Health System–Level Implementation of Digital Health Support for People Living With HIV and Substance Use Disorders: Protocol for a Cluster-Randomized, Stepped-Wedge Clinical Trial
Bibliographic record
Abstract
BACKGROUND: People living with HIV who are affected by substance use disorders and other social vulnerabilities are less likely to achieve and sustain viral suppression. Supporting these patients with mobile health systems can provide additional social and behavioral support that may improve HIV outcomes in these populations. OBJECTIVE: This study aims to implement and evaluate an evidence-based digital health system (antiretroviral therapy care coordination; ART-C) to improve HIV viral suppression and reduce missed clinic visits within a multisite HIV care program. METHODS: ART-C will be implemented in 8 HIV medical home clinics operated by Vivent Health in Colorado, Missouri, Texas, and Wisconsin. All patients receiving HIV care across the system will be invited to use a customized version of the Connections smartphone app to join a virtual community of peers living with HIV. A subset of patients with substance use disorder will be recruited to use enhanced features of the app, allowing sharing of information about substance use and other social determinants of health with their care team. Effectiveness and implementation outcomes will be evaluated using a stepped-wedge clinical trial design. Effectiveness will be evaluated by comparing missed visits and viral nonsuppression rates before and after the intervention. Implementation will be evaluated using a mixed methods approach based on the reach, effectiveness, adoption, implementation, and maintenance framework. RESULTS: Funding was received April 2022 with data collection beginning December 6, 2023. Data collection is anticipated to end in 2027 with data analysis and results expected late 2027. As of submission of the manuscript, 86 participants were recruited. Of the 6710 patients with health record data available 2 years before the trial began, 1723 (25.68%) had a substance use disorder, 2825 (42.1%) missed at least 2 care visits, and 1354 (20.18%) had at least 1 detectable HIV RNA test (viral load ≥200 copies/mL), leaving 3816 (56.87%) eligible individuals. Analysis of preimplementation data demonstrated that patients had a mean of 4.1 (SD 2.1) to 7.8 (SD 5.5) care visits across all 8 Vivent Health sites, with 45% to 70% of patients missing at least 1 visit. Patients underwent a mean of 2.2 to 4 HIV RNA tests, and 14% to 37% of patients across clinics had at least 1 nonsuppressed HIV RNA result. Primary analyses are expected to be completed by 2028. CONCLUSIONS: This study will evaluate the effectiveness of and implementation strategies for a mobile health-based intervention to support patients with HIV, substance use disorder, and related challenges in engaging in care and achieving viral suppression. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69842.
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.090 | 0.016 |
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".