背景:可穿戴生理监测设备是用于远程监测和早期检测感兴趣的潜在健康变化的有前途的工具。这种方法在社区和长时间内的广泛采用将需要一个自动化的数据收集平台,processing,并分析相关健康信息。
目的:在本研究中,我们探索通过自动数据收集对个人健康的前瞻性监测,提取度量,和健康异常分析管道在自由生活条件下连续监测几个月,重点是病毒性呼吸道感染,如流感或COVID-19。
方法:共有59名参与者在8个月的时间内每天提供智能手表数据以及健康症状和疾病报告。来自光电体积描记术传感器的生理和活动数据,包括高分辨率跳间间隔(IBI)和步数,直接从GarminFenix6智能手表上传,并使用独立设备在云中自动处理,开源分析引擎。根据心率和心率变异性指标与每个人的活动匹配基线值的偏差计算健康风险评分。并检查超过预定阈值的分数是否有相应的症状或疾病报告.相反,健康调查回复中的病毒性呼吸道疾病报告也被检查健康风险评分的相应变化,以定性评估作为急性呼吸道健康异常指标的风险评分.
结果:每天提供的指示智能手表佩戴合规性的传感器数据的中位数平均百分比为70%,调查答复表明健康报告依从性为46%。共检测到29个升高的健康风险评分,其中12人(41%)同时有调查数据,并表示有健康症状或疾病。研究参与者共报告了21种流感或COVID-19疾病;这些报告中有9种(43%)同时包含智能手表数据,其中6人(67%)的健康风险评分增加.
结论:我们演示了数据收集的协议,提取心率和心率变异性指标,和前瞻性分析,与使用可穿戴传感器进行连续监测的近实时健康评估兼容。用于数据收集和分析的模块化平台允许选择不同的可穿戴传感器和算法。这里,我们展示了其在自由生活条件下个人佩戴的GarminFenix6智能手表的高保真IBI数据收集中的实施,和潜在的,近实时的数据分析,最终计算健康风险分数。据我们所知,这项研究首次证明了使用智能手表近实时测量高分辨率心脏IBI和步数以在自由生活条件下长期监测期间进行呼吸系统疾病检测的可行性.
BACKGROUND: Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information.
OBJECTIVE: In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19.
METHODS: A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual\'s activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies.
RESULTS: The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score.
CONCLUSIONS: We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.