{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:17:43Z","timestamp":1779409063492,"version":"3.53.1"},"publisher-location":"New York, NY, USA","reference-count":127,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3772318.3791340","type":"proceedings-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:12:26Z","timestamp":1776053546000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Stress Mindset Matters: Rethinking Mental Stress Detection with Multimodal Wearable Sensors"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-6585","authenticated-orcid":false,"given":"Lakmal","family":"Meegahapola","sequence":"first","affiliation":[{"name":"Nokia Bell Labs, Cambridge, Cambridgeshire, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1454-0641","authenticated-orcid":false,"given":"Marios","family":"Constantinides","sequence":"additional","affiliation":[{"name":"CYENS Centre of Excellence, Nicosia, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2323-274X","authenticated-orcid":false,"given":"Zoran","family":"Radivojevic","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Cambridge, Cambridgeshire, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0749-001X","authenticated-orcid":false,"given":"Hongwei","family":"Li","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Cambridge, Cambridgeshire, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2255-4966","authenticated-orcid":false,"given":"Michael","family":"Eggleston","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Murray Hill, New Jersey, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9461-5804","authenticated-orcid":false,"given":"Daniele","family":"Quercia","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Cambridge, Cambridgeshire, United Kingdom and Politecnico di Torino, Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"EU\u00a0Artificial\u00a0Intelligence Act. 2024. The eu artificial intelligence act. European Union (2024)."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Maheen\u00a0M Adamson Angela Phillips Srija Seenivasan Julian Martinez Harlene Grewal Xiaojian Kang John Coetzee Ines Luttenbacher Ashley Jester Odette\u00a0A Harris et\u00a0al. 2020. International prevalence and correlates of psychological stress during the global COVID-19 pandemic. International journal of environmental research and public health 17 24 (2020) 9248.","DOI":"10.3390\/ijerph17249248"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Daniel\u00a0A Adler Emily Tseng Khatiya\u00a0C Moon John\u00a0Q Young John\u00a0M Kane Emanuel Moss David\u00a0C Mohr and Tanzeem Choudhury. 2022. Burnout and the quantified workplace: tensions around personal sensing interventions for stress in resident physicians. Proceedings of the ACM on Human-computer Interaction 6 CSCW2 (2022) 1\u201348.","DOI":"10.1145\/3555531"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Daniel\u00a0A Adler Vincent W-S Tseng Gengmo Qi Joseph Scarpa Srijan Sen and Tanzeem Choudhury. 2021. Identifying mobile sensing indicators of stress-resilience. Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies 5 2 (2021) 1\u201332.","DOI":"10.1145\/3463528"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3729176.3729177"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Mona Alhasani and Rita Orji. 2025. Exploring Trends Pitfalls and Future Directions in Digital Behaviour Change Interventions for Managing Student Stress. International Journal of Human\u2013Computer Interaction 41 16 (2025) 10379\u201310398.","DOI":"10.1080\/10447318.2024.2434172"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Wazzan\u00a0S Aljuhani Ziad\u00a0A Aljaafri Khalid\u00a0H Alhadlaq Abdullah\u00a0M Alanazi Abdulrahman\u00a0K Alhadlaq and Meshal\u00a0K Alaqeel. 2024. Assessment of stress level and depression among orthopaedic surgeons in Saudi Arabia. International Orthopaedics 48 11 (2024) 2785\u20132792.","DOI":"10.1007\/s00264-024-06288-0"},{"key":"e_1_3_3_2_9_2","unstructured":"LA Alker. 2019. Distress tolerance as a mediator of the relation between stress mindset and anxiety. B.S. thesis. University of Twente."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581190"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Mohamad Awada Burcin\u00a0Becerik Gerber Gale\u00a0M Lucas and Shawn\u00a0C Roll. 2024. Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis. Plos one 19 1 (2024) e0296468.","DOI":"10.1371\/journal.pone.0296468"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI52147.2023.10371835"},{"key":"e_1_3_3_2_13_2","unstructured":"Abeer Badawi Somayya Elmoghazy Samira Choudhury Khalid Elgazzar and Amer Burhan. 