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

Due to insufficient treatment options for insomnia, effective solutions are urgently needed. We evaluated the effects of a CBT-I-based app combining sleep training with subjective and objective sleep monitoring on (i) sleep and (ii) subjective-objective sleep discrepancies (SOSD). Fifty-seven volunteers (20-76 years; MAge  = 45.67 ± 16.38; 39 female) suffering from sleep problems were randomly assigned to an experimental group (EG, n = 28) or a waitlist control group (CG, n = 29). During the 6-week app phase, the EG used the CBT-I-based programme and a heart rate sensor for daily sleep monitoring and -feedback, while the CG used sleep monitoring only. Sleep was measured (i) subjectively via questionnaires (Insomnia Severity Index, ISI; Pittsburgh Sleep Quality Index, PSQI), (ii) objectively via ambulatory polysomnography (PSG), and (iii) continuously via heart-rate sensor and sleep diaries. Data revealed interactions for ISI (p = 0.003, ƞ2 part  = 0.11) and PSQI (p = 0.050, ƞ2 part  = 0.05), indicating training-specific improvements in EG, yet not in CG. While PSG-derived outcomes appear to be less training-specific, a tendential reduction in wake after sleep onset (WASO) was found in EG (p = 0.061, d = 0.55). Regarding changes in SOSD, the results indicate improvements during the app phase (EG) for sleep efficiency, sleep onset latency, and WASO (p ≤ 0.022, d ≥ 0.46); for total sleep time both groups showed a SOSD reduction. The findings indicate beneficial effects of a novel smartphone app on sleep and SOSD. More scientific evaluation of such digital programmes is needed to ultimately help in reducing the gap in non-pharmacological insomnia treatment.

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.

The Virtual Sleep Lab-A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables

Sleep staging based on polysomnography (PSG) performed by human experts is the de facto "gold standard" for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person's sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (<EUR 100) consumer wearables: an optical heart rate sensor (VS) and a breast belt (H10), both produced by POLAR®. The overall classification accuracy reached levels comparable to expert inter-rater reliability for both devices (VS: 81%, κ = 0.69; H10: 80.3%, κ = 0.69). In addition, we used the H10 and recorded daily ECG data from 49 participants with sleep complaints over the course of a digital CBT-I-based sleep training program implemented in the App NUKKUAA™. As proof of principle, we classified the IBIs extracted from H10 using the MCNN over the course of the training program and captured sleep-related changes. At the end of the program, participants reported significant improvements in subjective sleep quality and sleep onset latency. Similarly, objective sleep onset latency showed a trend toward improvement. Weekly sleep onset latency, wake time during sleep, and total sleep time also correlated significantly with the subjective reports. The combination of state-of-the-art machine learning with suitable wearables allows continuous and accurate monitoring of sleep in naturalistic settings with profound implications for answering basic and clinical research questions.

A low-threshold sleep intervention for improving sleep quality and well-being

BACKGROUND

Approximately one-third of the healthy population suffer from sleep problems, but only a small proportion of those affected receive professional help. Therefore, there is an urgent need for easily accessible, affordable, and efficacious sleep interventions.

OBJECTIVE

A randomized controlled study was conducted to investigate the efficacy of a low-threshold sleep intervention consisting of either (i) sleep data feedback plus sleep education or (ii) sleep data feedback alone in comparison with (iii) no intervention.

MATERIAL AND METHODS

A total of 100 employees of the University of Salzburg (age: 39.51 ± 11.43 years, range: 22-62 years) were randomly assigned to one of the three groups. During the 2-week study period, objective sleep parameters were assessed via actigraphy. In addition, an online questionnaire and a daily digital diary were used to record subjective sleep parameters, work-related factors, as well as mood and well-being. After 1 week, a personal appointment was conducted with participants of both experimental group 1 (EG1) and experimental group 2 (EG2). While the EG2 only received feedback about their sleep data from week 1, the EG1 additionally received a 45-min sleep education intervention containing sleep hygiene rules and recommendations regarding stimulus control. A waiting-list control group (CG) did not receive any feedback until the end of the study.

