Using mHealth to Predict Asthma Exacerbations in Children and Adolescents (Mobile Health for Kids With Asthma): Protocol for an Observational Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Asthma exacerbation is a major cause of emergency department visits in children and adolescents. Most of the existing asthma prediction scores and biomarkers are designed to predict severe exacerbations in the medium to long term. Mobile health (mHealth) is a promising approach for integrating real-time, multimodal data to improve the prediction of asthma exacerbation. Using mHealth can enable the identification of at-risk children and the implementation of timely interventions. OBJECTIVE The primary 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 epidemiological data. Furthermore, we will evaluate the association between the frequency of nocturnal cough measured in real time and asthma control and severe asthma exacerbation, and the acceptability of the RespiSentinel app in asthma self-management. METHODS This is a prospective cohort study with in-person and remote recruitment at 7 tertiary pediatric centers in Canada. Parents of children aged between 1 and 17 years, as well as children who have experienced at least one wheezing episode or asthma exacerbation during the 12 months before recruitment, will be eligible to participate (estimated number of children: n=2000). The planned duration of study participation is 6 months following the date of enrollment (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 or emergency department visit or hospitalization. The predictive model will be created using questionnaire data on asthma control via the RespiSentinel app as well as by 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 Detection Surveillance Program). Nocturnal cough frequency will be determined by using nighttime audio recordings, and their contribution to predict imminent asthma exacerbation will be evaluated. Acceptability of the RespiSentinel app 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 asthma control and take the necessary steps to prevent a severe asthma exacerbation. CONCLUSIONS The MoKA study will harness real-time mHealth data to identify children at imminent risk of asthma exacerbation with the ultimate goal of designing timely interventions to prevent morbidity in this group of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".