American Diabetes Association 83rd Scientific Sessions

June 23, 2023
June 26, 2023
San Diego Convention Center 111 W Harbor Drive San Diego, CA 92101

Leading physicians, scientists, and health care professionals from around the world are expected to participate in the American Diabetes Association’s (ADA) Scientific Sessions to unveil cutting-edge research, treatment recommendations, and advances toward a cure for diabetes. Diabetes affects more than 37 million children and adults in the United States and 537 million around the world. The global personal and economic costs are staggering, and addressing this crisis is the core purpose of Scientific Sessions.

Session Information:

Poster Session

Title: Body-worn motion sensors detect gait deficits in people with Impaired Fasting Glucose who have normal 400-meter walk time

Date: Saturday, June 24th, 2023

Time: 11:30 a.m. – 12:30 p.m. PST

Location: Hall B-C

Gait is increasingly recognized as a marker of general health status. Abnormal gait characteristics have been observed in people with diabetes, but it is unclear if subtle changes in specific gait characteristics can be found in people with impaired fasting glucose (IFG), who are not yet classified as diabetic (prediabetic). Join us for this poster session to learn about the aim and conclusions of this study to investigate if digital gait measures discriminate people with IFG from healthy control subjects (HC) who have a normal 400-meter walk time.


Vrutangkumar Shah headshot

Vrutangkumar Shah, Ph.D.

Director, Data Science and Biostatistics at Clario

Dr. Vrutangkumar Shah is a scientist with over 13 years of experience in clinical research. He is currently working as a Director, Data Science and Biostatistics at Clario, a global data and technology company that helps with decentralized clinical trials. He consults with clients sponsors and CROs in developing the Statistical Analysis Plan (SAP) package for Phase I-IV clinical studies. His expertise includes but not limited to data analytics, biostatistics, machine learning, and time-series analysis to develop digital biomarkers for clinical trials.