Can people living with and beyond colorectal cancer make lifestyle changes with the support of health technology: A feasibility study
Bibliographic record
Abstract
BACKGROUND: Rates of cancer survival are increasing, with more people living with and beyond cancer. Lifestyle recommendations for cancer survivors are based largely on extrapolation from cancer prevention recommendations. This feasibility study was designed to investigate diet and physical activity variables linked to primary prevention and digital behaviour change interventions in cancer survivors and delivered by an oncology dietitian to plan for future research. METHODS: In this 2-month feasibility study, participants who had completed treatment for colorectal cancer were invited to complete online food diaries, underwent physical activity assessment, attended fortnightly telephone consultations with an oncology dietitian and completed an evaluation form. The baseline food diaries were used to help participants pick two lifestyle changes to focus on throughout the intervention. Demographic and clinical data were analysed using descriptive statistics. RESULTS: In total, 996 patients were screened for eligibility; of these, 78 were eligible to approach and 69 were approached, resulting in 20 participants consenting to take part. Overall, the intervention was acceptable with 65% of participants completing an online food diary and 70% engaging with the dietitian over the telephone. The intervention received good feedback, with 100% of those completing the evaluation form reporting they felt supported and found it helpful. CONCLUSIONS: The present study offers preliminary evidence that a lifestyle intervention delivered by an oncology dietitian using digital behaviour change interventions (DBCIs) to cancer survivors is feasible and accepted by participants and providers.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".