Interactions between insomnia, sleep duration and emotional processes: An ecological momentary assessment of longitudinal influences combining self-report and physiological measures.

Previous studies indicated that further investigation is needed to understand how insomnia disorder interacts with emotional processes. The present study is an ecological momentary assessment evaluating the link between emotional and sleep alterations in patients with insomnia. Physiological (heart rate and heart rate variability) and subjective (sleep and emotions) indices were observed for 5 days in patients with insomnia disorder (n = 97), good sleepers under self-imposed sleep restriction (n = 41), and good sleepers with usual amount of sleep (n = 45). We evaluated differences in emotion regulation strategies and in valence and variability of emotional experiences. Over 5 days, patients with insomnia showed increased sleep and emotional difficulties compared with both control groups. Independent from group allocation, days with more negative emotions were associated with higher sleep alterations. Longer wake episodes at night and higher diurnal heart rate were associated with increased variations in emotion experienced during the day. Only in patients with insomnia, use of adaptive emotion regulation strategies was associated with higher sleep efficiency. Our data showed that alterations in sleep and emotional processes are closely linked. A combination of strategies targeting both sleep and emotional processes appears promising in the prevention and treatment of insomnia disorder.

Digital cognitive behavioural therapy for insomnia reduces insomnia in nurses suffering from shift work disorder: A randomised-controlled pilot trial.

Insomnia is a primary symptom of shift work disorder, yet it remains undertreated. This randomised-controlled pilot trial examined the efficacy of a digital, guided cognitive behavioural therapy for insomnia adapted to shift work (SleepCare) in nurses with shift work disorder. The hypothesis was that SleepCare reduces insomnia severity compared with a waitlist control condition. A total of 46 unmedicated nurses suffering from shift work disorder with insomnia (age: 39.7 ± 12.1 years; 80.4% female) were randomised to the SleepCare group or the waitlist control group. The primary outcome measure was the Insomnia Severity Index. Other questionnaires on sleep, mental health and occupational functioning, sleep diary data and actigraphy data were analysed as secondary outcomes. Assessments were conducted before (T0), after the intervention/waitlist period (T1), and 6 months after treatment completion (T2). The SleepCare group showed a significant reduction in insomnia severity from T0 to T1 compared with the control condition (ß = -4.73, SE = 1.12, p < 0.001). Significant improvements were observed in sleepiness, dysfunctional beliefs about sleep, pre-sleep arousal, sleep effort, self-reported sleep efficiency and sleep onset latency. No significant effect was found in actigraphy data. Depressive and anxiety symptoms, cognitive irritation and work ability improved significantly. Overall, satisfaction and engagement with the intervention was high. SleepCare improved insomnia severity, sleep, mental health and occupational functioning. This is the first randomised-controlled trial investigating the efficacy of digital cognitive behavioural therapy for insomnia in a population suffering from shift work disorder with insomnia. Future research should further explore these effects with larger sample sizes and active control conditions.

Adherence to sleep restriction therapy - An evaluation of existing measures.

Sleep restriction, a key element of cognitive behavioural therapy for insomnia, involves considerable behavioural changes in patients' lives, leading to side-effects like increased daytime sleepiness. Studies on sleep restriction rarely report adherence, and when assessed it is often limited to the average number of therapy sessions attended. This study aims to systematically evaluate different measures of adherence to cognitive behavioural therapy for insomnia and their relationship with treatment outcome. This is a secondary analysis of data from a randomized controlled trial investigating cognitive behavioural therapy for insomnia (Johann et al. (2020) Journal of Sleep Research, 29, e13102). The sample included 23 patients diagnosed with insomnia according to DSM-5 criteria who underwent 8 weeks of cognitive behavioural therapy for insomnia. The following adherence measures based on sleep diary data were used: number of sessions completed; deviations from agreed time in bed; average percentage of patients deviating from bedtime by 15, 30 or 60 min; variability of bedtime and wake-up time; change in time in bed from pre- to post-assessment. Treatment outcome was assessed using the Insomnia Severity Index. Multiple regression models were employed, and insomnia severity was controlled for. Results showed that none of the adherence measures predict insomnia severity. Baseline insomnia severity, dysfunctional thoughts and attitudes about sleep, depression or perfectionism did not predict adherence. The limited variance in the outcome parameter due to most patients benefiting from treatment and the small sample size may explain these findings. Additionally, using objective measures like actigraphy could provide a better understanding of adherence behaviour. Lastly, the presence of perfectionism in patients with insomnia may have mitigated adherence problems in this study.

Treatment effect heterogeneity of cognitive behavioral therapy for insomnia - A meta-analysis.

