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

New Methods (901) Mobile Health (mhealth) (2748) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1301) Depression and Mood Disorders; Suicide Prevention (1271)

Published on in Vol 17, No 7 (2015): July

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Authors of this article:

Sohrab Saeb1, 2 Author Orcid Image ;   Mi Zhang3 Author Orcid Image ;   Christopher J Karr1 Author Orcid Image ;   Stephen M Schueller1 Author Orcid Image ;   Marya E Corden1 Author Orcid Image ;   Konrad P Kording2 Author Orcid Image ;   David C Mohr1 Author Orcid Image
Article Authors Cited by (485) Tweetations (338) Metrics

Journals

  1. Reinertsen E, Clifford G. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiological Measurement 2018;39(5):05TR01 View
  2. Mei G, Xu W, Li L, Zhao Z, Li H, Liu W, Jiao Y. The Role of Campus Data in Representing Depression Among College Students: Exploratory Research. JMIR Mental Health 2020;7(1):e12503 View
  3. Gong J, Huang Y, Chow P, Fua K, Gerber M, Teachman B, Barnes L. Understanding behavioral dynamics of social anxiety among college students through smartphone sensors. Information Fusion 2019;49:57 View
  4. Ai P, Liu Y, Zhao X. Big Five personality traits predict daily spatial behavior: Evidence from smartphone data. Personality and Individual Differences 2019;147:285 View
  5. Wang W. Smartphones as Social Actors? Social dispositional factors in assessing anthropomorphism. Computers in Human Behavior 2017;68:334 View
  6. Rohani D, Tuxen N, Quemada Lopategui A, Kessing L, Bardram J. Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study. JMIR Mental Health 2018;5(2):e10122 View
  7. Razavi R, Gharipour A, Gharipour M. Depression screening using mobile phone usage metadata: a machine learning approach. Journal of the American Medical Informatics Association 2020;27(4):522 View
  8. Masud M, Mamun M, Thapa K, Lee D, Griffiths M, Yang S. Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. Journal of Biomedical Informatics 2020;103:103371 View
  9. Saeb S, Lonini L, Jayaraman A, Mohr D, Kording K. The need to approximate the use-case in clinical machine learning. GigaScience 2017;6(5) View
  10. Pratap A, Renn B, Volponi J, Mooney S, Gazzaley A, Arean P, Anguera J. Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial. Journal of Medical Internet Research 2018;20(8):e10130 View
  11. Otte C, Gold S, Penninx B, Pariante C, Etkin A, Fava M, Mohr D, Schatzberg A. Major depressive disorder. Nature Reviews Disease Primers 2016;2(1) View
  12. Johnson M, Jones M, Shervey M, Dudley J, Zimmerman N. Building a Secure Biomedical Data Sharing Decentralized App (DApp): Tutorial. Journal of Medical Internet Research 2019;21(10):e13601 View
  13. Wang R, Wang W, Aung M, Ben-Zeev D, Brian R, Campbell A, Choudhury T, Hauser M, Kane J, Scherer E, Walsh M. Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017;1(3):1 View
  14. Nugent N, Pendse S, Schatten H, Armey M. Innovations in Technology and Mechanisms of Change in Behavioral Interventions. Behavior Modification 2023;47(6):1292 View
  15. Majumder S, Deen M. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164 View
  16. Bauer M, Glenn T, Monteith S, Bauer R, Whybrow P, Geddes J. Ethical perspectives on recommending digital technology for patients with mental illness. International Journal of Bipolar Disorders 2017;5(1) View
  17. Luhmann M. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28 View
  18. Helbich M. Toward dynamic urban environmental exposure assessments in mental health research. Environmental Research 2018;161:129 View
  19. Aung M, Matthews M, Choudhury T. Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies. Depression and Anxiety 2017;34(7):603 View
  20. Becker D, van Breda W, Funk B, Hoogendoorn M, Ruwaard J, Riper H. Predictive modeling in e-mental health: A common language framework. Internet Interventions 2018;12:57 View
  21. 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
  22. Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
  23. Aung M, Alquaddoomi F, Hsieh C, Rabbi M, Yang L, Pollak J, Estrin D, Choudhury T. Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain. IEEE Journal of Selected Topics in Signal Processing 2016;10(5):962 View
  24. 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
  25. Fairburn C, Patel V. The impact of digital technology on psychological treatments and their dissemination. Behaviour Research and Therapy 2017;88:19 View
  26. Boukhechba M, Daros A, Fua K, Chow P, Teachman B, Barnes L. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health 2018;9-10:192 View
  27. Lee U, Han K, Cho H, Chung K, Hong H, Lee S, Noh Y, Park S, Carroll J. Intelligent positive computing with mobile, wearable, and IoT devices: Literature review and research directions. Ad Hoc Networks 2019;83:8 View
  28. 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
  29. 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
  30. Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H. Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study. Journal of Medical Internet Research 2016;18(3):e72 View
  31. Saha K, Chan L, De Barbaro K, Abowd G, De Choudhury M. Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017;1(3):1 View
  32. Berrouiguet S, Ramírez D, Barrigón M, Moreno-Muñoz P, Carmona Camacho R, Baca-García E, Artés-Rodríguez A. Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study. JMIR mHealth and uHealth 2018;6(12):e197 View
  33. Harari G, Müller S, Mishra V, Wang R, Campbell A, Rentfrow P, Gosling S. An Evaluation of Students’ Interest in and Compliance With Self-Tracking Methods. Social Psychological and Personality Science 2017;8(5):479 View
  34. Kamilaris A, Pitsillides A. Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal 2016;3(6):885 View
  35. 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
  36. Schneble C, Elger B, Shaw D. All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent. Journal of Medical Internet Research 2020;22(5):e16879 View
  37. Cao J, Truong A, Banu S, Shah A, Sabharwal A, Moukaddam N. Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study. JMIR Mental Health 2020;7(1):e14045 View
  38. Henson P, Barnett I, Keshavan M, Torous J. Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophrenia 2020;6(1) View
  39. Kirchner T, Shiffman S. Spatio-temporal determinants of mental health and well-being: advances in geographically-explicit ecological momentary assessment (GEMA). Social Psychiatry and Psychiatric Epidemiology 2016;51(9):1211 View
  40. Tuerk P, Schaeffer C, McGuire J, Adams Larsen M, Capobianco N, Piacentini J. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Current Psychiatry Reports 2019;21(10) View
  41. Kang Y. Ontology Components for the Depression Management based on Context. Journal of the Korea Institute of Information and Communication Engineering 2016;20(9):1785 View
  42. Schoedel R, Au Q, Völkel S, Lehmann F, Becker D, Bühner M, Bischl B, Hussmann H, Stachl C. Digital Footprints of Sensation Seeking. Zeitschrift für Psychologie 2018;226(4):232 View
  43. Price M, Van Stolk-Cooke K, Legrand A, Brier Z, Ward H, Connor J, Gratton J, Freeman K, Skalka C. Implementing assessments via mobile during the acute posttrauma period: feasibility, acceptability and strategies to improve response rates. European Journal of Psychotraumatology 2018;9(sup1) View
  44. Dogrucu A, Perucic A, Isaro A, Ball D, Toto E, Rundensteiner E, Agu E, Davis-Martin R, Boudreaux E. Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health 2020;17:100118 View
  45. Sabharwal A, Veeraraghavan A. Bio-Behavioral Sensing. GetMobile: Mobile Computing and Communications 2017;21(3):11 View
  46. DeMasi O, Feygin S, Dembo A, Aguilera A, Recht B. Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study. JMIR mHealth and uHealth 2017;5(10):e137 View
  47. Suffoletto B, Aguilera A. Expanding Adolescent Depression Prevention Through Simple Communication Technologies. Journal of Adolescent Health 2016;59(4):373 View
  48. Armstrong C, Ciulla R, Williams S, Micheel L. An Applied Test of Knowledge Translation Methods Using a Mobile Health Solution. Military Medicine 2020;185(Supplement_1):526 View
  49. Hung G, Yang P, Chang C, Chiang J, Chen Y. Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study. JMIR Research Protocols 2016;5(3):e160 View
