JMIR Publications

ABSTRACT

Background:

Asthma exacerbation is a major cause of emergency department (ED) visits in children and adolescents. Most existing predictive scores and biomarkers aim to predict severe exacerbations in the medium- to long-term perspective. Mobile health (mHealth) is a promising approach for integrating real-time multimodal data to improve prediction of asthma exacerbations. This can enable the identification of at-risk children and the implementation of timely interventions.

Objective:

The main objective of the Mobile Health for Kids with Asthma (MoKA) study is to develop a validated predictive model for imminent asthma exacerbation in children, using multimodal data, including participant-reported questionnaires through the RespiSentinel mobile app, augmented with publicly-sourced environmental and epidemiologic data. Furthermore, we will evaluate the association between the frequency of a real-time measured nocturnal cough and asthma control and severe asthma exacerbations, and the acceptability of RespiSentinel in asthma self-management.

Methods:

This is a prospective cohort study with in-person and remote recruitment at seven tertiary pediatric centers in Canada. Children aged between 1 and 17 years with at least one wheezing episode or asthma exacerbation over the previous 12 months prior to recruitment are eligible to participate (estimated n=2000). The planned duration of study participation is six months following the date of enrolment (cohort entry), regardless of the number of asthma exacerbations during the follow-up period. The primary outcome will be asthma exacerbation defined by asthma symptoms requiring systemic corticosteroid use and an urgent care/ED visit or hospitalization. The predictive model will be created using questionnaire data on asthma control via RespiSentinel as well as integrating publicly available local daily data on air pollutant levels (National Air Pollution Surveillance Program) and weekly prevalence of respiratory viruses (National Canadian Respiratory Virus Detections Surveillance Program). Nocturnal cough frequency will be determined by nighttime audio recordings and its contribution to predicting imminent asthma exacerbations will be evaluated. The acceptability of RespiSentinel will be assessed through an app-based questionnaire.

Results:

We will train and validate an asthma exacerbation prediction model using multimodal data sources. This approach may help patients, their families, and health professionals anticipate upcoming loss of control and take the necessary steps to prevent a severe asthma exacerbation.

Conclusions:

The MoKA study will harness mHealth real-time data to identify children at imminent risk of asthma exacerbations, with the ultimate goal of designing timely interventions to prevent morbidity in this group of patients. Clinical Trial: Non applicable