Location: 2203 SAS Hall, NC State Main Campus

Speaker: Dr. Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health

Title: Developing more sensitive endpoints by leveraging novel statistical methods for Digital

Health Technologies (DHTs) data

Abstract: Digital Health Technologies (DHT) are now used to continuously track physical

activity and sleep in many clinical studies. This DHT data provides tremendous opportunities to

develop novel more sensitive clinical trial endpoints. There is, however, a large gap between the

complexity of DHT data and statistical methodology for fully leveraging the potential of DHT.

This talk will discuss recent developments of novel DHT-centric statistical methods that can

provide more sensitive endpoints by extracting and fusing together information from temporal,

distributional, and time-series aspects of DHT data.

Bio: Dr. Vadim Zipunnikov is an Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. He co-leads the Wearable and Implantable Technology (WIT) group at Johns Hopkins University and serves as the Biostatistics Director of the NIMH-funded Motor Activity Research Consortium for Health (mMARCH), overseeing collection and analysis of large-scale digital health data across several clinical sites globally. Dr. Zipunnikov’s research focuses on developing advanced statistical methods for analyzing multimodal digital health data (DHT) from wearables and smartphones, including accelerometry, heart rate, glucose monitors, and ecological momentary assessment (EMA). In his work, Dr. Zipunnikov collaborates with the Food and Drug Administration on digital analytics for drug development, having developed a two-year course for the FDA’s Office of Biostatistics to equip drug reviewers with essential skills in DHT analytics. His research has garnered press coverage in NIH Research Matters, NIH Directors’ blog, TIME, Washington Post, Wall Street Journal, CNN, and BBC Radio. He has mentored 14 PhD students and 4 postdoctoral fellows and has authored over 100 peer-reviewed publications on digital biomarkers of physical/motor activity, sleep, and circadian rhythmicity in neurological and mental health disorders, including Alzheimer’s Disease, Multiple Sclerosis, and Bipolar Disorder. He is known for his collaborative approach, mentoring emerging researchers, developing novel DHT-centric methods, and his smile.