  50. Mandryk R, Birk M. Toward Game-Based Digital Mental Health Interventions: Player Habits and Preferences. Journal of Medical Internet Research 2017;19(4):e128 View
  51. Scott S, Munoz E, Mogle J, Gamaldo A, Smyth J, Almeida D, Sliwinski M. Perceived neighborhood characteristics predict severity and emotional response to daily stressors. Social Science & Medicine 2018;200:262 View
  52. Donker T, Van Esveld S, Fischer N, Van Straten A. 0Phobia – towards a virtual cure for acrophobia: study protocol for a randomized controlled trial. Trials 2018;19(1) View
  53. Chib A, Lin S. Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps. Journal of Health Communication 2018;23(10-11):909 View
  54. Tseng V, Sano A, Ben-Zeev D, Brian R, Campbell A, Hauser M, Kane J, Scherer E, Wang R, Wang W, Wen H, Choudhury T. Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia. Scientific Reports 2020;10(1) View
  55. 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
  56. 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
  57. Palmius N, Saunders K, Carr O, Geddes J, Goodwin G, De Vos M. Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study. Journal of Medical Internet Research 2018;20(10):e10194 View
  58. Zulueta J, Leow A, Ajilore O. Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. FOCUS 2020;18(2):175 View
  59. Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela J. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 2018;43(8):1660 View
  60. Khan S, Farhan A, Fahad L, Tahir S. Personal productivity monitoring through smartphones. Journal of Ambient Intelligence and Smart Environments 2020;12(4):327 View
  61. Boonstra T, Nicholas J, Wong Q, Shaw F, Townsend S, Christensen H. Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions. Journal of Medical Internet Research 2018;20(7):e10131 View
  62. Porras-Segovia A, Molina-Madueño R, Berrouiguet S, López-Castroman J, Barrigón M, Pérez-Rodríguez M, Marco J, Díaz-Oliván I, de León S, Courtet P, Artés-Rodríguez A, Baca-García E. Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study. Journal of Affective Disorders 2020;274:733 View
  63. Thomée S. Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure. International Journal of Environmental Research and Public Health 2018;15(12):2692 View
  64. Aguilera A, Bruehlman-Senecal E, Demasi O, Avila P. Automated Text Messaging as an Adjunct to Cognitive Behavioral Therapy for Depression: A Clinical Trial. Journal of Medical Internet Research 2017;19(5):e148 View
  65. 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
  66. Harari G, Müller S, Aung M, Rentfrow P. Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences 2017;18:83 View
  67. Ram N, Brinberg M, Pincus A, Conroy D. The Questionable Ecological Validity of Ecological Momentary Assessment: Considerations for Design and Analysis. Research in Human Development 2017;14(3):253 View
  68. 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
  69. Barrigón M, Baca-García E. Current challenges in research on suicide. Revista de Psiquiatría y Salud Mental (English Edition) 2018;11(1):1 View
  70. Mehrotra A, Musolesi M. Using Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(3):1 View
  71. Cai L, Boukhechba M, Gerber M, Barnes L, Showalter S, Cohn W, Chow P. An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients. Smart Health 2020;15:100086 View
  72. Saeb S, Lattie E, Schueller S, Kording K, Mohr D. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 2016;4:e2537 View
  73. Sultana M, Al-Jefri M, Lee J. Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: An Exploratory Study (Preprint). JMIR mHealth and uHealth 2020 View
  74. Torous J, Gershon A, Hays R, Onnela J, Baker J. Digital Phenotyping for the Busy Psychiatrist: Clinical Implications and Relevance. Psychiatric Annals 2019;49(5):196 View
  75. Shaffer J, Kronish I, Falzon L, Cheung Y, Davidson K. N-of-1 Randomized Intervention Trials in Health Psychology: A Systematic Review and Methodology Critique. Annals of Behavioral Medicine 2018;52(9):731 View
  76. Torous J, Kiang M, Lorme J, Onnela J. New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research. JMIR Mental Health 2016;3(2):e16 View
  77. 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
  78. Darvariu V, Convertino L, Mehrotra A, Musolesi M. Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
  79. Suffoletto B, Scaglione S. Using Digital Interventions to Support Individuals with Alcohol Use Disorder and Advanced Liver Disease: A Bridge Over Troubled Waters. Alcoholism: Clinical and Experimental Research 2018;42(7):1160 View
  80. Huckins J, daSilva A, Wang R, Wang W, Hedlund E, Murphy E, Lopez R, Rogers C, Holtzheimer P, Kelley W, Heatherton T, Wagner D, Haxby J, Campbell A. Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students. Frontiers in Neuroscience 2019;13 View
  81. Armontrout J, Torous J, Fisher M, Drogin E, Gutheil T. Mobile Mental Health: Navigating New Rules and Regulations for Digital Tools. Current Psychiatry Reports 2016;18(10) View
  82. Balicer R, Luengo-Oroz M, Cohen-Stavi C, Loyola E, Mantingh F, Romanoff L, Galea G. Using big data for non-communicable disease surveillance. The Lancet Diabetes & Endocrinology 2018;6(8):595 View
  83. Silvera-Tawil D, Hussain M, Li J. Emerging technologies for precision health: An insight into sensing technologies for health and wellbeing. Smart Health 2020;15:100100 View
  84. Narziev N, Goh H, Toshnazarov K, Lee S, Chung K, Noh Y. STDD: Short-Term Depression Detection with Passive Sensing. Sensors 2020;20(5):1396 View
  85. Busk J, Faurholt-Jepsen M, Frost M, Bardram J, Vedel Kessing L, Winther O. Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach. JMIR mHealth and uHealth 2020;8(4):e15028 View
  86. Barnett I, Onnela J. Inferring mobility measures from GPS traces with missing data. Biostatistics 2020;21(2):e98 View
  87. Ware S, Yue C, Morillo R, Lu J, Shang C, Kamath J, Bamis A, Bi J, Russell A, Wang B. Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(4):1 View
  88. Renn B, Pratap A, Atkins D, Mooney S, Areán P. Smartphone-based passive assessment of mobility in depression: Challenges and opportunities. Mental Health and Physical Activity 2018;14:136 View
  89. Cuttone A, Bækgaard P, Sekara V, Jonsson H, Larsen J, Lehmann S, Zhou W. SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events. PLOS ONE 2017;12(1):e0169901 View
  90. Rawtaer I, Mahendran R, Kua E, Tan H, Tan H, Lee T, Ng T. Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study. Journal of Medical Internet Research 2020;22(5):e16854 View
  91. 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
  92. Aubourg T, Demongeot J, Renard F, Provost H, Vuillerme N. Association between social asymmetry and depression in older adults: A phone Call Detail Records analysis. Scientific Reports 2019;9(1) View
  93. Meng J, Hussain S, Mohr D, Czerwinski M, Zhang M. Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study. Journal of Medical Internet Research 2018;20(7):e10139 View
  94. Holtz B, McCarroll A, Mitchell K. Perceptions and Attitudes Toward a Mobile Phone App for Mental Health for College Students: Qualitative Focus Group Study. JMIR Formative Research 2020;4(8):e18347 View
  95. Perle J. A Practical Guide for Health Service Providers on the Design, Development, and Deployment of Smartphone Apps for the Delivery of Clinical Services. Journal of Technology in Behavioral Science 2020;5(1):1 View
  96. Hekler E, Tiro J, Hunter C, Nebeker C. Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision. Annals of Behavioral Medicine 2020;54(11):805 View
  97. Cote D, Barnett I, Onnela J, Smith T. Digital Phenotyping in Patients with Spine Disease: A Novel Approach to Quantifying Mobility and Quality of Life. World Neurosurgery 2019;126:e241 View
  98. Fillekes M, Giannouli E, Kim E, Zijlstra W, Weibel R. Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research. International Journal of Health Geographics 2019;18(1) View
  99. Martinez-Martin N, Insel T, Dagum P, Greely H, Cho M. Data mining for health: staking out the ethical territory of digital phenotyping. npj Digital Medicine 2018;1(1) View
  100. Low C, Dey A, Ferreira D, Kamarck T, Sun W, Bae S, Doryab A. Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study. Journal of Medical Internet Research 2017;19(12):e420 View
