Technical Description of eHealth Tools in an Environmental Exposure Chamber: Implementation Study
Bibliographic record
Abstract
Background: Environmental exposure chambers (EECs) provide a controlled and reproducible setting for studying allergic rhinitis, allowing standardized exposure to aeroallergens. However, real-time symptom monitoring remains challenging, particularly as patients must be observed remotely while inside the chamber. Objective: This study aimed to design, implement, and evaluate an integrated eHealth system that leverages digital tools for data collection and real-time clinical monitoring during EEC exposures. The goal was to streamline workflow, enhance data reliability, and improve patient safety during allergen challenges. Methods: We conducted a prospective pilot study in a validated EEC at Ramón y Cajal University Hospital. A total of 34 provocation tests were conducted: 27 in patients with confirmed grass pollen allergy and 7 in nonallergic controls. All exposures took place outside the pollen season using standardized Phleum pratense (grass pollen) at concentrations of 940±100 grains/m³. Clinical data were collected using REDCap (Research Electronic Data Capture; Vanderbilt University), a secure electronic data capture platform. Surveys were automatically scheduled and included standardized instruments: Visual Analog Scale (VAS), Total Nasal Symptom Score (TNSS), Total Ocular Symptom Score (TOSS), asthma symptom questionnaires, peak expiratory flow (PEF), and peak nasal inspiratory flow (PNIF). Data were visualized in real time using Power BI dashboards (Microsoft Corp). Results: Among allergic patients, 85% had a positive response to the allergen exposure, with a median TNSS of 6 (IQR 6-7), while controls showed no positive reactions. Mean exposure time for positive cases was 48.9 minutes (SD 28.3), as patients were withdrawn early upon reaching symptom thresholds. The REDCap system was configured to collect symptom surveys at 15-minute intervals, allowing up to 6 entries per patient during exposure, depending on clinical tolerance. This setup enabled consistent symptom monitoring and integration with Power BI for real-time visualization. All records were successfully synchronized between REDCap and Power BI. Informal feedback from medical staff highlighted improved workflow and usability. Conclusions: The integration of REDCap with Power BI enabled automated, real-time tracking of symptoms during EEC exposures. This eHealth solution enhanced clinical oversight, supported timely decision-making, and ensured patient safety. The platform provides a flexible and reproducible model for use in allergen exposure studies and other clinical settings requiring structured remote monitoring. Clinical response data are reported in a separate validation study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".