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Home Blood Glucose Monitoring and Digital-Health in Diabetes

  • Andrew Farmer
  • Kingshuk Pal
Living reference work entry
Part of the Endocrinology book series (ENDOCR)

Abstract

Diabetes is a disorder of glucose metabolism and a major cause of death and disability. It currently affects 387 million people worldwide and is expected to affect 592 million by 2035. Monitoring of glucose levels is an essential component of treatment - providing feedback to clinician and patient on management through lifestyle and pharmacotherapy. This chapter provides an overview of the evidence that monitoring levels of glycaemia leads to improved outcomes for diabetes; a brief history of the technologies used for monitoring; and an update on recent research into ways in which people can be supported with use of their medication. Clinical support systems are now available and have been refined to improve their effectiveness, and combined with systems that enable personal support for self-monitoring can help make better use of the data available. The chapter includes a brief overview of recent developments with continuous glucose monitoring, flash monitoring and closed loop systems.

Keywords

Diabetes Glucose monitoring Digital technologies Insulin treatment Self-management support Adherence 

References

  1. Ahola AJ, et al. Many patients with Type 1 diabetes estimate their prandial insulin need inappropriately. J Diabetes. 2010;2(3):194–202.PubMedCrossRefGoogle Scholar
  2. Alkhaldi G, et al. The effectiveness of prompts to promote engagement with digital interventions: a systematic review. J Med Internet Res. 2016;18(1):e6.PubMedPubMedCentralCrossRefGoogle Scholar
  3. Arambepola C, et al. The impact of automated brief messages promoting lifestyle changes delivered via mobile devices to people with type 2 diabetes: a systematic literature review and meta-analysis of controlled trials. J Med Internet Res. 2016;18(4):e86–12.PubMedPubMedCentralCrossRefGoogle Scholar
  4. Arnhold M, Quade M, Kirch W. Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res. 2014;16(4):e104.PubMedPubMedCentralCrossRefGoogle Scholar
  5. Barnard K, et al. Future artificial pancreas technology for type 1 diabetes: what do users want? Diabetes Technol Ther. 2015;17(5):311–5.PubMedCrossRefGoogle Scholar
  6. Bin Abbas B, et al. Effect of mobile phone short text messages on glycemic control in type 2 diabetes. Int J Endocrinol Metab. 2015;13(1):e18791.PubMedPubMedCentralGoogle Scholar
  7. Bobrow K, et al. Mobile phone text messages to support treatment adherence in adults with high blood pressure (StAR): a single-blind, randomized trial. Circulation. 2016.; Available at: http://circ.ahajournals.org/content/133/6/592.full.html?ijkey=9HjK6o57zyKut6w&keytype=ref
  8. Boulos MN, Brewer, AC, Karimkhani C et al. Mobile medical and health apps: state of the art, concerns, regulatory control and certification. J Public Health Inform. 2014;5(3): 229. Google Scholar
  9. Breland JY, Yeh VM, Yu J. Adherence to evidence-based guidelines among diabetes self-management apps. Transl Behav Med. 2013;3(3):277–86.PubMedPubMedCentralCrossRefGoogle Scholar
  10. Brouwer W. Which intervention characteristics are related to more exposure to internet-delivered healthy lifestyle promotion interventions? A systematic review. J Med Internet Res. 2011;13(1):e2.PubMedPubMedCentralCrossRefGoogle Scholar
  11. Capozza K, Woolsey S, Georgsson M. Going mobile with diabetes support: a randomized study of a text message–based personalized behavioral intervention for type 2 diabetes self-care. Diabetes. 2015;28(2):83–91.Google Scholar
  12. Carver C, Scheier M. Control processes and self-organization as complementary principles underlying behavior. Personal Soc Psychol Rev. 2002;6(4):304–15.CrossRefGoogle Scholar
