The Effectiveness of Cognitive Restoration Therapy on Alexithymia and Sleep Regulation in Patients with Chronic Pain
Bibliographic record
Abstract
The objective of this study was to evaluate the effectiveness of Cognitive Restoration Therapy in reducing alexithymia and improving sleep regulation among patients with chronic pain. This randomized controlled trial employed a pre-test, post-test, and five-month follow-up design. Forty patients diagnosed with chronic pain were recruited from a hospital in Tehran and randomly assigned to either a Cognitive Restoration Therapy group (n=20) or a control group (n=20). The 20-item Toronto Alexithymia Scale and the Pittsburgh Sleep Quality Index were used as measurement tools. The intervention consisted of eight weekly sessions of Cognitive Restoration Therapy. Data were analyzed using repeated-measures ANOVA and Bonferroni post-hoc tests in SPSS-27. Repeated-measures ANOVA indicated significant time effects and significant time × group interactions for both alexithymia and sleep regulation (p<.001). Significant between-group effects were also observed, with large effect sizes (η²=.78–.83). Bonferroni post-hoc analyses showed significant differences between pre-test and post-test, as well as between pre-test and follow-up in the intervention group, while no significant difference was observed between post-test and follow-up, indicating stability of treatment outcomes. The findings demonstrate that Cognitive Restoration Therapy is effective in reducing alexithymia and improving sleep regulation in chronic pain patients, with treatment effects maintained at five-month follow-up. This intervention can serve as a valuable complementary approach within multidisciplinary chronic pain management programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".