Imrisek Steven D, Lee Matthew, Goldner Dan, Nagra Harpreet, Lavaysse Lindsey M, Hoy-Rosas Jamillah, Dachis Jeff, Sears Lindsay E
One Drop, New York, NY, United States.
JMIR Diabetes. 2022 May 3;7(2):e34624. doi: 10.2196/34624.
Personalized feedback is an effective behavior change technique frequently incorporated into mobile health (mHealth) apps. Innovations in data science create opportunities for leveraging the wealth of user data accumulated by mHealth apps to generate personalized health forecasts. One Drop's digital program is one of the first to implement blood glucose forecasts for people with type 2 diabetes. The impact of these forecasts on behavior and glycemic management has not been evaluated to date.
This study sought to evaluate the impact of exposure to blood glucose forecasts on blood glucose logging behavior, average blood glucose, and percentage of glucose points in range.
This retrospective cohort study examined people with type 2 diabetes who first began using One Drop to record their blood glucose between 2019 and 2021. Cohorts included those who received blood glucose forecasts and those who did not receive forecasts. The cohorts were compared to evaluate the effect of exposure to blood glucose forecasts on logging activity, average glucose, and percentage of glucose readings in range, after controlling for potential confounding factors. Data were analyzed using analysis of covariance (ANCOVA) and regression analyses.
Data from a total of 1411 One Drop users with type 2 diabetes and elevated baseline glucose were analyzed. Participants (60.6% male, 795/1311; mean age 50.2 years, SD 11.8) had diabetes for 7.1 years on average (SD 7.9). After controlling for potential confounding factors, blood glucose forecasts were associated with more frequent blood glucose logging (P=.004), lower average blood glucose (P<.001), and a higher percentage of readings in range (P=.03) after 12 weeks. Blood glucose logging partially mediated the relationship between exposure to forecasts and average glucose.
Individuals who received blood glucose forecasts had significantly lower average glucose, with a greater amount of glucose measurements in a healthy range after 12 weeks compared to those who did not receive forecasts. Glucose logging was identified as a partial mediator of the relationship between forecast exposure and week-12 average glucose, highlighting a potential mechanism through which glucose forecasts exert their effect. When administered as a part of a comprehensive mHealth program, blood glucose forecasts may significantly improve glycemic management among people living with type 2 diabetes.
个性化反馈是一种有效的行为改变技术，经常被纳入移动健康（mHealth）应用程序中。数据科学的创新为利用mHealth应用程序积累的大量用户数据来生成个性化健康预测创造了机会。One Drop的数字项目是最早为2型糖尿病患者实施血糖预测的项目之一。迄今为止，这些预测对行为和血糖管理的影响尚未得到评估。
本研究旨在评估接触血糖预测对血糖记录行为、平均血糖以及血糖值在正常范围内的百分比的影响。
这项回顾性队列研究调查了2019年至2021年间首次开始使用One Drop记录血糖的2型糖尿病患者。队列包括接受血糖预测的患者和未接受预测的患者。在控制潜在混杂因素后，比较各队列以评估接触血糖预测对记录活动、平均血糖以及血糖读数在正常范围内的百分比的影响。使用协方差分析（ANCOVA）和回归分析对数据进行分析。
共分析了1411名2型糖尿病且基线血糖升高的One Drop用户的数据。参与者（60.6%为男性，795/1311；平均年龄50.2岁，标准差11.8）平均患糖尿病7.1年（标准差7.9）。在控制潜在混杂因素后，12周后血糖预测与更频繁的血糖记录相关（P = 0.004）、平均血糖更低（P < 0.001）以及血糖值在正常范围内的读数百分比更高（P = 0.03）。血糖记录部分介导了预测暴露与平均血糖之间的关系。
与未接受预测的个体相比，接受血糖预测的个体平均血糖显著更低，且12周后在健康范围内的血糖测量次数更多。血糖记录被确定为预测暴露与第12周平均血糖之间关系的部分中介因素，突出了血糖预测发挥作用的潜在机制。当作为综合mHealth项目的一部分实施时，血糖预测可能会显著改善2型糖尿病患者的血糖管理。
