Deployment and Assessment of a Virtual Coaching Platform to Support Healthy Habits and Whole Health for Vulnerable People Living with HIV
Bibliographic record
Abstract
Background Office of Health and Human Services of Rhode Island (0HHS-RI) provides care to the most vulnerable people in the state living with HIV. While most of their clients achieve viral load suppression, the majority of these individuals still lack the support they need to tackle mental health issues, manage comorbidities, be physically active, and address other social determinants of Health. Digital health technologies have demonstrated value to provide this support and help people manage whole health. Digital platforms can deliver tailored health interventions and improved outcomes for people with HIV. However, this vulnerable population most in need of that support lacks access to current technology and platforms designed to meet their particular needs. Objective The objective wo design, deploy, and assess an evidence-based digital health platform to support healthy habits and improve outcomes for the target population, TAVIE Red. The platform is the product of two years of patient-centered iterative design and built upon theories of behavioral medicine. The core technology is a clinically-validated virtual nurse app that delivers tailored education to users. On the patient app, clients self-manage health (eg, monitor symptoms and receive treatment reminders), access social services (eg, locate food banks and clinics) and receive evidence-based interventions to promote mental and physical health (eg, decrease stress and increase physical activity). Users receive personalized feedback and rewards that incentivize and engage during each phase of treatment. On the professional portal, case managers can monitor clients remotely, receive actionable insights, and intervene appropriately. Administrators access real-time analytics on health status and delivery of services through a set of customizable dashboards. Methods Eligible clients received an Android phone preloaded with the app. Case managers received access to the professional platform on a desktop and in some cases, tablet devices to use the app in the field. Three generations of the platform were deployed over a two-year period. Participants completed a survey at baseline and follow-up on paper and digitally through the app. Thus far, 200 people living with HIV have participated in this program along with their case managers. Results Two years into the project, users are engaged with the app, enjoying it, and benefiting from it. Seventy-seven percent of users actively engage with the app, earning points and progressing through the coaching sessions with 67% completing self-assessments through the app and most track measures including physical activity, symptoms, and CD4 count and viral load. There was a 5% increase in viral suppression in this population over two years. Eighty-eight percent of users recommend the app to others. Conclusions To date, the TAVIE platform is an engaging appealing platform. Users remain on the app over time and report great benefits of use. Formal evaluation of outcomes ongoing.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".