An Evidence-Based HIV Risk–Reduction Intervention for Young African American Women in the US South Using mHealth: Adaptation and Development Study
Bibliographic record
Abstract
BACKGROUND: Young African American women have higher rates of sexually transmitted infections, including HIV, than those of young women of other racial and ethnic groups. Gender-, culture-, and age-specific interventions are needed to end the HIV epidemic. The Women's CoOp (WC) is an HIV risk-reduction intervention that is proven to be efficacious in various face-to-face formats. OBJECTIVE: This study aims to adapt the delivery method of an evidence-based intervention, the WC, from an in-person format to a self-guided mobile health (mHealth) format while ensuring that core elements are maintained for intervention comparability and fidelity. METHODS: Several adaptation phases were conducted by using the Personal Health Informatics and Intervention Toolkit (PHIT) as a guiding point to create the mobile app version of the WC. Throughout 5 phases, we established the implementation groundwork for the app; conducted formative research activities to test the initial draft of the app and obtain feedback; applied the PHIT toolkit programming structure to produce the mHealth version of the WC intervention; conducted usability testing and pretesting with interested parties, followed by in-house testing by WC interventionists and PHIT developers; and deployed the app to tablets and distributed it to study participants. The app underwent regular maintenance updates during the study. RESULTS: The team converted the seven elements of the WC as accurately as possible for comparability to determine efficacy in a mobile app format while changing little about the basic delivery methods. For instance, cue card presentations of the materials delivered by the intervention staff were presented within the app but with voice-over narration and in a self-guided format rather than being led by a staff member. Other aspects of the intervention did not lend themselves to such straightforward adaptation, such as hands-on condom proficiency practice and one-on-one goal-setting activities. In these cases, the subject matter experts and app developers worked together to find comparable analogs to be used within the app. Once developed, tested, and finalized, the mHealth WC app was deployed into local health departments as part of a randomized trial. CONCLUSIONS: This systematic adaptation process created an accurate mHealth equivalent of an existing, in-person behavioral health intervention. Although participants' reception of the app during the formative developmental phase was overall positive, maintaining fidelity to the in-person delivery compromised the natural capabilities of a mobile app, such as further gamification, different types of interactivity, and integrated notifications and messaging, which could be helpful for participants' adherence to the intervention schedule. Given the development and implementation of the app, the next step is to examine the impact of the app and its efficacy in HIV and substance use risk-reduction.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".