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

Bipolar Disorders (66) Mobile Health (mhealth) (3182) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1836) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1594)

Published on in Vol 3, No 1 (2016): Jan-Mar

Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study

Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study

Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study

Authors of this article:

Till Beiwinkel1 Author Orcid Image ;   Sally Kindermann2 Author Orcid Image ;   Andreas Maier1 Author Orcid Image ;   Christopher Kerl1 Author Orcid Image ;   Jörn Moock1 Author Orcid Image ;   Guido Barbian3 Author Orcid Image ;   Wulf Rössler1, 4 Author Orcid Image
Article Authors Cited by (139) Tweetations (12) Metrics

Journals

  1. Faurholt-Jepsen M, Bauer M, Kessing L. Smartphone-based objective monitoring in bipolar disorder: status and considerations. International Journal of Bipolar Disorders 2018;6(1) View
  2. Marshall J, Dunstan D, Bartik W. Clinical or gimmickal: The use and effectiveness of mobile mental health apps for treating anxiety and depression. Australian & New Zealand Journal of Psychiatry 2020;54(1):20 View
  3. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  4. Faurholt-Jepsen M, Geddes J, Goodwin G, Bauer M, Duffy A, Vedel Kessing L, Saunders K. Reporting guidelines on remotely collected electronic mood data in mood disorder (eMOOD)—recommendations. Translational Psychiatry 2019;9(1) View
  5. Goodday S, Cipriani A. Challenges in identifying behavioural markers of bipolar disorder through objective smartphone data. Australian & New Zealand Journal of Psychiatry 2019;53(2):168 View
  6. Reinertsen E, Clifford G. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiological Measurement 2018;39(5):05TR01 View
  7. Bauer A, Iles-Shih M, Ghomi R, Rue T, Grover T, Kincler N, Miller M, Katon W. Acceptability of mHealth augmentation of Collaborative Care: A mixed methods pilot study. General Hospital Psychiatry 2018;51:22 View
  8. Majumder S, Deen M. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164 View
  9. Meekan M, Duarte C, Fernández-Gracia J, Thums M, Sequeira A, Harcourt R, Eguíluz V. The Ecology of Human Mobility. Trends in Ecology & Evolution 2017;32(3):198 View
  10. Van Ameringen M, Turna J, Khalesi Z, Pullia K, Patterson B. There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and Anxiety 2017;34(6):526 View
  11. 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
  12. Moran J, Kelly G, Haberlin C, Mockler D, Broderick J. The use of eHealth to promote physical activity in patients with mental health conditions: a systematic review. HRB Open Research 2018;1:5 View
  13. Dogan E, Sander C, Wagner X, Hegerl U, Kohls E. Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review. Journal of Medical Internet Research 2017;19(7):e262 View
  14. Mühlbauer E, Bauer M, Ebner-Priemer U, Ritter P, Hill H, Beier F, Kleindienst N, Severus E. Effectiveness of smartphone-based ambulatory assessment (SBAA-BD) including a predicting system for upcoming episodes in the long-term treatment of patients with bipolar disorders: study protocol for a randomized controlled single-blind trial. BMC Psychiatry 2018;18(1) View
  15. Bourla A, Ferreri F, Ogorzelec L, Guinchard C, Mouchabac S. Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre. L'Encéphale 2018;44(2):168 View
  16. Faurholt-Jepsen M, Busk J, Þórarinsdóttir H, Frost M, Bardram J, Vinberg M, Kessing L. Objective smartphone data as a potential diagnostic marker of bipolar disorder. Australian & New Zealand Journal of Psychiatry 2019;53(2):119 View
  17. Jungmann S, Klan T, Kuhn S, Jungmann F. Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users. JMIR Formative Research 2019;3(4):e13863 View
