{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:29:12Z","timestamp":1793125752399,"version":"4.5.0"},"reference-count":81,"publisher":"BMJ","issue":"6","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"unspecified","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation (NRF) of Korea","doi-asserted-by":"crossref","award":["NRF-2021R1A5A8032895"],"award-info":[{"award-number":["NRF-2021R1A5A8032895"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation (NRF) of Korea","doi-asserted-by":"crossref","award":["NRF-2022M3C1B6080866"],"award-info":[{"award-number":["NRF-2022M3C1B6080866"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Information and Communications Promotion Fund through the National IT Industry Promotion Agency","award":["H0601-24-1017"],"award-info":[{"award-number":["H0601-24-1017"]}]},{"name":"Institute of Information & Communications Technology Planning & Evaluation","award":["RS-2023-00224823"],"award-info":[{"award-number":["RS-2023-00224823"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2025,6,9]]},"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Depression and anxiety are highly prevalent mental health conditions that significantly affect quality of life and cause societal burdens. However, their detection and diagnosis rates remain low owing to the limitations of the current screening methods. With rapid technological advancements and the proliferation of consumer-grade wearable devices and smartphones, their integration into digital phenotyping research has enabled the unobtrusive screening for depression and anxiety in natural settings. The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. By collecting comprehensive data using smartphones and smartwatches, this study aims to facilitate the translation of artificial intelligence-based early detection research into clinical impact, thereby potentially enhancing patient care through more accurate and timely interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>This cross-sectional observational study will enrol up to 2500 participants (at least 1000) aged 19-59 years from South Korea via social media outreach and clinical referrals. The eligible participants must use a compatible smartphone. Each participant will be followed up for 4 weeks. Data will be collected using a custom-developed smartphone application called PixelMood. Active data collection will include daily, weekly and monthly self-report questionnaires incorporating validated scales, such as the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7. Passive data from smartphones include information on physical activity, location, ambient light and smartphone usage patterns. Optionally, participants using the Apple Watch or Galaxy Watch devices can provide additional data on physiological responses and sleep health. The primary outcome will be the development of machine-learning algorithms to predict depression and anxiety based on these digital biomarkers. We will employ various machine-learning techniques, including random forest, support vector machine and deep-learning models. The secondary outcomes will include the association between digital biomarkers and clinical measures, and the feasibility and acceptability of data collection methods. Various features characterising mobile usage behaviours, physical\/social activity, sleep patterns, resting physiological states and circadian rhythms will be exploited to serve as potential digital phenotyping markers. Advanced machine-learning and deep-learning techniques will be applied to multimodal data for model generation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>This study protocol was reviewed and approved by the Institutional Review Board of the Korea University Anam Hospital (approval number: 2023AN0506). The results of this study will be disseminated via multiple channels. The findings will be presented at local, national and international conferences in relevant fields, such as psychiatry, psychology and digital health. Manuscripts detailing the study results will be submitted to peer-reviewed journals for publication.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration number<\/jats:title>\n                    <jats:p>\n                      The present study was registered with the Clinical Research Information Service (CRIS,\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/cris.nih.go.kr\">https:\/\/cris.nih.go.kr<\/jats:ext-link>\n                      ; identifier: KCT0009183).