Depressive and stress symptoms in insomnia patients predict group cognitive-behavioral therapy for insomnia long-term effectiveness: A data-driven analysis

The applicability and effectiveness of the cognitive behavioral therapy for insomnia (Smart CBT-I plus) online program in patients with insomnia disorder combined with anxiety and depression: a randomized controlled trial protocol

Background

Insomnia is often accompanied by depression and anxiety, which can seriously affect people's quality of life. Cognitive behavioral therapy for insomnia (CBT-I) is the first-line treatment, but the existing CBT-I ignores the intervention for anxiety-depressive symptoms, and has poor efficacy due to the lack of artificial support, poor compliance, the inability to spread widely and high dropping rate. A balance is needed between the convenience and efficiency of web-based technology and patient needs. Again in this context, an online WeChat applet (Smart CBT-I plus) will be developed with CBT-I technology as the core, integrating cognitive behavioral intervention modules for depression and anxiety.

Objectives

This study will validate the effectiveness and applicability of Smart CBT-I plus by examining whether the Smart CBT-I plus study group will significantly reduce the distress of people suffering from insomnia with anxiety and/or depression symptoms compared to the psychoeducational group.

Methods

In this parallel-group, randomized controlled trial, 180 patients seeking help for insomnia combined with anxiety and/or depression will be recruited, and they will be randomized with 60 patients being assigned to the psychoeducation group (control group), and 120 patients being assigned to the Smart CBT-I plus group (study group). Measurements will be taken at baseline, post-intervention, 6 and 12 month follow-up, at the same time, semi-structured qualitative interviews about the experience of using Smart CBT-I plus will be conducted with randomly selected patients from the study group.

Results

The results will involve insomnia, depression and anxiety to explore its effectiveness, in-treatment dropout rates and subjective patient feedback to explore the applicability of Smart CBT-I plus.

Future recommendations

Self-help platforms need to be more individually designed to reach a wider audience. Research aimed at a wider audience, such as the general public, will make the research more universal and the platform more meaningful.

Stress, Anxiety and Depressive Symptoms, Burnout and Insomnia Among Greek Nurses One Year After the End of the Pandemic: A Moderated Chain Mediation Model

Background/Objectives: Several studies have reported alarming rates of mental health issues and sleep problems among nurses even in the post-pandemic era. The objective was to investigate the prevalence of stress, anxiety and depressive symptoms, burnout and insomnia among nurses in Greece one year after the end of the pandemic and to construct a mediation model evaluating the impact of stress on insomnia, the chain mediating roles of depressive symptoms and burnout, as well as the moderating role of anxiety symptoms in the model. Methods: This cross-sectional study was conducted online in July 2024 and included 380 hospital nurses who completed the Depression Anxiety Stress Scale (DASS-21), the Copenhagen Burnout Inventory (CBI) and the Athens Insomnia Scale (AIS). Results: The prevalence rates of stress, anxiety and depressive symptoms, burnout and insomnia were 33.9% with 95% confidence interval (CI): [0.292, 0.390], 33.3% (95% CI: [0.284, 0.381]), 35% (95% CI: [0.302, 0.400]), 46.8% (95% CI: [0.399, 0.502]) and 56.1% (95% CI: [0.509, 0.611]), respectively. Multiple regression analysis indicated that the Depression subscale of the DASS-21 explained 40.6% of the variance in the AIS, while an additional 7.6% was explained by the CBI and another 1.3% rate by the Stress subscale of the DASS-21. Mediation analysis revealed that stress affected insomnia both directly and indirectly through the chain mediating roles of depressive symptoms and burnout. Anxiety symptoms moderated the chain mediation path by enhancing the negative impact of stress on depressive symptoms. Conclusions: The proposed moderated chain mediation model introduces certain factors influencing insomnia and explains how changes in any one of these factors effectuate changes in the other factors, offering insights for individualized interventions.

Exploring the associations between data-driven insomnia disorder combined with mild anxiety or/and depressive symptoms and the efficacy of Cognitive-Behavioral Therapy for insomnia

Objectives

The endophenotype of insomnia disorder is complex and the treatment is not targeted. The data-driven typing method might provide some bases for precise treatment. The present study was based on a post hoc analysis, aiming to explore the association between subtypes of insomnia disorder and the efficacy of cognitive-behavioral therapy for insomnia (CBT-I).