  101. Raugh I, James S, Gonzalez C, Chapman H, Cohen A, Kirkpatrick B, Strauss G. Geolocation as a Digital Phenotyping Measure of Negative Symptoms and Functional Outcome. Schizophrenia Bulletin 2020;46(6):1596 View
  102. Wicks P, Hotopf M, Narayan V, Basch E, Weatherall J, Gray M. It’s a long shot, but it just might work! Perspectives on the future of medicine. BMC Medicine 2016;14(1) View
  103. Jones M, Johnson M, Shervey M, Dudley J, Zimmerman N. Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept. Journal of Medical Internet Research 2019;21(8):e13600 View
  104. Faherty L, Hantsoo L, Appleby D, Sammel M, Bennett I, Wiebe D. Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy. Journal of the American Medical Informatics Association 2017;24(4):746 View
  105. Montag C, Sindermann C, Baumeister H. Digital phenotyping in psychological and medical sciences: a reflection about necessary prerequisites to reduce harm and increase benefits. Current Opinion in Psychology 2020;36:19 View
  106. Andrade A, Roughead E. Consumer‐directed technologies to improve medication management and safety. Medical Journal of Australia 2019;210(S6) View
  107. Aledavood T, Triana Hoyos A, Alakörkkö T, Kaski K, Saramäki J, Isometsä E, Darst R. Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype. JMIR Research Protocols 2017;6(6):e110 View
  108. Wang W, Harari G, Wang R, Müller S, Mirjafari S, Masaba K, Campbell A. Sensing Behavioral Change over Time. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(3):1 View
  109. Levinson C, Christian C, Shankar‐Ram S, Brosof L, Williams B. Sensor technology implementation for research, treatment, and assessment of eating disorders. International Journal of Eating Disorders 2019;52(10):1176 View
  110. Wu C, Boukhechba M, Cai L, Barnes L, Gerber M. Improving momentary stress measurement and prediction with bluetooth encounter networks. Smart Health 2018;9-10:219 View
  111. Sefidgar Y, Seo W, Kuehn K, Althoff T, Browning A, Riskin E, Nurius P, Dey A, Mankoff J. Passively-sensed Behavioral Correlates of Discrimination Events in College Students. Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1 View
  112. Tuarob S, Tucker C, Kumara S, Giles C, Pincus A, Conroy D, Ram N. How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information. Journal of Biomedical Informatics 2017;68:1 View
  113. Stachl C, Hilbert S, Au J, Buschek D, De Luca A, Bischl B, Hussmann H, Bühner M, Wrzus C. Personality Traits Predict Smartphone Usage. European Journal of Personality 2017;31(6):701 View
  114. Xu X, Chikersal P, Doryab A, Villalba D, Dutcher J, Tumminia M, Althoff T, Cohen S, Creswell K, Creswell J, Mankoff J, Dey A. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(3):1 View
  115. Chow P, Fua K, Huang Y, Bonelli W, Xiong H, Barnes L, Teachman B. Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students. Journal of Medical Internet Research 2017;19(3):e62 View
  116. Bhattacharya K, Kaski K. Social physics: uncovering human behaviour from communication. Advances in Physics: X 2019;4(1):1527723 View
  117. Turvey C, Fortney J. The Use of Telemedicine and Mobile Technology to Promote Population Health and Population Management for Psychiatric Disorders. Current Psychiatry Reports 2017;19(11) View
  118. Torous J, Levin M, Ahern D, Oser M. Cognitive Behavioral Mobile Applications: Clinical Studies, Marketplace Overview, and Research Agenda. Cognitive and Behavioral Practice 2017;24(2):215 View
  119. Morshed M, Saha K, Li R, D'Mello S, De Choudhury M, Abowd G, Plötz T. Prediction of Mood Instability with Passive Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(3):1 View
  120. Obuchi M, Huckins J, Wang W, daSilva A, Rogers C, Murphy E, Hedlund E, Holtzheimer P, Mirjafari S, Campbell A. Predicting Brain Functional Connectivity Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
  121. Hird N, Ghosh S, Kitano H. Digital health revolution: perfect storm or perfect opportunity for pharmaceutical R&D?. Drug Discovery Today 2016;21(6):900 View
  122. Bruehlman-Senecal E, Aguilera A, Schueller S. Mobile Phone–Based Mood Ratings Prospectively Predict Psychotherapy Attendance. Behavior Therapy 2017;48(5):614 View
  123. Boukhechba M, Chow P, Fua K, Teachman B, Barnes L. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Mental Health 2018;5(3):e10101 View
  124. Briffault X, Morgiève M, Courtet P. From e-Health to i-Health: Prospective Reflexions on the Use of Intelligent Systems in Mental Health Care. Brain Sciences 2018;8(6):98 View
  125. Barrigón M, Baca-García E. Retos actuales en la investigación en suicidio. Revista de Psiquiatría y Salud Mental 2018;11(1):1 View
  126. Huguet A, Rao S, McGrath P, Wozney L, Wheaton M, Conrod J, Rozario S, Choo K. A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression. PLOS ONE 2016;11(5):e0154248 View
  127. Singh V, Long T. Automatic assessment of mental health using phone metadata. Proceedings of the Association for Information Science and Technology 2018;55(1):450 View
  128. Barnett I, Torous J, Staples P, Keshavan M, Onnela J. Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data. Journal of the American Medical Informatics Association 2018;25(12):1669 View
  129. Christensen M, Bettencourt L, Kaye L, Moturu S, Nguyen K, Olgin J, Pletcher M, Marcus G, Romigi A. Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep. PLOS ONE 2016;11(11):e0165331 View
  130. Cho A, Lee H, Jo Y, Whang M. Embodied Emotion Recognition Based on Life-Logging. Sensors 2019;19(23):5308 View
  131. 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
  132. Klaas V, Troster G, Walt H, Jenewein J. Remotely Monitoring Cancer-Related Fatigue Using the Smart-Phone: Results of an Observational Study. Information 2018;9(11):271 View
  133. Basco M, Kyrarini M, Makedon F. Personal Devices and Smartphone Applications for Detection of Depression. Psychiatric Annals 2020;50(6):255 View
  134. Adler D, Ben-Zeev D, Tseng V, Kane J, Brian R, Campbell A, Hauser M, Scherer E, Choudhury T. Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks. JMIR mHealth and uHealth 2020;8(8):e19962 View
  135. Lu J, Shang C, Yue C, Morillo R, Ware S, Kamath J, Bamis A, Russell A, Wang B, Bi J. Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  136. Saeb S, Cybulski T, Kording K, Mohr D. Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles. Journal of Medical Internet Research 2017;19(4):e118 View
  137. Yim S, Lui L, Lee Y, Rosenblat J, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho R, Coles A, Majeed A, Wong E, Phan L, Nasri F, McIntyre R. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602 View
  138. Johansen B, Petersen M, Korzepa M, Larsen J, Pontoppidan N, Larsen J. Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data. Computers 2017;7(1):1 View
  139. Miloff A, Marklund A, Carlbring P. The challenger app for social anxiety disorder: New advances in mobile psychological treatment. Internet Interventions 2015;2(4):382 View
  140. 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
  141. Mohr D, Zhang M, Schueller S. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annual Review of Clinical Psychology 2017;13(1):23 View
  142. Nicholas J, Shilton K, Schueller S, Gray E, Kwasny M, Mohr D. The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2019;7(4):e12578 View
  143. Frank E, Pong J, Asher Y, Soares C. Smart phone technologies and ecological momentary data. Current Opinion in Psychiatry 2018;31(1):3 View
  144. Goodspeed R, Yan X, Hardy J, Vydiswaran V, Berrocal V, Clarke P, Romero D, Gomez-Lopez I, Veinot T. Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study. JMIR mHealth and uHealth 2018;6(8):e168 View
  145. Aledavood T, Lehmann S, Saramäki J. Digital daily cycles of individuals. Frontiers in Physics 2015;3 View
  146. Craske M. Honoring the Past, Envisioning the Future: ABCT’s 50th Anniversary Presidential Address. Behavior Therapy 2018;49(2):151 View
  147. Ware S, Yue C, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Predicting depressive symptoms using smartphone data. Smart Health 2020;15:100093 View
  148. Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker S, McInnis M, Ajilore O, Nelson P, Ryan K, Leow A. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research 2018;20(7):e241 View
