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Using a Wireless Electroencephalography Device to Evaluate E-Health and E-Learning Interventions.

Tanya Mailhot1, Patrick Lavoie, Marc-André Maheu-Cadotte

  • 1Tanya Mailhot, RN, PhD, is Postdoctoral Fellow, Faculty of Nursing and Faculty of Medicine, Université de Montréal, Montréal Heart Institute Research Center, Canada (''spell out province in bio). Patrick Lavoie, RN, PhD, is Postdoctoral Fellow, William F. Connell School of Nursing, Boston College, Massachusetts. Marc-André Maheu-Cadotte, RN, BSc, is Doctoral Student and Guillaume Fontaine, RN, MSc, is Doctoral Student, Faculty of Nursing, Université de Montréal, Montréal Heart Institute Research Center, Canada. Alexis Cournoyer, MD, is Doctoral Student, Université de Montréal, Hôpital du Sacré-Cœur de Montréal, Canada. José Côté, RN. PhD, is Professor, Faculty of Nursing, Université de Montréal, and Researcher, Centre Hospitalier de l'Université de Montréal Research Center, Canada. France Dupuis, RN, PhD, is Associate Professor, Faculty of Nursing, Université de Montréal, and Researcher, Sainte-Justine Research Center, Montréal, Canada. Thierry Karsenti, MA, MEd, PhD, is Professor, Faculty of Education Sciences, Université de Montréal, Canada. Sylvie Cossette, RN, PhD, is Professor, Faculty of Nursing, Université de Montréal, and Researcher, Montréal Heart Institute Research Center, Canada.

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Summary

Wireless electroencephalography (EEG) is a feasible and acceptable tool for measuring patient and professional engagement with e-health and e-learning. This technology provides real-time insights into affective and cognitive reactions during digital health interventions.

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Neuroscience

Background:

  • Measuring patient and health professional engagement with e-health and e-learning interventions presents research challenges.
  • Objective assessment of affective and cognitive states during digital health interventions is needed.

Purpose of the Study:

  • To evaluate the feasibility and acceptability of a wireless electroencephalography (EEG) device.
  • To measure affective (anxiety, enjoyment, relaxation) and cognitive (attention, engagement, interest) reactions.
  • To assess these reactions in patients and healthcare professionals during e-health or e-learning.

Main Methods:

  • A pilot study involved 6 patients and 7 health professionals using a wireless EEG device during a 10-minute e-health/e-learning session.
  • Feasibility and acceptability were assessed via consent rates, refusal reasons, setup time, completion rates, comfort, signal quality, and observation yield.
  • Wireless EEG readings were compared with self-reported participant reactions.

Main Results:

  • High observation yields (≥75%) were achieved for attention, engagement, enjoyment, and interest.
  • Wireless EEG scores generally aligned with self-reported scores, capturing real-time reactions.
  • Feasibility and acceptability indicators supported the use of the wireless EEG device for both patient and professional groups.

Conclusions:

  • Wireless EEG is a feasible and acceptable method for assessing reactions to e-health and e-learning interventions.
  • Future research should explore its application in diverse healthcare settings.
  • Combining wireless EEG with eye-tracking can further elucidate intervention component effects on participant responses.