{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T05:23:27Z","timestamp":1774329807368,"version":"3.50.1"},"reference-count":98,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Mood Disorders are a group of mental health conditions characterized by a disruption of the emotional state that affects the quality of life of the people living with them. Mental Disorders are difficult to diagnose and treat due to the complex processes involved and limitations of the healthcare system. Digital biomarkers have created accessible, long-term, non-invasive, and user-friendly alternatives for the diagnosis, treatment, and monitoring of these conditions. The use of everyday devices like smartphones and smartwatches and specialized tools like actigraphy, in conjunction with powerful statistical tools, artificial intelligence, and machine learning, represents a promising avenue for the implementation of personalized strategies to monitor and treat Mood Disorders, and potentially higher adherence to treatment. We conducted several studies that implement a variety of methodologies and tools to better understand Mood Disorders, using a patient-focused approach with the ultimate goal of identifying better strategies to improve their quality of life.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1595243","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T05:28:00Z","timestamp":1750138080000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review"],"prefix":"10.3389","volume":"7","author":[{"given":"Andrea P.","family":"Garz\u00f3n-Partida","sequence":"first","affiliation":[]},{"given":"Citlali B.","family":"Padilla-G\u00f3mez","sequence":"additional","affiliation":[]},{"given":"Diana Emilia","family":"Mart\u00ednez-Fern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"Joaqu\u00edn","family":"Garc\u00eda-Estrada","sequence":"additional","affiliation":[]},{"given":"Sonia","family":"Luquin","sequence":"additional","affiliation":[]},{"given":"David","family":"Fern\u00e1ndez-Quezada","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"B1","first-page":"1","article-title":"Mood disorder","volume-title":"StatPearls","author":"Sekhon","year":"2024"},{"key":"B2","first-page":"1","volume-title":"World Mental Health Report: Transforming Mental Health for All","year":"2022"},{"key":"B3","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40345-023-00290-y","article-title":"Stigma in people living with bipolar disorder and their families: a systematic review","volume":"11","author":"Latifian","year":"2023","journal-title":"Int J Bipolar Disord"},{"key":"B4","doi-asserted-by":"publisher","first-page":"115387","DOI":"10.1016\/j.bios.2023.115387","article-title":"The convergence of traditional and digital biomarkers through AI-assisted biosensing: a new era in translational diagnostics?","volume":"235","author":"Arya","year":"2023","journal-title":"Biosens Bioelectron"},{"key":"B5","article-title":"Diagnostic and Statistical Manual of Mental Disorders\u202f: Fifth Edition Text Revision DSM-5-TRTM","year":""},{"key":"B6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/B978-0-323-85751-2.00004-9","article-title":"Chapter 3\u2014cognitive internet of things (IoT) and computational intelligence for mental well-being.","volume-title":"Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data. Intelligent Data-Centric Systems","author":"Thapa","year":"2022"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmjhci-2023-100914","article-title":"Definitions of digital biomarkers: a systematic mapping of the biomedical literature","volume":"31","author":"Alonso","year":"2024","journal-title":"BMJ Health Care Inform"},{"key":"B8","doi-asserted-by":"publisher","first-page":"648190","DOI":"10.3389\/fdgth.2021.648190","article-title":"Discovering composite lifestyle biomarkers with artificial intelligence from clinical studies to enable smart eHealth and digital therapeutic services","volume":"3","author":"Kyriazakos","year":"2021","journal-title":"Front Digit Health"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3580252.3586977","article-title":"Parkinson\u2019s disease action tremor detection with supervised-leaning models","volume":"2023","author":"Sun","year":"2023","journal-title":"IEEE Int Conf Connect Health Appl Syst Eng Technol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-022-00583-z","article-title":"Digital biomarkers: convergence of digital health technologies and biomarkers","volume":"5","author":"Vasudevan","year":"2022","journal-title":"npj Digit Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"204e","DOI":"10.1097\/PRS.0000000000010572","article-title":"Artificial intelligence: singularity approaches","volume":"153","author":"TerKonda","year":"2024","journal-title":"Plast Reconstr Surg"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e030710","DOI":"10.1136\/bmjopen-2019-030710","article-title":"Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study","volume":"9","author":"Guthrie","year":"2019","journal-title":"BMJ Open"},{"key":"B13","doi-asserted-by":"publisher","first-page":"100350","DOI":"10.1016\/j.health.2024.100350","article-title":"A comprehensive review of predictive analytics models for mental illness using machine learning algorithms","volume":"6","author":"Islam","year":"2024","journal-title":"Healthc Anal"},{"key":"B14","doi-asserted-by":"publisher","first-page":"959","DOI":"10.3390\/brainsci13060959","article-title":"A review on smartphone keystroke dynamics as a digital biomarker for understanding neurocognitive functioning","volume":"13","author":"Nguyen","year":"2023","journal-title":"Brain Sci"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e21304","DOI":"10.2196\/21304","article-title":"Digital cognitive behavior therapy intervention for depression and anxiety: retrospective study","volume":"7","author":"Venkatesan","year":"2020","journal-title":"JMIR Ment Health"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e47515","DOI":"10.2196\/47515","article-title":"Comparison of the working alliance in blended cognitive behavioral therapy and treatment as usual for depression in Europe: secondary data analysis of the E-COMPARED randomized controlled trial","volume":"26","author":"Doukani","year":"2024","journal-title":"J Med Internet Res"},{"key":"B17","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1016\/j.jad.2024.01.099","article-title":"Livewell, a smartphone-based self-management intervention for bipolar disorder: intervention participation and usability analysis","volume":"350","author":"Jonathan","year":"2024","journal-title":"J Affect Disord"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1001\/jamapsychiatry.2020.1011","article-title":"Coached mobile app platform for the treatment of depression and anxiety among primary care patients","volume":"77","author":"Graham","year":"2020","journal-title":"JAMA Psychiatry"},{"key":"B19","doi-asserted-by":"publisher","first-page":"20552076241260409","DOI":"10.1177\/20552076241260409","article-title":"Digitally managing depression: a fully remote randomised attention-placebo controlled trial","volume":"10","author":"Kandola","year":"2024","journal-title":"Digital Health"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e29201","DOI":"10.2196\/29201","article-title":"A smartphone intervention for people with serious mental illness: fully remote randomized controlled trial of CORE","volume":"23","author":"Ben-Zeev","year":"2021","journal-title":"J Med Internet Res"},{"key":"B21","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1177\/07067437241245331","article-title":"A randomized evaluation of MoodFX, a patient-centred e-health tool to support outcome measurement for depression: une \u00e9valuation randomis\u00e9e de MoodFX, un outil de sant\u00e9 en ligne centr\u00e9 sur le patient pour soutenir la mesure du r\u00e9sultat dans la d\u00e9pression","volume":"69","author":"Li","year":"2024","journal-title":"Can J Psychiatry"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e47350","DOI":"10.2196\/47350","article-title":"Specifying the efficacy of digital therapeutic tools for depression and anxiety: retrospective, 2-cohort, real-world analysis","volume":"25","author":"Fundoiano-Hershcovitz","year":"2023","journal-title":"J Med Internet Res"},{"key":"B23","doi-asserted-by":"publisher","first-page":"e34","DOI":"10.1136\/ebmental-2021-300416","article-title":"Guided digital health intervention for depression in Lebanon: randomised trial","volume":"25","author":"Cuijpers","year":"2022","journal-title":"Evid Based Ment Health"},{"key":"B24","doi-asserted-by":"publisher","first-page":"e43385","DOI":"10.2196\/43385","article-title":"A fully automated self-help biopsychosocial transdiagnostic digital intervention to reduce anxiety and\/or depression and improve emotional regulation and well-being: pre\u2013follow-up single-arm feasibility trial","volume":"7","author":"Klein","year":"2023","journal-title":"JMIR Form Res"},{"key":"B25","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1007\/s11042-022-12315-2","article-title":"A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms","volume":"82","author":"Thati","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e40667","DOI":"10.2196\/40667","article-title":"Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings: retrospective analysis","volume":"10","author":"Zhang","year":"2022","journal-title":"JMIR Mhealth Uhealth"},{"key":"B27","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1111\/acps.13739","article-title":"Exploring actigraphy as a digital phenotyping measure: a study on differentiating psychomotor agitation and retardation in depression","volume":"151","author":"Maruani","year":"2024","journal-title":"Acta Psychiatr Scand"},{"key":"B28","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1001\/jamapsychiatry.2018.3546","article-title":"Real-time mobile monitoring of the dynamic associations among motor activity, energy, mood, and sleep in adults with bipolar disorder","volume":"76","author":"Merikangas","year":"2019","journal-title":"JAMA