Methods

The present study was conducted on data of 118 patients with chronic insomnia disorder combined mild anxiety or/and depressive symptoms, who had completed an 8-week randomized controlled trial of CBT-I vs CBT-I plus (CBT-I combined with modules targeting anxiety and depressive symptoms). The silhouette coefficient determined the optimal number of clusters, and a K-means clustering analysis was performed. T-tests were conducted to assess baseline differences at eight weeks, and the changes in self-reported total sleep time (sTST), Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Rating Scale (HRSD-17), and Hamilton Anxiety Rating Scale (HAMA) scores in order to explore the impact of subtypes and treatment approaches (CBT-I and CBT-I plus) on insomnia and emotional symptoms.

Results

The analysis revealed no significant demographic differences between the two clusters. Subtype 2 was characterized by significantly poorer baseline sleep quality (PSQI: 16.59 vs 12.74, t = -9.90, p < 0.01), higher depressive (HRSD: 18.47 vs 13.21, t = -8.37, p < 0.01), and anxiety levels (HAMA: 17.47 vs 13.46, t = -6.23, p < 0.01), and shorter sTST (4.67 vs 6.09 h, t = 8.31, p < 0.01) compared to Subtype 1. Post-treatment analyses showed significant improvements in both subtypes, with Subtype 2 experiencing a larger increase in sleep duration (csTST: 0.58 vs 1.77 h, t = -7.18, p < 0.01) and more pronounced improvements in sleep quality (cPSQI: 6.92 vs 8.88, t = -3.57, p < 0.001), depression (cHRSD: 8.07 vs 10.59, t = -2.71, p = 0.008), and anxiety (cHAMA: 9.28 vs 11.22, t = -2.56, p = 0.012). Despite these improvements, Subtype 1 maintained significantly better outcomes in sleep quality (PSQI: 5.81 vs 7.71, p < 0.01), depression (HRSD: 5.14 vs 7.89, p < 0.01), and anxiety (HAMA: 4.18 vs 6.25, p < 0.01) at 8 weeks. No significant differences in baseline characteristics were found between treatment groups within subtypes, indicating homogeneity. Within Cluster 1, CBT-I plus was more effective in reducing depressive symptoms (cHRSD: t = -2.48, p = 0.016), whereas CBT-I was superior in enhancing sTST in Cluster 2 (t = 2.01, p = 0.049), with no significant differences in other measures.

Conclusions

The study underscores the heterogeneity within ID subtypes and the differential response of sleep quality and depressive symptoms to CBT-I and CBT-I plus, highlighting the importance of personalized treatment strategies based on insomnia subtypes.

Cognitive-behavioural therapy for insomnia mechanism of action: Exploring the homeostatic K-complex involvement

Investigating the mechanisms of action of cognitive-behavioural therapy for insomnia (CBT-I), the first-line treatment for chronic insomnia disorder (ID), can contribute to the overall understanding of insomnia and its treatment. To date, no study has examined the relationship between K-complexes (KC) and CBT-I, despite the known homeostatic and protective function of this relevant sleep brainwave. This retrospective multicentre study aims to explore the relationship between electroencephalographic (EEG) indices and CBT-I, with a particular focus on evaluating an index of sleep homeostasis identified by KC. This research is designed to assess the predictive value of this index for treatment outcomes and to examine its variations before and after intervention. Ninety eight patients with ID underwent a 6-8 week in-person CBT-I programme, with pre-and post-treatment evaluation conducted using polysomnography (PSG) and the Insomnia Severity Index (ISI). The main outcome was determined by calculating the slope of the linear equation indexing the KC density (number of KC/minutes of N2) in each non-artifacted NREM stage 2 epoch throughout the night (KCSlope). Furthermore, the sample was categorised into Responders (ISIdecrease ≥8) and non-Responders (ISIdecrease <8). The results indicate that the KC Slope is effective not only to predict treatment response (one-way ANOVA, F = 7.831 p = 0.007; Responders = -2.954*10-5 ± 3.346*10-5, non-Responders = -5.583*10-5 ± 5.305*10-5; adjusted for PSG wake after sleep onset at the baseline), but also to detect a statistically significant improvement in sleep pressure following CBT-I (Wilcoxon signed-rank test W = 3074.000 p = 0.022; KCSlope pre-treatment = -4.054*10-5 ± 4.446*10-5, KCSlope post-treatment = -4.797*10-5 ± 5.710*10-5). These findings suggest that CBT-I increases sleep pressure in patients with chronic insomnia, highlighting a novel and relevant biomarker in this context.

