NIHR Exeter Biomedical Research Center, Medical School, Faculty of Health and Life sciences, University of Exeter, Exeter, UK.
Medical School, University of Exeter, Exeter, UK.
BMJ Open. 2023 Aug 8;13(8):e071428. doi: 10.1136/bmjopen-2022-071428.
A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms.
This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another.
The study was approved by the Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences.
ISRCTN15022307.
大量 COVID-19 幸存者在感染后 3 个月以上仍持续出现症状,尤其是那些需要医疗干预的患者。持续的症状广泛存在,且个体之间的表现和个体内部的症状波动存在差异。更好地了解症状波动和触发因素,可能会提高医疗保健提供者的疗效,并使患者能够更好地自我管理他们的长新冠。我们提出了一项方案,旨在开发和检验数字家庭监测在捕捉长新冠患者日常症状波动方面的可行性和可用性,并提供数据以促进长新冠症状的分类和管理的个性化方法。
这是一项针对英国 10 家国民保健服务(NHS)康复服务中患有长新冠的成年人的纵向前瞻性队列研究。我们计划从参与的 NHS 站点招募 400 人。在转诊至研究时、6 周和 12 周,参与者将完成人口统计学数据(转诊至研究)和临床结果测量,包括使用个人移动设备进行生态瞬时评估(EMA)。EMA 项目改编自 COVID-19 约克郡康复量表项目,包括自我报告的活动、症状和心理因素。被动活动数据将通过佩戴在手腕上的传感器收集。我们将使用潜在类别增长模型来识别体验轨迹、由症状共同出现定义的潜在表型以及压力源、症状和日常活动参与之间的相互关系。我们预计,n=300 名参与者提供 80%的效力,以检测与另一组患者相比,12 周内疲劳症状改善 20%。
该研究已获得约克郡和亨伯-布拉德福德利兹研究伦理委员会的批准(编号:21/YH/0276)。研究结果将发表在同行评议的期刊上,并在会议上展示。
ISRCTN81632257。