# Developing and testing a digital health tool for INterseCtional stigma assessment and reduction at multiple Levels and mUltiple DimEnsions (INCLUDE) to improve HIV care in ART centers in Nepal

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $187,491

## Abstract

Project Abstract
People living with HIV (PLWH) have poor clinical outcomes when they are excluded from care due to
intersectional stigma related to HIV, mental health (MH), and other dimensions. Recent studies and reviews
have highlighted three major challenges in identifying and addressing intersectional stigma: a lack of stigma
assessment strategies that are multi-dimensional and can be incorporated into routine clinical care, a lack of
tailored stigma-reduction activities, and a lack of implementation of multi-level interventions. These gaps make
it difficult to recognize and address intersectional stigma, leading to poor HIV care outcomes globally.
Digital health tools, co-designed with PLWH and healthcare workers (HCWs), have the potential to assist ART
centers in addressing these challenges. Guided by the principles of human-centered design, which our team
has utilized in a recent R34 study to improve adherence to HIV care in Nepal, we now propose to develop and
pilot test a digital tool with three components that can address the challenges in assessing, prioritizing, and
addressing intersectional stigma in ART centers. The components include: 1) a dynamic assessment strategy
that can be used during a clinic visit to collect both quantitative (i.e., ratings) and qualitative data (i.e., free text
of client’s perspectives) on stigma reported by PLWH; 2) a dashboard that incorporates this stigma
assessment data alongside routine clinical data (i.e., existing registry of clients in the ART center) so that ART
centers can directly link stigma with care engagement, and also identify relevant stigma-reduction activities;
and 3) a repository of evidence-based, culturally appropriate activities that can reduce stigma at the
intrapersonal-, interpersonal-, and clinic-levels.
The three components of the digital intervention are theoretically grounded and are based on prior studies and
consultations with local partners. The study’s Aim 1 is: To iteratively develop the digital health tool
INterseCtional stigma assessment and reduction at multiple Levels and mUltiple DimEnsions (INCLUDE) for
routine use in ART centers. We will achieve this by developing INCLUDE through a co-design process
involving PLWH, HCWs, researchers. We will then pre-pilot INCLUDE at a single ART center to prepare it for
Aim 2: To assess the acceptability and feasibility of INCLUDE among clients, HCWs, and ART center leads in
four ART centers. For this aim, we will conduct a pilot trial at four ART centers to assess the acceptability and
feasibility of INCLUDE. The human-centered co-design process ensures that INCLUDE meets the needs of
stakeholders and can be integrated into routine care. This project brings together our team’s longstanding
expertise and experience in HIV, stigma, MH, digital health, and in working closely with the local government. If
successful, this study will provide an intervention that can be incorporated into routine clinical practice to
systematically iden...

## Key facts

- **NIH application ID:** 11002820
- **Project number:** 1R01TW012682-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Bibhav Acharya
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $187,491
- **Award type:** 1
- **Project period:** 2024-08-05 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11002820

## Citation

> US National Institutes of Health, RePORTER application 11002820, Developing and testing a digital health tool for INterseCtional stigma assessment and reduction at multiple Levels and mUltiple DimEnsions (INCLUDE) to improve HIV care in ART centers in Nepal (1R01TW012682-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/11002820. Licensed CC0.

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