BME Researchers Contribute to National mHealth Covid Study

The Bio-MIBLab research team conntributed a diverse range of expertise to the study.
The Bio-MIBLab research team conntributed a diverse range of expertise to the study.

May Dongmei Wang, professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, is part of a 60-person expert task force that explored how mobile health technologies could be used to address Covid-19 and future pandemics.

Organized by researchers at Spaulding Rehabilitation Hospital (the teaching hospital for Harvard Medical School’s Department of Physical Medicine and Rehabilitation), the team set out to review mobile health (mHealth) technologies and wrote about it in a new study, “Can mHealth Technology Help Mitigate the Effects of the COVID 19 Pandemic?” in the latest issue of IEEE Open Journal of Engineering in Medicine and Biology. The team found that mHealth technologies are viable options to monitor Covid-19 patients and can be used to predict symptom escalation for earlier intervention.

Wang is the principal investigator of the Bio-MIBLab (Biomedical Informatics and Bioimaging Lab), where her team specializes in Biomedical and Health Informatics with a focus on predictive, preventative, pervasive, personalized, and precision health. The team’s specialty is developing advanced artificial intelligence (AI) algorithms for biomedical data quality improvement, data integration, causal inference, real-time decision making, and interpretable AI.

“We were invited to join to this large international task force to provide insight on mHealth data collection, harmonization, and infrastructure for analysis,” says Wang, a researcher in both the Petit Institute for Bioengineering and Bioscience, and the Institute for People and Technology at Georgia Tech. “The idea is to use mHealth effectively for Covid-19 patient precision staging, contact tracing, and monitoring to ultimately ease the effects of the global pandemic.”

Her lab has 10 years of research experience in mHealth data analytics for the Centers of Disease Control and Prevention, and Children's Healthcare of Atlanta. This made Wang’s team the perfect addition to the enterprise. During the pandemic, her lab has been developing advanced artificial intelligence techniques, such as deep learning-based algorithms for clinical decision support, assisting Covid-19 clinic physicians in fair resource allocation.

Paolo Bonato, director of the Spaulding Motion Analysis Lab, was the lead author on the study. “To be able to activate a diverse group of experts with such a singular focus speaks to the commitment the entire research and science community has in addressing this pandemic. Our goal is to quickly get important findings into the hands of the clinical community, so we continue to build effective interventions,” said Dr. Bonato.

Telehealth usage and mobile health technologies commonly called mHealth, has gained the attention of the public at large. While telehealth has allowed patients to stay connected for ongoing appointments and check-ins, wearable mHealth technologies provide a significant opportunity for data collection and mHealth technology could be used to monitor patients with mild symptoms who have tested positive for Covid-19. These patients are typically instructed to self-quarantine at home or undergo monitoring at community treatment centers.

However, a portion of them eventually experience an exacerbation, namely the sudden occurrence of severe symptoms, and require hospitalization. In this context, mHealth technology could enable early detection of such exacerbations, allowing clinicians to deliver necessary interventions in a timely manner thus improving clinical outcomes.

The Task Force paper concluded that Smartphone applications enabling self-reports and wearable sensors enabling physiological data collection could be used to monitor clinical personnel and detect early signs of an outbreak in the hospital/healthcare settings. They also reported similarly, in the community, early detection of Covid-19 cases could be achieved by building upon prior studies which showed that by using wearable sensors to capture resting heart rate and sleep duration it is possible to predict influenza-like illness rates as well as Covid-19 epidemic trends.

“The better data and tracking we can collect using mHealth technologies can help public health experts understand the scope and spread of this virus and most importantly hopefully help more people get the care they need earlier,” said Bonato. “Our hope is to build on more studies from here and continue to expand our understanding.”


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Jerry Grillo


Georgia Tech