{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T11:15:01Z","timestamp":1783336501710,"version":"3.54.6"},"reference-count":87,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Management Science"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases is, however, understudied. We draw on the medical literature to support depression detection using motion sensor data. To connect humans in this decision making, safeguard trust, and ensure algorithm transparency, we develop an interpretable deep learning model: temporal prototype network (TempPNet). TempPNet is built on the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing temporal progressions of prototypes. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression detection. Moreover, TempPNet interprets its decision by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further employ a user study and a medical expert panel to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good\u2014collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression detection from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients\u2019 depression risks in real time. Our model\u2019s interpretability also allows human experts to participate in the decision making by reviewing the interpretation and making informed interventions.<\/jats:p>\n                  <jats:p>This paper was accepted by D. J. Wu, information systems.<\/jats:p>\n                  <jats:p>Funding: J. Xie and X. Fang are supported by the University of Delaware Research Foundation Strategic Initiatives Grant and Alfred Lerner College of Business and Economics Research Grant, X. Zhao acknowledges financial support from the National Natural Science Foundation of China [Grant 72401172] and the Fundamental Research Funds for the Central Universities [Grant 2023110139, 2023110318]. J. Xie and X. Fang did not receive any form of support from, nor do they have any affiliation with, X. Zhao\u2019s funding sources.<\/jats:p>\n                  <jats:p>Supplemental Material: The online appendices and data files are available at https:\/\/doi.org\/10.1287\/mnsc.2023.04183 .<\/jats:p>","DOI":"10.1287\/mnsc.2023.04183","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T17:51:22Z","timestamp":1761673882000},"page":"5894-5915","source":"Crossref","is-referenced-by-count":3,"title":["Care for the Mind amid Chronic Diseases: An Interpretable AI Approach Using IoT"],"prefix":"10.1287","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9415-3726","authenticated-orcid":false,"given":"Jiaheng","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Accounting & MIS, Lerner College of Business and Economics, University of Delaware, Newark, Delaware 19716"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7766-6730","authenticated-orcid":false,"given":"Xiaohang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai 200437, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7324-3378","authenticated-orcid":false,"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Accounting & MIS, Lerner College of Business and Economics, University of Delaware, Newark, Delaware 19716"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9429-5748","authenticated-orcid":false,"given":"Xiao","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Accounting & MIS, Lerner College of Business and Economics, University of Delaware, Newark, Delaware 19716"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","unstructured":"AHQR (2022) Healthcare expenditures for treatment of mental disorders: Estimates for adults ages 18 and older, U.S. civilian noninstitutionalized population, 2019. 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Chronic Diseases\n                      6(4):86\u201389."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2020\/14644"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001531"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpr.2015.02.003"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.11"},{"key":"B8","unstructured":"Britannica (2023) U.S. States ranked by population: Which is largest? Accessed August 16, 2024, https:\/\/www.britannica.com\/topic\/largest-U-S-state-by-population."},{"key":"B9","doi-asserted-by":"crossref","unstructured":"Canzian L, Musolesi M (2015) Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis.\n                      Proc. ACM Internat. Joint Conf. Pervasive Ubiquitous Comput\n                      . (ACM, New York).","DOI":"10.1145\/2750858.2805845"},{"key":"B10","doi-asserted-by":"crossref","unstructured":"Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission.\n                      Proc. KDD\n                      (ACM, New York).","DOI":"10.1145\/2783258.2788613"},{"key":"B11","unstructured":"CDC (2012) Mental health and chronic diseases CDC fact sheet. Technical report, Centers for Disease Control and Prevention, Atlanta, GA."},{"key":"B12","unstructured":"CDC (2021) U.S. healthcare spending attributable to cigarette smoking in 2014. Technical report, Centers for Disease Control and Prevention, Atlanta, GA."},{"key":"B13","unstructured":"CDC (2022) Chronic diseases in America. Technical report, Centers for Disease Control and Prevention, Atlanta, GA."},{"key":"B14","doi-asserted-by":"crossref","unstructured":"Chau M, Li TM, Wong PW, Xu JJ, Yip PS, Chen H (2020) Finding people with emotional distress in online social media: A design combining machine learning and rule-based classification.\n                      MIS Quart.\n                      44(2):933\u2013955.","DOI":"10.25300\/MISQ\/2020\/14110"},{"key":"B15","unstructured":"Chen C, Li O, Tao C, Barnett AJ, Su J, Rudin C (2019) This looks like that: Deep learning for interpretable image recognition.\n                      Proc. 33rd Internat. Conf. Neural Inform. Processing Systems\n                      (ACM, New York)."},{"key":"B16","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches.\n                      Proc. 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Systems\n                      (Association for Information Systems, Atlanta), 1069."},{"key":"B29","unstructured":"Hoffman D (2022) Commentary on Chronic Disease Prevention in the US in 2022. chronicdisease.org. Accessed August 16, 2024, https:\/\/chronicdisease.org\/wp-content\/uploads\/2022\/04\/FS_ChronicDiseaseCommentary2022FINAL.pdf."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0123705"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.3390\/s20123572"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1207\/S15327566IJCE0401_04"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1003955"},{"key":"B34","doi-asserted-by":"crossref","unstructured":"Kaur H, Nori H, Jenkins S, Caruana R, Wallach H, Wortman Vaughan J (2020) Interpreting interpretability: Understanding data scientists\u2019 use of interpretability tools for machine learning.\n                      Proc. CHI Conf. Human Factors Comput. 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