Sleep effort and its measurement: A scoping review

Insomnia disorder is characterized by disruption in sleep continuity and an overall dissatisfaction with sleep. A relevant feature of insomnia is sleep effort, which refers to both cognitive and behavioural conscious attempts to initiate sleep. The Glasgow Sleep Effort Scale is a self-report tool developed to assess this construct. The objective of the current scoping review was to map how sleep effort has been discussed in the literature and operationalized through its respective measure. Medline/PubMed, Scopus, Web of Science and PsycInfo databases were used to search for potential studies. The search query used in databases was the specific name of the self-reported tool itself (Glasgow Sleep Effort Scale) and "sleep effort" term. This scoping review followed JBI guidelines. To be included, records pertaining to any type of study that mentioned the Glasgow Sleep Effort Scale were considered. No language constraint was used. At the end, 166 initial records were retrieved. From those, 46 records met eligibility criteria and were analysed. Among the main findings, it was observed that the Glasgow Sleep Effort Scale has been increasingly used in recent years, with a notable observed upward trend, especially in the last 2 years. In addition to the original measure, only three published adapted versions of the instrument were identified. This suggests that there is limited research on adapting the scale for different populations or contexts. Sleep effort has been increasingly studied in the last few years. Nonetheless, more research on the Glasgow Sleep Effort Scale tool is recommended, including cross-cultural adaptations.

Digital cognitive behavioural therapy for patients with insomnia and depression: A systematic review and meta-analysis

WHAT IS KNOWN ON THE SUBJECT

Sleep problems are common among those with depression, and there is increasing evidence that sleep problems should be addressed during treatment simultaneously rather than treating depression alone. The first-line treatment for insomnia is cognitive behavioural therapy for insomnia (CBT-I), due to a lack of well-trained therapists and patient time constraints (travelling, work), CBT-I has not been popularized. The development of digital cognitive behavioural therapy for insomnia (dCBT-I) is making the treatment more accessible.

WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE

Interventions for dCBT-I were significantly better than other control conditions in both reducing insomnia and improving depression in patients with depression and insomnia comorbidities. The effect was found to be related to the duration of the intervention and the severity of insomnia before the intervention and therapist-involved dCBT-I has less shedding than self-help.

WHAT ARE THE IMPLICATIONS FOR PRACTICE

It's important for mental health practitioners to realize that insomnia in depressed people needs to be treated. Future trials may explore the effectiveness of therapist-guided dCBT-I in depressed populations and analyse the cost-effectiveness of this treatment.

ABSTRACT

Aim The aim of the study was to systematically identify and synthesize the evidence for the effectiveness of digital cognitive behavioural therapy in insomnia with comorbid depression. Design Systematic review and metaanalysis. Methods A search was conducted on five English and four non-English databases from the inception of the databases to November 2023. This review adhered to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-analysis Statement 2020 and the included studies were evaluated using version 2 of the Cochrane risk of bias tool. This review examined sleep-related outcomes, including insomnia severity and sleep diaries, along with psychological outcomes, such as depression. We conducted a meta-analysis of each outcome using a random effects model. Heterogeneity was assessed by the I2 statistic. Results A total of seven articles with 1864 participants were included in this review. The results showed that the digital cognitive behavioural therapy group demonstrated a statistically significant amelioration in the severity of insomnia symptoms, as well as a reduction in depressive symptomatology compared with the control groups. The post-intervention effect was found to be related to the duration of the intervention and the severity of insomnia before the intervention. Conclusions Digital cognitive behavioural therapy for insomnia application in patients with depression and insomnia was demonstrated to be effective, less time-consuming and more accessible. Relevance to Clinical Practice We may consider incorporating nurses into treatment plans and conducting nurse-led interventions in specific programs. In the future, nurses may be able to provide exclusive digital behavioural therapy for insomnia to patients with depression to achieve greater effectiveness.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia

STUDY OBJECTIVES

The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response.

METHODS

Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample.

RESULTS

19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ21 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation.

CONCLUSIONS

Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value.

CITATION

Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2024;20(6):921-931.

Subtyping at-risk adolescents for predicting response toward insomnia prevention program

BACKGROUND

Previous study has shown that a brief cognitive-behavioral prevention insomnia program could reduce 71% risk of developing insomnia among at-risk adolescents. This study aimed to evaluate the differential response to insomnia prevention in subgroups of at-risk adolescents.

METHODS

Adolescents with a family history of insomnia and subthreshold insomnia symptoms were randomly assigned to a 4-week insomnia prevention program or nonactive control group. Assessments were conducted at baseline, 1 week, and 6- and 12-month after the intervention. Baseline sleep, daytime, and mood profiles were used to determine different subgroups by using latent class analysis (LCA). Analyses were conducted based on the intention-to-treat approach.

