The Science of Digital Health

Technology is changing the way we research and deliver health care

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Why Conduct Digital Behavioral Health Research?

Collect data in daily life to capture accurate, valid, context-specific information

Model dynamic processes as they naturally unfold

Build, test, and disseminate therapies that are accessible and inexpensive

Provide on-demand support for people in their everyday lives

Uncover unique insights and push the field forward via machine learning and smart technologies

Exciting Projects Leveraging our Platform

At Colliga Apps, the science of psychology comes first. Here are a few examples of innovative projects, collaborations, and initiatives on our platform.

Using Artificial Intelligence to Intervene in Racial and Gender Prejudice

Tracking Real-Life Microaggressions against Minoritized Students in STEM

Dr. Theodora Chaspari, Dr. Verna Keith, Dr. Srividya Ramasubramanian, and Dr. Ruihong Huang

HUman Bio-Behavioral Signals (HUBBS) Lab

This project uses machine learning and wearable technology to identify and intervene upon microaggressions toward racial and gender minoritized students in STEM classes

Investigating racial bias in machine learning models of mental health: Implications for equitable telebehavioral health. Body Sensor Networks.

Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations. IEEE Transactions on Signal Processing.

Sparse Representation of Electrodermal Activity with Knowledge-Driven Dictionaries IEEE Transactions on Biomedical Engineering.

Training the Next Generation of Psychologists in Digital Mental Health Best Practices

The Development of a Formalized Training Program in Digital Mental Health

Dr. Daniel Bagner, Dr. Adela Timmons, Dr. Jonathan Comer, and Dr. Justin Parent

Supported by the Association of Psychology Postdoctoral and Internship Centers (APPIC)

This grant supports the development and testing of a pilot training program for doctoral students in clinical psychology in the use of remote health monitoring to support and augment in-person care.

Improving Family Functioning in Everyday Life via Machine Learning

The Development and Systematic Evaluation of an AI-Assisted Just-in-Time Adaptive Intervention for Improving Child Mental Health

Dr. Adela Timmons and Dr. Jonathan Comer

Supported by the National Institute of Mental Health

Technological Interventions for Ecological Systems Lab
Mental Health Interventions and Technology Lab

The goal of this project is to develop machine learning models of child and family behavior and to conduct a micro-randomized clinical trial of a just-in-time adaptive intervention to improve child and family mental health.

Sub-population specific machine learning models of couples’ interpersonal conflict. Association for Computing Machinery Transactions of Internet Technology

New frontiers in ambulatory assessment: Big data methods for capturing couples’ emotions, vocalizations, and physiology in daily life. Social Psychological and Personality Science.

Using multimodal wearable technology to detect conflict among couples. IEEE Computer.

Understanding Teen Depression through Smartphones

Dyadographic Network Modeling of the Intergenerational Transmission of Depression: Use of Parent-Adolescent Active and Passive Smartphone Data

Dr. Mei Yi Ng and Dr. Justin Parent

Mechanisms Underlying Treatment Technologies Lab
Child and Family Well-Being Clinic and Lab

This study aims to understand patterns of depression and conflict in mothers and adolescent girls by collecting data from smartphones and wearable sensors.

Annual research review: Building a science of personalized intervention for youth mental health Journal of Child Psychology and Psychiatry.

Assessing fit between evidence-based psychotherapies for youth depression and real-life coping in early adolescence Journal of Clinical Child and Adolescent Psychology.

Youth top problems: Using idiographic, consumer-guided assessment to identify treatment needs and track change during psychotherapy Journal of Consulting and Clinical Psychology.

Improving ADHD Outcomes in Kids via Technology

Personalizing Behavioral Parent Training: Improving Reach and Outcomes for Families of Children with ADHD

Dr. Brittany Merrill

Center for Children and Families of Western New York

This project uses mobile devices and data science to personalize treatment programs for kids with ADHD to increase the effectiveness of care.

Psychosocial interventions for Attention-Deficit /Hyperactivity Disorder: Systematic review with evidence and gap maps. Journal of Developmental & Behavioral Pediatrics.

Improving homework performance among children with ADHD: A randomized clinical trial. Journal of Consulting and Clinical Psychology

Family burden of raising a child with ADHD. Journal of Abnormal Child Psychology.

Using Technology to Make Advances in Parenting Science for At-Risk Families

Leveraging Innovations in Machine Learning to Accelerate our Understanding of the Dynamic and Transactional Nature of Parenting in Low-Income Families

Dr. Deborah Jones

Deborah Jones Lab

This study uses mobile technology and artificial intelligence to determine how stress impacts interpersonal processes in low-income and at-risk families.

Parent-therapist alliance and technology use in behavioral parent training: A brief report. Psychological Services.

Caregiver use of the core components of technology-enhanced HNC: A case series analysis of low-income families Cognitive and Behavioral Practice.

Adoption of Technology-Enhanced Treatments: Conceptual and Practical Considerations. Clinical Psychology: Science and Practice.