Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis.

Journal: Biosensors
PMID: 39852064

Abstract

This study investigates the impact of patient stress on COVID-19 screening. An attempt was made to measure the level of anxiety of individuals undertaking rapid tests for SARS-CoV-2. To this end, a galvanic skin response (GSR) sensor that was connected to a microcontroller was used to record the individual stress levels. GSR data were collected from 51 individuals at SARS-CoV-2 testing sites. The recorded data were then compared with theoretical estimates to draw insights into stress patterns. Machine learning analysis was applied for the optimization of the sensor results. Classification algorithms allowed the automatic reading of the sensor results and individual identification as "stressed" or "not stressed". The findings confirmed the initial hypothesis that there was a significant increase in stress levels during the rapid test. This observation is critical, as heightened anxiety may influence a patient's willingness to participate in screening procedures, potentially reducing the effectiveness of public health screening strategies.

Authors

  • Antonios Georgas
    Laboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, Greece.
  • Anna Panagiotakopoulou
    Laboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, Greece.
  • Grigorios Bitsikas
    Laboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, Greece.
  • Katerina Vlantoni
    Department of History and Philosophy of Science, School of Science, National and Kapodistrian University of Athens, 15771 Athens, Greece.
  • Angelo Ferraro
    Laboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, Greece.
  • Evangelos Hristoforou
    Laboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, Greece.

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