The Role of Sleep Quality and Fatigue on the Benefits of an Interdisciplinary Treatment for Adults With Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Interdisciplinary chronic pain treatment is effective for reducing pain intensity and pain-related disability, and for improving psychological function. However, the mechanisms that underlie these treatment-related benefits are not yet well understood. Sleep problems and fatigue are modifiable factors often comorbid with chronic pain. The goal of this study was to evaluate the role that changes in sleep quality and fatigue might have on the benefits of an interdisciplinary chronic pain treatment. METHODS: A total of 125 adults with chronic pain participated in a 4-week interdisciplinary pain management program. Measures of depression, sleep disturbance, fatigue, pain intensity, and physical function were administered at pre- and post-treatment. Three regression analyses were conducted to evaluate the contribution of pre- to post-treatment improvements in fatigue and sleep disturbance to the pre- to post-treatment improvements in pain intensity, disability, and depression, while controlling for demographic characteristics (age and sex) and pain intensity. RESULTS: Changes in fatigue and sleep disturbance made independent and significant contributions to the prediction of treatment-related benefits in pain intensity; improvements in depressive symptoms were predicted by improvements in fatigue, and improvements in disability were only predicted by pre-treatment and pre- to post-treatment decreases in pain intensity (one of the control variables). CONCLUSIONS: In addition to sleep, fatigue emerged as a key potential mechanism of multidisciplinary chronic pain treatment-related improvements, suggesting that interventions including elements that effectively target sleep and fatigue may enhance the efficacy of interdisciplinary chronic pain programs. This possibility should be evaluated in future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".