\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2024-096773","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T13:00:43Z","timestamp":1750424443000},"page":"e096773","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":9,"title":["Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety (SWARTS-DA) observational study in Korea"],"prefix":"10.1136","volume":"15","author":[{"given":"Yu-Bin","family":"Shin","sequence":"first","affiliation":[{"name":"Department of Psychiatry, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ah Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Medical Information Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea"},{"name":"Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seonmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Korea University College of Medicine, Seoul, Korea"},{"name":"Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min-Sup","family":"Shin","sequence":"additional","affiliation":[{"name":"Korea University School of Psychology, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhwa","family":"Choi","sequence":"additional","affiliation":[{"name":"Batoners Inc, Daegu, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung Lyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Batoners Inc, Daegu, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jisu","family":"Lee","sequence":"additional","affiliation":[{"name":"Batoners Inc, Daegu, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangwon","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Incheon National University, Incheon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heon-Jeong","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1663-9680","authenticated-orcid":false,"given":"Chul-Hyun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Korea University College of Medicine, Seoul, Korea"},{"name":"Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"2025121221515748000_15.6.e096773.1","unstructured":"Dattani S , Rod\u00e9s-Guirao L , Ritchie H , et al . \"Mental health\u201d published online at OurWorldinData.org. 2023. Available: https:\/\/ourworldindata.org\/mental-health [Accessed 18 Nov 2024]."},{"key":"2025121221515748000_15.6.e096773.2","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.62.6.617"},{"key":"2025121221515748000_15.6.e096773.3","unstructured":"Maser JD , Cloninger C . Comorbidity of mood and anxiety disorders. American Psychiatric Association, 1990."},{"key":"2025121221515748000_15.6.e096773.4","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.49.1.377"},{"key":"2025121221515748000_15.6.e096773.5","unstructured":"Regier D , Burke J , Burke K . Comorbidity of affective and anxiety disorders in the NIMH Epidemiologic Catchment Area Program. In: Maser JD , Cloninger CR , eds. Comorbidity of mood and anxiety disorders. Washington, D.C: American Psychiatric Press, 1990."},{"key":"2025121221515748000_15.6.e096773.6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1093\/tbm\/ibab133","article-title":"Trends in mental health symptoms, service use, and unmet need for services among US adults through the first 8 months of the COVID-19 pandemic","volume":"12","author":"Coley","year":"2022","journal-title":"Transl Behav Med"},{"key":"2025121221515748000_15.6.e096773.7","doi-asserted-by":"publisher","DOI":"10.1037\/hea0001259"},{"key":"2025121221515748000_15.6.e096773.8","doi-asserted-by":"crossref","DOI":"10.1186\/s12888-023-05481-6","article-title":"The global prevalence of depression, anxiety, and sleep disorder among patients coping with Post COVID-19 syndrome (long COVID): a systematic review and meta-analysis","volume":"24","author":"Seighali","year":"2024","journal-title":"BMC Psychiatry"},{"key":"2025121221515748000_15.6.e096773.9","doi-asserted-by":"publisher","DOI":"10.1177\/070674371005501003"},{"key":"2025121221515748000_15.6.e096773.10","doi-asserted-by":"publisher","DOI":"10.1016\/S0272-7358(99)00026-4"},{"key":"2025121221515748000_15.6.e096773.11","doi-asserted-by":"crossref","unstructured":"Birchwood M , Gleeson J , Chanen A , et al . Emotional and personality dysfunctions in early psychosis. In: Jackson HJ , McGorry PD , eds. The recognition and management of psychosis: a preventive approach. Cambridge: Cambridge University Press, 2009: 283\u2013302.","DOI":"10.1017\/CBO9780511576287.017"},{"key":"2025121221515748000_15.6.e096773.12","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1111\/psyg.12490","article-title":"Anxiety symptoms are quantitatively and qualitatively different in dementia with Lewy bodies than in Alzheimer\u2019s disease in the years preceding clinical diagnosis","volume":"20","author":"Segers","year":"2020","journal-title":"Psychogeriatrics"},{"key":"2025121221515748000_15.6.e096773.13","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1016\/j.sbspro.2010.07.335","article-title":"A study of relationship between suicidal ideas, depression, anxiety, resiliency, daily stresses and mental health among Tehran university students","volume":"5","author":"Izadinia","year":"2010","journal-title":"Procedia Soc Behav Sci"},{"key":"2025121221515748000_15.6.e096773.14","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1176\/ajp.156.12.1915","article-title":"Impairment