Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design
Peter Cho, Jaehan Yi, Ethan Ho, Md Mobashir Hasan Shandhi, Yen Dinh, Aneesh Patil, Leatrice Martin, Geetika Singh, Brinnae Bent, Geoffrey S. Ginsburg, Matthew Smuck, Christopher W. Woods, Ryan J. Shaw, Jessilyn Dunn
Abstract
Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, and treatment. Recently, there has been a surge of digital health studies where data are collected through a bring-your-own-device (BYOD) approach, in which participants who already own a specific technology may voluntarily sign up for the study and provide their digital health data. BYOD study design accelerates the collection of data from a larger number of participants than cohort design; this is possible because researchers are not limited in the study population size based on the number of devices afforded by their budget or the number of people familiar with the technology. However, the BYOD study design may not support the collection of data from a representative random sample of the target population where digital health technologies are intended to be deployed. This may result in biased study results and biased downstream technology development, as has occurred in other fields. In this viewpoint paper, we describe demographic imbalances discovered in existing BYOD studies, including our own, and we propose the Demographic Improvement Guideline to address these imbalances.