Mobile Phone Apps for HIV Prevention Among College-Aged Black Women in Atlanta: Mixed Methods Study and User-Centered Prototype
Bibliographic record
Abstract
BACKGROUND: Black women in college are disproportionately affected by HIV diagnoses. Mobile apps can facilitate the innovative delivery of accurate HIV and sexual and reproductive health information. However, mobile health interventions are severely underused in this population. OBJECTIVE: We aimed to quantitatively and qualitatively explore the perspectives of college-aged Black women on using a mobile health app for HIV prevention and sexual and reproductive health. The data obtained from Black women were used to design preliminary mobile app wireframes and features. METHODS: This explanatory, sequential mixed methods study took place from 2019 to 2020 and targeted Black women who were enrolled in college or who had recently graduated from college. Convenience sampling was used during the quantitative phase, followed by purposive sampling in the qualitative phase. A cross-sectional web-based survey evaluating the willingness to use a mobile app for HIV prevention was conducted in the quantitative phase. Descriptive statistics were used for all variables. A separate focus group discussion was conducted with Black women in college to expand on the quantitative results. Focus group discussions explored their perceptions on HIV and health content delivered through a mobile app along with potential features that participants desired within the app. Using the data obtained, we selected the primary features for the app prototype. RESULTS: In total, we enrolled 34 participants in the survey, with 6 participating in focus group discussions. Over half of the respondents reported a willingness to use an app that contained pre-exposure prophylaxis content. Women who claimed recent sexual activity reported being more likely to use an app feature that would allow them to order an at-home HIV testing kit than their non-sexually active counterparts. The emerging themes from the focus group session were Black women's health concerns, HIV risk, sources of health information, and preferred app features. The content in our prototype included speaking with a specialist, HIV and pre-exposure prophylaxis information, holistic wellness, and features promoting engagement and retention. CONCLUSIONS: The results of our study guided the design of wireframes for an app prototype targeting HIV prevention in college-aged Black women. The rapid growth of mobile devices in Black communities, coupled with high rates of smartphone ownership among Black youth, makes mobile health interventions a promising strategy for addressing sexual and reproductive health disparities. Participants in our sample were willing to use a culturally appropriate and gender-considerate app for their sexual health needs. Our findings indicate that Black women in college may be excellent candidates for mobile app-based interventions.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".