Article

Digital CBT for Insomnia Shows Effectiveness in Patients with Chronic Fatigue

Author(s):

Digital Cognitive Behavior Therapy for Insomnia (CBT-I) is equally effective in treating insomnia in individuals with or without co-occurring chronic fatigue, according to a recent study.

Digital CBT for Insomnia Shows Effectiveness in Patients with Chronic Fatigue

Lina Stålesen Ramfjord, PhD candidate

Results from a recent investigation suggest that co-occurring chronic fatigue does not moderate the effectiveness of digital Cognitive Behavior Therapy for Insomnia (CBT-I) in adults with the condition.1

According to the study, the data established digital CBT-I as a promising approach for intervention when treating patients with physical and mental disorders including chronic fatigue.

CBT-I is a non-pharmacological treatment that's exhibited effectiveness in the improvement of sleep quality and reducing symptoms of insomnia.

Oftentimes, insomnia is accompanied by chronic fatigue, which is particularly complex and can have a debilitating impact on affected individuals. Those plagued by persistent fatigue can't alleviate their symptoms with rest, significantly influencing their quality of life.

Previously, it was unclear whether patients with co-existing symptoms of insomnia and chronic fatigue responded differently to CBT-I compared with those without chronic fatigue.

To futher the understanding of these relationships, a large community-based sample of adults with insomnia were evaluated by a team of investigators, including Lina Stålesen Ramfjord, Department of Mental Health, Norwegian University of Science and Technology.

Comparing CBT and Patient Education for Insomnia

The team of investigators conducted a secondary analysis of data from a community-based randomized controlled trial of digital CBT-I compared with patient education. The study included 1717 participants with self-reported insomnia, of which 592 reported symptoms of chronic fatigue while 1125 did not.

Patients with chronic fatigue reported significantly greater improvements following digital CBT-I for insomnia compared with patient education on the Insomnia Severity Index, Short Form-12 mental health, and Hospital Anxiety and Depression Scale.

However, findings showed no significant differences in the effectiveness of digital CBT-I for insomnia between patients with chronic fatigue and those without chronic fatigue on any outcome.

Impact of Co-existing Chronic Fatigue

For the analysis, linear mixed models were employed with baseline ratings of the Chalder Fatigue Questionnaire used to identify patients with more or fewer symptoms of self-reported chronic fatigue.

The main outcomes assessed in the study were the Insomnia Severity Index, Short Form-12 mental health, Short Form-12 physical health, and the Hospital Anxiety and Depression Scale.

Some limitations were noted, such as relying on self-reported measures of insomnia and chronic fatigue, and the use of a specific digital CBT-I program, which may limit the generalizability of the findings. Further research is needed to confirm these results and explore the potential benefits of digital CBT-I in individuals with other comorbid conditions.

Overall, the research demonstrated significant improvements in insomnia symptoms among the adults who reported high levels of fatigue after they participated in CBT-I.

"This has clinical implications that are relevant to the management of insomnia, as fatigue is among the most commonly reported comorbid symptoms. Moreover, this may further establish dCBT-I as an adjunctive intervention in individuals with physical and mental disorders."

References:

  1. Ramfjord, L. S., Faaland, P., Scott, J., Saksvik, S. B., Lydersen, S., Vedaa, Ø., Kahn, N., Langsrud, K., Stiles, T. C., Ritterband, L. M., Harvey, A. G., Sivertsen, B., & Kallestad, H. (2023). Digital cognitive behaviour therapy for insomnia in individuals with self-reported insomnia and chronic fatigue: A secondary analysis of a large scale randomized controlled trial. Journal of Sleep Research, e13888. https://doi.org/10.1111/jsr.13888

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