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. 2022 Jun 27;5:78. doi: 10.1038/s41746-022-00626-5

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — A T1D subject is equipped with CGM and the wristband to measure glucose levels and vital signs, both of which communicate with the ARISES app via Bluetooth connectivity and provide input data for the deep learning models. The wearable devices in the system are marked by *. The data collected in phase I are used to train a population model with meta-learning, which is then fine-tuned in phase II to develop personalized models.