2024. Leveraging self-training and variational autoencoder for agitation detection in people with dementia using wearable sensors. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.19254 (2024)."},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Jakob\u00a0E Bardram and Aleksandar Matic. 2020. A decade of ubiquitous computing research in mental health. IEEE Pervasive Computing 19 1 (2020) 62\u201372.","DOI":"10.1109\/MPRV.2019.2925338"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSCE60160.2023.00432"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Ananya Bhattacharjee Pan Chen Abhijoy Mandal Anne Hsu Katie O\u2019Leary Alex Mariakakis Joseph\u00a0Jay Williams et\u00a0al. 2024. Exploring user perspectives on brief reflective questioning activities for stress management: Mixed methods study. JMIR Formative Research 8 1 (2024) e47360.","DOI":"10.2196\/47360"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640471.3680447"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871165"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Brandon\u00a0M Booth Hana Vrzakova Stephen\u00a0M Mattingly Gonzalo\u00a0J Martinez Louis Faust and Sidney\u00a0K D\u2019Mello. 2022. Toward robust stress prediction in the age of wearables: Modeling perceived stress in a longitudinal study with information workers. IEEE Transactions on Affective Computing 13 4 (2022) 2201\u20132217.","DOI":"10.1109\/TAFFC.2022.3188006"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2011.6090412"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858498"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Luxi Chen Li Qu and Ryan\u00a0Y Hong. 2022. Pathways linking the big five to psychological distress: exploring the mediating roles of stress mindset and coping flexibility. Journal of Clinical Medicine 11 9 (2022) 2272.","DOI":"10.3390\/jcm11092272"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Sheldon Cohen Tom Kamarck and Robin Mermelstein. 1983. A global measure of perceived stress. Journal of health and social behavior (1983) 385\u2013396.","DOI":"10.2307\/2136404"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Marios Constantinides Edyta\u00a0Paulina Bogucka Sanja Scepanovic and Daniele Quercia. 2024. Good intentions risky inventions: A method for assessing the risks and benefits of AI in mobile and wearable uses. Proceedings of the ACM on Human-Computer Interaction 8 MHCI (2024) 1\u201328.","DOI":"10.1145\/3676507"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Marios Constantinides and Daniele Quercia. 2022. Good intentions bad inventions: How employees judge pervasive technologies in the workplace. IEEE Pervasive Computing 22 1 (2022) 69\u201376.","DOI":"10.1109\/MPRV.2022.3217408"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Dajana \u010copec Matea Karlovi\u0107\u00a0Vragolov and Vesna Bu\u0161ko. 2022. Individual dispositions and situational stressors in competitive sport: The role of stress mindset in the cognitive appraisals processes. Frontiers in psychology 13 (2022) 829053.","DOI":"10.3389\/fpsyg.2022.829053"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Alia\u00a0J Crum Modupe Akinola Ashley Martin and Sean Fath. 2017. The role of stress mindset in shaping cognitive emotional and physiological responses to challenging and threatening stress. Anxiety stress & coping 30 4 (2017) 379\u2013395.","DOI":"10.1080\/10615806.2016.1275585"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Alia\u00a0J Crum Peter Salovey and Shawn Achor. 2013. Rethinking stress: the role of mindsets in determining the stress response. Journal of personality and social psychology 104 4 (2013) 716.","DOI":"10.1037\/a0031201"},{"key":"e_1_3_3_2_29_2","unstructured":"Jason\u00a0Peres da Silva. 2023. Privacy Data Ethics of Wearable Digital Health Technology. https:\/\/digitalhealth.med.brown.edu\/news\/2023-05-04\/ethics-wearables. Accessed: 2025-09-10."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Carolina Del-Valle-Soto Juan\u00a0Carlos L\u00f3pez-Pimentel Javier V\u00e1zquez-Castillo Juan\u00a0Arturo Nolazco-Flores Ramiro Vel\u00e1zquez Jos\u00e9 Varela-Ald\u00e1s and Paolo Visconti. 2024. A comprehensive review of behavior change techniques in wearables and IoT: implications for health and well-being. Sensors 24 8 (2024) 2429.","DOI":"10.3390\/s24082429"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Allison\u00a0E Diamond and Aaron\u00a0J Fisher. 