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

Mobile Health (mhealth) (2765) mHealth for Data Collection and Research (914) Depression and Mood Disorders; Suicide Prevention (1282) Methods and New Tools in Mental Health Research (424) Psychotic Disorders (157) Diagnostic Tools in Mental Health (222) Digital Biomarkers and Digital Phenotyping (178) Ecological Momentary Assessment (EMA) (295)

Published on in Vol 20, No 7 (2018): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9775, first published .
Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study

Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study

Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study

Authors of this article:

John Zulueta1 Author Orcid Image ;   Andrea Piscitello1 Author Orcid Image ;   Mladen Rasic1 Author Orcid Image ;   Rebecca Easter1 Author Orcid Image ;   Pallavi Babu2 Author Orcid Image ;   Scott A Langenecker1 Author Orcid Image ;   Melvin McInnis2 Author Orcid Image ;   Olusola Ajilore1 Author Orcid Image ;   Peter C Nelson1 Author Orcid Image ;   Kelly Ryan2 Author Orcid Image ;   Alex Leow1 Author Orcid Image
Article Authors Cited by (126) Tweetations (36) Metrics

Journals

  1. Liu G, Henson P, Keshavan M, Pekka-Onnela J, Torous J. Assessing the potential of longitudinal smartphone based cognitive assessment in schizophrenia: A naturalistic pilot study. Schizophrenia Research: Cognition 2019;17:100144 View
  2. Allen S. Artificial Intelligence and the Future of Psychiatry. IEEE Pulse 2020;11(3):2 View
  3. Potier R. The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective. Frontiers in Psychology 2020;11 View
  4. Birnbaum M, Ernala S, Rizvi A, Arenare E, R. Van Meter A, De Choudhury M, Kane J. Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. npj Schizophrenia 2019;5(1) View
  5. Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819 View
  6. Daus H, Bloecher T, Egeler R, De Klerk R, Stork W, Backenstrass M. Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder. JMIR Mental Health 2020;7(7):e14267 View
  7. 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
  8. Mastoras R, Iakovakis D, Hadjidimitriou S, Charisis V, Kassie S, Alsaadi T, Khandoker A, Hadjileontiadis L. Touchscreen typing pattern analysis for remote detection of the depressive tendency. Scientific Reports 2019;9(1) View
  9. Stange J, Kleiman E, Mermelstein R, Trull T. Using ambulatory assessment to measure dynamic risk processes in affective disorders. Journal of Affective Disorders 2019;259:325 View
  10. Rashidisabet H, Thomas P, Ajilore O, Zulueta J, Moore R, Leow A. A systems biology approach to the digital behaviorome. Current Opinion in Systems Biology 2020;20:8 View
  11. Radhakrishnan K, Kim M, Burgermaster M, Brown R, Xie B, Bray M, Fournier C. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;68(5):548 View
  12. Campbell L, Tang B, Watson W, Higgins M, Cherner M, Henry B, Moore R. Cannabis Use is Associated with Greater Total Sleep Time in Middle-Aged and Older Adults with and without HIV: A Preliminary Report Utilizing Digital Health Technologies. Cannabis 2020;3(2):180 View
  13. Walker W, Walton J, DeVries A, Nelson R. Circadian rhythm disruption and mental health. Translational Psychiatry 2020;10(1) View
  14. Victory A, Letkiewicz A, Cochran A. Digital solutions for shaping mood and behavior among individuals with mood disorders. Current Opinion in Systems Biology 2020;21:25 View
  15. Piau A, Wild K, Mattek N, Kaye J. Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review. Journal of Medical Internet Research 2019;21(8):e12785 View
  16. Brietzke E, Hawken E, Idzikowski M, Pong J, Kennedy S, Soares C. Integrating digital phenotyping in clinical characterization of individuals with mood disorders. Neuroscience & Biobehavioral Reviews 2019;104:223 View
  17. Zulueta J, Leow A, Ajilore O. Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. FOCUS 2020;18(2):175 View
  18. Bidmon S, Elshiewy O, Terlutter R, Boztug Y. What Patients Value in Physicians: Analyzing Drivers of Patient Satisfaction Using Physician-Rating Website Data. Journal of Medical Internet Research 2020;22(2):e13830 View
  19. Purswani J, Dicker A, Champ C, Cantor M, Ohri N. Big Data From Small Devices: The Future of Smartphones in Oncology. Seminars in Radiation Oncology 2019;29(4):338 View
  20. Rudd B, Beidas R. Digital Mental Health: The Answer to the Global Mental Health Crisis?. JMIR Mental Health 2020;7(6):e18472 View
  21. Severus E, Ebner-Priemer U, Beier F, Mühlbauer E, Ritter P, Hill H, Bauer M. Ambulantes Monitoring und digitale Phänotypisierung in Diagnostik und Therapie bipolarer Erkrankungen. Der Nervenarzt 2019;90(12):1215 View
