SCH: A multimodal wearable device to measure physiologic coupling

Acute exacerbations, bouts of disease worsening, are common in many chronic conditions like chronic obstructive pulmonary disease (COPD). These exacerbations have drastic impacts on whole person health, and with prompt treatment and intervention can reduce morbidity and mortality. However, determining between an acute exacerbation versus daily variation in disease symptomology is

problematic. In COPD acute exacerbations can be objectively detected using patient-reported outcome questionnaires that are completed daily. However, due to daily variation, these tools require 2-3 days to establish a diagnosis. Moreover, daily questionnaires burden patients, making this approach impractical

for routine monitoring. The long-term goal of the proposed research is to provide a complementary objective diagnostic measure to facilitate analysis of multicomponent interventions effects on the interconnected physiological systems of the whole person within diverse social and environmental contexts. The research objective is to create and validate a multimodal physiologically-based passive

monitoring system and analytic approach based on biorhythm interconnectivity using three specific aims: 1) integrate heterogeneous sensing modalities and extract key features from high-dimensional data; 2) integrate an electronic nose sensor with the wearable device to improve diagnostic accuracy and

specificity; and 3) test the hypothesis that biorhythm interconnectivity can distinguish changes in health status as identified by validated patient-reported outcomes (i.e. EXACT-RS and CAT). Complementary and integrative digital innovations designed for remote monitoring of whole person health can improve

clinical outcomes by stratifying the risk of exacerbation and offer many advantages, including continuous collection of whole person data, remote monitoring of data by clinicians, and the opportunity to guide multimodality management to improve whole person health. Our technical platform has been designed to

be flexible with reconfiguration and integration of additional sensors. This enables utilization of study findings across diverse chronic health conditions including asthma, heart disease, and other inflammatory disorders marked by acute exacerbations necessitating prompt treatment. Our research team that

includes a physician, engineer, statistician, bioinformatician, machine learning/artificial intelligence expert, and human movement scientist, is uniquely positioned to successfully complete this research.