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Cognitive Behavioral Therapy In Patients With Insomnia And Chronic Fatigue

Cognitive Behavioral Therapy In Patients With Insomnia And Chronic Fatigue


This study delves into the relationship between insomnia, chronic fatigue, and the response to cognitive behavioral therapy for insomnia (CBT-I). The research, a secondary analysis of data from a community-based randomized controlled trial, encompassed 1717 participants with self-reported insomnia. The primary aim was to assess whether individuals experiencing both insomnia and chronic fatigue responded differently to digital CBT-I compared to those without chronic fatigue.


Participants were categorized into two groups: one with chronic fatigue (n=592) and another without chronic fatigue (n=1125), based on baseline Chalder Fatigue Questionnaire ratings. The study employed various outcome measures, including the Insomnia Severity Index, Short Form-12 mental and physical health assessments, and the Hospital Anxiety and Depression Scale.


The findings revealed that individuals with chronic fatigue experienced significantly greater improvements when undergoing digital CBT-I compared to those receiving patient education, as indicated by improvements in the Insomnia Severity Index, Short Form-12 mental health, and the Hospital Anxiety and Depression Scale. However, there were no substantial differences in the effectiveness of digital CBT-I between participants with chronic fatigue and those without on any of the assessed outcomes.


This study, conducted on a sizable community-based sample of adults with insomnia, suggests that co-existing chronic fatigue does not moderate the effectiveness of digital CBT-I. These findings contribute to the growing body of evidence supporting digital CBT-I as a valuable adjunctive intervention for individuals dealing with both physical and mental health challenges.



Insomnia is a prevalent condition affecting approximately 10% of the population, characterized by difficulties in initiating or maintaining sleep and resulting in negative daytime consequences, most notably, daytime fatigue. Cognitive behavioral therapy for insomnia (CBT-I), delivered face-to-face or digitally, has consistently shown its effectiveness in treating insomnia and related symptoms in various populations, including the general public and those with psychiatric conditions or higher fatigue risk, such as cancer patients. While CBT-I doesn’t directly target daytime fatigue, studies have indicated that it can lead to improvements in fatigue.


The significance of these findings lies in the potential implications for treating other mental and physical disorders that are associated with both sleep disturbances and fatigue. Many individuals diagnosed with chronic fatigue (fatigue persisting for at least six months) are often offered therapies like exercise or CBT-like interventions that prioritize daytime functioning. However, the benefits of these treatments are limited, with minimal improvement in sleep disturbances. CBT-I, which requires behavioral changes, can be particularly challenging for individuals with both chronic fatigue and insomnia, potentially leading to increased fatigue as a side effect.


Despite this, there is a lack of knowledge regarding the comparative effectiveness of digital CBT-I (dCBT-I) in individuals with insomnia who also experience chronic fatigue in the general population. The study hypothesized that the presence of self-reported chronic fatigue at baseline may moderate the effectiveness of dCBT-I. Therefore, it aimed to investigate whether individuals with chronic fatigue responded differently to dCBT-I compared to those without chronic fatigue across various outcomes.


This secondary analysis of a substantial randomized controlled trial dataset sought to determine whether chronic fatigue moderated the effects of dCBT-I on insomnia severity, mental and physical health, and psychological distress at a 9-week follow-up compared to patient education about insomnia. This research contributes to a deeper understanding of the potential benefits of dCBT-I in individuals dealing with both insomnia and chronic fatigue, which could have broader implications for the treatment of mental and physical disorders associated with these symptoms.



The study under review is a secondary analysis of data from a previously conducted randomized controlled trial (RCT) that investigated the effectiveness of digital cognitive behavioral therapy for insomnia (dCBT-I) compared to a patient education (PE) intervention. The RCT involved 1721 participants with self-reported insomnia from the Norwegian community. The trial was registered with (NCT02558647) and approved by the Regional Committee for Medical and Health Research Ethics in Southeast Norway (2015/134).


Trial Design

– Participants and Recruitment: Recruitment took place between February 2016 and July 2018 through various channels, including healthcare facilities, general practitioners’ offices, and social media. Eligible participants, aged 18 years or older, with an Insomnia Severity Index (ISI) score of ≥12, provided informed consent and completed baseline assessments, including sleep diaries.


