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Diabetes. Epidemiology, Genetics, Pathogenesis, Diagnosis, Prevention, and Treatment

Part of the book series: Endocrinology ((ENDOCR))

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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.

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References

  • Ahola AJ, et al. Many patients with Type 1 diabetes estimate their prandial insulin need inappropriately. J Diabetes. 2010;2(3):194–202.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Barnard K, et al. Future artificial pancreas technology for type 1 diabetes: what do users want? Diabetes Technol Ther. 2015;17(5):311–5.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    PubMed  PubMed Central  Google Scholar 

  • 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

  • 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 

  • Breland JY, Yeh VM, Yu J. Adherence to evidence-based guidelines among diabetes self-management apps. Transl Behav Med. 2013;3(3):277–86.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 

  • Carver C, Scheier M. Control processes and self-organization as complementary principles underlying behavior. Personal Soc Psychol Rev. 2002;6(4):304–15.

    Article  Google Scholar 

  • Health and Social Care Information Centre. National diabetes audit 2014–2015 report 1: care processes and treatment targets; 2016 HSCIC Leeds UK. 

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Coonrod BA, Betschart J, Harris MI. Frequency and determinants of diabetes patient education among adults in the US population. Diabetes Care. 1994;

    Google Scholar 

  • 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 

  • Couper MP. Engagement and retention: measuring breadth and depth of participant use of an online intervention. J Med Internet Res. 2010;12(4):e52.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27(5):1218–24.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Del Toro V, Parker SR. Principles of control systems engineering: McGraw Hill; New York;1960.

    Google Scholar 

  • van Dijk JAGM. Digital divide research, achievements and shortcomings. Poetics. 2006;34(4-5):221–35.

    Article  Google Scholar 

  • Donkin L. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res. 2011;13(3):e52.

    Article  PubMed  PubMed Central  Google Scholar 

  • Egede LE, et al. Medication nonadherence in diabetes. Diabetes Care. 2012;35(12):2533–9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Eng DS, Lee JM. The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes. 2013;14(4):231–8.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Free AH, et al. Simple specific test for urine glucose. Clin Chem. 1957;3(3):163–8.

    CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Grol R. Personal paper. Beliefs and evidence in changing clinical practice. Br Med J. 1997;315(7105):418–21.

    Article  CAS  Google Scholar 

  • Guariguata L, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–49.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Holman R, et al. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359:1577–89.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • International Diabetes Federation. Diabetes: facts and figures. n.d.. Available at: http://www.idf.org/worlddiabetesday/toolkit/gp/facts-figures.

  • Juvenile Diabetes Resarch Foundation. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008;359:1464–1476

    Google Scholar 

  • Karter AJ, et al. New prescription medication gaps: a comprehensive measure of adherence to new prescriptions. Health Serv Res. 2009;44(5p1):1640–61.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  PubMed  Google Scholar 

  • Liang X, et al. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabet Med. 2011;28(4):455–63.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    PubMed  Google Scholar 

  • Murray E, et al. Normalisation process theory: a framework for developing, evaluating and implementing complex interventions. BMC Med. 2010;8:63.

    Article  PubMed  PubMed Central  Google Scholar 

  • National Institute for Health and Clinical Excellence. Type 1 diabetes in adults: diagnosis and management. NICE London; 2015.

    Google Scholar 

  • 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 

  • O’Connor PJ, et al. Outpatient diabetes clinical decision support: current status and future directions. Diabet Med. 2016;33(6):734–41.

    Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pereira K, et al. Internet delivered diabetes self-management education: a review. Diabetes Technol Ther. 2015;17(1):55–63.

    Article  PubMed  Google Scholar 

  • 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.

    CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Riazi H, et al. Managing diabetes mellitus using information technology: a systematic review. J Diabetes Metab Disord. 2015;14(1):35.

    Article  Google Scholar 

  • Roberts LG. Beyond Moore’s law: internet growth trends. Computer. 2000;33(1):117–9.

    Article  Google Scholar 

  • van Rooij T, Marsh S. eHealth: past and future perspectives. Personalized Medicine 2016;13(1):15–40

    Google Scholar 

  • Roshanov PS, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. Br Med J. 2013;346:f657.

    Article  Google Scholar 

  • Samy GN, Ahmad R, Ismail Z. Security threats categories in healthcare information systems. Health Informatics J. 2010;16(3):201–9.

    Article  PubMed  Google Scholar 

  • 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 

  • Schaller RR. Moore’s law: past, present and future. IEEE Spectr. 1997;34(6):52–57.

    Google Scholar 

  • Schmidt S, et al. Use of an automated bolus calculator in MDI-treated type 1 diabetes. Diabetes Care. 2012;35(5):984–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

  • 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 

  • Office for National Statistics. Internet users. Office for National Statistics; London; 2015.

    Google Scholar 

  • Tabak RG, Khoong EC, Chambers DA. et al. Bridging research and practice. Am J Prev Med 2012; 43:337–350.

    Google Scholar 

  • Tattersall RB. Home blood glucose monitoring. Diabetologia. 1979;16(2):71–4.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Walford S, et al. Self-monitoring of blood-glucose – improvement of diabetic control. Lancet. 1978;1(8067):732–5.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Woolf SH. The meaning of translational research and why it matters. JAMA. 2008;299(2):211–3.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

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Farmer, A., Pal, K. (2018). Home Blood Glucose Monitoring and Digital-Health in Diabetes. In: Bonora, E., DeFronzo, R. (eds) Diabetes. Epidemiology, Genetics, Pathogenesis, Diagnosis, Prevention, and Treatment. Endocrinology. Springer, Cham. https://doi.org/10.1007/978-3-319-27317-4_13-1

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