  149. Palmius N, Tsanas A, Saunders K, Bilderbeck A, Geddes J, Goodwin G, De Vos M. Detecting Bipolar Depression From Geographic Location Data. IEEE Transactions on Biomedical Engineering 2017;64(8):1761 View
  150. Place S, Blanch-Hartigan D, Rubin C, Gorrostieta C, Mead C, Kane J, Marx B, Feast J, Deckersbach T, Pentland A, Nierenberg A, Azarbayejani A. Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. Journal of Medical Internet Research 2017;19(3):e75 View
  151. Saeb S, Lattie E, Kording K, Mohr D. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112 View
  152. Spaiser V, Luzzatti D, Gregoriou A, Ferrara E, Chadefaux T. Advancing sustainability: Using smartphones to study environmental behavior in a field-experimental setup. Data Science 2019;2(1-2):277 View
  153. Leonard N, Silverman M, Sherpa D, Naegle M, Kim H, Coffman D, Ferdschneider M. Mobile Health Technology Using a Wearable Sensorband for Female College Students With Problem Drinking: An Acceptability and Feasibility Study. JMIR mHealth and uHealth 2017;5(7):e90 View
  154. Harari G, Lane N, Wang R, Crosier B, Campbell A, Gosling S. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 2016;11(6):838 View
  155. Torous J, Rodriguez J, Powell A. The New Digital Divide For Digital Biomarkers. Digital Biomarkers 2017;1(1):87 View
  156. Roberts L, Chan S, Torous J. New tests, new tools: mobile and connected technologies in advancing psychiatric diagnosis. npj Digital Medicine 2018;1(1) View
  157. Jim H, Hoogland A, Brownstein N, Barata A, Dicker A, Knoop H, Gonzalez B, Perkins R, Rollison D, Gilbert S, Nanda R, Berglund A, Mitchell R, Johnstone P. Innovations in research and clinical care using patient‐generated health data. CA: A Cancer Journal for Clinicians 2020;70(3):182 View
  158. 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
  159. 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
  160. Webb C, Rosso I, Rauch S. Internet-Based Cognitive-Behavioral Therapy for Depression: Current Progress and Future Directions. Harvard Review of Psychiatry 2017;25(3):114 View
  161. Kleiman E, Nock M. Real-time assessment of suicidal thoughts and behaviors. Current Opinion in Psychology 2018;22:33 View
  162. Berrouiguet S, Perez-Rodriguez M, Larsen M, Baca-García E, Courtet P, Oquendo M. From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health. Journal of Medical Internet Research 2018;20(1):e2 View
  163. Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild. JMIR mHealth and uHealth 2016;4(3):e111 View
  164. Bhugra D, Tasman A, Pathare S, Priebe S, Smith S, Torous J, Arbuckle M, Langford A, Alarcón R, Chiu H, First M, Kay J, Sunkel C, Thapar A, Udomratn P, Baingana F, Kestel D, Ng R, Patel A, Picker L, McKenzie K, Moussaoui D, Muijen M, Bartlett P, Davison S, Exworthy T, Loza N, Rose D, Torales J, Brown M, Christensen H, Firth J, Keshavan M, Li A, Onnela J, Wykes T, Elkholy H, Kalra G, Lovett K, Travis M, Ventriglio A. The WPA- Lancet Psychiatry Commission on the Future of Psychiatry. The Lancet Psychiatry 2017;4(10):775 View
  165. DeMasi O, Kording K, Recht B, Jan Y. Meaningless comparisons lead to false optimism in medical machine learning. PLOS ONE 2017;12(9):e0184604 View
  166. Šimon M, Vašát P, Poláková M, Gibas P, Daňková H. Activity spaces of homeless men and women measured by GPS tracking data: A comparative analysis of Prague and Pilsen. Cities 2019;86:145 View
  167. Singh V, Goyal R, Wu S. Riskalyzer. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  168. Eichstaedt J, Smith R, Merchant R, Ungar L, Crutchley P, Preoţiuc-Pietro D, Asch D, Schwartz H. Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences 2018;115(44):11203 View
  169. Piau A, Rumeau P, Nourhashemi F, Martin M. Information and Communication Technologies, a Promising Way to Support Pharmacotherapy for the Behavioral and Psychological Symptoms of Dementia. Frontiers in Pharmacology 2019;10 View
  170. Li B, Sano A. Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(2):1 View
  171. Bourla A, Mouchabac S, El Hage W, Ferreri F. e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD). European Journal of Psychotraumatology 2018;9(sup1) View
  172. 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
  173. Kennedy S, Ceniti A. Unpacking Major Depressive Disorder: From Classification to Treatment Selection. The Canadian Journal of Psychiatry 2018;63(5):308 View
  174. 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
  175. Bader C, Skurla M, Vahia I. Technology in the Assessment, Treatment, and Management of Depression. Harvard Review of Psychiatry 2020;28(1):60 View
  176. Harari G. A process-oriented approach to respecting privacy in the context of mobile phone tracking. Current Opinion in Psychology 2020;31:141 View
  177. Arean P, Hallgren K, Jordan J, Gazzaley A, Atkins D, Heagerty P, Anguera J. The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial. Journal of Medical Internet Research 2016;18(12):e330 View
  178. 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
  179. Doryab A, Villalba D, Chikersal P, Dutcher J, Tumminia M, Liu X, Cohen S, Creswell K, Mankoff J, Creswell J, Dey A. Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data. JMIR mHealth and uHealth 2019;7(7):e13209 View
  180. 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
  181. Singh V, Ghosh I. Inferring Individual Social Capital Automatically via Phone Logs. Proceedings of the ACM on Human-Computer Interaction 2017;1(CSCW):1 View
  182. Jongs N, Jagesar R, van Haren N, Penninx B, Reus L, Visser P, van der Wee N, Koning I, Arango C, Sommer I, Eijkemans M, Vorstman J, Kas M. A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data. Translational Psychiatry 2020;10(1) View
  183. Pratap A, Atkins D, Renn B, Tanana M, Mooney S, Anguera J, Areán P. The accuracy of passive phone sensors in predicting daily mood. Depression and Anxiety 2019;36(1):72 View
  184. Sarikaya R. The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components. IEEE Signal Processing Magazine 2017;34(1):67 View
  185. 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
  186. Ha Q, Chen J, Uy H, Capistrano E. Exploring the Privacy Concerns in Using Intelligent Virtual Assistants under Perspectives of Information Sensitivity and Anthropomorphism. International Journal of Human–Computer Interaction 2021;37(6):512 View
  187. Thakur S, Roy R. Predicting mental health using smart-phone usage and sensor data. Journal of Ambient Intelligence and Humanized Computing 2021;12(10):9145 View
  188. Bertoa M, Moreno N, Perez-Vereda A, Bandera D, Álvarez-Palomo J, Canal C, Linaje M. Digital Avatars: Promoting Independent Living for Older Adults. Wireless Communications and Mobile Computing 2020;2020:1 View
  189. Wang Y, Mao H. Intelligent soccer system based on biosensor network technology. Measurement 2021;173:108564 View
  190. Fischer F, Kleen S. Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review. Journal of Medical Internet Research 2021;23(1):e17691 View
  191. 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
  192. Fulford D, Mote J, Gonzalez R, Abplanalp S, Zhang Y, Luckenbaugh J, Onnela J, Busso C, Gard D. Smartphone sensing of social interactions in people with and without schizophrenia. Journal of Psychiatric Research 2021;137:613 View
  193. 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
  194. Aubourg T, Demongeot J, Vuillerme N. Novel statistical approach for assessing the persistence of the circadian rhythms of social activity from telephone call detail records in older adults. Scientific Reports 2020;10(1) View
  195. Zulueta J, Ajilore O. Beyond non-inferior: how telepsychiatry technologies can lead to superior care. International Review of Psychiatry 2021;33(4):366 View
  196. Kumar D, Jeuris S, Bardram J, Dragoni N. Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications. ACM Transactions on Computing for Healthcare 2021;2(1):1 View
  197. Thongnopakun S, Visanuyothin S, Manwong M, Rodjarkpai Y, Patipat P. <p>Promoting Health Literacy to Prevent Depression Among Workers in Industrial Factories in the Eastern Economic Corridor of Thailand</p>. Journal of Multidisciplinary Healthcare 2020;Volume 13:1443 View
  198. 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
