Pediatric asthma, asthma, bronchial hyperreactivity, exercise-induced bronchoconstriction
Conditions
Interventions
The main research product is an AI model capable of predicting asthma exacerbations based on patient data and environmental factors. Furthermore, this model can explain which factors are important for
Additionally, the study will assess how digital tools, such as the Puffer app, can contribute to improved self-management and personalized support for children with asthma.
Sponsors
Medisch Spectrum Twente (MST)
Eligibility
Inclusion criteria
Inclusion criteria: A child with asthma under 18 years old who is under treatment for Asthma in Medisch Spectrum Twente hospital.
Exclusion criteria
Exclusion criteria: Patient does not want his/her data to be used in the research (opt-out).
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The key outcome of the study is the accuracy of the AI model in predicting asthma exacerbations, which will be evaluated using metrics like:Precision: How often does the model correctly predict an asthma exacerbation?Sensitivity: How well does the model detect actual exacerbations?Specificity: How well does the model identify children who will NOT experience an exacerbation? | — |
Secondary
| Measure | Time frame |
|---|---|
| In addition to evaluating the model’s predictive accuracy, the study will assess:Clinical applicability: How well can the AI model be integrated into routine asthma care?Impact of eHealth data: Does adding home-monitoring and symptom tracking improve predictions?Prediction horizon: How far in advance can the model accurately predict an exacerbation?Acceptance by healthcare professionals: How do doctors and nurses perceive the usability and reliability of AI-assisted asthma care? | — |
Countries
Netherlands
Contacts
Public ContactM.R. van der Kamp
Medisch Spectrum Twente (MST)
Outcome results
None listed