Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
School of Philosophical, Historical, and International Studies, Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, VIC, Australia.
Sleep. 2023 Sep 8;46(9). doi: 10.1093/sleep/zsad167.
Insomnia is a disorder diagnosed based on self-reported sleep complaints. Differences between self-reported and sensor-based sleep parameters (sleep-wake state discrepancy) are common but not well-understood in individuals with insomnia. This two-arm, parallel-group, single-blind, superiority randomized-controlled trial examined whether monitoring sleep using wearable devices and providing support for interpretation of sensor-based sleep data improved insomnia symptoms or impacted sleep-wake state discrepancy.
A total of 113 (age M = 47.53; SD = 14.37, 64.9% female) individuals with significant insomnia symptoms (Insomnia Severity Index(ISI) ≥10) from the community were randomized 1:1 (permuted block randomization) to receive 5 weeks (1) Intervention (n = 57): feedback about sensor-based sleep (Fitbit and EEG headband) with guidance for data interpretation and ongoing monitoring, and (2) Control (n = 56): sleep education and hygiene. Both groups received one individual session and two check-in calls. The ISI (primary outcome), sleep disturbance (SDis), sleep-related impairment (SRI), depression, and anxiety were assessed at baseline and post-intervention.
In total, 103 (91.2%) participants completed the study. Intention-to-treat multiple regression with multiple imputations showed that after controlling for baseline values, compared to the Control group (n = 51), the Intervention group (n = 52) had lower ISI (p = .011, d = 0.51) and SDis (p = .036, d = 0.42) post-intervention, but differences in SRI, depression, anxiety, and sleep-wake state discrepancy parameters (total sleep time, sleep onset latency, and wake after sleep onset) were not meaningful (P-values >.40).
Providing feedback and guidance about sensor-based sleep parameters reduced insomnia severity and sleep disturbance but did not alter sleep-wake state discrepancy in individuals with insomnia more than sleep hygiene and education. The role of sleep wearable devices among individuals with insomnia requires further research.
The Novel Insomnia Treatment Experiment (NITE): the effectiveness of incorporating appropriate guidance for sleep wearables in users with insomnia. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378452, Australia New Zealand Clinical Trials Registry: ACTRN12619001636145.
失眠是一种根据自我报告的睡眠问题诊断的疾病。在失眠患者中，自我报告的睡眠参数和基于传感器的睡眠参数（睡眠-觉醒状态差异）之间存在差异，但尚未得到很好的理解。这项双臂、平行组、单盲、优效性随机对照试验检验了使用可穿戴设备监测睡眠并为解释基于传感器的睡眠数据提供支持是否能改善失眠症状或影响睡眠-觉醒状态差异。
共有 113 名（年龄 M = 47.53；SD = 14.37，64.9%为女性）来自社区的有显著失眠症状（失眠严重程度指数（ISI）≥10）的个体被随机分为 1:1（随机区组分组）接受 5 周（1）干预（n = 57）：基于传感器的睡眠（Fitbit 和 EEG 头带）反馈，包括数据解释和持续监测指导，和（2）对照（n = 56）：睡眠教育和卫生。两组均接受一次个体会议和两次随访电话。在基线和干预后评估 ISI（主要结局）、睡眠障碍（SDis）、睡眠相关损害（SRI）、抑郁和焦虑。
共有 103 名（91.2%）参与者完成了研究。意向治疗多重回归分析与多重插补显示，与对照组（n = 51）相比，在控制基线值后，干预组（n = 52）的 ISI（p =.011，d = 0.51）和 SDis（p =.036，d = 0.42）在干预后降低，但 SRI、抑郁、焦虑和睡眠-觉醒状态差异参数（总睡眠时间、睡眠潜伏期和睡眠后觉醒）的差异无意义（P 值>.40）。
提供基于传感器的睡眠参数的反馈和指导可降低失眠严重程度和睡眠障碍，但在失眠患者中，与睡眠卫生和教育相比，不会改变睡眠-觉醒状态差异。在失眠患者中，睡眠可穿戴设备的作用需要进一步研究。
新型失眠治疗实验（NITE）：在有失眠症的用户中纳入适当的睡眠可穿戴设备指导的效果。https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378452，澳大利亚新西兰临床试验注册中心：ACTRN12619001636145。