2017. Comparative autonomic responses to diagnostic interviewing between individuals with GAD MDD SAD and healthy controls. Frontiers in human neuroscience 10 (2017) 677.","DOI":"10.3389\/fnhum.2016.00677"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445763"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2017.7917644"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Stephen\u00a0A Erath Mona El-Sheikh J\u00a0Benjamin Hinnant and E\u00a0Mark Cummings. 2011. Skin conductance level reactivity moderates the association between harsh parenting and growth in child externalizing behavior. Developmental Psychology 47 3 (2011) 693.","DOI":"10.1037\/a0021909"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642557"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Simon F\u00f6ll Martin Maritsch Federica Spinola Varun Mishra Filipe Barata Tobias Kowatsch Elgar Fleisch and Felix Wortmann. 2021. FLIRT: A feature generation toolkit for wearable data. Computer Methods and Programs in Biomedicine 212 (2021) 106461.","DOI":"10.1016\/j.cmpb.2021.106461"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Shruti Gedam and Sanchita Paul. 2021. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access 9 (2021) 84045\u201384066.","DOI":"10.1109\/ACCESS.2021.3085502"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2968306"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"David\u00a0S Goldstein Oladi Bentho Mee-Yeong Park and Yehonatan Sharabi. 2011. Low-frequency power of heart rate variability is not a measure of cardiac sympathetic tone but may be a measure of modulation of cardiac autonomic outflows by baroreflexes. Experimental physiology 96 12 (2011) 1255\u20131261.","DOI":"10.1113\/expphysiol.2010.056259"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Wuwei Gong and Susan\u00a0A Geertshuis. 2023. Distress and eustress: an analysis of the stress experiences of offshore international students. Frontiers in Psychology 14 (2023) 1144767.","DOI":"10.3389\/fpsyg.2023.1144767"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"crossref","unstructured":"Araceli Gonzalez Michelle Rozenman Pauline Goger and Sarah\u00a0E Velasco. 2024. Autonomic reactivity during acute social stress: exploratory investigation of an interaction by threat interpretation bias and emotion regulation difficulties. Anxiety Stress & Coping 37 2 (2024) 251\u2013264.","DOI":"10.1080\/10615806.2023.2235283"},{"key":"e_1_3_3_2_42_2","first-page":"2369","volume-title":"Healthcare","author":"Gonz\u00e1lez\u00a0Ram\u00edrez Maria\u00a0Luisa","year":"2023","unstructured":"Maria\u00a0Luisa Gonz\u00e1lez\u00a0Ram\u00edrez, Juan\u00a0Pablo Garc\u00eda\u00a0V\u00e1zquez, Marcela\u00a0D Rodr\u00edguez, Luis\u00a0Alfredo Padilla-L\u00f3pez, Gilberto\u00a0Manuel Galindo-Aldana, and Daniel Cuevas-Gonz\u00e1lez. 2023. Wearables for stress management: a scoping review. In Healthcare , Vol.\u00a011. MDPI, 2369."},{"key":"e_1_3_3_2_43_2","volume-title":"How we trained Fitbit\u2019s Body Response feature to detect stress","author":"Blog Google","year":"2023","unstructured":"Google Blog. 2023. How we trained Fitbit\u2019s Body Response feature to detect stress. https:\/\/blog.google\/products\/fitbit\/how-we-trained-fitbits-body-response-feature-to-detect-stress\/ Accessed: 2025-12-02."},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"crossref","unstructured":"Samuel\u00a0D Gosling Peter\u00a0J Rentfrow and William\u00a0B Swann\u00a0Jr. 2003. A very brief measure of the Big-Five personality domains. Journal of Research in personality 37 6 (2003) 504\u2013528.","DOI":"10.1016\/S0092-6566(03)00046-1"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544793.3560324"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"crossref","unstructured":"James\u00a0A Hanley Abdissa Negassa Michael D\u00a0deB Edwardes and Janet\u00a0E Forrester. 2003. Statistical analysis of correlated data using generalized estimating equations: an orientation. American journal of epidemiology 157 4 (2003) 364\u2013375.","DOI":"10.1093\/aje\/kwf215"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3167132.3167395"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"Odin Hjemdal Oddgeir Friborg Tore\u00a0C Stiles Jan\u00a0H Rosenvinge and Monica Martinussen. 