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mHealth Intervention Integrating Personal PM2.5 Monitoring and Deep Learning to Reduce Pediatric Asthma Exacerbations: Pilot Study
ABSTRACT
Background:
Fine particulate matter (PM2.5) is a major trigger of pediatric asthma exacerbations, yet individual sensitivity varies considerably. Existing interventions often adopt a uniform approach, despite this heterogeneity.
Objective:
This study aimed to evaluate the feasibility and effectiveness of a pilot mobile health (mHealth) intervention that integrates personal PM2.5 monitoring, deep learning (DL)–based prediction and tailored behavioral recommendations to mitigate exacerbation risks in children.
Methods:
In this 3-year pilot study, 272 pediatric patients with asthma were enrolled across nine tertiary hospitals in Korea. Using asthma symptom reports and personal PM monitoring data collected via smartphones and portable devices, a 1D CNN–LSTM model was developed to identify PM-sensitive patients and predict exacerbations. After model construction, 109 participants entered the intervention phase and were allocated to three groups (model-based intervention, forecast-based intervention, or no intervention) through initial screening and model-based grouping. The model-based group received individualized alerts with behavioral recommendations based on DL predictions, while the forecast-based group received the same recommendations based on regional air quality forecasts. The primary outcome was change in asthma exacerbation rates (measured by Intervention Effectiveness Ratio, IER).
Results:
The model-based group demonstrated significant reductions in exacerbation rates (median IER decrease: 6.6%; mean IER decrease: 11.5%; P < 0.05), whereas no significant changes were observed in the other groups. Odds ratio analysis indicated that the model-based group had 5.92- and 4.22-fold lower odds of PM2.5-related exacerbations compared with the no-intervention and forecast-based groups, respectively. Stratified and adjusted analyses confirmed that the benefit of model-based alerts remained robust despite baseline differences in asthma severity and control status.
Conclusions:
This pilot study demonstrates the feasibility and potential effectiveness of an mHealth intervention that integrates personal PM monitoring, DL–based prediction and tailored behavioral recommendations in pediatric asthma. This approach shows promise for reducing PM-related exacerbations and warrants validation in larger, longer-term studies. Clinical Trial: The Clinical Research Information Service KCT0010530; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=30032&search_page=L
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