Assessing the Impact of Home Environmental Exposures on Allergic Rhinitis Using Real-Time Air Quality Monitoring and Symptom Assessment: Observational Feasibility Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».