Active Mental Health Psychology & Behaviour

AVATAR therapy implementation and innovation: Testing delivery of standard AVATAR therapy across settings in sub-Saharan Africa and South Asia alongside UK-based automation

In plain English

AI plain-English summary

People who hear voices that others do not will soon be able to confront those voices through a digital avatar, delivered in their own language in Ethiopia and India, or via an automated AI system in the UK. Hearing distressing voices (auditory verbal hallucinations) affects millions of people worldwide, yet most evidence-based therapies exist only in high-income countries and require specialist therapists. This project tackles two gaps: the lack of culturally adapted voice-hearing therapy in low- and middle-income countries, and the shortage of trained therapists everywhere. The researchers will establish training hubs in Ethiopia and India, adapting AVATAR therapy—where a patient speaks to a computer-generated representation of their voice—into Amharic, Oromo, Hindi, and other languages spoken by over half a billion people. In parallel, they will build an English-language version where AI generates the avatar’s responses automatically, then test whether non-specialist mental health workers can deliver it effectively. If successful, this work could make a proven therapy accessible to populations currently excluded from it—people in low-resource settings and those on long waiting lists in the UK. It would also demonstrate that AI can handle complex therapeutic dialogues, potentially reshaping how mental health care is delivered at scale.

View original technical description
In this project we will establish AVATAR therapy Implementation and Training Hubs in two distinct LMIC settings (Ethiopia and India). For the first time, we will test the feasibility and cultural acceptability of standard AVATAR therapy delivered in Indian and Ethiopian languages, spoken by over half a billion people. In parallel work, we will develop an English-language version of AVATAR therapy in which automated dialogues are delivered using Artificial Intelligence (AVATAR-AI) and test the feasibility of delivery by a non- specialist UK workforce. We will establish Hub leadership within each LMIC setting, building capacity for sustainable implementation of standard AVATAR therapy (now) and AVATAR-AI (future). This study has 5 key aims, delivered over 5 workpackages: Aim 1: To develop, through diverse stakeholder engagement, ethically responsible and culturally acceptable adaptation of AVATAR therapy across LMIC and UK sites. Aim 2: To establish Implementation Hubs in Ethiopia and India, to deliver and train AVATAR therapy. Aim 3: To develop and conduct user testing of AI-powered conversational agents to deliver AVATAR therapy dialogues (in English). Aim 4: To deliver AVATAR-AI, as a blended digital therapeutic. Aim 5: To test the feasibility, acceptability, and clinical impact of AVATAR-AI (UK), delivered by non-specialist mental health workers.

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Researchers

Andrew Gumley (EPMC Awardee)Atalay Alem (EPMC Awardee)Awoke Mihretu (EPMC Awardee)Koushik Sinha Deb (EPMC Awardee)Lucia Valmaggia (EPMC Awardee)Mamta Sood (EPMC Awardee)Mark Huckvale (EPMC Awardee)Philippa Garety (EPMC Awardee)Rakesh Chadda (EPMC Awardee)Rohit Verma (EPMC Awardee)Thomas Craig (EPMC Awardee)Thomas Ward (EPMC Awardee)Tsegahun Manyazewal (EPMC Awardee)Vaibhav Patil (EPMC Awardee)Yumnam Surbala Devi (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

AVATAR therapy in the Real World (AVATAR-RW): A process evaluation, effectiveness and cost-effectiveness study in routine care of AVATAR therapy, a digitally supported therapy for distressing voices.
Optimising AVATAR therapy for distressing voices: a multi-centre randomised controlled trial
AVATAR2: Optimising AVATAR therapy for distressing voices: a multi-centre randomised controlled trial
Aphasia Partnership Training: A programme of work to co-design and evaluate the effectiveness of training a person with aphasia and their communication partner to communicate better together
Can AI-powered, self-guided digital therapy improve access while reducing NHS workforce strain?

Original classification

Discretionary Award

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