Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.

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.

Changes in Brain Activity Immediately Post-Exercise Indicate a Role for Central Fatigue in the Volitional Termination of Exercise.

Electroencephalography (EEG) allows for the evaluation of real time changes in brain (electrocortical) activity during exercise. A few studies have examined changes in electrocortical activity using stationary cycling, but the findings have been mixed. Some of these studies have found increases in brain activity following exercise, while others have found decreases in brain activity following exercise. Hence, it is of importance to identify post-exercise changes in brain activity. Sixteen healthy, untrained subjects (8 males; 8 females) participated in the study. All 16 participants performed a graded exercise test (GXT) to volitional exhaustion on an upright cycle ergometer. Continuous EEG recordings were sampled before (PRE) and immediately following (IP) the GXT. Regions of interest were primarily the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), and left and right motor cortex (MC). In the DLPFC, a frontal asymmetry index was also identified. There was a statistically significant increase in theta power in the DLPFC, VLPFC, and left and right MC from PRE to IP (all p < 0.05). There was also a shift towards right hemisphere asymmetry at the IP time point in the DLPFC (p < 0.05). Finally, there was an increase in alpha power from PRE to IP in the right MC (p < 0.05). EEG could prove to be an important way to measure the effects of central fatigue on brain activity before and immediately following exercise.

Assessing the pattern electroretinogram as a proxy measure for dopamine in the context of iron deficiency.

OBJECTIVES: Animal studies have suggested that dietary iron deficiency (ID) negatively affects dopamine (DA) synthesis and re-uptake, which in turn negatively affects memory and cognition. This study was intended to assess whether the pattern electroretinogram (pattern ERG) could be used as an indirect measure of DA in college-age women with and without ID by determining the extent to which features of the ERG were sensitive to iron status and were related to other indirect measures of DA. METHODS: The pattern ERG was measured in 21 iron deficient non-anemic (IDNA) and 21 iron sufficient (IS) women, who also performed a contrast detection and probabilistic selection task, both with concurrent electroencephalography (EEG). Both spontaneous and task-related blink rates were also measured. RESULTS: The implicit times of the A- and B-waves were significantly longer for the IDNA than for the IS women. Both the amplitudes and implicit times of the A- and B-waves were significantly correlated with levels of serum ferritin (sFt). Only the amplitude of the A-wave was correlated with spontaneous blink rate. It was possible to accurately identify a woman's iron status solely on the basis of the implicit time of the B-wave. Finally, the implicit times of the ERG features mediated the relationship between iron levels and accuracy in the probabilistic selection task. CONCLUSIONS: Results suggest the utility of the pattern ERG in testing the hypothesis that iron deficiency affects DA levels in humans and that this may be one of the mechanisms by which iron deficiency negatively affects cognition.

Report of the European Medicines Agency Conference on RNA-Based Medicines.

RNA-based medicines have potential to treat a large variety of diseases, and research in the field is very dynamic. Proactively, The European Medicines Agency (EMA) organized a virtual conference on February 2, 2023 to promote the development of RNA-based medicines. The initiative addresses the goal of the EMA Regulatory Science Strategy to 2025 to "catalyse the integration of science and technology in medicines development." The conference focused on RNA technologies (excluding RNA vaccines) and involved different stakeholders, including representatives from academia, industry, regulatory authorities, and patient organizations. The conference comprised presentations and discussion sessions conducted by panels of subject matter experts. In this meeting report, we summarize the presentations and recap the main themes of the panel discussions.