Using a Wireless Electroencephalography Device to Evaluate E-Health and E-Learning Interventions
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Bibliographic record
Abstract
BACKGROUND: Measuring engagement and other reactions of patients and health professionals to e-health and e-learning interventions remains a challenge for researchers. OBJECTIVE: The aim of this pilot study was to assess the feasibility and acceptability of using a wireless electroencephalography (EEG) device to measure affective (anxiety, enjoyment, relaxation) and cognitive (attention, engagement, interest) reactions of patients and healthcare professionals during e-health or e-learning interventions. METHODS: Using a wireless EEG device, we measured patient (n = 6) and health professional (n = 7) reactions during a 10-minute session of an e-health or e-learning intervention. The following feasibility and acceptability indicators were assessed and compared for patients and healthcare professionals: number of eligible participants who consented to participate, reasons for refusal, time to install and calibrate the wireless EEG device, number of participants who completed the full 10-minute sessions, participant comfort when wearing the device, signal quality, and number of observations obtained for each reaction. The wireless EEG readings were compared to participant self-rating of their reactions. RESULTS: We obtained at least 75% of possible observations for attention, engagement, enjoyment, and interest. EEG scores were similar to self-reported scores, but they varied throughout the sessions, which gave information on participants' real-time reactions to the e-health/e-learning interventions. Results on the other indicators support the feasibility and acceptability of the wireless EEG device for both patients and professionals. DISCUSSION: Using the wireless EEG device was feasible and acceptable. Future studies must examine its use in other contexts of care and explore which components of the interventions affected participant reactions by combining wireless EEG and eye tracking.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it