2006. Resilience predicting psychiatric symptoms: A prospective study of protective factors and their role in adjustment to stressful life events. Clinical Psychology & Psychotherapy: An International Journal of Theory & Practice 13 3 (2006) 194\u2013201.","DOI":"10.1002\/cpp.488"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713174"},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Melanie\u00a0S Houle and George\u00a0E Billman. 1999. Low-frequency component of the heart rate variability spectrum: a poor marker of sympathetic activity. American Journal of Physiology-Heart and Circulatory Physiology 276 1 (1999) H215\u2013H223.","DOI":"10.1152\/ajpheart.1999.276.1.H215"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2807526"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3502027"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"publisher","unstructured":"Stanley\u00a0H. Hung Kelsey Serwa Gillian Rosenthal and Janice\u00a0J. Eng. 2025. Validity of heart rate measurements in wrist-based monitors across skin tones during exercise. PloS One 20 2 (2025) e0318724. 10.1371\/journal.pone.0318724","DOI":"10.1371\/journal.pone.0318724"},{"key":"e_1_3_3_2_54_2","doi-asserted-by":"crossref","unstructured":"Stephanie\u00a0L Hyland Martin Faltys Matthias H\u00fcser Xinrui Lyu Thomas Gumbsch Crist\u00f3bal Esteban Christian Bock Max Horn Michael Moor Bastian Rieck et\u00a0al. 2020. Early prediction of circulatory failure in the intensive care unit using machine learning. Nature medicine 26 3 (2020) 364\u2013373.","DOI":"10.1038\/s41591-020-0789-4"},{"key":"e_1_3_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3714029"},{"key":"e_1_3_3_2_56_2","doi-asserted-by":"crossref","unstructured":"Tanvir Islam and Peter Washington. 2023. Individualized stress mobile sensing using self-supervised pre-training. Applied Sciences 13 21 (2023) 12035.","DOI":"10.3390\/app132112035"},{"key":"e_1_3_3_2_57_2","unstructured":"Oussama Jlassi. 2023. Predicting viral respiratory tract infections using wearable garment biosensors. Ph.\u00a0D. Dissertation. Universit\u00e9 de Montr\u00e9al."},{"key":"e_1_3_3_2_58_2","doi-asserted-by":"crossref","unstructured":"Konstantinos Karampas Christos Pezirkianidis and Anastassios Stalikas. 2022. ReStress mindset: An internet-delivered intervention that changes university students\u2019 mindset about stress in the shadow of the COVID-19 pandemic. Frontiers in Psychology 13 (2022) 1036564.","DOI":"10.3389\/fpsyg.2022.1036564"},{"key":"e_1_3_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6439-6_102001-1"},{"key":"e_1_3_3_2_60_2","volume-title":"Distress vs. Eustress: Positive & Negative Stress Explained","author":"Keohan Elizabeth","year":"2023","unstructured":"Elizabeth Keohan. 2023. Distress vs. Eustress: Positive & Negative Stress Explained. Retrieved September 6, 2025 from https:\/\/www.talkspace.com\/blog\/distress-vs-eustress\/"},{"key":"e_1_3_3_2_61_2","doi-asserted-by":"crossref","unstructured":"Justin Khasentino Anastasiya Belyaeva Xin Liu Zhun Yang Nicholas\u00a0A Furlotte Chace Lee Erik Schenck Yojan Patel Jian Cui Logan\u00a0Douglas Schneider et\u00a0al. 2025. A personal health large language model for sleep and fitness coaching. Nature Medicine (2025) 1\u201310.","DOI":"10.1038\/s41591-025-03888-0"},{"key":"e_1_3_3_2_62_2","doi-asserted-by":"crossref","unstructured":"Christopher\u00a0J Kilby and Kerry\u00a0A Sherman. 2016. Delineating the relationship between stress mindset and primary appraisals: preliminary findings. Springerplus 5 1 (2016) 336.","DOI":"10.1186\/s40064-016-1937-7"},{"key":"e_1_3_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3517701"},{"key":"e_1_3_3_2_64_2","doi-asserted-by":"crossref","unstructured":"Zachary\u00a0D King Judith Moskowitz Begum Egilmez Shibo Zhang Lida Zhang Michael Bass John Rogers Roozbeh Ghaffari Laurie Wakschlag and Nabil Alshurafa. 2019. Micro-stress EMA: A passive sensing framework for detecting in-the-wild stress in pregnant mothers. Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies 3 3 (2019) 1\u201322.","DOI":"10.1145\/3351249"},{"key":"e_1_3_3_2_65_2","doi-asserted-by":"crossref","unstructured":"Clemens Kirschbaum Karl-Martin Pirke and Dirk\u00a0H Hellhammer. 1993. The \u2018Trier Social Stress Test\u2019\u2013a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 28 1-2 (1993) 76\u201381.","DOI":"10.1159\/000119004"},{"key":"e_1_3_3_2_66_2","doi-asserted-by":"crossref","unstructured":"Rafal Kocielnik and Natalia Sidorova. 