Cooking for Vitality: Pilot Study of an Innovative Culinary Nutrition Intervention for Cancer-Related Fatigue in Cancer Survivors
Bibliographic record
Abstract
(1) Background: Cancer-related fatigue (CRF) is one of the most prevalent and distressing side effects experienced by patients with cancer during and after treatment, and this negatively impacts all aspects of quality of life. An increasing body of evidence supports the role of poor nutritional status in the etiology of CRF and of specific diets in mitigating CRF. We designed a group-based two session culinary nutrition intervention for CRF, Cooking for Vitality (C4V), aimed at increasing understanding of how food choices can impact energy levels and establishing basic food preparation and cooking skills as well as the application of culinary techniques that minimize the effort/energy required to prepare meals. The purpose of this pilot mixed-method study was to evaluate: Feasibility of the experimental methods and intervention; acceptability and perceived helpfulness of intervention; and to obtain a preliminary estimate of the effectiveness of the intervention on fatigue (primary outcome), energy, overall disability, and confidence to manage fatigue (secondary outcomes). (2) Methods: Prospective, single arm, embedded mixed-methods feasibility study of cancer survivors with cancer-related fatigue was conducted. Participants completed measures at baseline (T0), immediately following the intervention (T1), and three months after the last session (T2). Qualitative interviews were conducted at T2. (3) Results: Recruitment (70%) and retention (72%) rates along with qualitative findings support the feasibility of the C4V intervention for cancer survivors living with CRF (program length and frequency, ease of implementation, and program flexibility). Acceptability was also high and participants provided useful feedback for program improvements. Fatigue (FACT-F) scores significantly improved from T0–T1 and T0–T2 (p < 0.001). There was also a significant decrease in disability scores (WHO-DAS 2.0) from T0–T2 (p = 0.006) and an increase in POMS-Vigor (Profile of Mood States) from T0–T1 (p = 0.018) and T0–T2 (p = 0.013). Confidence in managing fatigue improved significantly from T0–T1 and T0–T2 (p < 0.001). (4) Conclusions: The results suggest that the C4V program was acceptable and helpful to patients and may be effective in improving fatigue levels and self-management skills. A randomized controlled trial is required to confirm these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".