  13. Health and Social Care Information Centre. National diabetes audit 2014–2015 report 1: care processes and treatment targets; 2016 HSCIC Leeds UK. Google Scholar
  14. Chaudoir SR, Dugan AG, Barr CH. Measuring factors affecting implementation of health innovations: a systematic review of structural, organizational, provider, patient, and innovation level measures. Implement Sci. 2013;8(1):724.CrossRefGoogle Scholar
  15. Clar C, et al. Self-monitoring of blood glucose in type 2 diabetes: systematic review. Health Technol Assess (Winch, Eng). 2010;14(12):1–140.Google Scholar
  16. Coonrod BA, Betschart J, Harris MI. Frequency and determinants of diabetes patient education among adults in the US population. Diabetes Care. 1994;Google Scholar
  17. Coster S, et al. Monitoring blood glucose control in diabetes mellitus: a systematic review. Health Technol Assess (Winch, Eng). 2000;4(12):1–93.Google Scholar
  18. Couper MP. Engagement and retention: measuring breadth and depth of participant use of an online intervention. J Med Internet Res. 2010;12(4):e52.PubMedPubMedCentralCrossRefGoogle Scholar
  19. Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27(5):1218–24.PubMedCrossRefGoogle Scholar
  20. Cresswell K, Sheikh A. Organizational issues in the implementation and adoption of health information technology innovations: an interpretative review. Int J Med Inform. 2013;82(5):e73–86.PubMedCrossRefGoogle Scholar
  21. Davidson M, et al. The effect of self monitoring of blood glucose concentrations on glycated hemoglobin levels in diabetic patients not taking insulin: a blinded, randomized trial. Am J Med. 2005;118(4):422–5.PubMedCrossRefGoogle Scholar
  22. Del Toro V, Parker SR. Principles of control systems engineering: McGraw Hill; New York;1960.Google Scholar
  23. van Dijk JAGM. Digital divide research, achievements and shortcomings. Poetics. 2006;34(4-5):221–35.CrossRefGoogle Scholar
  24. Donkin L. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res. 2011;13(3):e52.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Egede LE, et al. Medication nonadherence in diabetes. Diabetes Care. 2012;35(12):2533–9.PubMedPubMedCentralCrossRefGoogle Scholar
  26. Eng DS, Lee JM. The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes. 2013;14(4):231–8.PubMedCrossRefGoogle Scholar
  27. Farmer A, et al. A systematic review of telemedicine interventions to support blood glucose self-monitoring in diabetes. Diabet Med. 2005;22(10):1372–8.PubMedCrossRefGoogle Scholar
  28. Farmer A, et al. Impact of self monitoring of blood glucose in the management of patients with non-insulin treated diabetes: open parallel group randomised trial. Br Med J. 2007;335(7611):132.CrossRefGoogle Scholar
  29. Farmer AJ, Rodgers LR, Lonergan M, et al. Adherence to oral glucose–lowering therapies and associations with 1-year HbA 1c: a retrospective cohort analysis in a large primary care database. Diabetes Care. 2016; 39(2):258–263.Google Scholar
  30. Franciosi M, et al. The impact of blood glucose self-monitoring on metabolic control and quality of life in type 2 diabetic patients: an urgent need for better educational strategies. Diabetes Care. 2001;24(11):1870–7.PubMedCrossRefGoogle Scholar
  31. Franciosi M, et al. ROSES: role of self-monitoring of blood glucose and intensive education in patients with Type 2 diabetes not receiving insulin. A pilot randomized clinical trial. Diabet Med. 2011;28(7):789–96.PubMedCrossRefGoogle Scholar
  32. Free AH, et al. Simple specific test for urine glucose. Clin Chem. 1957;3(3):163–8.PubMedGoogle Scholar
  33. Free C, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review T. Cornford, ed. PLoS Med. 2013; 10(1):e1001362.Google Scholar