  18. Quiroz J, Geangu E, Yong M. Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study. JMIR Mental Health 2018;5(3):e10153 View
  19. Bidargaddi N, Musiat P, Makinen V, Ermes M, Schrader G, Licinio J. Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Molecular Psychiatry 2017;22(2):164 View
  20. . Digital Sensory Phenotyping for Psychiatric Disorders. Journal of Psychiatry and Brain Science 2020 View
  21. Fraccaro P, Beukenhorst A, Sperrin M, Harper S, Palmier-Claus J, Lewis S, Van der Veer S, Peek N. Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review. Journal of the American Medical Informatics Association 2019;26(11):1412 View
  22. Moran J, Kelly G, Haberlin C, Mockler D, Broderick J. The use of eHealth to promote physical activity in people with mental health conditions: a systematic review. HRB Open Research 2018;1:5 View
  23. Ryan K, Babu P, Easter R, Saunders E, Lee A, Klasnja P, Verchinina L, Micol V, Doil B, McInnis M, Kilbourne A. A Smartphone App to Monitor Mood Symptoms in Bipolar Disorder: Development and Usability Study. JMIR Mental Health 2020;7(9):e19476 View
  24. Haines-Delmont A, Chahal G, Bruen A, Wall A, Khan C, Sadashiv R, Fearnley D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR mHealth and uHealth 2020;8(6):e15901 View
  25. Rajagopalan A, Shah P, Zhang M, Ho R. Digital Platforms in the Assessment and Monitoring of Patients with Bipolar Disorder. Brain Sciences 2017;7(11):150 View
  26. Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
  27. Parker G, Tavella G. Design limitations to bipolar II treatment efficacy studies: A challenge and a revisionist strategy. Journal of Affective Disorders 2018;229:334 View
  28. Faurholt-Jepsen M, Busk J, Vinberg M, Christensen E, Þórarinsdóttir H, Frost M, Bardram J, Kessing L. Daily mobility patterns in patients with bipolar disorder and healthy individuals. Journal of Affective Disorders 2021;278:413 View
  29. Saunders K, Bilderbeck A, Panchal P, Atkinson L, Geddes J, Goodwin G. Experiences of Remote Mood and Activity Monitoring in Bipolar Disorder: A Qualitative Study. European Psychiatry 2017;41(1):115 View
  30. Pennou A, Lecomte T, Potvin S, Khazaal Y. Mobile Intervention for Individuals With Psychosis, Dual Disorders, and Their Common Comorbidities: A Literature Review. Frontiers in Psychiatry 2019;10 View
  31. 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
  32. Meyer T, Crist N, La Rosa N, Ye B, Soares J, Bauer I. Are existing self‐ratings of acute manic symptoms in adults reliable and valid?—A systematic review. Bipolar Disorders 2020;22(6):558 View
  33. Owen J, Jaworski B, Kuhn E, Hoffman J, Schievelbein L, Chang A, Ramsey K, Rosen C. Development of a mobile app for family members of Veterans with PTSD: identifying needs and modifiable factors associated with burden, depression, and anxiety. Journal of Family Studies 2020;26(2):286 View
  34. Rohani D, Faurholt-Jepsen M, Kessing L, Bardram J. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR mHealth and uHealth 2018;6(8):e165 View
  35. Marshall J, Dunstan D, Bartik W. Effectiveness of Using Mental Health Mobile Apps as Digital Antidepressants for Reducing Anxiety and Depression: Protocol for a Multiple Baseline Across-Individuals Design. JMIR Research Protocols 2020;9(7):e17159 View
  36. Coelho Y, Bastos-Filho T. A Bipolar Disorder Monitoring System Based on Wearable Device and Smartphone. IFAC-PapersOnLine 2016;49(30):216 View
  37. Moran J, Kelly G, Haberlin C, Mockler D, Broderick J. The use of eHealth to promote physical activity in people with mental health conditions: a systematic review. HRB Open Research 2018;1:5 View
  38. Torous J, Rodriguez J, Powell A. The New Digital Divide For Digital Biomarkers. Digital Biomarkers 2017;1(1):87 View
