Skip to main content

Automated Insulin Dosing for Type 1 Diabetes

  • Living reference work entry
  • First Online:
Encyclopedia of Systems and Control
  • 356 Accesses

Abstract

The development of automated insulin delivery (also known as a closed-loop artificial pancreas) systems has been an active research area since the 1960s, with an intense focus since 2005. In the United States in 2019, there is currently one commercial device available, with others under development. There is also a strong do-it-yourself community of individuals developing and using closed-loop technology. In this chapter we provide an overview of the challenges in developing automated insulin delivery systems and the algorithms that are commonly used to regulate blood glucose.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Bibliography

  • Atlas E, Nimri R, Miller S, Grunberg EA, Phillip M (2010) MD-logic artificial pancreas systems. Diabetes Care 33(5):1072–1076

    Article  Google Scholar 

  • Barnard KD, Ziegler R, Klonoff DC, Braune K, Petersen B, Rendschmidt T, Finan D, Kowalski A, Heinemann L (2018). Open source closed-loop insulin delivery systems: a clash of cultures or merging of diverse approaches? J Diabetes Sci Technol 12(6):1223–1226

    Article  Google Scholar 

  • Baysal N, Cameron F, Buckingham BA, Wilson DM, Chase HP, Maahs DM, Bequette BW (2014) A novel method to detect pressure-induced sensor attenuations (PISA) in an artificial pancreas. J Diabetes Sci Technol 8(6):1091–1096

    Article  Google Scholar 

  • Bequette BW (2009) Glucose clamp algorithms and insulin time-action profiles. J Diabetes Sci Technol 3(5):1005–1013

    Article  Google Scholar 

  • Bequette BW (2012) Challenges and progress in the development of a closed-loop artificial pancreas. Annu Rev Control 36:255–266

    Article  Google Scholar 

  • Bequette BW (2013) Algorithms for a closed-loop artificial pancreas: the case for model predictive control (MPC). J Diabetes Sci Technol 7(6):1632–1643

    Article  Google Scholar 

  • Bequette BW (2014) Fault detection and safety in closed-loop artificial pancreas systems. J Diabetes Sci Technol 8(6):1204–1214

    Article  Google Scholar 

  • Bequette BW, Cameron F, Baysal N, Howsmon DP, Buckingham BA, Maahs DM, Levy CJ (2016) Algorithms for a single hormone closed-loop artificial pancreas: challenges pertinent to chemical process operations and control. Processes 4(4):39. https://doi.org/10.3390/pr4040039

    Article  Google Scholar 

  • Bequette BW, Cameron F, Buckingham BA, Maahs DM, Lum J (2018) Overnight hypoglycemia and hyperglycemia mitigation for individuals with type 1 diabetes. How risks can be reduced. IEEE Control Syst 38(1):125–134. https://doi.org/10.1109/MCS.2017.2767119

    Article  MathSciNet  Google Scholar 

  • Blauw H, van Bon AC, Koops R, DeVries JH (2016) Performance and safety of an integrated artificial pancreas for fully automated glucose control at home. Diabetes Obes Metab 18:671–677

    Article  Google Scholar 

  • Bondia J, Romero-Vivo S, Ricaret B, Diez JL (2018) Insulin estimation and prediction. IEEE Control Syst Mag 38(1):47–66

    Article  Google Scholar 

  • Boughton CK, Hovorka R (2019) Advances in artificial pancreas systems. Sci Transl Med 11:484. eaaw4949. https://doi.org/10.1126/scitranslmed.aaw4949

  • Breton MD, Brown SA, Karvetski CH, Kollar L, Topchyan KA, Anderson SM, Kovatchev BP (2014) Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes. Diabetes Technol Ther 16(8):506–11. https://doi.org/10.1089/dia.2013.0333

    Article  Google Scholar 

  • Brown S, Raghinaru D, Emory E, Kovatchev B (2018) First look at control-IQ: a new-generation automated insulin delivery system. Diabetes Care 41(12):2634–2636. https://doi.org/10.2337/dc18-1249

    Article  Google Scholar 

  • Buckingham BA, Block J, Burdick J, Kalajian A, Kollman C, Choy M et al (2005) Response to nocturnal alarms using a real-time glucose sensor. Diabetes Technol Ther 7:440–447

    Article  Google Scholar 

  • Buckingham B, Chase HP, Dassau E, Cobry E, Clinton P, Gage V, Caswell K, Wilkinson J, Cameron F, Lee H, Bequette BW, Doyle FJ III (2010) Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care 33(5):1013–1018