Psychiatry"},{"key":"B29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1038\/s41398-018-0125-7","article-title":"Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder","volume":"8","author":"Carr","year":"2018","journal-title":"Transl Psychiatry"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyt.2023.1232433","article-title":"Optomyography-based sensing of facial expression derived arousal and valence in adults with depression","volume":"14","author":"Broulidakis","year":"2023","journal-title":"Front Psychiatry"},{"key":"B31","doi-asserted-by":"publisher","first-page":"e34898","DOI":"10.2196\/34898","article-title":"Longitudinal relationships between depressive symptom severity and phone-measured mobility: dynamic structural equation modeling study","volume":"9","author":"Zhang","year":"2022","journal-title":"JMIR Ment Health"},{"key":"B32","doi-asserted-by":"publisher","first-page":"e107","DOI":"10.1192\/bjo.2023.77","article-title":"Real-time air pollution and bipolar disorder symptoms: remote-monitored cross-sectional study","volume":"9","author":"Kandola","year":"2023","journal-title":"BJPsych Open"},{"key":"B33","doi-asserted-by":"publisher","first-page":"e18751","DOI":"10.2196\/18751","article-title":"The relationship between smartphone-recorded environmental audio and symptomatology of anxiety and depression: exploratory study","volume":"4","author":"Matteo","year":"2020","journal-title":"JMIR Form Res"},{"key":"B34","doi-asserted-by":"publisher","first-page":"100631","DOI":"10.1016\/j.jadr.2023.100631","article-title":"The social cost of depression: investigating the impact of impaired social emotion regulation, social cognition, and interpersonal behavior on social functioning","volume":"14","author":"Kupferberg","year":"2023","journal-title":"J Affect Disord Rep"},{"key":"B35","doi-asserted-by":"publisher","first-page":"751629","DOI":"10.3389\/fdgth.2021.751629","article-title":"Redefining and validating digital biomarkers as fluid, dynamic multi-dimensional digital signal patterns","volume":"3","author":"Au","year":"2022","journal-title":"Front Digit Health"},{"key":"B36","doi-asserted-by":"publisher","first-page":"105094","DOI":"10.1016\/j.ebiom.2024.105094","article-title":"Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data","volume":"103","author":"Song","year":"2024","journal-title":"eBioMedicine"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-023-00827-6","article-title":"Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response","volume":"6","author":"Ali","year":"2023","journal-title":"npj Digit Med"},{"key":"B38","doi-asserted-by":"publisher","first-page":"e46895","DOI":"10.2196\/46895","article-title":"Characterizing longitudinal patterns in cognition, mood, and activity in depression with 6-week high-frequency wearable assessment: observational study","volume":"11","author":"Cormack","year":"2024","journal-title":"JMIR Ment Health"},{"key":"B39","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1080\/13803395.2021.1975656","article-title":"Relationships between daily mood states and real-time cognitive performance in individuals with bipolar disorder and healthy comparators: a remote ambulatory assessment study","volume":"43","author":"Bomyea","year":"2021","journal-title":"J Clin Exp Neuropsychol"},{"key":"B40","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.jad.2023.04.139","article-title":"Mood instability and activity\/energy instability in patients with bipolar disorder according to day-to-day smartphone-based data\u2014an exploratory post hoc study","volume":"334","author":"Faurholt-Jepsen","year":"2023","journal-title":"J Affect Disord"},{"key":"B41","doi-asserted-by":"publisher","first-page":"e24333","DOI":"10.2196\/24333","article-title":"Smartphone-based self-reports of depressive symptoms using the remote monitoring application in psychiatry (ReMAP): interformat validation study","volume":"8","author":"Goltermann","year":"2021","journal-title":"JMIR Ment Health"},{"key":"B42","doi-asserted-by":"publisher","first-page":"e37061","DOI":"10.2196\/37061","article-title":"Digital content-free speech analysis tool to measure affective distress in mental health: evaluation study","volume":"6","author":"Tonn","year":"2022","journal-title":"JMIR Form Res"},{"key":"B43","doi-asserted-by":"publisher","first-page":"e28244","DOI":"10.2196\/28244","article-title":"Behavioral activation and depression symptomatology: longitudinal assessment of linguistic indicators in text-based therapy sessions","volume":"23","author":"Burkhardt","year":"2021","journal-title":"J Med Internet Res"},{"key":"B44","doi-asserted-by":"publisher","first-page":"e23146","DOI":"10.1590\/s2175-97902023e23146","article-title":"Advancements in artificial intelligence and machine learning in revolutionising biomarker discovery","volume":"59","author":"Raikar","year":"2023","journal-title":"Braz J Pharm Sci"},{"key":"B45","doi-asserted-by":"publisher","first-page":"00133","DOI":"10.1051\/bioconf\/20249700133","article-title":"An overview