  Objective: Asthma is a chronic pulmonary disease with multiple triggers
manifesting as symptoms with various intensities. This paper evaluates the
suitability of long-term monitoring of pediatric asthma using diverse data to
qualify and quantify triggers that contribute to the asthma symptoms and
control to enable a personalized management plan.
  Materials and Methods: Asthma condition, environment, and adherence to the
prescribed care plan were continuously tracked for 97 pediatric patients using
kHealth-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, as
the 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 the
28% of the patients deployed in winter, PM2.5 was identified as the major
contributor for 80% of them. One patient across each season has been chosen to
explain the determination of personalized triggers by observing correlations
between triggers and asthma symptoms gathered from anecdotal evidence.
  Discussion and Conclusion: Both public and personal health signals including
compliance to prescribed care plan have been captured through continuous
monitoring using the kHealth-Asthma technology which generated insights on
causes of asthma symptoms across different seasons. Collectively, they can form
the underlying basis for personalized management plan and intervention.
  KEYWORDS: Personalized Digital Health, Medical Internet of Things, Pediatric
Asthma Management, Patient Generated Health Data, Personalized Triggers,
Telehealth,
