A Mobile Health Intervention for HIV Prevention Among Racially and Ethnically Diverse Young Men: Usability Evaluation
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps have the potential to be a useful mode of delivering HIV prevention information, particularly for young men (13-24 years) who account for 21% of new HIV diagnoses in the United States. We translated an existing evidence-based, face-to-face HIV prevention curriculum into a portable platform and developed a mobile Web app: MyPEEPS Mobile. OBJECTIVE: The purpose of this study was to assess the usability of MyPEEPS Mobile from both expert and end user perspectives. METHODS: We conducted a heuristic evaluation with five experts in informatics to identify violations of usability principles and end user usability testing with 20 young men aged 15 to 18 years in New York, NY, Birmingham, AL, and Chicago, IL to identify potential obstacles to their use of the app. RESULTS: Mean scores of the overall severity of the identified heuristic violations rated by experts ranged from 0.4 and 2.6 (0=no usability problem to 4=usability catastrophe). Overall, our end users successfully completed the tasks associated with use case scenarios and provided comments/recommendations on improving usability of MyPEEPS Mobile. The mean of the overall Post-Study System Usability Questionnaire scores rated by the end users was 1.63 (SD 0.65), reflecting strong user acceptance of the app. CONCLUSIONS: The comments made by experts and end users will be used to refine MyPEEPS Mobile prior to a pilot study assessing the acceptability of the app across diverse sexual minority young men in their everyday lives.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".