of machine learning classification techniques","volume":"97","author":"Alnuaimi","year":"2024","journal-title":"BIO Web Conf"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0078-0","article-title":"Digital biomarkers of mood disorders and symptom change","volume":"2","author":"Jacobson","year":"2019","journal-title":"npj Digit Med"},{"key":"B47","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1111\/acps.13148","article-title":"Actigraphic patterns, impulsivity and mood instability in bipolar disorder, borderline personality disorder and healthy controls","volume":"141","author":"McGowan","year":"2020","journal-title":"Acta Psychiatr Scand"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-024-02876-1","article-title":"Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number","volume":"14","author":"Corponi","year":"2024","journal-title":"Transl Psychiatry"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-023-00977-7","article-title":"Classifying, and clustering mood disorder patients using smartphone data from a feasibility study","volume":"6","author":"Langholm","year":"2023","journal-title":"npj Digit Med"},{"key":"B50","doi-asserted-by":"publisher","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":"B51","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.jad.2023.10.125","article-title":"Electrodermal activity in bipolar disorder: differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting","volume":"345","author":"Anmella","year":"2024","journal-title":"J Affect Disord"},{"key":"B52","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1111\/acps.13718","article-title":"Sleep\u2013wake variations of electrodermal activity in bipolar disorder","volume":"151","author":"Valenzuela-Pascual","year":"2025","journal-title":"Acta Psychiatr Scand"},{"key":"B53","doi-asserted-by":"publisher","first-page":"104078","DOI":"10.1016\/j.actpsy.2023.104078","article-title":"Longitudinal interactions between residual symptoms and physiological stress in the remitted symptom network structure of depression","volume":"241","author":"Whiston","year":"2023","journal-title":"Acta Psychol (Amst)"},{"key":"B54","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1109\/TBME.2016.2611862","article-title":"Detecting bipolar depression from geographic location data","volume":"64","author":"Palmius","year":"2017","journal-title":"IEEE Trans Biomed Eng"},{"key":"B55","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.1093\/schbul\/sbaa121","article-title":"Geolocation as a digital phenotyping measure of negative symptoms and functional outcome","volume":"46","author":"Raugh","year":"2020","journal-title":"Schizophr Bull"},{"key":"B56","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1017\/S0033291716002166","article-title":"A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression","volume":"47","author":"O\u2019Brien","year":"2017","journal-title":"Psychol Med"},{"key":"B57","doi-asserted-by":"publisher","first-page":"640741","DOI":"10.3389\/fpsyt.2021.640741","article-title":"A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression","volume":"12","author":"Sverdlov","year":"2021","journal-title":"Front Psychiatry"},{"key":"B58","doi-asserted-by":"publisher","first-page":"e24699","DOI":"10.2196\/24699","article-title":"Acoustic and facial features from clinical interviews for machine learning\u2013based psychiatric diagnosis: algorithm development","volume":"9","author":"Birnbaum","year":"2022","journal-title":"JMIR Ment Health"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyt.2024.1342835","article-title":"Validating the efficacy and value proposition of mental fitness vocal biomarkers in a psychiatric population: prospective cohort study","volume":"15","author":"Larsen","year":"2024","journal-title":"Front Psychiatry"},{"key":"B60","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1093\/jamia\/ocaa057","article-title":"Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: a BiAffect iOS study","volume":"27","author":"Vesel","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"B61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyt.2020.584711","article-title":"Monitoring changes in depression severity using wearable and mobile sensors","volume":"11","author":"Pedrelli","year":"2020","journal-title":"Front Psychiatry"},{"key":"B62","doi-asserted-by":"publisher","first-page":"e27908","DOI":"10.2196\/27908","article-title":"Toward an extended definition of major depressive disorder symptomatology: digital assessment and cross-validation study","volume":"5","author":"Martin-Key","year":"2021","journal-title":"JMIR Form Res"},{"key":"B63","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.jad.2021.04.039","article-title":"Internet-based cognitive assessment tool: sensitivity and validity of a new online cognition screening tool for patients with bipolar disorder","volume":"289","author":"Miskowiak","year":"2021","journal-title":"J Affect Disord"},{"key":"B64","doi-asserted-by":"publisher","first-page":"590","DOI":"10.3390\/encyclopedia3020042","article-title":"Predictive