Enhancing Adolescent Asthma Control and Self-Efficacy: A Decision Tree Analysis of a Mobile Health Application in a Randomized Controlled Trial

Aims and Objectives

To evaluate the efficacy of YoungAsthma, a nurse-led, web-based mHealth intervention on asthma control and self-efficacy among adolescents with asthma utilizing decision tree analysis.

Background

Asthma is a prevalent chronic condition in pediatric populations, necessitating sustained management for optimal disease control.

Design

A randomized controlled clinical trial.

Methods

Fifty-four eligible adolescents were randomly assigned to either the intervention group (YoungAsthma + Usual care, n = 27) or the control group (Usual care, n = 27) for 4 weeks. Primary outcomes—asthma control and self-efficacy—were assessed using the Information Form, Asthma Control Test, Self-Efficacy Scale for Children and Adolescents with Asthma. Statistical analyses included Fisher's exact test, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, and Intention-to-Treat (ITT) analysis.

Results

Forty-eight participants completed the study (11% dropout per group). The intervention group exhibited a greater improvement in asthma control than the control group. While both groups showed increased self-efficacy, the intervention group's improvement was significantly higher. Decision tree analysis identified key predictors, indicating that lower scores were associated with a higher likelihood of remaining in the control group.

Conclusions

Nurse-led, technology-supported interventions significantly enhance asthma control and self-efficacy in adolescents. Decision tree analysis provided valuable insights into key factors influencing asthma control and self-efficacy improvements, identifying subgroups that benefited most from the intervention. Interdisciplinary collaboration facilitated a user-centered approach grounded in Bandura's Self-Efficacy Theory, offering a data-driven framework for personalized asthma management.

Relevance to Clinical Practice

Decision tree analysis aids in identifying patients who would benefit most, enabling precision-targeted interventions.

Reporting Method

This study was conducted in accordance with Consolidated Standards of Reporting Trials and with the Mobile Health Evidence Reporting and Assessment guidelines.

Clinical Trial Registration Number

Clinicaltrials. gov, ID: NCT04691557 & Date of first recruitment: December, 2020. https://register.clinicaltrials.gov/prs/beta/studies/S000AJ5B00000102/recordSummary.