    Article  Google Scholar 

  • Buckingham BA, Forlenza GP, Pinsker JE, Christiansen MP, Wadwa RP, Schneider J, Peyser TA, Dassau E, Lee JB, O’Connor J, Layne JE, Ly TT (2018) Safety and feasibility of the OmniPod hybrid closed-system in adult, adolescent, and pediatric patients with type 1 diabetes using a personalized model predictive control algorithm. Diabetes Technol Ther 20(4):257–262

    Article  Google Scholar 

  • Cameron F, Bequette BW, Wilson DM, Buckingham BA, Lee H, Niemeyer G (2011) A closed-loop artificial pancreas based on risk management. J Diabetes Sci Technol 5(2):368–379

    Article  Google Scholar 

  • Cameron F, Wilson DM, Buckingham BA, Arzumanyan H, Clinton P, Chase HP, Lum J, Maahs DM, Calhoun PM, Bequette BW (2012a) In-patient studies of a Kalman filter based predictive pump shut-off algorithm. J Diabetes Sci Technol 6(5):1142–1147

    Article  Google Scholar 

  • Cameron F, Niemeyer G, Bequette BW (2012b) Extended multiple model prediction with application to blood glucose regulation. J Process Control 22(7):1422–1432

    Article  Google Scholar 

  • Cameron F, Niemeyer G, Wilson DM, Bequette BW, Benassi KS, Clinton P, Buckingham BA (2014) Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control (MMPPC) with repeated large unannounced meals. Diabetes Technol Ther 16(11):728–734

    Article  Google Scholar 

  • Cameron FM, Ly TT, Buckingham BA, Maahs DM, Forlenza GP, Levy CJ, Lam D, Clinton P, Messer LH, Westfall E, Levister C, Xie YY, Baysal N, Howsmon D, Patek SD, Bequette BW (2017) Closed-loop control without meal announcement in type 1 diabetes. Diabetes Technol Ther 19(9):527–532. https://doi.org/10.1089/dia.2017.0078

    Article  Google Scholar 

  • Castle JR, DeVries JH, Kovatchev B (2017) Future of automated insulin delivery. Diabetes Technol Ther 19(S3):S-67–S-72

    Google Scholar 

  • Cinar A (2018) Artificial pancreas systems. IEEE Control Syst Mag 38(1):26–29

    Article  Google Scholar 

  • Dassau E, Pinsker JE, Kudva YC, Brown SA, Gondhalekar R, Dalla Man C, Patek S, Schiavon M, Dadlani V, Dasanayake I, Church MM, Carter RE, Bevier WC, Huyett LM, Hughes J, Anderson S, Lv D, Schertz E, Emory E, McCrady-Spitzer SK, Jean T, Bradley PK, Hinshaw L, Sanz AJL, Basu A, Kovatchev B, Cobelli C, Doyle FJ III (2017) Twelve-week 24/7 ambulatory artificial pancreas with weekly adaptation of insulin delivery settings: effect on hemoglobin A1c an hypoglycemia. Diabetes Care 40:1719–1726

    Article  Google Scholar 

  • Diabetes Control and Complications Trial (DCCT) Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329:977–986

    Article  Google Scholar 

  • Diamond T, Bequette BW, Cameron F (2019) A control systems analysis of “DIY looping.” Presented at the 2019 Diabetes Technology Meeting, Bethesda

    Google Scholar 

  • Doyle FJ III, Huyett LM, Lee JB, Zisser HC, Dassau E (2014) Closed-loop artificial pancreas systems: engineering the algorithms. Diabetes Care 37:1191–1197

    Article  Google Scholar 

  • El-Khatib FH, Russell SJ, Nathan DM, Sutherlin RG, Damiano ER (2010) A bihormonal closed-loop artificial pancreas for type 1 diabetes. Sci Transl Med 2:27ra27

    Google Scholar 

  • El-Khatib F, Balliro C, Hillar MA, Magyar KL, Ekhlaspour L, Sinha M, Mondesir D, Esmaeili A, Hartigan C, Thompson MJ, Malkani S, Lock JP, Harlan DM, Clinton P, Frank E, Wilson DM, DeSalvo D, Norlander L, Ly T, Buckingham BA, Diner J, Dezube M, Young LA, Goley A, Kirkman MS, Buse JB, Zheng H, Selagamsetty RR, Damiano ER, Russell SJ (2017) Home use of bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial. Lancet 389:369–380

    Article  Google Scholar 

  • El Fathi A, Smaqui MR, Gingras V, Bolout B, Haidar A (2018) The artificial pancreas and meal control. IEEE Control Syst Mag 38(1):67–85

