Ranjan Yatharth, Althobiani Malik, Jacob Joseph, Orini Michele, Dobson Richard Jb, Porter Joanna, Hurst John, Folarin Amos A
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Royal Free Campus, University College London Respiratory, University College London, London, United Kingdom.
JMIR Res Protoc. 2021 Oct 7;10(10):e28873. doi: 10.2196/28873.
Chronic lung disorders like chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by exacerbations. They are unpleasant for patients and sometimes severe enough to cause hospital admission and death. Moreover, due to the COVID-19 pandemic, vulnerable populations with these disorders are at high risk, and their routine care cannot be done properly. Remote monitoring offers a low cost and safe solution for gaining visibility into the health of people in their daily lives, making it useful for vulnerable populations.
The primary objective is to assess the feasibility and acceptability of remote monitoring using wearables and mobile phones in patients with pulmonary diseases. The secondary objective is to provide power calculations for future studies centered around understanding the number of exacerbations according to sample size and duration.
Twenty participants will be recruited in each of three cohorts (COPD, IPF, and posthospitalization COVID). Data collection will be done remotely using the RADAR-Base (Remote Assessment of Disease And Relapse) mobile health (mHealth) platform for different devices, including Garmin wearable devices and smart spirometers, mobile app questionnaires, surveys, and finger pulse oximeters. Passive data include wearable-derived continuous heart rate, oxygen saturation, respiration rate, activity, and sleep. Active data include disease-specific patient-reported outcome measures, mental health questionnaires, and symptom tracking to track disease trajectory. Analyses will assess the feasibility of lung disorder remote monitoring (including data quality, data completeness, system usability, and system acceptability). We will attempt to explore disease trajectory, patient stratification, and identification of acute clinical events such as exacerbations. A key aspect is understanding the potential of real-time data collection. We will simulate an intervention to acquire responses at the time of the event to assess model performance for exacerbation identification.
The Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH) study provides a unique opportunity to assess the use of remote monitoring in the evaluation of lung disorders. The study started in the middle of June 2021. The data collection apparatus, questionnaires, and wearable integrations were setup and tested by the clinical teams prior to the start of recruitment. While recruitment is ongoing, real-time exacerbation identification models are currently being constructed. The models will be pretrained daily on data of previous days, but the inference will be run in real time.
The RALPMH study will provide a reference infrastructure for remote monitoring of lung diseases. It specifically involves information regarding the feasibility and acceptability of remote monitoring and the potential of real-time data collection and analysis in the context of chronic lung disorders. It will help plan and inform decisions in future studies in the area of respiratory health.
ISRCTN Registry ISRCTN16275601; https://www.isrctn.com/ISRCTN16275601.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/28873.
慢性肺部疾病，如慢性阻塞性肺疾病（COPD）和特发性肺纤维化（IPF），其特征是病情加重。这对患者来说很痛苦，有时严重到足以导致住院和死亡。此外，由于新冠疫情，患有这些疾病的弱势群体面临高风险，他们的常规护理无法妥善进行。远程监测为了解人们日常生活中的健康状况提供了一种低成本且安全的解决方案，对弱势群体很有用。
主要目的是评估在肺部疾病患者中使用可穿戴设备和手机进行远程监测的可行性和可接受性。次要目的是为未来围绕根据样本量和持续时间了解病情加重次数的研究进行功效计算。
将在三个队列（COPD、IPF和新冠住院后患者）中各招募20名参与者。使用RADAR-Base（疾病与复发远程评估）移动健康（mHealth）平台，通过不同设备远程收集数据，包括佳明可穿戴设备、智能肺活量计、移动应用问卷、调查和手指脉搏血氧仪。被动数据包括可穿戴设备获取的连续心率、血氧饱和度、呼吸频率、活动和睡眠数据。主动数据包括特定疾病的患者报告结局指标、心理健康问卷以及用于跟踪疾病轨迹的症状跟踪。分析将评估肺部疾病远程监测的可行性（包括数据质量、数据完整性、系统可用性和系统可接受性）。我们将尝试探索疾病轨迹、患者分层以及识别急性临床事件，如病情加重。一个关键方面是了解实时数据收集的潜力。我们将模拟一种干预措施，以便在事件发生时获取响应，以评估病情加重识别模型的性能。
肺部疾病远程评估及其对身心健康的影响（RALPMH）研究为评估远程监测在肺部疾病评估中的应用提供了独特机会。该研究于2021年6月中旬开始。在招募开始前，临床团队对数据收集设备、问卷和可穿戴设备集成进行了设置和测试。在招募过程中，目前正在构建实时病情加重识别模型。这些模型将每天根据前几天的数据进行预训练，但推理将实时运行。
RALPMH研究将为肺部疾病的远程监测提供参考基础设施。它特别涉及远程监测的可行性和可接受性以及在慢性肺部疾病背景下实时数据收集和分析的潜力等信息。它将有助于规划和为呼吸健康领域的未来研究提供决策依据。
ISRCTN注册库ISRCTN16275601；https://www.isrctn.com/ISRCTN16275601。
国际注册报告识别码（IRRID）：PRR1-10.2196/28873。
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