Effect of Mobile-Based Application Usage on Time in Range and Time above Range in Patients with Diabetes Mellitus: A Pilot Cohort Retrospective Study
Main Article Content
Mikhalina Akulova, Ms
Anna Nesterova, Ms
http://orcid.org/0000-0003-4100-2558
Diana Isaacs, Dr
http://orcid.org/0000-0002-5743-9458
Eugene Molodkin, Mr
http://orcid.org/0000-0002-9287-9758
Mike Ushakov, Mr
http://orcid.org/0000-0002-9429-7313
http://orcid.org/0000-0003-4100-2558
Diana Isaacs, Dr
http://orcid.org/0000-0002-5743-9458
Eugene Molodkin, Mr
http://orcid.org/0000-0002-9287-9758
Mike Ushakov, Mr
http://orcid.org/0000-0002-9429-7313
Abstract
Achieving recommended glucose targets is challenging for many people with diabetes. An application that assists in self-management through continuous glucose monitoring may help reduce glycemic variability and help people with diabetes reach glucose targets. We aimed to evaluate how a digital collection of meal photos and postprandial continuous glucose monitoring data may impact glycemic stability. We assessed people with type 1 or type 2 diabetes and a time in range of <70%. Glucose parameters were measured at the beginning of application usage and after 14 days. To exclude time in range improvement due to continuous glucose monitoring use, the preceding glucose data of each user were collected in a control cohort. The intervention was via a photo-based food diary combined with continuous glucose monitoring data history. Users (n=21) demonstrated significant improvements in time in range (11.0%±5.0, P=.001) and time above range (-12.0%±5.0, P=.001). Combining continuous glucose monitoring data with meal photos in a mobile application could help improve time in range and time above range. A photo-based diabetes management application with visualized continuous glucose monitoring data and connection to specific meals allows better understanding of different meal-related decisions among individuals, thus decreasing fluctuations in glucose levels. Further research is needed to evaluate time in range and time above range changes caused by the regular use of the digital food diary combined with continuous glucose monitoring data.
Keywords:
continuous glucose monitoring, diabetes, food diary, mobile apps, self-management, time in range
Article Details
How to Cite
AKULOVA, Mikhalina et al.
Effect of Mobile-Based Application Usage on Time in Range and Time above Range in Patients with Diabetes Mellitus: A Pilot Cohort Retrospective Study.
Medical Research Archives, [S.l.], v. 11, n. 7.1, july 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4047>. Date accessed: 27 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i7.1.4047.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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33. Vigersky RA, McMahon C. The relationship of hemoglobin A1C to time-in-range in patients with diabetes. Diabetes Technol Ther. 2019;21(2):81-85. doi:10.1089/dia.2018.0310
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41. Hou C, Carter B, Hewitt J, Francisa T, Mayor S. Do mobile phone applications improve glycemic control (HbA1c) in the self-management of diabetes? A Systematic Review, Meta-analysis, and GRADE of 14 Randomized Trials. Diabetes Care. 2016;39(11):2089-2095. doi:10.2337/dc16-0346
42. Fonda SJ, Lewis DG, Vigersky RA. Minding the gaps in continuous glucose monitoring: a method to repair gaps to achieve more accurate glucometrics. J Diabetes Sci Technol. 2013;7(1):88-92. doi:10.1177/193229681300700110
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46. Boyle L, Grainger R, Hall RM, Krebs JD. Use of and beliefs about mobile phone apps for diabetes self-management: surveys of people in a hospital diabetes clinic and diabetes health professionals in New Zealand. JMIR Mhealth Uhealth. 2017;5(6):e85. doi:10.2196/mhealth.7263
47. American Diabetes Association. Standards of medical care in diabetes--2014. Diabetes Care. 2014;37 Suppl 1:S14-80. doi:10.2337/dc14-S014
48. Stehouwer CDA. Microvascular dysfunction and hyperglycemia: a vicious cycle with widespread consequences. Diabetes. 2018;67(9):1729-1741. doi:10.2337/dbi17-0044
49. Schwingshackl L, Hoffmann G. Comparison of the long-term effects of high-fat v. low-fat diet consumption on cardiometabolic risk factors in subjects with abnormal glucose metabolism: a systematic review and meta-analysis. Br J Nutr. 2014;111(12):2047-2058. doi:10.1017/S0007114514000464
50. Rodbard D. Metrics to evaluate quality of glycemic control: comparison of time in target, hypoglycemic, and hyperglycemic ranges with "risk indices". Diabetes Technol Ther. 2018;20(5):325-334. doi:10.1089/dia.2017.0416
51. Beck RW, Riddlesworth TD, Ruedy K, et al. Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: a randomized trial. Ann Intern Med. 2017;167(6):365-374. doi:10.7326/M16-2855
2. Monnier L, Colette C, Wojtusciszyn A, et al. Toward defining the threshold between low and high glucose variability in diabetes. Diabetes Care. 2017;40(7):832-838. doi:10.2337/dc16-1769
3. Glycemic Targets: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S66-S76. doi:10.2337/dc20-S006
4. Schade DS, Lorenzi GM, Braffett BH, et al. Hearing impairment and type 1 diabetes in the diabetes control and complications trial/epidemiology of diabetes interventions and complications (DCCT/EDIC) cohort. Diabetes Care. 2018;41(12):2495-2501. doi:10.2337/dc18-0625
5. Albernaz PL. Hearing loss, dizziness, and carbohydrate Metabolism. Int Arch Otorhinolaryngol. 2016;20(3):261-270. doi:10.1055/s-0035-1558450
6. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537-547. doi:10.1016/S2213-8587(16)30010-9
7. Fasil A, Biadgo B, Abebe M. Glycemic control and diabetes complications among diabetes mellitus patients attending at University of Gondar Hospital, Northwest Ethiopia. Diabetes Metab Syndr Obes. 2019;12:75-83. doi:10.2147/DMSO.S185614
8. Hirsch JD, Morello CM. Economic impact of and treatment options for type 2 diabetes. Am J Manag Care. 2017;23(13 Suppl):S231-S240.
