Not All Cancer Survivors Respond to a 4-Week mHealth Exercise Fatigue Intervention: Who Are the Responders?
Bibliographic record
Abstract
Cancer-related fatigue (CRF) is prevalent and onerous for cancer survivors. Not all survivors respond equally to interventions, but the characteristics distinguishing responders and non-responders are often unknown. This secondary analysis study compared baseline characteristics for responders (CRF reduction ≥2 points), non-responders, and those lost to follow-up using data from a two-group pre-test/post-test trial of a four-week exercise intervention compared to usual care. Included were 278 adult cancer survivors, with a mean age of 52.2 ± 11.9, 65% (180/278) female, and 90% (250/278) Caucasian. Of these, 77 (28%) were responders, 153 (55%) were non-responders, and 48 (17%) were lost to follow-up. At baseline, participants completed the 6-item Schwartz Cancer Fatigue Scale, with responses from 1 (not at all) to 5 (extremely fatigued) and a total score ranging 6-30. In the intervention group, 35% (49/141) reported decreased fatigue, 24% (34/141) reported increased fatigue, 25% (35/141) had minimal change, and 16% (23/141) were lost to follow-up. In the control group, 20% (28/137) reported decreased fatigue, 39% (53/137) reported increased fatigue, 23% (31/137) had minimal change, and 18% (25/137) were lost to follow-up. Responders in both groups reported higher baseline fatigue than non-responders, with mean differences of 5.2 (95% CI: 3.6-6.8) and 5.4 (95% CI: 3.4-7.3) for intervention and usual care, respectively. Higher baseline fatigue was found in responders compared to non-responders, regardless of group assignment, suggesting that those with a greater fatigue burden may have derived more benefit from exercise for CRF or a regression to the mean effect.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".