  199. Mendu S, Baglione A, Baee S, Wu C, Ng B, Shaked A, Clore G, Boukhechba M, Barnes L. A Framework for Understanding the Relationship between Social Media Discourse and Mental Health. Proceedings of the ACM on Human-Computer Interaction 2020;4(CSCW2):1 View
  200. Chikersal P, Doryab A, Tumminia M, Villalba D, Dutcher J, Liu X, Cohen S, Creswell K, Mankoff J, Creswell J, Goel M, Dey A. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing. ACM Transactions on Computer-Human Interaction 2021;28(1):1 View
  201. Wang Y, Ren X, Liu X, Zhu T. Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study. JMIR mHealth and uHealth 2021;9(1):e19046 View
  202. Wen H, Sobolev M, Vitale R, Kizer J, Pollak J, Muench F, Estrin D. mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study. JMIR Mental Health 2021;8(1):e25019 View
  203. Aubourg T, Demongeot J, Provost H, Vuillerme N. Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study. JMIR mHealth and uHealth 2020;8(11):e13535 View
  204. He-Yueya J, Buck B, Campbell A, Choudhury T, Kane J, Ben-Zeev D, Althoff T. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 2020;6(1) View
  205. Low C. Harnessing consumer smartphone and wearable sensors for clinical cancer research. npj Digital Medicine 2020;3(1) View
  206. Asuzu K, Rosenthal M. Mobile device use among inpatients on a psychiatric unit: A preliminary study. Psychiatry Research 2021;297:113720 View
  207. Hafiz P, Miskowiak K, Maxhuni A, Kessing L, Bardram J. Wearable Computing Technology for Assessment of Cognitive Functioning of Bipolar Patients and Healthy Controls. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(4):1 View
  208. Martinez-Martin N, Dasgupta I, Carter A, Chandler J, Kellmeyer P, Kreitmair K, Weiss A, Cabrera L. Ethics of Digital Mental Health During COVID-19: Crisis and Opportunities. JMIR Mental Health 2020;7(12):e23776 View
  209. 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
  210. Elhai J, Sapci O, Yang H, Amialchuk A, Rozgonjuk D, Montag C. Objectively‐measured and self‐reported smartphone use in relation to surface learning, procrastination, academic productivity, and psychopathology symptoms in college students. Human Behavior and Emerging Technologies 2021;3(5):912 View
  211. Klein A, Clucas J, Krishnakumar A, Ghosh S, Van Auken W, Thonet B, Sabram I, Acuna N, Keshavan A, Rossiter H, Xiao Y, Semenuta S, Badioli A, Konishcheva K, Abraham S, Alexander L, Merikangas K, Swendsen J, Lindner A, Milham M. Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study. Journal of Medical Internet Research 2021;23(11):e22369 View
  212. Labus A, Radenković B, Rodić B, Barać D, Malešević A. Enhancing smart healthcare in dentistry: an approach to managing patients’ stress. Informatics for Health and Social Care 2021;46(3):306 View
  213. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  214. Sadeghian A, Kaedi M. Happiness recognition from smartphone usage data considering users’ estimated personality traits. Pervasive and Mobile Computing 2021;73:101389 View
  215. Wang X, Vouk N, Heaukulani C, Buddhika T, Martanto W, Lee J, Morris R. HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning. Journal of Medical Internet Research 2021;23(3):e23984 View
  216. Bai R, Xiao L, Guo Y, Zhu X, Li N, Wang Y, Chen Q, Feng L, Wang Y, Yu X, Wang C, Hu Y, Liu Z, Xie H, Wang G. Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study. JMIR mHealth and uHealth 2021;9(3):e24365 View
  217. 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
  218. Xu X, Chikersal P, Dutcher J, Sefidgar Y, Seo W, Tumminia M, Villalba D, Cohen S, Creswell K, Creswell J, Doryab A, Nurius P, Riskin E, Dey A, Mankoff J. Leveraging Collaborative-Filtering for Personalized Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1 View
  219. Gloster A, Meyer A, Klotsche J, Villanueva J, Block V, Benoy C, Rinner M, Walter M, Lang U, Karekla M. The spatiotemporal movement of patients in and out of a psychiatric hospital: an observational GPS study. BMC Psychiatry 2021;21(1) View
  220. Balaskas A, Schueller S, Cox A, Doherty G, Myers B. Ecological momentary interventions for mental health: A scoping review. PLOS ONE 2021;16(3):e0248152 View
  221. Ríssola E, Losada D, Crestani F. A Survey of Computational Methods for Online Mental State Assessment on Social Media. ACM Transactions on Computing for Healthcare 2021;2(2):1 View
  222. Poudyal A, van Heerden A, Hagaman A, Islam C, Thapa A, Maharjan S, Byanjankar P, Kohrt B. What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment. Frontiers in Public Health 2021;9 View
  223. Low C, Li M, Vega J, Durica K, Ferreira D, Tam V, Hogg M, Zeh III H, Doryab A, Dey A. Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study. JMIR Cancer 2021;7(2):e27975 View
  224. Baglione A, Clemens M, Maestre J, Min A, Dahl L, Shih P. Understanding the Technological Practices and Needs of Music Therapists. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW1):1 View
  225. Tonti S, Marzolini B, Bulgheroni M. Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study. JMIR Biomedical Engineering 2021;6(2):e15417 View
  226. Ziepert B, de Vries P, Ufkes E. “Psyosphere”: A GPS Data-Analysing Tool for the Behavioural Sciences. Frontiers in Psychology 2021;12 View
  227. Baumeister H, Bauereiss N, Zarski A, Braun L, Buntrock C, Hoherz C, Idrees A, Kraft R, Meyer P, Nguyen T, Pryss R, Reichert M, Sextl T, Steinhoff M, Stenzel L, Steubl L, Terhorst Y, Titzler I, Ebert D. Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders. Frontiers in Psychiatry 2021;12 View
  228. Sedano-Capdevila A, Porras-Segovia A, Bello H, Baca-García E, Barrigon M. Use of Ecological Momentary Assessment to Study Suicidal Thoughts and Behavior: a Systematic Review. Current Psychiatry Reports 2021;23(7) View
  229. 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
  230. Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Fusing Location Data for Depression Prediction. IEEE Transactions on Big Data 2021;7(2):355 View
  231. Adler D, Tseng V, Qi G, Scarpa J, Sen S, Choudhury T. Identifying Mobile Sensing Indicators of Stress-Resilience. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(2):1 View
  232. Chandran A, Selva Kumar S, Hairi N, Low W, Mustapha F. Non-communicable Disease Surveillance in Malaysia: An Overview of Existing Systems and Priorities Going Forward. Frontiers in Public Health 2021;9 View
  233. Müller S, Chen X, Peters H, Chaintreau A, Matz S. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports 2021;11(1) View
  234. Xu X, Mankoff J, Dey A. Understanding practices and needs of researchers in human state modeling by passive mobile sensing. CCF Transactions on Pervasive Computing and Interaction 2021;3(4):344 View
  235. Gillan C, Rutledge R. Smartphones and the Neuroscience of Mental Health. Annual Review of Neuroscience 2021;44(1):129 View
  236. Taliaz D, Souery D. A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder. Journal of Clinical Medicine 2021;10(14):3109 View
  237. Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840 View
  238. 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
  239. Nickels S, Edwards M, Poole S, Winter D, Gronsbell J, Rozenkrants B, Miller D, Fleck M, McLean A, Peterson B, Chen Y, Hwang A, Rust-Smith D, Brant A, Campbell A, Chen C, Walter C, Arean P, Hsin H, Myers L, Marks Jr W, Mega J, Schlosser D, Conrad A, Califf R, Fromer M. Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling. JMIR Mental Health 2021;8(8):e27589 View
  240. 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
  241. Virginia Anikwe C, Friday Nweke H, Chukwu Ikegwu A, Adolphus Egwuonwu C, Uchenna Onu F, Rita Alo U, Wah Teh Y. Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications 2022;202:117362 View
  242. Lee H, Kang S, Lee U. Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  243. Müller S, Bayer J, Ross M, Mount J, Stachl C, Harari G, Chang Y, Le H. Analyzing GPS Data for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science 2022;5(2):251524592210826 View
  244. Adler D, Wang F, Mohr D, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516 View
  245. Hagaman A, Lopez Mercado D, Poudyal A, Bemme D, Boone C, van Heerden A, Byanjankar P, Man Maharjan S, Thapa A, Kohrt B, Wasti S. “Now, I have my baby so I don’t go anywhere”: A mixed method approach to the ‘everyday’ and young motherhood integrating qualitative interviews and passive digital data from mobile devices. PLOS ONE 2022;17(7):e0269443 View
