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Enhancing Adolescent Asthma Control and Self-Efficacy: A Decision Tree Analysis of a Mobile Health Application in a
Nimet Karataş1, Ayşegül İşler1, Ayşen Bingöl2
1Pediatric Nursing Department, Faculty of Nursing, Akdeniz University, Antalya, Türkiye.
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.
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