Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus

Evgenii Pustozerov, Polina Popova, Aleksandra Tkachuk, Yana Bolotko, Zafar Yuldashev, Elena Grineva, Evgenii Pustozerov, Polina Popova, Aleksandra Tkachuk, Yana Bolotko, Zafar Yuldashev, Elena Grineva

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: blood glucose prediction; gestational diabetes mellitus; mHealth; mobile app; personalized medicine; recommender system.

Conflict of interest statement

Conflicts of Interest: None declared.

©Evgenii Pustozerov, Polina Popova, Aleksandra Tkachuk, Yana Bolotko, Zafar Yuldashev, Elena Grineva. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 09.01.2018.

Figures

Figure 1
Figure 1
Conceptual scheme of the gestational diabetes mellitus recommender system. BG: blood glucose; CGM: continuous glucose monitoring.
Figure 2
Figure 2
Ensemble of blood glucose (BG) curves collected 3 hours before and after meals for one of the patients. Different colors represent different meals.
Figure 3
Figure 3
Example of a standardized report exported from the app.
Figure 4
Figure 4
Result of data matching between the continuous glucose monitoring system signal and the electronic diary.

References

    1. HAPO Study Cooperative Research Group. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, Hadden DR, McCance DR, Hod M, McIntyre HD, Oats JJN, Persson B, Rogers MS, Sacks DA. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008 May 08;358(19):1991–2002. doi: 10.1056/NEJMoa0707943.
    1. Silverman BL, Rizzo TA, Cho NH, Metzger BE. Long-term effects of the intrauterine environment. The Northwestern University Diabetes in Pregnancy Center. Diabetes Care. 1998 Aug;21 Suppl 2:B142–B149.
    1. El Hajj N, Schneider E, Lehnen H, Haaf T. Epigenetics and life-long consequences of an adverse nutritional and diabetic intrauterine environment. Reproduction. 2014 Dec;148(6):R111–R120. doi: 10.1530/REP-14-0334.
    1. Bonoto BC, de Araújo VE, Godói IP, de Lemos LL, Godman B, Bennie M, Diniz LM, Junior AAG. Efficacy of mobile apps to support the care of patients with diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth. 2017 Mar 01;5(3):e4. doi: 10.2196/mhealth.6309.
    1. Liang X, Wang Q, Yang X, Cao J, Chen J, Mo X, Huang J, Wang L, Gu D. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabet Med. 2011 Apr;28(4):455–463. doi: 10.1111/j.1464-5491.2010.03180.x.
    1. Holtz B, Lauckner C. Diabetes management via mobile phones: a systematic review. Telemed J E Health. 2012 Apr;18(3):175–184. doi: 10.1089/tmj.2011.0119.
    1. Borgen I, Garnweidner-Holme LM, Jacobsen AF, Bjerkan K, Fayyad S, Joranger P, Lilleengen AM, Mosdøl A, Noll J, Småstuen MC, Terragni L, Torheim LE, Lukasse M. Smartphone application for women with gestational diabetes mellitus: a study protocol for a multicentre randomised controlled trial. BMJ Open. 2017 Mar 27;7(3):e013117. doi: 10.1136/bmjopen-2016-013117.
    1. American Diabetes Association Standards of medical care in diabetes-2016 abridged for primary care providers. Clin Diabetes. 2016 Jan;34(1):3–21. doi: 10.2337/diaclin.34.1.3.
    1. American Diabetes Association (4) Foundations of care: education, nutrition, physical activity, smoking cessation, psychosocial care, and immunization. Diabetes Care. 2015 Jan;38 Suppl:S20–S30. doi: 10.2337/dc15-S007.
    1. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalová L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized nutrition by prediction of glycemic responses. Cell. 2015 Nov 19;163(5):1079–1094. doi: 10.1016/j.cell.2015.11.001.
    1. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, Bowling AC, Newman HC, Jenkins AL, Goff DV. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981 Mar;34(3):362–366.
    1. Dodd H, Williams S, Brown R, Venn B. Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index. Am J Clin Nutr. 2011 Oct;94(4):992–996. doi: 10.3945/ajcn.111.012138.
    1. Vrolix R, Mensink RP. Variability of the glycemic response to single food products in healthy subjects. Contemp Clin Trials. 2010 Jan;31(1):5–11. doi: 10.1016/j.cct.2009.08.001.
    1. Dunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, Shaw JE, Bertovic DA, Zimmet PZ, Salmon J, Owen N. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care. 2012 May;35(5):976–983. doi: 10.2337/dc11-1931.
    1. Carpenter D, Dhar S, Mitchell LM, Fu B, Tyson J, Shwan NA, Yang F, Thomas MG, Armour JA. Obesity, starch digestion and amylase: association between copy number variants at human salivary (AMY1) and pancreatic (AMY2) amylase genes. Hum Mol Genet. 2015 Jun 15;24(12):3472–3480. doi: 10.1093/hmg/ddv098.