  246. 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
  247. Abdullah S, Choudhury T. Sensing Technologies for Monitoring Serious Mental Illnesses. IEEE MultiMedia 2018;25(1):61 View
  248. 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
  249. Zhou J, Lamichhane B, Ben-Zeev D, Campbell A, Sano A. Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis. JMIR mHealth and uHealth 2022;10(4):e31006 View
  250. Messner E, Sariyska R, Mayer B, Montag C, Kannen C, Schwerdtfeger A, Baumeister H. Insights – Future Implications of Passive Smartphone Sensing in the Therapeutic Context. Verhaltenstherapie 2022;32(Suppl. 1):86 View
  251. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  252. Nicolaidou I, Aristeidis L, Lambrinos L. A gamified app for supporting undergraduate students’ mental health: A feasibility and usability study. DIGITAL HEALTH 2022;8:205520762211090 View
  253. 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
  254. Choudhary S, Thomas N, Ellenberger J, Srinivasan G, Cohen R. A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study. JMIR Formative Research 2022;6(5):e37736 View
  255. Ware S, Yue C, Morillo R, Shang C, Bi J, Kamath J, Russell A, Song D, Bamis A, Wang B. Automatic depression screening using social interaction data on smartphones. Smart Health 2022;26:100356 View
  256. 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
  257. Hart A, Reis D, Prestele E, Jacobson N. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment. Journal of Medical Internet Research 2022;24(4):e34015 View
  258. Kempermann G, Lopes J, Zocher S, Schilling S, Ehret F, Garthe A, Karasinsky A, Brandmaier A, Lindenberger U, Winter Y, Overall R. The individuality paradigm: Automated longitudinal activity tracking of large cohorts of genetically identical mice in an enriched environment. Neurobiology of Disease 2022;175:105916 View
  259. Rohani D, Faurholt-Jepsen M, Kessing L, Bardram J. Benefits of Using Activity Recommender Technology for Self-management of Depressive Symptoms. ACM Transactions on Computing for Healthcare 2021;2(4):1 View
  260. Conceição M, Monteiro M, Kasraian D, van den Berg P, Haustein S, Alves I, Azevedo C, Miranda B. The effect of transport infrastructure, congestion and reliability on mental wellbeing: a systematic review of empirical studies. Transport Reviews 2023;43(2):264 View
  261. Bae S, Suffoletto B, Zhang T, Chung T, Ozolcer M, Islam M, Dey A. Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study. JMIR Formative Research 2023;7:e39862 View
  262. Xia Y, Hu J, Zhao S, Tao L, Li Z, Yue T, Kong J. Build-in sensors and analysis algorithms aided smartphone-based sensors for point-of-care tests. Biosensors and Bioelectronics: X 2022;11:100195 View
  263. 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
  264. Coghlan S, D’Alfonso S. Digital Phenotyping: an Epistemic and Methodological Analysis. Philosophy & Technology 2021;34(4):1905 View
  265. Griffiths C, da Silva K, Leathlean C, Jiang H, Ang C, Searle R. Investigation of physical activity, sleep, and mental health recovery in treatment resistant depression (TRD) patients receiving repetitive transcranial magnetic stimulation (rTMS) treatment. Journal of Affective Disorders Reports 2022;8:100337 View
  266. 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
  267. Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatric Disease and Treatment 2021;Volume 17:3415 View
  268. 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
  269. Boulos L, Mendes A, Delmas A, Chraibi Kaadoud I. An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health. Frontiers in Psychiatry 2021;12 View
  270. Areàn P, Hoa Ly K, Andersson G. Mobile technology for mental health assessment. Dialogues in Clinical Neuroscience 2016;18(2):163 View
  271. Hong J, Kim J, Kim S, Oh J, Lee D, Lee S, Uh J, Yoon J, Choi Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare 2022;10(7):1189 View
  272. 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
  273. Eagle T, Mehrotra A, Sharma A, Zuniga A, Whittaker S. "Money Doesn't Buy You Happiness": Negative Consequences of Using the Freemium Model for Mental Health Apps. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  274. Choi J, Lee S, Kim S, Kim D, Kim H. Depressed Mood Prediction of Elderly People with a Wearable Band. Sensors 2022;22(11):4174 View
  275. Bettis A, Burke T, Nesi J, Liu R. Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review. Clinical Psychological Science 2022;10(1):3 View
  276. Santillán Cooper M, Armentano M. Predicting future sedentary behaviour using wearable and mobile devices. Information Processing & Management 2022;59(6):103104 View
  277. 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
  278. Ayranci P, Bandera C, Phan N, Jin R, Li D, Kenne D. Distinguishing the Effect of Time Spent at Home during COVID-19 Pandemic on the Mental Health of Urban and Suburban College Students Using Cell Phone Geolocation. International Journal of Environmental Research and Public Health 2022;19(12):7513 View
  279. 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
  280. Pellegrini A, Huang E, Staples P, Hart K, Lorme J, Brown H, Perlis R, Onnela J. Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort. Brain and Behavior 2022;12(2) View
  281. Kaplan D. Social-Ecological Measurement of Daily Life: How Relationally Focused Ambulatory Assessment Can Advance Clinical Intervention Science. Review of General Psychology 2023;27(2):206 View
  282. Park J, Arunachalam R, Silenzio V, Singh V. Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study. JMIR Formative Research 2022;6(6):e34366 View
  283. Kamath J, Barriera R, Jain N, Keisari E, Wang B. Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World Journal of Psychiatry 2022;12(3):393 View
  284. Xu X, Liu X, Zhang H, Wang W, Nepal S, Sefidgar Y, Seo W, Kuehn K, Huckins J, Morris M, Nurius P, Riskin E, Patel S, Althoff T, Campbell A, Dey A, Mankoff J. GLOBEM. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(4):1 View
  285. Alamoudi D, Breeze E, Crawley E, Nabney I. The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review. JMIR mHealth and uHealth 2023;11:e44123 View
  286. Montag C, Elhai J, Dagum P. On Blurry Boundaries When Defining Digital Biomarkers: How Much Biology Needs to Be in a Digital Biomarker?. Frontiers in Psychiatry 2021;12 View
  287. Mansoor H, Gerych W, Alajaji A, Buquicchio L, Chandrasekaran K, Agu E, Rundensteiner E, Rodriguez A. INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping. Visual Informatics 2023;7(2):13 View
  288. De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr D, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Medicine 2022;5(1) View
  289. Zlatintsi A, Filntisis P, Garoufis C, Efthymiou N, Maragos P, Menychtas A, Maglogiannis I, Tsanakas P, Sounapoglou T, Kalisperakis E, Karantinos T, Lazaridi M, Garyfalli V, Mantas A, Mantonakis L, Smyrnis N. E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures. Sensors 2022;22(19):7544 View
  290. Wu C, McMahon M, Fritz H, Schnyer D. circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking. DIGITAL HEALTH 2022;8:205520762211142 View
  291. Ferrás Sexto C, García Y. Los datos georreferenciados con teléfonos móviles para las terapias psicosociales. MEDICA REVIEW. International Medical Humanities Review / Revista Internacional de Humanidades Médicas 2019;7(2):83 View
  292. Mandryk R, Birk M, Vedress S, Wiley K, Reid E, Berger P, Frommel J. Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks. Frontiers in Psychology 2021;12 View
  293. 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
  294. Wu C, Fritz H, Miller M, Craddock C, Kinney K, Castelli D, Schnyer D. Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study. Frontiers in Digital Health 2021;3 View
  295. Yan R, Liu X, Dutcher J, Tumminia M, Villalba D, Cohen S, Creswell D, Creswell K, Mankoff J, Dey A, Doryab A. A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams. ACM Transactions on Intelligent Systems and Technology 2022;13(3):1 View
  296. Timakum T, Xie Q, Song M. Analysis of E-mental health research: mapping the relationship between information technology and mental healthcare. BMC Psychiatry 2022;22(1) View
