Does providing feedback and guidance on sleep perceptions using sleep wearables improve insomnia? Findings from “Novel Insomnia Treatment Experiment”: a randomized controlled trial

Abstract

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 randomised 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 113 (age M=47.53; SD=14.37, 64.9% female) individuals with significant insomnia symptoms (Insomnia Severity Index >=10; ISI) from the community were randomised 1:1 (permuted block randomization) to receive 5-week (a) Intervention (n=57): feedback about sensor-based sleep (Fitbit and EEG headband) with guidance for data interpretation and ongoing monitoring; (b) 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 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 (TST, SOL, WASO) 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 require further research.

From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability

“…As Western health systems increasingly adopt digital health interventions including sleep (see Arroyo and Zawadzki [38] for a systematic review on mHealth interventions in sleep), objective and reliable tracking of the effects of such interventions becomes more and more relevant and allows for ecologically valid and continuous measurements [9] in natural home settings. Recently, for example, Spina et al [11] used sensor-based sleep feedback in a sample of 103 participants suffering from sleep disturbances and found that such sensor-based sleep feedback can already reduce some of the insomnia symptoms. Interestingly, such feedback alone was however not enough to induce changes in the sleepwake misperception, which may need additional interventions (see Hinterberger et al [39]).…”

“…At the same time, there are undeniably benefits of using accurate wearable devices that affordably capture daily sleep changes in natural environments, outside of the laboratory. Especially in light of recent studies showing that implementing such technologies together with sleep intervention protocols can have positive therapy outcomes [10][11][12][13]. It is our opinion that such technologies, if optimized and carefully validated, will soon play a central role in research and clinical practice as they allow continuous sleep measurements (and feedback) in ecologically valid home environments and at an affordable cost.…”

Investigating the subjective and objective efficacy of a cognitive behavioural therapy for insomnia (CBT‐I)‐based smartphone app on sleep: A randomised controlled trial

“…Generally, providing feedback on sleep seems to be beneficial regarding insomnia severity and sleep disturbances in individuals with significant insomnia symptoms (Spina et al, 2023). However, it is also important to note, that despite potential improvements in sleep parameters and SOSD, it is not yet fully understood, whether reporting feedback on objective sleep might also possibly result in negative effects, for example, an increase of obsession with sleep due to selected attention, a key pathological feature of insomnia.…”

Tailoring cognitive behavioural therapy for insomnia across contexts, conditions, and individuals: What do we know, where do we go?

“…Further, recent development of sleep-measuring wearables sees these devices increasingly integrated into sleep interventions (Glazer Baron et al, 2022). For example, one study provided regular feedback on wearable-measured sleep for individuals with insomnia (Spina et al, 2023), and another added wearable-enabled sleep data synchronisation to a digital behavioural therapy to demonstrate greater engagement and improvements in sleep (Aji et al, 2022). Real-world sleep-wake patterns from wearable devices have also been implemented to recommend personalised sleep schedules to maximise alertness (Song et al, 2023).…”