  39. Knight A, Bidargaddi N. Commonly available activity tracker apps and wearables as a mental health outcome indicator: A prospective observational cohort study among young adults with psychological distress. Journal of Affective Disorders 2018;236:31 View
  40. Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman M, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751 View
  41. White B, Martin A, White J, Burns S, Maycock B, Giglia R, Scott J. Theory-Based Design and Development of a Socially Connected, Gamified Mobile App for Men About Breastfeeding (Milk Man). JMIR mHealth and uHealth 2016;4(2):e81 View
  42. Pham Q, Graham G, Carrion C, Morita P, Seto E, Stinson J, Cafazzo J. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11941 View
  43. Gillett G, Saunders K. Remote Monitoring for Understanding Mechanisms and Prediction in Psychiatry. Current Behavioral Neuroscience Reports 2019;6(2):51 View
  44. Park D, Goering E, Head K, Bartlett Ellis R. Implications for Training on Smartphone Medication Reminder App Use by Adults With Chronic Conditions: Pilot Study Applying the Technology Acceptance Model. JMIR Formative Research 2017;1(1):e5 View
  45. Song K, Lee S, Yoon W, Kim C, Joo Y, Lee J, Chon M. Developing and Clinical Application of a Smartphone Mobile Mood Chart Application in Korean for Patients with Bipolar Disorder. Journal of Korean Neuropsychiatric Association 2018;57(3):244 View
  46. Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454 View
  47. 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
  48. Wang E, Zhou L, Chen S, Hill K, Parmanto B. An mHealth Platform for Supporting Clinical Data Integration into Augmentative and Alternative Communication Service Delivery: User-Centered Design and Usability Evaluation. JMIR Rehabilitation and Assistive Technologies 2018;5(2):e14 View
  49. Bruen A, Wall A, Haines-Delmont A, Perkins E. Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users. JMIR Mental Health 2020;7(9):e18407 View
  50. Taeger J, Bischoff S, Hagen R, Rak K. Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study. JMIR mHealth and uHealth 2021;9(1):e19346 View
  51. Liu J, Xu K, Zhu G, Zhang Q, Li X. Effects of smartphone-based interventions and monitoring on bipolar disorder: A systematic review and meta-analysis. World Journal of Psychiatry 2020;10(11):272 View
  52. Sela Y, Santamaria L, Amichai-Hamburge Y, Leong V. Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories. Sensors 2020;20(20):5781 View
  53. de Moura I, Teles A, Endler M, Coutinho L, da Silva e Silva F. Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals. Sensors 2020;21(1):86 View
  54. 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
  55. Stanislaus S, Vinberg M, Melbye S, Frost M, Busk J, Bardram J, Kessing L, Faurholt-Jepsen M. Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals. International Journal of Bipolar Disorders 2020;8(1) View
  56. Moshe I, Terhorst Y, Opoku Asare K, Sander L, Ferreira D, Baumeister H, Mohr D, Pulkki-Råback L. Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data. Frontiers in Psychiatry 2021;12 View
  57. Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu D, Bhathena D, Fisher L, Cusin C, Nyer M, Yeung A, Sangermano L, Mischoulon D, Alpert J, Picard R. Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors. Frontiers in Psychiatry 2020;11 View
  58. Gutierrez L, Rabbani K, Ajayi O, Gebresilassie S, Rafferty J, Castro L, Banos O. Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management. International Journal of Environmental Research and Public Health 2021;18(3):1327 View
  59. Fiorinelli M, Di Mario S, Surace A, Mattei M, Russo C, Villa G, Dionisi S, Di Simone E, Giannetta N, Di Muzio M. Smartphone distraction during nursing care: Systematic literature review. Applied Nursing Research 2021;58:151405 View
