Defining insomnia: Quantitative criteria for insomnia severity and frequency
Defining insomnia: Quantitative criteria for insomnia severity and frequency
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DOI：
10.1093/sleep/29.4.479
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发表时间：
2006-04-01
期刊：
影响因子：
5.6
通讯作者：
Means, MK
中科院分区：
文献类型：
--
作者：
Lineberger, MD;Carney, CE;Means, MK
研究目的：近年来，人们努力开发定量频率、持续时间和失眠严重程度的标准。目前的研究是为了测试一系列频率和严重程度的标准，以区分原发性失眠症患者和正常睡眠者。参与者：72名患有原发性失眠症的成年人和88名年龄匹配的正常睡眠者。方法：参与者完成了连续14个晚上的睡眠记录，以监测他们的家庭睡眠模式。接受者-操作者特征曲线分析用于比较一系列严重程度和频率标准集，以区分失眠症和正常睡眠组。此外，根据2周平均睡眠记录数据，对一系列清醒时间严重程度临界值进行敏感性和特异性测试。结果：患者-操作者特征曲线分析显示，没有任何一种严重程度和频率标准的组合具有最大的敏感性和特异性。相反，最佳频率截止随着严重程度标准的增加而降低。对平均睡眠记录数据的分析表明，在2周的睡眠记录监测中，平均睡眠开始潜伏期或午夜醒来时间（即从睡眠开始到最终早晨醒来之间的清醒时间）截止时间为20分钟或更长，对失眠分类的灵敏度（94.4%）和特异性（79.6%）最高。结论：本文所建立的最佳定量失眠标准不同于以往提出的标准。尽管如此，研究结果表明，从睡眠记录数据中得出的定量标准可能对原发性失眠的分类有用。
Study Objective: Recent efforts have been made to develop quantitative frequency, duration, and severity criteria for insomnia. The current study was conducted to test a range of frequency and severity criteria sets for discriminating primary insomnia sufferers from normal sleepers.Participants: Seventy-two adults with primary insomnia and 88 age-matched normal sleepers.Methods: Participants completed 14 consecutive nights of sleep logs to monitor their home sleep patterns. Receiver-operator characteristic curve analyses were used to compare a range of severity and frequency criteria sets for discriminating the insomnia and normal-sleeper groups. In addition, sensitivity and specificity tests were conducted for a range of wake-time severity cutoffs based on 2-week mean sleep-log data.Results: Receiver-operator characteristic curve analyses showed that no 1 combination of severity and frequency criteria maximized sensitivity and specificity. Rather, the optimal frequency cutoff decreased as the severity criterion increased. Analyses of mean sleep-log data showed that an average sleep-onset latency or middle-of-the-night wake time (ie, time awake between sleep onset and final morning awakening) cutoff of 20 minutes or longer over 2 weeks of sleep-log monitoring appeared to best maximize sensitivity (94.4%) and specificity (79.6%) for insomnia classification.Conclusions: The optimal quantitative insomnia criteria found herein differ from those previously proposed. Nonetheless, results suggest that quantitative criteria derived from sleep-log data may be useful for classification of primary insomnia.