Development of an Intervention Targeting Multiple Health Behaviors Among High School Students: Participatory Design Study Using Heuristic Evaluation and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Mobile electronic platforms provide exciting possibilities for health behavior promotion. For instance, they can promote smoking cessation, moderate alcohol consumption, healthy eating, and physical activity. Young adults in Sweden are proficient in the use of technology, having been exposed to computers, smartphones, and the internet from an early age. However, with the high availability of mobile health (mHealth) interventions of varying quality, it is critical to optimize the usability of mHealth interventions to ensure long-term use of these health promotion interventions. OBJECTIVE: This study aims to investigate the usability of an mHealth intervention (LIFE4YOUth) targeting health behaviors among high school students through heuristic evaluation and usability testing. METHODS: A preliminary version of the LIFE4YOUth mHealth intervention, which was aimed at promoting healthy eating, physical activity, smoking cessation, and nonrisky drinking among high school students, was developed in early 2019. We completed a total of 15 heuristic evaluations and 5 usability tests to evaluate the usability of the mHealth intervention prototype to improve its functioning, content, and design. RESULTS: Heuristic evaluation from a total of 15 experts (10 employees and 5 university students, both women and men, aged 18-25 years) revealed that the major usability problems and the worst ratings, a total of 17 problems termed usability catastrophes, concerned shortcomings in displaying easy-to-understand information to the users or technical errors. The results of the usability testing including 5 high school students (both girls and boys, aged 15-18 years) showed that the design, quality, and quantity of content in the intervention may impact the users' level of engagement. Poor functionality was considered a major barrier to usability. Of the 5 participants, one rated the LIFE4YOUth intervention as poor, 2 rated as average, and 2 assessed it as good, according to the System Usability Scale. CONCLUSIONS: High school students have high expectations of digital products. If an mHealth intervention does not offer optimal functions, they may cease to use it. Optimizing the usability of mHealth interventions is a critical step in the development process. Heuristic evaluation and usability testing in this study provided valuable knowledge about the prototype from a user's perspective. The findings may lead to the development of similar interventions targeting the high school population.
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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 | 0.037 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".