Jahrami Haitham, Trabelsi Khaled, Husain Waqar, Ammar Achraf, BaHammam Ahmed S, Pandi-Perumal Seithikurippu R, Saif Zahra, Vitiello Michael V
Government Hospitals, Manama 329, Bahrain.
Department of Psychiatry, College of Medicine and Medical Sciences, Arabian Gulf University, Manama 329, Bahrain.
Brain Sci. 2024 Nov 6;14(11):1123. doi: 10.3390/brainsci14111123.
BACKGROUND/OBJECTIVES: Orthosomnia has become a concern in the field of sleep medicine. The purpose of this cross-sectional study was to estimate the prevalence of orthosomnia in the general population.
We collected data from 523 participants via the Generalized Anxiety Disorder Scale, Anxiety and Preoccupation about Sleep Questionnaire, and Athens Insomnia Scale. Additionally, we gathered information about participants' use of commercial sleep-tracking wearable devices.
We developed a four-criteria algorithm to identify cases of orthosomnia: ownership of a wearable sleep-tracking device, AIS score ≥ 6, GAD-7 score ≤ 14, and APSQ score ≥ 40 or APSQ score ≥ 35 or APSQ score ≥ 30, for conservative, moderate, and lenient prevalence estimates, respectively. One hundred seventy-six (35.8%) (95% CI 34.6-40.1%) participants regularly used sleep-tracking devices. The prevalence rates of algorithm-identified orthosomnia in the study sample were: 16 participants (3.0%, 95% CI 1.6-4.5%), 45 participants (8.6%, 95% CI 6.2-11.0%), 73 participants (14.0%, 95% CI 10.9-16.9%) for the for conservative, moderate, and lenient prevalence estimates, respectively. Individuals with orthosomnia were not significantly different in terms of age and sex. The cases consistently had higher AIS scores than non-cases across all APSQ cutoffs, indicating more severe insomnia symptoms, with significant differences observed at each cutoff point.
This study offers initial insights into the prevalence of orthosomnia within our sample at a specific time. The findings reveal notable rates of orthosomnia among individuals using sleep-tracking devices; however, we must acknowledge the limitations inherent in a cross-sectional design.
背景/目的：矫正性失眠已成为睡眠医学领域关注的问题。这项横断面研究的目的是估计普通人群中矫正性失眠的患病率。
我们通过广泛性焦虑障碍量表、睡眠焦虑和关注问卷以及雅典失眠量表收集了523名参与者的数据。此外，我们收集了参与者使用商业睡眠追踪可穿戴设备的信息。
我们开发了一种四标准算法来识别矫正性失眠病例：拥有可穿戴睡眠追踪设备、雅典失眠量表（AIS）得分≥6、广泛性焦虑障碍量表（GAD-7）得分≤14，以及睡眠焦虑和关注问卷（APSQ）得分≥40或APSQ得分≥35或APSQ得分≥30，分别用于保守、中等和宽松患病率估计。176名（35.8%）（95%置信区间34.6-40.1%）参与者经常使用睡眠追踪设备。研究样本中算法识别的矫正性失眠患病率分别为：16名参与者（3.0%，95%置信区间1.6-4.5%）、45名参与者（8.6%，95%置信区间6.2-11.0%）、73名参与者（14.0%，95%置信区间10.9-16.9%），分别对应保守、中等和宽松患病率估计。矫正性失眠患者在年龄和性别方面无显著差异。在所有APSQ临界值下，病例组的AIS得分始终高于非病例组，表明失眠症状更严重，在每个临界值点均观察到显著差异。
本研究为特定时间内我们样本中矫正性失眠的患病率提供了初步见解。研究结果显示，使用睡眠追踪设备的人群中矫正性失眠患病率较高；然而，我们必须承认横断面设计存在的固有局限性。
