{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:12:27Z","timestamp":1772820747032,"version":"3.50.1"},"reference-count":82,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>In theory, wearable physiological sensing devices offer an opportunity for institutions to monitor and manage the health and well-being of a group of people. For instance, schools or universities could leverage these devices to track rising stress levels or detect signs of illness among students. Advances in sensing accuracy and utility design in wearables might make this feasible; however, real-world adoption faces challenges, as users often fail to wear or use these devices consistently and correctly. Additionally, institutional monitoring raises privacy concerns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we analyze real-world data from a cohort of 103 Japanese university students to identify periods of cyclical stress while ensuring individual privacy through aggregation. We identify potential stress patterns by observing elevated waking heart rate (HR) and maximum waking HR, supported by related metrics such as sleep HR, sleep heart rate variability (HRV), activity patterns, and sleep phases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The physiological changes align with significant academic and societal events, indicating a strong link to stress.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our findings demonstrate the potential of consumer wearables to detect collective changes in stress biomarkers within a cohort using in-the-wild data, i.e., data that is noisy and has gaps. Furthermore, we explore how universities could implement such monitoring in practice, highlighting both the potential benefits and challenges of real-world application.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcomp.2025.1575404","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T05:28:12Z","timestamp":1755494892000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Unobtrusive stress detection using wearables: application and challenges in a university setting"],"prefix":"10.3389","volume":"7","author":[{"given":"Peter","family":"Neigel","sequence":"first","affiliation":[]},{"given":"Andrew","family":"Vargo","sequence":"additional","affiliation":[]},{"given":"Benjamin","family":"Tag","sequence":"additional","affiliation":[]},{"given":"Koichi","family":"Kise","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"B1","first-page":"1","article-title":"\u201cHuman acute stress detection via integration of physiological signals and thermal imaging,\u201d","volume-title":"Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA '16","author":"Abouelenien","year":"2016"},{"key":"B2","first-page":"423","article-title":"\u201cCan smartphones detect stress-related changes in the behaviour of individuals?,\u201d","author":"Bauer","year":"2012","journal-title":"2012 IEEE International Conference on Pervasive Computing and Communications Workshops"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e0000473","DOI":"10.1371\/journal.pdig.0000473","article-title":"Predicting stress in first-year college students using sleep data from wearable devices","volume":"3","author":"Bloomfield","year":"2024","journal-title":"PLOS Digit. 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