Oral, gut, and skin microbiota characterization in patients with heart valve disease with or without infective endocarditis: a pilot study.

Advances in non-invasive wearable and ambient sensor technologies, including developments in sensor networks, communication protocols, and feature classification, have created a landscape of opportunity for the clinical use of health-related information captured in daily life [–]. A shift toward remote or ‘free-living’ measurement outside of a clinic or laboratory with digital health technologies, enables monitoring of behaviors and symptoms as they occur throughout the day, reducing reliance on patient self-report [–] and minimizing the opportunity for observer effects [, ]. Continuous remote measurement can also capture infrequent events that are unlikely to occur under the observation of a healthcare provider [, –]. With continued advances, clinically relevant information derived from remotely acquired data could positively impact health outcomes and decrease healthcare costs by identifying declines in health that can inform early diagnosis and timeliness of care [–]. Unfortunately, while consumer use of wearable technologies (‘wearables’) for health monitoring has grown tremendously – largely driven by the adoption of smartwatches for general health and fitness tracking – clinical uptake has lagged. Integration of wearables into the healthcare system has been limited, in part, by the need for analytical and clinical validation of digital endpoints [, ] as well as the relative absence of technical, training, and change management processes to support this transition [, ]. The current work describes an open-access, easy-to-use analytics pipeline (NiMBaLWear) designed to minimize these barriers and maximize the utility of wearables for clinical application. NiMBaLWear was designed and developed according to four criteria deemed essential for clinically meaningful remote data capture and analysis within our populations of interest, which primarily include older adults and persons living with complex health conditions: 1) multi-domain measurement, 2) device independence, 3) pipeline modularity and extensibility, and 4) pipeline and data usability. NiMBaLWear adopts a flexible and integrated approach to health monitoring that considers multiple, inter-related physiological and behavioral domains (e.g., mobility, sleep, activity, cardiovascular function) to provide a more holistic view of health status. This multi-domain approach necessitates a multi-sensor model that accommodates a variety of sensor types (‘modes’) worn at appropriate body locations (‘nodes’) to optimize the type and quality of raw data that is used to construct the valid metrics of health required for clinical purposes [, , –]. NiMBaLWear development has prioritized a ‘low-burden’ multi-modal and multi-nodal model not afforded by currently available software that is either limited to processing data from a particular device manufacturer or uses a single-sensor approach. Currently, NiMBaLWear utilizes accelerometer, gyroscope, and temperature sensor data from wrist- and ankle-worn devices across its data preparation and analytics algorithms. However, to accommodate the future integration of additional sensor modes (e.g., electrocardiography (ECG), Global Positioning System (GPS)) and nodes (e.g., thigh, chest), the NiMBaLWear pipeline maintains device independence by supporting data ingestion from various devices and including pre-processing modules to temporally synchronize incoming data. This model makes it possible to unify the measurement and analysis of multiple health domains by optimizing the information captured from individual devices and, when appropriate, fusing data from multiple inputs.