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.