RESULTS

LCA identified three subgroups: (a) insomnia symptoms only, (b) insomnia symptoms with daytime sleepiness and mild anxiety, and (c) insomnia symptoms with daytime sleepiness, mild anxiety, and depression. The incidence rate of insomnia disorder over the 12-month follow-up was significantly reduced for adolescents receiving intervention in subgroup 3 compared with the controls (hazard ratio [HR] = 0.37; 95% confidence interval [CI]: 0.13-0.99; p = .049) and marginally for subgroup 2 (HR = 0.14; 95% CI: 0.02-1.08; p = .059). In addition, adolescents who received intervention in subgroups 2 and 3 had a reduced risk of excessive daytime sleepiness (subgroup 2: adjusted OR [AdjOR] = 0.45, 95% CI: 0.23-0.87; subgroup 3: AdjOR = 0.32, 95% CI: 0.13-0.76) and possible anxiety (subgroup 2: AdjOR = 0.47, 95% CI: 0.27-0.82; subgroup 3: AdjOR = 0.33, 95% CI: 0.14-0.78) compared with the controls over the 12-month follow-up.

CONCLUSIONS

Adolescents at risk for insomnia can be classified into different subgroups according to their psychological profiles, which were associated with differential responses to the insomnia prevention program. These findings indicate the need for further phenotyping and subgrouping at-risk adolescents to develop personalized insomnia prevention.

Baseline symptoms of depression and anxiety negatively impact the effectiveness of CBTi in treating acute insomnia among young adults

Background

Cognitive-behavioural therapy for insomnia (CBTi) is the first-line treatment for those with this sleep disorder. However, depressive and anxiety symptoms often co-occur with acute insomnia, which may affect the effectiveness of CBTi treatment.

Aims

This study aimed to determine the impact of depressive and anxiety symptoms on the efficacy of CBTi in treating acute insomnia.

Methods

A single-arm clinical trial was conducted among individuals who have acute insomnia. Participants underwent self-guided CBTi for 1-week. Their insomnia, depressive symptoms and anxiety symptoms were evaluated using the Insomnia Severity Index and the Hospital Anxiety and Depression Scale at baseline, post-treatment and 3-month follow-up. Repeated measures analysis of variance was used to assess the effectiveness of CBTi in treating insomnia, depressive symptoms and anxiety symptoms. A multivariate Cox regression model was used to determine the impact of depressive and anxiety symptoms on insomnia.

Results

The study found significant reductions in insomnia, depressive symptoms and anxiety symptoms at both post-treatment and 3-month follow-up (F=17.45, p<0.001; F=36.37, p=0.001; and F=81.51, p<0.001, respectively). The duration of CBTi treatment had a positive impact on insomnia recovery (hazard ratio (HR)=0.94, p=0.018). However, baseline depressive symptoms (HR=1.83, p=0.004) and baseline anxiety symptoms (HR=1.99, p=0.001) had significant negative effects on insomnia recovery.

Conclusions

The study showed that a 1-week self-guided CBTi treatment is effective in treating acute insomnia and comorbid depressive and anxiety symptoms. However, baseline depressive and anxiety symptoms negatively impact treatment effectiveness. Therefore, clinicians should assess for depressive and anxiety symptoms before treating acute insomnia with monotherapy CBTi.

Improving mental health and daytime function in adult insomnia patients predict cognitive behavioral therapy for insomnia effectiveness: A case-control study

Objective

This study investigated demographic, sleep related symptoms and mental health status as predictors of clinically significant treatment responses to cognitive behavioral therapy in adults who have good adherence for the cognitive behavioral therapy for insomnia (CBT-I) program in primary insomnia.

Methods

A total of 42 adults with primary insomnia disorder were treated with CBT-I at a university hospital from June 2020 to January 2021. Demographic variables were surveyed and sleep-related symptoms were measured using self-reported questionnaires before and after the intervention, comprising a 6-week interval. The treatment responder group was defined as patients with an Insomnia Severity Index change score >7 compared to baseline. Logistic regression and paired t-test examined whether these factors predicted treatment outcomes for CBT-I.

Results

Demographic variables did not predict treatment outcomes. Higher levels of anxiety were associated with a higher likelihood of treatment response (odds ratio [OR] = 1.234; confidence interval [CI]: 1.008-1.511). More severe insomnia at baseline was associated with a greater likelihood of treatment response (OR = 1.450; CI: 1.121-1.875). The lesser the dysfunctional beliefs and attitudes about sleep, the more effective the treatment response (OR = 0.943; CI: 0.904-0.984). Unlike the group of treatment responders, daytime function, depressive mood, and anxiety status did not improve in the group of treatment non-responders after CBT-I intervention.