  22. Vesel C, Rashidisabet H, Zulueta J, Stange J, Duffecy J, Hussain F, Piscitello A, Bark J, Langenecker S, Young S, Mounts E, Omberg L, Nelson P, Moore R, Koziol D, Bourne K, Bennett C, Ajilore O, Demos A, Leow A. Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study. Journal of the American Medical Informatics Association 2020;27(7):1007 View
  23. Diamantaris M, Marcantoni F, Ioannidis S, Polakis J. The Seven Deadly Sins of the HTML5 WebAPI. ACM Transactions on Privacy and Security 2020;23(4):1 View
  24. Birnbaum M, Kulkarni P, Van Meter A, Chen V, Rizvi A, Arenare E, De Choudhury M, Kane J. Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study. JMIR Mental Health 2020;7(9):e19348 View
  25. Asensio-Cuesta S, Sánchez-García Á, Conejero J, Saez C, Rivero-Rodriguez A, García-Gómez J. Smartphone Sensors for Monitoring Cancer-Related Quality of Life: App Design, EORTC QLQ-C30 Mapping and Feasibility Study in Healthy Subjects. International Journal of Environmental Research and Public Health 2019;16(3):461 View
  26. van der Watt A, Odendaal W, Louw K, Seedat S. Distant mood monitoring for depressive and bipolar disorders: a systematic review. BMC Psychiatry 2020;20(1) View
  27. Antosik-Wójcińska A, Dominiak M, Chojnacka M, Kaczmarek-Majer K, Opara K, Radziszewska W, Olwert A, Święcicki Ł. Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling. International Journal of Medical Informatics 2020;138:104131 View
  28. Mouchabac S, Adrien V, Falala-Séchet C, Bonnot O, Maatoug R, Millet B, Peretti C, Bourla A, Ferreri F. Psychiatric Advance Directives and Artificial Intelligence: A Conceptual Framework for Theoretical and Ethical Principles. Frontiers in Psychiatry 2021;11 View
  29. Zulueta J, Ajilore O. Beyond non-inferior: how telepsychiatry technologies can lead to superior care. International Review of Psychiatry 2021;33(4):366 View
  30. Burgess-Hull A, Epstein D. Ambulatory Assessment Methods to Examine Momentary State-Based Predictors of Opioid Use Behaviors. Current Addiction Reports 2021;8(1):122 View
  31. Sagorac Gruichich T, David Gomez J, Zayas‐Cabán G, McInnis M, Cochran A. A digital self‐report survey of mood for bipolar disorder. Bipolar Disorders 2021;23(8):810 View
  32. Druijff‐van de Woestijne G, McConchie H, de Kort Y, Licitra G, Zhang C, Overeem S, Smolders K. Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest–activity patterns. Journal of Sleep Research 2021;30(5) View
  33. 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
  34. Hilty D, Armstrong C, Luxton D, Gentry M, Krupinski E. A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions. Journal of Technology in Behavioral Science 2021;6(2):278 View
  35. 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
  36. Lhaksampa T, Nanavati J, Chisolm M, Miller L. Patient electronic communication data in clinical care: what is known and what is needed. International Review of Psychiatry 2021;33(4):372 View
  37. Davidson B. The crossroads of digital phenotyping. General Hospital Psychiatry 2022;74:126 View
  38. Kohli M, Moore D, Moore R. Using health technology to capture digital phenotyping data in HIV-associated neurocognitive disorders. AIDS 2021;35(1):15 View
  39. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  40. Vlisides-Henry R, Gao M, Thomas L, Kaliush P, Conradt E, Crowell S. Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk. Frontiers in Psychiatry 2021;12 View
  41. Schueller S, Neary M, Lai J, Epstein D. Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview Study. JMIR Mental Health 2021;8(8):e29368 View
  42. Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, Haug J, Schlee W, Holfelder M, Stach M, Schickler M, Baumeister H, Cohrdes C, Deckert J, Deserno L, Edler J, Eichner F, Greger H, Hein G, Heuschmann P, John D, Kestler H, Krefting D, Langguth B, Meybohm P, Probst T, Reichert M, Romanos M, Störk S, Terhorst Y, Weiß M, Pryss R. Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(14):7395 View
  43. Martinez-Martin N, Greely H, Cho M. Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study. JMIR mHealth and uHealth 2021;9(7):e27343 View
  44. 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
  45. Chen M, Leow A, Ross M, DeLuca J, Chiaravalloti N, Costa S, Genova H, Weber E, Hussain F, Demos A. Associations between smartphone keystroke dynamics and cognition in MS. DIGITAL HEALTH 2022;8:205520762211432 View
  46. Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?. Psychologische Rundschau 2023;74(2):89 View
  47. Hampel H, Gao P, Cummings J, Toschi N, Thompson P, Hu Y, Cho M, Vergallo A. The foundation and architecture of precision medicine in neurology and psychiatry. Trends in Neurosciences 2023;46(3):176 View
