Pustozerov Evgenii, Popova Polina, Tkachuk Aleksandra, Bolotko Yana, Yuldashev Zafar, Grineva Elena
Department of Biomedical Engineering, Saint Petersburg State Electrotechnical University, Saint Petersburg, Russian Federation.
Institute of Endocrinology, Almazov National Medical Research Centre, Saint Petersburg, Russian Federation.
JMIR Mhealth Uhealth. 2018 Jan 9;6(1):e6. doi: 10.2196/mhealth.9236.
Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage.
This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM.
A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models.
The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions.
The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.
为糖尿病患者进行个性化血糖（BG）预测是全球众多研究人员追求的重要目标。尽管有许多提议，但只有少数项目致力于开发完整的推荐系统基础设施，其中纳入了针对糖尿病患者的BG预测算法。借助移动技术开发和实施这样一个系统，对于妊娠期糖尿病（GDM）患者尤为重要，特别是考虑到在短时间内（即孕期）迅速实现这些患者的血糖充分控制具有重大意义，以及移动健康（mHealth）解决方案在短期至中期使用中通常具有较高的接受率。
本研究旨在开发包含数据处理算法、BG预测模型以及用于患者电子记录管理的合适移动应用程序的基础设施，以指导针对GDM患者基于BG预测的个性化推荐。
开发了一个用于电子日记管理的移动应用程序以及数据交换和连续BG信号处理软件。这两个组件相互耦合，以获取用于个性化BG预测系统所需的数据。通过实施的移动应用程序和连续血糖监测（CGM）系统处理软件收集有关膳食、BG测量和其他事件的必要数据。这些数据用于调整和评估BG预测模型，该模型包括动态系数调整算法。在临床研究中，62名参与者（GDM：n = 49；对照组：n = 13）参加了为期1周的监测试验，在此期间他们使用移动应用程序跟踪膳食以及自我测量的BG，并使用CGM系统进行连续BG监测。将909次食物摄入量及相应的餐后BG曲线数据以及患者特征集（如糖化血红蛋白、体重指数[BMI]、年龄和生活方式参数）作为BG预测模型的输入。
模型对进食后1小时BG水平的预测结果为均方根误差=0.87 mmol/L，平均绝对误差=0.69 mmol/L，平均绝对百分比误差=12.8%，这对于BG控制决策而言具有足够的预测准确性。
为推荐系统开发了用于收集和处理相关数据的移动应用程序、用于CGM系统信号处理的合适软件以及BG预测模型。所开发的系统可能有助于改善GDM患者的BG控制；这将是后续研究评估的主题。