  297. van der Zee-Neuen A, Seymer A, Schaffler-Schaden D, Herfert J, ÓBrien J, Johansson T, Kutschar P, Ludwig S, Stöggl T, Keeley D, Flamm M, Osterbrink J. Association of national COVID-19 cases with objectively and subjectively measured mental health proxies in the Austrian Football league – an epidemiological study. All Life 2021;14(1):1011 View
  298. Zhang Y, Folarin A, Sun S, Cummins N, Vairavan S, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Vilella E, Simblett S, Rintala A, Bruce S, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study. JMIR Mental Health 2022;9(3):e34898 View
  299. Lamichhane B, Moukaddam N, Patel A, Sabharwal A. ECoNet: Estimating Everyday Conversational Network From Free-Living Audio for Mental Health Applications. IEEE Pervasive Computing 2022;21(2):32 View
  300. Nisenson M, Lin V, Gansner M. Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective. Harvard Review of Psychiatry 2021;29(6):401 View
  301. Messner E, Sariyska R, Mayer B, Montag C, Kannen C, Schwerdtfeger A, Baumeister H. Insights: Anwendungsmöglichkeiten von passivem Smartphone-Tracking im therapeutischen Kontext. Verhaltenstherapie 2019;29(3):155 View
  302. Lee H, Cho C, Lee T, Jeong J, Yeom J, Kim S, Jeon S, Seo J, Moon E, Baek J, Park D, Kim S, Ha T, Cha B, Kang H, Ahn Y, Lee Y, Lee J, Kim L. Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study. Psychological Medicine 2023;53(12):5636 View
  303. 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
  304. Rout A, Nitoslawski S, Ladle A, Galpern P. Using smartphone-GPS data to understand pedestrian-scale behavior in urban settings: A review of themes and approaches. Computers, Environment and Urban Systems 2021;90:101705 View
  305. D’Mello R, Melcher J, Torous J. Similarity matrix-based anomaly detection for clinical intervention. Scientific Reports 2022;12(1) View
  306. Lim J, Jeong C, Lim J, Chung S, Kim G, Noh K, Jeong H. Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges. IEEE Access 2022;10:2063 View
  307. Adler D, Tseng E, Moon K, Young J, Kane J, Moss E, Mohr D, Choudhury T. Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  308. Coffey M, Coffey C. The emerging story of emerging technologies in neuropsychiatry. Dialogues in Clinical Neuroscience 2016;18(2):127 View
  309. Kathan A, Harrer M, Küster L, Triantafyllopoulos A, He X, Milling M, Gerczuk M, Yan T, Rajamani S, Heber E, Grossmann I, Ebert D, Schuller B. Personalised depression forecasting using mobile sensor data and ecological momentary assessment. Frontiers in Digital Health 2022;4 View
  310. Xiang Y, Li S, Zhang P. An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management. Fundamental Research 2022;2(1):154 View
  311. Kringle E, Tucker D, Wu Y, Lv N, Kannampallil T, Barve A, Dosala S, Wittels N, Dai R, Ma J. Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression. Mental Health and Physical Activity 2023;24:100512 View
  312. Laiou P, Kaliukhovich D, Folarin A, Ranjan Y, Rashid Z, Conde P, Stewart C, Sun S, Zhang Y, Matcham F, Ivan A, Lavelle G, Siddi S, Lamers F, Penninx B, Haro J, Annas P, Cummins N, Vairavan S, Manyakov N, Narayan V, Dobson R, Hotopf M. The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones. JMIR mHealth and uHealth 2022;10(1):e28095 View
  313. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  314. Newn J, Kelly R, D'Alfonso S, Lederman R. Examining and Promoting Explainable Recommendations for Personal Sensing Technology Acceptance. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(3):1 View
  315. Yao B, Chen Y, Yang H. Constrained Markov Decision Process Modeling for Optimal Sensing of Cardiac Events in Mobile Health. IEEE Transactions on Automation Science and Engineering 2022;19(2):1017 View
  316. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  317. Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Automatic depression prediction using Internet traffic characteristics on smartphones. Smart Health 2020;18:100137 View
  318. Memon A, Kilby J, Breñosa J, Espinosa J, Ashraf I. Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix. Sensors 2022;22(24):9898 View
  319. Panicheva P, Mararitsa L, Sorokin S, Koltsova O, Rosso P. Predicting subjective well-being in a high-risk sample of Russian mental health app users. EPJ Data Science 2022;11(1) View
  320. Meyerhoff J, Liu T, Kording K, Ungar L, Kaiser S, Karr C, Mohr D. Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(9):e22844 View
  321. 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
  322. Ilyas Y, Hassanbeigi Daryani S, Kiriella D, Pachwicewicz P, Boley R, Reyes K, Smith D, Zalta A, Schueller S, Karnik N, Stiles-Shields C. Geolocation Patterns, Wi-Fi Connectivity Rates, and Psychiatric Symptoms Among Urban Homeless Youth: Mixed Methods Study Using Self-report and Smartphone Data. JMIR Formative Research 2023;7:e45309 View
  323. Čermák J, Pietrucha S, Nawka A, Lipone P, Ruggieri A, Bonelli A, Comandini A, Cattaneo A. An Observational Pilot Study using a Digital Phenotyping Approach in Patients with Major Depressive Disorder Treated with Trazodone. Frontiers in Psychiatry 2023;14 View
  324. Smail E, Alpert J, Mardini M, Kaufmann C, Bai C, Gill T, Fillingim R, Cenko E, Zapata R, Karnati Y, Marsiske M, Ranka S, Manini T, Lipsitz L. Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and Mobility Monitor. The Journals of Gerontology: Series A 2023;78(5):821 View
  325. Lee H, Cho C, Lee T, Jeong J, Yeom J, Kim S, Jeon S, Seo J, Moon E, Baek J, Park D, Kim S, Ha T, Cha B, Kang H, Ahn Y, Lee Y, Lee J, Kim L. Prediction of Impending Mood Episode Recurrence Using Real-Time Digital Phenotypes in Major Depression and Bipolar Disorders in South Korea: A Prospective Nationwide Cohort Study. SSRN Electronic Journal 2022 View
  326. Lee K, Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas M, Wright A, Demiris G, Ritchie C, Pickering C, Nicholas Dionne-Odom J. Using digital phenotyping to understand health-related outcomes: A scoping review. International Journal of Medical Informatics 2023;174:105061 View
  327. Zou B, Zhang X, Xiao L, Bai R, Li X, Liang H, Ma H, Wang G. Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:1786 View
  328. Ahmed M, Ahmed N. A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach. JMIR Formative Research 2023;7:e28848 View
  329. 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
  330. große Deters F, Schoedel R. Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being. Computers in Human Behavior 2024;150:107977 View
  331. Akbarova S, Im M, Kim S, Toshnazarov K, Chung K, Chun J, Noh Y, Kim Y. Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach. Sensors 2023;23(21):8866 View
  332. Thaxton C, Dardik A. Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence. Seminars in Vascular Surgery 2023;36(3):419 View
  333. Wang W, Xu W, Chander A, Nepal S, Buck B, Pakhomov S, Cohen T, Ben-Zeev D, Campbell A. The Power of Speech in the Wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1 View
  334. 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
  335. Rajkishan S, Meitei A, Singh A. Role of AI/ML in the study of mental health problems of the students: a bibliometric study. International Journal of System Assurance Engineering and Management 2023 View
  336. Deng S, Cheng X, Hu R. Detecting depression and its severity based on social media digital cues. Industrial Management & Data Systems 2023;123(12):3038 View
  337. Taliaz D, Serretti A. Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path. CNS & Neurological Disorders - Drug Targets 2023;22(9):1267 View
  338. Shende C, Sahoo S, Sam S, Patel P, Morillo R, Wang X, Ware S, Bi J, Kamath J, Russell A, Song D, Wang B. Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1 View
  339. Moukaddam N, Lamichhane B, Salas R, Goodman W, Sabharwal A, Emanuele E. Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder. Behavioural Neurology 2023;2023:1 View
  340. Sun S, Folarin A, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Matcham F, Leightley D, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Nica R, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Vairavan S, Narayan V, Annas P, Hotopf M, Dobson R. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis. Journal of Medical Internet Research 2023;25:e45233 View
