联合连续血糖监测数字健康解决方案对2型糖尿病成年人血糖指标和自我管理行为的影响:真实世界,观察研究。

Q2 Medicine JMIR Diabetes Pub Date : 2023-09-11 DOI:10.2196/47638
Abhimanyu B Kumbara, Anand K Iyer, Courtney R Green, Lauren H Jepson, Keri Leone, Jennifer E Layne, Mansur Shomali
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引用次数: 0

摘要

背景:针对糖尿病患者的蓝星(Welldoc)数字健康解决方案整合了来自多个设备的数据,并使用人工智能生成指导信息。BlueStar应用程序同步G6(Dexcom)实时连续血糖监测(RT-CGM)系统的血糖数据,该系统每5分钟提供一次血糖测量。目的:这项使用数字健康解决方案和RT-CGM对2型糖尿病(T2D)患者进行的真实世界研究的目的是评估3个月内血糖控制和参与该项目的变化。方法:参与者是雇主赞助的健康计划的现任或前任参与者,年龄在18岁或以上,诊断为T2D,并且没有使用餐前胰岛素。结果包括基于CGM的血糖指标和使用BlueStar应用程序,包括记录所服用的药物、锻炼、食物细节、血压、体重和睡眠时间。结果:符合我们分析标准的项目参与者(n=52)的平均年龄为53岁(SD 9);37%(19/52)为女性,约50%(25/52)服用糖尿病药物。RT-CGM系统在3个月内有90%(SD8%)的时间磨损。在基线血糖控制不理想(定义为平均血糖>180 mg/dL)的个体中,观察到血糖控制有临床意义的改善,包括血糖管理指标降低(-0.8个百分点),高于181-250 mg/dL范围的时间(-4.4个百分点)和高于>250 mg/dL的时间(-14个百分点);所有结论:人工智能数字健康解决方案和RT-CGM的结合帮助T2D患者改善了血糖结果和糖尿病自我管理行为。
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Impact of a Combined Continuous Glucose Monitoring-Digital Health Solution on Glucose Metrics and Self-Management Behavior for Adults With Type 2 Diabetes: Real-World, Observational Study.

Background: The BlueStar (Welldoc) digital health solution for people with diabetes incorporates data from multiple devices and generates coaching messages using artificial intelligence. The BlueStar app syncs glucose data from the G6 (Dexcom) real-time continuous glucose monitoring (RT-CGM) system, which provides a glucose measurement every 5 minutes.

Objective: The objective of this real-world study of people with type 2 diabetes (T2D) using the digital health solution and RT-CGM was to evaluate change in glycemic control and engagement with the program over 3 months.

Methods: Participants were current or former enrollees in an employer-sponsored health plan, were aged 18 years or older, had a T2D diagnosis, and were not using prandial insulin. Outcomes included CGM-based glycemic metrics and engagement with the BlueStar app, including logging medications taken, exercise, food details, blood pressure, weight, and hours of sleep.

Results: Participants in the program that met our analysis criteria (n=52) were aged a mean of 53 (SD 9) years; 37% (19/52) were female and approximately 50% (25/52) were taking diabetes medications. The RT-CGM system was worn 90% (SD 8%) of the time over 3 months. Among individuals with suboptimal glycemic control at baseline, defined as mean glucose >180 mg/dL, clinically meaningful improvements in glycemic control were observed, including reductions in a glucose management indicator (-0.8 percentage points), time above range 181-250 mg/dL (-4.4 percentage points) and time above range >250 mg/dL (-14 percentage points; all P<.05). Time in range 70-180 mg/dL also increased by 15 percentage points (P=.016) in this population, which corresponds to an increase of approximately 3.5 hours per day in the target range. Over the 3-month study, 29% (15/52) of participants completed at least one engagement activity per week. Medication logging was completed most often by participants (23/52, 44%) at a rate of 12.1 (SD 0.8) events/week, and this was closely followed by exercise and food logging.

Conclusions: The combination of an artificial intelligence-powered digital health solution and RT-CGM helped people with T2D improve their glycemic outcomes and diabetes self-management behaviors.

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来源期刊
JMIR Diabetes
JMIR Diabetes Computer Science-Computer Science Applications
CiteScore
4.00
自引率
0.00%
发文量
35
审稿时长
16 weeks
期刊最新文献
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