Investigating the subjective and objective efficacy of a cognitive behavioral therapy for insomnia (CBT-I)-based smartphone app on sleep: a randomized controlled trial

Hinterberger, Alexandra; Eigl, Esther-Sevil; Schwemlein, Robyn Nina; Topalidis, Pavlos; Schabus, Manuel

doi:10.31219/osf.io/rp2qf

Cited by 2 publications

References 42 publications

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Non-guided, Mobile, CBT-I-based Sleep Training in War-torn Ukraine: A Feasibility Study

Kurapov,

Blechert,

Hinterberger

et al. 2024

Preprint

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ObjectivesTo study whether a mobile, unguided Cognitive Behavior Therapy-based Intervention for Sleep Disturbance, Sleep2is feasible, acceptable, and reduces mental health/sleep disturbance symptoms among the Ukrainian population during the ongoing war.MethodsA single-arm, open-label, uncontrolled pre-post evaluation study was conducted with 487 registered participants: 283 started, out of which 95 completed without an ambulatory heart rate (HR) sensor and 65 with. Assessments were conducted using online questionnaires and continuous objective measurements via HR sensors. Key outcome measures included sleep disturbance, insomnia, fear of sleep, anxiety, depression, PTSD, perceived stress, and somatic symptoms.ResultsEngagement with the program was robust, achieving an 80.72% compliance rate, alongside high levels of feasibility and acceptance. Participants reported significant pre- post reductions in the severity of sleep disturbance (by 22.60%), insomnia (by 35.08%), fear of sleep (by 32.43%), anxiety (by 27.72%), depression (by 28.67%), PTSD (by 32.41%), somatic symptoms (by 24.52%), and perceived stress (by 17.90%), all with medium to high effect sizes. Objective sleep measurements showed a slight reduction in sleep onset latency.ConclusionThe ‘Sleep2Ukraine’ program demonstrated high feasibility and acceptance, with significant improvements in subjective sleep and mental health measures among participants. These findings demonstrate the potential of scalable mobile-based CBT-I interventions in war-torn regions with or without the instrument, based on the heart rate assessment.

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Non-guided, Mobile, CBT-I-based Sleep Training in War-torn Ukraine: A Feasibility Study

Kurapov,

Blechert,

Hinterberger

et al. 2024

Preprint

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

Topalidis,

Baron,

Heib

et al. 2023

Sensors

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More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, κ = 0.79), as well as the H10 (84.4%, κ = 0.76), and VS (84.2%, κ = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.

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Investigating the subjective and objective efficacy of a cognitive behavioral therapy for insomnia (CBT-I)-based smartphone app on sleep: a randomized controlled trial

Cited by 2 publications

References 42 publications

Non-guided, Mobile, CBT-I-based Sleep Training in War-torn Ukraine: A Feasibility Study

Non-guided, Mobile, CBT-I-based Sleep Training in War-torn Ukraine: A Feasibility Study

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

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