Using the behavior change wheel to develop text messages to promote diet and physical activity adherence following a diabetes prevention program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it