# VITAL Start (Video-intervention to Inspire Treatment Adherence for Life): Brief facility based video intervention to improve retention and adherence to ART among pregnant and breastfeeding women

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $364,275

## Abstract

Abstract
We propose to evaluate the impact, implementation and cost effectiveness of VITAL Start, a brief facility-
based video intervention for HIV-positive (HIV+) pregnant women, in a multisite randomized controlled trial
(RCT) in Malawi with the primary composite outcome of retention and adherence (viral suppression) 12
months after starting antiretroviral therapy (ART).
 Universal HIV testing and treatment can accelerate population-level ART initiation and is critical to
realize the UNAIDS 90-90-90 goals. Malawi pioneered Option B+ (B+), a novel application of test-and-treat
that provided life-long ART for HIV+ pregnant and breastfeeding women. While maternal ART uptake
improved 7-fold, retention and adherence remained suboptimal: only 59% were retained after two years,
and of these, only two-thirds achieved adequate ART adherence. Other B+ countries are observing steep
drops in retention. Reasons are multi-factorial; women who feel healthy are unprepared to commit to
lifelong ART; inadequate counseling due to increased demands on healthcare workers; and limited partner
disclosure affecting retention and adherence. There is an urgent need for evidence-based interventions to
improve retention and adherence that can be easily incorporated into overextended health systems.
 Using formative participatory research, applied theoretical frameworks, and evidence-based message
framing techniques, we created an innovative 37-minute video-based intervention for pregnant women
newly initiating ART. The video promotes retention and adherence by providing a VITAL Start (Video
intervention to Inspire Treatment Adherence for Life) at the critical teachable moment before committing to
lifelong ART. Preliminary findings suggest high levels of patient and HCW satisfaction, and improved ART
knowledge, rates of partner disclosure, and retention. With these promising results, the proposed study
aims to rigorously evaluate the impact, implementation, and cost-effectiveness of VITAL Start. A sample of
796 HIV+ pregnant women will be randomized on a 1:1 basis to receive VITAL Start or standard of care
counseling prior to ART initiation. The primary endpoint will be a composite of retention and adherence
(virologic suppression), at 12 months post ART initiation. Secondary outcomes include self-reported
adherence, pharmacy data, and tenofovir diphosphate testing. We will also examine the delivery of VITAL
Start via surveys and interviews with patients, partners, and HCW and conduct cost-effectiveness analyses.
If successful, VITAL Start will provide an intervention that (1) standardizes and improves counseling at a
critical teaching moment through an engaging and culturally sensitive experience, (2) is inexpensive and
rapidly scalable without decelerating ART expansion, and (3) allows more efficient use of precious HCW
time. Building on a longstanding collaboration with the Malawi Ministry of Health, we are poised to support
rapid dissemination of VITAL Start if the re...

## Key facts

- **NIH application ID:** 10335141
- **Project number:** 5R01MH115793-05
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Saeed Ahmed
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $364,275
- **Award type:** 5
- **Project period:** 2018-04-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10335141

## Citation

> US National Institutes of Health, RePORTER application 10335141, VITAL Start (Video-intervention to Inspire Treatment Adherence for Life): Brief facility based video intervention to improve retention and adherence to ART among pregnant and breastfeeding women (5R01MH115793-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10335141. Licensed CC0.

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