Infectious Diseases Area, University of Navarra Clinic, Pamplona, Spain
Research and Development Department, Hyfe, Wilmington, Delaware, USA.
BMJ Open. 2021 Jul 2;11(7):e051278. doi: 10.1136/bmjopen-2021-051278.
Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms.
Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions.
Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l'Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences.
NCT04762693.
咳嗽是 COVID-19 和其他呼吸道疾病的常见症状。然而,由于缺乏可靠和可扩展的监测系统,客观测量其频率和演变受到阻碍。这可以通过利用智能手机便携性开发的新人工智能模型来克服。在持续的 COVID-19 大流行背景下,针对呼吸疾病综合征的咳嗽检测代表了早期发现爆发和疾病监测的简单手段。在本方案中,我们评估基于人群的数字咳嗽监测在预测西班牙纳瓦拉人群中呼吸道疾病发病率方面的能力,同时评估这些平台使用的个体决定因素。
将邀请 Cendea de Cizur、Zizur Mayor 的参与者或当地纳瓦拉大学(潘普洛纳)的参与者使用智能手机应用程序 Hyfe Cough Tracker 监测他们夜间的咳嗽。检测到的咳嗽将在时间和空间上进行汇总。参与者队列中 COVID-19 和其他诊断出的呼吸道疾病的发病率,以及研究区域和人口将从当地卫生设施收集,并用于对这些独立时间序列进行自回归移动平均分析。在混合方法设计中,我们将通过评估参与模式和社会人口特征,探索连续数字咳嗽监测的障碍和促进因素。参与者将填写一份可接受性问卷,一个小组将参加焦点小组讨论。
加拿大蒙特利尔大学医院伦理委员会和西班牙纳瓦拉医学研究伦理委员会已批准该伦理。初步结果将与民政和卫生当局分享,并报告给个别参与者。结果将提交给同行评议的科学期刊和国际会议发表。
NCT04762693。