in pure and comorbid generalized anxiety disorder and major depression at 12 months in two national surveys","volume":"156","author":"Kessler","year":"1999","journal-title":"Am J Psychiatry"},{"key":"2025121221515748000_15.6.e096773.15","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.10m06176blu"},{"key":"2025121221515748000_15.6.e096773.16","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.v67n0803"},{"key":"2025121221515748000_15.6.e096773.17","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1002\/da.20386","article-title":"Comorbid depression, but not comorbid anxiety disorders, predicts poor outcome in anxiety disorders","volume":"25","author":"van Balkom","year":"2008","journal-title":"Depress Anxiety"},{"key":"2025121221515748000_15.6.e096773.18","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jad.2016.03.025","article-title":"A randomised controlled trial of the effectiveness of a program for early detection and treatment of depression in primary care","volume":"198","author":"Picardi","year":"2016","journal-title":"J Affect Disord"},{"key":"2025121221515748000_15.6.e096773.19","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.jad.2020.09.131","article-title":"Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review","volume":"279","author":"Costantini","year":"2021","journal-title":"J Affect Disord"},{"key":"2025121221515748000_15.6.e096773.20","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.166.10.1092"},{"key":"2025121221515748000_15.6.e096773.21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0155431","article-title":"Screening for Depression in the General Population with the Center for Epidemiologic Studies Depression (CES-D): A Systematic Review with Meta-Analysis","volume":"11","author":"Vilagut","year":"2016","journal-title":"PLoS One"},{"key":"2025121221515748000_15.6.e096773.22","doi-asserted-by":"publisher","DOI":"10.1097\/NMD.0b013e3181d6141f"},{"key":"2025121221515748000_15.6.e096773.23","doi-asserted-by":"publisher","DOI":"10.1016\/j.annepidem.2018.03.016"},{"key":"2025121221515748000_15.6.e096773.24","doi-asserted-by":"crossref","unstructured":"Paulhus DL . Socially desirable responding on self-reports. In: Zeigler-Hill V , Shackelford TK , eds. Encyclopedia of personality and individual differences. Cham: Springer International Publishing, 2017: 1\u20135.","DOI":"10.1007\/978-3-319-28099-8_1349-1"},{"key":"2025121221515748000_15.6.e096773.25","doi-asserted-by":"publisher","DOI":"10.1207\/S15324796ABM2403_09"},{"key":"2025121221515748000_15.6.e096773.26","doi-asserted-by":"crossref","DOI":"10.2196\/mental.5165","article-title":"New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research","volume":"3","author":"Torous","year":"2016","journal-title":"JMIR Ment Health"},{"key":"2025121221515748000_15.6.e096773.27","doi-asserted-by":"crossref","unstructured":"DeMasi O , Recht B . A step towards quantifying when an algorithm can and cannot predict an individual\u2019s wellbeing. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers; Maui, Hawaii: Association for Computing Machinery, 2017:63\u201371.","DOI":"10.1145\/3123024.3125609"},{"key":"2025121221515748000_15.6.e096773.28","doi-asserted-by":"crossref","first-page":"10653","DOI":"10.1007\/s11042-015-3128-x","article-title":"Using linguistic and topic analysis to classify sub-groups of online depression communities","volume":"76","author":"Nguyen","year":"2017","journal-title":"Multimed Tools Appl"},{"key":"2025121221515748000_15.6.e096773.29","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.3502","article-title":"Machine Learning-Based Prediction of Attention-Deficit\/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children","volume":"6","author":"Kim","year":"2023","journal-title":"JAMA Netw Open"},{"key":"2025121221515748000_15.6.e096773.30","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291722002847"},{"key":"2025121221515748000_15.6.e096773.31","doi-asserted-by":"crossref","DOI":"10.1007\/s11920-018-0914-y","article-title":"Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps","volume":"20","author":"Torous","year":"2018","journal-title":"Curr Psychiatry Rep"},{"key":"2025121221515748000_15.6.e096773.32","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1093\/jamia\/ocz221","article-title":"Depression screening using mobile phone usage metadata: a machine learning approach","volume":"27","author":"Razavi","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2025121221515748000_15.6.e096773.33","doi-asserted-by":"crossref","DOI":"10.2196\/37736","article-title":"A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study","volume":"6","author":"Choudhary","year":"2022","journal-title":"JMIR Form Res"},{"key":"2025121221515748000_15.6.e096773.34","doi-asserted-by":"crossref","DOI":"10.2196\/26540","article-title":"Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study","volume":"9","author":"Opoku