Hancock David G, Kicic-Starcevich Elizabeth, Sondag Thijs, Rivers Rael, McGee Kate, Karpievitch Yuliya V, D'Vaz Nina, Agudelo-Romero Patricia, Caparros-Martin Jose A, Iosifidis Thomas, Kicic Anthony, Stick Stephen M
Wal-yan Respiratory Research Centre, Telethon Kids Institute, Nedlands WA 6009, Australia.
Department of Respiratory and Sleep Medicine, Perth Children's Hospital, Nedlands WA 6009, Australia.
iScience. 2024 Sep 17;27(10):110912. doi: 10.1016/j.isci.2024.110912. eCollection 2024 Oct 18.
Cohort studies investigating respiratory disease pathogenesis aim to pair mechanistic investigations with longitudinal virus detection but are limited by the burden of methods tracking illness over time. In this study, we explored the utility of a purpose-built AERIAL TempTracker smartphone app to assess real-time data collection and adherence monitoring and overall burden to participants, while identifying symptomatic respiratory illnesses in two birth cohort studies. We observed strong adherence with daily app usage over the six-month study period, with positive feedback from participant families. A total of 648 symptomatic respiratory illness events were identified with significant variability between individuals in the frequency, duration, and virus detected. Collectively, our data show that a smartphone app provides a reliable method to capture the longitudinal virus data in cohort studies which facilitates the understanding of early life infections in chronic respiratory disease development.
调查呼吸道疾病发病机制的队列研究旨在将机制研究与纵向病毒检测相结合，但受限于长期追踪疾病的方法带来的负担。在本研究中，我们探索了一款专门构建的AERIAL TempTracker智能手机应用程序在两项出生队列研究中评估实时数据收集、依从性监测以及对参与者的总体负担，同时识别有症状的呼吸道疾病的效用。我们观察到在为期六个月的研究期间，参与者对每日应用程序使用的依从性很高，且得到了参与者家庭的积极反馈。共识别出648例有症状的呼吸道疾病事件，个体之间在频率、持续时间和检测到的病毒方面存在显著差异。总体而言，我们的数据表明，智能手机应用程序为在队列研究中获取纵向病毒数据提供了一种可靠的方法，这有助于理解慢性呼吸道疾病发展中的早期生命感染情况。