Conclusions

Patients with severe insomnia and anxiety at baseline should be treated more aggressively with CBT-I. During treatment, patients' mental health problems and daytime activities should be continuously monitored, in order to help improve these problems which might strengthen the effectiveness of CBT-I.

The top 100 most cited papers in insomnia: A bibliometric analysis

Objective

The number of citations to a paper represents the weight of that work in a particular area of interest. Several highly cited papers are listed in the bibliometric analysis. This study aimed to identify and analyze the 100 most cited papers in insomnia research that might appeal to researchers and clinicians.

Methods

We reviewed the Web of Science (WOS) Core Collection database to identify articles from 1985 to 24 March 2022. The R bibliometric package was used to further analyze citation counts, authors, year of publication, source journal, geographical origin, subject, article type, and level of evidence. Word co-occurrence in 100 articles was visualized using VOS viewer software.

Results

A total of 44,654 manuscripts were searched on the Web of Science. Between 2001 and 2021, the top 100 influential manuscripts were published, with a total citation frequency of 38,463. The top countries and institutions contributing to the field were the U.S. and Duke University. Morin C.M. was the most productive author, ranking first in citations. Sleep had the highest number of manuscripts published in the top 100 (n = 31), followed by Sleep Medicine Reviews (n = 9). The most cited manuscript (Bastien et al., Sleep Medicine, 2001; 3,384 citations) reported clinical validation of the Insomnia Severity Index (ISI) as a brief screening indicator for insomnia and as an outcome indicator for treatment studies. Co-occurrence analyses suggest that psychiatric disorders combined with insomnia and cognitive behavioral therapy remain future research trends.

Conclusion

This study provides a detailed list of the most cited articles on insomnia. The analysis provides researchers and clinicians with a detailed overview of the most cited papers on insomnia over the past two decades. Notably, COVID-19, anxiety, depression, CBT, and sleep microstructure are potential areas of focus for future research.

[Insomnia in the context of affective disorders]

The article analyzes the current literature on the relationship of insomnia with affective disorders, in particular with depression and anxiety. Research shows that there is a strong multi-channel relationship between insomnia, depression, and anxiety, with insomnia being considered a risk factor for mood disorders more often than vice versa. The so-called insomnia paradox of bipolar disorder is described, the essence of which is that in manic episodes the frequency of insomnia is higher than in depressive episodes. The data of a network meta-analysis, which found an evidence base for the use of a variety of drugs used for the pharmacological treatment of insomnia in adults, are presented. Efficiency and convenience in taking the drug Valocordin-Doxylamine are noted.

Using Latent Profile Analysis to Evaluate Preferences for Chronic Wasting Disease Management Options among Different Hunter Types

Simple Summary

Extensive research has explored hunters’ support for chronic wasting disease management, but many studies do not account for differences between types of hunters nor the tradeoffs hunters make in their decision-making about management alternatives. To address this, we used data from a survey of Texas hunters to create categories of hunters and explore their preferences for chronic wasting disease management based on these categories. Across five hunter categories, most hunters support disease management, although their attitudes towards Texas Parks and Wildlife Department varied. Different hunter categories had different preferences for chronic wasting disease management policies. Wildlife agencies can refer to our study findings to create preferred chronic wasting disease management policies and better communicate about them.

Abstract

Wildlife agencies seek to understand how hunters have and will respond to chronic wasting disease (CWD) management policies because of the vital role hunters play in deer management efforts. As such, dozens of studies have examined the human dimensions of CWD management and policy to assess stakeholder support for management alternatives and reveal what drives support. However, most of these studies have not (1) fully explored the heterogeneity that exists among hunters, and (2) accounted for the tradeoffs that agencies and hunters must make with regard to deer management and potential CWD policy alternatives. To address these deficiencies, we used latent profile analysis to create different typologies of hunters based on a survey of Texas hunters, then analyzed discrete choice experiments investigating the CWD management preferences of these typologies. Across five hunter typologies, we found strong overall support for CWD management, although attitudes towards Texas Parks and Wildlife Department were variable. Preferences for CWD management policies greatly differed between each hunter typology. Wildlife agencies can refer to our findings to better develop hunter-preferred CWD management policies and identify areas of compromise between typologies. Our results also provide agencies with insights regarding how to better communicate about CWD management with different types of hunters.