RESULTS

Results indicate positive effects on sleep and well-being following sleep monitoring over the course of 2 weeks and minimal intervention with a single in-person appointment including sleep data feedback. Improvements are seen in sleep quality, mood, vitality, and actigraphy-measured sleep efficiency (SE; EG1), as well as in well-being and sleep onset latency (SOL) in EG2. The inactive CG did not improve in any parameter.

CONCLUSION

Results suggest small and beneficial effects on sleep and well-being in people being continuously monitored and receiving (actigraphy-based) sleep feedback when paired with a single-time personal intervention.

Self-reported changes in sleep patterns and behavior in children and adolescents during COVID-19

The COVID-19 pandemic and lockdowns worldwide forced children and adolescents to change and adapt their lives to an unprecedented situation. Using an online survey, we investigated whether they showed changes in sleep quality and other related factors due to this event. Between February 21st, 2021 and April 19th, 2021, a total of 2,290 Austrian children and adolescents (6-18 years) reported their sleep habits and quality of sleep as well as physical activity, daylight exposure and usage of media devices during and, retrospectively, before the pandemic. Results showed an overall delay of sleep and wake times. Almost twice as many respondents reported having sleeping problems during the pandemic as compared to before, with insomnia, nightmares and daytime sleepiness being the most prevalent problems. Furthermore, sleeping problems and poor quality of sleep correlated positively with COVID-19 related anxiety. Lastly, results showed a change from regular to irregular bedtimes during COVID-19, higher napping rates, a strong to very strong decrease in physical activity and daylight exposure, as well as a high to very high increase in media consumption. We conclude that the increase in sleeping problems in children and adolescent during COVID-19 is concerning. Thus, health promoting measures and programs should be implemented and enforced.

Burdens and psychosocial consequences of the COVID-19 pandemic for Austrian children and adolescents

OBJECTIVES

The negative psychosocial effects of the COVID-19 pandemic are becoming increasingly apparent. Children and adolescents in particular, were affected and torn away from their daily life routines. The aim of our survey is to evaluate the psychosocial burden and impairments of children and adolescents in Austria during the COVID-19 pandemic by using cross-sectional analysis.

SETTING

An Austrian-wide online survey was conducted from 21 February to 19 April 2021 for children and adolescents. The questionnaire was distributed widely using the national press agency and public media.

PARTICIPANTS

Using an online questionnaire, 5,483 children and adolescents between 6 and 18 years of age were sampled.

OUTCOME MEASURE

Quantitative responses to questions regarding the children's feelings, worries, and needs concerning the COVID-19 pandemic were measured. Furthermore, the children were sampled for subjective risk perception as well as their sleep quality.

RESULTS

Most children reported a high degree of fear due to the pandemic, especially female (48.1%) participants being under more emotional strain than their male (35.9%) counterparts. Associated with this, we found a strong overestimation of COVID-19-associated hospitalization likelihood (>100-fold) across all age groups. In addition, an alarming lack of positive perspective during the ongoing pandemic is evident across all age groups, including the youngest participants aged 6-10 years. Feelings of anger and annoyance (58.2%), loneliness (46%), and sadness (42.7%) are reported much more frequently than before the pandemic. On the other hand, only 15.6% reported feeling well (or even better; 2%) since the COVID-19 pandemic. Last but not least, our study shows an alarming 37% of children and adolescents who now report poorer sleep quality than before the pandemic.

CONCLUSION

The results of this survey indicate the high burden and emotional strain for children and adolescents during the pandemic. Personal contact with friends and family is mentioned as the most protective factor for their mental health. The study results underscore the need for immediate action to limit the collateral damage that has already occurred on a psychosocial and developmental level among younger generations worldwide.

The coronavirus pandemic: Psychosocial burden, risk-perception, and attitudes in the Austrian population and its relation to media consumption

Objective

The aim was to assess the psychosocial burden, risk-perception and attitudes regarding the coronavirus pandemic among the Austrian population after the second infection wave in Austria.

