{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:33:31Z","timestamp":1791909211590,"version":"4.3.4"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["Law 232\/2016 (Initiative \"Departments of Excellence\")"],"award-info":[{"award-number":["Law 232\/2016 (Initiative \"Departments of Excellence\")"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1\u20135 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-\/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient\u2019s data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.<\/jats:p>","DOI":"10.3390\/s20143870","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T09:30:49Z","timestamp":1594719049000},"page":"3870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":168,"title":["Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors"],"prefix":"10.3390","volume":"20","author":[{"given":"Martina","family":"Vettoretti","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4358-9268","authenticated-orcid":false,"given":"Giacomo","family":"Cappon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8041-2280","authenticated-orcid":false,"given":"Andrea","family":"Facchinetti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3248-1393","authenticated-orcid":false,"given":"Giovanni","family":"Sparacino","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1001\/archinternmed.2009.193","article-title":"Diabetes Control and Complications Trial\/Epidemiology of Diabetes Interventions and Complications (DCCT\/EDIC) Research Group; Nathan, D.M.; Zinman, B.; Modern-day clinical course of type 1 diabetes mellitus after 30 years\u2019 duration: The diabetes control and complications trial\/epidemiology of diabetes interventions and complications and pittsburgh epidemiology of diabetes complications experience (1983\u20132005)","volume":"169","author":"Nathan","year":"2009","journal-title":"Arch. Intern. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.4158\/EP.12.S1.34","article-title":"Insights from the diabetes control and complications trial\/epidemiology of diabetes interventions and complications study on the use of intensive glycemic treatment to reduce the risk of complications of type 1 diabetes","volume":"12","author":"Genuth","year":"2006","journal-title":"Endocr. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41572-019-0092-1","article-title":"Diabetic neuropathy","volume":"5","author":"Feldman","year":"2019","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1177\/1932296814532906","article-title":"Bolus Calculators","volume":"8","author":"Schmidt","year":"2014","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11892-018-1097-y","article-title":"App-based insulin calculators: Current and future state","volume":"18","author":"Eiland","year":"2018","journal-title":"Curr. Diabetes Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.4158\/EP.14.9.1095","article-title":"Analysis of guidelines for Basal-Bolus insulin dosing: Basal insulin, correction factor, and carbohydrate-to-insulin ratio","volume":"14","author":"Davidson","year":"2008","journal-title":"Endocr. Pract."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/dia.2014.0192","article-title":"Circadian variability of insulin sensitivity: Physiological input for in silico artificial pancreas","volume":"17","author":"Visentin","year":"2015","journal-title":"Diabetes Technol. Ther."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"American Diabetes Association 7 (2019). Diabetes technology: Standards of medical care in diabetes\u20142020. Diabetes Care, 43, S77\u2013S88.","DOI":"10.2337\/dc20-S007"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.diabres.2017.08.005","article-title":"Continuous glucose monitoring: A review of the technology and clinical use","volume":"133","author":"Klonoff","year":"2017","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"383","DOI":"10.4093\/dmj.2019.0121","article-title":"Continuous glucose monitoring sensors for diabetes management: A review of technologies and applications","volume":"43","author":"Cappon","year":"2019","journal-title":"Diabetes Metab. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"215","DOI":"10.2147\/MDER.S110121","article-title":"Devices for continuous monitoring of glucose: Update in technology","volume":"10","author":"Velandia","year":"2017","journal-title":"Med. Devices"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.amjms.2019.07.003","article-title":"Continuous glucose monitoring: Review of an innovation in diabetes management","volume":"358","author":"Mian","year":"2019","journal-title":"Am. J. Med. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1177\/193229681000400222","article-title":"Continuous glucose monitoring: Real-Time algorithms for calibration, filtering, and alarms","volume":"4","author":"Bequette","year":"2010","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1089\/dia.2019.0234","article-title":"Real-world data from the first U.S. commercial users of an implantable continuous glucose sensor","volume":"21","author":"Sanchez","year":"2019","journal-title":"Diabetes Technol. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mancini, G., Berioli, M.G., Santi, E., Rogari, F., Toni, G., Tascini, G., Crispoldi, R., Ceccarini, G., and Esposito, S. (2018). Flash glucose monitoring: A review of the literature with a special focus on type 1 diabetes. Nutrients, 10.","DOI":"10.3390\/nu10080992"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1056\/NEJMoa1002853","article-title":"STAR 3 Study Group. Effectiveness of sensor-augmented insulin-pump therapy in type 1 diabetes","volume":"363","author":"Bergenstal","year":"2010","journal-title":"N. Engl. J. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group, Tamborlane, W.V., Beck, R.W., Bode, B.W., Buckingham, B., Chase, H.P., Clemons, R., Fiallo-Scharer, R., Fox, L.A., and Gilliam, L.K. (2008). Continuous glucose monitoring and intensive treatment of type 1 diabetes. N. Engl. J. Med., 359, 1464\u20131476.","DOI":"10.1056\/NEJMoa0805017"},{"key":"ref_18","unstructured":"Abbott (2020, July 02). Abbott\u2032s Freestyle\u00ae Libre 2, with Optional Real-Time Alarms, Secures CE Mark for Use in Europe. Available online: https:\/\/abbott.mediaroom.com\/2018-10-01-Abbott-s-FreeStyle-R-Libre-2-with-Optional-Real-Time-Alarms-Secures-CE-Mark-for-Use-in-Europe."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1177\/1932296816667749","article-title":"Regulation Catches Up to Reality","volume":"11","author":"Edelman","year":"2016","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s12938-019-0658-x","article-title":"Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: A review","volume":"18","author":"Vettoretti","year":"2019","journal-title":"Biomed. Eng. Online"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s40265-019-01149-2","article-title":"Artificial pancreas: Current progress and future outlook in the treatment of type 1 diabetes","volume":"79","author":"Ramli","year":"2019","journal-title":"Drugs"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"181","DOI":"10.2337\/dc16-1536","article-title":"Diabetes device use in adults with type 1 diabetes: Barriers to uptake and potential intervention targets","volume":"40","author":"Tanenbaum","year":"2016","journal-title":"Diabetes Care"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1177\/1932296818759845","article-title":"Smart pens will improve insulin therapy","volume":"12","author":"Klonoff","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_24","first-page":"170","article-title":"Decision support systems for insulin treatment adjustment in people with type 1 diabetes","volume":"17","author":"Nimri","year":"2020","journal-title":"Pediatr. Endocrinol. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e10775","DOI":"10.2196\/10775","article-title":"Artificial intelligence for diabetes management and decision support: Literature review","volume":"20","author":"Contreras","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1089\/152091503322250640","article-title":"Design, methods, and evaluation directions of a multi-access service for the management of diabetes mellitus patients","volume":"5","author":"Bellazzi","year":"2003","journal-title":"Diabetes Technol. Ther."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1109\/TITB.2009.2039711","article-title":"SMARTDIAB: A communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients","volume":"14","author":"Mougiakakou","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1089\/dia.2017.0007","article-title":"\u201cIt is definitely a game changer\u201d: A qualitative study of experiences with in-home overnight closed-loop technology among adults with type 1 diabetes","volume":"19","author":"Hendrieckx","year":"2017","journal-title":"Diabetes Technol. Ther."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1111\/dme.13268","article-title":"Psychological outcomes of evening and night closed-loop insulin delivery under free living conditions in people with Type 1 diabetes: A 2-month randomized crossover trial","volume":"34","author":"Kropff","year":"2016","journal-title":"Diabet. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1089\/dia.2018.0079","article-title":"Continuous glucose monitoring and insulin informed advisory system with automated titration and dosing of insulin reduces glucose variability in type 1 diabetes mellitus","volume":"20","author":"Breton","year":"2018","journal-title":"Diabetes Technol. Ther."