9. Foundations of Care and Comprehensive Medical Evaluation. Diabetes Care. 2016;39 Suppl 1:S23-35. doi:10.2337/dc16-S006
10. American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917-928. doi:10.2337/dci18-0007
11. Clarke SF, Foster JR. A history of blood glucose meters and their role in self-monitoring of diabetes mellitus. Br J Biomed Sci. 2012;69(2):83-93. doi:10.1080/09674845.2012.12002443
12. Weinstock RS, Aleppo G, Bailey TS, et al. The role of blood glucose monitoring in diabetes management. Arlington (VA): American Diabetes Association; 2020 Oct. doi: 10.2337/db2020-31
13. Gunst J, De Bruyn A, Van den Berghe G. Glucose control in the ICU. Curr Opin Anaesthesiol. 2019;32(2):156-162. doi:10.1097/ACO.0000000000000706
14. Beck RW, Connor CG, Mullen DM, Wesley DM, Bergenstal RM. The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading. Diabetes Care. 2017;40(8):994-999. doi:10.2337/dc17-0636
15. Azhar A, Gillani SW, Mohiuddin G, Majeed RA. A systematic review on clinical implication of continuous glucose monitoring in diabetes management. J Pharm Bioallied Sci. 2020;12(2):102-111. doi:10.4103/jpbs.JPBS_7_20
16. Brazeau AS, Mircescu H, Desjardins K, et al. Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. Diabetes Res Clin Pract. 2013;99(1):19-23. doi:10.1016/j.diabres.2012.10.024
17. McArdle PD, Mellor D, Rilstone S, Taplin J. The role of carbohydrate in diabetes management. Pract Diabetes. 2016;33(7):237-42. doi:10.1002/pdi.2048
18. Petrie JR, Peters AL, Bergenstal RM, Holl RW, Fleming GA, Heinemann L. Improving the clinical value and utility of CGM systems: issues and recommendations : A joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group. Diabetologia. 2017;60(12):2319-2328. doi:10.1007/s00125-017-4463-4
19. Danne T, Nimri R, Battelino T, et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40(12):1631-1640. doi:10.2337/dc17-1600
20. Moström P, Ahlén E, Imberg H, Hansson PO, Lind M. Adherence of self-monitoring of blood glucose in persons with type 1 diabetes in Sweden. BMJ Open Diabetes Res Care. 2017;5(1):e000342. doi:10.1136/bmjdrc-2016-000342
21. Zhong VW, Crandell JL, Shay CM, et al. Dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes. J Diabetes Complications. 2017;31(8):1340-1347. doi:10.1016/j.jdiacomp.2017.04.017
22. Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. doi:10.1016/j.cell.2015.11.001
23. Bell KJ, Smart CE, Steil GM, Brand-Miller JC, King B, Wolpert HA. Impact of fat, protein, and glycemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era. Diabetes Care. 2015;38(6):1008-1015. doi:10.2337/dc15-0100
24. Stone JY, Bailey TS. Benefits and limitations of continuous glucose monitoring in type 1 diabetes. Expert Rev Endocrinol Metab. 2020;15(1):41-49. doi:10.1080/17446651.2020.1706482
25. Ziegler R, Heinemann L, Freckmann G, Schnell O, Hinzmann R, Kulzer B. Intermittent use of continuous glucose monitoring: expanding the clinical value of CGM. J Diabetes Sci Technol. 2021;15(3):684-694. doi:10.1177/1932296820905577
27. James S, Perry L, Gallagher R, Lowe J. Diabetes educators: perceived experiences, supports and barriers to use of common diabetes-related technologies. J Diabetes Sci Technol. 2016;10(5):1115-1121. doi:10.1177/1932296816660326
28. Gabbay MAL, Rodacki M, Calliari LE, et al. Time in range: a new parameter to evaluate blood glucose control in patients with diabetes. Diabetol Metab Syndr. 2020;12:22. doi: 10.1186/s13098-020-00529-z
29. Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range. Diabetes Care. 2019;42(8):1593-1603. doi:10.2337/dci19-0028
30. American Diabetes Association. 6. Glycemic targets: standards of medical care in diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S73-S84. doi:10.2337/dc21-S006
31. Hirsch IB, Battelino T, Peters AL, Chamberlain JJ, Aleppo G, Bergenstal RM. Role of continuous glucose monitoring in diabetes treatment. Arlington, VA: American Diabetes Association; 2018.