    Article  Google Scholar 

  • Forlenza GP, Deshpande S, Ly TT, Howsmon DP, Cameron F, Baysal N, Mauritzen E, Marcal T, Towers L, Bequette BW, Huyett LM, Pinsker JE, Gondhalekar R, Doyle FJ III, Maahs DM, Buckingham BA, Dassau E (2017) Application of zone model predictive control artificial pancreas during extended use of infusion set and sensor: a randomized crossover-controlled home-use trial. Diabetes Care 40:1096–1102. https://doi.org/10.2337/dc17-0500

    Article  Google Scholar 

  • Forlenza GP, Cameron FM, Ly TT, Lam D, Howsmon DP, Baysal N, Kulina G, Messer L, Clinton P, Levister C, Patek SD, Levy CJ, Wadwa RP, Maahs DM, Bequette BW, Buckingham BA (2018) Fully closed-loop multiple model predictive controller (MMPPC) artificial pancreas (AP) performance in adolescents and adults in a supervised hotel setting. Diabetes Technol Ther 20(5):335–343

    Article  Google Scholar 

  • Garg SK, Weinzimer SA, Tamborlane WV, Buckingham BA, Bode BW, Bailey TS, Brazg RL, Ilany J, Slover RH, Anderson SM, Bergentaal RM, Grosman B, Roy A, Cordero TL, Shin J, Lee SW, Kaufman FR (2017) Glucose outcomes with the in-hoe use of a hybrid closed-loop insulin delivery system in adolescents and adults with type 1 diabetes. Diabetes Technol Ther 19(3):156–163

    Article  Google Scholar 

  • Grosman B, Dassau E, Zisser HC, Jovanovic L, Doyle FJ III (2010) Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events. J Diabetes Sci Technol 4:961–975

    Article  Google Scholar 

  • Hirsch IB (2004) Treatment of patients with severe insulin deficiency: what we have learned over the past 2 years. Am J Med 116(3A):17S–22S

    Article  Google Scholar 

  • Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Fedrici MO et al (2004) Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 25(4):905–920

    Article  Google Scholar 

  • Howsmon DP, Cameron F, Baysal N, Ly TT, Forlenza GP, Maahs DM, Buckingham BA, Hahn J, Bequette BW (2017) Continuous glucose monitoring enables the detection of losses in infusion set actuation (LISAs). Sensors 17:161. https://doi.org/10.3390/s17010161

    Article  Google Scholar 

  • Howsmon DP, Baysal N, Buckingham BA, Forlenza GP, Ly TT, Maahs DM, Marcal T, Towers L, Mauritzen E, Deshpande S, Huyett LM, Pinsker JE, Gondhalekar R, Doyle FJ III, Dassau E, Hahn J, Bequette BW (2018) Real-time detection of infusion site failures in a closed-loop artificial pancreas. J Diabetes Sci Technol 12(3):599–607. https://doi.org/10.1177/19322968187551

    Article  Google Scholar 

  • Huyett LM, Dassau E, Zisser HC, Doyle FJ III (2018) Glucose sensor dynamics and the artificial pancreas. IEEE Control Syst Mag 39(1):30–46

    Google Scholar 

  • Kovatchev BP, Breton M, Dalla Man C, Cobelli C (2009a) In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 3(1):44–55

    Article  Google Scholar 

  • Kovatchev B, Patek S, Dassau E, Doyle FJ III, Magni L, De Nicolao G et al (2009b) The juvenile diabetes research foundation artificial pancreas consortium. Control to range for diabetes: functionality and modular architecture. J Diabetes Sci Technol 3(5):1058–1065

    Article  Google Scholar 

  • Lee JM, Newman MW, Gebremariam A, Choi P, Lewis D, Nordgren W, Costik J, Wedding J, West B, Gilby N, Benovich C, Pasek J, Garrity A, Hirschfeld E (2017) Real-world use and self-reported health outcomes of a patient-designed do-it-yourself mobile technology system for diabetes: lessons for mobile health. Diabetes Technol Ther 19(4):209219

    Article  Google Scholar 

  • Lewis D (2018) History and perspective on DIY closed looping. J Diabetes Sci Technol 13(4):790–793

    Article  Google Scholar 

  • Maahs DM, Buckingham BA, Castle JR, Cinar A, Damiano ER, Dassau E, DeVries JH, DoyleIII FJ, Griffen SC, Haidar A et al (2016) Outcome measures for artificial pancreas clinical trials: a consensus report. Diabetes Care 39:1175–1179