  34. French DP, et al. Self-monitoring of blood glucose changed non-insulin-treated Type 2 diabetes patients’ beliefs about diabetes and self-monitoring in a randomized trial. Diabet Med. 2008;25(10):1218–28.PubMedCrossRefGoogle Scholar
  35. Garabedian LF, Ross-Degnan D, Wharam JF. Mobile phone and smartphone technologies for diabetes care and self-management. Curr Diab Rep. 2015;15(12):109.PubMedCrossRefGoogle Scholar
  36. Greenhalgh T, et al. Storylines of research in diffusion of innovation: a meta-narrative approach to systematic review. Soc Sci Med. 2005;61(2):417–30.PubMedCrossRefGoogle Scholar
  37. Grol R. Personal paper. Beliefs and evidence in changing clinical practice. Br Med J. 1997;315(7105):418–21.CrossRefGoogle Scholar
  38. Guariguata L, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–49.PubMedCrossRefGoogle Scholar
  39. Hertz RP, Unger AN, Lustik MB. Adherence with pharmacotherapy for type 2 diabetes: a retrospective cohort study of adults with employer-sponsored health insurance. Clin Ther. 2005;27(7):1064–73.PubMedCrossRefGoogle Scholar
  40. Hex N, et al. Estimating the current and future costs of Type 1 and Type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet Med. 2012;29(7):855–62.PubMedCrossRefGoogle Scholar
  41. Holman R, et al. Addition of biphasic, prandial, or basal insulin to oral therapy in type 2 diabetes. N Engl J Med. 2007;357:1716–30.PubMedCrossRefGoogle Scholar
  42. Holman R, et al. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359:1577–89.PubMedCrossRefGoogle Scholar
  43. Hovorka R, et al. Overnight closed-loop insulin delivery in young people with type 1 diabetes: a free-living, randomized clinical trial. Diabetes Care. 2014;37(5):1204–11.PubMedPubMedCentralCrossRefGoogle Scholar
  44. Hunt DL, et al. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA. 1998;280(15):1339–46.PubMedCrossRefGoogle Scholar
  45. International Diabetes Federation. Diabetes: facts and figures. n.d.. Available at: http://www.idf.org/worlddiabetesday/toolkit/gp/facts-figures.
  46. Juvenile Diabetes Resarch Foundation. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008;359:1464–1476Google Scholar
  47. Karter AJ, et al. New prescription medication gaps: a comprehensive measure of adherence to new prescriptions. Health Serv Res. 2009;44(5p1):1640–61.PubMedPubMedCentralCrossRefGoogle Scholar
  48. Kawamoto K, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Br Med J. 2005;330(7494):765–0.CrossRefGoogle Scholar
  49. Klonoff DC. The current status of mhealth for diabetes: will it be the next big thing? J Diabetes Sci Technol. 2013;7(3):749–58.PubMedPubMedCentralCrossRefGoogle Scholar
  50. Law GR, et al. Analysis of continuous glucose monitoring in pregnant women with diabetes: distinct temporal patterns of glucose associated with large-for-gestational-age infants. Diabetes Care. 2015;38(7):1319–25.PubMedCrossRefGoogle Scholar
  51. Leon N, et al. Improving treatment adherence for blood pressure lowering via mobile phone SMS-messages in South Africa: a qualitative evaluation of the SMS-text Adherence SuppoRt (StAR) trial. BMC Fam Pract. 2015;16:80.Google Scholar
  52. Lester RT, et al. Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet. 2010;376(9755):1838–45.PubMedCrossRefGoogle Scholar
  53. Liang X, et al. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabet Med. 2011;28(4):455–63.PubMedCrossRefGoogle Scholar
  54. Malanda UL, Welschen LM, Riphagen II, Dekker JM, Nijpels G, Bot SD. Self-monitoring of blood glucose in patients with type 2 diabetes mellitus who are not using insulin. Cochrane Database Syst Rev. 2012;1:CD005060.Google Scholar
  55. Misono AS, et al. Healthcare information technology interventions to improve cardiovascular and diabetes medication adherence. Am J Manag Care. 2010;16(12 Suppl HIT):SP82–92.PubMedGoogle Scholar
