The mChoice App, an mHealth Tool for the Monitoring of Preexposure Prophylaxis Adherence and Sexual Behaviors in Young Men Who Have Sex With Men: Usability Evaluation
Bibliographic record
Abstract
Background: Mobile health (mHealth) apps provide easy and quick access for end users to monitor their health-related activities. Features such as medication reminders help end users adhere to their medication schedules and automatically record these actions, thereby helping manage their overall health. Due to insufficient mHealth tools tailored for HIV preventive care in young men who have sex with men (MSM), our study evaluated the usability of the mChoice app, a tool designed to enhance preexposure prophylaxis (PrEP) adherence and promote sexual health (eg, encouraging the use of condoms and being aware of the partner's HIV status and PrEP use). Objective: This study aimed to apply systematic usability evaluations to test the mChoice app and to refine the visualizations to better capture and display patient-reported health information. Methods: Usability testing involved heuristic evaluations conducted with 5 experts in informatics and user testing with 20 young MSM who were taking or were eligible to take PrEP. Results: End users demonstrated satisfaction with the appearance of the mChoice app, reporting that the app has an intuitive interface to track PrEP adherence. However, participants highlighted areas needing improvement, including chart titles and the inclusion of "undo" and "edit" buttons to improve user control when recording PrEP use. Conclusions: Usability evaluations involving heuristic experts and end users provided valuable insights into the mChoice app's design. Areas for improvement were identified, such as enhancing chart readability and providing additional user controls. These findings will guide iterative refinements, ensuring that future versions of the app better address the needs of its target audience and effectively support HIV prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".