{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:47:40Z","timestamp":1782283660851,"version":"3.54.5"},"reference-count":34,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Scandinavian Med Sci Sports"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Continuous monitoring of core temperature is crucial for optimizing exercise performance, promoting health, and ensuring safety. Here, we evaluated the validity of a machine learning\u2010based earbud\u2010type core temperature sensor during exercise. At ambient temperatures of 10\u00b0C, 20\u00b0C, and 30\u00b0C, participants rested and then cycled for 25\u2009min (90\u2009W for males and 60\u2009W for females), with an artificial wind of ~3.0\u2009m\/s directed at the face during the final 10\u2009min of exercise. Throughout the experiment, two thermistors embedded in the earbud\u2010type device monitored both ambient temperature and internal ear temperature. Additionally, an infrared temperature sensor integrated into the earbud\u2010type thermistor measured internal ear temperature. Core temperature was measured through a machine learning\u2010based approach, and esophageal temperature served as the reference. We created mixed\u2010effects Bland\u2013Altman plots for the relationship between estimated and esophageal temperature, and found that the bias was \u22120.003\u00b0C with a limit of agreement of \u22120.52\u00b0C\u20130.51\u00b0C. Root mean squared error and Pearson's\n                    <jats:italic>r<\/jats:italic>\n                    for the two\u2010temperature relationship were 0.26\u00b0C and 0.81, and 76.9% of the data exhibited temperature differences within a margin of \u2264\u20090.3\u00b0C between the two measurements. Similar results were also observed with artificial wind. Including infrared temperature measurements did not enhance the aforementioned variables. We show that a machine learning\u2010based wearable earbud\u2010type thermometer can validly estimate core temperature in exercising individuals across 10\u00b0C\u201330\u00b0C indoor ambient temperatures with and without exposure to a wind speed of ~3.0\u2009m\/s. However, further improvement of the algorithm is needed to enhance estimation accuracy.\n                  <\/jats:p>","DOI":"10.1111\/sms.70315","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T13:15:39Z","timestamp":1780924539000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Core Temperature Estimation Using Wearable Earbud\u2010Type Thermometer and Machine Learning During Light\u2010Intensity Cycling Under Varying Indoor Ambient Temperatures"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9696-8291","authenticated-orcid":false,"given":"Naoto","family":"Fujii","sequence":"first","affiliation":[{"name":"Institute of Health and Sport Sciences University of Tsukuba  Tsukuba Japan"},{"name":"Advanced Research Initiative for Human High Performance (ARIHHP)  Tsukuba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1675-3141","authenticated-orcid":false,"given":"Akira","family":"Katagiri","sequence":"additional","affiliation":[{"name":"Institute of Health and Sport Sciences University of Tsukuba  Tsukuba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kouichi","family":"Nakagome","sequence":"additional","affiliation":[{"name":"Casio Computer Co., Ltd  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5004-640X","authenticated-orcid":false,"given":"Akira","family":"Hamada","sequence":"additional","affiliation":[{"name":"Casio Computer Co., Ltd  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Yamaya","sequence":"additional","affiliation":[{"name":"Casio Computer Co., Ltd  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takanori","family":"Ishihama","sequence":"additional","affiliation":[{"name":"Casio Computer Co., Ltd  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Maeda","sequence":"additional","affiliation":[{"name":"Casio Computer Co., Ltd  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6040-7756","authenticated-orcid":false,"given":"Takeshi","family":"Nishiyasu","sequence":"additional","affiliation":[{"name":"Institute of Health and Sport Sciences University of Tsukuba  Tsukuba Japan"},{"name":"Advanced Research Initiative for Human High Performance (ARIHHP)  Tsukuba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"1","article-title":"Effects of High\u2010Intensity Exercise Repetition Number 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