{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T14:41:36Z","timestamp":1776523296040,"version":"3.51.2"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00440371"],"award-info":[{"award-number":["RS-2024-00440371"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02180-5","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T01:14:23Z","timestamp":1744161263000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Personalized Health Prediction AI Models Using Transfer Learning and Strategic Overfitting on Wearable Device Data"],"prefix":"10.1007","volume":"49","author":[{"given":"Inyong","family":"Jeong","sequence":"first","affiliation":[]},{"given":"Seokjin","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Yeongmin","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Yihyun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Byeongsu","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Se-Jin","family":"Ahn","sequence":"additional","affiliation":[]},{"given":"Ju-Wan","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hwamin","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"2180_CR1","doi-asserted-by":"publisher","unstructured":"Baig MM, Afifi S, Gholam Hosseini H, Mirza F (2019) A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults - a Focus on Ageing Population and Independent Living. J Med Syst 43(8):233. doi: https:\/\/doi.org\/10.1007\/s10916-019-1365-7. PMID: 31203472.","DOI":"10.1007\/s10916-019-1365-7"},{"key":"2180_CR2","doi-asserted-by":"publisher","unstructured":"Luo J, Zhang K, Xu Y, Tao Y, Zhang Q (2021) Effectiveness of Wearable Device-based Intervention on Glycemic Control in Patients with Type 2 Diabetes: A System Review and Meta-Analysis. J Med Syst 46(1):11. doi: https:\/\/doi.org\/10.1007\/s10916-021-01797-6. PMID: 34951684.","DOI":"10.1007\/s10916-021-01797-6"},{"issue":"1","key":"2180_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10916-024-02081-z","volume":"48","author":"ME Letton","year":"2024","unstructured":"Letton ME, Tr\u1ea7n TB, Flower S, Wewege MA, Wang AY, Sandler CX, Sen S, Arnold R (2024) Digital Physical Activity and Exercise Interventions for People Living with Chronic Kidney Disease: A Systematic Review of Health Outcomes and Feasibility. J Med Syst 48(1):63. doi: https:\/\/doi.org\/10.1007\/s10916-024-02081-z. PMID: 38951385; PMCID: PMC11217122.","journal-title":"J Med Syst"},{"issue":"10","key":"2180_CR4","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1016\/j.cjca.2024.07.009","volume":"40","author":"TB Marvasti","year":"2024","unstructured":"Marvasti TB, Gao Y, Murray KR, Hershman S, McIntosh C, Moayedi Y (2024) Unlocking Tomorrow\u2019s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence. Can J Cardiol 40(10):1934\u20131945. doi: 10.1016\/j.cjca.2024.07.009. Epub 2024 Jul 25. PMID: 39025363.","journal-title":"Can J Cardiol"},{"key":"2180_CR5","doi-asserted-by":"publisher","first-page":"1504190","DOI":"10.3389\/fpsyt.2024.1504190","volume":"15","author":"JH Park","year":"2025","unstructured":"Park JH, Shin YB, Jung D, Hur JW, Pack SP, Lee HJ, Lee HM, Cho CH (2025) Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions. Front Psychiatry 15:1504190. doi: https:\/\/doi.org\/10.3389\/fpsyt.2024.1504190","journal-title":"Front Psychiatry"},{"key":"2180_CR6","doi-asserted-by":"publisher","unstructured":"Goetz L, Seedat N, Vandersluis R, van der Schaar M (2024) Generalization-a key challenge for responsible AI in patient-facing clinical applications. NPJ Digit Med 21;7(1):126. doi: https:\/\/doi.org\/10.1038\/s41746-024-01127-3. PMID: 38773304; PMCID: PMC11109198.","DOI":"10.1038\/s41746-024-01127-3"},{"issue":"1","key":"2180_CR7","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41746-024-01118-4","volume":"7","author":"C Ong Ly","year":"2024","unstructured":"Ong Ly C, Unnikrishnan B, Tadic T, Patel T, Duhamel J, Kandel S, Moayedi Y, Brudno M, Hope A, Ross H, McIntosh C (2024) Shortcut learning in medical AI hinders generalization: method for estimating AI model generalization without external data. NPJ Digit Med 7(1):124. doi: https:\/\/doi.org\/10.1038\/s41746-024-01118-4. PMID: 38744921; PMCID: PMC11094145.","journal-title":"NPJ Digit