Addressing cancer-related fatigue through sleep: A secondary analysis of a randomized trial comparing acupuncture and cognitive behavioral therapy for insomnia
Bibliographic record
Abstract
Fatigue is a troublesome symptom in cancer survivors that often results from disrupted sleep. We sought to assess whether two insomnia-focused non-pharmacological interventions are also effective for improving fatigue. We analyzed data from a randomized clinical trial comparing cognitive behavioral therapy for insomnia (CBT-I) versus acupuncture for insomnia among cancer survivors. Participants were 109 patients who reported insomnia and moderate or worse fatigue. Interventions were delivered over eight weeks. Fatigue was evaluated at baseline, week 8, and week 20 using the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). We used both mediation analysis and t-tests to explore the extent to which fatigue reduction was attributable to insomnia response. Compared to baseline, both CBT-I and acupuncture produced significant reductions in total MFSI-SF scores at week 8 (−17.1 points; 95% confidence interval [CI]: −21.1 to −13.1, and −13.2 points; 95% CI: -17.2 to -9.2, respectively, all p<0.001) and week 20 (-14.6 points; 95% CI: -18.6 to -10.6, and −14.2 points; 95% CI: -18.1 to -10.3. respectively, all p<0.001), with no significant between-group differences. MFSI-SF total scores at week 8 were significantly associated with sleep improvements in both CBT-I and acupuncture groups (p<0.001 and p=0.011, respectively). Insomnia responders demonstrated significantly greater improvements in mean MFSI-SF total scores compared with non-responders in the CBT-I group (p=0.016) but not in the acupuncture group. CBT-I and acupuncture produced similar, clinically meaningful, and durable fatigue reductions in cancer survivors with insomnia, primarily through improvements in sleep. Acupuncture may also reduce fatigue through additional pathways. ClinicalTrials.gov, identifier: NCT02356575
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".