ESRA 2027 sessions by theme

Back to Overview of Sessions

Session Details

The ubiquitousness of smartphones combined with the growing availability of wearable devices has transformed the collection of intensive longitudinal data in survey research. Ecological Momentary Assessment (EMA), also known as the Experience Sampling Method (ESM), enables researchers to capture participants' experiences, behaviors, and contexts in real time through repeated smartphone-based self-reports. At the same time, wearable devices provide continuous, objective measurements of physical activity, physiological processes (e.g., heart rate, heart rate variability, temperature), sleep, and environmental conditions. Combining EMA with wearable sensor data offers unique opportunities to study dynamic relationships between subjective experiences and objective behavioral and physiological states in everyday life. Sensor information can also be used to trigger context-aware EMA prompts based on detected behaviors, activities, locations, or physiological changes, thereby increasing the ecological validity and relevance of self-reports.
Despite this promise, integrating EMA and wearable sensing into survey research raises important methodological challenges related to participation, measurement, and data quality. Frequent EMA prompts may increase respondent burden and attrition, while wearable studies face challenges related to consent, compliance, device heterogeneity, data harmonization, and the use of participant-owned versus study-provided devices. We invite methodological contributions addressing these and related issues, including the design of participant communication strategies, incentive schemes, app and user interface design, gamification, participant feedback, sensor-triggered EMA protocols, representativeness of wearable data, scalability of combined study designs, and measurement differences across devices and population subgroups. This session aims to bring together researchers developing and evaluating innovative methods for combining self-reported and sensor-based data to advance the quality, efficiency, and scientific value of survey research.