The impact of mobile technologies on healthcare is particularly evident in the case of self-management of chronic diseases, where they can decrease spending and improve life quality of patients. We propose the adoption of agent-based modeling and simulation techniques as built-in tools to dynamically monitor the state of patient health and provide recommendations for self-management. To demonstrate the feasibility of our proposal we focus on Type 1 diabetes mellitus as our case study, and provide simulation results where the dynamic evolution of signal parameters is shown in the case of healthy and Type 1 diabetes mellitus patients, focussing in particular on the beneficial effects that self-management interventions have on plasma glucose values.
SilvaBMRodriguesJJDe la Torre DíezIet al. Mobilehealth: A review of current state in 2015. J Biomed Inform2015; 56: 265–272. DOI: 10.1016/j.jbi.2015.06.003.
2.
VarshneyU. Mobile health: Four emerging themes of research. Decis Support Syst2014; 66: 20–35. DOI: 10.1016/j.dss.2014.06.001.
3.
UllahSHigginsHBraemBet al. A comprehensive survey of wireless body area networks. J Med Syst2012; 36: 1065–1094. DOI:10.1007/s10916–010–9571–3.
4.
LaiXLiuQWeiXet al. A survey of body sensor networks. Sensors2013; 13: 5406–5447. DOI:10.3390/s130505406.
5.
FortinoGGiannantonioRGravinaRet al. Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Trans Hum Mach Syst2013; 43: 115–133. DOI: 10.1109/TSMCC.2012.2215852.
6.
BarcelóAEpping-JordanJOrduñezPet al. Innovative care for chronic conditions: Organizing and delivering high quality care for chronic noncommunicable diseases in the Americas. Washington, D.C: The PAHO Headquarters Library and Information Services, 2013.
7.
RuggieroLGlasgowRDryfoosJMet al. Diabetes self-management: Self-reported recommendations and patterns in a large population. Diabetes Care1997; 20: 568–576. DOI:10.2337/diacare.20.4.568.
8.
NewmanSSteedLMulliganK. Self-management interventions for chronic illness. Lancet2004; 364: 1523–1537. DOI:10.1016/S0140–6736(04)17277–2.
9.
AkterSRayP. mHealth - an ultimate platform to serve the unserved. IMIA Yearb Med Inform2010; 2010: 94–100.
10.
ZhengHNugentCMcCullaghPet al. Smart self-management: assistive technology to support people with chronic disease. J Telemed Telecare2010; 16: 224–227. DOI:10.1258/jtt.2010.004017.
11.
GuytonACHallJE. Textbook of medical physiology. 11th ed.Philadelphia, PA: Elsevier Sounders, 2006. pp. 1064.
12.
MontagnaSOmiciniAAngeliFDet al. Towards the adoption of agent-based modelling and simulation in mobile health systems for the self-management of chronic diseases. In: 17th workshop “from objects to agents” (WOA 2016) (eds SantoroCMessinaFDe BenedettiM), Catania, Italy, July 29–30 2016, volume 1664, pp. 100–105. CEUR-WS.org. Technical University of Aachen (RWTH): Sun SITE Central Europe (CEUR).
13.
PopowCHornWRamiBet al. VIE-DIAB: A support program for telemedical glycaemic control. In: Artificial intelligence in medicine: 9th conference on artificial intelligence in medicine in Europe, AIME 2003 (eds DojatMKeravnouETBarahonaP), Protaras, Cyprus, October 18–22 2003, pp. 350–354. Berlin: Springer. DOI:10.1007/978–3–540–39 907–0 48.
14.
VarshneyU. Pervasive healthcare computing. EMR/EHR, wireless and health monitoring. 1st ed. Berlin: Springer, 2009. DOI:10.1007/978–1–4419–0215–3.
15.
ElissenANolteEKnaiCet al. Is Europe putting theory into practice? A qualitative study of the level of self-management support in chronic care management approaches. BMC Health Serv Res2013; 13: 1–9. DOI:10.1186/1472–6963–13–117.
