‘Not to Be Harsh but Try Less to Relate to ‘the Teens’ and You’ll Relate to Them More’: Co-Designing Obesity Prevention Text Messages with Adolescents
Bibliographic record
Abstract
Text messages remain a preferred way for adolescents to communicate, and recent evidence suggests adolescents would like access to digital healthcare options. However, there is limited evidence for text messages to engage adolescent populations in obesity prevention behaviors. We aimed to co-design a bank of text messages that are evidence-based, acceptable, and engaging for adolescents. An established iterative mixed methods process, consisting of three phases, was used to develop the text message program. The first bank of 145 text messages was drafted based on current evidence, behavior change techniques, and input from researchers and health professionals. A survey was then administered to adolescents and professionals for review of text message content, usefulness, understanding, and age-appropriateness. An adolescent research assistant collaborated with the research team on all three phases. Forty participants (25 adolescents and 15 professionals) reviewed the initial bank of 145 text messages. On average, all reviewers agreed the text messages were easy to understand (13.6/15) and useful (13.1/15). In total, 107 text messages were included in the final text message bank to support behavior change and prevent obesity. This study may guide other researchers or health professionals who are seeking to engage adolescents in the co-design of health promotion or intervention content. Effectiveness of the text message program will be tested in a randomized controlled trial.
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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 | 0.041 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".