  56. Murray E, et al. Normalisation process theory: a framework for developing, evaluating and implementing complex interventions. BMC Med. 2010;8:63.PubMedPubMedCentralCrossRefGoogle Scholar
  57. National Institute for Health and Clinical Excellence. Type 1 diabetes in adults: diagnosis and management. NICE London; 2015.Google Scholar
  58. Norris SL, et al. Self-management education for adults with type 2 diabetes: a meta-analysis of the effect on glycemic control. Diabetes Care 2002;25(7):1159–71.Google Scholar
  59. O’Connor PJ, et al. Outpatient diabetes clinical decision support: current status and future directions. Diabet Med. 2016;33(6):734–41.Google Scholar
  60. O’Kane MJ, et al. Efficacy of self monitoring of blood glucose in patients with newly diagnosed type 2 diabetes (ESMON study): randomised controlled trial. 2008;336(7654):1174–7.Google Scholar
  61. Pal K, et al. Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2013;3:CD008776.Google Scholar
  62. Pal K, et al. Computer-based interventions to improve self-management in adults with type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2014;37(6):1759–66.PubMedCrossRefGoogle Scholar
  63. Payne HE, et al. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR mHealth uHealth. 2015;3(1):e20.PubMedPubMedCentralCrossRefGoogle Scholar
  64. Pereira K, et al. Internet delivered diabetes self-management education: a review. Diabetes Technol Ther. 2015;17(1):55–63.PubMedCrossRefGoogle Scholar
  65. Pickup JC, Ford Holloway M, Samsi K. Real-time continuous glucose monitoring in type 1 diabetes: a qualitative framework analysis of patient narratives. Diabetes Care. 2015;38(4):544–50.PubMedGoogle Scholar
  66. Pladevall M, et al. Clinical outcomes and adherence to medications measured by claims data in patients with diabetes. Diabetes Care. 2004;27(12):2800–5.PubMedPubMedCentralCrossRefGoogle Scholar
  67. Polonsky WH, Fisher L. Self-monitoring of blood glucose in noninsulin-using type 2 diabetic patients: right answer, but wrong question: self-monitoring of blood glucose can be clinically valuable for noninsulin users. Diabetes Care. 2013;36(1):179–82.PubMedCrossRefGoogle Scholar
  68. Powers MA, Bardsley J, Cypress M, et al. Diabetes Self-Management Education and Support in Type 2 Diabetes: A Joint Position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. J Acad Nutr Diet. 2015;115(8):1323–34. Google Scholar
  69. Quinn CC, et al. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011;34(9):1934–42.PubMedPubMedCentralCrossRefGoogle Scholar
  70. Riazi H, et al. Managing diabetes mellitus using information technology: a systematic review. J Diabetes Metab Disord. 2015;14(1):35.CrossRefGoogle Scholar
  71. Roberts LG. Beyond Moore’s law: internet growth trends. Computer. 2000;33(1):117–9.CrossRefGoogle Scholar
  72. van Rooij T, Marsh S. eHealth: past and future perspectives. Personalized Medicine 2016;13(1):15–40Google Scholar
  73. Roshanov PS, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. Br Med J. 2013;346:f657.CrossRefGoogle Scholar
  74. Samy GN, Ahmad R, Ismail Z. Security threats categories in healthcare information systems. Health Informatics J. 2010;16(3):201–9.PubMedCrossRefGoogle Scholar
  75. Sarkar U, Lyles CR, Parker MM,et al. Use of the refill function through an online patient portal is associated with improved adherence to statins in an integrated health system. Med Care. 2014;52(3):194–201.Google Scholar
  76. Schaller RR. Moore’s law: past, present and future. IEEE Spectr. 1997;34(6):52–57.Google Scholar
  77. Schmidt S, et al. Use of an automated bolus calculator in MDI-treated type 1 diabetes. Diabetes Care. 2012;35(5):984–90.PubMedPubMedCentralCrossRefGoogle Scholar
  78. Schwedes U, et al. Meal-related structured self-monitoring of blood glucose: effect on diabetes control in non-insulin-treated type 2 diabetic patients. Diabetes Care. 2002;25(11):1928–32.PubMedCrossRefGoogle Scholar
