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

New Methods (944) Mobile Health (mhealth) (2939) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) Depression and Mood Disorders; Suicide Prevention (1363)

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 (511) Tweetations (322) Metrics

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  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
  370. Terhorst Y, Knauer J, Philippi P, Baumeister H. The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e51875 View
  371. Mohr D, Weingardt K, Reddy M, Schueller S. Three Problems With Current Digital Mental Health Research . . . and Three Things We Can Do About Them. Psychiatric Services 2017;68(5):427 View
  372. Rodman A, Burns J, Cotter G, Ohashi Y, Rich R, McLaughlin K. Within-Person Fluctuations in Objective Smartphone Use and Emotional Processes During Adolescence: An Intensive Longitudinal Study. Affective Science 2024 View
  373. Bidargaddi N, Leibbrandt R, Paget T, Verjans J, Looi J, Lipschitz J. Remote sensing mental health: A systematic review of factors essential to clinical translation from validation research. DIGITAL HEALTH 2024;10 View
  374. Fairburn C, Patel V. The Impact of Digital Technology on Psychological Treatments and Their Dissemination. Focus 2018;16(4):449 View
  375. 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: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. GetMobile: Mobile Computing and Communications 2024;28(2):23 View
  376. Shin D, Kim H, Lee S, Cho Y, Jung W. Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study. Journal of Medical Internet Research 2024;26:e54617 View
  377. Shukla A, Srivastava A. Immersive Healing: Examining the Effectiveness of Cognitive Behavioral Therapy Using Virtual Reality to Reduce Cognitive Distortions. Augmented Human Research 2024;9(1) View
  378. Lamichhane B, Moukaddam N, Sabharwal A. Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions. Scientific Reports 2024;14(1) View
  379. Shvetcov A, Funke Kupper J, Zheng W, Slade A, Han J, Whitton A, Spoelma M, Hoon L, Mouzakis K, Vasa R, Gupta S, Venkatesh S, Newby J, Christensen H. Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping. Frontiers in Psychiatry 2024;15 View
  380. Thomas E, Yang M, Contractor A, Weiss N. Examining the proximal relationship between religious coping and depression among trauma-exposed adults. Mental Health, Religion & Culture 2024:1 View
  381. Guerreiro J, Garriga R, Lozano Bagén T, Sharma B, Karnik N, Matić A. Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises. npj Digital Medicine 2024;7(1) View
  382. Chitale V, Henry J, Liang H, Matthews B, Baghaei N. Virtual reality analytics map (VRAM): A conceptual framework for detecting mental disorders using virtual reality data. New Ideas in Psychology 2025;76:101127 View
  383. Müller S, Peters H, Matz S, Wang W, Harari G. Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking. European Journal of Personality 2020;34(5):714 View
  384. Rodman A, Vidal Bustamante C, Dennison M, Flournoy J, Coppersmith D, Nook E, Worthington S, Mair P, McLaughlin K. A Year in the Social Life of a Teenager: Within-Persons Fluctuations in Stress, Phone Communication, and Anxiety and Depression. Clinical Psychological Science 2021;9(5):791 View
  385. Avramidis K, Kunc D, Perz B, Adsul K, Feng T, Kazienko P, Saganowski S, Narayanan S. Scaling Representation Learning From Ubiquitous ECG With State-Space Models. IEEE Journal of Biomedical and Health Informatics 2024;28(10):5877 View
  386. Stiles-Shields C, Montague E, Lattie E, Kwasny M, Mohr D. What might get in the way: Barriers to the use of apps for depression. DIGITAL HEALTH 2017;3 View
  387. Mohammed Mahdi Allarakhia , Mubashira Shaikh , Hussain Sidhpurwala , Ayesha Sayyed , Dr. Ashfaq Shaikh . Depression Detection through Integrative Multimodal Signals: Exploring Advanced Computational Techniques. International Journal of Advanced Research in Science, Communication and Technology 2024:194 View
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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
  46. Shaker R, Ibrahim N, Abdennadher S. Proceedings of the Third International Conference on Innovations in Computing Research (ICR’24). View
  47. Zafeiridi E, Qirtas M, Bantry White E, Pesch D. Bridging the Gap Between AI and Reality. View
  48. Farnaz N, Guru S, Prakash A, Tripathy H, Yang T, Wang L, Rathore B. Proceedings of Fifth Doctoral Symposium on Computational Intelligence. View