Quante Mirja, Kaplan Emily R, Cailler Michael, Rueschman Michael, Wang Rui, Weng Jia, Taveras Elsie M, Redline Susan
Department of Neonatology, University of Tuebingen, Tuebingen, Germany.
Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.
Nat Sci Sleep. 2018 Jan 18;10:13-20. doi: 10.2147/NSS.S151085. eCollection 2018.
Actigraphy is widely used to estimate sleep-wake time, despite limited information regarding the comparability of different devices and algorithms. We compared estimates of sleep-wake times determined by two wrist actigraphs (GT3X+ versus Actiwatch Spectrum [AWS]) to in-home polysomnography (PSG), using two algorithms (Sadeh and Cole-Kripke) for the GT3X+ recordings.
Participants included a sample of 35 healthy volunteers (13 school children and 22 adults, 46% male) from Boston, MA, USA. Twenty-two adults wore the GT3X+ and AWS simultaneously for at least five consecutive days and nights. In addition, actigraphy and PSG were concurrently measured in 12 of these adults and another 13 children over a single night. We used intraclass correlation coefficients (ICCs), epoch-by-epoch comparisons, paired -tests, and Bland-Altman plots to determine the level of agreement between actigraphy and PSG, and differences between devices and algorithms.
Each actigraph showed comparable accuracy (0.81-0.86) for sleep-wake estimation compared to PSG. When analyzing data from the GT3X+, the Cole-Kripke algorithm was more sensitive (0.88-0.96) to detect sleep, but less specific (0.35-0.64) to detect wake than the Sadeh algorithm (sensitivity: 0.82-0.91, specificity: 0.47-0.68). Total sleep time measured using the GT3X+ with both algorithms was similar to that obtained by PSG (ICC=0.64-0.88). In contrast, agreement between the GT3X+ and PSG wake after sleep onset was poor (ICC=0.00-0.10). In adults, the GT3X+ using the Cole-Kripke algorithm provided data comparable to the AWS (mean bias=3.7±19.7 minutes for total sleep time and 8.0±14.2 minutes for wake after sleep onset).
The two actigraphs provided comparable and accurate data compared to PSG, although both poorly identified wake episodes (i.e., had low specificity). Use of actigraphy scoring algorithm influenced the mean bias and level of agreement in sleep-wake times estimates. The GT3X+, when analyzed by the Cole-Kripke, but not the Sadeh algorithm, provided comparable data to the AWS.
尽管关于不同设备和算法的可比性信息有限,但活动记录仪仍被广泛用于估计睡眠-觉醒时间。我们使用两种算法(萨德算法和科尔-克里普克算法)对GT3X+的记录进行分析,比较了两款腕部活动记录仪(GT3X+与Actiwatch Spectrum [AWS])所确定的睡眠-觉醒时间估计值与家庭多导睡眠图(PSG)的差异。
参与者包括来自美国马萨诸塞州波士顿的35名健康志愿者样本(13名学童和22名成年人,46%为男性)。22名成年人同时佩戴GT3X+和AWS至少连续五个日夜。此外,对其中12名成年人和另外13名儿童在一个晚上同时进行了活动记录仪监测和PSG监测。我们使用组内相关系数(ICC)、逐时段比较、配对t检验和布兰德-奥特曼图来确定活动记录仪与PSG之间的一致性水平,以及不同设备和算法之间的差异。
与PSG相比,各活动记录仪在睡眠-觉醒估计方面显示出相当的准确性(0.81 - 0.86)。在分析GT3X+的数据时,科尔-克里普克算法在检测睡眠方面更敏感(0.88 - 0.96),但在检测觉醒方面的特异性(0.35 - 0.64)低于萨德算法(敏感性:0.82 - 0.91,特异性:0.47 - 0.68)。使用两种算法通过GT3X+测量的总睡眠时间与PSG获得的总睡眠时间相似(ICC = 0.64 - 0.88)。相比之下,GT3X+与PSG在睡眠开始后觉醒时间的一致性较差(ICC = 0.00 - 0.10)。在成年人中,使用科尔-克里普克算法的GT3X+提供的数据与AWS相当(总睡眠时间的平均偏差为3.7±19.7分钟,睡眠开始后觉醒时间的平均偏差为8.0±14.2分钟)。
与PSG相比,两款活动记录仪提供了相当且准确的数据,尽管两者在识别觉醒事件方面都较差(即特异性较低)。活动记录仪评分算法的使用影响了睡眠-觉醒时间估计的平均偏差和一致性水平。当使用科尔-克里普克算法而非萨德算法进行分析时,GT3X+提供的数据与AWS相当。