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While real-world studies provide ecological validity, controlled studies ensure structured, high-quality data with minimal missing values, making them ideal for developing generalized and personalized anxiety detection models.; AB@Existing publicly available datasets related to anxiety research are limited to developed nations and focus on one or two anxiety-inducing activities. However, cultural differences significantly influence how anxiety is experienced and expressed, highlighting the need for datasets from diverse populations. This work presents\n                    <jats:bold>MAD,<\/jats:bold>\n                    a novel dataset collected in a low-to-middle-income country that addresses this gap. Our study involved participants engaging in three anxiety-inducing activities\u2014speech, group discussion, and interview\u2014each structured into three phases: anticipation, performance, and reflection. Physiological data were collected using wearable sensors, including electrocardiogram, electrodermal activity, and photoplethysmography, along with self-reported anxiety levels.; AB@Our dataset\n                    <jats:italic toggle=\"yes\">(N = 97)<\/jats:italic>\n                    is unique in its inclusion of multiple anxiety-inducing activities, comprehensive phase-wise assessment, and representation of an underrepresented population. It provides a valuable resource for developing generalizable anxiety detection models, designing personalized interventions, and studying cultural variations in anxiety responses. By making\n                    <jats:bold>MAD<\/jats:bold>\n                    available, we aim to facilitate future research in machine learning-based mental health analysis, cross-cultural studies, and privacy-preserving anxiety detection approaches.\n                  <\/jats:p>","DOI":"10.1145\/3770632","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:42:32Z","timestamp":1764704552000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["MAD: A Multimodal Physiological and Self-Reported Dataset for Anxiety Research from a Low-to-Middle-Income Country"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1675-7270","authenticated-orcid":false,"given":"Nilesh Kumar","family":"Sahu","sequence":"first","affiliation":[{"name":"Indian Institute of Science Education and Research Bhopal, Bhopal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5498-2917","authenticated-orcid":false,"given":"Snehil","family":"Gupta","sequence":"additional","affiliation":[{"name":"All India Institute of Medical Sciences Bhopal, Bhopal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1245-2974","authenticated-orcid":false,"given":"Haroon R.","family":"Lone","sequence":"additional","affiliation":[{"name":"Indian Institute of Science Education and Research Bhopal, Bhopal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"issue":"3","key":"e_1_2_2_1_1","first-page":"1","article-title":"Detecting social contexts from mobile sensing indicators in virtual interactions with socially anxious individuals. 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