A Trauma-Informed, Geospatially Aware, Just-in-Time Adaptive mHealth Intervention to Support Effective Coping Skills Among People Living With HIV in New Orleans: Development and Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: In 2020, Greater New Orleans, Louisiana, was home to 7048 people living with HIV-1083 per 100,000 residents, 2.85 times the US national rate. With Louisiana routinely ranked last in indexes of health equity, violent crime rates in Orleans Parish quintupling national averages, and in-care New Orleans people living with HIV surviving twice the US average of adverse childhood experiences, accessible, trauma-focused, evidence-based interventions (EBIs) for violence-affected people living with HIV are urgently needed. OBJECTIVE: To meet this need, we adapted Living in the Face of Trauma, a well-established EBI tailored for people living with HIV, into NOLA GEM, a just-in-time adaptive mobile health (mHealth) intervention. This study aimed to culturally tailor and refine the NOLA GEM app and assess its acceptability; feasibility; and preliminary efficacy on care engagement, medication adherence, viral suppression, and mental well-being among in-care people living with HIV in Greater New Orleans. METHODS: The development of NOLA GEM entailed identifying real-time tailoring variables via a geographic ecological momentary assessment (GEMA) study (n=49; aim 1) and place-based and user-centered tailoring, responsive to the unique cultural contexts of HIV survivorship in New Orleans, via formative interviews (n=12; aim 2). The iOS- and Android-enabled NOLA GEM app leverages twice-daily GEMA prompts to offer just-in-time, in-app recommendations for effective coping skills practice and app-delivered Living in the Face of Trauma session content. For aim 3, the pilot trial will enroll an analytic sample of 60 New Orleans people living with HIV individually randomized to parallel NOLA GEM (intervention) or GEMA-alone (control) arms at a 1:1 allocation for a 21-day period. Acceptability and feasibility will be assessed via enrollment, attrition, active daily use through paradata metrics, and prevalidated usability measures. At the postassessment time point, primary end points will be assessed via a range of well-validated, domain-specific scales. Care engagement and viral suppression will be assessed via past missed appointments and self-reported viral load at 30 and 90 days, respectively, and through well-demonstrated adherence self-efficacy measures. RESULTS: Aims 1 and 2 have been achieved, NOLA GEM is in Beta, and all aim-3 methods have been reviewed and approved by the institutional review board of Tulane University. Recruitment was launched in July 2023, with a target date for follow-up assessment completion in December 2023. CONCLUSIONS: By leveraging user-centered development and embracing principles that elevate the lived expertise of New Orleans people living with HIV, mHealth-adapted EBIs can reflect community wisdom on posttraumatic resilience. Sustainable adoption of the NOLA GEM app and a promising early efficacy profile will support the feasibility of a future fully powered clinical trial and potential translation to new underserved settings in service of holistic survivorship and well-being of people living with HIV. TRIAL REGISTRATION: ClinicalTrials.gov NCT05784714; https://clinicaltrials.gov/ct2/show/NCT05784714. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47151.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".