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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.
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. METHODS A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological data, including high-resolution interbeat interval (IBI), were uploaded directly from the smartwatches and processed automatically using a modular software architecture. A health risk algorithm developed in a previous influenza challenge study using electrocardiogram (ECG) sensors was applied to data collected with the wrist-worn photoplethysmography (PPG) sensors. Health risk scores exceeding a predefined threshold were flagged and checked for corresponding symptom or illness reports. From the self-reported health survey responses, illness reports of influenza and COVID-19 were noted and checked for corresponding changes in health risk score. RESULTS The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 69%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health scores were detected, of which 41% had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported; 43% of these reports had concurrent smartwatch data, of which 67% had an increase in health score that was above or below threshold. CONCLUSIONS We demonstrate a protocol for data collection, extraction of metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform allows for choice of different wearable sensors and algorithms for health anomaly detection. Although data reporting compliance was at a sufficient level in general for accurate calculation of health risk scores, limited wear compliance or health survey reporting limited confirmation of illness in many cases. To our knowledge, the study demonstrates for the first time the feasibility of measuring high-resolution heart interbeat interval and step count using smartwatches in real time for illness detection over a long-term monitoring period in free-living conditions.
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