RC2 – Adaptive Ambulatory Assessment in Digital Health and MORE: Presenting and Experimenting with the Modular Open Research Platform for Situated and Longitudinal Human-Subject Research

Lecturer: David Haag
Fields: Psychology / Cognitive Science / Data Science

Content

Ambulatory assessment has been an emerging paradigm in psychological and health research, allowing for the intensive longitudinal study of individuals\’ behavior, physiology, and experiences in their natural environments. By capturing data in the flow of daily life, ambulatory assessment allows us to gain insights into the dynamic processes that shape an individual’s behavior and to consecutively adapt interventions to the individual and their current context. The integration of digital technologies has revolutionized ambulatory assessment, offering robust methodologies for investigating complex systems ranging from individual behavior to broader socio-ecological interactions.

The course will provide an introduction into ambulatory assessment, its significance in current research, particularly with modern approaches such as Ecological Momentary Assessment (EMA) and its potential for future applications within psychology, digital health and beyond. By examining various studies, we will explore how this methodology contributes to our understanding of human behavior and system interactions in situ, furthering the discourse on adaptive capacities within complex environments.

Additionally, we will introduce the open-source platform MORE (Modular Open Research Platform; https://dhp.lbg.ac.at/more/?lang=en), an innovative tool developed at the Ludwig Boltzmann Institute for Digital Health and Prevention in Salzburg in a research and development effort led by Dr. Jan David Smeddinck. The system is aimed at establishing a digital infrastructure capable of handling the complexities of modern health studies enabling researchers to carry out ambitious ambulatory assessment studies. It addresses the integration of external devices, real-time data collection, participant administration, and data security standards. Notably, the platform contains near real-time data capture and interpretation features, thus allowing for the ecologically valid investigation of momentary antecedents of health behaviors or even testing interventions that adapt to an individual and their current context. This is crucial e.g. for research into personalization in digital health and further investigations along the concept of “precision health”. MORE comprises a web application for study management and a smartphone app for participant engagement. The smartphone app is configured to guide participants through study procedures, enabling the collection of questionnaire data and sensor information over extended periods in a minimally intrusive and minimally burdensome manner. The app\’s design reflects a commitment to sustainability, user-friendliness, and technological relevance.

In summary, this course will offer participants a blend of a theoretical introduction to ambulatory assessment and a comprehensive overview of the MORE platform, including its conception, key features, and examples of its capabilities to carry out ambulatory assessment studies. Additionally, attendees will have the opportunity for hands-on testing of the free and open-source system, providing a practical understanding of the platform.

Literature

Lecturer

David Haag
David Haag

David Haag is a PhD student in psychology working at the Ludwig Boltzmann Institute for Digital Health and Prevention in Salzburg (Austria). He received his master’s degree in psychology from the University of Graz in 2021. The same year, he started his PhD aiming to develop adaptive digital interventions to support individuals in achieving their physical activity goals. Following his initial investigation on momentary antecedents of physical activity using ambulatory assessment, he narrowed his research focus to action planning based behavior change interventions such as implementation intentions. His current work mostly deals with planning based Just-in-Time Adaptive Interventions (JITAIs) and how the latest developments in Large Language Models could tie in with JITAI implementation.

Affiliation: Ludwig Boltzmann Institute for Digital Health and Prevention
Homepage: https://dhp.lbg.ac.at/team/david-haag-msc/?lang=en