Using the behavior change wheel to develop text messages to promote diet and physical activity adherence following a diabetes prevention program
Bibliographic record
Abstract
Improving diet and physical activity (PA) can reduce the risk of developing type 2 diabetes (T2D); however, long-term diet and PA adherence is poor. To impact population-level T2D risk, scalable interventions facilitating behavior change adherence are needed. Text messaging interventions supplementing behavior change interventions can positively influence health behaviors including diet and PA. The Behavior Change Wheel (BCW) provides structure to intervention design and has been used extensively in health behavior change interventions. Describe the development process of a bank of text messages targeting dietary and PA adherence following a diabetes prevention program using the BCW. The BCW was used to select the target behavior, barriers and facilitators to engaging in the behavior, and associated behavior change techniques (BCTs). Messages were written to map onto BCTs and were subsequently coded for BCT fidelity. The target behaviors were adherence to diet and PA recommendations. A total of 16 barriers/facilitators and 28 BCTs were selected for inclusion in the messages. One hundred and twenty-four messages were written based on selected BCTs. Following the fidelity check a total of 43 unique BCTs were present in the final bank of messages. This study demonstrates the application of the BCW to guide the development of a bank of text messages for individuals with prediabetes. Results underscore the potential utility of having independent coders for an unbiased expert evaluation of what active components are in use. Future research is needed to demonstrate the feasibility and effectiveness of resulting bank of messages.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".