Digital health application integrating wearable data and behavioral patterns improves metabolic health

被引：24

作者：

Zahedani, Ashkan Dehghani ^{[1
]}

Veluvali, Arvind ^{[1
]}

McLaughlin, Tracey ^{[2
]}

Aghaeepour, Nima ^{[2
]}

Hosseinian, Amir ^{[1
]}

Agarwal, Saransh ^{[1
]}

Ruan, Jingyi ^{[1
]}

Tripathi, Shital ^{[1
]}

Woodward, Mark ^{[1
]}

Hashemi, Noosheen ^{[1
]}

Snyder, Michael ^{[1
,2
]}

机构：

[1] January AI, Menlo Pk, CA 94025 USA

[2] Stanford Univ, Stanford, CA 94305 USA

来源：

NPJ DIGITAL MEDICINE | 2023年 / 6卷 / 01期

关键词：

IMPAIRED GLUCOSE-TOLERANCE; LIFE-STYLE INTERVENTION; DIABETES PREVENTION; FOLLOW-UP; FASTING GLUCOSE; SELF-MANAGEMENT; TYPE-1; ADULTS; NUTRITION; METFORMIN;

D O I：

10.1038/s41746-023-00956-y

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

The effectiveness of lifestyle interventions in reducing caloric intake and increasing physical activity for preventing Type 2 Diabetes (T2D) has been previously demonstrated. The use of modern technologies can potentially further improve the success of these interventions, promote metabolic health, and prevent T2D at scale. To test this concept, we built a remote program that uses continuous glucose monitoring (CGM) and wearables to make lifestyle recommendations that improve health. We enrolled 2,217 participants with varying degrees of glucose levels (normal range, and prediabetes and T2D ranges), using continuous glucose monitoring (CGM) over 28 days to capture glucose patterns. Participants logged food intake, physical activity, and body weight via a smartphone app that integrated wearables data and provided daily insights, including overlaying glucose patterns with activity and food intake, macronutrient breakdown, glycemic index (GI), glycemic load (GL), and activity measures. The app furthermore provided personalized recommendations based on users' preferences, goals, and observed glycemic patterns. Users could interact with the app for an additional 2 months without CGM. Here we report significant improvements in hyperglycemia, glucose variability, and hypoglycemia, particularly in those who were not diabetic at baseline. Body weight decreased in all groups, especially those who were overweight or obese. Healthy eating habits improved significantly, with reduced daily caloric intake and carbohydrate-to-calorie ratio and increased intake of protein, fiber, and healthy fats relative to calories. These findings suggest that lifestyle recommendations, in addition to behavior logging and CGM data integration within a mobile app, can enhance the metabolic health of both nondiabetic and T2D individuals, leading to healthier lifestyle choices. This technology can be a valuable tool for T2D prevention and treatment.

引用

页数：15

共 46 条

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