Asare","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"2025121221515748000_15.6.e096773.35","doi-asserted-by":"crossref","DOI":"10.2196\/22844","article-title":"Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study","volume":"23","author":"Meyerhoff","year":"2021","journal-title":"J Med Internet Res"},{"key":"2025121221515748000_15.6.e096773.36","doi-asserted-by":"crossref","DOI":"10.2196\/35807","article-title":"Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning-Based Exploratory Study","volume":"6","author":"Mullick","year":"2022","journal-title":"JMIR Form Res"},{"key":"2025121221515748000_15.6.e096773.37","doi-asserted-by":"crossref","DOI":"10.1038\/s44184-023-00041-y","article-title":"Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study","volume":"3","author":"Stamatis","year":"2024","journal-title":"Npj Ment Health Res"},{"key":"2025121221515748000_15.6.e096773.38","doi-asserted-by":"crossref","DOI":"10.2196\/38943","article-title":"A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study","volume":"10","author":"Choudhary","year":"2022","journal-title":"JMIR Med Inform"},{"key":"2025121221515748000_15.6.e096773.39","doi-asserted-by":"crossref","DOI":"10.1038\/s41398-022-02038-1","article-title":"Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life","volume":"12","author":"Jacobson","year":"2022","journal-title":"Transl Psychiatry"},{"key":"2025121221515748000_15.6.e096773.40","doi-asserted-by":"publisher","DOI":"10.5665\/SLEEP.2810"},{"key":"2025121221515748000_15.6.e096773.41","doi-asserted-by":"publisher","DOI":"10.1067\/mhj.2000.109981"},{"key":"2025121221515748000_15.6.e096773.42","doi-asserted-by":"crossref","DOI":"10.1038\/s41398-020-0694-0","article-title":"Circadian rhythm disruption and mental health","volume":"10","author":"Walker","year":"2020","journal-title":"Transl Psychiatry"},{"key":"2025121221515748000_15.6.e096773.43","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1111\/acps.13712","article-title":"Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample","volume":"151","author":"Cohen","year":"2025","journal-title":"Acta Psychiatr Scand"},{"key":"2025121221515748000_15.6.e096773.44","doi-asserted-by":"crossref","DOI":"10.2196\/56874","article-title":"Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study","volume":"26","author":"Ik\u00e4heimonen","year":"2024","journal-title":"J Med Internet Res"},{"key":"2025121221515748000_15.6.e096773.45","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1111\/acps.13765","article-title":"Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology","volume":"151","author":"Lipschitz","year":"2025","journal-title":"Acta Psychiatr Scand"},{"key":"2025121221515748000_15.6.e096773.46","doi-asserted-by":"crossref","first-page":"101621","DOI":"10.1016\/j.pmcj.2022.101621","article-title":"Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: a longitudinal data analysis","volume":"83","author":"Opoku Asare","year":"2022","journal-title":"Pervasive Mob Comput"},{"key":"2025121221515748000_15.6.e096773.47","doi-asserted-by":"crossref","DOI":"10.2196\/24872","article-title":"Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling","volume":"9","author":"Rykov","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"2025121221515748000_15.6.e096773.48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2016.08.006"},{"key":"2025121221515748000_15.6.e096773.49","doi-asserted-by":"publisher","DOI":"10.1046\/j.1525-1497.2001.016009606.x"},{"key":"2025121221515748000_15.6.e096773.50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2010.04.011"},{"key":"2025121221515748000_15.6.e096773.51","first-page":"27211","article-title":"Rates of detection of mood and anxiety disorders in primary care: a descriptive, cross-sectional study","volume":"13","author":"Vermani","year":"2011","journal-title":"Prim Care Companion CNS Disord"},{"key":"2025121221515748000_15.6.e096773.52","doi-asserted-by":"crossref","first-page":"168","DOI":"10.2174\/1570159X17666191001142934","article-title":"An Overview of the Heterogeneity of Major Depressive Disorder: Current Knowledge and Future Prospective","volume":"18","author":"Athira","year":"2020","journal-title":"Curr Neuropharmacol"},{"key":"2025121221515748000_15.6.e096773.53","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2010.10.019"},{"key":"2025121221515748000_15.6.e096773.54","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2015.12.030"},{"key":"2025121221515748000_15.6.e096773.55","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1037\/abn0000884","article-title":"Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder","volume":"133","author":"Nemesure","year":"2024","journal-title":"J Psychopathol Clin