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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. 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. 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. 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. 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 Sexual assault prevention is a priority for the military and is likely to be most effective when tailored to specific needs and individual experiences. Technology advances make it possible to integrate individualized programming into group education settings common to military training, but this approach is not without potential challenges. Prior to implementing and evaluating a novel prevention program, it is critical to conduct a feasibility study to assess the extent to which the program can be successfully implemented, is acceptable to participants, and can be rigorously evaluated. This paper presents the results of a large feasibility study of the Sexual Communication and Consent (SCC) program in Air Force Basic Military Training. The objectives were to understand (1) the suitability and acceptability of SCC and study procedures, (2) the research team’s resources and ability to manage and implement the study and SCC, and (3) the appropriateness of the data collection procedures and outcome measures. Methods The 26-week feasibility study involved more than 9000 trainees. At pretest, the sample was 73% male and 90% straight. Half of trainees (51%) reported not being in a romantic relationship, and 46% reported being in an exclusive romantic relationship. SCC involved 6 h of training over two sessions delivered 2 weeks apart. Sessions combined tablet-based training that was tailored to individual sexual assault risk (for revictimization, first-time victimization, or perpetration based on a screening instrument) with instructor-led classroom activities and discussion. Results Trainee, instructor, and observer data indicated that the program was feasible, implemented with high fidelity, and acceptable to trainees. Technology and data collection procedures worked well, but time constraints led to program changes, missed content, and missing survey data. Staff time and skill requirements for SCC implementation exceeded those of standard training. Conclusions Our results suggest that high-quality implementation of tailored sexual assault prevention training is feasible in a military environment, and they inform recommendations for program revisions, future evaluation, and sustainability.

Wearable devices, such as smartwatches integrating heart rate and activity sensors, have the potential to transform health monitoring by enabling continuous, near real-time data collection and analytics. In this paper, we present a novel modular architecture for collecting and end-to-end processing of high-resolution signals from wearable sensors. The system obtains minimally processed data directly from the smartwatch and further processes and analyzes the data stream without transmitting it to the device vendor cloud. The standalone operation is made possible by a software stack that provides data cleaning, extraction of physiological metrics, and standardization of the metrics to enable person-to-person and rest-to-activity comparisons. To illustrate the operation of the system, we present examples of datasets from volunteers wearing Garmin Fenix smartwatches for several weeks in free-living conditions. As collected, the datasets contain time series of each interbeat interval and the respiration rate, blood oxygen saturation, and step count every 1 min. From the high-resolution datasets, we extract heart rate variability metrics, which are a source of information about the heart’s response to external stressors. These biomarkers can be used for the early detection of a range of diseases and the assessment of physical and mental performance of the individual. The data collection and analytics system has the potential to broaden the use of smartwatches in continuous near to real-time monitoring of health and well-being.

Psychological resilience is critical to minimize the health effects of traumatic events. Trauma may induce a chronic state of hyperarousal, resulting in problems such as anxiety, insomnia, or posttraumatic stress disorder. Mind-body practices, such as relaxation breathing and mindfulness meditation, help to reduce arousal and may reduce the likelihood of such psychological distress. To better understand resilience-building practices, we are conducting the Biofeedback-Assisted Resilience Training (BART) study to evaluate whether the practice of slow, paced breathing with or without heart rate variability biofeedback can be effectively learned via a smartphone app to enhance psychological resilience. Our objective was to conduct a limited, interim review of user interactions and study data on use of the BART resilience training app and demonstrate analyses of real-time sensor-streaming data. We developed the BART app to provide paced breathing resilience training, with or without heart rate variability biofeedback, via a self-managed 6-week protocol. The app receives streaming data from a Bluetooth-linked heart rate sensor and displays heart rate variability biofeedback to indicate movement between calmer and stressful states. To evaluate the app, a population of military personnel, veterans, and civilian first responders used the app for 6 weeks of resilience training. We analyzed app usage and heart rate variability measures during rest, cognitive stress, and paced breathing. Currently released for the BART research study, the BART app is being used to collect self-reported survey and heart rate sensor data for comparative evaluation of paced breathing relaxation training with and without heart rate variability biofeedback. To date, we have analyzed the results of 328 participants who began using the BART app for 6 weeks of stress relaxation training via a self-managed protocol. Of these, 207 (63.1%) followed the app-directed procedures and completed the training regimen. Our review of adherence to protocol and app-calculated heart rate variability measures indicated that the BART app acquired high-quality data for evaluating self-managed stress relaxation training programs. The BART app acquired high-quality data for studying changes in psychophysiological stress according to mind-body activity states, including conditions of rest, cognitive stress, and slow, paced breathing.