This paper is in the following e-collection/theme issue:

Digital Biomarkers and Digital Phenotyping (178) Machine Learning (1385) Depression and Mood Disorders; Suicide Prevention (1282) mHealth for Wellness, Behavior Change and Prevention (2743) Wearable Devices and Sensors (818) Fitness Trackers and Smart Pedometers/Accelerometers (541)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24872, first published .
Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Authors of this article:

Yuri Rykov1 Author Orcid Image ;   Thuan-Quoc Thach2 Author Orcid Image ;   Iva Bojic3 Author Orcid Image ;   George Christopoulos4 Author Orcid Image ;   Josip Car3, 5 Author Orcid Image
Article Authors Cited by (44) Tweetations (7) Metrics

Journals

  1. Ahmed A, Ramesh J, Ganguly S, Aburukba R, Sagahyroon A, Aloul F. Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning. Information 2022;13(9):406 View
  2. Abd-alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. Journal of Medical Internet Research 2023;25:e42672 View
  3. Chakrabarti S, Biswas N, Jones L, Kesari S, Ashili S. Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics 2022;12(9):2110 View
  4. Lee T, Kim G, Choi M. Identification of Geriatric Depression and Anxiety Using Activity Tracking Data and Minimal Geriatric Assessment Scales. Applied Sciences 2022;12(5):2488 View
  5. Ono T, Sakurai T, Kasuno S, Murai T. Novel 3-D action video game mechanics reveal differentiable cognitive constructs in young players, but not in old. Scientific Reports 2022;12(1) View
  6. Tonn P, Seule L, Degani Y, Herzinger S, Klein A, Schulze N. Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study. JMIR Formative Research 2022;6(8):e37061 View
  7. Neumann D, Tiberius V, Biendarra F. Adopting wearables to customize health insurance contributions: a ranking-type Delphi. BMC Medical Informatics and Decision Making 2022;22(1) View
  8. Cotes R, Boazak M, Griner E, Jiang Z, Kim B, Bremer W, Seyedi S, Bahrami Rad A, Clifford G. Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study. JMIR Research Protocols 2022;11(7):e36417 View
  9. Dhinagaran D, Martinengo L, Ho M, Joty S, Kowatsch T, Atun R, Tudor Car L. Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework. JMIR mHealth and uHealth 2022;10(10):e38740 View
  10. Choi J, Lee S, Kim S, Kim D, Kim H. Depressed Mood Prediction of Elderly People with a Wearable Band. Sensors 2022;22(11):4174 View
  11. Phiri B, Fèvre D, Hidano A. Uptrend in global managed honey bee colonies and production based on a six-decade viewpoint, 1961–2017. Scientific Reports 2022;12(1) View
  12. Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Frontiers in Psychiatry 2022;13 View
  13. Dillenseger A, Weidemann M, Trentzsch K, Inojosa H, Haase R, Schriefer D, Voigt I, Scholz M, Akgün K, Ziemssen T. Digital Biomarkers in Multiple Sclerosis. Brain Sciences 2021;11(11):1519 View
  14. Modde Epstein C, McCoy T. Linking Electronic Health Records With Wearable Technology From the All of Us Research Program. Journal of Obstetric, Gynecologic & Neonatal Nursing 2023;52(2):139 View
  15. Watanabe K, Tsutsumi A. The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study. JMIR Formative Research 2022;6(11):e40339 View
  16. Takano A, Ono K, Nozawa K, Sato M, Onuki M, Sese J, Yumoto Y, Matsushita S, Matsumoto T. Wearable Sensor and Mobile App–Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study. JMIR Research Protocols 2023;12:e44275 View
  17. Anmella G, Corponi F, Li B, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young A, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study. JMIR mHealth and uHealth 2023;11:e45405 View
  18. Kim E, Jenness J, Miller A, Halabi R, de Zambotti M, Bagot K, Baker F, Pratap A. Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children. JAMA Network Open 2023;6(3):e235681 View
  19. Wang S, Feng M, Fang Y, Lv L, Sun G, Cheng S, Huang W, Yang S, Guo P, Qian M, Chen H. Effects of chronotype on antidepressant treatment and symptoms in patients with depression: a multicenter, parallel, controlled study. BMC Psychiatry 2023;23(1) View
  20. Ricka N, Pellegrin G, Fompeyrine D, Lahutte B, Geoffroy P. Predictive biosignature of major depressive disorder derived from physiological measurements of outpatients using machine learning. Scientific Reports 2023;13(1) View
  21. Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine 2023;6(1) View
  22. McIntyre R, Greenleaf W, Bulaj G, Taylor S, Mitsi G, Saliu D, Czysz A, Silvesti G, Garcia M, Jain R. Digital health technologies and major depressive disorder. CNS Spectrums 2023;28(6):662 View
