349 Cognitive Behavioral Therapy for Insomnia in Patients with Chronic Pain - A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction Patients with chronic non-cancer pain often report insomnia as a significant comorbidity. Cognitive behavioral therapy for insomnia (CBT-I) is recommended as the first line of treatment for insomnia, and several randomized controlled trials (RCTs) have examined the efficacy of CBT-I on various health outcomes in patients with comorbid insomnia and chronic non-cancer pain. We conducted a systematic review and meta-analysis on the effectiveness of CBT-I on sleep, pain, depression, anxiety and fatigue in adults with comorbid insomnia and chronic non-cancer pain. Methods A systematic search was conducted using ten electronic databases. The duration of the search was set between database inception to April 2020. Included studies must be RCTs assessing the effects of CBT-I on at least patient-reported sleep outcomes in adults with chronic non-cancer pain. Quality of the studies was assessed using the Cochrane risk of bias assessment and Yates quality rating scale. Continuous data were extracted and summarized using standard mean difference (SMD) with 95% confidence intervals (CIs). Results The literature search resulted in 7,772 articles, of which 14 RCTs met the inclusion criteria. Twelve of these articles were included in the meta-analysis. The meta-analysis comprised 762 participants. CBT-I demonstrated a large significant effect on patient-reported sleep (SMD = 0.87, 95% CI [0.55–1.20], p < 0.00001) at post-treatment and final follow-up (up to 9 months) (0.59 [0.31–0.86], p < 0.0001); and moderate effects on pain (SMD = 0.20 [0.06, 0.34], p = 0.006) and depression (0.44 [0.09–0.79], p= 0.01) at post-treatment. The probability of improving sleep and pain following CBT-I at post-treatment was 81% and 58%, respectively. The probability of improving sleep and pain at final follow-up was 73% and 57%, respectively. There were no statistically significant effects on anxiety and fatigue. Conclusion This systematic review and meta-analysis showed that CBT-I is effective for improving sleep in adults with comorbid insomnia and chronic non-cancer pain. Further, CBT-I may lead to short-term moderate improvements in pain and depression. However, there is a need for further RCTs with adequate power, longer follow-up periods, CBT for both insomnia and pain, and consistent scoring systems for assessing patient outcomes. Support (if any):
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".