2015. Personalized stress management: enabling stress monitoring with lifelogexplorer. KI-K\u00fcnstliche Intelligenz 29 2 (2015) 115\u2013122.","DOI":"10.1007\/s13218-015-0348-1"},{"key":"e_1_3_3_2_67_2","doi-asserted-by":"crossref","unstructured":"Shakar Kutal Lauri\u00a0Juhani Tulkki Tomi Sarkanen Petra Redfors Katarina Jood Annika Nordanstig Nil\u00fcfer Ye\u015filot Mine Sezgin Pauli Ylikotila Marialuisa Zedde et\u00a0al. 2025. Association between self-perceived stress and cryptogenic ischemic stroke in young adults: a case-control study. Neurology 104 6 (2025) e213369.","DOI":"10.1212\/WNL.0000000000213369"},{"key":"e_1_3_3_2_68_2","doi-asserted-by":"crossref","unstructured":"Liang Ky. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73 (1986) 13\u201322.","DOI":"10.1093\/biomet\/73.1.13"},{"key":"e_1_3_3_2_69_2","doi-asserted-by":"crossref","unstructured":"Feng Lin Kathi Heffner Mark Mapstone Ding-Geng\u00a0Din Chen and Anton Porsteisson. 2014. Frequency of mentally stimulating activities modifies the relationship between cardiovascular reactivity and executive function in old age. The American Journal of Geriatric Psychiatry 22 11 (2014) 1210\u20131221.","DOI":"10.1016\/j.jagp.2013.04.002"},{"key":"e_1_3_3_2_70_2","doi-asserted-by":"crossref","unstructured":"Yunfei Luo Iman Deznabi Abhinav Shaw Natcha Simsiri Tauhidur Rahman and Madalina Fiterau. 2024. Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data. Scientific Reports 14 1 (2024) 6631.","DOI":"10.1038\/s41598-024-56674-2"},{"key":"e_1_3_3_2_71_2","doi-asserted-by":"crossref","unstructured":"Xinrui Lyu Bowen Fan Matthias H\u00fcser Philip Hartout Thomas Gumbsch Martin Faltys Tobias\u00a0M Merz Gunnar R\u00e4tsch and Karsten Borgwardt. 2024. An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinformatics 40 Supplement_1 (2024) i247\u2013i256.","DOI":"10.1093\/bioinformatics\/btae212"},{"key":"e_1_3_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/2504335.2504406"},{"key":"e_1_3_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642444"},{"key":"e_1_3_3_2_74_2","doi-asserted-by":"crossref","unstructured":"Matteo Malgaroli and Daniel McDuff. 2024. An overview of diagnostics and therapeutics using large language models. Journal of Traumatic Stress 37 5 (2024) 754\u2013760.","DOI":"10.1002\/jts.23082"},{"key":"e_1_3_3_2_75_2","doi-asserted-by":"crossref","unstructured":"Paul\u00a0C Mansell and Martin\u00a0J Turner. 2023. The mediating role of proactive coping in the relationships between stress mindset challenge appraisal tendencies and psychological wellbeing. Frontiers in Psychology 14 (2023) 1140790.","DOI":"10.3389\/fpsyg.2023.1140790"},{"key":"e_1_3_3_2_76_2","unstructured":"Stacy Marsella Jonathan Gratch Paolo Petta et\u00a0al. 2010. Computational models of emotion. A Blueprint for Affective Computing-A sourcebook and manual 11 1 (2010) 21\u201346."},{"key":"e_1_3_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858247"},{"key":"e_1_3_3_2_78_2","doi-asserted-by":"crossref","unstructured":"Lakmal Meegahapola William Droz Peter Kun Amalia De\u00a0G\u00f6tzen Chaitanya Nutakki Shyam Diwakar Salvador\u00a0Ruiz Correa Donglei Song Hao Xu Miriam Bidoglia et\u00a0al. 2023. Generalization and personalization of mobile sensing-based mood inference models: an analysis of college students in eight countries. Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies 6 4 (2023) 1\u201332.","DOI":"10.1145\/3569483"},{"key":"e_1_3_3_2_79_2","doi-asserted-by":"crossref","unstructured":"Lakmal Meegahapola and Daniel Gatica-Perez. 2020. Smartphone sensing for the well-being of young adults: A review. IEEE Access 9 (2020) 3374\u20133399.","DOI":"10.1109\/ACCESS.2020.3045935"},{"key":"e_1_3_3_2_80_2","doi-asserted-by":"crossref","unstructured":"Lakmal Meegahapola Hamza Hassoune and Daniel Gatica-Perez. 2024. M3BAT: Unsupervised domain adaptation for multimodal mobile sensing with multi-branch adversarial training. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 8 2 (2024) 1\u201330.","DOI":"10.1145\/3659591"},{"key":"e_1_3_3_2_81_2","doi-asserted-by":"crossref","unstructured":"Lakmal Meegahapola Salvador Ruiz-Correa Viridiana del\u00a0Carmen Robledo-Valero Emilio\u00a0Ernesto Hernandez-Huerfano Leonardo Alvarez-Rivera Ronald Chenu-Abente and Daniel Gatica-Perez. 2021. One more bite? Inferring food consumption level of college students using smartphone sensing and self-reports. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 5 1 (2021) 1\u201328.","DOI":"10.1145\/3448120"},{"key":"e_1_3_3_2_82_2","doi-asserted-by":"crossref","unstructured":"Dorota Mierzejewska-Floreani Mateusz Banaszkiewicz and Ewa Gruszczy\u0144ska. 2022. Psychometric properties of the Stress Mindset Measure (SMM) in the Polish population. Plos one 17 3 (2022) e0264853.","DOI":"10.1371\/journal.pone.0264853"},{"key":"e_1_3_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3267537"},{"key":"e_1_3_3_2_84_2","doi-asserted-by":"crossref","unstructured":"Varun Mishra Sougata Sen Grace Chen Tian Hao Jeffrey Rogers Ching-Hua Chen and David Kotz. 2020. Evaluating the reproducibility of physiological stress detection models. Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies 4 4 (2020) 1\u201329.","DOI":"10.1145\/3432220"},{"key":"e_1_3_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACII55700.2022.9953824"},{"key":"e_1_3_3_2_86_2","doi-asserted-by":"crossref","unstructured":"Martin\u00a0Karl Moser Maximilian Ehrhart and Bernd Resch. 2024. An explainable deep learning approach for stress detection in wearable sensor measurements. Sensors 24 16 (2024) 5085.","DOI":"10.3390\/s24165085"},{"key":"e_1_3_3_2_87_2","unstructured":"Luis Moya-Albiol Alicia Salvador Raquel Costa Sonia Mart\u00ednez-Sanch\u00eds and Esperanza Gonz\u00e1lez-Bono. 2003. Psychophysiological responses to acute stress in two groups of healthy women differing in fitness. Psicothema (2003) 563\u2013568."},{"key":"e_1_3_3_2_88_2","doi-asserted-by":"crossref","unstructured":"Md\u00a0Sakibul\u00a0Hasan Nahid Tahrim\u00a0Zaman Tila and Turuna\u00a0S Seecharan. 2024. Detecting emotional arousal and aggressive driving using neural networks: A pilot study involving young drivers in Duluth. Sensors 24 22 (2024) 7109.","DOI":"10.3390\/s24227109"},{"key":"e_1_3_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642662"},{"key":"e_1_3_3_2_90_2","doi-asserted-by":"crossref","unstructured":"Matthias Norden Amin\u00a0Gerard Hofmann Martin Meier Felix Balzer Oliver\u00a0T Wolf Erwin B\u00f6ttinger and Hanna Drimalla. 2022. Inducing and recording acute stress responses on a large scale with the digital stress test (DST): development and evaluation study. Journal of medical Internet research 24 7 (2022) e32280.","DOI":"10.2196\/32280"},{"key":"e_1_3_3_2_91_2","doi-asserted-by":"crossref","unstructured":"Aleksandr Ometov Anzhelika Mezina Hsiao-Chun Lin Otso Arponen Radim Burget and Jari Nurmi. 2025. Stress and Emotion Open Access Data: A Review on Datasets Modalities Methods Challenges and Future Research Perspectives. Journal of Healthcare Informatics Research (2025) 1\u201333.","DOI":"10.1007\/s41666-025-00200-0"},{"key":"e_1_3_3_2_92_2","doi-asserted-by":"crossref","unstructured":"Joseph Orji Gerry Chan and Rita Orji. 2025. Revitalizing Wellbeing: App Design for Stress Reduction through Artificial Intelligence and Persuasive Technology. International Journal of Human-Computer Studies (2025) 103600.","DOI":"10.1016\/j.ijhcs.2025.103600"},{"key":"e_1_3_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713515"},{"key":"e_1_3_3_2_94_2","doi-asserted-by":"crossref","unstructured":"Evan\u00a0M Peck Emily Carlin and Robert Jacob. 2015. Designing brain-computer interfaces for attention-aware systems. Computer 48 10 (2015) 34\u201342.","DOI":"10.1109\/MC.2015.315"},{"key":"e_1_3_3_2_95_2","doi-asserted-by":"crossref","unstructured":"Anuja Pinge Vinaya Gad Dheryta Jaisighani Surjya Ghosh and Sougata Sen. 2024. Detection and monitoring of stress using wearables: a systematic review. Frontiers in Computer Science 6 (2024) 1478851.","DOI":"10.3389\/fcomp.2024.1478851"},{"key":"e_1_3_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491101.3519758"},{"key":"e_1_3_3_2_97_2","first-page":"575","volume-title":"European Conference on Computer Vision","author":"Rafi Houda","year":"2022","unstructured":"Houda Rafi, Yannick Benezeth, Philippe Reynaud, Emmanuel Arnoux, Fan\u00a0Yang Song, and Cedric Demonceaux. 2022. Personalization of AI models based on federated learning for driver stress monitoring. In European Conference on Computer Vision. Springer, 575\u2013585."},{"key":"e_1_3_3_2_98_2","unstructured":"Pranav Rao Maryam Taj Alex Mariakakis Joseph\u00a0Jay Williams and Ananya Bhattacharjee. 2025. Fitting the Message to the Moment: Designing Calendar-Aware Stress Messaging with Large Language Models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2505.23997 (2025)."},{"key":"e_1_3_3_2_99_2","unstructured":"Pranav Rao Sarah\u00a0Yi Xu Ananya Bhattacharjee Yuchen Zeng Alex Mariakakis and Joseph\u00a0Jay Williams. 2024. Integrating digital calendars with large language models for stress management interventions. (2024)."