Alcohol Use Behavioral Phenotyping Test for Global Populations (2024-2025)


Alcohol use is a leading risk factor for premature mortality and life-long disability. Multiple biological, psychological and social factors interact with each other and contribute to harmful alcohol use. Alcohol use disorders (AUD) are associated with changes in psychological constructs such as reward prediction, reward learning and cognitive control that can be mapped to neural circuits. 

However, current AUD assessments rely on self-reports of consumption and harm. Further, any AUD assessment will face difficulties with transcultural adaptation and translinguistic translation in global health settings. The growing AUD burden in low- and middle-income countries calls for the development and translation of tools allowing for the systematic, comprehensive and coherent study of the determinants of alcohol use. 

Project Description

This project builds on the work of previous teams that created the Alcohol Use Behavioral Phenotyping Test (AUBPT), a virtual tool that uses games and tasks to assess the user’s risk of AUD. Previous teams developed computational models to explain and predict behaviors on AUBPT tasks. This team will expand on previous work to further data collection at multiple global sites and pilot the AUBPT app in Kenya, Brazil and India.

Team members will deploy, evaluate and culturally adapt AUBPT in new global settings. The project will use crowdsourced samples from online recruitment platforms in addition to clinical and general population samples from the U.S. and India.

In phase one of the project, data collection and content validation will take place in Maringá, Brazil, Nagpur, India, and Eldoret, Kenya on 15-20 general population participants. The team will collect demographic and alcohol use responses. To validate content, team members will perform semi-structured interviews on 10-12 local hospital staff and local stakeholders.

In phase two of the project, the team will evaluate the validity of AUBPT task-based paradigms in clinical settings. Team members will implement AUBPT into clinical population samples in medical centers in India, Brazil, and Kenya. Additionally, the project will recruit 10-12 healthcare providers to participate in semi-structured interviews after the implementation is complete to assess the implementation strategy. 

Anticipated Outputs

Open-source multilingual computer application for AUBPT; global research experiences; research papers; data for NIH application; secondary data analyses of datasets from National Institute of Mental Health’s Data Archive; conference presentations

Student Opportunities

Ideally, this project team will include 4 graduate students and 4 undergraduate students from diverse backgrounds and academic and cultural experiences. All team members should have a strong interest in global mental health, an aptitude for quantitative and qualitative research methods, and enthusiasm for working with global collaborators. Graduate students should have backgrounds in psychology, neuroscience, global health, public policy, interdisciplinary data science, computer science, cognitive science or biostatistics. 

Undergraduates with a major or minor in global health will be preferred for selection to the Implementation subteam. For the App Development subteam, students who have prior web development experience in open-source languages (Python, Java, C++, etc.) will be preferred. For the Analysis subteam, those with an interest or background in biostatistics, theoretical neuroscience and machine learning will be preferred. 

By participating in this project, team members will gain experience in project coordination, oversight and management, hands-on research with human participants, study design, international collaboration, systematic review and manuscript writing, data collection and conference presentations. Undergraduate students will gain first-hand experience working in translational research and an introduction to global health. Graduate students will lead the subteams, draft research papers as first/second authors and work independently on advanced data analyses. Paige O’Leary will serve as project manager.

In an optional summer component, students will perform fieldwork and must be willing to commit 40 hours per week for 4-6 weeks at one of the partner sites in Maringa, Brazil; Nagpur, India; or Eldoret, Kenya.


Fall 2024 – Spring 2025

  • Summer 2024 (optional): Perform fieldwork in Brazil, India and Kenya; pilot AUBPT; validate content
  • Fall 2024: Start data analysis; adapt AUBPT versions to Portuguese, Hindi/Marathi and Kenyan Swahili based on feedback
  • Spring 2025: Draft manuscripts; initiate data collection in clinical settings
  • Summer 2025 (optional): Complete data collection from clinical sites; analyze clinical validation data


Academic credit available for fall and spring semesters; summer funding available

See earlier related team, Alcohol Use Behavioral Phenotyping Test for Global Populations (2023-2024).


Team Leaders

  • Paige O'Leary, School of Medicine-Surgery: Emergency Medicine
  • Catherine Staton, School of Medicine-Surgery
  • Joao Vissoci, School of Medicine-Surgery: Emergency Medicine
  • Siddhesh Zadey, School of Medicine-Surgery

/yfaculty/staff Team Members

  • Eric Green, Duke Global Health Institute
  • Ashley Phillips, School of Medicine-Surgery: Emergency Medicine
  • Eve Puffer, Arts & Sciences-Psychology and Neuroscience
  • Anna Tupetz, School of Medicine-Surgery: Emergency Medicine

/zcommunity Team Members

  • Editruda Gamassa, Kilimajaro Christian Medical Center
  • Florence Jaguga, Moi Teaching and Referral Hospital
  • Suyog Jaiswal, AIIMS-Nagpur
  • Leonardo Oliveira, University of Sao Paulo, Maringa Campus, Brazil
  • Francis Sakita, Kilimanjaro Christian Medical Center