Dr. Xiao Li’s, Natural Affinity Toward Science Paved the Way for Her to Explore Technology and Health Disparities


Li’s Bold Hypothesis Helps Generate More Hypotheses in Fittech

Xiao Li, PhD, is an Assistant Professor in the Department of Biochemistry and Member of the Center for RNA Science and Therapeutics in the Case School of Medicine and Assistant Professor in the Department of Biochemistry in the Case School of Medicine. Dr. Li combined her passion for science with a love for math and statistics as the field of informatics was blossoming. 

“I had a bold hypothesis, at the time, that we could use fittech for health,” said Dr. Li. 

Dr. Li most recently used technology for COVID-19 detection. She collected data from people who wear Fitbits or Apple watches and looked for abnormal patterns compared to their own baseline (not the entire population). Dr. Li made sure that the abnormalities were not caused by activity or circadian rhythm. She was able to confirm that the data from the wearable could capture a COVID-related signal seven days before a person would test positive for COVID-19. Dr. Li emphasized the importance of being able to capitalize on a commercial device to flag people–avoiding unnecessary exposure and increasing the likelihood that someone could access time-sensitive drugs for mild cases. 

Using fittech wearables to detect COVID-19 was just one of many ways Dr. Li has explored the intersection between fittech and health disparities. One of the common questions Dr. Li uses to guide her health disparities research is, “Will this study help the entire population?” Some concerns she has received regarding her focus on wearable devices are ideas that the devices are only accessible by people with financial means–introducing bias in study results. However, Dr. Li acknowledged that the price drop over the years and various price points for different versions does make the technology more accessible. She proposes that the potential impact of using wearables has implications for the entire population. 

“If we can understand how to use wearables more efficiently, it could help everyone. Insurance companies would be happy because it could help lower insurance costs and health events. One day insurance companies could even provide devices wide-scale,” Dr. Li said. 

Dr. Li also highlighted that wearables are also good for people without access to hospitals or doctors–especially in remote or rural communities. She added that they can wear their fittech with AI for monitoring as an added layer of prevention or protection (e.g., alerting the wearer on when they should go to the hospital based on a perceived or actual medical event). The passive collection of information is an added bonus for patient populations that are already inundated with demands (e.g., caregiving, working, self-care, etc.). 

As with all meaningful work, Dr. Li shared that health disparities research does not come without challenges. Dr. Li is motivated when she sees progress in research. She added, “Lack of resources, managing a team (e.g., communication, troubleshooting, human resources), and running a human study (i.e., managing the unexpected) all contribute to what makes health disparities research – or research in general – demanding, but also worth it when you have a diverse team with high energy and urgency to contribute to advancement in science and medicine.” 


Dr. Li received a CTSC Annual Pilot Award in 2022 to study wearable technology for the early detection of and mapping of COVID-19 and similar viral pandemics. 

Learn more about Dr. Li’s research here

Fast Five with Dr. Li

  1. Favorites thing to do in Northeast Ohio: Walking around in the fall (so many great colors) 
  2. Favorite research innovation: Something with wearables and cancer (even if it doesn’t exist yet) 
  3. Favorite restaurant in Northeast Ohio: Any restaurant in AsiaTown
  4. Favorite place to go in Northeast Ohio: Cleveland Museum of Natural History 
  5. If I wasn’t a researcher, I’d be a…: baker–I’m really good at making cakes. 
CTSA Program In Action Goals
Goal 1: Train and Cultivate the Translational Science Workforce