and number of awakenings (D) between polysomnography (PSG) and Lötjönen et al's method algorithm (LötMt) in nocturnal sleep (NS), day recovery sleep (DRS) and caffeine day recovery sleep (CDRS) conditions. Sleep latency and number of awakenings was log-transformed. Asterisks denote a significant difference (P<0.05) between the PSG and LötMt algorithms.

Figure 2

Bland-Altman plot for each subject of the difference between polysomnography (PSG) and Lötjönen et al's method algorithm (LötMt) for total sleep time in the caffeine daytime recovery sleep condition. Mean bias (dashed line) and 2 standard deviations of bias (dotted lines) are presented.

Figure 3

Mean bias and standard deviation of mean bias from Bland-Altman's plot for sleep latency (A), total sleep time (B), sleep efficiency (C), and number of awakenings (D) between polysomnography (PSG) and Actiware medium threshold algorithm (ACT40), Actiware low threshold algorithm (ACT20), Lötjönen et al's equation algorithm (LötEq), and Lötjönen et al's method algorithm (LötMt) in the nocturnal sleep (NS), daytime recovery sleep (DRS), and caffeine daytime recovery sleep (CDRS) conditions.