Med"},{"issue":"1","key":"2180_CR8","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1186\/s12874-023-02014-3","volume":"23","author":"Kympouropoulos Stelios","year":"2023","unstructured":"Stelios, Kympouropoulos (2023) Real World Evidence: methodological issues and opportunities from the European Health Data Space. BMC Med Res Methodol 23(1):185. doi: https:\/\/doi.org\/10.1186\/s12874-023-02014-3","journal-title":"BMC Med Res Methodol"},{"key":"2180_CR9","first-page":"191","volume":"2020","author":"M Ghassemi","year":"2020","unstructured":"Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R (2020) A Review of Challenges and Opportunities in Machine Learning for Health. AMIA Jt Summits Transl Sci Proc 2020:191\u2013200. PMID: 32477638; PMCID: PMC7233077.","journal-title":"AMIA Jt Summits Transl Sci Proc"},{"key":"2180_CR10","doi-asserted-by":"publisher","unstructured":"Zhou DW, Sun HL, Ning J, Ye HJ, Zhan DC (2024) Continual learning with pre-trained models: a survey. arXiv [preprint]. arXiv:2401.16386v2. doi: https:\/\/doi.org\/10.48550\/arXiv.2401.16386.","DOI":"10.48550\/arXiv.2401.16386"},{"key":"2180_CR11","doi-asserted-by":"publisher","unstructured":"Adaimi R, Bedri A, Gong J, Kang R, Arreaza-Taylor J, Pascual GM, Ralph M, Laput G (2024) Advancing location-invariant and device-agnostic motion activity recognition on wearable devices. arXiv [preprint]. arXiv:2402.03714v1. doi: https:\/\/doi.org\/10.48550\/arXiv.2402.03714.","DOI":"10.48550\/arXiv.2402.03714"},{"key":"2180_CR12","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12682","author":"R Dama\u0161evi\u010dius","year":"2024","unstructured":"Dama\u0161evi\u010dius R, Jagatheesaperumal SK, Kandala RNVS, Hussain S, Alizadehsani R, Gorriz JM (2024) Deep learning for personalized health monitoring and prediction: a review. Comp Intell. doi: https:\/\/doi.org\/10.1111\/coin.12682.","journal-title":"Comp Intell."},{"key":"2180_CR13","doi-asserted-by":"publisher","unstructured":"Lee SY, Ku MY, Tsai YH, Lin CC (2024) RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in Wearable Applications. IEEE Trans Biomed Circuits Syst. doi: https:\/\/doi.org\/10.1109\/TBCAS.2024.3442250. Epub ahead of print. PMID: 39137083.","DOI":"10.1109\/TBCAS.2024.3442250"},{"key":"2180_CR14","doi-asserted-by":"publisher","first-page":"128722","DOI":"10.1109\/ACCESS.2021.3113133","volume":"9","author":"J Lu","year":"2021","unstructured":"Lu J, Qi X (2021) Pre-trained-based individualization model for real-time spatial audio rendering system. IEEE Access. 9:128722\u2013128733. doi: https:\/\/doi.org\/10.1109\/ACCESS.2021.3113133.","journal-title":"IEEE Access."},{"issue":"2","key":"2180_CR15","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3390\/math12020237","volume":"12","author":"I Jeong","year":"2024","unstructured":"Jeong I, Kim Y, Cho NJ, Gil HW, Lee H (2024) A novel method for medical predictive models in small data using out-of-distribution data and transfer learning. Mathematics. 12(2):237. doi: https:\/\/doi.org\/10.3390\/math12020237.","journal-title":"Mathematics."},{"issue":"3","key":"2180_CR16","doi-asserted-by":"publisher","first-page":"426","DOI":"10.4218\/etrij.2022-44.3","volume":"44","author":"S Chung","year":"2022","unstructured":"Chung S, Jeong CY, Lim JM, Lim J, Noh KJ, Kim G, Jeong H (2022) Real-world multimodal lifelog dataset for human behavior study. ETRI J 44(3):426\u2013437. doi: https:\/\/doi.org\/10.4218\/etrij.2022-44.3.","journal-title":"ETRI J"},{"key":"2180_CR17","doi-asserted-by":"publisher","first-page":"108968","DOI":"10.1016\/j.knosys.2022.108968","volume":"249","author":"MD Samad","year":"2022","unstructured":"Samad MD, Abrar S, Diawara N (2022) Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework. Knowl Based Syst 249:108968. doi: https:\/\/doi.org\/10.1016\/j.knosys.2022.108968. Epub 2022 May 10. PMID: 36159738; PMCID: PMC9503087.","journal-title":"Knowl Based Syst"},{"issue":"3","key":"2180_CR18","doi-asserted-by":"publisher","first-page":"035041","DOI":"10.1088\/2632-2153\/ad65b5","volume":"5","author":"M Paiano","year":"2024","unstructured":"Paiano M, Martina S, Giannelli C, Caruso F. (2024) Transfer learning with generative models for object detection on limited datasets. Mach Learn