Investigation of the heterogeneity of the treatment effect (HTE) might guide the optimization of cognitive behavioral therapy for insomnia (CBT-I). This study examined HTE in CBT-I thereby analyzing if treatment setting, control group, different CBT-I components, and patient characteristics drive HTE. Randomized controlled trials investigating CBT-I were included. Bayesian random effect meta-regressions were specified to examine variances between the intervention and control groups regarding post-treatment symptom severity. Subgroup analyses analyzing treatment setting and control groups and covariate analysis analyzing treatment components and patient characteristics were specified. No significant HTE in CBT-I was found for the overall data set, settings and control groups. The covariate analyses yielded significant results for baseline severity and the treatment component relaxation therapy. Thus, this study identified potential causes for HTE in CBT-I for the first time, showing that it might be worthwhile to further examine possibilities for precision medicine in CBT-I.

The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior.

Introduction: Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the correlations between smartphone usage features (SUF) and insomnia symptoms and their predictive value for detecting insomnia symptoms. Methods: In an observational study of a German convenience sample, the Insomnia Severity Index (ISI) and smartphone usage data (e.g., time the screen was active, longest time the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlation analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to training, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neighbor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores ≥15. Results: 752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SUF and insomnia symptoms were found. In the ML models, sensitivity was low, ranging from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes were the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in the testing subsample indicated a low discrimination capacity. Conclusions: Given the small magnitude of the correlations and low discrimination capacity of the ML models, SUFs, as measured in this study, do not appear to be sufficient for detecting insomnia symptoms. Further research is necessary to explore whether examining intra-individual variations and subpopulations or employing alternative smartphone sensors yields more promising outcomes.

Comparative efficacy of onsite, digital, and other settings for cognitive behavioral therapy for insomnia: a systematic review and network meta-analysis.

Given the limited availability and accessibility of onsite cognitive behavioral therapy for insomnia (CBT-I), other CBT-I settings, such as internet-delivered CBT-I (iCBT-I), have been proposed. The primary aim of the study was to compare the efficacy of available CBT-I settings on insomnia severity. A systematic review and frequentist network meta-analysis of available CBT-I settings was performed. PsycINFO, PsycARTICLES, MEDLINE, PubMed, and CINAHL were searched for randomized controlled trials (RCTs) investigating any CBT-I settings in adults with insomnia disorder. The systematic literature search (3851 references) resulted in 52 RCTs. For the primary outcome insomnia severity, all examined CBT-I settings except smartphone-delivered CBT-I yielded significant effects when compared to WL. Large standardized mean differences were found for individual onsite CBT-I (- 1.27;95%CI - 1.70, - 0.84), group-delivered CBT-I (- 1.00;95%CI - 1.42. - 0.59), telehealth (- 1.28;95%CI - 2.06, - 0.50), and guided bibliotherapy (- 0.99;95%CI - 1.67, - 0.32). Both guided iCBT-I (- 0.71;95%CI - 1.18, - 0.24) and unguided iCBT-I (- 0.78;95%CI - 1.18, - 0.38) yielded medium effect sizes. The results underline that health care systems should intensify their efforts to provide synchronously-delivered CBT-I (individual onsite, group-delivered, and telehealth), and particularly individual onsite CBT-I, given its solid evidence base. Medium to large effect sizes for iCBT-I and guided bibliotherapy indicate that self-help settings may be a viable alternative when synchronously-delivered CBT-I is not available.

The Effects of Cognitive Behavioral Therapy for Insomnia on Multidimensional Perfectionism.

Perfectionism is related to insomnia and objective markers of disturbed sleep. This study examined whether multidimensional perfectionism is related to dysfunctional beliefs about sleep, sleep-effort, pre-sleep arousal, and polysomnography-determined markers of sleep among individuals with insomnia. The effects of cognitive behavioral therapy for insomnia (CBT-I) on perfectionism was also examined. This was a secondary analysis of a randomized controlled trial on CBT-I. Forty-three insomnia patients were randomized to treatment (receiving CBT-I) or waitlist control groups. Sleep was recorded using polysomnography at baseline. Participants completed measures of perfectionism, dysfunctional beliefs about sleep, sleep-effort and pre-sleep arousal at baseline and posttreatment. Total perfectionism scores and doubts about action, concern over mistakes and personal standards were each significantly related to increased sleep effort, pre-sleep arousal and dysfunctional beliefs about sleep at baseline. Patients receiving treatment displayed increased total perfectionism scores posttreatment d = .49. In those receiving treatment, levels of organization d = .49 and parental expectations d = .47 were significantly increased posttreatment, relative to baseline. In line with the literature, our results confirm that perfectionism is related to insomnia. Here, insomnia was related to increased sleep effort, pre-sleep arousal and dysfunctional beliefs about sleep. The propensity to maintain a high standard of order and organization may be elevated following CBT-I, considering the treatment protocol expects patients to strictly adhere to a set of clearly defined rules. Levels of parental expectations may be increased following CBT-I since the patient-therapist-relationship may trigger implicit expectations in patients which are reminiscent of their relationship to their parents.