Efficacy of eHealth monitoring in pediatric asthma

Abstract

Asthma, affecting roughly 10% of children globally, requires vigilant control to maintain normal activities, reduce flare-ups, and minimize medication side effects.¹ So far, clinical practice mostly relies on retrospective history taking, often leading to recall bias and misinterpretation of symptom severity, due to the adaptive nature of children and the occurrence of poor perceivers.² Despite the increasing application of eHealth monitoring for children with asthma, the overall benefits remain unclear, and no consensus exists on the most effective monitoring parameters. This letter highlights the results from our recent systematic review evaluating the efficacy of eHealth interventions in improving asthma control among children.

The systematic review followed PRISMA guidelines, incorporating a comprehensive search strategy across PubMed, EMBASE, and Web of Science databases.³ Two reviewers independently screened the studies and extracted the data based on pre-defined inclusion and exclusion criteria (Data S1). The final analysis comprised 21 studies out of an initial 3983 reports (Table 1). The studies were categorized into two main topics: eHealth interventions for improving asthma control, and digital prediction models of asthma control.

The review highlights the potential of eHealth interventions to enhance asthma management by improving adherence, real-time feedback, and comprehensive monitoring. Home spirometry shows potential, yet its results are affected by patient population and accuracy of test performance. Multiparameter eHealth interventions with direct feedback appear to be most effective in improving asthma control, knowledge, and adherence to monitoring and treatment plans, although adherence sometimes declines due to “monitor fatigue”.^{4, 5} This aligns with the concept that pediatric asthma management must be tailored to individual needs and contexts, emphasizing age-appropriate (eHealth) interventions and specialized care settings (Figure 1). However, the included studies are heterogenous in design and outcomes, and display bias related to confounding, participant selection, and missing data (Data S4 and S5). As a result, a meta-analysis could not be performed, limiting the ability to draw definitive conclusions and recommendations. Further research should focus on addressing confounders, enhancing randomization procedures, and improving data management to strengthen the reliability of the results.⁶

In conclusion, eHealth interventions and predictive models are promising in improving pediatric asthma control, particularly through comprehensive platforms that offer direct feedback, education, and home spirometry. Future research should aim to standardize methodologies, interventions, and outcome measures to enhance comparability and generalizability. Continued exploration of wearables and predictive models may further refine our ability to manage asthma in a more proactive and effective way.

Casper E.W. Gijsen, Thijs Bogers, Marieke W.P. van Horck and Edward Dompeling conceptualized and designed the study. Casper E.W. Gijsen and Thijs Bogers were responsible for data collection, analysis, and interpretation of the results. Jean W.M. Muris, Marieke W.P. van Horck and Edward Dompeling contributed to the interpretation of the results and were responsible for drafting the manuscript. All authors critically reviewed the content, approved the final version for submission, and provided consent for publication. Each author takes responsibility for the accuracy and integrity of the work presented in this article.

C.Gijsen received a salary from Zorginstituut Nederland. No foundation was involved in the study design, data synthesis, analysis or submission for publication. The remaining authors did not receive support from any organization for the submitted work.

The authors have no conflict of interest to declare.