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

New Methods (956) Mobile Health (mhealth) (2972) mHealth for Data Collection and Research (973) Depression and Mood Disorders; Suicide Prevention (1384) Wearable Devices and Sensors (900) Posttraumatic Stress Disorder (PTSD) (116) Anxiety and Stress Disorders (1072)

Published on in Vol 19, No 3 (2017): March

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders

Authors of this article:

Skyler Place1 Author Orcid Image ;   Danielle Blanch-Hartigan2 Author Orcid Image ;   Channah Rubin1 Author Orcid Image ;   Cristina Gorrostieta1 Author Orcid Image ;   Caroline Mead1 Author Orcid Image ;   John Kane1 Author Orcid Image ;   Brian P Marx3, 4 Author Orcid Image ;   Joshua Feast1 Author Orcid Image ;   Thilo Deckersbach5 Author Orcid Image ;   Alex “Sandy” Pentland6 Author Orcid Image ;   Andrew Nierenberg5 Author Orcid Image ;   Ali Azarbayejani1 Author Orcid Image
Article Authors Cited by (84) Tweetations (28) Metrics

Journals

  1. Laird E, Ryan A, McCauley C, Bond R, Mulvenna M, Curran K, Bunting B, Ferry F, Gibson A. Using Mobile Technology to Provide Personalized Reminiscence for People Living With Dementia and Their Carers: Appraisal of Outcomes From a Quasi-Experimental Study. JMIR Mental Health 2018;5(3):e57 View
  2. Palmer K, Burrows V. Ethical and Safety Concerns Regarding the Use of Mental Health–Related Apps in Counseling: Considerations for Counselors. Journal of Technology in Behavioral Science 2021;6(1):137 View
  3. Walker W, Walton J, DeVries A, Nelson R. Circadian rhythm disruption and mental health. Translational Psychiatry 2020;10(1) View
  4. Lind M, Byrne M, Wicks G, Smidt A, Allen N. The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health 2018;5(3):e10334 View
  5. Ryan P, Luz S, Albert P, Vogel C, Normand C, Elwyn G. Using artificial intelligence to assess clinicians’ communication skills. BMJ 2019:l161 View
  6. Werner-Seidler A, Huckvale K, Larsen M, Calear A, Maston K, Johnston L, Torok M, O’Dea B, Batterham P, Schweizer S, Skinner S, Steinbeck K, Ratcliffe J, Oei J, Patton G, Wong I, Beames J, Wong Q, Lingam R, Boydell K, Salmon A, Cockayne N, Mackinnon A, Christensen H. A trial protocol for the effectiveness of digital interventions for preventing depression in adolescents: The Future Proofing Study. Trials 2020;21(1) View
  7. Wang R, Wang W, daSilva A, Huckins J, Kelley W, Heatherton T, Campbell A. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  8. Marsch L. Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology 2021;46(1):191 View
  9. Bagot K, Matthews S, Mason M, Squeglia L, Fowler J, Gray K, Herting M, May A, Colrain I, Godino J, Tapert S, Brown S, Patrick K. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience 2018;32:121 View
  10. Sened H, Lazarus G, Gleason M, Rafaeli E, Fleeson W. The Use of Intensive Longitudinal Methods in Explanatory Personality Research. European Journal of Personality 2018;32(3):269 View
  11. Sarda A, Munuswamy S, Sarda S, Subramanian V. Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study. JMIR mHealth and uHealth 2019;7(1):e11041 View
  12. 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
  13. Place S, Blanch-Hartigan D, Smith V, Erb J, Marci C, Ahern D. Effect of a Mobile Monitoring System vs Usual Care on Depression Symptoms and Psychological Health. JAMA Network Open 2020;3(1):e1919403 View
  14. Malhi G, Hamilton A, Morris G, Mannie Z, Das P, Outhred T. The promise of digital mood tracking technologies: are we heading on the right track?. Evidence Based Mental Health 2017;20(4):102 View
  15. Rashid H, Mendu S, Daniel K, Beltzer M, Teachman B, Boukhechba M, Barnes L. Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1 View
  16. Betthauser L, Stearns-Yoder K, McGarity S, Smith V, Place S, Brenner L. Mobile App for Mental Health Monitoring and Clinical Outreach in Veterans: Mixed Methods Feasibility and Acceptability Study. Journal of Medical Internet Research 2020;22(8):e15506 View
  17. Chan S, Godwin H, Gonzalez A, Yellowlees P, Hilty D. Review of Use and Integration of Mobile Apps Into Psychiatric Treatments. Current Psychiatry Reports 2017;19(12) View
  18. Di Matteo D, Fine A, Fotinos K, Rose J, Katzman M. Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire. JMIR Mental Health 2018;5(3):e56 View