modeling in medicine","volume":"3","author":"Toma","year":"2023","journal-title":"Encyclopedia"},{"key":"B65","doi-asserted-by":"publisher","first-page":"891","DOI":"10.2147\/IJGM.S268093","article-title":"The increasing role of artificial intelligence in health care: will robots replace doctors in the future?","volume":"13","author":"Shuaib","year":"2020","journal-title":"Int J Gen Med"},{"key":"B66","doi-asserted-by":"publisher","first-page":"e11029","DOI":"10.2196\/11029","article-title":"Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study","volume":"21","author":"Cho","year":"2019","journal-title":"J Med Internet Res"},{"key":"B67","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jad.2019.02.017","article-title":"Network dynamics of positive and negative affect in bipolar disorder","volume":"249","author":"Curtiss","year":"2019","journal-title":"J Affect Disord"},{"key":"B68","doi-asserted-by":"publisher","first-page":"18596","DOI":"10.1038\/s41598-023-44592-8","article-title":"Personalized relapse prediction in patients with major depressive disorder using digital biomarkers","volume":"13","author":"Vairavan","year":"2023","journal-title":"Sci Rep"},{"key":"B69","doi-asserted-by":"publisher","first-page":"e15028","DOI":"10.2196\/15028","article-title":"Forecasting mood in bipolar disorder from smartphone self-assessments: hierarchical Bayesian approach","volume":"8","author":"Busk","year":"2020","journal-title":"JMIR Mhealth Uhealth"},{"key":"B70","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.3390\/s20123572","article-title":"Passive sensing of prediction of moment-to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones","volume":"20","author":"Jacobson","year":"2020","journal-title":"Sensors"},{"key":"B71","doi-asserted-by":"publisher","first-page":"114425","DOI":"10.1016\/j.psychres.2022.114425","article-title":"Associations among smartphone app-based measurements of mood, sleep and activity in bipolar disorder","volume":"310","author":"Tseng","year":"2022","journal-title":"Psychiatry Res"},{"key":"B72","doi-asserted-by":"publisher","first-page":"e10194","DOI":"10.2196\/10194","article-title":"Group-personalized regression models for predicting mental health scores from objective Mobile phone data streams: observational study","volume":"20","author":"Palmius","year":"2018","journal-title":"J Med Internet Res"},{"key":"B73","doi-asserted-by":"publisher","first-page":"5636","DOI":"10.1017\/S0033291722002847","article-title":"Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study","volume":"53","author":"Lee","year":"2023","journal-title":"Psychol Med"},{"key":"B74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-024-01035-6","article-title":"Personalized mood prediction from patterns of behavior collected with smartphones","volume":"7","author":"Balliu","year":"2024","journal-title":"npj Digit Med"},{"key":"B75","doi-asserted-by":"publisher","first-page":"e2363","DOI":"10.1002\/brb3.2363","article-title":"Naturalistic smartphone keyboard typing reflects processing speed and executive function","volume":"11","author":"Ross","year":"2021","journal-title":"Brain Behav"},{"key":"B76","doi-asserted-by":"publisher","first-page":"e241","DOI":"10.2196\/jmir.9775","article-title":"Predicting mood disturbance severity with mobile phone keystroke metadata: a BiAffect digital phenotyping study","volume":"20","author":"Zulueta","year":"2018","journal-title":"J Med Internet Res"},{"key":"B77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fdgth.2022.964582","article-title":"Personalised depression forecasting using mobile sensor data and ecological momentary assessment","volume":"4","author":"Kathan","year":"2022","journal-title":"Front Digit Health"},{"key":"B78","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s40345-020-00210-4","article-title":"Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders","volume":"8","author":"Ebner-Priemer","year":"2020","journal-title":"Int J Bipolar Disord"},{"key":"B79","doi-asserted-by":"publisher","first-page":"121845","DOI":"10.1109\/ACCESS.2023.3328342","article-title":"Automatic bipolar disorder assessment using machine learning with smartphone-based digital phenotyping","volume":"11","author":"Wu","year":"2023","journal-title":"IEEE Access"},{"key":"B80","doi-asserted-by":"publisher","first-page":"e24365","DOI":"10.2196\/24365","article-title":"Tracking and monitoring mood stability of patients with Major depressive disorder by machine learning models using passive digital data: prospective naturalistic multicenter study","volume":"9","author":"Bai","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"B81","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyt.2021.625247","article-title":"Predicting symptoms of depression and anxiety using smartphone and wearable data","volume":"12","author":"Moshe","year":"2021","journal-title":"Front