  60. Krichen M. Anomalies Detection Through Smartphone Sensors: A Review. IEEE Sensors Journal 2021;21(6):7207 View
  61. Gillett G, McGowan N, Palmius N, Bilderbeck A, Goodwin G, Saunders K. Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations. Frontiers in Psychiatry 2021;12 View
  62. Maharjan S, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt B, Hagaman A. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Medical Informatics and Decision Making 2021;21(1) View
  63. 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
  64. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  65. Anýž J, Bakštein E, Dally A, Kolenič M, Hlinka J, Hartmannová T, Urbanová K, Correll C, Novák D, Španiel F. Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study. JMIR Mental Health 2021;8(8):e26348 View
  66. 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
  67. Patoz M, Hidalgo-Mazzei D, Pereira B, Blanc O, de Chazeron I, Murru A, Verdolini N, Pacchiarotti I, Vieta E, Llorca P, Samalin L. Patients’ adherence to smartphone apps in the management of bipolar disorder: a systematic review. International Journal of Bipolar Disorders 2021;9(1) View
  68. Morton E, Nicholas J, Yang L, Lapadat L, Barnes S, Provencher M, Depp C, Chan M, Kulur R, Michalak E. Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management. International Journal of Bipolar Disorders 2022;10(1) View
  69. Anthes E. Quoi de neuf docteur smartphone ?. Cerveau & Psycho 2017;N° 91(8):44 View
  70. Young A, Choi A, Cannedy S, Hoffmann L, Levine L, Liang L, Medich M, Oberman R, Olmos-Ochoa T. Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study. JMIR Research Protocols 2022;11(8):e39010 View
  71. Forchuk C, Serrato J, Lizotte D, Mann R, Taylor G, Husni S. Developing a Smart Home Technology Innovation for People With Physical and Mental Health Problems: Considerations and Recommendations. JMIR mHealth and uHealth 2022;10(4):e25116 View
  72. Miller M, Raugh I, Strauss G, Harvey P. Remote digital phenotyping in serious mental illness: Focus on negative symptoms, mood symptoms, and self-awareness. Biomarkers in Neuropsychiatry 2022;6:100047 View
  73. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042 View
  74. Sargazi S, Zahedi Abghari A, Mirinejad S, Heidari Nia M, Majidpour M, Danesh H, Saravani R, Sheervalilou R, Shakiba M, Zahedi Abghari F. Long noncoding RNA HOTAIR polymorphisms and susceptibility to bipolar disorder: a preliminary case–control study. Nucleosides, Nucleotides & Nucleic Acids 2022;41(7):684 View
  75. Mullick T, Radovic A, Shaaban S, Doryab A. Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study. JMIR Formative Research 2022;6(6):e35807 View
  76. Dominiak M, Kaczmarek-Majer K, Antosik-Wójcińska A, Opara K, Olwert A, Radziszewska W, Hryniewicz O, Święcicki Ł, Wojnar M, Mierzejewski P. Behavioral and Self-reported Data Collected From Smartphones for the Assessment of Depressive and Manic Symptoms in Patients With Bipolar Disorder: Prospective Observational Study. Journal of Medical Internet Research 2022;24(1):e28647 View
  77. Melbye S, Stanislaus S, Vinberg M, Frost M, Bardram J, Kessing L, Faurholt-Jepsen M. Automatically Generated Smartphone Data in Young Patients With Newly Diagnosed Bipolar Disorder and Healthy Controls. Frontiers in Psychiatry 2021;12 View
  78. Sangha N, Lyall L, Wyse C, Cullen B, Whalley H, Smith D. The nosological status of unipolar mania and hypomania within UK Biobank according to objective and subjective measures of diurnal rest and activity. Bipolar Disorders 2022;24(7):726 View
  79. Tatham I, Clarke E, Grieve K, Kaushal P, Smeddinck J, Millar E, Sharma A. Process and Outcome Evaluations of Smartphone Apps for Bipolar Disorder: Scoping Review. Journal of Medical Internet Research 2022;24(3):e29114 View
