Informing behavioural counselling efforts in cancer survivors: Evidence from a systematic review on multiple health behaviour change research
Bibliographic record
Abstract
Multiple-behaviour change interventions (MBC) may have greater impact on health and wellbeing than single-behaviour interventions, especially when behaviours are related to higher-level goals such as improved survivorship experiences following cancer. Based on social cognitive theory, initial success in one behavioural domain may lead to increased perceived self-efficacy and foster subsequent mastery and motivation for change in another domain. MBC may be ideal for informing behavioural counselling, as many cancer survivors accumulate multiple behavioural risk factors (i.e., not meeting physical activity [PA] guidelines and poor diet). A systematic review of randomized controlled trials (N = 25 analyzed) was conducted using electronic databases to identify the MBC design approaches – sequential (one behaviour after the other) or simultaneous – and examined effectiveness on diet and PA in survivors. Post-intervention treatment effect sizes (standardized mean difference [SMD]) were calculated for fruit and vegetable consumption (F&V), fat intake (%fat), diet quality (DQ), and PA. Studies simultaneously targeting behaviours (n = 23), SMD ranges: 0.14 to 1.66 (F&V), -2.29 to 0.28 (%fat) and 0.04 to 0.92 (DQ), and -0.43 to 1.22 (PA). Sequential interventions (n = 2), SMD ranges: 0.21 to 0.22 (F&V), -0.41 to -0.07 (%fat) and 0.36 to 0.38 (DQ) and 0.11 to 0.24 (PA). Given study heterogeneity and low number of sequential studies, further research is needed to determine the most effective approach for improving health behaviours among cancer survivors. With more definitive information on intervention approach, behavioural counselling strategies can be tailored to the approach for maximal health benefit.
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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 | 0.030 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".