Digital intervention (Renewed) to support symptom management, wellbeing, and quality of life among cancer survivors in primary care: a randomised controlled trial
Bibliographic record
Abstract
Background Many cancer survivors following primary treatment have prolonged poor quality of life. Aim To determine the effectiveness of a bespoke digital intervention to support cancer survivors. Design and setting This was a pragmatic parallel open randomised trial in UK general practices (ISRCTN:96374224). Method People having finished primary treatment (≤10 years previously) for colorectal, breast, or prostate cancers, with European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30) score ≤85, were randomised by online software to: 1) detailed ‘generic’ digital NHS support (‘LiveWell’;n= 906); 2) a bespoke complex digital intervention (‘Renewed’;n= 903) addressing symptom management, physical activity, diet, weight loss, and distress; or 3) ‘Renewed with support’ (n= 903): ‘Renewed’ with additional brief email and telephone support. Results Mixed linear regression provided estimates of the differences between each intervention group and generic advice. At 6 months all groups improved (primary time point:nfor the generic, Renewed groups, and Renewed with support were 806, 749, and 705, respectively), with no significant between-group differences for EORTC QLQ-C30, but global health improved more in both the Renewed groups. By 12 months there were small improvements in EORTC QLQ-C30 for Renewed with support (versus generic advice: 1.42, 95% confidence interval [CI] = 0.33 to 2.51); both Renewed groups improved global health (12 months: Renewed: 3.06, 95% CI = 1.39 to 4.74; Renewed with support: 2.78, 95% CI = 1.08 to 4.48), dyspnoea, constipation and enablement, and lower primary care NHS costs (in comparison with generic advice [£265]: Renewed was −£141 [95% CI = −£153 to–£128] and Renewed with Support was −£77 [95% CI = −£90 to −£65]); and for Renewed with support improvement in several other symptom subscales. No harms were identified. Conclusion Cancer survivors’ quality of life improved with detailed generic online support. Robustly developed bespoke digital support provides limited additional short-term benefit, but additional longer-term improvement in global health, enablement, and symptom management, with substantially lower NHS costs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".