Sci"},{"key":"2025121221515748000_15.6.e096773.56","doi-asserted-by":"crossref","first-page":"S52","DOI":"10.1002\/mpr.215","article-title":"Supplementary dimensional assessment in anxiety disorders","volume":"16 Suppl 1","author":"Shear","year":"2007","journal-title":"Int J Methods Psychiatr Res"},{"key":"2025121221515748000_15.6.e096773.57","doi-asserted-by":"publisher","DOI":"10.1037\/0021-843X.105.2.181"},{"key":"2025121221515748000_15.6.e096773.58","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/S0140-6736(72)92090-9","article-title":"Interval between onset of sleep and rapid-eye-movement sleep as an indicator of depression","volume":"2","author":"Kupfer","year":"1972","journal-title":"Lancet"},{"key":"2025121221515748000_15.6.e096773.59","doi-asserted-by":"publisher","DOI":"10.31887\/DCNS.2008.10.4\/plfranzen)"},{"key":"2025121221515748000_15.6.e096773.60","doi-asserted-by":"crossref","DOI":"10.3390\/ijms20030607","article-title":"Depression and Sleep","volume":"20","author":"Steiger","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2025121221515748000_15.6.e096773.61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2011.01.011"},{"key":"2025121221515748000_15.6.e096773.62","doi-asserted-by":"publisher","DOI":"10.1016\/j.smrv.2018.10.006"},{"key":"2025121221515748000_15.6.e096773.63","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-021-00548-8","article-title":"Digital health tools for the passive monitoring of depression: a systematic review of methods","volume":"5","author":"De Angel","year":"2022","journal-title":"NPJ Digit Med"},{"key":"2025121221515748000_15.6.e096773.64","doi-asserted-by":"crossref","DOI":"10.1038\/s41398-023-02669-y","article-title":"Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data","volume":"13","author":"Price","year":"2023","journal-title":"Transl Psychiatry"},{"key":"2025121221515748000_15.6.e096773.65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2020.12.086"},{"key":"2025121221515748000_15.6.e096773.66","doi-asserted-by":"crossref","DOI":"10.3390\/healthcare10071189","article-title":"Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone","volume":"10","author":"Hong","year":"2022","journal-title":"Healthcare (Basel)"},{"key":"2025121221515748000_15.6.e096773.67","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2015.10.013"},{"key":"2025121221515748000_15.6.e096773.68","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3223(00)01000-3"},{"key":"2025121221515748000_15.6.e096773.69","doi-asserted-by":"publisher","DOI":"10.1037\/h0027806"},{"key":"2025121221515748000_15.6.e096773.70","doi-asserted-by":"publisher","DOI":"10.1002\/da.10049"},{"key":"2025121221515748000_15.6.e096773.71","doi-asserted-by":"publisher","DOI":"10.1016\/0005-7967(77)90042-0"},{"key":"2025121221515748000_15.6.e096773.72","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-016-3703-5"},{"key":"2025121221515748000_15.6.e096773.73","doi-asserted-by":"publisher","DOI":"10.2307\/2136404"},{"key":"2025121221515748000_15.6.e096773.74","doi-asserted-by":"publisher","DOI":"10.1080\/10705500802222972"},{"key":"2025121221515748000_15.6.e096773.75","doi-asserted-by":"publisher","DOI":"10.1207\/s15327752jpa5101_6"},{"key":"2025121221515748000_15.6.e096773.76","doi-asserted-by":"publisher","DOI":"10.1037\/1040-3590.19.2.176"},{"key":"2025121221515748000_15.6.e096773.77","doi-asserted-by":"publisher","DOI":"10.1016\/0022-3999(94)90005-1"},{"key":"2025121221515748000_15.6.e096773.78","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.beth.2011.12.001","article-title":"Refining the measurement of distress intolerance","volume":"43","author":"McHugh","year":"2012","journal-title":"Behav Ther"},{"key":"2025121221515748000_15.6.e096773.79","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-9457(00)00065-4"},{"key":"2025121221515748000_15.6.e096773.80","first-page":"122","article-title":"A preliminary study on the Korean translation of composite scale (KtCS) to measure morningness: eveningness","volume":"36","author":"Yoon","year":"1997","journal-title":"J Korean Neuropsych Assoc"},{"key":"2025121221515748000_15.6.e096773.81","doi-asserted-by":"publisher","DOI":"10.1207\/s15327752jpa4901_13"}],"container-title":["BMJ Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjopen-2024-096773","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T05:52:10Z","timestamp":1765605130000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopen.bmj.com\/lookup\/doi\/10.1136\/bmjopen-2024-096773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":81,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6,20]]},"published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10.1136\/bmjopen-2024-096773"],"URL":"https:\/\/doi.org\/10.1136\/bmjopen-2024-096773","relation":{},"ISSN":["2044-6055","2044-6055"],"issn-type":[{"value":"2044-6055","type":"print"},{"value":"2044-6055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}