  19. Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digital Medicine 2019;2(1) View
  20. Snyder C, Dorsey E, Atreja A. The Best Digital Biomarkers Papers of 2017. Digital Biomarkers 2018;2(2):64 View
  21. Lovejoy C. Technology and mental health: The role of artificial intelligence. European Psychiatry 2019;55:1 View
  22. Nierenberg A. Bipolar II Disorder Is NOT a Myth. The Canadian Journal of Psychiatry 2019;64(8):537 View
  23. Reinertsen E, Clifford G. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiological Measurement 2018;39(5):05TR01 View
  24. Galvin H, DeMuro P. Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019. Yearbook of Medical Informatics 2020;29(01):032 View
  25. Poudyal A, van Heerden A, Hagaman A, Maharjan S, Byanjankar P, Subba P, Kohrt B. Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal. JMIR Research Protocols 2019;8(8):e14734 View
  26. 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
  27. Barnett S, Huckvale K, Christensen H, Venkatesh S, Mouzakis K, Vasa R. Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications. Journal of Medical Internet Research 2019;21(11):e16399 View
  28. Chan S, Li L, Torous J, Gratzer D, Yellowlees P. Review of Use of Asynchronous Technologies Incorporated in Mental Health Care. Current Psychiatry Reports 2018;20(10) View
  29. Sano A, Taylor S, McHill A, Phillips A, Barger L, Klerman E, Picard R. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study. Journal of Medical Internet Research 2018;20(6):e210 View
  30. Bond R, Moorhead A, Mulvenna M, O'Neill S, Potts C, Murphy N. Exploring temporal behaviour of app users completing ecological momentary assessments using mental health scales and mood logs. Behaviour & Information Technology 2019;38(10):1016 View
  31. H. Birk R, Samuel G. Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’. Sociology of Health & Illness 2020;42(8):1873 View
  32. Kulakli A, Shubina I. Scientific Publication Patterns of Mobile Technologies and Apps for Posttraumatic Stress Disorder Treatment: Bibliometric Co-Word Analysis. JMIR mHealth and uHealth 2020;8(11):e19391 View
  33. 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
  34. 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
  35. Suruliraj B, Bessenyei K, Bagnell A, McGrath P, Wozney L, Orji R, Meier S. Mobile Sensing Apps and Self-management of Mental Health During the COVID-19 Pandemic: Web-Based Survey. JMIR Formative Research 2021;5(4):e24180 View
  36. 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
  37. Flanagan O, Chan A, Roop P, Sundram F. Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review. JMIR mHealth and uHealth 2021;9(9):e24352 View
  38. 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
  39. 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
  40. Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman M, Rose J. Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study. Journal of Medical Internet Research 2021;23(8):e28918 View
  41. Inomata T, Nakamura M, Sung J, Midorikawa-Inomata A, Iwagami M, Fujio K, Akasaki Y, Okumura Y, Fujimoto K, Eguchi A, Miura M, Nagino K, Shokirova H, Zhu J, Kuwahara M, Hirosawa K, Dana R, Murakami A. Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study. npj Digital Medicine 2021;4(1) View
  42. Otte Andersen T, Skovlund Dissing A, Rosenbek Severinsen E, Kryger Jensen A, Thanh Pham V, Varga T, Hulvej Rod N. Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults. Sleep 2022;45(6) View
  43. 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
  44. 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
  45. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  46. 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
  47. Nisenson M, Lin V, Gansner M. Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective. Harvard Review of Psychiatry 2021;29(6):401 View
  48. Leenings R, Winter N, Dannlowski U, Hahn T. Recommendations for machine learning benchmarks in neuroimaging. NeuroImage 2022;257:119298 View
  49. Kalanadhabhatta M, Santana A, Zhang Z, Ganesan D, Grabell A, Rahman T. EarlyScreen. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  50. Cho Y, Lim K, Lee S, Kim Y, Kim M, Kim C, Kim Y, Kim H. Developing a Multimodal Monitoring System for Geriatric Depression. CIN: Computers, Informatics, Nursing 2023;41(1):46 View
