VirtualSelf: A computer-based learning environment for patient education using physiological and trace data

Abstract : Healthcare education systems play an important role in helping patients to understand medical information related to diagnosis, treatment options, and disease management. However, little research has explored how patients navigate biomedical information and the types of emotions elicited during learning. The purpose of this study was to examine patients’ cognitive, metacognitive, and affective processes while learning about breast cancer using {VirtualSelf}, a computer-based learning environment ({CBLE}). Diagnosed breast cancer patients (N=5) and a comparison group of healthy university students (N=5) completed several self-report measures prior to the learning session (i.e., task-value, demographics, prior knowledge, emotion regulation strategies). They then used the system for 45 minutes while we collected trace data (e.g., video of facial expressions, log-file data for navigational behaviors, and galvanic skin response for changes in arousal levels). Immediately following the session they completed several questionnaires (i.e., perceived usefulness, satisfaction, understanding). Results from product and process data focus on learning trajectories and regulatory processes across both groups. This study represents an important area of interdisciplinary research that aims to better understand how patients learn about complex disease processes. Our findings have implications for patient education, decision-making, and human factors involved in healthcare systems, including the design of {CBLEs}.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01217198
Contributor : Lip6 Publications <>
Submitted on : Monday, October 19, 2015 - 10:59:50 AM
Last modification on : Friday, March 22, 2019 - 1:38:06 AM

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  • HAL Id : hal-01217198, version 1

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Melissa Duffy, Roger Azevedo, Sarkis Meterissian, François Bouchet. VirtualSelf: A computer-based learning environment for patient education using physiological and trace data. IIE Annual 2014, Jun 2014, Montreal, QC, Canada. ⟨hal-01217198⟩

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