Dominic Ehrmann, Bernhard Ruch, Michael Mitter, Johanna Kober, Norbert Hermanns, Vanessa Schäfer, Bernhard Kulzer, Stephan Silbermann
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A secondary objective was to evaluate the long-term impact of the logbook on mean blood glucose levels over 3 and 12 months using observational, real-world data (RWD).</p><p><strong>Methods: </strong>Data from 342 participants with type 1 or type 2 diabetes enrolled in the mySugr PRO-RCT were analyzed. A robust logistic regression was performed to examine the likelihood of achieving optimal glycemic control, defined as an HbA1c value ≤6.5% at the 3-month follow-up. The dependent variable was the dichotomous outcome indicating whether this threshold was met. The primary independent variable was group allocation, with baseline HbA1c included as a covariate. For the analysis of RWD, a total of 2861 participants with type 1 or type 2 diabetes were identified using propensity score matching to align their characteristics with those of the RCT participants closely. One-sample t tests were conducted to analyze changes in mean blood glucose separately for each diabetes type, from baseline to 3 months of app use, and from baseline to 12 months of app use (in a subcohort of 1176 participants).</p><p><strong>Results: </strong>The RCT data showed that the likelihood of achieving optimal glycemic control was nearly doubled in the intervention group compared with the control group (odds ratio 2.24, 95% CI 1.12-4.47; P=.02). RWD indicated that mean blood glucose levels significantly improved over 3 months of app use in both groups (type 1: -16.3 mg/dL; 95% CI -20.6 to -12.4; P<.001 and type 2: -27.3 mg/dL, 95% CI -28.7 to -25.9; P<.001). Participants with an estimated HbA<sub>1c</sub>>8.5% at baseline (before app use) showed the greatest reductions in mean blood glucose (type 1: -82.2 mg/dL; 95% CI -102.0 to -61.8; P<.001; type 2: -104.6 mg/dL, 95% CI -109.1 to -100.3; P<.001). 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引用次数: 0
摘要
背景:糖尿病的治疗需要大量的自我管理。数字工具可以帮助减轻自我管理的负担,并可能改善血糖控制。目的:本研究旨在通过对随机对照试验(RCT)数据的二次分析,确定数字糖尿病日志的使用是否增加了3个月后实现最佳血糖控制(糖化血红蛋白[HbA1c]≤6.5%)的可能性。第二个目标是使用观察性真实世界数据(RWD)评估日志对3个月和12个月平均血糖水平的长期影响。方法:对参加mysugar PRO-RCT的342名1型或2型糖尿病患者的数据进行分析。采用稳健的逻辑回归来检验实现最佳血糖控制的可能性,定义为3个月随访时HbA1c值≤6.5%。因变量是指示是否满足阈值的二分类结果。主要自变量是组分配,包括基线HbA1c作为协变量。对于RWD的分析,共有2861名1型或2型糖尿病患者使用倾向评分匹配来确定他们的特征与RCT参与者的特征密切相关。在1176名参与者的亚队列中,进行了单样本t检验,分别分析了每种糖尿病类型、应用程序使用基线至3个月以及应用程序使用基线至12个月的平均血糖变化。结果:RCT数据显示,与对照组相比,干预组实现最佳血糖控制的可能性几乎增加了一倍(优势比2.24,95% CI 1.12-4.47;P = .02点)。RWD显示,两组的平均血糖水平在使用app 3个月后显著改善(1型:-16.3 mg/dL;95% CI -20.6 ~ -12.4;基线(应用程序使用前)P1c为8.5%,平均血糖下降幅度最大(1型:-82.2 mg/dL;95% CI -102.0 ~ -61.8;结论:该随机对照试验的二次分析显示,使用mysugar日志3个月后,实现最佳血糖控制的可能性显著增加。这一发现得到了观察性真实数据的支持,该数据显示,在使用应用程序3个月和12个月后,平均血糖显著降低,尤其是在基线HbA1c水平升高的个体中。试验注册:德国临床试验注册中心DRKS00022923;https://drks.de/search/en/trial/DRKS00022923/details。
Improvements in Glycemic Control With a Digital Diabetes Logbook: Secondary Analysis of a Randomized Controlled Trial Enriched by Observational, Real-World Data.
Background: The treatment of diabetes requires substantial self-management. Digital tools can help reduce the burden of self-management and may improve glycemic control.
Objective: This study aims to determine whether the use of a digital diabetes logbook increased the likelihood of achieving optimal glycemic control (glycated hemoglobin [HbA1c] ≤6.5%) after 3 months, based on a secondary analysis of randomized controlled trial (RCT) data. A secondary objective was to evaluate the long-term impact of the logbook on mean blood glucose levels over 3 and 12 months using observational, real-world data (RWD).
Methods: Data from 342 participants with type 1 or type 2 diabetes enrolled in the mySugr PRO-RCT were analyzed. A robust logistic regression was performed to examine the likelihood of achieving optimal glycemic control, defined as an HbA1c value ≤6.5% at the 3-month follow-up. The dependent variable was the dichotomous outcome indicating whether this threshold was met. The primary independent variable was group allocation, with baseline HbA1c included as a covariate. For the analysis of RWD, a total of 2861 participants with type 1 or type 2 diabetes were identified using propensity score matching to align their characteristics with those of the RCT participants closely. One-sample t tests were conducted to analyze changes in mean blood glucose separately for each diabetes type, from baseline to 3 months of app use, and from baseline to 12 months of app use (in a subcohort of 1176 participants).
Results: The RCT data showed that the likelihood of achieving optimal glycemic control was nearly doubled in the intervention group compared with the control group (odds ratio 2.24, 95% CI 1.12-4.47; P=.02). RWD indicated that mean blood glucose levels significantly improved over 3 months of app use in both groups (type 1: -16.3 mg/dL; 95% CI -20.6 to -12.4; P<.001 and type 2: -27.3 mg/dL, 95% CI -28.7 to -25.9; P<.001). Participants with an estimated HbA1c>8.5% at baseline (before app use) showed the greatest reductions in mean blood glucose (type 1: -82.2 mg/dL; 95% CI -102.0 to -61.8; P<.001; type 2: -104.6 mg/dL, 95% CI -109.1 to -100.3; P<.001). Long-term analyses revealed a sustained reduction in mean blood glucose over a 12-month period, with a mean decrease of -19.8 mg/dL (95% CI -21.8 to -17.9; P<.001) after 12 months of app use in the total RWD sample.
Conclusions: The secondary analysis of the RCT demonstrated a significant increase in the likelihood of achieving optimal glycemic control after 3 months of using the mySugr logbook. This finding was supported by observational, real-world data, which showed significant reductions in mean blood glucose after 3 and 12 months of app use-particularly among individuals with elevated baseline HbA1c levels.
Trial registration: German Clinical Trials Register DRKS00022923; https://drks.de/search/en/trial/DRKS00022923/details.
期刊介绍:
The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades.
As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor.
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