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

Mobile Health (mhealth) (2215) New Methods (747) Digital Biomarkers and Digital Phenotyping (150) mHealth for Data Collection and Research (744) Social Anxiety Disorder (SAD) (40)

Published on in Vol 22 , No 5 (2020) :May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16875, first published .
Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

Authors of this article:

Nicholas C Jacobson 1 Author Orcid Image ;   Berta Summers 2 Author Orcid Image ;   Sabine Wilhelm 2 Author Orcid Image
Article Authors Cited by (43) Tweetations (12) Metrics

Journals

  1. Jacobson N, Chung Y. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020;20(12):3572 View
  2. Orsolini L, Fiorani M, Volpe U. Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?. International Journal of Molecular Sciences 2020;21(20):7684 View
  3. Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health 2020;23(4):161 View
  4. Jayakumar P, Lin E, Galea V, Mathew A, Panda N, Vetter I, Haynes A. Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review. Journal of Personalized Medicine 2020;10(4):282 View
  5. Jacobson N, Lekkas D, Huang R, Thomas N. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. Journal of Affective Disorders 2021;282:104 View
  6. Lekkas D, Jacobson N. Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Scientific Reports 2021;11(1) View
  7. Skorburg J, Yam J. Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19. AJOB Neuroscience 2022;13(3):177 View
  8. Neethirajan S, Kemp B. Digital Phenotyping in Livestock Farming. Animals 2021;11(7):2009 View
  9. Ryu J, Sükei E, Norbury A, H Liu S, Campaña-Montes J, Baca-Garcia E, Artés A, Perez-Rodriguez M. Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study. JMIR Mental Health 2021;8(9):e30833 View
  10. Daniel K, Mendu S, Baglione A, Cai L, Teachman B, Barnes L, Boukhechba M. Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life. Anxiety, Stress, & Coping 2022;35(3):298 View
  11. Ye S, Cheng H, Zhai Z, Liu H. Relationship Between Social Anxiety and Internet Addiction in Chinese College Students Controlling for the Effects of Physical Exercise, Demographic, and Academic Variables. Frontiers in Psychology 2021;12 View
  12. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  13. Langener A, Stulp G, Kas M, Bringmann L. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Mental Health 2023;10:e42646 View
  14. Choudhary S, Thomas N, Alshamrani S, Srinivasan G, Ellenberger J, Nawaz U, Cohen R. A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study. JMIR Medical Informatics 2022;10(8):e38943 View
  15. Jacobson N, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry 2022;12(1) View
  16. Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. Journal of Medical Internet Research 2022;24(5):e35951 View
  17. Pastre M, Lopez-Castroman J. Actigraphy monitoring in anxiety disorders: A mini-review of the literature. Frontiers in Psychiatry 2022;13 View
  18. Girousse E, Vuillerme N. The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(12):e38785 View
  19. Dechant M, Birk M, Shiban Y, Schnell K, Mandryk R. How Avatar Customization Affects Fear in a Game-based Digital Exposure Task for Social Anxiety. Proceedings of the ACM on Human-Computer Interaction 2021;5(CHI PLAY):1 View
  20. Qirtas M, Zafeiridi E, Pesch D, White E. Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review. JMIR mHealth and uHealth 2022;10(4):e34638 View
  21. Bejarano C, Hesse D, Cushing C. Hedonic Appetite, Affect, and Loss of Control Eating: Macrotemporal and Microtemporal Associations in Adolescents. Journal of Pediatric Psychology 2023 View
  22. Shetty A, Delanerolle G, Zeng Y, Shi J, Ebrahim R, Pang J, Hapangama D, Sillem M, Shetty S, Shetty B, Hirsch M, Raymont V, Majumder K, Chong S, Goodison W, O’Hara R, Hull L, Pluchino N, Shetty N, Elneil S, Fernandez T, Brownstone R, Phiri P. A systematic review and meta-analysis of digital application use in clinical research in pain medicine. Frontiers in Digital Health 2022;4 View
  23. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  24. Ash G, Nally L, Stults-Kolehmainen M, De Los Santos M, Jeon S, Brandt C, Gulanski B, Spanakis E, Baker J, Weinzimer S, Fucito L. Personalized Digital Health Information to Substantiate Human-Delivered Exercise Support for Adults With Type 1 Diabetes. Clinical Journal of Sport Medicine 2023;Publish Ahead of Print View
  25. Bonnechère B, Timmermans A, Michiels S. Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review. Sensors 2023;23(2):875 View
  26. Jeong H, Jeong Y, Park Y, Kim K, Park J, Kang D. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. DIGITAL HEALTH 2022;8:205520762211366 View
  27. Senaratne H, Oviatt S, Ellis K, Melvin G. A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  28. MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638 View
  29. Bartolome A, Prioleau T. A computational framework for discovering digital biomarkers of glycemic control. npj Digital Medicine 2022;5(1) View
  30. Akbarialiabad H, Bastani B, Taghrir M, Paydar S, Ghahramani N, Kumar M. Threats to Global Mental Health From Unregulated Digital Phenotyping and Neuromarketing: Recommendations for COVID-19 Era and Beyond. Frontiers in Psychiatry 2021;12 View
  31. Jacobson N, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy 2022;149:104013 View
  32. Harvey P, Depp C, Rizzo A, Strauss G, Spelber D, Carpenter L, Kalin N, Krystal J, McDonald W, Nemeroff C, Rodriguez C, Widge A, Torous J. Technology and Mental Health: State of the Art for Assessment and Treatment. American Journal of Psychiatry 2022;179(12):897 View
  33. Dechant M, Frommel J, Mandryk R. The Development of Explicit and Implicit Game-Based Digital Behavioral Markers for the Assessment of Social Anxiety. Frontiers in Psychology 2021;12 View
  34. Kilshaw R, Adamo C, Butner J, Deboeck P, Shi Q, Bulik C, Flatt R, Thornton L, Argue S, Tregarthen J, Baucom B. Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study. JMIR Research Protocols 2022;11(6):e38294 View
  35. Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica K, Kunta A, Low C. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Frontiers in Digital Health 2021;3 View
  36. Parziale A, Mascalzoni D. Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem. Frontiers in Psychiatry 2022;13 View
  37. Schmidt S, D'Alfonso S. Clinician perspectives on how digital phenotyping can inform client treatment. Acta Psychologica 2023;235:103886 View
  38. Giebel G, Speckemeier C, Abels C, Plescher F, Börchers K, Wasem J, Blase N, Neusser S. Problems and Barriers Related to the Use of Digital Health Applications: Scoping Review. Journal of Medical Internet Research 2023;25:e43808 View

Books/Policy Documents

  1. Keller O, Budney A, Struble C, Teepe G. Digital Therapeutics for Mental Health and Addiction. View
  2. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
  3. Hidayah N, Ramli M, Kirana K, Hanafi H, Yunita M, Rofiqoh R. Proceedings of the International Conference on Educational Management and Technology (ICEMT 2022). View