Message Frame–Tailoring in Digital Health Communication: Intervention Redesign and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Message frame-tailoring based on the need for autonomy is a promising strategy to improve the effectiveness of digital health communication interventions. An example of a digital health communication intervention is Personal Advice in Stopping smoking (PAS), a web-based content-tailored smoking cessation program. PAS was effective in improving cessation success rates, but its effect sizes were small and disappeared after 6 months. Therefore, investigating whether message frame-tailoring based on the individual's need for autonomy might improve effect rates is worthwhile. However, to our knowledge, this has not been studied previously. OBJECTIVE: To investigate whether adding message frame-tailoring based on the need for autonomy increases the effectiveness of content-tailored interventions, the PAS program was redesigned to incorporate message frame-tailoring also. This paper described the process of redesigning the PAS program to include message frame-tailoring, providing smokers with autonomy-supportive or controlling message frames-depending on their individual need for autonomy. Therefore, we aimed to extend framing theory, tailoring theory, and self-determination theory. METHODS: Extension of the framing theory, tailoring theory, and self-determination theory by redesigning the PAS program to include message frame-tailoring was conducted in close collaboration with scientific and nonscientific smoking cessation experts (n=10), smokers (n=816), and communication science students (n=19). Various methods were used to redesign the PAS program to include message frame-tailoring with optimal usability: usability testing, think-aloud methodology, heuristic evaluations, and a web-based experiment. RESULTS: The most autonomy-supportive and controlling message frames were identified, the cutoff point for the need for autonomy to distinguish between people with high and those with low need for autonomy was determined, and the usability was optimized. CONCLUSIONS: This resulted in a redesigned digital health communication intervention that included message frame-tailoring and had optimal usability. A detailed description of the redesigning process of the PAS program is provided. TRIAL REGISTRATION: Netherlands Trial Register NL6512 (NRT6700); https://www.trialregister.nl/trial/6512.
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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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".