},{"key":"ref_31","unstructured":"Herrero, P., Lopez, B., and Martin, C. (2016, January 30). PEPPER: Patient empowerment through predictive personalized decision support. Proceedings of the AID: Artificial Intelligence for Diabetes: 1st ECAI Workshop on Artificial Intelligence for Diabetes at the 22nd European Conference on Artificial Intelligence (ECAI 2016), The Hague, The Netherlands."},{"key":"ref_32","unstructured":"DreaMed Diabeted Ltd (2020, April 15). On DreaMed Advisor Pro. Available online: https:\/\/dreamed-diabetes.com\/advisor\/."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Diabetes Research In Children Network (DirecNet) Study Group, Tsalikian, E., Buckingham, B., Xing, N., Weinzimer, S., Fiallo-Scharer, R., Kollman, C., Mauras, N., Tsalikian, E., and Tamborlane, W. (2008). Use of the DirecNet Applied Treatment Algorithm (DATA) for diabetes management with a real-time continuous glucose monitor (the FreeStyle Navigator). Pediatr. Diabetes, 9, 142\u2013147.","DOI":"10.1111\/j.1399-5448.2007.00301.x"},{"key":"ref_34","unstructured":"Scheiner, G. (2015). Practical CGM: Improving Patient Outcomes through Continuous Glucose Monitoring, American Diabetes Association."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1177\/1932296816663747","article-title":"Recommendations for using Real-Time Continuous Glucose Monitoring (rtCGM) data for insulin adjustments in type 1 diabetes","volume":"11","author":"Pettus","year":"2016","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1177\/1932296818777524","article-title":"In Silico Assessment of literature insulin bolus calculation methods accounting for glucose rate of change","volume":"13","author":"Cappon","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1177\/1932296818759558","article-title":"A Neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring","volume":"12","author":"Cappon","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1177\/1932296813514502","article-title":"The UVA\/PADOVA type 1 diabetes simulator: New features","volume":"8","author":"Man","year":"2014","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1177\/1932296815599177","article-title":"Are risk indices derived from CGM interchangeable with SMBG-Based indices?","volume":"10","author":"Fabris","year":"2015","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., del Favero, S., and Facchinetti, A. (2020). Machine-learning based model to improve insulin bolus calculation in type 1 diabetes therapy. IEEE Trans. Biomed. Eng., 1.","DOI":"10.1109\/TBME.2020.3004031"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2015.02.003","article-title":"Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability","volume":"119","author":"Herrero","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_42","first-page":"1","article-title":"Advanced insulin bolus advisor based on run-to-run control and case-based reasoning","volume":"19","author":"Herrero","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3233\/AIC-1994-7104","article-title":"Case-based reasoning: Foundational issues, methodological variations, and system approaches","volume":"7","author":"Aamodt","year":"1994","journal-title":"AI Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JBHI.2015.2464088","article-title":"An advanced bolus calculator for type 1 diabetes: System architecture and usability results","volume":"20","author":"Pesl","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1089\/dia.2015.0413","article-title":"Clinical safety and feasibility of the advanced bolus calculator for type 1 diabetes based on case-based reasoning: A 6-week nonrandomized single-arm pilot study","volume":"18","author":"Reddy","year":"2016","journal-title":"Diabetes Technol. Ther."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/JBHI.2018.2813424","article-title":"Personalized adaptive cbr bolus recommender system for type 1 diabetes","volume":"23","author":"Ibanez","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.artmed.2017.09.007","article-title":"Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support","volume":"85","author":"Brown","year":"2018","journal-title":"Artif. Intell. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1109\/JBHI.2018.2887067","article-title":"A dual mode adaptive basal-bolus advisor based on reinforcement learning","volume":"23","author":"Sun","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Abraham, S.B., Arunachalam, S., Zhong, A., Agrawal, P., Cohen, O., and McMahon, C.M. (2019). Improved real-world glycemic control with continuous glucose monitoring system predictive alerts. J. Diabetes Sci. Technol.","DOI":"10.1177\/1932296819859334"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1089\/dia.2019.0139","article-title":"A real-time continuous glucose monitoring-based algorithm to trigger hypotreatments to prevent\/mitigate hypoglycemic events","volume":"21","author":"Camerlingo","year":"2019","journal-title":"Diabetes Technol. Ther."