  48. Nisenson M, Lin V, Gansner M. Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective. Harvard Review of Psychiatry 2021;29(6):401 View
  49. Opoku Asare K, Moshe I, Terhorst Y, Vega J, Hosio S, Baumeister H, Pulkki-Råback L, Ferreira D. Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis. Pervasive and Mobile Computing 2022;83:101621 View
  50. Bougeard A, Guay Hottin1 R, Houde V, Jean T, Piront T, Potvin S, Bernard P, Tourjman V, De Benedictis L, Orban P. Le phénotypage digital pour une pratique clinique en santé mentale mieux informée. Santé mentale au Québec 2021;46(1):135 View
  51. Bennett C, Ross M, Baek E, Kim D, Leow A. Predicting clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics. Pervasive and Mobile Computing 2022;83:101598 View
  52. Kasyanov E, Kovaleva Y, Mazo G. Digital phenotyping as a new method of screening for mental disorders. V.M. BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY 2022;56(4):96 View
  53. Khazanov G, Forbes C, Dunn B, Thase M. Addressing anhedonia to increase depression treatment engagement. British Journal of Clinical Psychology 2022;61(2):255 View
  54. Alfalahi H, Khandoker A, Chowdhury N, Iakovakis D, Dias S, Chaudhuri K, Hadjileontiadis L. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific Reports 2022;12(1) View
  55. Hillis J, Bizzo B. Use of Artificial Intelligence in Clinical Neurology. Seminars in Neurology 2022;42(01):039 View
  56. Nguyen V, Lu N, Kane J, Birnbaum M, De Choudhury M. Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study. JMIR Mental Health 2022;9(12):e39747 View
  57. van Berkel N, D’Alfonso S, Kurnia Susanto R, Ferreira D, Kostakos V. AWARE-Light: a smartphone tool for experience sampling and digital phenotyping. Personal and Ubiquitous Computing 2023;27(2):435 View
  58. Yu J, Chiu C, Wang Y, Dzubur E, Lu W, Hoffman J. A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis. Journal of Medical Internet Research 2021;23(8):e27709 View
  59. Davidson B, Ellis D, Stachl C, Taylor P, Joinson A. Measurement practices exacerbate the generalizability crisis: Novel digital measures can help. Behavioral and Brain Sciences 2022;45 View
  60. De La Fabián R, Jiménez-Molina Á, Pizarro Obaid F. A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry. Big Data & Society 2023;10(1):205395172211490 View
  61. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  62. Saccaro L, Amatori G, Cappelli A, Mazziotti R, Dell'Osso L, Rutigliano G. Portable technologies for digital phenotyping of bipolar disorder: A systematic review. Journal of Affective Disorders 2021;295:323 View
  63. Hoeijmakers A, Licitra G, Meijer K, Lam K, Molenaar P, Strijbis E, Killestein J. Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis. Scientific Reports 2023;13(1) View
  64. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  65. Van Assche E, Antoni Ramos-Quiroga J, Pariante C, Sforzini L, Young A, Flossbach Y, Gold S, Hoogendijk W, Baune B, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100 View
  66. Zhang D, Lim J, Zhou L, Dahl A. Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study. JMIR Mental Health 2021;8(12):e31633 View
  67. DAUS H, FINK-LAMOTTE J, BACKENSTRASS M. Mobile-based, emotion-sensitive video diaries and ambulatory third-party assessments as indicators of mood states in bipolar disorder. Minerva Psychiatry 2022;63(4) View
  68. Orr M, MacLeod L, Bagnell A, McGrath P, Wozney L, Meier S. The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. Computers in Human Behavior 2023;140:107603 View
  69. Abdul Rashid N, Martanto W, Yang Z, Wang X, Heaukulani C, Vouk N, Buddhika T, Wei Y, Verma S, Tang C, Morris R, Lee J. Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia: protocol for the HOPE-S observational study. BMJ Open 2021;11(10):e046552 View
  70. Liu T, Meyerhoff J, Eichstaedt J, Karr C, Kaiser S, Kording K, Mohr D, Ungar L. The relationship between text message sentiment and self-reported depression. Journal of Affective Disorders 2022;302:7 View
  71. Chen Z, Kulkarni P, Galatzer-Levy I, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3(11):100602 View
  72. Fonseka L, Woo B. Wearables in Schizophrenia: Update on Current and Future Clinical Applications. JMIR mHealth and uHealth 2022;10(4):e35600 View
  73. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  74. 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
  75. Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Formative Research 2023;7:e42935 View
  76. Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'Évolution Psychiatrique 2022;87(4):729 View
  77. Montag C, Dagum P, Hall B, Elhai J. How the study of digital footprints can supplement research in behavioral genetics and molecular psychology. Molecular Psychology: Brain, Behavior, and Society 2022;1:2 View
