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

Wearables and MHealth Reviews (310) Digital Biomarkers and Digital Phenotyping (283) Reviews in Digital Mental Health (289) mHealth for Wellness, Behavior Change and Prevention (3313) mHealth for Data Collection and Research (1196) Anxiety and Stress Disorders (1327) Depression and Mood Disorders; Suicide Prevention (1688) Wearable Devices and Sensors (1133)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40689, first published .
Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review

Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review

Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review

Authors of this article:

Adrien Choi1 Author Orcid Image ;   Aysel Ooi1 Author Orcid Image ;   Danielle Lottridge1 Author Orcid Image
Article Authors Cited by (26) Tweetations (4) Metrics

Journals

  1. Karas M, Huang D, Clement Z, Millner A, Kleiman E, Bentley K, Zuromski K, Fortgang R, DeMarco D, Haim A, Donovan A, Buonopane R, Bird S, Smoller J, Nock M, Onnela J. Smartphone Screen Time Characteristics in People With Suicidal Thoughts: Retrospective Observational Data Analysis Study. JMIR mHealth and uHealth 2024;12:e57439 View
  2. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View
  3. Odom J, Lee K, Currie E, Allen-Watts K, Harrell E, Bechthold A, Engler S, Curry K, Kamal A, Ritchie C, Demiris G, Wright A, Bakitas M, Azuero A. Feasibility and Acceptability of Collecting Passive Smartphone Data for Potential Use in Digital Phenotyping Among Family Caregivers and Patients With Advanced Cancer. JCO Clinical Cancer Informatics 2025;(9) View
  4. Rocchi G, Vocaj E, Moawad S, Antonucci A, Grigioni C, Giuffrida V, Bordini J. Optimizing personalized psychological well-being interventions through digital phenotyping: results from a randomized non-clinical trial. Frontiers in Psychology 2025;15 View
  5. Bello C, Eisler P, Heidegger T. Perioperative Anxiety: Current Status and Future Perspectives. Journal of Clinical Medicine 2025;14(5):1422 View
  6. Heckler W, Feijó L, de Carvalho J, Barbosa J. Digital phenotyping for mental health based on data analytics: A systematic literature review. Artificial Intelligence in Medicine 2025;163:103094 View
  7. Odom J, Lee K, Harrell E, Watts K, Bechthold A, Engler S, Puga F, Bibriescas N, Kamal A, Ritchie C, Demiris G, Wright A, Bakitas M, Azuero A. Associations between smartphone GPS data and changes in psychological health and burden outcomes among family caregivers and patients with advanced cancer: an exploratory longitudinal cohort study. BMC Cancer 2025;25(1) View
  8. Lialiou P, Maglogiannis I. Students’ Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review. AI Sensors 2025;1(1):2 View
  9. Beltrán J, Jacob Y, Mehta M, Hossain T, Adams A, Fontaine S, Torous J, McDonough C, Johnson M, Delgado A, Murrough J, Morris L. Digital measures of activity and motivation impact depression and anxiety in the real world. npj Digital Medicine 2025;8(1) View
  10. Torous J, Linardon J, Goldberg S, Sun S, Bell I, Nicholas J, Hassan L, Hua Y, Milton A, Firth J. The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality. World Psychiatry 2025;24(2):156 View
  11. Ciharova M, Amarti K, van Breda W, Gevonden M, Ghassemi S, Kleiboer A, Vinkers C, Sep M, Trofimova S, Cooper A, Peng X, Schulte M, Karyotaki E, Cuijpers P, Riper H. Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned. Frontiers in Psychiatry 2025;16 View
  12. Beames J, Dabash O, Spoelma M, Shvetcov A, Zheng W, Slade A, Han J, Hoon L, Kupper J, Parker R, Mitchell B, Martin N, Newby J, Whitton A, Christensen H. Feasibility of Collecting and Linking Digital Phenotyping, Clinical, and Genetics Data for Mental Health Research: Pilot Observational Study. JMIR Formative Research 2025;9:e71377 View
  13. Ringwald W, King G, Vize C, Wright A. Passive Smartphone Sensors for Detecting Psychopathology. JAMA Network Open 2025;8(7):e2519047 View
  14. Shen S, Qi W, Zeng J, Li S, Liu X, Zhu X, Dong C, Wang B, Shi Y, Yao J, Wang B, Lou X, Gu S, Li P, Wang J, Jiang G, Cao S. Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review. Journal of Medical Internet Research 2025;27:e77066 View
  15. Leimhofer J, Petrovic M, Dominik A, Heider D, Hegerl U. Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review. Interactive Journal of Medical Research 2025;14:e69686 View
  16. Zakai J, Alharthi S. Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress. Healthcare 2025;13(16):2008 View
  17. Jiang B, Zhang Y, Xie Z, Wu Z, Ma Y, Zhang X, Feng Y. A novel non-contact screening tool based on Vibraimage technology for detecting depressive disorder in psychiatric outpatients: A diagnostic accuracy study. Journal of Affective Disorders 2026;392:120232 View
  18. Jung H, Kim D, Lee I, Kim O, Lee S, Lee S, Chung U, Kim J, Kim S, Kim J, Shin A, Lee J. Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2025;27:e77331 View
  19. Moreira L, Sá M, de Mendonça L, Raddaoui L, Elabd N, Rahman Z, Silva Júnior J, Campos L, Baltatu O. Predictive modeling of depression and anxiety after myocardial infarction: a study on influential factors and heart rate variability. Health Information Science and Systems 2025;13(1) View
  20. Choi A, Lottridge D, Warren J. Personalised modelling of routine variability and affective states. npj Digital Medicine 2025;8(1) View
  21. Mok C, Cheng C, Chu M. Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis. Frontiers in Psychiatry 2025;16 View
  22. Ospina-Pinillos L, Shambo-Rodríguez D, Riaño-Fonseca M, Sánchez Nítola M, Ramírez-Castro M, Calvo-Valderrama M, Camacho S, Gómez-Restrepo C, Navarro-Mancilla A, Hickie I, Occhipinti J. Implementing digital tools for mental health support in young individuals in Colombia: a mixed-methods feasibility study (Preprint). JMIR Formative Research 2024 View
  23. Félix G, Silva G, Meneses D, Lopez L. Efeito preditivo do fenótipo digital no diagnóstico do Transtorno do Espectro Autista: uma revisão sistemática. Saúde em Debate 2025;49(spe1) View
  24. Bögemann S, Krause F, van Kraaij A, Marciniak M, van Leeuwen J, Weermeijer J, Mituniewicz J, Puhlmann L, Zerban M, Reppmann Z, Kobylińska D, Yuen K, Kleim B, Walter H, Myin-Germeys I, Kalisch R, Veer I, Roelofs K, Hermans E. Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress: Methodological development and longitudinal multicenter evaluation. Behavior Research Methods 2025;58(1) View

Books/Policy Documents

  1. Goyal S, Fiorini L. Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises. View

Conference Proceedings

  1. Joshi Y, Gupta N, Mishra D. 2025 World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS). The Role of Artificial Intelligence in Mental Health Support: Present Implementations, Innovative Tools, and Research Horizons View