ESRA 2027 sessions by theme
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Next-Generation Survey Measurement: Integrating Wearables, Biosensors, and Other Emerging Technologies to Track GlP-1/GIP Medication Use |
| Coordinator 1 | Dr Tadeja Gracner (University of Southern California) |
| Coordinator 2 | Dr Ritika Chaturvedi (University of Southern California) |
| Coordinator 3 | Dr Francisco Perez-Arce (University of Southern California) |
Session Details
This session explores innovations in the integration of surveys, wearables, and biosensors within GLIMMER (GLP Influence on Metabolism and Mind Examined in Real-world, Real-time), a surveillance platform embedded in the Understanding America Study (UAS), a nationally representative probability panel. GLIMMER layers monthly brief surveys and an annual core survey with passive, device-based data on the behavioral pathways these medications affect: diet via ecological momentary assessment (EMA), continuous biobehavioral smartwatch metrics, glycemic response via continuous glucose monitors (CGM), and adiposity via smart scales.
The rapid adoption of GLP-1/GIPs has created an urgent need for real-world surveillance, yet current data fall short: claims and administrative records miss much of the population and cannot open the black box of the behavioral mechanisms, while traditional survey self-report alone cannot capture the fine-grained behavioral change these medications produce.
Presentations will cover the design of this multi-modal infrastructure, such as fielding decisions, deployment and validation, and tradeoffs between burden and precision, and substantive analyses applying causal inference and machine learning methods to the linked survey and device data, estimating how diet, activity, glycemic control, and weight shift following medication initiation, discontinuation, and dose changes.
Beyond the GLP-1 context, the session speaks to broader questions facing survey methodologists integrating passive data into panel infrastructure: How should panels incorporate wearable and biosensor streams without compromising representativeness or inflating attrition? What can EMA and continuous device data reveal about mechanisms and timing that periodic self-report cannot, and where does self-report remain indispensable? How do we validate new instruments against device-based "ground truth" within a probability sample?
We welcome additional papers addressing the integration of EMA, wearables, biomarkers, or other passive data streams into survey infrastructure, and work on the challenges that arise when combining these sources.
