{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:33:54Z","timestamp":1791999234054,"version":"4.4.0"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T00:00:00Z","timestamp":1621987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.<\/jats:p>","DOI":"10.3390\/bioengineering8060072","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T10:30:32Z","timestamp":1622025032000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques"],"prefix":"10.3390","volume":"8","author":[{"given":"Benedetta","family":"De Paoli","sequence":"first","affiliation":[{"name":"Unit of Computer Systems and Bioinformatics, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7377-5462","authenticated-orcid":false,"given":"Federico","family":"D\u2019Antoni","sequence":"additional","affiliation":[{"name":"Unit of Computer Systems and Bioinformatics, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9406-2397","authenticated-orcid":false,"given":"Mario","family":"Merone","sequence":"additional","affiliation":[{"name":"Unit of Computer Systems and Bioinformatics, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Pieralice","sequence":"additional","affiliation":[{"name":"Unit of Diabetology and Endocrinology, Department of Medicine, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-3938","authenticated-orcid":false,"given":"Vincenzo","family":"Piemonte","sequence":"additional","affiliation":[{"name":"Unit of Chemical Engineering, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Pozzilli","sequence":"additional","affiliation":[{"name":"Unit of Diabetology and Endocrinology, Department of Medicine, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.2337\/dc13-2112","article-title":"The diabetes control and complications trial\/epidemiology of diabetes interventions and complications study at 30 years: Overview","volume":"37","author":"Nathan","year":"2014","journal-title":"Diabetes Care"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.2337\/dc16-1728","article-title":"Physical activity\/exercise and diabetes: A position statement of the American Diabetes Association","volume":"39","author":"Colberg","year":"2016","journal-title":"Diabetes Care"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1111\/pedi.12176","article-title":"Exercise in children and adolescents with diabetes","volume":"15","author":"Robertson","year":"2014","journal-title":"Pediatr. 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