Applying Human-Centered Design to Develop Smartphone-Based Intervention Messages to Help Young Adults Quit Using E-Cigarettes and Cigarettes: A Remote User Testing Study
Bibliographic record
Abstract
Background: Despite the popularity of concurrent use of electronic cigarettes (e-cigarettes) and cigarettes (dual tobacco use) among young adults, few interventions address the cessation of both tobacco products. The application of a human-centered design (HCD) approach in the development of such interventions remains limited. Objective: This study used an HCD approach to develop smartphone-based intervention messages for dual tobacco cessation for young adults. Methods: Intervention messages were developed based on theories, cessation guidelines, existing messages, and our previous formative study. Three rounds of message testing were conducted asynchronously via an online platform with 35 young adults (18-29 years old) who currently used both e-cigarettes and cigarettes and were motivated to quit either smoking or vaping in the next 6 months. In each round, a new sample of 10-12 participants evaluated the messages individually. For the quantitative assessment, participants viewed and rated each message on a scale from 1 ("very low degree") to 5 ("very high degree") across 4 components: Comprehension ("This message is easy to understand"), Usefulness ("This message is useful for encouraging tobacco cessation"), Tone ("The language is clear and non-judgmental"), and Design ("The design is appealing"). For the qualitative assessment, participants used a platform-enabled feature to place markers on specific parts of messages they liked, disliked, or found confusing and then provided brief explanations for their feedback. Initial messages were assessed during the first 2 rounds of testing, and those with low mean scores were revised based on participants' feedback and retested in the third round. Results: We found significant improvements in message ratings after refinement. The overall mean score increased from 3.6 (SD 0.4) to 4.6 (SD 0.2) (P<.001), using paired t tests. Specifically, the mean score of "Comprehension" improved from 4.0 (SD 0.5) to 4.9 (SD 0.2) (P<.001), the mean score of "Usefulness" increased from 3.0 (SD 0.6) to 4.4 (SD 0.4) (P<.001), the mean score of "Tone" increased from 3.8 (SD 0.6) to 4.8 (SD 0.2) (P<.001), and the mean score of "Design" increased from 3.4 (SD 0.48) to 4.4 (SD 0.3) (P<.001). The qualitative assessments highlighted design elements related to message liking, such as clear layout, minimalistic imagery, italicized quotes, and highlighted keywords. Conversely, design features related to message dislike included color shades, lengthy text, and confusing wording. Conclusions: This study demonstrated the use of HCD in developing smartphone-based intervention messages to support dual tobacco cessation among young adults. Integrating remote message testing improved the feasibility of rapid prototyping while enhancing the relevance and appeal of message content and design. Future interventions targeting emerging health behaviors among young adults may benefit from incorporating a remote testing method to efficiently gather user feedback and refine intervention messages in a timely manner.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".