Methods

A self-designed questionnaire was available online from 17th January to 19th February 2021. Knowledge, attitudes, fears, and psychosocial burdens were collected in a comprehensive convenience sample of 3,848 adults from the Austrian general population.

Results

67.2% reported their greatest fear was that a close relative could be infected; the fear of dying from COVID-19 oneself, however, was mentioned least frequently (15.2%). Isolation from family and friends (78%), homeschooling for parents (68.4%), and economic consequences (67.7%) were perceived as most stressful factors during the pandemic. Personal risk for COVID-19-associated (ICU) hospitalization was overestimated 3- to 97-fold depending on age group. Depending on the media mainly consumed, the sample could be divided into two subsamples whose estimates were remarkably opposite to each other, with regular public media users overestimating hospitalization risk substantially more.

Conclusion

The results show a high degree of psychosocial burden in the Austrian population and emphasize the need for more objective risk communication in order to counteract individually perceived risk and consequently anxiety. Altogether data call for a stronger focus and immediate action for supporting mental well-being and general health in the aftermath of the coronavirus pandemic.

["Now You're Talking!"]

Die Umfrage „Jetzt Sprichst Du!“ veranschaulicht eindrücklich die psychosozialen Belastungen und die Beeinträchtigung von Kindern und Jugendlichen in Österreich während der aktuellen Coronapandemie. Im Rahmen einer Online-Umfrage wurden 5483 Kinder und Jugendliche im Alter von 6 bis 18 Jahren bezüglich ihrer Gefühle, Ängste, Sorgen und Einschätzungen im Zusammenhang mit der Coronapandemie befragt. Es zeigt sich, dass die Kinder und Jugendlichen durch die Situation geängstigt sind und Mädchen hierbei über alle Altersgruppen hinweg stärker belastet sind. In diesem Zusammenhang wird auch deutlich, dass das Risiko einer COVID-19-assoziierten Hospitalisierung ähnlich wie bei Erwachsenen von den Kindern wie Jugendlichen massiv überschätzt wird. Auch macht sich in allen Altersgruppen eine hohe Perspektivenlosigkeit aufgrund der anhaltend herausfordernden Situation bemerkbar. Ferner häufen sich Gefühle von Wut, Ärger, Einsamkeit und Traurigkeit und es zeigt sich eine alarmierende Verschlechterung der Schlafqualität und eine Zunahme der Schlafproblematiken. Die Daten der Umfrage „Jetzt Sprichst Du!“ betonen die Notwendigkeit eines unabdingbaren und raschen Handelns, um sowohl die psychosozialen, entwicklungspsychologischen als auch gesundheitlichen Kollateralschäden in dieser jungen Altersgruppe einzudämmen, soweit dies heute noch möglich ist.

Evaluation of a Low-Cost Commercial Actigraph and Its Potential Use in Detecting Cultural Variations in Physical Activity and Sleep

The purpose of the present study was to evaluate the performance of a low-cost commercial smartwatch, the Xiaomi Mi Band (MB), in extracting physical activity and sleep-related measures and show its potential use in addressing questions that require large-scale real-time data and/or intercultural data including low-income countries. We evaluated physical activity and sleep-related measures and discussed the potential application of such devices for large-scale step and sleep data acquisition. To that end, we conducted two separate studies. In Study 1, we evaluated the performance of MB by comparing it to the GT3X (ActiGraph, wGT3X-BT), a scientific actigraph used in research, as well as subjective sleep reports. In Study 2, we distributed the MB across four countries (Austria, Germany, Cuba, and Ukraine) and investigated physical activity and sleep among these countries. The results of Study 1 indicated that MB step counts correlated highly with the scientific GT3X device, but did display biases. In addition, the MB-derived wake-up and total-sleep-times showed high agreement with subjective reports, but partly deviated from GT3X predictions. Study 2 revealed similar MB step counts across countries, but significant later wake-up and bedtimes for Ukraine than the other countries. We hope that our studies will stimulate future large-scale sensor-based physical activity and sleep research studies, including various cultures.