Assessing the Impact of Home Environmental Exposures on Allergic Rhinitis Using Real-Time Air Quality Monitoring and Symptom Assessment: Observational Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Rhinitis is the most common sinonasal condition and poses a significant cost burden. Rhinitis symptom control is associated with exposure to environmental triggers (eg, aeroallergens, pollutants, and irritants). While people spend much of their time at home, studies examining the association of rhinitis symptoms with home environmental exposures, especially in low-income, urban, and racial or ethnic minorities, are limited. Frequently, 3 types of surveys are used in ecological momentary assessment (EMA): a survey conducted at a predetermined rate, an event-triggered survey, and a follow-up survey to gauge behavioral changes in response to the event. OBJECTIVE: This study aims to determine the feasibility and usability of daily and triggered EMA paired with an indoor air quality monitor to collect exposure and rhinitis symptom data. METHODS: Participants were recruited from the Allergy and Ear, Nose, and Throat clinics at 2 academic centers. Participants had to have a rhinitis diagnosis with active symptoms, be 18 years of age or older, self-identify as a racial or ethnic minority, live in the city of Chicago, be able to read and speak English, and have a smartphone. Participants received the Awair Omni air quality monitor to measure volatile organic compounds, particulate matter, and humidity. EMA data were collected using a personal smartphone using the PiLR Health app. Participants were sent daily scheduled surveys, random check-in surveys, and air quality event-triggered survey EMA notifications to assess rhinitis symptoms, environmental exposures, and mitigation strategies for 14 days. After the 14-day data collection period, participants completed the acceptability, appropriateness, and feasibility survey items. Feasibility metrics captured included recruitment and retention, demographics, rhinitis symptoms, and the usability of the PiLR Health App and Awair Omni. Barriers and challenges were identified and captured by the study staff. Descriptive statistics were performed using Excel (Microsoft Corp). RESULTS: A total of 24 participants were approached, 15 participants consented and 12 participants completed the study. Participants received an average of 62.42 (SD 14.26) total surveys during their study period, and of those surveys, an average of 36.83 (SD 22.18; 59%) surveys were completed. All 12 participants met the threshold for successful home air monitoring (11 days of continuous environmental data assessment). The usability of study components and integration into the overall study was high (usability scale≥68), indicating participants considered each of the devices to be usable. Participant feedback on the study was positive; yet, they did identify areas for improvement including getting air quality data in real time, providing more detailed instructions for device setup, and doing more check-ins. CONCLUSIONS: A real-time assessment of home environmental exposures and subjective rhinitis symptoms was feasible to conduct. This study will support the development of targeted interventions to address disparities in sinonasal disease care and outcomes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".