Promoting Healthy Eating and Physical Activity Behaviors: A Systematic Review of Multiple Health Behavior Change Interventions Among Cancer Survivors
Bibliographic record
Abstract
Multiple health behavior change (MHBC) interventions have great potential for enhancing health and well-being following cancer diagnosis and treatment. However, the characteristics and effects of MHBC interventions remain elusive for cancer survivors. The main purpose of this study was to evaluate the effectiveness of MHBC interventions on healthy eating and physical activity behaviors among cancer survivors. A secondary aim was to examine the effect of using a simultaneous and sequential design approach to MHBC (ie, changing both behaviors at the same time or one after the other). Randomized controlled trials reporting the impact of a MHBC intervention on both healthy eating and physical activity behaviors among cancer survivors were retrieved from MEDLINE, Cochrane Library, and PsycINFO. A total of 27 MHBC interventions were identified; most (92.6%) were designed to promote simultaneous change in both behaviors and assessed end-of-treatment effect among breast cancer survivors. MHBC interventions led by nurses or multidisciplinary teams showed the most compelling evidence for small to moderate improvement in both behaviors, with interventions that lasted ≥17 weeks more likely to improve both behaviors. This study identifies research priorities and provides preliminary evidence for clinical decision making and advancements in MHBC intervention design and delivery for clinical oncology.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".