  341. Montag C, Hall B. Enhancing real-time digital surveillance can guide evidence-based policymaking to improve global mental health. Nature Mental Health 2023;1(10):697 View
  342. Zimmermann F, Filser A, Haas G, Bähr S. The IAB-SMART-Mobility Module: An Innovative Research Dataset with Mobility Indicators Based on Raw Geodata. Jahrbücher für Nationalökonomie und Statistik 2023;0(0) View
  343. Bo Y, Liu Q, Tong Y. The Effects of Adopting Mobile Health and Fitness Apps on Hospital Visits: Quasi-Experimental Study. Journal of Medical Internet Research 2023;25:e45681 View
  344. 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
  345. Robbins M, Jonnalagadda P, Spahr C. Rebalancing social & personality psychology methods: The case for naturalistic observation. Social and Personality Psychology Compass 2024;18(1) View
  346. 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
  347. Breitinger S, Gardea-Resendez M, Langholm C, Xiong A, Laivell J, Stoppel C, Harper L, Volety R, Walker A, D'Mello R, Byun A, Zandi P, Goes F, Frye M, Torous J. Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study. Journal of Medical Internet Research 2023;25:e47006 View
  348. 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
  349. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  350. Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
  351. Alamoudi D, Nabney I, Crawley E. Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances. Sensors 2024;24(3):722 View
  352. Torous J, Haim A. Dichotomies in the Development and Implementation of Digital Mental Health Tools. Psychiatric Services 2018;69(12):1204 View
  353. Kazemzadeh K. The Role of Artificial Intelligence in Depression Diagnosis, Prognosis, and Treatment: Gaps and Future Directions. Neurology Letters 2024;3(1):20 View
  354. 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
  355. Shi K, Chen Z, Sun W, Hu W. Measuring regularity of human physical activities with entropy models. Journal of Big Data 2024;11(1) View
  356. Tlachac M, Heinz M, Reisch M, Ogden S. Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(1):1 View
  357. Kilshaw R, Boggins A, Everett O, Butner E, Leifker F, Baucom B. Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study. JMIR Research Protocols 2024;13:e53857 View
  358. Adler D, Stamatis C, Meyerhoff J, Mohr D, Wang F, Aranovich G, Sen S, Choudhury T. Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data. npj Mental Health Research 2024;3(1) View
  359. Hossain M, Yang S. A decision integration strategy algorithm to detect the depression severity level using wearable and profile data. Iran Journal of Computer Science 2024 View
  360. Atrey A, Zakaria C, Balan R, Shenoy P. W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing. Proceedings of the ACM on Human-Computer Interaction 2024;8(CSCW1):1 View
  361. Englhardt Z, Ma C, Morris M, Chang C, Xu X, Qin L, McDuff D, Liu X, Patel S, Iyer V. From Classification to Clinical Insights. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(2):1 View
  362. 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
  363. Shaikh M, Dong X, Zheng G, Wang C, Lin Y. An Improved Expeditious Meta-Heuristic Clustering Method for Classifying Student Psychological Issues with Homogeneous Characteristics. Mathematics 2024;12(11):1620 View
  364. Mullick T, Shaaban S, Radovic A, Doryab A. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling. JMIR AI 2024;3:e47805 View
  365. Knauer J, Baumeister H, Schmitt A, Terhorst Y. Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  366. Hull G. Infrastructure, Modulation, Portal: Thinking with Foucault about how Internet Architecture Shapes Subjects. SSRN Electronic Journal 2021 View
  367. Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Stewart C, Conde P, Sankesara H, Laiou P, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Vairavan S, Myin-Germeys I, Mohr D, Wykes T, Haro J, Annas P, Penninx B, Narayan V, Hotopf M, Dobson R. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis. Journal of Medical Internet Research 2024;26:e55302 View
  368. 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
  369. Zheng L, Kwan M, Liu Y, Liu D, Huang J, Kan Z. How mobility pattern shapes the association between static green space and dynamic green space exposure. Environmental Research 2024;258:119499 View

Books/Policy Documents

  1. Dagum P, Montag C. Digital Phenotyping and Mobile Sensing. View
  2. Derksen J. Preventie psychische aandoeningen. View
  3. Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  4. Vayena E, Gasser U. The Ethics of Biomedical Big Data. View
  5. Lee H, Jo Y, Kim H, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  6. . The Cambridge Handbook of Research Methods in Clinical Psychology. View
  7. Losada D, Crestani F. Experimental IR Meets Multilinguality, Multimodality, and Interaction. View
  8. Ferguson S, Jahnel T, Elliston K, Shiffman S. The Cambridge Handbook of Research Methods in Clinical Psychology. View
  9. Chanchaichujit J, Tan A, Meng F, Eaimkhong S. Healthcare 4.0. View
  10. Fang Y, Mao R. Depressive Disorders: Mechanisms, Measurement and Management. View
  11. Maglogiannis I, Zlatintsi A, Menychtas A, Papadimatos D, Filntisis P, Efthymiou N, Retsinas G, Tsanakas P, Maragos P. Artificial Intelligence Applications and Innovations. View
  12. Cho A, Lee H, Hwang H, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  13. Klaas V, Calatroni A, Hardegger M, Guckenberger M, Theile G, Tröster G. Wireless Mobile Communication and Healthcare. View
  14. Thakur S, Roy R. Computational Intelligence: Theories, Applications and Future Directions - Volume I. View
  15. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
  16. Rabbi M. Encyclopedia of Behavioral Medicine. View
  17. Cummins N, Matcham F, Klapper J, Schuller B. Artificial Intelligence in Precision Health. View
  18. Duke É, Montag C. Internet Addiction. View
  19. Pérez-Vereda A, Flores-Martín D, Canal C, Murillo J. Gerontechnology. View
  20. Theilig M, Blankenhagel K, Zarnekow R. Information Systems and Neuroscience. View
  21. Wolfer J. Online Engineering & Internet of Things. View
  22. Rabbi M, Hane Aung M, Choudhury T. Mobile Health. View
  23. Singh V, Ghosh I. Encyclopedia of Behavioral Medicine. View
  24. Rustagi A, Manchanda C, Sharma N, Kaushik I. International Conference on Innovative Computing and Communications. View
  25. Castro L, Rodríguez M, Martínez F, Rodríguez L, Andrade Á, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. View
  26. Singh V, Ghosh I. Encyclopedia of Behavioral Medicine. View
  27. Rabbi M. Encyclopedia of Behavioral Medicine. View
  28. Harari G, Stachl C, Müller S, Gosling S. The Handbook of Personality Dynamics and Processes. View
  29. Tushar A, Kabir M, Ahmed S. Signal Processing Techniques for Computational Health Informatics. View
  30. Beierle F. Integrating Psychoinformatics with Ubiquitous Social Networking. View
  31. Beierle F. Integrating Psychoinformatics with Ubiquitous Social Networking. View
  32. Flores-Martin D, Laso S, Berrocal J, Murillo J. Gerontechnology III. View
  33. Bickmore T, O'Leary T. Digital Therapeutics for Mental Health and Addiction. View
  34. Dagum P, Montag C. Digital Phenotyping and Mobile Sensing. View
  35. Krajchevska E, Petreska N, Handjiski O, Andovska S, Ilijoski B, Lameski P, Ribarski P, Tojtovska B. ICT Innovations 2021. Digital Transformation. View
  36. Baumeister H, Montag C. Digital Phenotyping and Mobile Sensing. View
  37. Mansoor H, Gerych W, Alajaji A, Buquicchio L, Chandrasekaran K, Agu E, Rundensteiner E. Computer Vision, Imaging and Computer Graphics Theory and Applications. View
  38. Garatva P, Terhorst Y, Messner E, Karlen W, Pryss R, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  39. Marchionatti L, Mastella N, Bouvier V, Passos I. Digital Mental Health. View
  40. Tlachac M, Flores R, Toto E, Rundensteiner E. Deep Learning Applications, Volume 4. View
  41. Ahmed M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
  42. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
  43. Devi D, Naresh R, Kumar C, Senthilkumar S, Jovin A. Technological Tools for Predicting Pregnancy Complications. View
  44. Mondragón-González S, Burguière E, N’diaye K. Machine Learning for Brain Disorders. View
  45. Bhasin H, Chirag , Kumar N, Thakur H. Advanced Computing. View