Study of InSpark Technologies’ Pattern Identification System Published in the Journal of Diabetes Science and Technology
Data demonstrates the utility of InSpark’s advanced blood glucose pattern recognition technology.
“As shown by these study results, InSpark is taking a leadership role in transforming blood glucose data into relevant information that can empower patients and clinicians to make better diabetes management decisions in real-time."
InSpark Technologies, Inc. announced today that the peer-reviewed Journal of Diabetes Science and Technology has published the paper “Evaluation of the Utility of a Glycemic Pattern Identification System” in its July 2014 issue. The system that is the subject of this paper is being incorporated into InSpark’s Vigilant™† iPhone-enabled diabetes management software.
The retrospective study evaluated the Vigilant daily pattern identification system in data from 536 patients with diabetes. It was found that patterns identified by Vigilant algorithms were predictive of future hyperglycemia and hypoglycemia across diabetes types and glycemic control groups, and were significantly more predictive than six other pattern identification techniques evaluated. Episodes of hypoglycemia, hyperglycemia, severe hypoglycemia, and severe hyperglycemia were 120 percent, 46 percent, 123 percent and 76 percent more likely in the next seven days after pattern identification, respectively, compared to intraday periods when no pattern was identified. Identified patterns continued to be predictive of glycemic events up to three months later.
“This is a significant validation of our technology platform,” said Erik Otto, president and co-founder of InSpark Technologies. “An important measure of the utility of identified blood glucose patterns is the degree to which they are indicative of your risk of upcoming high or low blood glucose events. In this study we have shown that our pattern messages are not only highly predictive of future glycemic events, but more so than common ways glucose patterns are identified today. We are excited about bringing this technology to patients and clinicians so they can benefit from this pattern information to improve diabetes management.”
“There are now many ways patients can transmit readings to mobile phones or cloud-based systems, but unfortunately little value is added to that data in terms of concise summaries or prospective actionable messages about glucose patterns,” said Dr. Stacey Anderson, medical director at the University of Virginia Center for Diabetes Technology. “As shown by these study results, InSpark is taking a leadership role in transforming blood glucose data into relevant information that can empower patients and clinicians to make better diabetes management decisions in real-time.”
About InSpark Technologies, Inc.
InSpark Technologies is developing tools that help people with diabetes transform the significant amount of blood glucose data captured on a daily basis into useful insight. InSpark’s technology pushes actionable pattern-related information to patients and health care providers at the time they need it most (such as when a patient is testing) rather than requiring them to pull the information from detailed charts, graphs, and log books. InSpark’s technology is derived from a broad suite of pattern recognition intellectual property licensed from the University of Virginia Licensing and Ventures Group. More information can be found at www.insparktech.com.
Vigilant™ is an iPhone-enabled software device that analyzes blood glucose data and provides feedback about daily patterns of hyperglycemia, hypoglycemia, glucose variability and test frequency in real-time, as well as patterns indicative of increased risk of severe hypoglycemia in the next 24 hours. It is designed to provide concise messages prior to periods of risk so that users can take action to improve their glucose control.
† Vigilant™ is an investigational use software device that is not currently available for sale in the United States.
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