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

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;48(5):448 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;33(5):512 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
  39. Clay I, De Luca V, Sano A. Editorial: Multimodal digital approaches to personalized medicine. Frontiers in Big Data 2023;6 View
  40. Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V, Sarmiento R. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS Digital Health 2023;2(10):e0000347 View
  41. 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
  42. Paromita P, Mundnich K, Nadarajan A, Booth B, Narayanan S, Chaspari T. Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers. Frontiers in Digital Health 2023;5 View
  43. Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2023;25:e46778 View
  44. Lane E, D’Arcey J, Kidd S, Onyeaka H, Alon N, Joshi D, Torous J. Digital Phenotyping in Adults with Schizophrenia: A Narrative Review. Current Psychiatry Reports 2023;25(11):699 View
  45. Stamatis C, Liu T, Meyerhoff J, Meng Y, Cho Y, Karr C, Curtis B, Ungar L, Mohr D. Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety. Internet Interventions 2023;34:100683 View
  46. Lee S, Hwang H, Kim S, Hwang J, Park J, Park S. Clinical Implication of Maumgyeol Basic Service–the 2 Channel Electroencephalography and a Photoplethysmogram–based Mental Health Evaluation Software. Clinical Psychopharmacology and Neuroscience 2023;21(3):583 View
  47. Rigatti M, Chapman B, Chai P, Smelson D, Babu K, Carreiro S. Digital biomarker applications across the spectrum of opioid use disorder. Cogent Mental Health 2023;2(1) View
  48. Frank A, Li R, Peterson B, Narayanan S. Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review. JMIR Mental Health 2023;10:e45572 View
  49. Wang Z, Larrazabal M, Rucker M, Toner E, Daniel K, Kumar S, Boukhechba M, Teachman B, Barnes L. Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1 View
  50. Buck B, Wingerson M, Tauscher J, Enkema M, Wang W, Campbell A, Ben-Zeev D. Using Smartphones to Identify Momentary Characteristics of Persecutory Ideation Associated With Functional Disability. Schizophrenia Bulletin Open 2023;4(1) View
  51. Nestor B, Chimoff J, Koike C, Weitzman E, Riley B, Uhl K, Kossowsky J. Adolescent and Parent Perspectives on Digital Phenotyping in Youths With Chronic Pain: Cross-Sectional Mixed Methods Survey Study. Journal of Medical Internet Research 2024;26:e47781 View
  52. Hong W. Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones. JMIR mHealth and uHealth 2024;12:e48803 View
  53. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View
  54. Stamatis C, Meyerhoff J, Meng Y, Lin Z, Cho Y, Liu T, Karr C, Liu T, Curtis B, Ungar L, Mohr D. Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study. npj Mental Health Research 2024;3(1) View
  55. Langener A, Bringmann L, Kas M, Stulp G. Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks. Administration and Policy in Mental Health and Mental Health Services Research 2024;51(4):455 View
  56. Popp Z, Low S, Igwe A, Rahman M, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang P, Sunderaraman P, Shih L, Lin H, Kolachalama V, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. Journal of the American Heart Association 2024;13(2) View
  57. Gopalakrishnan A, Gururajan R, Zhou X, Venkataraman R, Chan K, Higgins N. A survey of autonomous monitoring systems in mental health. WIREs Data Mining and Knowledge Discovery 2024;14(3) View
  58. Gültekin M, Şahin M. The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think?. Cyberpsychology: Journal of Psychosocial Research on Cyberspace 2024;18(1) View
  59. Fernández-Álvarez J, Colombo D, Gómez Penedo J, Pierantonelli M, Baños R, Botella C. Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review. JMIR Mental Health 2024;11:e46593 View
  60. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  61. Chadaga K, Prabhu S, Sampathila N, Chadaga R, Bhat D, Sharma A, Swathi K. SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques. SLAS Technology 2024;29(2):100129 View
  62. Harris C, Tang Y, Birnbaum E, Cherian C, Mendhe D, Chen M. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies. Archives of Clinical Neuropsychology 2024;39(3):290 View
  63. 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
  64. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Ibrahim O, Rosenberg J, O’Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12-25 Years): A Scoping Review. SSRN Electronic Journal 2024 View
  65. Choi H, Cho Y, Min C, Kim K, Kim E, Lee S, Kim J. Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning. DIGITAL HEALTH 2024;10 View
  66. O’Leary A, Lahey T, Lovato J, Loftness B, Douglas A, Skelton J, Cohen J, Copeland W, McGinnis R, McGinnis E. Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges. Sensors 2024;24(10):3214 View
  67. Choi A, Ooi A, Lottridge D. Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review. JMIR mHealth and uHealth 2024;12:e40689 View
  68. Jaiswal A, Shah A, Harjadi C, Windgassen E, Washington P. Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping. JMIR Formative Research 2024;8:e59794 View
  69. Jafarlou S, Azimi I, Lai J, Wang Y, Labbaf S, Nguyen B, Qureshi H, Marcotullio C, Borelli J, Dutt N, Rahmani A, Chang L. Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications. PLOS ONE 2024;19(6):e0298949 View
  70. D’Alfonso S, Coghlan S, Schmidt S, Mangelsdorf S. Ethical Dimensions of Digital Phenotyping Within the Context of Mental Healthcare. Journal of Technology in Behavioral Science 2024 View
  71. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Dabash O, Rosenberg J, O'Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review. Heliyon 2024;10(15):e35472 View
  72. Mlakar I, Arioz U, Smrke U, Plohl N, Šafran V, Rojc M. An End-to-End framework for extracting observable cues of depression from diary recordings. Expert Systems with Applications 2024;257:125025 View
  73. Cho M, Park D, Choo M, Kim J, Han D. Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study. JMIR Formative Research 2024;8:e59623 View
  74. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  75. Gyorda J, Lekkas D, Jacobson N. Detecting Longitudinal Trends between Passively Collected Phone Use and Anxiety among College Students. Digital Biomarkers 2024;8(1):181 View
  76. Mulinari S. Aligning digital biomarker definitions in psychiatry with the National Institute of Mental Health Research Domain Criteria framework. NPP—Digital Psychiatry and Neuroscience 2024;2(1) View
  77. Rashid Z, Folarin A, Zhang Y, Ranjan Y, Conde P, Sankesara H, Sun S, Stewart C, Laiou P, Dobson R. Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform. JMIR Mental Health 2024;11:e51259 View
  78. Alt A, Pascher A, Seizer L, von Fraunberg M, Conzelmann A, Renner T. Psychotherapy 2.0 - Application context and effectiveness of sensor technology in psychotherapy with children and adolescents: A systematic review. Internet Interventions 2024;38:100785 View
  79. Patel J, Hung C, Katapally T. Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review. Psychiatry Research 2025;343:116277 View
  80. Adler D, Yang Y, Viranda T, Xu X, Mohr D, Van Meter A, Tartaglia J, Jacobson N, Wang F, Estrin D, Choudhury T. Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(4):1 View
  81. Smrke U, Mlakar I, Rehberger A, Žužek L, Plohl N. Decoding anxiety: A scoping review of observable cues. DIGITAL HEALTH 2024;10 View
  82. Ikäheimonen A, Luong N, Baryshnikov I, Darst R, Heikkilä R, Holmen J, Martikkala A, Riihimäki K, Saleva O, Isometsä E, Aledavood T. Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study. Journal of Medical Internet Research 2024;26:e56874 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
  4. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View
  5. Davies A, Fried E, Costilla-Reyes O, Aung H. Pervasive Computing Technologies for Healthcare. View
  6. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View