Continuous sleep tracking in digital CBT-I: Efficacy and insights from a naturalistic-environment study

数字认知行为疗法(CBT-I)中的持续睡眠追踪:一项自然环境研究的疗效和启示

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Abstract

BACKGROUND: Insomnia is highly prevalent, yet few receive cognitive behavioral therapy for insomnia (CBT-I) due to limited treatment availability. Unguided digital CBT-I offers an accessible alternative to traditional face-to-face therapy. Research in this area often relies on either subjective sleep measurements (e.g., sleep diaries) or controlled single-night lab studies. This study examines the effectiveness of a novel app-based CBT-I program combining therapy with continuous subjective and objective sleep tracking via a heart rate (HR) sensor in a naturalistic setting. METHODS: Eighty-eight participants (56.8 % female) aged 20-85 years (M = 49.9 ± 13.10) completed an 8-week app-based CBT-I intervention with continuous sleep tracking (sleep diaries and HR sensor), followed by a 2-week follow-up. Assessments at baseline, post-intervention, and follow-up included sleep related problems, psychological strain, quality of life, and dysfunctional beliefs. RESULTS: Insomnia prevalence dropped from 92 % at pre-intervention to 67 % at follow-up. Improvements were observed in subjective sleep quality (p < .001, r = 0.59), dysfunctional beliefs (p < .001, r = 0.48), quality of life (p's < .002, r's > 0.33), psychological strain (p < .001, r = 0.43), depression (p = .010, r = 0.27), and anxiety (p = .003, r = 0.32). While sleep diary data showed improvements in various sleep parameters, objective data revealed statistical trends towards a reduced total sleep time (TST; p = .083, r = 0.19), driven by sleep restriction, and light sleep (p = .089, r = 0.18). Using continuous sleep monitoring we additionally found relevant changes during the intervention levels for subjective wake after sleep onset, sleep efficiency as well as objective TST. CONCLUSION: Findings support the effectiveness of app-based CBT-I and suggest that continuous objective sleep tracking over weeks can reveal previously undetected sleep and well-being improvements in real-world settings.

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