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Citing this Article

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Published on 16.03.17 in Vol 19, No 3 (2017): March

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

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

Skyler Place, Danielle Blanch-Hartigan, Channah Rubin, Cristina Gorrostieta, Caroline Mead, John Kane, Brian P Marx, Joshua Feast, Thilo Deckersbach, Alex “Sandy” Pentland, Andrew Nierenberg, Ali Azarbayejani

J Med Internet Res 2017 (Mar 16); 19(3):e75 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.6678):

(note that this is only a small subset of citations)

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  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
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  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
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  13. Place S, Blanch-Hartigan D, Smith V, Erb J, Marci CD, Ahern DK. Effect of a Mobile Monitoring System vs Usual Care on Depression Symptoms and Psychological Health. JAMA Network Open 2020;3(1):e1919403
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  14. Malhi GS, 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
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  15. Rashid H, Mendu S, Daniel KE, Beltzer ML, Teachman BA, Boukhechba M, Barnes LE. 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
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  16. Betthauser LM, Stearns-Yoder KA, McGarity S, Smith V, Place S, Brenner LA. 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
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  25. Poudyal A, van Heerden A, Hagaman A, Maharjan SM, Byanjankar P, Subba P, Kohrt BA. 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
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  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
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  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
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  28. Chan S, Li L, Torous J, Gratzer D, Yellowlees PM. Review of Use of Asynchronous Technologies Incorporated in Mental Health Care. Current Psychiatry Reports 2018;20(10)
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  29. Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, 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
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  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
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  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
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  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
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  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
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  34. Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu DF, Bhathena D, Fisher LB, Cusin C, Nyer M, Yeung A, Sangermano L, Mischoulon D, Alpert JE, Picard RW. Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors. Frontiers in Psychiatry 2020;11
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  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
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  36. Hilty DM, Armstrong CM, Luxton DD, Gentry MT, Krupinski EA. 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
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  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
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  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
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  39. Daniel KE, Mendu S, Baglione A, Cai L, Teachman BA, Barnes LE, 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
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  40. Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman MA, 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
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  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)
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  42. Otte Andersen T, Skovlund Dissing A, Rosenbek Severinsen E, Kryger Jensen A, Thanh Pham V, Varga TV, Hulvej Rod N. Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults. Sleep 2022;45(6)
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  43. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson NC. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1)
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  44. Young AS, Choi A, Cannedy S, Hoffmann L, Levine L, Liang L, Medich M, Oberman R, Olmos-Ochoa TT. 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
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  45. . Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78
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  46. Winkler T, Büscher R, Larsen ME, Kwon S, Torous J, Firth J, Sander LB. Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(11):e42146
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  47. Nisenson M, Lin V, Gansner M. Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective. Harvard Review of Psychiatry 2021;29(6):401
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  48. Leenings R, Winter NR, Dannlowski U, Hahn T. Recommendations for machine learning benchmarks in neuroimaging. NeuroImage 2022;257:119298
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  49. Kalanadhabhatta M, Santana AM, Zhang Z, Ganesan D, Grabell AS, Rahman T. EarlyScreen. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1
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  50. Cho Y, Lim K, Lee S, Kim Y, Kim M, Kim CO, Kim Y, Kim H. Developing a Multimodal Monitoring System for Geriatric Depression. CIN: Computers, Informatics, Nursing 2023;41(1):46
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  51. Lou SS, Liu H, Warner BC, 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
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  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
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  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
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  54. Wade NE, Ortigara JM, Sullivan RM, Tomko RL, Breslin FJ, Baker FC, Fuemmeler BF, Delrahim Howlett K, Lisdahl KM, Marshall AT, Mason MJ, Neale MC, Squeglia LM, Wolff-Hughes DL, Tapert SF, Bagot KS. Passive Sensing of Preteens’ Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy. JMIR Mental Health 2021;8(10):e29426
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  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
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  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
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  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
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  62. Shin J, Bae SM. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984
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According to Crossref, the following books are citing this article (DOI 10.2196/jmir.6678):

  1. Nishiyama Y, Ferreira D, Eigen Y, Sasaki W, Okoshi T, Nakazawa J, Dey AK, Sezaki K. Distributed, Ambient and Pervasive Interactions. 2020. Chapter 17:223
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  2. Turner B, Eslami A. High-Performance Computing and Big Data Analysis. 2019. Chapter 9:109
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  3. Hamilton JL, Coulter RW, Radovic A. Technology and Adolescent Health. 2020. :305
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  4. Sivak E, Smirnov I. Social Informatics. 2020. Chapter 26:352
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  5. . Artificial Intelligence in Medicine. 2021. Chapter 56-1:1
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  6. . Artificial Intelligence in Medicine. 2022. Chapter 56:115
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  7. Heinz MV, Price GD, Song SH, Bhattacharya S, Jacobson NC. Digital Mental Health. 2023. Chapter 2:13
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  8. Podina IR, Caculidis-Tudor D. Brain, Decision Making and Mental Health. 2023. Chapter 17:347
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  9. . Artificial Intelligence in Medicine. 2022. Chapter 56-2:1
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  10. Marchionatti LE, Mastella NDS, Bouvier VDA, Passos IC. Digital Mental Health. 2023. Chapter 3:35
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  11. Ceja JMO, Arenas AG, Romero CDG, Rodríguez SR, Luna GLM. Information Technology and Systems. 2024. Chapter 19:203
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