University of North Carolina Gillings School of Global Public Health researchers have proposed a new statistical method for developing tailored treatment data using data from mobile devices.
Study co-authors include Dr. Daniel Luckett, postdoctoral research associate in biostatistics; Ms. Anna Kahkoska, doctoral student in nutrition; Dr. Elizabeth Mayer-Davis, Cary C. Boshamer Distinguished Professor of nutrition and medicine; and Dr. Michael Kosorok, W.R. Kenan Jr. Distinguished Professor and chair of biostatistics.
Their paper, “Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning,” was published in the Journal of the American Statistical Association.
The researchers hope to continue their work by putting their algorithm into a mobile app that eventually can help people with Type 1 diabetes live healthier lives.
By using mobile health, or mHealth, technologies, patient status can be monitored in real time, and data produced by the mobile technologies have great potential to inform personalized health care decisions.
The Gillings School team set out to develop a statistical method that could be used in conjunction with data from continuous glucose monitors and accelerometers to help people with Type 1 diabetes better manage their disease. Using data from a previous study, they were able to show that their new method could learn strategies to help patients maintain stable glucose levels over time.
“This is a fabulous example of how precision medicine approaches can be applied to subpopulations to improve health,” Dr. Mayer-Davis said. “In other words, this is an example of how a vision of precision public health can be realized.”Tags: Friday Letter Submission