16.
VelikovaMLucasPJVan der HeijdenM. Intelligent disease self-management with mobile technology. Computer2015; 48: 32–39. DOI:10.1109/MC.2015.48.
17.
MooreSLFischerHHSteeleAWet al. A mobile health infrastructure to support underserved patients with chronic disease. Healthcare2014; 2: 63–68. DOI: 10.1016/j.hjdsi.2013.12.016.
18.
The Mount Hood 4 Modeling Group. Computer modeling of diabetes and its complications. Diabetes Care2007; 30: 1638–1646. DOI:10.2337/dc07–9919.
19.
MacalCMNorthMJ. Tutorial on agent-based modelling and simulation. J Simulat2010; 4: 151–162. DOI:10.1057/jos.2010.3.
20.
OmiciniAZambonelliF. MAS as complex systems: A view on the role of declarative approaches. In: LeiteJAOmiciniASterlingLet al. (eds) Declarative agent languages and technologies, lecture notes in computer science. Berlin: Springer, 2004, volume 2990, pp. 1–17. DOI:10.1007/978–3–540–25932–9 1.
21.
MontagnaSOmiciniA. Agent-based modelling in multicellular systems biology. In: AdamattiDF (ed) Multi-agent based simulations applied to biological and environmental systems. Advances in Computational Intelligence and Robotics (ACIR), Hershey, PA: IGI Global, 2017, pp. 159–178. DOI:10.4018/978–1–5225–1756–6.ch007.
22.
CannataNCorradiniFMerelliEet al. An agent-oriented conceptual framework for systems biology. In: MerelliEGonz′alez PerezPPOmiciniA (eds) Transactions on computational systems biology III, lecture notes in computer science. Berlin: Springer, 2005, volume 3737, pp. 105–122. DOI:10.1007/11599128 8.
23.
MichelFFerberJDrogoulA. Multi-Agent Systems and Simulation: A Survey from the Agent Community’s Perspective; in UhrmacherAMWeynsD (eds.) Multi-Agent Systems: Simulation and Applications. Boca Raton, FL, USA: CRC Press, 2009. ISBN 978-1-4200-7023-1. DOI: 10.1201/9781420070248.
24.
GelfandA. The biology of interacting things: The intuitive power of agent-based models. Biomed Comput Rev2013; Fall: 20–27.
25.
MontagnaSOmiciniAPianiniD. Extending the Gillespie’s stochastic simulation algorithm for integrating discrete-event and multi-agent based simulation. In: GaudouBSichmanJS (eds) Multi-agent based simulation XVI, lecture notes in computer science. Berlin: Springer, 2016, volume 9568, pp. 3–18. DOI: 10.1007/978–3–319–31447–1_1.
26.
BonabeauE. Agent-based modeling: Methods and techniques for simulating human systems. Proc Nat Acad Sci USA2002; 99(suppl. 3): 7280–7287. DOI:10.1073/pnas.082080899.
27.
FortinoGGarroAMascillaroSet al. Using event-driven lightweight DSC-based agents for MAS modelling. Int J Agent-Oriented Softw2010; 4: 113–140. DOI:10.1504/IJAOSE.2010.032798.
28.
PianiniDMontagnaSViroliM. Chemical-oriented simulation of computational systems with ALCHEMIST. J Simulat2013; 7: 202.
29.
FortinoGGarroARussoW. An integrated approach for the development and validation of multi-agent systems. Int J Comput Sys Sci Eng2005; 20: 259–271.
30.
FortinoGNorthMJ. Simulation-based development and validation of multi-agent systems: AOSE and ABMS approaches. J Simulat2013; 7: 137–143. DOI: 10.1057/jos.2013.12.
31.
FortinoGRussoW. ELDAMeth: An agent-oriented methodology for simulation-based prototyping of distributed agent systems. Inform Software Tech2012; 54: 608–624. DOI:10.1016/j.infsof.2011.08.006.