Eligibility Criteria


Inclusion criteria included age 18 or older and an ISI score ≥12. Exclusion criteria comprised an Epworth Sleepiness Scale score >10 (indicating sleep apnea or hypersomnia risk), risk of sleep apnea assessed through screening questions, self-reported medical conditions contraindicated for dCTB-I, and current night-time shift work.



  1. Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I): This intervention involved an interactive web-based program consisting of six treatment components commonly associated with CBT-I, except relaxation training. Participants received tailored feedback based on their sleep diaries and treatment goals, and the program was designed to be completed within 9 weeks.


  1. Patient Education about Insomnia (PE): PE was delivered through an online platform, offering written text with information about sleep hygiene advice and psychoeducation regarding insomnia. Unlike dCBT-I, PE did not provide feedback. Participants in the PE group could access the website as often as they liked during the intervention period.


Data Analyzed in This Study


For the moderator analysis in this study, data from baseline and the 9-week follow-up assessments were extracted, focusing on the following measures:


Primary Outcome Measure


The Insomnia Severity Index (ISI), a self-report measure comprising seven items to assess overall insomnia severity, with scores ranging from 0 to 28. A decrease of eight points on the ISI from baseline to 9-week follow-up indicated a treatment response, while a score below eight indicated remission.


Secondary Outcome Measures

– The 12-item Short-Form Health Survey (SF-12), a self-report measure assessing perceived physical and mental health status. It provides summary scores for mental and physical health, with higher scores indicating better functioning.

– The Hospital Anxiety and Depression Scale (HADS), a self-report measure comprising 14 items to assess psychological distress, with total scores ranging from 0 to 42, where higher scores indicate greater psychological distress.


This secondary analysis aimed to explore the effectiveness of dCBT-I and PE, focusing on their impact on insomnia severity, mental and physical health, and psychological distress at the 9-week follow-up compared to baseline. The study contributes valuable insights into the treatment of insomnia and its associated outcomes in a community-based population.


Statistical Analysis

In this study, participants were categorized into two groups: those with more self-reported symptoms of chronic fatigue (CF) and those with fewer symptoms, referred to as non-CF individuals (nCF). To ensure that the CF category aligned with the diagnostic criteria for CF syndrome, several criteria were applied to participants’ responses on the Chalder Fatigue Questionnaire (CFQ). These criteria included:


  1. Duration: CF would only be considered in individuals reporting a duration of fatigue of at least 6 months.
  2. Persistence: Participants needed to report that at least 50% of their wake time was affected by fatigue.
  3. Severity of Impairment/Symptoms: A bimodal scoring system was applied to the other nine CFQ items, reducing the scores to 0 (indicating no or lower severity of impairment) or 1 (indicating worse symptoms or higher severity). To be classified as CF, participants had to score >5 on these nine items.


In contrast, participants who did not meet the CF criteria were categorized as nCF, based on a bimodal score of ≤5, duration of less than 6 months, and persistence of less than 50%. This categorization approach closely aligned with the diagnostic criteria for CF syndrome and had been used previously in a Norwegian population.


Planned Analyses

The study conducted various planned analyses to investigate the impact of CF on the outcomes of digital cognitive behavioral therapy for insomnia (dCBT-I). These analyses included:


  1. Baseline Differences: Independent samples t-tests were used to examine baseline differences in outcomes between CF and nCF categorizations.


  1. Intervention Completion: The proportions of participants who completed dCBT-I in CF and nCF categorizations were compared, along with the proportions of participants in each categorization and intervention group who met the criteria for response and remission on the Insomnia Severity Index (ISI) using Pearson chi-square tests.


  1. Linear Mixed Models: To investigate intervention response, linear mixed models were employed, with ISI, Hospital Anxiety and Depression Scale (HADS), Short-Form 12 (SF-12) mental health score, and SF-12 physical health score as dependent variables. The models included time (baseline vs. 9-week follow-up), intervention (dCBT-I vs. PE), and categorization (CF vs. nCF) as covariates, involving main effects, two-way interactions (intervention × time and time × categorization), and a three-way interaction (intervention × time × categorization). These models were adjusted for baseline assessments, age, and sex.


  1. Effect Sizes: Standard effect sizes (Cohen’s d) were calculated for between-group assessments using estimated differences and pooled standard deviations.


  1. Statistical Significance: Statistical significance was determined using two-sided p-values less than 0.05.


All statistical analyses were conducted using SPSS version 26. The study aimed to discern how the presence of chronic fatigue (CF) influenced the outcomes of digital cognitive behavioral therapy for insomnia (dCBT-I), contributing valuable insights into the treatment’s efficacy in individuals with insomnia and CF symptoms.



In this study involving 1721 participants, 592 individuals met the criteria for chronic fatigue (CF), while the remaining participants were categorized as non-CF individuals (nCF). Among those with CF, half were assigned to receive digital cognitive behavioral therapy for insomnia (dCBT-I), and the other half to patient education (PE). Baseline characteristics revealed that most participants were women, married, and over 45% were employed full-time.


Analysis of baseline assessments indicated significant differences between CF and nCF groups in various measures, including the Insomnia Severity Index (ISI), Short-Form 12 (SF-12) mental health, SF-12 physical health, and the Hospital Anxiety and Depression Scale (HADS).


Regarding intervention completion, a similar proportion of CF and nCF participants completed the first core of dCBT-I (85% vs. 87%), with approximately half completing all six cores.


Primary Outcome (ISI)

The study examined the effectiveness of dCBT-I in individuals with or without CF. The three-way interaction analysis (categorization × intervention × time) revealed no significant differences in the efficacy of dCBT-I on the ISI between individuals with or without CF (p = 0.184). Remission rates from insomnia were not significantly different between CF and nCF participants receiving dCBT-I (31.3% vs. 40.8%, p = 0.061), but for those receiving PE, fewer CF participants achieved remission compared to nCF participants (5% vs. 10%, p = 0.026).


Secondary Outcomes (SF-12 Mental Health, SF-12 Physical Health, HADS)

The three-way interaction analyses showed no significant differences in the effectiveness of dCBT-I on the SF-12 mental health score (p = 0.110), SF-12 physical health score (p = 0.618), or HADS (p = 0.952) between individuals with or without CF. However, there were significant between-group differences in favor of dCBT-I compared to PE for participants in both CF and nCF categorizations after 9 weeks. Effect sizes for these differences were generally small to small-to-medium.


The study found that the presence of chronic fatigue did not significantly affect the effectiveness of dCBT-I in improving insomnia symptoms and related outcomes. Both CF and nCF participants benefited from dCBT-I, and the differences in outcomes between the two groups were generally small. These findings suggest that dCBT-I can be an effective treatment for individuals with insomnia, regardless of the presence of chronic fatigue.



In a secondary analysis of a large community-based randomized controlled trial (RCT) involving individuals with self-reported insomnia, the study aimed to understand whether the benefits of digital cognitive behavioral therapy for insomnia (dCBT-I) were influenced by the presence of self-reported symptoms of chronic fatigue (CF) at baseline.


The primary finding of the study was that dCBT-I proved to be an effective and acceptable intervention for addressing both nighttime and daytime symptoms in individuals with and without co-occurring self-reported CF. Both CF and non-CF individuals experienced reductions in insomnia levels, improvements in mental health, and decreases in psychological distress following dCBT-I. However, the intervention did not have a significant effect on physical health as measured by the SF-12.


The study population had a relatively high prevalence of self-reported CF (34%), and this overlap between insomnia and CF suggests a potential interrelationship and shared mechanisms. The study’s results indicate that insomnia treatment should be adapted with fatigue-specific interventions when patients present with CF at baseline, and conversely, the management of CF should include an assessment of the patient’s sleep difficulties and appropriate interventions.


The study’s findings also highlighted the importance of reducing psychological distress as a potential mediator for the positive treatment outcomes in individuals with both CF and insomnia. Psychological distress was closely related to both conditions, and addressing it through dCBT-I could have contributed to improvements in other areas, such as insomnia severity.


However, several limitations should be considered when interpreting these results. This study was a secondary analysis, and the categorization of CF relied on self-reported levels of fatigue rather than clinical diagnostic assessments. Additionally, insomnia was assessed using the Insomnia Severity Index (ISI) rather than clinical diagnosis. The study focused on immediate post-intervention outcomes, and further research is needed to investigate long-term changes in both conditions.


This study suggests that dCBT-I is an effective intervention for individuals with both self-reported CF and insomnia. It underscores the potential overlap and interrelatedness of these conditions and highlights the importance of addressing psychological distress in treatment. Future research should explore fatigue-specific components in insomnia treatment and investigate potential mechanisms of change during extended follow-up.

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