  80. Jameel L, Valmaggia L, Barnes G, Cella M. mHealth technology to assess, monitor and treat daily functioning difficulties in people with severe mental illness: A systematic review. Journal of Psychiatric Research 2022;145:35 View
  81. Braund T, Zin M, Boonstra T, Wong Q, Larsen M, Christensen H, Tillman G, O’Dea B. Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study. JMIR Mental Health 2022;9(5):e35549 View
  82. Ross M, Tulabandhula T, Bennett C, Baek E, Kim D, Hussain F, Demos A, Ning E, Langenecker S, Ajilore O, Leow A. A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity. Sensors 2023;23(3):1585 View
  83. Fletcher K, Lindblom K, Seabrook E, Foley F, Murray G. Pilot Testing in the Wild: Feasibility, Acceptability, Usage Patterns, and Efficacy of an Integrated Web and Smartphone Platform for Bipolar II Disorder. JMIR Formative Research 2022;6(5):e32740 View
  84. Sarni N. Nouvelles influences pour la nosographie psychiatrique. Annales Médico-psychologiques, revue psychiatrique 2022;180(1):85 View
  85. White K, Williamson C, Bergou N, Oetzmann C, de Angel V, Matcham F, Henderson C, Hotopf M. A systematic review of engagement reporting in remote measurement studies for health symptom tracking. npj Digital Medicine 2022;5(1) View
  86. Anmella G, Faurholt‐Jepsen M, Hidalgo‐Mazzei D, Radua J, Passos I, Kapczinski F, Minuzzi L, Alda M, Meier S, Hajek T, Ballester P, Birmaher B, Hafeman D, Goldstein T, Brietzke E, Duffy A, Haarman B, López‐Jaramillo C, Yatham L, Lam R, Isometsa E, Mansur R, McIntyre R, Mwangi B, Vieta E, Kessing L. Smartphone‐based interventions in bipolar disorder: Systematic review and meta‐analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force. Bipolar Disorders 2022;24(6):580 View
  87. Svensson M, Erhardt S, Hållmarker U, James S, Deierborg T. A physically active lifestyle is associated with lower long-term incidence of bipolar disorder in a population-based, large-scale study. International Journal of Bipolar Disorders 2022;10(1) View
  88. Bjella T, Collier Høegh M, Holmstul Olsen S, Aminoff S, Barrett E, Ueland T, Icick R, Andreassen O, Nerhus M, Myhre Ihler H, Hagen M, Busch-Christensen C, Melle I, Lagerberg T. Developing “MinDag” – an app to capture symptom variation and illness mechanisms in bipolar disorder. Frontiers in Medical Technology 2022;4 View
  89. Abdullah S, Choudhury T. Sensing Technologies for Monitoring Serious Mental Illnesses. IEEE MultiMedia 2018;25(1):61 View
  90. Tsai C, Chen P, Liu D, Kuo Y, Hsieh T, Chiang D, Lai F, Wu C. Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study. JMIR Medical Informatics 2022;10(2):e33063 View
  91. Torous J, Bucci S, Bell I, Kessing L, Faurholt‐Jepsen M, Whelan P, Carvalho A, Keshavan M, Linardon J, Firth J. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021;20(3):318 View
  92. 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
  93. Vega J, Bell B, Taylor C, Xie J, Ng H, Honary M, McNaney R. Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating. JMIR Mental Health 2022;9(4):e32146 View
  94. 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
  95. Hoel S, Victory A, Sagorac Gruichich T, Stowe Z, McInnis M, Cochran A, Thomas E. A Mixed-Methods Analysis of Mobile ACT Responses From Two Cohorts. Frontiers in Digital Health 2022;4 View
  96. Arya S, Dias S, Jelinek H, Hadjileontiadis L, Pappa A. The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?. Biosensors and Bioelectronics 2023;235:115387 View
  97. Nash C, Nair R, Naqvi S. Machine Learning in ADHD and Depression Mental Health Diagnosis: A Survey. IEEE Access 2023;11:86297 View
  98. Kadirvelu B, Bellido Bel T, Wu X, Burmester V, Ananth S, Cabral C C Branco B, Girela-Serrano B, Gledhill J, Di Simplicio M, Nicholls D, Faisal A. Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study. JMIR Formative Research 2023;7:e44877 View
