Eileen R. Faulds is an assistant professor, diabetes researcher, endocrinology nurse practitioner, and certified diabetes educator at The Ohio State University College of Nursing. Dr. Faulds has worked in both pediatric and adult endocrinology as well as in the outpatient and inpatient setting. Her research focuses on understanding the experience of individuals with type 1 diabetes, their relationship with technology, and the development of innovative approaches to diabetes technology self-management.
My interest in endocrinology came unexpectedly when my eldest child was born with a rare endocrine disorder. The experience of caring for a child with a complex chronic condition compelled me to become an endocrinology nurse practitioner (NP).
Once in the field, I found myself drawn to the care of people with diabetes and their families. Over my 10 years of experience as an NP and certified diabetes educator, I have practiced in inpatient and outpatient settings with both children and adults with type 1 diabetes. My consistent focus has been on technology self-management.
My clinical experiences and my interactions with people with diabetes as they managed this incredibly complex condition fueled my desire to further my career as a diabetes researcher. In the spring of 2020, I successfully defended my dissertation and obtained my PhD.
My research is focused on understanding the novel aspects of diabetes self-management and the experience of individuals with diabetes and their interaction with technology. My work employs innovative and advanced methodologies in the study of diabetes self-management that incorporates use of personal diabetes device data.
Despite recent advancements and increased use of technologies to manage diabetes, 70% of individuals with type 1 diabetes do not meet their hemoglobin A1c targets. This is in part because benefits associated with diabetes technologies depend in part on individual utilization and self-management behaviors.
Diabetes device downloads provide a wealth of information and offer a profile of each person’s self-management behaviors and utilization. The long term goal is to use these rich pools of device data to identify individual self-management needs and facilitate targeted interventions.
Using diabetes device download data allows me to study objective measures of self-management. Typically, researchers have used subjective self-report measures for self-management research, however these have been shown to have high rates of mis-reporting. This is especially true among adolescents with type 1 diabetes where mis-reporting is as high as 40–70%.
While using device downloads offers a superior way to measure and analyze self-management, it isn’t without its challenges. The cleaning and data management associated with using raw device data can be very time consuming and challenging. While cleaning the device data was likely the most challenging part of my dissertation study, it provided an amazing training opportunity.
I looked into using other device download platforms for my dissertation study, but there were some sizable barriers to all of those other products. Most other download products will only download a specific manufacturer brand, and I knew I wanted to compare and contrast self-management across device brands.
While there are a few agnostic download platforms, Tidepool was the only product that allowed me to download all commercially available pump and CGM brands.
Cost was an even larger barrier I encountered. The cost of other products was prohibitive on a small NIH predoctoral training budget.
I can say with near certainty that without access to Tidepool’s platform, because of its capabilities and free access, I would not have been able to complete my dissertation study.
I have now used Tidepool’s Clinical Study Platform in two studies. In both, I collected data prospectively, meaning I consented patients prior to data collection and created anonymous accounts for the downloads. Collecting data prospectively allowed me to combine other data (e.g. A1c, years diagnosed, socioeconomic, insurance, household structure, race/ethnicity) with the device data for a comprehensive look at factors that affect self-management.
Security is a major concern as a researcher. We are tasked with ensuring that the information that participants generously entrust with us stays confidential and private. Additionally, institutions like The Ohio State University where I work are very vigilant about data security and scrutinize products used for research data collection.
I have seen fellow researchers abort proposed research because the institution would not grant approval for the use of data collection software due to security concerns. I was able to use Tidepool at OSU because of the platform's strong data security and PHI protections.
Early stage investigators like myself typically don’t have large budgets — meaning we don’t have a team of research assistants and are often doing much of the data collection and analysis ourselves.
I have found the platform incredibly easy to use, and, with the new data extraction feature built directly into the platform, Tidepool has really torn down so many research data collection barriers.
Through my clinical work and relationships with people with diabetes, I’ve learned that each person’s diabetes is unique and very personal. Through diabetes device data, we can glimpse that unique profile of each person’s self-management behaviors and utilization. In the future, I’m hoping that device data can help us discover personalized self-management and technology solutions to meet people with diabetes’ unique needs.
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