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

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.

On the Efficacy of a CBT-I-Based Online Program for Sleep Problems: A Randomized Controlled Trial.

There is an urgent need for easily accessible treatment options for sleep problems to reduce the current treatment gap in receiving cognitive behavioral therapy for insomnia (CBT-I). Using a randomized controlled trial, we evaluated the efficacy of a CBT-I-based online program on sleep. Fifty-three volunteers (21-71 years; MAge = 44.6 ± 12.5; 27 female) suffering from impaired sleep were randomly allocated either to the experimental group (EG, n = 27) or to an active control group (CG, n = 26). The EG participated in a 6-week CBT-I-based online program, while the CG received psychoeducation and sleep hygiene instructions. Sleep was assessed both objectively via ambulatory polysomnography (PSG) as well as subjectively via questionnaires at three time points (baseline, pre- and post-intervention). A one-month follow-up assessment was performed using questionnaires. The EG showed small but reliable improvements from pre- to post-intervention in PSG-derived wake after sleep onset (from 58.6 min to 42.5 min; p < 0.05) and sleep efficiency (from 86.0% to 89.2%; p < 0.05). Furthermore, subjective sleep quality (assessed via Pittsburgh Sleep Quality Index) improved significantly during intervention (p = 0.011) and follow-up (p = 0.015) in the EG alone. The Insomnia Severity Index decreased from pre- to post-intervention in both groups (EG: p = 0.003, CG: p = 0.008), while it further improved during follow-up (p = 0.035) in the EG alone. We show that a CBT-I-based online program can improve sleep not only subjectively but also objectively and can be a viable alternative when face-to-face interventions are not available.