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Objective: Asthma is a chronic pulmonary disease with multiple triggersmanifesting as symptoms with various intensities. This paper evaluates thesuitability of long-term monitoring of pediatric asthma using diverse data toqualify and quantify triggers that contribute to the asthma symptoms andcontrol to enable a personalized management plan.  Materials and Methods: Asthma condition, environment, and adherence to theprescribed care plan were continuously tracked for 97 pediatric patients usingkHealth-Asthma technology for one or three months.  Result: At the cohort level, among 21% of the patients deployed in spring,63% and 19% indicated pollen and Particulate Matter (PM2.5), respectively, asthe major asthma contributors. Of the 18% of the patients deployed in fall, 29%and 21% found pollen and PM2.5 respectively, to be the contributors. For the28% of the patients deployed in winter, PM2.5 was identified as the majorcontributor for 80% of them. One patient across each season has been chosen toexplain the determination of personalized triggers by observing correlationsbetween triggers and asthma symptoms gathered from anecdotal evidence.  Discussion and Conclusion: Both public and personal health signals includingcompliance to prescribed care plan have been captured through continuousmonitoring using the kHealth-Asthma technology which generated insights oncauses of asthma symptoms across different seasons. Collectively, they can formthe underlying basis for personalized management plan and intervention.  KEYWORDS: Personalized Digital Health, Medical Internet of Things, PediatricAsthma Management, Patient Generated Health Data, Personalized Triggers,Telehealth,

Determination of Personalized Asthma Triggers from Evidence based on Multimodal Sensing and Mobile Application