    1. Gibbs EM, Stock JL, McCoid SC, Stukenbrok HA, Pessin JE, Stevenson RW, Milici AJ, McNeish JD. Glycemic improvement in diabetic db/db mice by overexpression of the human insulin-regulatable glucose transporter (GLUT4) J Clin Invest. 1995 Apr;95(4):1512–1518. doi: 10.1172/JCI117823. doi: 10.1172/JCI117823.
    1. Oviedo S, Vehí J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. Int J Numer Method Biomed Eng. 2017 Jun;33(6):e1. doi: 10.1002/cnm.2833.
    1. Chomutare T, Fernandez-Luque L, Arsand E, Hartvigsen G. Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res. 2011;13(3):e65. doi: 10.2196/jmir.1874.
    1. Årsand E, Frøisland DH, Skrøvseth SO, Chomutare T, Tatara N, Hartvigsen G, Tufano JT. Mobile health applications to assist patients with diabetes: lessons learned and design implications. J Diabetes Sci Technol. 2012 Sep;6(5):1197–1206.
    1. Goyal S, Cafazzo JA. Mobile phone health apps for diabetes management: current evidence and future developments. QJM. 2013 Dec;106(12):1067–1069. doi: 10.1093/qjmed/hct203.
    1. Caburnay CA, Graff K, Harris JK, McQueen A, Smith M, Fairchild M, Kreuter MW. Evaluating diabetes mobile applications for health literate designs and functionality, 2014. Prev Chronic Dis. 2015;12:E61. doi: 10.5888/pcd12.140433.
    1. United States Department of Agriculture, Agricultural Research Service. [2017-12-08]. USDA Food Composition Databases
    1. Russian Academy of Medical Sciences [Institute of Nutrition] [2017-12-08].
    1. Dedov II, Krasnopol'skiy VI, Sukhikh GT. Russian National Consensus Statement on gestational diabetes: diagnostics, treatment and postnatal care. DM. 2012 Dec 15;15(4):4. doi: 10.14341/2072-0351-5531.
    1. International Association of Diabetes and Pregnancy Study Groups Consensus Panel. Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, Dyer AR, Leiva AD, Hod M, Kitzmiler JL, Lowe LP, McIntyre HD, Oats JJN, Omori Y, Schmidt MI. International Association of Diabetes and Pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010 Mar;33(3):676–682. doi: 10.2337/dc09-1848.
    1. Durán A, Martín P, Runkle I, Pérez N, Abad R, Fernández M, Del VL, Sanz MF, Calle-Pascual AL. Benefits of self-monitoring blood glucose in the management of new-onset Type 2 diabetes mellitus: the St Carlos Study, a prospective randomized clinic-based interventional study with parallel groups. J Diabetes. 2010 Sep;2(3):203–211. doi: 10.1111/j.1753-0407.2010.00081.x.
    1. IBM. [2017-11-10]. SPSS Statistics, Version 22 .
    1. MathWorks. [2017-11-10]. MATLAB and Statistics Toolbox Release 2016a .
    1. R Core Team. 2017. Nov 10, R: A language and environment for statistical computing, Release 3A
    1. Python. [2017-11-10]. Python Language Reference, Version 2
    1. Tibshirani R. Regression shrinkage selection via the lasso. J R Stat Soc B. 1996;58(1):267–288. doi: 10.1111/j.1467-9868.2011.00771.
    1. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
    1. Whitehead N, White H. Systematic review of randomised controlled trials of the effects of caffeine or caffeinated drinks on blood glucose concentrations and insulin sensitivity in people with diabetes mellitus. J Hum Nutr Diet. 2013 Apr;26(2):111–125. doi: 10.1111/jhn.12033.
    1. Yamada Y, Hosoya S, Nishimura S, Tanaka T, Kajimoto Y, Nishimura A, Kajimoto O. Effect of bread containing resistant starch on postprandial blood glucose levels in humans. Biosci Biotechnol Biochem. 2005 Mar;69(3):559–566.
    1. Schmidt MI, Hadji-Georgopoulos A, Rendell M, Margolis S, Kowarski A. The dawn phenomenon, an early morning glucose rise: implications for diabetic intraday blood glucose variation. Diabetes Care. 1981;4(6):579–585.
    1. Plis K, Bunescu RC, Marling CR, Shubrook J. A machine learning approach to predicting blood glucose levels for diabetes management. Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence; AAAI Workshop: Modern Artificial Intelligence for Health Analytics; Jul 27-31, 2014; Québec City, QC. 2014. Jun 18, pp. 35–39.
    1. Wang Y, Wu X, Mo X. A novel adaptive-weighted-average framework for blood glucose prediction. Diabetes Technol Ther. 2013 Oct;15(10):792–801. doi: 10.1089/dia.2013.0104.
    1. Pérez-Gandía C, Facchinetti A, Sparacino G, Cobelli C, Gómez EJ, Rigla M, de Leiva A, Hernando ME. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther. 2010 Jan;12(1):81–88. doi: 10.1089/dia.2009.0076.
    1. Stahl F. Diabetes Mellitus Glucose Prediction by Linear and Bayesian Ensemble Modeling [PhD Thesis] Lund, Sweden: Department of Automatic Control, Lund Institute of Technology, Lund University; 2012. p. 127.

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera