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

Mobile Health (mhealth) (2748) Digital Biomarkers and Digital Phenotyping (177) Methods and New Tools in Mental Health Research (424) mHealth for Data Collection and Research (907) Wearable Devices and Sensors (811) Behavior Change (652) Mobile Health in Psychiatry (399) Depression and Mood Disorders; Suicide Prevention (1271) Anxiety and Stress Disorders (999)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22844, first published .
Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

Authors of this article:

Jonah Meyerhoff1 Author Orcid Image ;   Tony Liu2 Author Orcid Image ;   Konrad P Kording3, 4 Author Orcid Image ;   Lyle H Ungar2 Author Orcid Image ;   Susan M Kaiser1 Author Orcid Image ;   Chris J Karr5 Author Orcid Image ;   David C Mohr1 Author Orcid Image
Article Authors Cited by (33) Tweetations (25) Metrics

Journals

  1. Hashemi S, Ghazanfari F, Ebrahimzadeh F, Ghavi S, Badrizadeh A. Investigate the relationship between cell-phone over-use scale with depression, anxiety and stress among university students. BMC Psychiatry 2022;22(1) View
  2. Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Frontiers in Psychiatry 2022;13 View
  3. 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
  4. Adler D, Wang F, Mohr D, Estrin D, Livesey C, Choudhury T. A call for open data to develop mental health digital biomarkers. BJPsych Open 2022;8(2) View
  5. 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
  6. Currey D, Torous J. Digital phenotyping correlations in larger mental health samples: analysis and replication. BJPsych Open 2022;8(4) View
  7. Watanabe K, Tsutsumi A. The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study. JMIR Formative Research 2022;6(11):e40339 View
  8. Stamatis C, Meyerhoff J, Liu T, Sherman G, Wang H, Liu T, Curtis B, Ungar L, Mohr D. Prospective associations of text‐message‐based sentiment with symptoms of depression, generalized anxiety, and social anxiety. Depression and Anxiety 2022;39(12):794 View
  9. Schütz N, Knobel S, Botros A, Single M, Pais B, Santschi V, Gatica-Perez D, Buluschek P, Urwyler P, Gerber S, Müri R, Mosimann U, Saner H, Nef T. A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust. npj Digital Medicine 2022;5(1) View
  10. Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Formative Research 2023;7:e42935 View
  11. de Angel V, Lewis S, White K, Matcham F, Hotopf M. Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians. JMIR Mental Health 2022;9(8):e38934 View
  12. 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
  13. D’Mello R, Melcher J, Torous J. Similarity matrix-based anomaly detection for clinical intervention. Scientific Reports 2022;12(1) View
  14. Ono T, Sakurai T, Kasuno S, Murai T. Novel 3-D action video game mechanics reveal differentiable cognitive constructs in young players, but not in old. Scientific Reports 2022;12(1) View
  15. 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
  16. 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
  17. McIntyre R, Greenleaf W, Bulaj G, Taylor S, Mitsi G, Saliu D, Czysz A, Silvesti G, Garcia M, Jain R. Digital health technologies and major depressive disorder. CNS Spectrums 2023;28(6):662 View
  18. Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2023;25:e46778 View
  19. ZhuParris A, de Goede A, Yocarini I, Kraaij W, Groeneveld G, Doll R. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. Sensors 2023;23(11):5243 View
  20. Stamatis C, Liu T, Meyerhoff J, Meng Y, Cho Y, Karr C, Curtis B, Ungar L, Mohr D. Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety. Internet Interventions 2023;34:100683 View
  21. 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
  22. Marin-Dragu S, Forbes A, Sheikh S, Iyer R, Pereira dos Santos D, Alda M, Hajek T, Uher R, Wozney L, Paulovich F, Campbell L, Yakovenko I, Stewart S, Corkum P, Bagnell A, Orji R, Meier S. Associations of active and passive smartphone use with measures of youth mental health during the COVID-19 pandemic. Psychiatry Research 2023;326:115298 View
  23. Kornfield R, Stamatis C, Bhattacharjee A, Pang B, Nguyen T, Williams J, Kumar H, Popowski S, Beltzer M, Karr C, Reddy M, Mohr D, Meyerhoff J. A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype. Internet Interventions 2023;34:100667 View
  24. Nghiem J, Adler D, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians’ Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Formative Research 2023;7:e47380 View
  25. Meyerhoff J, Liu T, Stamatis C, Liu T, Wang H, Meng Y, Curtis B, Karr C, Sherman G, Ungar L, Mohr D. Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?. Behaviour Research and Therapy 2023;166:104342 View
  26. 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
  27. 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
  28. Zierer C, Behrendt C, Lepach-Engelhardt A. Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering. Journal of Affective Disorders 2024;356:438 View
  29. 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
  30. Park S, Song J, Karger D, Malone T. Who2chat: A Social Networking System for Academic Researchers in Virtual Social Hours Enabling Coordinating, Overcoming Barriers and Social Signaling. Proceedings of the ACM on Human-Computer Interaction 2024;8(CSCW1):1 View
  31. Choi A, Ooi A, Lottridge D. Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review. JMIR mHealth and uHealth 2024;12:e40689 View