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/JBHI.2018.2840690","article-title":"An ARIMA model with adaptive orders for predicting blood glucose concentrations and hypoglycemia","volume":"23","author":"Yang","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e2833","DOI":"10.1002\/cnm.2833","article-title":"A review of personalized blood glucose prediction strategies for T1DM patients","volume":"33","author":"Oviedo","year":"2016","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1177\/1932296816654161","article-title":"How much is short-term glucose prediction in type 1 diabetes improved by adding insulin delivery and meal content information to CGM data? A Proof-of-Concept Study","volume":"10","author":"Zecchin","year":"2016","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.artmed.2019.07.007","article-title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes","volume":"98","author":"Woldaregay","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1089\/dia.2009.0076","article-title":"Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring","volume":"12","author":"Facchinetti","year":"2010","journal-title":"Diabetes Technol. Ther."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1177\/1932296818761457","article-title":"Decision support in diabetes care: The challenge of supporting patients in their daily living using a mobile glucose predictor","volume":"12","author":"Rigla","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_57","unstructured":"(2020, April 15). The OhioT1DM Dataset. Available online: http:\/\/smarthealth.cs.ohio.edu\/OhioT1DM-dataset.html."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xie, J., and Wang, Q. (2020). Benchmarking machine learning algorithms on blood glucose prediction for Type 1 Diabetes in comparison with classical time-series models. IEEE Trans. Biomed. Eng., 1.","DOI":"10.1109\/TBME.2020.2975959"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cappon, G., Facchinetti, A., Sparacino., G., Georgiou, P., and Herrero, P. (2019). Classification of postprandial glycemic status with application to insulin dosing in type 1 diabetes\u2014An in silico proof-of-concept. Sensors, 19.","DOI":"10.3390\/s19143168"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1177\/1932296818823792","article-title":"Prediction of hypoglycemia during aerobic exercise in adults with type 1 diabetes","volume":"13","author":"Reddy","year":"2019","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1177\/1460458219850682","article-title":"Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning","volume":"26","author":"Vehi","year":"2019","journal-title":"Health Inform. J."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Contreras, I., Oviedo, S., Vettoretti, M., Visentin, R., and Vehi, J. (2017). Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187754"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijmedinf.2019.03.008","article-title":"Risk-based postprandial hypoglycemia forecasting using supervised learning","volume":"126","author":"Oviedo","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1093\/jamia\/ocv104","article-title":"A case study in open source innovation: Developing the Tidepool Platform for interoperability in type 1 diabetes management","volume":"23","author":"Neinstein","year":"2015","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1177\/1932296815592409","article-title":"Early detection of physical activity for people with type 1 diabetes mellitus","volume":"9","author":"Dasanayake","year":"2015","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/1932296815622453","article-title":"A digital ecosystem of diabetes data and technology: Services, systems, and tools enabled by wearables, sensors, and apps","volume":"10","author":"Heintzman","year":"2015","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1177\/1932296818774078","article-title":"Continuous glucose monitoring: Current use in diabetes management and possible future applications","volume":"12","author":"Vettoretti","year":"2018","journal-title":"J. Diabetes Sci. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3870\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:05Z","timestamp":1760176205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,10]]},"references-count":67,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20143870"],"URL":"https:\/\/doi.org\/10.3390\/s20143870","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,10]]}}}