{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:50:01Z","timestamp":1740149401255,"version":"3.37.3"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T00:00:00Z","timestamp":1583280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017M3C4A7083534"],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices\u2019 sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).<\/jats:p>","DOI":"10.3390\/s20051396","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T15:46:08Z","timestamp":1583336768000},"page":"1396","source":"Crossref","is-referenced-by-count":78,"title":["STDD: Short-Term Depression Detection with Passive Sensing"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7070-2126","authenticated-orcid":false,"given":"Nematjon","family":"Narziev","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Inha University, Incheon 22212, Korea"}]},{"given":"Hwarang","family":"Goh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Inha University, Incheon 22212, Korea"}]},{"given":"Kobiljon","family":"Toshnazarov","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Inha University, Incheon 22212, Korea"}]},{"given":"Seung Ah","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Psychology, Yonsei University, Seoul 03722, Korea"}]},{"given":"Kyong-Mee","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Psychology, Yonsei University, Seoul 03722, Korea"}]},{"given":"Youngtae","family":"Noh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Inha University, Incheon 22212, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,4]]},"reference":[{"key":"ref_1","first-page":"617","article-title":"Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication","volume":"62","author":"Kessler","year":"2005","journal-title":"JAMA Psychiatry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/S0006-3223(03)00420-7","article-title":"Social and economic burden of mood disorders","volume":"54","author":"Simon","year":"2003","journal-title":"Biol. Psychiatry"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/S0022-3999(02)00313-6","article-title":"Impact of major depression on chronic medical illness","volume":"53","author":"Katon","year":"2002","journal-title":"J. Psychosom. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s10597-012-9589-8","article-title":"Development of Telehealth Dialogues for Monitoring Suicidal Patients with Schizophrenia: Consumer Feedback","volume":"50","author":"Kasckow","year":"2013","journal-title":"Community Ment. Health J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e55","DOI":"10.2196\/jmir.1838","article-title":"Harnessing Context Sensing to Develop a Mobile Intervention for Depression","volume":"13","author":"Burns","year":"2011","journal-title":"J. Med. Internet Res."},{"unstructured":"American Psychiatric Association (2019, August 01). Available online: http:\/\/dissertation.argosy.edu\/chicago\/fall07\/pp7320_f07schreier.doc.","key":"ref_6"},{"doi-asserted-by":"crossref","unstructured":"Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., and Campbell, A.T. (2014, January 13\u201317). Studentlife: Assessing mental health, academic performance and behavioural trends of college students using smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Ser. UbiComp \u201914, Seattle, WA, USA.","key":"ref_7","DOI":"10.1145\/2632048.2632054"},{"doi-asserted-by":"crossref","unstructured":"Van Breda, W., Pastor, J., Hoogendoorn, M., Ruwaard, J., Asselbergs, J., and Riper, H. (2016, January 15\u201317). Exploring and Comparing Machine Learning Approaches for Predicting Mood Over Time. Proceedings of the International Conference on Innovation in Medicine and Healthcare, Puerto de la Cruz, Spain.","key":"ref_8","DOI":"10.1007\/978-3-319-39687-3_4"},{"unstructured":"Becker, D., Bremer, V., Funk, B., Asselbergs, J., Riper, H., and Ruwaard, J. (2016, January 11\u201314). How to predict mood? delving into features of smartphone-based data. Proceedings of the 22nd Americas Conference on Information Systems, San Diego, CA, USA.","key":"ref_9"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e11661","DOI":"10.2196\/11661","article-title":"Preliminary Effectiveness of a Smartphone App to Reduce Depressive Symptoms in the Workplace: Feasibility and Acceptability Study","volume":"6","author":"Deady","year":"2018","journal-title":"JMIR mHealth uHealth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e10131","DOI":"10.2196\/10131","article-title":"Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions","volume":"20","author":"Boonstra","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e26","DOI":"10.2196\/mental.9480","article-title":"Impact of Mental Health Screening on Promoting Immediate Online Help-Seeking: Randomized Trial Comparing Normative Versus Humor-Driven Feedback","volume":"5","author":"Choi","year":"2018","journal-title":"JMIR Ment. Health"},{"doi-asserted-by":"crossref","unstructured":"Crosby, R.D., Lavender, J.M., Engel, S.G., and Wonderlich, S.A. (2016). Ecological Momentary Assessment, Springer.","key":"ref_13","DOI":"10.1007\/978-981-287-087-2_159-1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1046\/j.1525-1497.2001.016009606.x","article-title":"The PHQ-9: Validity of a brief depression severity measure","volume":"16","author":"Kroenke","year":"2001","journal-title":"J. Gen. Intern. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e175","DOI":"10.2196\/jmir.4273","article-title":"Mobile phone sensor correlates of depressive symptom severity in daily-life behaviour: An exploratory study","volume":"17","author":"Saeb","year":"2015","journal-title":"J. Med. Internet Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e181","DOI":"10.2196\/jmir.3376","article-title":"Purple: A modular system for developing and deploying behavioural intervention technologies","volume":"16","author":"Schueller","year":"2014","journal-title":"J. Med. Internet Res."