Alcohol Use Behavioral Phenotyping Test for Global Populations (2026-2027)

Background

Alcohol use is a leading contributor to early death and long-term disability around the world. Harmful alcohol use arises from a complex interplay of biological, psychological and social factors. Yet most assessments of alcohol use rely on self-reported behaviors, which are often unreliable and difficult to adapt across cultures and languages.

Alcohol use disorders are associated with altered reward learning, fear responses and cognitive control — behaviors that are linked to specific brain circuits. The National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC) framework to study such constructs in a systematic, cross-cultural way. However, RDoC principles have not yet been broadly implemented or validated internationally. There is a growing need for tools that can measure behavioral patterns related to alcohol use consistently across global populations.

Project Description

This project builds on a multiyear effort to design, test and refine the Alcohol Use Behavioral Phenotyping Test (AUBPT) — a gamified, multilingual digital tool that assesses behavioral constructs related to alcohol use. AUBPT includes tasks that capture reward processing, fear responses and cognitive control, and it has already been translated into Swahili, Portuguese, Hindi and Spanish.

The 2026-2027 team will advance AUBPT through three major aims:

Aim 1: Global pilot testing and content validation
Students will help complete pilot data collection in Tanzania, India and Durham, building on completed work in Brazil. Pilot activities include quantitative assessments (15-20 participants per site) and qualitative interviews with local experts (10-12 per site), allowing the team to evaluate content validity and cultural fit.

Aim 2: Psychometric testing and app refinement
The team will collect data from general population samples of at least 150 participants each in Durham, Tanzania, Brazil and India. Participants will complete questionnaires and play through the AUBPT tasks. Students will collaborate with partners at Duke National University of Singapore (NUS), who specialize in mHealth and implementation science, to refine the AUBPT interface and improve usability for clinical settings.

Aim 3: Clinical implementation
The refined AUBPT will be deployed with clinical populations (N=50 per site) in India, Brazil, Tanzania and Durham. Students will analyze behavioral data, evaluate implementation strategies and conduct interviews with 10-12 healthcare providers per site to assess usability and barriers to adoption.

Students will work in small subteams, each responsible for coordinating tasks across one or more global study sites. Weekly meetings will bring the full team together to share progress, problem-solve and prepare manuscripts and presentations.

Anticipated Outputs

Open-source multilingual AUBPT app
Five research papers, including a systematic review and meta-analysis
Data to support NIH grant submissions
Secondary analyses of NIMH RDoC datasets
Student presentations at international conferences
Strengthened partnerships with collaborators in Brazil, Tanzania, India and Singapore

Student Opportunities

Ideally, this project team will include 4 graduate students and 4 undergraduate students. Students with backgrounds in psychology, neuroscience, global health, public policy, data science, computer science, cognitive science, biostatistics or machine learning are encouraged to apply.

All team members should be eager to collaborate internationally and work in interdisciplinary settings that integrate mental health, global health and digital health. Students will gain hands-on experience in study design and implementation, literature synthesis, global collaboration, quantitative and qualitative data collection, computational modeling, statistical analysis and manuscript development.

Undergraduates will participate in data collection and analysis, contribute to systematic reviews and assist with conference presentations. Students with computer science experience will work directly with Duke NUS experts to refine the AUBPT interface. Graduate students will lead subteams, mentor undergraduates and take on advanced analytic tasks.

The project includes international fieldwork opportunities, though only a select number of students will travel based on project needs and partner availability.

Timing

Fall 2026 – Spring 2027

Fall 2026:

Finalize pilot data analyses and manuscript preparation
Launch Aim 2 data collection across global study sites
Collaborate with Duke NUS to begin app refinements

Spring 2027:

Complete Aim 2 data collection and conduct psychometric analyses
Finalize new AUBPT application for clinical implementation
Prepare conference presentations and refined study materials

Crediting

Academic credit available for fall and spring semesters

Team Leaders

Mia Buono, Franklin Humanities Institute
Editruda Gamassa, Kilimajaro Christian Medical Center
Suyog Jaiswal, AIIMS-Nagpur
Joao Ricardo Nickening Vissoci, Duke Global Health Institute, School of Medicine
Paige O’Leary, School of Medicine
Leonardo Oliveira, University of Sao Paulo, Maringa Campus, Brazil
Marcus Ong, Duke National University of Singapore
Francis Sakita, Kilimanjaro Christian Medical Center
Catherine Staton, Duke Global Health Institute, School of Medicine, School of Medicine: Surgery
Siddhesh Zadey, School of Medicine: Surgery

Team Contributors

Eric Green, Duke Global Health Institute
Eve Puffer, Arts & Sciences: Psychology and Neuroscience, Duke Global Health Institute