Experience sampling in mental health research 2500-EN-CS-EM-05

Experience Sampling Method (ESM), also known as Ecological Momentary Assessment (EMA), is an intensive longitudinal methodology that involves collecting data on individuals' thoughts, emotions, and behaviors in real-time or near real-time as they go about their daily lives. This is typically achieved by gathering multiple daily entries through short queries sent to participants' smartphones. By minimizing post-hoc recall bias and providing a rich, in-the-moment description of subjective experiences, ESM can be effectively combined with laboratory or neuroimaging methods to develop sophisticated models of mental health disorders.
During the subsequent lectures, we will:
• Introduce the ESM method and demonstrate how it contributes to mental health (and loneliness) research.
• Briefly explore its history and emerging trends in ecological assessment.
• Discuss what types of research questions can be addressed using ESM and how to design corresponding studies.
• Examine key issues in questionnaire design and evaluation.
• Highlight ethical considerations in ESM research.
• Address practical aspects, such as ESM platforms, study briefing, and preregistration.
• Provide an introduction to multilevel modeling and the analysis of ESM data.
• Introduce the concept of digital phenotyping and discuss challenges associated with combining ESM data with physiological and wearable data.
The workshop part will provide participants with the practical skills needed to design, implement, and analyze ESM studies.
Participants will work in groups and choose from a list of potential research topics to design a basic ESM study. Each group will collectively progress through all stages of ESM study preparation, including:
• Defining research questions and objectives.
• Designing appropriate questionnaires tailored to the study's goals.
• Setting up the study on an ESM platform, using the free trial version of Movisens XS.
• Testing the study to ensure functionality and clarity for participants.
• Simulating data collection by running the study in real-time.
• Performing an initial analysis of the collected data using multilevel modeling techniques.