{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T14:17:32Z","timestamp":1787753852044,"version":"build-2784847793"},"reference-count":58,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"The National Center for Mental Health, China","award":["GXWH2025B-072"],"award-info":[{"award-number":["GXWH2025B-072"]}]},{"name":"The Research Fund of Vanke School of Public Health, Tsinghua University","award":["20220102"],"award-info":[{"award-number":["20220102"]}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2024C03007"],"award-info":[{"award-number":["2024C03007"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["20240484742"],"award-info":[{"award-number":["20240484742"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Adolescent suicide is a critical public health challenge. Traditional risk screening relies on self-report measures limited by various biases, while passive EDA monitoring during daily activities, often yields limited predictive validity due to environmental confounding such as motion artifacts, thermoregulatory sweating, and ambient temperature fluctuations. This study proposes an innovative \u201cinterview-embedded\u201d framework to capture physiological signatures of suicide ideation (SI) during a standardized clinical probe.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 151 adolescents (102 with active suicide ideation, 49 matched controls) were enrolled. Their electrodermal activity signals were continuously recorded throughout clinical interview (Mini International Neuropsychiatric Interview for Children and Adolescents) and analyzed through advanced machine learning approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      XGBoost model achieved superior classification performance (AUC=0.802, sensitivity=0.857, specificity=0.8) compared to CatBoost, and Balanced Random Forest, significantly outperforming resting-state models and clinical symptom baselines (Resting EDA Model: AUC = 0.598, sensitivity = 0.571, specificity = 0.4; Full Clinical Interview Baseline: AUC = 0.926, sensitivity = 0.476, specificity = 1; Symptom-only Clinical Baseline Model: AUC = 0.712, sensitivity = 0.619, specificity = 0.8). Feature importance analysis revealed that dynamic features reflecting physiological reactivity were the most discriminative markers, providing predictive information potentially beyond self-report. EDA features did not significantly correlate with continuous BSS severity scores (all \u03c1 &lt; 0.15, all p &gt; 0.05; regression R\n                      <jats:sup>2<\/jats:sup>\n                      &lt; 0), supporting a threshold rather than dose-dependent physiological response to suicidal ideation. The results of robustness checks and subgroup analyses showed modest but clinically relevant utility of the model, even when accounting for highly comorbid factors such as depression and non-suicidal self-injury.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates that a task-embedded biosensing framework, integrating wearable biosensing into standardized clinical interviews is a feasible and effective approach for adolescent suicide risk detection. Embedding physiological data collection within established clinical workflows offers a scalable solution to improve early suicide risk screening in real-world settings, such as schools and primary care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261483391","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T13:53:05Z","timestamp":1787579585000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating wearable biosensing with clinical interviews for suicide risk detection in adolescents"],"prefix":"10.1177","volume":"12","author":[{"given":"Xin","family":"Huang","sequence":"first","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Zou","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihang","family":"Su","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Lei","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinan","family":"Duan","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Wu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyan","family":"Yan","sequence":"additional","affiliation":[{"name":"Wuhan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Wang","sequence":"additional","affiliation":[{"name":"National Center for Mental Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Ding","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengling","family":"Feng","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Jin","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3398-5750","authenticated-orcid":false,"given":"Runsen","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/jcpp.13106"},{"key":"e_1_3_6_3_2","volume-title":"Suicide worldwide in 2021: Global health estimates","author":"World Health Organization","year":"2025","unstructured":"World Health Organization. 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