Sci Technol 5(3):035041. doi: https:\/\/doi.org\/10.1088\/2632-2153\/ad65b5.","journal-title":"Mach Learn Sci Technol"},{"issue":"4","key":"2180_CR19","doi-asserted-by":"publisher","first-page":"103946","DOI":"10.1016\/j.drudis.2024.103946","volume":"29","author":"W Guo","year":"2024","unstructured":"Guo W, Dong Y, Hao GF (2024) Transfer learning empowers accurate pharmacokinetics prediction of small samples. Drug Discov Today 29(4):103946. doi: https:\/\/doi.org\/10.1016\/j.drudis.2024.103946. Epub 2024 Mar 8. PMID: 38460571.","journal-title":"Drug Discov Today"},{"key":"2180_CR20","doi-asserted-by":"publisher","first-page":"103502","DOI":"10.1016\/j.artint.2021.103502","volume":"298","author":"K Aas","year":"2021","unstructured":"Aas K, Jullum M, L\u00f8land A (2021) Explaining individual predictions when features are dependent: more accurate approximations to Shapley values. Artif Intell 298:103502. doi: https:\/\/doi.org\/10.1016\/j.artint.2021.103502.","journal-title":"Artif Intell"},{"issue":"4","key":"2180_CR21","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TRPMS.2022.3231702","volume":"7","author":"JB Hopson","year":"2023","unstructured":"Hopson JB, Neji R, Dunn JT, McGinnity CJ, Flaus A, Reader AJ, Hammers A (2023) Pre-training via Transfer Learning and Pretext Learning a Convolutional Neural Network for Automated Assessments of Clinical PET Image Quality. IEEE Trans Radiat Plasma Med Sci 7(4):372\u2013381. doi: 10.1109\/TRPMS.2022.3231702. PMID: 37051163; PMCID: PMC7614424.","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"2180_CR22","doi-asserted-by":"publisher","unstructured":"Cho CH, Lee T, Kim MG, In HP, Kim L, Lee HJ (2019) Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study. J Med Internet Res 21(4):e11029. doi: 10.2196\/11029. Erratum in: J Med Internet Res. 2019;21(10):e15966. doi: https:\/\/doi.org\/10.2196\/15966. PMID: 30994461; PMCID: PMC6492069.","DOI":"10.2196\/15966"},{"issue":"12","key":"2180_CR23","doi-asserted-by":"publisher","first-page":"9298","DOI":"10.1109\/TPAMI.2021.3129870","volume":"44","author":"T Mensink","year":"2022","unstructured":"Mensink T, Uijlings J, Kuznetsova A, Gygli M, Ferrari V. Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types (2022) IEEE Trans Pattern Anal Mach Intell 44(12):9298\u20139314. doi: 10.1109\/TPAMI.2021.3129870. Epub 2022 Nov 7. PMID: 34813469.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2180_CR24","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.neucom.2021.10.051","volume":"469","author":"S Nagae","year":"2022","unstructured":"Nagae S, Kanda D, Kawai S, Nobuhara H (2022) Automatic layer selection for transfer learning and quantitative evaluation of layer effectiveness. Neurocomputing 469:151\u2013162. doi: https:\/\/doi.org\/10.1016\/j.neucom.2021.10.051.","journal-title":"Neurocomputing"},{"key":"2180_CR25","doi-asserted-by":"publisher","unstructured":"Zahedani AD, McLaughlin T, Veluvali A, Aghaeepour N, Hosseinian A, Agarwal S, Ruan J, Tripathi S, Woodward M, Hashemi N, Snyder M (2023) Digital health application integrating wearable data and behavioral patterns improves metabolic health. NPJ Digit Med 6(1):216. doi: https:\/\/doi.org\/10.1038\/s41746-023-00956-y. Erratum in: NPJ Digit Med. 2024;7(1):9. doi: 10.1038\/s41746-024-00996-y. PMID: 38001287; PMCID: PMC10673832.","DOI":"10.1038\/s41746-023-00956-y"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02180-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02180-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02180-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T01:14:25Z","timestamp":1744161265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02180-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2180"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02180-5","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]},"assertion":[{"value":"27 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and Consent To Participate Declarations"}},{"value":"The authors declare no potential conflicts of interest, no competing financial interests or personal relationships, that could have influenced the work reported in this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"45"}}