  78. Chen M, Cherian C, Elenjickal K, Rafizadeh C, Ross M, Leow A, DeLuca J. Real-time associations among MS symptoms and cognitive dysfunction using ecological momentary assessment. Frontiers in Medicine 2023;9 View
  79. Lam K, Twose J, Lissenberg-Witte B, Licitra G, Meijer K, Uitdehaag B, De Groot V, Killestein J. The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis. Journal of Medical Internet Research 2022;24(11):e37614 View
  80. Ross M, Demos A, Zulueta J, Piscitello A, Langenecker S, McInnis M, Ajilore O, Nelson P, Ryan K, Leow A. Naturalistic smartphone keyboard typing reflects processing speed and executive function. Brain and Behavior 2021;11(11) View
  81. Winkler T, Büscher R, Larsen M, Kwon S, Torous J, Firth J, Sander L. Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(11):e42146 View
  82. Bennett C, Ross M, Baek E, Kim D, Leow A. Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. npj Digital Medicine 2022;5(1) View
  83. Bilal A, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén K, Elofsson U, Axfors C, Skalkidou A, Papadopoulos F. Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ Open 2022;12(4):e059033 View
  84. Block V, Bove R, Nourbakhsh B. The Role of Remote Monitoring in Evaluating Fatigue in Multiple Sclerosis: A Review. Frontiers in Neurology 2022;13 View
  85. Hackett K, Giovannetti T. Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools. JMIR Aging 2022;5(3):e38130 View
  86. Ortiz A, Maslej M, Husain M, Daskalakis Z, Mulsant B. Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction. Journal of Affective Disorders 2021;295:1190 View
  87. Kalinich M, Ebrahim S, Hays R, Melcher J, Vaidyam A, Torous J. Applying machine learning to smartphone based cognitive and sleep assessments in schizophrenia. Schizophrenia Research: Cognition 2022;27:100216 View
  88. Braund T, O’Dea B, Bal D, Maston K, Larsen M, Werner-Seidler A, Tillman G, Christensen H. Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study. JMIR Mental Health 2023;10:e44986 View
  89. Wetherell M, Lau S, Maxion R. The effect of socially evaluated multitasking stress on typing rhythms. Psychophysiology 2023;60(8) View
  90. Cochran A, Maronge J, Victory A, Hoel S, McInnis M, Thomas E. Mobile Acceptance and Commitment Therapy in Bipolar Disorder: Microrandomized Trial. JMIR Mental Health 2023;10:e43164 View
  91. Ortiz A, Park Y, Gonzalez-Torres C, Alda M, Blumberger D, Burnett R, Husain M, Sanches M, Mulsant B. Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models. International Journal of Bipolar Disorders 2023;11(1) View
  92. Nguyen T, Leow A, Ajilore O. A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning. Brain Sciences 2023;13(6):959 View
  93. Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. Journal of Medical Internet Research 2023;25:e44502 View
  94. Heydarian S, Shakiba A, Rostam Niakan Kalhori S. The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements. Journal of Healthcare Informatics Research 2023;7(2):254 View
  95. Wu C, Hsu J, Liou C, Su H, Lin E, Chen P. Automatic Bipolar Disorder Assessment Using Machine Learning With Smartphone-Based Digital Phenotyping. IEEE Access 2023;11:121845 View
  96. Neznanov N, Vasileva A. Digitalization in mental health care. New opportunities for specialists and patients. National Health Care (Russia) 2023;4(2):15 View
  97. Lenze E, Torous J, Arean P. Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments. Neuropsychopharmacology 2024;49(1):205 View
  98. Lim S, Kim C, Cho B, Choi S, Lee H, Jang D. Investigation of daily patterns for smartphone keystroke dynamics based on loneliness and social isolation. Biomedical Engineering Letters 2023 View
  99. Kim S, Bennett C, Henkel Z, Lee J, Stanojevic C, Baugus K, Bethel C, Piatt J, Šabanović S. Generative replay for multi-class modeling of human activities via sensor data from in-home robotic companion pets. Intelligent Service Robotics 2024;17(2):277 View
  100. Funkhouser C, Trivedi E, Li L, Helgren F, Zhang E, Sritharan A, Cherner R, Pagliaccio D, Durham K, Kyler M, Tse T, Buchanan S, Allen N, Shankman S, Auerbach R. Detecting adolescent depression through passive monitoring of linguistic markers in smartphone communication. Journal of Child Psychology and Psychiatry 2024;65(7):932 View
  101. 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
  102. Knol L, Nagpal A, Leaning I, Idda E, Hussain F, Ning E, Eisenlohr-Moul T, Beckmann C, Marquand A, Leow A. Smartphone keyboard dynamics predict affect in suicidal ideation. npj Digital Medicine 2024;7(1) View
  103. 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