Psychiatry"},{"key":"B82","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1038\/s41398-024-02873-4","article-title":"Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese","volume":"14","author":"Chen","year":"2024","journal-title":"Transl Psychiatry"},{"key":"B83","doi-asserted-by":"publisher","first-page":"e29840","DOI":"10.2196\/29840","article-title":"Predicting depressive symptom severity through Individuals\u2019 nearby bluetooth device count data collected by Mobile phones: preliminary longitudinal study","volume":"9","author":"Zhang","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"B84","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1017\/S0033291721005377","article-title":"The dynamic interplay between sleep and mood: an intensive longitudinal study of individuals with bipolar disorder","volume":"53","author":"Lewis","year":"2023","journal-title":"Psychol Med"},{"key":"B85","doi-asserted-by":"publisher","first-page":"11203","DOI":"10.1073\/pnas.1802331115","article-title":"Facebook Language predicts depression in medical records","volume":"115","author":"Eichstaedt","year":"2018","journal-title":"Proc Natl Acad Sci USA"},{"key":"B86","doi-asserted-by":"publisher","first-page":"e5870","DOI":"10.2196\/jmir.5870","article-title":"Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view","volume":"18","author":"Luo","year":"2016","journal-title":"J Med Internet Res"},{"key":"B87","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2196\/24872","article-title":"Digital biomarkers for depression screening with wearable devices: cross-sectional study with machine learning modeling\u2014pMC","volume":"9","author":"Rykov","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"B88","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1007\/s11999-008-0346-9","article-title":"Statistics in brief: the importance of sample size in the planning and interpretation of medical research","volume":"466","author":"Biau","year":"2008","journal-title":"Clin Orthop Relat Res"},{"key":"B89","doi-asserted-by":"publisher","first-page":"106504","DOI":"10.1016\/j.cmpb.2021.106504","article-title":"Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models","volume":"213","author":"Bailly","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"B90","doi-asserted-by":"publisher","first-page":"7283","DOI":"10.1038\/s41467-023-42992-y","article-title":"Exploiting redundancy in large materials datasets for efficient machine learning with less data","volume":"14","author":"Li","year":"2023","journal-title":"Nat Commun"},{"key":"B91","doi-asserted-by":"publisher","first-page":"936","DOI":"10.3390\/jpm12060936","article-title":"Development of digital biomarkers of mental illness via Mobile apps for personalized treatment and diagnosis","volume":"12","author":"Chen","year":"2022","journal-title":"J Pers Med"},{"key":"B92","doi-asserted-by":"publisher","first-page":"e41042","DOI":"10.2196\/41042","article-title":"Digital biomarker\u2013based interventions: systematic review of systematic reviews","volume":"24","author":"Motahari-Nezhad","year":"2022","journal-title":"J Med Internet Res"},{"key":"B93","doi-asserted-by":"publisher","first-page":"9164","DOI":"10.3390\/s23229164","article-title":"Evaluation of leading smartwatches for the detection of hypoxemia: comparison to reference oximeter","volume":"23","author":"Walzel","year":"2023","journal-title":"Sensors"},{"key":"B94","doi-asserted-by":"publisher","first-page":"1069410","DOI":"10.3389\/fdgth.2023.1069410","article-title":"Stuck in translation: stakeholder perspectives on impediments to responsible digital health","volume":"5","author":"Landers","year":"2023","journal-title":"Front Digit Health"},{"key":"B95","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.3390\/jpm12111914","article-title":"Ethical conundrums in the application of artificial intelligence (AI) in healthcare\u2014A scoping review of reviews","volume":"12","author":"Prakash","year":"2022","journal-title":"J Pers Med"},{"key":"B96","doi-asserted-by":"publisher","first-page":"e0000519","DOI":"10.1371\/journal.pdig.0000519","article-title":"Mapping the ethical landscape of digital biomarkers: a scoping review","volume":"3","author":"Andreoletti","year":"2024","journal-title":"PLOS Digit Health"},{"key":"B97","article-title":"GDPR Law","year":""},{"key":"B98","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1111\/1468-0009.12712","article-title":"Targeting machine learning and artificial intelligence algorithms in health care to reduce bias and improve population health","volume":"102","author":"Hurd","year":"2024","journal-title":"Milbank Q"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1595243\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T05:28:01Z","timestamp":1750138081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1595243\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,17]]},"references-count":98,"alternative-id":["10.3389\/fdgth.2025.1595243"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1595243","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,17]]},"article-number":"1595243"}}