  51. Lou S, Liu H, Warner B, Harford D, Lu C, Kannampallil T. Predicting physician burnout using clinical activity logs: Model performance and lessons learned. Journal of Biomedical Informatics 2022;127:104015 View
  52. Goetz C, Bavaresco R, Kunst R, Barbosa J. Industrial intelligence in the care of workers’ mental health: A review of status and challenges. International Journal of Industrial Ergonomics 2022;87:103234 View
  53. Psaltos D, Chappie K, Karahanoglu F, Chasse R, Demanuele C, Kelekar A, Zhang H, Marquez V, Kangarloo T, Patel S, Czech M, Caouette D, Cai X. Multimodal Wearable Sensors to Measure Gait and Voice. Digital Biomarkers 2019;3(3):133 View
  54. Wade N, Ortigara J, Sullivan R, Tomko R, Breslin F, Baker F, Fuemmeler B, Delrahim Howlett K, Lisdahl K, Marshall A, Mason M, Neale M, Squeglia L, Wolff-Hughes D, Tapert S, Bagot K. Passive Sensing of Preteens’ Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy. JMIR Mental Health 2021;8(10):e29426 View
  55. Yamada Y, Shinkawa K, Nemoto M, Arai T. Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions. Frontiers in Psychiatry 2021;12 View
  56. Wang W, Nepal S, Huckins J, Hernandez L, Vojdanovski V, Mack D, Plomp J, Pillai A, Obuchi M, daSilva A, Murphy E, Hedlund E, Rogers C, Meyer M, Campbell A. First-Gen Lens. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  57. 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
  58. Jossou T, Medenou D, Et-tahir A, Ahouandjinou H, Edoh T, Houessouvo R, Pecchia L, Sbihi M, Mounadi A, Garoum M. A review about Technology in mental health sensing and assessment. ITM Web of Conferences 2022;46:01005 View
  59. Andrić-Petrović S, Marić N. Improvement of the psychiatric care through outsourcing artificial intelligence technologies: Where are we now?. Medicinska istrazivanja 2022;55(2):19 View
  60. Katsaros D, Hawthorne J, Patel J, Pothier K, Aungst T, Franzese C. Optimizing Social Support in Oncology with Digital Platforms. JMIR Cancer 2022;8(2):e36258 View
  61. De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. Journal of Medical Internet Research 2023;25:e39546 View
  62. 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
  63. 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
  64. Ernst M, Hyldig Nielsen J, Runge E, Bouchard S, Clemmensen L. Biomarkers in exposure-based treatment of anxiety in virtual reality: a systematic review. Frontiers in Virtual Reality 2024;5 View
  65. Shin J, Bae S. Use of voice features from smartphones for monitoring depressive disorders: Scoping review. DIGITAL HEALTH 2024;10 View
  66. Kaczmarek‐Majer K, Dominiak M, Antosik A, Hryniewicz O, Kamińska O, Opara K, Owsiński J, Radziszewska W, Sochacka M, Święcicki Ł. Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study. Acta Psychiatrica Scandinavica 2024 View
  67. Marx B, Rothbaum B, Vermetten E. What I was thinking/what I would do differently: Technology‐enabled traumatic stress support. Journal of Traumatic Stress 2024;37(5):739 View
  68. 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
  69. Edler J, Winter M, Steinmetz H, Cohrdes C, Baumeister H, Pryss R. Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study. Interactive Journal of Medical Research 2024;13:e53248 View

Books/Policy Documents

  1. Nishiyama Y, Ferreira D, Eigen Y, Sasaki W, Okoshi T, Nakazawa J, Dey A, Sezaki K. Distributed, Ambient and Pervasive Interactions. View
  2. Turner B, Eslami A. High-Performance Computing and Big Data Analysis. View
  3. Hamilton J, Coulter R, Radovic A. Technology and Adolescent Health. View
  4. Sivak E, Smirnov I. Social Informatics. View
  5. Mistry P. Artificial Intelligence in Medicine. View
  6. Mistry P. Artificial Intelligence in Medicine. View
  7. Heinz M, Price G, Song S, Bhattacharya S, Jacobson N. Digital Mental Health. View
  8. Podina I, Caculidis-Tudor D. Brain, Decision Making and Mental Health. View
  9. Mistry P. Artificial Intelligence in Medicine. View
  10. Marchionatti L, Mastella N, Bouvier V, Passos I. Digital Mental Health. View
  11. Ceja J, Arenas A, Romero C, Rodríguez S, Luna G. Information Technology and Systems. View