Books/Policy Documents

  1. Hussain F, Stange J, Langenecker S, McInnis M, Zulueta J, Piscitello A, Cao B, Huang H, Yu P, Nelson P, Ajilore O, Leow A. Digital Phenotyping and Mobile Sensing. View
  2. Chentsova Dutton Y, Lyons S. Emotion Measurement. View
  3. Mao S, Khalifa Y, Zhang Z, Shu K, Suri A, Bouzid Z, Sejdic E. Digital Health. View
  4. Hussain F, Stange J, Langenecker S, McInnis M, Zulueta J, Piscitello A, Ross M, Demos A, Vesel C, Rashidisabet H, Cao B, Huang H, Yu P, Nelson P, Ajilore O, Leow A. Digital Phenotyping and Mobile Sensing. View
  5. . The Cambridge Handbook of Community Psychology. View
  6. Tugade M, Tan T, Wachsmuth L, Bradley E. The Cambridge Handbook of Community Psychology. View
  7. Carmi L, Abbas A, Schultebraucks K, Galatzer-Levy I. Mental Health in a Digital World. View
  8. El rhatassi F, El Ghali B, Daoudi N. Proceedings of the 6th International Conference on Big Data and Internet of Things. View
  9. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View
  10. Lee Y, Pham V, Zhang J, Chung T. Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. View
  11. Bodenstein K, Paquin V, Sekhon K, Lesage M, Cinalioglu K, Rej S, Vahia I, Sekhon H. Biomarkers in Neuropsychiatry. View