  79. Secher AL, Ringholm L, Andersen HU, et al. The effect of real-time continuous glucose monitoring in pregnant women with diabetes: a randomized controlled trial. Diabetes Care. 2013;36(7):1877–83. Google Scholar
  80. Seuring T, Archangelidi O, Suhrcke M. The economic costs of type 2 diabetes: a global systematic review. Pharmacoeconomics. 2015;33(8):811–31.Google Scholar
  81. Sherifali D, et al. Evaluating the effect of a diabetes health coach in individuals with type 2 diabetes. Can J Diabetes. 2016;40(1):84–94.PubMedCrossRefGoogle Scholar
  82. van der Sijs H, et al. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138–47.Google Scholar
  83. Simon J, et al. Cost effectiveness of self monitoring of blood glucose in patients with non-insulin treated type 2 diabetes: economic evaluation of data from the DiGEM trial. Br Med J. 2008;336(7654):1177–80.Google Scholar
  84. Office for National Statistics. Internet users. Office for National Statistics; London; 2015.Google Scholar
  85. Tabak RG, Khoong EC, Chambers DA. et al. Bridging research and practice. Am J Prev Med 2012; 43:337–350.Google Scholar
  86. Tattersall RB. Home blood glucose monitoring. Diabetologia. 1979;16(2):71–4.PubMedCrossRefGoogle Scholar
  87. The Diabetes Control and Complications Trial Epidemiology of Diabetes Interventions and Complications DCCT/EDIC Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353(25):2643–53.CrossRefGoogle Scholar
  88. Thom DH, et al. Impact of peer health coaching on glycemic control in low-income patients with diabetes: a randomized controlled trial. Ann Fam Med. 2013;11(2):137–44.PubMedPubMedCentralCrossRefGoogle Scholar
  89. U.S. Department of Commerce, National Telecommunications and Information Administration (NTIA). Falling through the net: a survey of the have nots in rural and urban America. NTIA, Washington, DC; 1995.Google Scholar
  90. van Vugt M, et al. Uptake and effects of the e-vita personal health record with self-management support and coaching, for type 2 diabetes patients treated in primary care. J Diabetes Res. 2016;2016(2):1–9.CrossRefGoogle Scholar
  91. Walford S, et al. Self-monitoring of blood-glucose – improvement of diabetic control. Lancet. 1978;1(8067):732–5.PubMedCrossRefGoogle Scholar
  92. Wayne N, Ritvo P. Smartphone-enabled health coach intervention for people with diabetes from a modest socioeconomic strata community: single-arm longitudinal feasibility study. J Med Internet Res. 2006;16(6):e149.CrossRefGoogle Scholar
  93. Wing R, et al. Does self-monitoring of blood glucose levels improve dietary compliance for obese patients with type II diabetes? Am J Med. 1986;81:830–6.PubMedCrossRefGoogle Scholar
  94. Winkley K, et al. Patient explanations for non-attendance at structured diabetes education sessions for newly diagnosed Type 2 diabetes: a qualitative study. Diabet Med. 2015;32(1):120–8.PubMedCrossRefGoogle Scholar
  95. Woolf SH. The meaning of translational research and why it matters. JAMA. 2008;299(2):211–3.PubMedCrossRefGoogle Scholar
  96. van der Wulp I, et al. Effectiveness of peer-led self-management coaching for patients recently diagnosed with Type 2 diabetes mellitus in primary care: a randomized controlled trial. Diabet Med. 2012;29(10):e390–7.PubMedCrossRefGoogle Scholar
  97. Yu L, Mishra A. An empirical study of Lehman’s law on software quality evolution. Int J Software Informatics. 2013;7(3):469–481.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nuffield Department of Primary Care Health SciencesUniversity of OxfordOxfordUK
  2. 2.Research Department of Primary Care and Population HealthUniversity College LondonLondonUK

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