32. Lanspa MJ, Krinsley JS, Hersh AM, et al. Percentage of time in range 70 to 139 mg/dl is associated with reduced mortality among critically ill patients receiving IV insulin infusion. Chest. 2019;156(5):878-886. doi:10.1016/j.chest.2019.05.016
33. Vigersky RA, McMahon C. The relationship of hemoglobin A1C to time-in-range in patients with diabetes. Diabetes Technol Ther. 2019;21(2):81-85. doi:10.1089/dia.2018.0310
34. Cutruzzolà A, Irace C, Parise M, et al. Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes. Nutr Metab Cardiovasc Dis. 2020;30(10):1800-1805. doi:10.1016/j.numecd.2020.06.009
35. Dovc K, Battelino T. Evolution of diabetes technology. Endocrinol Metab Clin North Am. 2020;49(1):1-18. doi:10.1016/j.ecl.2019.10.009
36. Hui CY, Walton R, McKinstry B, Jackson T, Parker R, Pinnock H. The use of mobile applications to support self-management for people with asthma: a systematic review of controlled studies to identify features associated with clinical effectiveness and adherence. J Am Med Inform Assoc. 2017;24(3):619-632. doi:10.1093/jamia/ocw143
37. Haase J, Farris KB, Dorsch MP. Mobile applications to improve medication adherence. Telemed J E Health. 2017;23(2):75-79. doi:10.1089/tmj.2015.0227
38. Choi A, Lovett AW, Kang J, Lee K, Choi L. Mobile applications to improve medication adherence: existing apps, quality of life and future directions. Adv Pharmacol Pharm. 2015;3(3):64-74. doi:10.13189/app.2015.030302
39. Liang X, Wang Q, Yang X, et al. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabet Med. 2011;28(4):455-463. doi:10.1111/j.1464-5491.2010.03180.x
40. Yang Y, Lee EY, Kim HS, Lee SH, Yoon KH, Cho JH. Effect of a mobile phone-based glucose-monitoring and feedback system for type 2 diabetes management in multiple primary care clinic settings: cluster randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(2):e16266. doi:10.2196/16266
41. Hou C, Carter B, Hewitt J, Francisa T, Mayor S. Do mobile phone applications improve glycemic control (HbA1c) in the self-management of diabetes? A Systematic Review, Meta-analysis, and GRADE of 14 Randomized Trials. Diabetes Care. 2016;39(11):2089-2095. doi:10.2337/dc16-0346
42. Fonda SJ, Lewis DG, Vigersky RA. Minding the gaps in continuous glucose monitoring: a method to repair gaps to achieve more accurate glucometrics. J Diabetes Sci Technol. 2013;7(1):88-92. doi:10.1177/193229681300700110
43. Raybaut P. Spyder-documentation. Available online at: pythonhosted org. 2009
44. Glantz SA Primer of biostatistics (fourth edn.). New York: McGraw-Hill; 1997. pp. 350–360
45. Kebede MM, Pischke CR. Corrigendum: popular diabetes apps and the impact of diabetes app use on self-care behaviour: a survey among the digital community of persons with diabetes on social media. Front Endocrinol (Lausanne). 2019;10:220. doi:10.3389/fendo.2019.00220
46. Boyle L, Grainger R, Hall RM, Krebs JD. Use of and beliefs about mobile phone apps for diabetes self-management: surveys of people in a hospital diabetes clinic and diabetes health professionals in New Zealand. JMIR Mhealth Uhealth. 2017;5(6):e85. doi:10.2196/mhealth.7263
47. American Diabetes Association. Standards of medical care in diabetes--2014. Diabetes Care. 2014;37 Suppl 1:S14-80. doi:10.2337/dc14-S014
48. Stehouwer CDA. Microvascular dysfunction and hyperglycemia: a vicious cycle with widespread consequences. Diabetes. 2018;67(9):1729-1741. doi:10.2337/dbi17-0044
49. Schwingshackl L, Hoffmann G. Comparison of the long-term effects of high-fat v. low-fat diet consumption on cardiometabolic risk factors in subjects with abnormal glucose metabolism: a systematic review and meta-analysis. Br J Nutr. 2014;111(12):2047-2058. doi:10.1017/S0007114514000464
50. Rodbard D. Metrics to evaluate quality of glycemic control: comparison of time in target, hypoglycemic, and hyperglycemic ranges with "risk indices". Diabetes Technol Ther. 2018;20(5):325-334. doi:10.1089/dia.2017.0416
51. Beck RW, Riddlesworth TD, Ruedy K, et al. Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: a randomized trial. Ann Intern Med. 2017;167(6):365-374. doi:10.7326/M16-2855