},{"key":"ref_17","first-page":"195","article-title":"Accompanying Depression with FINE - A Smartphone-Based Approach","volume":"228","author":"Dang","year":"2016","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ajp.2016.08.003","article-title":"Smartphone-based ecological momentary assessment for chinese patients with depression: An exploratory study in Taiwan","volume":"23","author":"Hung","year":"2016","journal-title":"Asian J. Psychiatry"},{"unstructured":"Bardram, J.E., Frost, M., Sz\u00e1nt\u00f3, K., Faurholt-Jepsen, M., Vinberg, M., and Kessing, L.V. (May, January 27). Designing mobile health technology for bipolar disorder: A field trial of the Monarca system. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ser. CHI \u201913, Paris, France.","key":"ref_19"},{"doi-asserted-by":"crossref","unstructured":"Gideon, J., Provost, E., and McInnis, M. (2016, January 20\u201325). Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","key":"ref_20","DOI":"10.1109\/ICASSP.2016.7472099"},{"doi-asserted-by":"crossref","unstructured":"Frost, M., Doryab, A., Faurholt-Jepsen, M., Kessing, L.V., and Bardram, J.E. (2013, January 8\u201312). Supporting disease insight through data analysis. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Zurich, Switzerland.","key":"ref_21","DOI":"10.1145\/2493432.2493507"},{"doi-asserted-by":"crossref","unstructured":"Voida, S., Matthews, M., Abdullah, S., Xi, M.C., Green, M., Jang, W.J., Hu, D., Weinrich, J., Patil, P., and Rabbi, M. (2013, January 8\u201312). Moodrhythm: Tracking and supporting daily rhythms. Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, Ser. UbiComp \u201913 Adjunct, Zurich, Switzerland.","key":"ref_22","DOI":"10.1145\/2494091.2494111"},{"doi-asserted-by":"crossref","unstructured":"Hidalgo-Mazzei, D., Mateu, A., Reinares, M., Undurraga, J., del Mar Bonn\u00edn, C., S\u00e1nchez-Moreno, J., Vieta, E., and Colom, F. (2015). Self-monitoring and psychoeducation in bipolar patients with a smart-phone application (sim-ple) project: Design, development and studies protocols. BMC Psychiatry, 15.","key":"ref_23","DOI":"10.1186\/s12888-015-0437-6"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e013518","DOI":"10.1136\/bmjopen-2016-013518","article-title":"Ibobbly mobile health intervention for suicide prevention in Australian Indigenous youth: A pilot randomised controlled trial","volume":"7","author":"Tighe","year":"2017","journal-title":"BMJ Open"},{"unstructured":"(2019, September 10). Qualtrics. Available online: https:\/\/www.qualtrics.com\/.","key":"ref_25"},{"doi-asserted-by":"crossref","unstructured":"Gellman, M.D., and Turner, J.R. (2013). Beck Depression Inventory (BDI). Encyclopedia of Behavioral Medicine, Springer.","key":"ref_26","DOI":"10.1007\/978-1-4419-1005-9"},{"unstructured":"(2019, August 15). State-Trait Anxiety Inventory (STAI). Available online: https:\/\/www.apa.org\/pi\/about\/publications\/caregivers\/practice-settings\/assessment\/tools\/trait-state.","key":"ref_27"},{"key":"ref_28","first-page":"22","article-title":"Comorbid anxiety and depression","volume":"66","author":"Pollack","year":"2005","journal-title":"J. Clin. Psychiatry"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/1475-2891-8-31","article-title":"Food consumption frequency and perceived stress and depressive symptoms among students in three European countries","volume":"8","author":"Mikolajczyk","year":"2009","journal-title":"Nutr. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/j.1600-0447.1994.tb05800.x","article-title":"Food and mood: Relationship between food, serotonin and affective disorders","volume":"89","author":"Wallin","year":"1994","journal-title":"Acta Psychiatr. Scand."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"E191","DOI":"10.1503\/cmaj.110829","article-title":"Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): A meta-analysis","volume":"184","author":"Manea","year":"2011","journal-title":"Can. Med. Assoc. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.genhosppsych.2013.04.006","article-title":"Standardization of the depression screener Patient Health Questionnaire (PHQ-9) in the general population","volume":"35","author":"Kocalevent","year":"2013","journal-title":"Gen. Hosp. Psychiatry"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.genhosppsych.2014.09.009","article-title":"A diagnostic meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) algorithm scoring method as a screen for depression","volume":"37","author":"Manea","year":"2015","journal-title":"Gen. Hosp. Psychiatry"},{"unstructured":"(2019, July 01). Django. Available online: https:\/\/www.djangoproject.com\/.","key":"ref_34"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"doi-asserted-by":"crossref","unstructured":"Quiroz, J.C., Yong, M.H., and Geangu, E. (2017, January 11\u201315). Emotion recognition using smart watch accelerometer data: Preliminary findings. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, Ser. UbiComp \u201917, Maui, HI, USA.","key":"ref_36","DOI":"10.1145\/3123024.3125614"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e10153","DOI":"10.2196\/10153","article-title":"Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study","volume":"5","author":"Quiroz","year":"2018","journal-title":"JMIR Ment. Health"},{"unstructured":"(2019, July 20). KakaoTalk. Available online: https:\/\/www.kakaocorp.com\/service\/KakaoTalk?lang=en.","key":"ref_38"},{"doi-asserted-by":"crossref","unstructured":"Canzian, L., and Musolesi, M. (2015, January 7\u201311). Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ser. UbiComp \u201915, Osaka, Japan.","key":"ref_39","DOI":"10.1145\/2750858.2805845"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T03:20:32Z","timestamp":1719285632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,4]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051396"],"URL":"https:\/\/doi.org\/10.3390\/s20051396","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,3,4]]}}}