An mHealth Intervention to Improve Pre-Exposure Prophylaxis Knowledge Among Young Black Women in Family Planning Clinics: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Young Black women between the ages of 18 and 24 years are disproportionately impacted by HIV, yet they have a low self-perception of HIV risk and limited exposure to prevention strategies. Pre-exposure prophylaxis (PrEP) is a safe and effective biomedical HIV prevention strategy for those at risk for HIV infection, but uptake has been slow among cisgender women. Family planning clinics are a primary source of health care access for young women, providing an ideal opportunity to integrate PrEP information and care into existing clinic practices. OBJECTIVE: The aim of this study was to use a multistage, community-engaged process to develop a mobile health app and to evaluate the feasibility and acceptability of the app. METHODS: Using user-centered design, the In the Loop app was developed in collaboration with a community advisory board of young Black women. This study employed a multistage design, which included community-engaged app development, user testing, and evaluation of the app's feasibility and acceptability. A pre- and postdesign was used to assess the impact of the app on PrEP knowledge immediately after app use. Descriptive statistics (eg, mean, SD, and percentage values) were used to describe the sample, and Wilcoxon matched-pairs signed-ranks test was used to detect changes in PrEP knowledge before and immediately after using the app. RESULTS: A total of 50 sexually active, young Black women, aged 18-24 (mean 21, SD 1.9) years, were enrolled in this study. Analysis comparing scores before and immediately after use of the app revealed a significant increase in PrEP content knowledge scores on a 7-item true or false scale (z=-6.04, P<.001). Overall, participants considered the In the Loop app feasible and acceptable to use while waiting for a family planning visit. The majority of participants (n=46, 92%) agreed that they would recommend In the Loop to friends to learn more about PrEP. Participants rated the overall quality of the app 4.3 on a 1-5 scale (1=very poor and 5=very good). Of 50 participants, 40 (80%) agreed that the app was easy to use, and 48 (96%) agreed that they found the information in the app easy to understand. Finally, 40 (80%) agreed that they had enjoyed using the app while waiting for their family planning visit. CONCLUSIONS: Our findings suggest that young Black women waiting for family planning visits found the In the Loop app to be feasible and acceptable. This study demonstrates the value of engaging young Black women in the app design process. As family planning clinics are a primary source of health care access for young women, they provide an ideal setting to integrate PrEP information and care into existing clinic practices. Next steps in the development of the In the Loop app include implementing user-suggested improvements and conducting efficacy testing in a randomized controlled trial to determine the app's impact on PrEP uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".