Behavior change theory and behavior change technique use in cancer rehabilitation interventions: a secondary analysis
Bibliographic record
Abstract
BACKGROUND: There is limited evidence depicting ways that behavioral theory and techniques have been incorporated into cancer rehabilitation interventions. Examining their use within cancer rehabilitation interventions may provide insight into the active ingredients that can maximize patient engagement and intervention effectiveness. AIM: This secondary analysis aimed to describe the use of behavior change theory and behavior change techniques (BCTs) in two previously conducted systematic reviews of cancer rehabilitation interventions. DESIGN: Secondary analysis of randomized controlled trials (RCTs) drawn from two systematic reviews examining the effect of cancer rehabilitation interventions on function and disability. SETTING: In-person and remotely delivered rehabilitation interventions. POPULATION: Adult cancer survivors. METHODS: Data extraction included: behavior change theory use, functional outcome data, and BCTs using the Behavior Change Technique Taxonomy (BCTTv1). Based on their effects on function, interventions were categorized as "very", "quite" or "non-promising". To assess the relative effectiveness of coded BCTs, a BCT promise ratio was calculated (the ratio of promising to non-promising interventions that included the BCT). RESULTS: Of 180 eligible RCTs, 25 (14%) reported using a behavior change theory. Fifty-four (58%) of the 93 BCTs were used in least one intervention (range 0-29). Interventions reporting theory use utilized more BCTs (median=7) compared to those with no theory (median=3.5; U=2827.00, P=0.001). The number of BCTs did not differ between the very, quite, and non-promising intervention groups (H(2)=0.24, P=0.85). 20 BCTs were considered promising (promise ratio >2) with goal setting, graded tasks, and social support (unspecified) having the highest promise ratios. CONCLUSIONS: While there was a wide range of BCTs utilized, they were rarely based on theoretically-proposed pathways and the number of BCTs reported was not related to intervention effectiveness. CLINICAL REHABILITATION IMPACT: Clinicians should consider basing new interventions upon a relevant behavior change theory. Intentionally incorporating the BCTs of goal setting, graded tasks, and social support may improve intervention efficacy.
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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 | 0.065 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".