Feasibility and Usability of an mHealth App (mLab+) to Guide Users Through HIV and Syphilis Self-Testing: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background: HIV self-testing is an important strategy in the US Ending the HIV Epidemic initiative. To facilitate uptake of self-testing, we developed the mLab app, which complements existing self-test options to support the potential for higher uptake of the HIV self-test. Syphilis, a sexually transmitted infection with currently rising prevalence and overlap in risk profiles with HIV, could similarly benefit from the advantages of companion diagnostic mobile apps such as mLab. Due to the success of the mLab app in promoting HIV self-testing during a randomized controlled trial and the scientific evidence of need for at-home syphilis testing, our team developed the mLab+ app, which supports both HIV and syphilis testing through an image processing algorithm that incorporates a duplex HIV and syphilis point-of-care test. Objective: We conducted a pilot study to assess the feasibility and usability of the mLab+ app for HIV and syphilis testing. Methods: We recruited participants who were assigned male sex at birth and reported sex with another man. Participants came to the Nurse Practitioner Group clinic for baseline and follow-up visits. Participants rated the usability of the app using the Health Information Technology Usability Evaluation Scale and the Post-Study System Usability Questionnaire at their 3-month follow-up visit. The primary outcome was the number of participants who were able to self-administer the DPP HIV-Syphilis test with the assistance of the mLab+ app. Feasibility was measured through recruitment pace, retention over 3 months, app usability, and paradata. Results: Of the 20 participants, 19 identified as male and 1 identified as nonbinary. Most participants (n=16) were able to complete the DPP HIV-Syphilis test with facilitation support from the mLab+ app. The average duration of an app session, from after authentication until log-out or abandonment, was 30 minutes and 33 seconds (SD 21 minutes and 40 seconds). Apart from the 27% (13/48) of sessions that were 5 minutes or less, the distribution of session durations was approximately normal. Users spent the longest time viewing testing screens (ie, timer screens, initial testing screen, test guided walkthroughs, test results, and picture and result upload). The overall mean scores on the Post-Study System Usability Questionnaire (2.65, SD 1.06) and Health Information Technology Usability Evaluation Scale (3.62, SD 1.07) indicated medium to high usability. The retention rate for the 3-month trial was 80% (16/20). Conclusions: The findings support the use of the mLab+ app as a tool for assisting consumers in self-testing for HIV and syphilis. The limitations of the study design warrant further examination outside of clinic settings to better understand the utility of these tools for improving consumer health outcomes.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".