SLEEP · 2023 · 23 citations · 71 references
MedicineBehavioral SciencesPsychologySleep Disorders In Older AdultsSleep MedicineSleep PsychiatryInsomnia DisordersInsomnia ManagementSleep AssessmentSleep DisturbanceSleep HealthSleep-wake DisordersInsomnia Symptom AssessmentInsomnia SymptomsSleep - Wake StateSleep ImpairmentSleep Symptom AssessmentSleep Symptom ManagementSleep-wake State MonitoringSubjective Sleep Measurement
STUDY OBJECTIVES: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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. CLINICAL TRIAL REGISTRATION: 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.
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Paul A. Harris, Robert Taylor, Robert J. Thielke et al. · Journal of Biomedical Informatics · 2008 · 51.7K citations · Full text
A New Method for Measuring Daytime Sleepiness: The Epworth Sleepiness Scale
Murray W. Johns · SLEEP · 1991 · 17.2K citations · Full text
Medicine, Neurology, Psychometrics +16
Lauren Krupp · Archives of Neurology · 1989 · 6K citations