  23. Glaus J, Kang S, Guo W, Lamers F, Strippoli M, Leroux A, Dey D, Plessen K, Vaucher J, Vollenweider P, Zipunnikov V, Merikangas K, Preisig M. Objectively assessed sleep and physical activity in depression subtypes and its mediating role in their association with cardiovascular risk factors. Journal of Psychiatric Research 2023;163:325 View
  24. Aneni K, Chen C, Meyer J, Cho Y, Lipton Z, Kher S, Jiao M, Gomati de la Vega I, Umutoni F, McDougal R, Fiellin L. Identifying Game-Based Digital Biomarkers of Cognitive Risk for Adolescent Substance Misuse: Protocol for a Proof-of-Concept Study. JMIR Research Protocols 2023;12:e46990 View
  25. Yeung A, Torkamani A, Butte A, Glicksberg B, Schuller B, Rodriguez B, Ting D, Bates D, Schaden E, Peng H, Willschke H, van der Laak J, Car J, Rahimi K, Celi L, Banach M, Kletecka-Pulker M, Kimberger O, Eils R, Islam S, Wong S, Wong T, Gao W, Brunak S, Atanasov A. The promise of digital healthcare technologies. Frontiers in Public Health 2023;11 View
  26. Tao Z, Sun N, Yuan Z, Chen Z, Liu J, Wang C, Li S, Ma X, Ji B, Li K. Research on a New Intelligent and Rapid Screening Method for Depression Risk in Young People Based on Eye Tracking Technology. Brain Sciences 2023;13(10):1415 View
  27. Bertl M, Bignoumba N, Ross P, Yahia S, Draheim D. Evaluation of deep learning-based depression detection using medical claims data. Artificial Intelligence in Medicine 2024;147:102745 View
  28. Price G, Heinz M, Song S, Nemesure M, Jacobson N. 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. Translational Psychiatry 2023;13(1) View
  29. Piccin J, Viduani A, Buchweitz C, Pereira R, Zimerman A, Amando G, Cosenza V, Ferreira L, McMahon N, Melo R, Richter D, Reckziegel F, Rohrsetzer F, Souza L, Tonon A, Costa-Valle M, Zajkowska Z, Araújo R, Hauser T, van Heerden A, Hidalgo M, Kohrt B, Mondelli V, Swartz J, Fisher H, Kieling C. Prospective Follow-Up of Adolescents With and at Risk for Depression: Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort Longitudinal Assessments. JAACAP Open 2024;2(2):145 View
  30. Price G, Heinz M, Collins A, Jacobson N. Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample. Psychiatry Research 2024;332:115693 View
  31. Sarwar A, Almadani A, Agu E. Few-shot meta-learning for pre-symptomatic detection of Covid-19 from limited health tracker data. Smart Health 2024;32:100459 View
  32. Zierer C, Behrendt C, Lepach-Engelhardt A. Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering. Journal of Affective Disorders 2024;356:438 View
  33. Barata F, Shim J, Wu F, Langer P, Fleisch E. The Bitemporal Lens Model—toward a holistic approach to chronic disease prevention with digital biomarkers. JAMIA Open 2024;7(2) View
  34. Choo M, Park D, Cho M, Bae S, Kim J, Han D. Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening. Frontiers in Psychiatry 2024;15 View
  35. Dong T, Yu C, Mao Q, Han F, Yang Z, Yang Z, Pires N, Wei X, Jing W, Lin Q, Hu F, Hu X, Zhao L, Jiang Z. Advances in biosensors for major depressive disorder diagnostic biomarkers. Biosensors and Bioelectronics 2024;258:116291 View
  36. Ahmed M, Hasan T, Islam S, Ahmed N. Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study. JMIR Research Protocols 2024;13:e51540 View
  37. Hurwitz E, Butzin-Dozier Z, Master H, O'Neil S, Walden A, Holko M, Patel R, Haendel M. Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study. JMIR mHealth and uHealth 2024;12:e54622 View
  38. Huang J, Wang H, Wu Q, Yin J, Zhou H, He Y. Clinical research on neurological and psychiatric diagnosis and monitoring using wearable devices: A literature review. Interdisciplinary Medicine 2024 View
  39. Newby D, Taylor N, Joyce D, Winchester L. Optimising the use of electronic medical records for large scale research in psychiatry. Translational Psychiatry 2024;14(1) View
  40. Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. Journal of Affective Disorders 2024;361:445 View
  41. Li B, Guo S, Xu H, Zhou Y, Zhang M, Wang J, Chen Y, Chen H, Song J, Tan S. Abnormal circadian rhythm of heart rate variability and their association with symptoms in patients with major depressive disorder. Journal of Affective Disorders 2024;362:14 View
  42. Janssen Daalen J, van den Bergh R, Prins E, Moghadam M, van den Heuvel R, Veen J, Mathur S, Meijerink H, Mirelman A, Darweesh S, Evers L, Bloem B. Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Friedrich O, Schleidgen S, Seifert J. Medizin – Technik – Ethik. View