},{"key":"e_1_3_3_2_100_2","doi-asserted-by":"crossref","unstructured":"Patrice Renaud and Jean-Pierre Blondin. 1997. The stress of Stroop performance: Physiological and emotional responses to color\u2013word interference task pacing and pacing speed. International Journal of Psychophysiology 27 2 (1997) 87\u201397.","DOI":"10.1016\/S0167-8760(97)00049-4"},{"key":"e_1_3_3_2_101_2","doi-asserted-by":"crossref","unstructured":"Vincenzo Ronca Ana\u00a0C Martinez-Levy Alessia Vozzi Andrea Giorgi Pietro Aric\u00f2 Rossella Capotorto Gianluca Borghini Fabio Babiloni and Gianluca Di\u00a0Flumeri. 2023. Wearable technologies for electrodermal and cardiac activity measurements: a comparison between fitbit sense empatica E4 and shimmer GSR3+. Sensors 23 13 (2023) 5847.","DOI":"10.3390\/s23135847"},{"key":"e_1_3_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-18914-7_55"},{"key":"e_1_3_3_2_103_2","doi-asserted-by":"crossref","unstructured":"Federica Scarpina and Sofia Tagini. 2017. The stroop color and word test. Frontiers in psychology 8 (2017) 557.","DOI":"10.3389\/fpsyg.2017.00557"},{"key":"e_1_3_3_2_104_2","doi-asserted-by":"crossref","unstructured":"Sanja \u0160\u0107epanovi\u0107 Marios Constantinides Daniele Quercia and Seunghyun Kim. 2023. Quantifying the impact of positive stress on companies from online employee reviews. Scientific Reports 13 1 (2023) 1603.","DOI":"10.1038\/s41598-022-26796-6"},{"key":"e_1_3_3_2_105_2","doi-asserted-by":"crossref","unstructured":"Mhd\u00a0Saeed Sharif Madhav Raj\u00a0Theeng Tamang Cynthia Fu Ahmed\u00a0Ibrahim Alzahrani and Fahad Alblehai. 2025. Innovative computation to detect stress in working people based on mode of commute. Journal of Transport & Health 41 (2025) 101979.","DOI":"10.1016\/j.jth.2024.101979"},{"key":"e_1_3_3_2_106_2","unstructured":"Abhinav Shaw Natcha Simsiri Iman Deznaby Madalina Fiterau and Tauhidur Rahaman. 2019. Personalized student stress prediction with deep multitask network. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1906.11356 (2019)."},{"key":"e_1_3_3_2_107_2","doi-asserted-by":"crossref","unstructured":"Pragya Singh Ankush Gupta Mohan Kumar and Pushpendra Singh. 2025. AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 9 3 (2025) 1\u201347.","DOI":"10.1145\/3749519"},{"key":"e_1_3_3_2_108_2","doi-asserted-by":"crossref","unstructured":"\u017diga Str\u017einar Araceli Sanchis Agapito Ledezma Oscar Sipele Bo\u0161tjan Pregelj and Igor \u0160krjanc. 2023. Stress detection using frequency spectrum analysis of wrist-measured electrodermal activity. Sensors 23 2 (2023) 963.","DOI":"10.3390\/s23020963"},{"key":"e_1_3_3_2_109_2","doi-asserted-by":"crossref","unstructured":"Yi-Yuan Tang Britta\u00a0K H\u00f6lzel and Michael\u00a0I Posner. 2015. The neuroscience of mindfulness meditation. Nature reviews neuroscience 16 4 (2015) 213\u2013225.","DOI":"10.1038\/nrn3916"},{"key":"e_1_3_3_2_110_2","unstructured":"Tahrim\u00a0Zaman Tila. 2023. Exploring Relationship Between Electrodermal Activity and Driving Behavior. Master\u2019s thesis. University of Minnesota."},{"key":"e_1_3_3_2_111_2","first-page":"333","volume-title":"World Congress on Engineering Asset Management","author":"Tila Tahrim\u00a0Zaman","year":"2022","unstructured":"Tahrim\u00a0Zaman Tila and Turuna\u00a0S Seecharan. 2022. Using wearable sensors to form a relationship between driver stress and aggressive driving habits. In World Congress on Engineering Asset Management. Springer, 333\u2013342."},{"key":"e_1_3_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713802"},{"key":"e_1_3_3_2_113_2","doi-asserted-by":"crossref","unstructured":"DJ Van\u00a0der Mee Q Duivestein MJ Gevonden JHDM Westerink and EJC de Geus. 2020. The short Sing-a-Song Stress Test: A practical and valid test of autonomic responses induced by social-evaluative stress. Autonomic Neuroscience 224 (2020) 102612.","DOI":"10.1016\/j.autneu.2019.102612"},{"key":"e_1_3_3_2_114_2","doi-asserted-by":"crossref","unstructured":"Gideon Vos Maryam Ebrahimpour Liza van Eijk Zoltan Sarnyai and Mostafa\u00a0Rahimi Azghadi. 2025. Stress monitoring using low-cost electroencephalogram devices: A systematic literature review. International Journal of Medical Informatics (2025) 105859.","DOI":"10.1016\/j.ijmedinf.2025.105859"},{"key":"e_1_3_3_2_115_2","doi-asserted-by":"crossref","unstructured":"Gideon Vos Kelly Trinh Zoltan Sarnyai and Mostafa\u00a0Rahimi Azghadi. 2023. Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review. International Journal of Medical Informatics 173 (2023) 105026.","DOI":"10.1016\/j.ijmedinf.2023.105026"},{"key":"e_1_3_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3501835"},{"key":"e_1_3_3_2_117_2","doi-asserted-by":"crossref","unstructured":"Yuna Watanabe Natasha Yamane Aarti Sathyanarayana Varun Mishra and Matthew\u00a0S Goodwin. 2025. Beyond Motion Artifacts: Optimizing PPG Preprocessing for Accurate Pulse Rate Variability Estimation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2510.06158 (2025).","DOI":"10.1145\/3714394.3756241"},{"key":"e_1_3_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-93418-6_22"},{"key":"e_1_3_3_2_119_2","doi-asserted-by":"crossref","unstructured":"Mark\u00a0A Wetherell Olivia Craw and Michael\u00a0A Smith. 2018. Techniques for inducing stress. The Routledge international handbook of psychobiology (2018) 47.","DOI":"10.4324\/9781315642765-10"},{"key":"e_1_3_3_2_120_2","doi-asserted-by":"crossref","unstructured":"Brent\u00a0D Winslow George\u00a0L Chadderdon Sara\u00a0J Dechmerowski David\u00a0L Jones Solomon Kalkstein Jennifer\u00a0L Greene and Philip Gehrman. 2016. Development and clinical evaluation of an mHealth application for stress management. Frontiers in psychiatry 7 (2016) 130.","DOI":"10.3389\/fpsyt.2016.00130"},{"key":"e_1_3_3_2_121_2","unstructured":"World Health Organization. 2023. Stress. https:\/\/www.who.int\/news-room\/questions-and-answers\/item\/stress. https:\/\/www.who.int\/news-room\/questions-and-answers\/item\/stress Accessed: 2025-09-08."},{"key":"e_1_3_3_2_122_2","unstructured":"Lingxi Wu Arlene John and Jorge\u00a0Piano Simoes. 2025. Predicting Positive Psychological States using Machine Learning and Digital Biomarkers from Everyday Wearable Data. medRxiv (2025) 2025\u201304."},{"key":"e_1_3_3_2_123_2","unstructured":"Yi Xiao Harshit Sharma Sawinder Kaur Dessa Bergen-Cico and Asif Salekin. 2025. Human Heterogeneity Invariant Stress Sensing. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2506.02256 (2025)."},{"key":"e_1_3_3_2_124_2","doi-asserted-by":"crossref","unstructured":"Yi Xiao Harshit Sharma Zhongyang Zhang Dessa Bergen-Cico Tauhidur Rahman and Asif Salekin. 2024. Reading between the heat: Co-teaching body thermal signatures for non-intrusive stress detection. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 7 4 (2024) 1\u201330.","DOI":"10.1145\/3631441"},{"key":"e_1_3_3_2_125_2","doi-asserted-by":"crossref","unstructured":"Xuhai Xu Xin Liu Han Zhang Weichen Wang Subigya Nepal Yasaman Sefidgar Woosuk Seo Kevin\u00a0S Kuehn Jeremy\u00a0F Huckins Margaret\u00a0E Morris et\u00a0al. 2023. Globem: Cross-dataset generalization of longitudinal human behavior modeling. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 6 4 (2023) 1\u201334.","DOI":"10.1145\/3569485"},{"key":"e_1_3_3_2_126_2","doi-asserted-by":"crossref","unstructured":"Camellia Zakaria Rajesh Balan and Youngki Lee. 2019. StressMon: scalable detection of perceived stress and depression using passive sensing of changes in work Routines and group interactions. Proceedings of the ACM on human-computer interaction 3 CSCW (2019) 1\u201329.","DOI":"10.1145\/3359139"},{"key":"e_1_3_3_2_127_2","doi-asserted-by":"crossref","unstructured":"Panyu Zhang Gyuwon Jung Jumabek Alikhanov Uzair Ahmed and Uichin Lee. 2024. A reproducible stress prediction pipeline with mobile sensor data. Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies 8 3 (2024) 1\u201335.","DOI":"10.1145\/3678578"},{"key":"e_1_3_3_2_128_2","doi-asserted-by":"crossref","unstructured":"Howe\u00a0Yuan Zhu Hsiang-Ting Chen and Chin-Teng Lin. 2021. The effects of virtual and physical elevation on physiological stress during virtual reality height exposure. IEEE Transactions on Visualization and Computer Graphics 29 4 (2021) 1937\u20131950.","DOI":"10.1109\/TVCG.2021.3134412"}],"event":{"name":"CHI 2026: CHI Conference on Human Factors in Computing Systems","location":"Barcelona Spain","acronym":"CHI '26","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3772318.3791340","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T23:29:06Z","timestamp":1779406146000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3772318.3791340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":127,"alternative-id":["10.1145\/3772318.3791340","10.1145\/3772318"],"URL":"https:\/\/doi.org\/10.1145\/3772318.3791340","relation":{},"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"2026-04-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}