32.
MolesiniACasadeiMOmiciniAet al. Simulation in agent-oriented software engineering: The SODA case study. Sci Comput Program2013; 78: 705–714. DOI:10.1016/j.scico.2011.09.007.
33.
Foster-PowellKHoltSHABrand-MillerJC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr2002; 76: 5–56.
34.
AtkinsonFSFoster-PowellKBrand-MillerJC. International tables of glycemic index and glycemic load values: 2008. Diabetes Care2008; 31: 2281–2283. DOI: 10.2337/dc08–1239.
35.
FunnellMMBrownTLChildsBPet al. National standards for diabetes self-management education. Diabetes Care2011; 34(Suppl. 1): S89–S96. DOI:10.2337/dc11-S089.
GeorgssonMStaggersN. An evaluation of patients’ experienced usability of a diabetes mhealth system using a multi-method approach. J Biomed Inform2016; 59: 115–129. DOI:10.1016/j.jbi.2015.11.008.
39.
LiangXWangQYangXet al. Effect of mobile phone intervention for diabetes on glycaemic control: A metaanalysis. Diabetic Med2011; 28: 455–463. DOI: 10.1111/j.1464–5491.2010.03180.x.
40.
SutcliffePMartinSSturtJet al. Systematic review of communication technologies to promote access and engagement of young people with diabetes into healthcare. BMC Endocr Disord2011; 11: 1. DOI: 10.1186/1472–6823–11–1.
41.
DayerLHeldenbrandSAndersonPet al. Smartphone medication adherence apps: Potential benefits to patients and providers. J Am Pharm Assoc2013; 53: 172–181. DOI:10.1331/JAPhA.2013.12202.
42.
MulvaneySAAndersSSmithAKet al. A pilot test of a tailored mobile and web-based diabetes messaging system for adolescents. J Telemed Telecare2012; 18: 115–118. DOI:10.1258/jtt.2011.111006.
43.
ChungYSKimYLeeCH. Effectiveness of the smart care service for diabetes management. Healthc Inform Res2011; 20: 288–294. DOI: 10.4258/hir.2014.20.4.288.
44.
FioravantiAFicoGSalviDet al. Automatic messaging for improving patients engagement in diabetes management: an exploratory study. Med Biol Eng Comput2015; 53: 1285–1294. DOI:10.1007/s11517–014–1237–8.
45.
WakiKFujitaHUchimuraYet al. Dialbetics: A novel smartphone-based self-management support system for Type 2 diabetes patients. J Diabetes Sci Technol2014; 8: 209–215. DOI: 10.1177/1932296814526495.
46.
MessoriMCobelliCMagniL. Artificial pancreas: from in-silico to in-vivo. 9th IFAC symposium on advanced control of chemical processes, ADCHEM 2015. IFAC-PapersOnLine 2015; 48: 1300–1308. DOI:10.1016/j.ifacol.2015.09.148.
47.
YasiniSNaghibi-SistaniMBKarimpourA. Agent-based simulation for blood glucose control in diabetic patients. Int J Med Health Biomed Bioeng Pharm Eng2009; 3: 41–48.
48.
GuytonACHallJE. Textbook of medical physiology. 11th ed.Philadelphia, PA: Elsevier Sounders, 2006, chapter 78, pp. 961–977.
49.
WilmoreJHCostillDL. Physiology of sport and exercise. 2nd ed.Champaign, IL: Human Kinetics Publishers, 2004.
KingABArmstrongDU. A prospective evaluation of insulin dosing recommendations in patients with Type 1 diabetes at near normal glucose control: Bolus dosing. J Diabetes Sci Technol2007; 1: 42–46. DOI:10.1177/193229680700100107.
52.
LukeSCioffi-RevillaCPanaitLet al. MASON: A multiagent simulation environment. Simulation2005; 81: 517–527.
53.
MariebEN. Human anatomy and physiology, chapter 16. San Francisco, CA: Benjamin Cummings, 2001.