  99. Medich M, Cannedy S, Hoffmann L, Chinchilla M, Pila J, Chassman S, Calderon R, Young A. Clinician and Patient Perspectives on the Use of Passive Mobile Monitoring and Self-Tracking for Patients With Serious Mental Illness: User-Centered Approach. JMIR Human Factors 2023;10:e46909 View
  100. Shin J, Bae S. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984 View
  101. Caratù M, Pigliautile I, Piselli C, Fabiani C. A perspective on managing cities and citizens' well-being through smart sensing data. Environmental Science & Policy 2023;147:169 View
  102. 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
  103. 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
  104. Hsu J, Wu C, Lin E, Chen P. MoodSensing: A smartphone app for digital phenotyping and assessment of bipolar disorder. Psychiatry Research 2024;334:115790 View
  105. Halabi R, Mulsant B, Alda M, DeShaw A, Hintze A, Husain M, O'Donovan C, Patterson R, Ortiz A. Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder. Journal of Psychiatric Research 2024;174:326 View
  106. Castro M, Zavod M, Rutgersson A, Jörntén-Karlsson M, Dutta B, Hagger L. iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control. Journal of Asthma and Allergy 2024;Volume 17:653 View
  107. Bidargaddi N, Leibbrandt R, Paget T, Verjans J, Looi J, Lipschitz J. Remote sensing mental health: A systematic review of factors essential to clinical translation from validation research. DIGITAL HEALTH 2024;10 View
  108. Castro M, Zavod M, Rutgersson A, Jörntén-Karlsson M, Dutta B, Hagger L. iPREDICT: proof-of-concept study to develop a predictive model of changes in asthma control. Therapeutic Advances in Respiratory Disease 2024;18 View
  109. Shvetcov A, Funke Kupper J, Zheng W, Slade A, Han J, Whitton A, Spoelma M, Hoon L, Mouzakis K, Vasa R, Gupta S, Venkatesh S, Newby J, Christensen H. Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping. Frontiers in Psychiatry 2024;15 View
  110. Hsu J, Wu C, Wang W, Su H, Lin E, Chen P. Digital Phenotyping-Based Bipolar Disorder Assessment Using Multiple Correlation Data Imputation and Lasso-MLP. IEEE Transactions on Affective Computing 2024;15(3):885 View
  111. Lee T, Chen C, Chen I, Chen H, Liu C, Wu S, Hsiao C, Kuo P. Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study. Journal of Medical Internet Research 2024;26:e55635 View
  112. Sidani L, Nadar S, Tfaili J, El Rayes S, Sharara F, Elhage J, Fakhoury M. Digital Psychiatry: Opportunities, Challenges, and Future Directions. Journal of Psychiatric Practice 2024;30(6):400 View
  113. Humberg C, Neß V, Rau L, Wager J. Is There a Long-Term Link Between Digital Media Use and Adolescent Headaches? A Longitudinal School-Based Study. Children 2024;11(12):1549 View
  114. Hassan L, Milton A, Sawyer C, Casson A, Torous J, Davies A, Ruiz-Yu B, Firth J. Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review. JMIR Mental Health 2025;12:e65143 View
  115. Ortiz A, Halabi R, Alda M, Burgos A, DeShaw A, Gonzalez-Torres C, Husain M, O'Donovan C, Tolend M, Hintze A, Mulsant B. Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder. Journal of Affective Disorders 2025;374:75 View
  116. Linardon J, Chen K, Gajjar S, Eadara A, Wang S, Flathers M, Burns J, Torous J. Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features. Psychiatry Research 2025;348:116483 View
  117. Yang M, Ngai E, Hu X, Hu B, Liu J, Gelenbe E, Leung V. Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection. Proceedings of the IEEE 2024;112(12):1773 View
  118. Lodewyk K, Wiebe M, Dennett L, Larsson J, Greenshaw A, Hayward J, Ayatollahi H. Wearables research for continuous monitoring of patient outcomes: A scoping review. PLOS Digital Health 2025;4(5):e0000860 View

