Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms.
Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms.
复制标题
青少年和成年人的基于Attraphy的睡眠估计：使用两种评分算法与多个评分算法进行比较。
DOI：
10.2147/nss.s151085
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发表时间：
2018
影响因子：
3.4
通讯作者：
Redline S
中科院分区：
文献类型：
--
作者：
Quante M;Kaplan ER;Cailler M;Rueschman M;Wang R;Weng J;Taveras EM;Redline S
尽管关于不同设备和算法的可比性的信息有限，但活动描记术被广泛用于估计睡眠-觉醒时间。我们使用两种算法（Sadeh和Cole-Kripke）对GT3X+记录进行比较，将两个手腕活动仪（GT3X+与Actiwatch Spectrum [AWS]）确定的睡眠-觉醒时间与家庭多导睡眠描记仪（PSG）进行比较。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（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5个昼夜。此外，在一个晚上对其中12名成年人和另外13名儿童同时进行了活动描记和PSG测量。我们使用类内相关系数（ICCs）、逐时代比较、配对t检验和Bland-Altman图来确定活动描画和PSG之间的一致程度，以及设备和算法之间的差异。与PSG相比，每个活动图在睡眠-觉醒估计方面显示出相当的准确性（0.81-0.86）。在分析GT3X+数据时，Cole-Kripke算法对睡眠的检测灵敏度（0.88-0.96）高于Sadeh算法（灵敏度：0.82-0.91，特异性：0.47-0.68），但对清醒的检测特异性（0.35-0.64）低于Sadeh算法（灵敏度：0.82-0.91，特异性：0.47-0.68）。两种算法下使用GT3X+测量的总睡眠时间与PSG测量的结果相似（ICC= 0.64-0.88）。相比之下，睡眠开始后GT3X+和PSG唤醒之间的一致性较差（ICC= 0.00-0.10）。在成人中，使用Cole-Kripke算法的GT3X+提供的数据与AWS相当（总睡眠时间平均偏差为3.7±19.7分钟，睡眠开始后醒来时间平均偏差为8.0±14.2分钟）。与PSG相比，这两种活动仪提供了可比性和准确性的数据，尽管它们对尾流发作的识别都很差（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Cole-Kripke算法分析GT3X+而不是Sadeh算法时，它提供了与AWS相当的数据。
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 t-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.