Home spirometry telemonitoring in pediatric patients with asthma: a mixed study.

Background: To evaluate the feasibility and practicality of home spirometry telemonitoring for pediatric patients with asthma, including both motivators and barriers, as well as the requirements for effective implementation. Methods: This single-arm, prospective study involved three phases: outpatient spirometry examination, home spirometry telemonitoring, and semi-structured interviews. A total of 110 children aged 5-12 years, who required spirometry monitoring at the pediatric outpatient clinic of the Second Affiliated Hospital of Chongqing Medical University, were enrolled. The PF286 (referred to as PF286), a home spirometry telemonitoring device was used for this study. Upon enrollment, each child initially underwent spirometry with a clinical-grade spirometer (Jaeger). Subsequently, they were tested using the PF286 under both supervised and unsupervised settings. To assess the consistency between PF286 and the clinical spirometer, we employed Pearson correlation coefficients. Children diagnosed with mild-to-moderate asthma, based on specified tests, participated in a four-week home spirometry telemonitoring program. After the telemonitoring period, semi-structured interviews were conducted with children, their guardians, and healthcare professionals to evaluate their experiences and identify the motivators and barriers in integrating home spirometry telemonitoring into clinical care. Spirometry data were collected using the PF286. Results: The findings suggest that the PF286 is suitable for home spirometry telemonitoring in children, with unsupervised use yielding reliable data for clinical application. Semi-structured interviews with eight groups of children and their guardians, five nurses, and four physicians identified five key themes: "benefits of telemonitoring", "data accuracy and reliability", "barriers", "emotions", and " expectations". Conclusion: The study concludes that home spirometry telemonitoring is feasible and acceptable for pediatric asthma management. However, several challenges, such as cost, insurance coverage, data security, health education, and healthcare workload, need to be addressed prior to its widespread implementation. Future research should focus on leveraging artificial intelligence for early disease detection, treatment guidance, and improving the quality of life for pediatric asthma patients.