Books/Policy Documents

  1. Senders J, Maher N, Hulsbergen A, Lamba N, Bredenoord A, Broekman M. Ethics of Innovation in Neurosurgery. View
  2. Hegerl U, Dogan E, Oehler C, Sander C, Stöber F. Gesundheit digital. View
  3. Teles A, Barros F, Rodrigues I, Barbosa A, Silva F, Coutinho L, Teixeira S. IoT and ICT for Healthcare Applications. View
  4. Rajagopalan A, Ho R. Major Depressive Disorder. View
  5. Tushar A, Kabir M, Ahmed S. Signal Processing Techniques for Computational Health Informatics. View
  6. Rosenfeld A, Benrimoh D, Armstrong C, Mirchi N, Langlois-Therrien T, Rollins C, Tanguay-Sela M, Mehltretter J, Fratila R, Israel S, Snook E, Perlman K, Kleinerman A, Saab B, Thoburn M, Gabbay C, Yaniv-Rosenfeld A. Applications of Big Data in Healthcare. View
  7. Mao S, Khalifa Y, Zhang Z, Shu K, Suri A, Bouzid Z, Sejdic E. Digital Health. View
  8. Anmella G, Hidalgo-Mazzei D, Vieta E. Digital Mental Health. View
  9. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View
  10. Devi D, Naresh R, Kumar C, Senthilkumar S, Jovin A. Technological Tools for Predicting Pregnancy Complications. View
  11. Emmert K, Maetzler W. Gerontechnology. A Clinical Perspective. View
  12. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View
  13. Zimerman A, Zimmer C, Montezano B, Passos I. Bipolar Disorder. View

Conference Proceedings

  1. Rhim S, Lee U, Han K. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Tracking and Modeling Subjective Well-Being Using Smartphone-Based Digital Phenotype View
  2. Gielis K, Kennes J, Dobbeleer C, Puttemans S, Abeele V. 2019 IEEE International Conference on Healthcare Informatics (ICHI). Collecting Digital Biomarkers on Cognitive Health Through Computer Vision and Gameplay: an Image Processing Toolkit for Card Games View
  3. Majid S, Morriss R, Figueredo G, Reeves S. Designing Interactive Systems Conference. Exploring self-tracking practices for those with lived experience of bipolar disorder View
  4. Amate N, Patil S, Jojan P, Morankar S. 2021 International Conference on Innovative Trends in Information Technology (ICITIIT). Use of Social Media and Smartwatch Data Analytics for Mental Health Diagnosis View
  5. Hamieh S, Heiries V, Al Osman H, Godin C. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Relapse Detection in Patients with Psychotic Disorders Using Unsupervised Learning on Smartwatch Signals View
  6. Teng S, D'Alfonso S, Kostakos V. Proceedings of the CHI Conference on Human Factors in Computing Systems. A Tool for Capturing Smartphone Screen Text View
  7. Jyothi V, Varma P. 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). Analysis of Bipolar Student Performance Prediction using a Machine Learning Model View
  8. Wu Z, Nishiyama Y, Shibuya Y. 2024 IEEE International Conference on Big Data (BigData). The Bidirectional Relationship between Emotional Change and Physical Movement Activities: An Analysis Using Propensity Score Matching Methods View