    Article  Google Scholar 

  • Mauseth R, Wang Y, Dassau E, Kircher R, Matheson D, Zisser H et al (2010) Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. J Diabetes Sci Technol 4:913–922

    Article  Google Scholar 

  • Messori M, Incremona CP, Cobelli C, Magni (2018) Individualized model predictive control for the artificial pancreas. IEEE Control Syst Mag 38(1):86–104

    Google Scholar 

  • Musolino G, Allen JM, Hartnell S, Wilinska ME, Tauschmann M, Boughton C, Campbell F, Denvir L, Trevelyan N, Wadwa P, DiMeglio L, Buckingham BA, Weinzimer S, Acerini CL, Hood K, Fox S, Kollman C, Sibayan J, Borgman S, Cheng P, Hovorka R (2019) Assessing the efficacy, safety and utility of 6-month day-and-night automated closed-loop insulin delivery under free-living conditions compared with insulin pump therapy in children and adolescents with type 1 diabetes: an open-label, multicenter, multinational, single-period, randomized, parallel group study protocol. BMJ Open 9:e027856. https://doi.org/10.1136/bmjopen-2018-027856

    Article  Google Scholar 

  • Navarathna P, Bequette BW, Cameron F (2018) Device based activity recognition and prediction for improved feedforward control. 2018 American control conference, Milwaukee, pp 3571–3576. https://doi.org/10.23919/ACC.2018.8430775

  • Palerm CC (2011) Physiologic insulin delivery with insulin feedback: a control systems perspective. Comput Methods Prog Biomed 102(2):130–137

    Article  MathSciNet  Google Scholar 

  • Patek SD, Bequette BW, Breton M, Buckingham BA, Dassau E, Doyle FJ III, Lum J, Magni L, Zisser H (2009) In silico preclinical trials: methodology and engineering guide to closed-loop control. J Diabetes Sci Technol 3(2):269–282

    Article  Google Scholar 

  • Pinsker JE, Lee JB, Dassau E, Seborg DE, Bradley PK, Gondhalekar R, Bevier WC, Huyett L, Zisser HC, Doyle FJ 3rd (2016) Randomized crossover comparison of personalized MPC and PID control algorithms for the artificial pancreas. Diabetes Care 39(7):1135–1142. https://doi.org/10.2337/dc15-2344. PubMed PMID: 27289127; PMCID: PMC4915560

  • Ramkissoon C, Aufderheide B, Bequette BW, Vehi J (2017) Safety and hazards associated with the artificial pancreas. IEEE Rev Biomed Eng 10:44–62. https://doi.org/10.1109/RBME.2017.2749038

    Article  Google Scholar 

  • Russell SJ, El-Khatib F, Manasi S, Magyar KL, McKeon K, Goergen LG, …Damiano ER (2014) Outpatient glycemic control with a bionic pancreas in type 1 diabetes. N Engl J Med 371(4):313–325. https://doi.org/10.1056/NEJMoa1314474

  • Steil GM (2013) Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control. J Diabetes Sci Technol 7(6):1621–1631

    Article  Google Scholar 

  • Steil GM, Palerm CC, Kurtz N, Voskanyan G, Roy A, Paz S, Kandeel FR (2011) The effect of insulin feedback on closed loop glucose control. J Clin Endocrinol Metab 96(5):1402–1408

    Article  Google Scholar 

  • Stenerson M, Cameron F, Wilson DM, Harris B, Payne S, Bequette BW, Buckingham BA (2014) The impact of accelerometer and heart rate data on hypoglycemia mitigation in type 1 diabetes. J Diabetes Sci Technol 8(1):64–69

    Article  Google Scholar 

  • Turksoy K, Paulino TM, Zaharieva DP, Yavelberg L, Jamnik V, Riddell MC, Cinar A (2015) Classification of physical activity: information to artificial pancreas control systems in real time. J Diabetes Sci Technol 9(6):1200–1207. https://doi.org/10.1177/1932296815609369

    Article  Google Scholar 

  • Turksoy T, Littlejohn E, Cinar A (2018) Multimodule, multivariable artificial pancreas for patients with type 1 diabetes. IEEE Control Syst Mag 38(1):105–124

    Article  Google Scholar 

  • Wilinska ME, Chassin LJ, Acerini CL, Allen JM, Dunger DB, Hovorka R (2010) Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes. J Diabetes Sci Technol 4:132–144

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Wayne Bequette .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer-Verlag London Ltd., part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Bequette, B.W. (2020). Automated Insulin Dosing for Type 1 Diabetes. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100131-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_100131-1

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5102-9

  • Online ISBN: 978-1-4471-5102-9

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics