{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T04:39:19Z","timestamp":1777264759925,"version":"3.51.4"},"reference-count":82,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011073","name":"Nutrition Obesity Research Center, University of North Carolina","doi-asserted-by":"publisher","award":["P30DK072476"],"award-info":[{"award-number":["P30DK072476"]}],"id":[{"id":"10.13039\/100011073","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIH National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["U54 GM104940"],"award-info":[{"award-number":["U54 GM104940"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["CUP F53D23000720006"],"award-info":[{"award-number":["CUP F53D23000720006"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007028","name":"Leona M and Harry B Helmsley Charitable Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.engappai.2025.111758","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T16:43:33Z","timestamp":1753289013000},"page":"111758","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PC","title":["A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes"],"prefix":"10.1016","volume":"159","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5129-8829","authenticated-orcid":false,"given":"Eleonora M.","family":"Aiello","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1288-3456","authenticated-orcid":false,"given":"Chiara","family":"Toffanin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael C.","family":"Riddell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8125-4015","authenticated-orcid":false,"given":"Corby K.","family":"Martin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susana R.","family":"Patton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin L.","family":"Gal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3293-9114","authenticated-orcid":false,"suffix":"III","given":"Francis J.","family":"Doyle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2025.111758_b1","series-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi","year":"2015"},{"key":"10.1016\/j.engappai.2025.111758_b2","article-title":"Review of automated insulin delivery systems for individuals with type 1 diabetes: tailored solutions for subpopulations","volume":"19","author":"Aiello","year":"2021","journal-title":"Curr. Opin. Biomed. Eng."},{"key":"10.1016\/j.engappai.2025.111758_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.conengprac.2023.105810","article-title":"Model predictive control (MPC) of an artificial pancreas with data-driven learning of multi-step-ahead blood glucose predictors","volume":"144","author":"Aiello","year":"2024","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.engappai.2025.111758_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2019.103255","article-title":"Therapy-driven deep glucose forecasting","volume":"87","author":"Aiello","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"8","key":"10.1016\/j.engappai.2025.111758_b5","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1249\/MSS.0b013e31821ece12","article-title":"2011 Compendium of Physical Activities: a second update of codes and MET values","volume":"43","author":"Ainsworth","year":"2011","journal-title":"Med. Sci. Sport. Exerc."},{"issue":"4","key":"10.1016\/j.engappai.2025.111758_b6","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.ijforecast.2021.12.015","article-title":"Hierarchical forecasting with a top-down alignment of independent-level forecasts","volume":"38","author":"Anderer","year":"2022","journal-title":"Int. J. Forecast."},{"issue":"5","key":"10.1016\/j.engappai.2025.111758_b7","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1177\/193229680700100522","article-title":"Recent advances in free-living physical activity monitoring: a review","volume":"1","author":"Andre","year":"2007","journal-title":"J. Diabetes Sci. Technol."},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b8","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ijforecast.2008.07.004","article-title":"Hierarchical forecasts for Australian domestic tourism","volume":"25","author":"Athanasopoulos","year":"2009","journal-title":"Int. J. Forecast."},{"issue":"3","key":"10.1016\/j.engappai.2025.111758_b9","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1109\/TII.2012.2219063","article-title":"Evaluating combined load forecasting in large power systems and smart grids","volume":"9","author":"Borges","year":"2012","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"24","key":"10.1016\/j.engappai.2025.111758_b10","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1136\/bjsports-2020-102955","article-title":"World health organization 2020 guidelines on physical activity and sedentary behaviour","volume":"54","author":"Bull","year":"2020","journal-title":"Br. J. Sports Med."},{"issue":"3","key":"10.1016\/j.engappai.2025.111758_b11","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1109\/TII.2019.2924326","article-title":"Two-layer transfer-learning-based architecture for short-term load forecasting","volume":"16","author":"Cai","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.engappai.2025.111758_b12","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.econmod.2010.01.006","article-title":"Multi-horizon inflation forecasts using disaggregated data","volume":"27","author":"Capistr\u00e1n","year":"2010","journal-title":"Econ. Model."},{"issue":"12","key":"10.1016\/j.engappai.2025.111758_b13","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.2337\/diab.42.12.1700","article-title":"Intensive insulin therapy and weight gain in IDDM","volume":"42","author":"Carlson","year":"1993","journal-title":"Diabetes"},{"issue":"8","key":"10.1016\/j.engappai.2025.111758_b14","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1038\/s41430-024-01446-4","article-title":"24-H energy expenditure in people with type 1 diabetes: impact on equations for clinical estimation of energy expenditure","volume":"78","author":"Carnero","year":"2024","journal-title":"Eur. J. Clin. Nutr."},{"key":"10.1016\/j.engappai.2025.111758_b15","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3389\/fnut.2016.00056","article-title":"Energy expenditure in people with diabetes mellitus: a review","volume":"3","author":"Caron","year":"2016","journal-title":"Front. Nutr."},{"key":"10.1016\/j.engappai.2025.111758_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijnonlinmec.2022.103984","article-title":"Intelligent modeling of nonlinear dynamical systems by machine learning","volume":"142","author":"Chen","year":"2022","journal-title":"Int. J. Non-Linear Mech."},{"key":"10.1016\/j.engappai.2025.111758_b17","article-title":"Design of a real-time physical activity detection and classification framework for individuals with type 1 diabetes","author":"Cho","year":"2023","journal-title":"J. Diabetes Sci. Technol."},{"key":"10.1016\/j.engappai.2025.111758_b18","series-title":"Keras: Deep Learning Library for Theano and Tensorflow","first-page":"T1","author":"Chollet","year":"2015"},{"key":"10.1016\/j.engappai.2025.111758_b19","article-title":"Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models","volume":"87","author":"Cortes","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"issue":"8","key":"10.1016\/j.engappai.2025.111758_b20","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1249\/01.MSS.0000135794.01507.48","article-title":"Accuracy of polar S410 heart rate monitor to estimate energy cost of exercise","volume":"36","author":"Crouter","year":"2004","journal-title":"Med. Sci. Sport. Exerc."},{"key":"10.1016\/j.engappai.2025.111758_b21","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00421-006-0307-5","article-title":"Estimating energy expenditure using accelerometers","volume":"98","author":"Crouter","year":"2006","journal-title":"Eur. J. Appl. Physiol."},{"issue":"4","key":"10.1016\/j.engappai.2025.111758_b22","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1109\/JBHI.2015.2432911","article-title":"Estimating energy expenditure with multiple models using different wearable sensors","volume":"20","author":"Cvetkovi\u0107","year":"2015","journal-title":"IEEE J. Biomed. Heal. Inform."},{"issue":"11","key":"10.1016\/j.engappai.2025.111758_b23","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1249\/MSS.0b013e318299d2eb","article-title":"A comparison of energy expenditure estimation of several physical activity monitors","volume":"45","author":"Dannecker","year":"2013","journal-title":"Med. Sci. Sports Exerc."},{"key":"10.1016\/j.engappai.2025.111758_b24","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.ijcard.2015.03.038","article-title":"Fitbit\u00ae: An accurate and reliable device for wireless physical activity tracking","volume":"185","author":"Diaz","year":"2015","journal-title":"Int. J. Cardiol."},{"issue":"6","key":"10.1016\/j.engappai.2025.111758_b25","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1152\/japplphysiol.00309.2011","article-title":"Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample","volume":"111","author":"Freedson","year":"2011","journal-title":"J. Appl. Physiol."},{"key":"10.1016\/j.engappai.2025.111758_b26","first-page":"1","article-title":"Towards interpretable LSTM-based modelling of hydrological systems","volume":"2023","author":"De la Fuente","year":"2023","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"issue":"10","key":"10.1016\/j.engappai.2025.111758_b27","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1089\/dia.2023.0528","article-title":"Online classification of unstructured free-living exercise sessions in people with type 1 diabetes","volume":"26","author":"Fushimi","year":"2024","journal-title":"Diabetes Technol. Ther."},{"issue":"10","key":"10.1016\/j.engappai.2025.111758_b28","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"issue":"Aug","key":"10.1016\/j.engappai.2025.111758_b29","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"10.1016\/j.engappai.2025.111758_b30","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b31","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1046\/j.1523-1755.2002.00421.x","article-title":"Effect of dietary protein restriction on prognosis in patients with diabetic nephropathy","volume":"62","author":"Hansen","year":"2002","journal-title":"Kidney Int."},{"key":"10.1016\/j.engappai.2025.111758_b32","series-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009"},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.clnu.2009.09.001","article-title":"Protein and energy metabolism in type 1 diabetes","volume":"29","author":"Hebert","year":"2010","journal-title":"Clin. Nutr."},{"issue":"9 Suppl","key":"10.1016\/j.engappai.2025.111758_b34","doi-asserted-by":"crossref","first-page":"S442","DOI":"10.1097\/00005768-200009001-00002","article-title":"Validity of accelerometry for the assessment of moderate intensity physical activity in the field","volume":"32","author":"Hendelman","year":"2000","journal-title":"Med. Sci. Sports Exerc."},{"key":"10.1016\/j.engappai.2025.111758_b35","article-title":"Training and analysing deep recurrent neural networks","volume":"26","author":"Hermans","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b36","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.jshs.2023.10.010","article-title":"The 2024 compendium of physical activities and its expansion","volume":"13","author":"Herrmann","year":"2024","journal-title":"J. Sport. Heal. Sci."},{"key":"10.1016\/j.engappai.2025.111758_b37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3389\/fnut.2014.00005","article-title":"Assessment of physical activity and energy expenditure: an overview of objective measures","volume":"1","author":"Hills","year":"2014","journal-title":"Front. Nutr."},{"key":"10.1016\/j.engappai.2025.111758_b38","first-page":"3","article-title":"Measuring physical activity and energy expenditure","author":"Hj","year":"1996","journal-title":"Hum. Kinet."},{"issue":"8","key":"10.1016\/j.engappai.2025.111758_b39","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"issue":"6","key":"10.1016\/j.engappai.2025.111758_b40","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1177\/1932296815609371","article-title":"Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate","volume":"9","author":"Jacobs","year":"2015","journal-title":"J. Diabetes Sci. Technol."},{"issue":"8","key":"10.1016\/j.engappai.2025.111758_b41","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1002\/clc.4960130809","article-title":"Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity","volume":"13","author":"Jett\u00e9","year":"1990","journal-title":"Clin. Cardiol."},{"issue":"7","key":"10.1016\/j.engappai.2025.111758_b42","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1002\/oby.23454","article-title":"Interindividual variability of human thermoregulation: Toward personalized ergonomics of the indoor thermal environment","volume":"30","author":"Khovalyg","year":"2022","journal-title":"Obes."},{"key":"10.1016\/j.engappai.2025.111758_b43","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"L\u00e4ngkvist","year":"2014","journal-title":"Pattern Recognit. Lett."},{"issue":"6555","key":"10.1016\/j.engappai.2025.111758_b44","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1136\/bmj.293.6555.1121","article-title":"Effect of optimal glycaemic control with continuous subcutaneous insulin infusion on energy expenditure in type I diabetes mellitus","volume":"293","author":"Leslie","year":"1986","journal-title":"Br. Med. J. ( Clin. Res. Ed.)"},{"key":"10.1016\/j.engappai.2025.111758_b45","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.insmatheco.2019.02.011","article-title":"A forecast reconciliation approach to cause-of-death mortality modeling","volume":"86","author":"Li","year":"2019","journal-title":"Insurance Math. Econom."},{"issue":"5","key":"10.1016\/j.engappai.2025.111758_b46","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.3390\/s21051805","article-title":"Forecasting nonlinear systems with LSTM: analysis and comparison with EKF","volume":"21","author":"Llerena Ca\u00f1a","year":"2021","journal-title":"Sens."},{"issue":"11","key":"10.1016\/j.engappai.2025.111758_b47","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1109\/TNNLS.2019.2891257","article-title":"Multistep prediction of dynamic systems with recurrent neural networks","volume":"30","author":"Mohajerin","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"10","key":"10.1016\/j.engappai.2025.111758_b48","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1088\/0967-3334\/37\/10\/1770","article-title":"Wrist-independent energy expenditure prediction models from raw accelerometer data","volume":"37","author":"Montoye","year":"2016","journal-title":"Physiol. Meas."},{"issue":"3","key":"10.1016\/j.engappai.2025.111758_b49","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/02701367.2019.1603989","article-title":"Validity of activity trackers in estimating energy expenditure during high-intensity functional training","volume":"90","author":"Morris","year":"2019","journal-title":"Res. Q. Exerc. Sport"},{"issue":"12","key":"10.1016\/j.engappai.2025.111758_b50","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1089\/dia.2022.0253","article-title":"Relationship between moderate-to-vigorous physical activity and glycemia among young adults with type 1 diabetes and overweight or obesity: results from the advancing care for type 1 diabetes and obesity network (ACT1ON) study","volume":"24","author":"Muntis","year":"2022","journal-title":"Diabetes Technol. Ther."},{"key":"10.1016\/j.engappai.2025.111758_b51","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/BF00253494","article-title":"Increased energy expenditure in poorly controlled type 1 (insulin-dependent) diabetic patients","volume":"27","author":"Nair","year":"1984","journal-title":"Diabetol."},{"key":"10.1016\/j.engappai.2025.111758_b52","series-title":"KerasTuner","author":"O\u2019Malley","year":"2019"},{"key":"10.1016\/j.engappai.2025.111758_b53","series-title":"International Conference on Machine Learning","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","author":"Pascanu","year":"2013"},{"key":"10.1016\/j.engappai.2025.111758_b54","series-title":"2014 14th International Conference on Frontiers in Handwriting Recognition","first-page":"285","article-title":"Dropout improves recurrent neural networks for handwriting recognition","author":"Pham","year":"2014"},{"issue":"19","key":"10.1016\/j.engappai.2025.111758_b55","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1001\/jama.2018.14854","article-title":"The physical activity guidelines for Americans","volume":"320","author":"Piercy","year":"2018","journal-title":"Jama"},{"issue":"6","key":"10.1016\/j.engappai.2025.111758_b56","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1007\/s12551-020-00776-4","article-title":"Modeling of dynamical systems through deep learning","volume":"12","author":"Rajendra","year":"2020","journal-title":"Biophys. Rev."},{"issue":"5","key":"10.1016\/j.engappai.2025.111758_b57","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/S2213-8587(17)30014-1","article-title":"Exercise management in type 1 diabetes: a consensus statement","volume":"5","author":"Riddell","year":"2017","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"5","key":"10.1016\/j.engappai.2025.111758_b58","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1089\/dia.2020.0495","article-title":"More time in glucose range during exercise days than sedentary days in adults living with type 1 diabetes","volume":"23","author":"Riddell","year":"2021","journal-title":"Diabetes Technol. Ther."},{"issue":"6","key":"10.1016\/j.engappai.2025.111758_b59","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1177\/1932296815609370","article-title":"Exercise and the development of the artificial pancreas: one of the more difficult series of hurdles","volume":"9","author":"Riddell","year":"2015","journal-title":"J. Diabetes Sci. Technol."},{"issue":"8","key":"10.1016\/j.engappai.2025.111758_b60","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1088\/1361-6579\/aa7cdf","article-title":"An original piecewise model for computing energy expenditure from accelerometer and heart rate signals","volume":"38","author":"Romero-Ugalde","year":"2017","journal-title":"Physiol. Meas."},{"issue":"13","key":"10.1016\/j.engappai.2025.111758_b61","doi-asserted-by":"crossref","first-page":"4379","DOI":"10.3390\/s21134379","article-title":"Deep LSTM-based transfer learning approach for coherent forecasts in hierarchical time series","volume":"21","author":"Sagheer","year":"2021","journal-title":"Sens."},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b62","doi-asserted-by":"crossref","first-page":"19038","DOI":"10.1038\/s41598-019-55320-6","article-title":"Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems","volume":"9","author":"Sagheer","year":"2019","journal-title":"Sci. Rep."},{"issue":"5","key":"10.1016\/j.engappai.2025.111758_b63","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.jsams.2011.04.003","article-title":"Validation and comparison of ActiGraph activity monitors","volume":"14","author":"Sasaki","year":"2011","journal-title":"J. Sci. Med. Sport."},{"issue":"4","key":"10.1016\/j.engappai.2025.111758_b64","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0093520","article-title":"Estimating energy expenditure from heart rate in older adults: a case for calibration","volume":"9","author":"Schrack","year":"2014","journal-title":"PLoS One"},{"issue":"21","key":"10.1016\/j.engappai.2025.111758_b65","doi-asserted-by":"crossref","first-page":"12859","DOI":"10.1109\/JSEN.2020.3000772","article-title":"Determining physical activity characteristics from wristband data for use in automated insulin delivery systems","volume":"20","author":"Sevil","year":"2020","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.engappai.2025.111758_b66","doi-asserted-by":"crossref","first-page":"439","DOI":"10.2165\/00007256-200131060-00004","article-title":"Physical activity assessment in children and adolescents","volume":"31","author":"Sirard","year":"2001","journal-title":"Sports Med."},{"key":"10.1016\/j.engappai.2025.111758_b67","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s00421-010-1672-7","article-title":"A comparison of energy expenditure estimates from the Actiheart and Actical physical activity monitors during low intensity activities, walking, and jogging","volume":"111","author":"Spierer","year":"2011","journal-title":"Eur. J. Appl. Physiol."},{"issue":"9","key":"10.1016\/j.engappai.2025.111758_b68","doi-asserted-by":"crossref","first-page":"S450","DOI":"10.1097\/00005768-200009001-00003","article-title":"Estimation of energy expenditure using CSA accelerometers at hip and wrist sites","volume":"32","author":"Swartz","year":"2000","journal-title":"Med. Sci. Sport. Exerc."},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2015.11.010","article-title":"Supply chain forecasting: Theory, practice, their gap and the future","volume":"252","author":"Syntetos","year":"2016","journal-title":"European J. Oper. Res."},{"issue":"7","key":"10.1016\/j.engappai.2025.111758_b70","doi-asserted-by":"crossref","DOI":"10.2196\/15576","article-title":"Effectiveness of wearable trackers on physical activity in healthy adults: systematic review and meta-analysis of randomized controlled trials","volume":"8","author":"Tang","year":"2020","journal-title":"JMIR MHealth UHealth"},{"key":"10.1016\/j.engappai.2025.111758_b71","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jprocont.2019.03.007","article-title":"Multiple models for artificial pancreas predictions identified from free-living condition data: A proof of concept study","volume":"77","author":"Toffanin","year":"2019","journal-title":"J. Process Control"},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b72","first-page":"1181","article-title":"MPC model individualization in free-living conditions: a proof-of-concept case study","volume":"50","author":"Toffanin","year":"2017","journal-title":"IFAC-Pap."},{"issue":"10","key":"10.1016\/j.engappai.2025.111758_b73","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1162\/08997660260293319","article-title":"Many-layered learning","volume":"14","author":"Utgoff","year":"2002","journal-title":"Neural Comput."},{"key":"10.1016\/j.engappai.2025.111758_b74","series-title":"2017 American Control Conference","first-page":"5324","article-title":"A new concept using LSTM neural networks for dynamic system identification","author":"Wang","year":"2017"},{"issue":"1","key":"10.1016\/j.engappai.2025.111758_b75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"issue":"9 Suppl","key":"10.1016\/j.engappai.2025.111758_b76","doi-asserted-by":"crossref","first-page":"S489","DOI":"10.1097\/00005768-200009001-00008","article-title":"A comparative evaluation of three accelerometry-based physical activity monitors","volume":"32","author":"Welk","year":"2000","journal-title":"Med. Sci. Sports Exerc."},{"key":"10.1016\/j.engappai.2025.111758_b77","doi-asserted-by":"crossref","first-page":"90","DOI":"10.3389\/fphys.2013.00090","article-title":"Physical activity and physical activity induced energy expenditure in humans: measurement, determinants, and effects","volume":"4","author":"Westerterp","year":"2013","journal-title":"Front. Physiol."},{"issue":"526","key":"10.1016\/j.engappai.2025.111758_b78","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1080\/01621459.2018.1448825","article-title":"Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization","volume":"114","author":"Wickramasuriya","year":"2019","journal-title":"J. Amer. Statist. Assoc."},{"issue":"2","key":"10.1016\/j.engappai.2025.111758_b79","doi-asserted-by":"crossref","DOI":"10.1002\/btm2.10201","article-title":"A review of biomarkers in the context of type 1 diabetes: biological sensing for enhanced glucose control","volume":"6","author":"Wolkowicz","year":"2021","journal-title":"Bioeng. Transl. Med."},{"issue":"6","key":"10.1016\/j.engappai.2025.111758_b80","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1152\/japplphysiol.01163.2007","article-title":"Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry","volume":"104","author":"Zakeri","year":"2008","journal-title":"J. Appl. Physiol."},{"issue":"2","key":"10.1016\/j.engappai.2025.111758_b81","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s40565-018-0435-z","article-title":"A learning framework based on weighted knowledge transfer for holiday load forecasting","volume":"7","author":"Zeng","year":"2019","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"10.1016\/j.engappai.2025.111758_b82","series-title":"2015 17th International Conference on E-Health Networking, Application & Services","first-page":"501","article-title":"Using deep learning for energy expenditure estimation with wearable sensors","author":"Zhu","year":"2015"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197625017609?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197625017609?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T03:56:55Z","timestamp":1777262215000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197625017609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":82,"alternative-id":["S0952197625017609"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2025.111758","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2025.111758","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111758"}}