“Sleep-wake state discrepancy”: toward a common understanding and standardized nomenclature

Bensen-Boakes, Darah-Bree; Lovato, Nicole; Meaklim, Hailey; Bei, Bei; Scott, Hannah

doi:10.1093/sleep/zsac187

Cited by 7 publications

(2 citation statements)

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“…The difference between self-reported and objectively measured sleep parameters is known in the literature as sleep-state misperception [ 12–14 ], paradoxical insomnia [ 15–17 ], or sleep discrepancy [ 18–20 ]. In this manuscript, we will use the term sleep–wake state discrepancy [ 21 ] to define differences that occur when comparing objectively recorded versus self-reported sleep data.…”

Section: Introductionmentioning

confidence: 99%

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Does changing perceptions of sleep by incorporating sleep wearables improve insomnia? Protocol for a randomized study (the Novel Insomnia Treatment Experiment)

et al. 2023

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Study Objectives Insomnia is common in the general population and is diagnosed based on self-reported sleep complaints. There is often discrepancy between objectively recorded and self-reported sleep (sleep-wake state discrepancy), especially in individuals with insomnia. Although sleep-wake state discrepancy is well-documented in the literature, it is not well understood. This protocol describes the methodology of a randomised controlled which will examine whether providing monitoring and feedback about objectively recorded sleep with support for interpretation of sleep-wake state discrepancy improves insomnia symptoms and will explore the potential mechanisms of change. Methods Participants are 90 individuals with insomnia symptoms (Insomnia Severity Index ≥ 10). Participants will be randomized to one of two conditions: (a) Intervention: feedback about objectively recorded sleep (actigraph and optional EEG headband) with guidance for data interpretation; (b) Control: sleep hygiene session. Both conditions will involve individual sessions and two check-in calls. The primary outcome is Insomnia Severity Index score. Secondary outcomes include sleep-related impairment, symptoms of anxiety and depression, and other sleep and quality of life measures. Outcomes will be assessed using validated instruments at baseline and post-intervention. Discussion With increasing wearable devices that measure sleep, there is a need to understand how sleep data provided by these devices could be utilised in the treatment of insomnia. Findings from this study have the potential to better understand sleep-wake state discrepancy in insomnia and uncover new approaches to supplement current insomnia treatment.

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Section: Introductionmentioning

confidence: 99%

“…Measurement-related aspects include differences in how tools assess sleep, as each tool infers sleep in a different way (e.g. polysomnography from electroencephalogram signals, actigraphy from activity and movement) [ 21 ]. Cognitive-related aspects include sleep-related worries and selective monitoring [ 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%