Behavior Change Techniques for the Maintenance of Physical Activity in Cancer
Bibliographic record
Abstract
Ester et al report the findings from a 2-arm cluster randomized controlled trial nested within a hybrid effectiveness-implementation study, which involved a 12-week exercise and behavior change program for rural and remote Canadians (Exercise for Cancer to Enhance Living Well [EXCEL]). The addition of 23 weeks of app-based physical activity monitoring to the EXCEL program did not result in significant between-group differences in physical activity at 6 months. While several behavior change techniques were included in the initial 12-week intervention, additional techniques were embedded within the mobile app. However, there is currently a lack of evidence regarding how many and which behavior change techniques are the most effective for people with cancer and if these differ based on individual characteristics. Potentially, the use of the mobile app was not required in addition to the behavior change support delivered to both groups as part of the EXCEL program. Further research should involve participants who may be in most need of behavioral support, for example, those with lower levels of self-efficacy. Suggestions for future research to tailor behavior change support for people with cancer are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".