Sanjay Arora, Chun Nok Lam, Elizabeth Burner, Michael Menchine
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引用次数: 0
Abstract
Background: Despite the efficacy of diabetes prevention programs, only an estimated 5% of people with pre-diabetes actually participate. Mobile health (mHealth) holds promise to engage patients with pre-diabetes into lifestyle modification programs by decreasing the referral burden, centralizing remote enrollment, removing the physical requirement of a brick-and-mortar location, lowering operating costs through automation, and reducing time and transportation barriers.
Methods: Non-randomized implementation study enrolling patients with pre-diabetes from a large health care organization. Patients were exposed to a text message-based program combining live human coaching guidance and support with automated scheduled, interactive, data-driven, and on-demand messages. The primary analysis examined predicted weight outcomes at 6 and 12 months. Secondary outcomes included predicted changes in HbA1c and minutes of exercise at 6 and 12 months.
Results: Of the 163 participants included in the primary analysis, participants had a mean predicted weight loss of 5.5% at six months (P < .001) and of 4.3% at 12 months (P < .001). We observed a decrease in predicted HbA1c from 6.1 at baseline to 5.8 at 6 and 12 months (P < .001). Activity minutes were statistically similar from a baseline of 155.5 minutes to 146.0 minutes (P = .567) and 142.1 minutes (P = .522) at 6 and 12 months, respectively, for the overall cohort.
Conclusions: In this real-world implementation of the myAgileLife Diabetes Prevention Program among patients with pre-diabetes, we observed significant decreases in weight and HbA1c at 6 and 12 months. mHealth may represent an effective and easily scalable potential solution to deliver impactful diabetes prevention curricula to large numbers of patients.
期刊介绍：
The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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