The Tidepool Big Data Donation Project (TBDDP) enables people with diabetes using the Tidepool platform to opt-in to donate their data to help fuel the next generation of diabetes research and innovation. Through the Tidepool Big Data Donation Project, students, academics, and industry are able to innovate faster and expand the boundaries of existing knowledge about diabetes by providing broader access to critical data. To date, over 48,000+ individuals have opted to share their data with Tidepool. In turn, Tidepool either licenses these datasets to data partners or provides the data at no cost to citizen scientists without the resources to acquire this type of data.
But what happens after you click ‘donate your anonymized data’? We had the opportunity to connect with researchers who have used the datasets we have created with your generously donated data, and we’re so excited to share more about what we learned.
Dr. DeSalvo is a pediatric endocrinologist who completed his postdoctoral fellowship at Stanford. He is committed to compassionate patient care and advancing clinical care through patient-centered care and quality improvement endeavors. Dr. DeSalvo has been fueled by his own personal experiences with type 1 diabetes and his active involvement in emerging diabetes technology and therapies dedicated to reducing the burden of care. He is also one of the newest Tidepool medical advisors and we are so grateful for all his expertise!
“Having a dataset readily available for an academic group of up-and-coming investigators is really impactful for their academic and career development, but ultimately it's about what we can do for the people who donated this data.”
What was your biggest takeaway from the research?
“Having real-world data sets is really helpful, and having a combined approach with data scientists and clinicians allows us to better understand and characterize how people living with diabetes can successfully optimize their glycemic outcomes. Based off of these findings, we’re able to develop new best practices and guidelines and algorithms to allow people living with diabetes to thrive and optimize their glucose outcomes, specifically with their insulin and exercise management.”
Dana Lewis is a trailblazer in the diabetes community, known for founding the #OpenAPS movement—an open-source artificial pancreas system. In 2013, she created #DIYPS to address personal glucose monitor challenges, collaborating with Scott Leibrand to develop a predictive algorithm. Dana advocates for patient-centered diabetes research, leading diverse projects and securing funding from organizations like the Robert Wood Johnson Foundation and JDRF.
“The conversations we’ve been having about increasing access to these real-world datasets and using them more commonly will only benefit the diabetes community and ecosystem. The more anonymized datasets we have, the more innovation we can see in the diabetes community.”
What was your biggest takeaway from the research?
“One of the things we were able to do was compare other people with diabetes from the Tidepool dataset, allowing us to further validate the findings around real-world evidence. Using multiple data sets out in the wild allows us to study the lack of evidence regarding hypotheses related to data integrity in real-world datasets.”
David Scheinker is the Executive Director of Systems Design and Collaborative Research at Stanford Lucile Packard Children's Hospital. He is also the Founder and Director of SURF Stanford Medicine, a group that connects university students and faculty with healthcare professionals to implement and publish projects improving healthcare quality and efficiency.
“The only way to do something useful for diabetes is to have data we can analyze. When we started working on this five years ago, very little data were available, so we wouldn't have had any kind of project without a large retrospective dataset.”
What was your biggest takeaway from the research?
“We wanted to develop a tool that our clinic could use to identify when patients were in trouble so we could provide proactive care. We processed CGM data and extracted relevant metrics to build an algorithm that wasn’t restricted to one type of diabetes device and find gaps in data.
You don’t need anything fancy; you just need simple ways to show care teams which patients need their help in a quick and straightforward method that reduces their time in front of the screen and gives them more time with the patients. The Tidepool data was really helpful in building out a system and a tool that worked for our team.”
You can learn more about the Tidepool Big Data Donation Project (TBDDP) or how to opt-in to donate your data here.
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