Published on in Vol 24 , No 2 (2022) :February

Preprints (earlier versions) of this paper are available at, first published .
Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review


  1. De La Fabián R, Jiménez-Molina Á, Pizarro Obaid F. A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry. Big Data & Society 2023;10(1):205395172211490 View
  2. Dlima S, Shevade S, Menezes S, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR Bioinformatics and Biotechnology 2022;3(1):e39618 View
  3. Diniz E, Fontenele J, de Oliveira A, Bastos V, Teixeira S, Rabêlo R, Calçada D, dos Santos R, de Oliveira A, Teles A. Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation. Healthcare 2022;10(4):698 View
  4. 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
  5. Elmer T, Lodder G. Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness. Journal of Social and Personal Relationships 2023;40(2):654 View
  6. de Oliveira A, Diniz E, Teixeira S, Teles A. How can machine learning identify suicidal ideation from user's texts? Towards the explanation of the Boamente system. Procedia Computer Science 2022;206:141 View
  7. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  8. Chen Z, Kulkarni P, Galatzer-Levy I, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3(11):100602 View
  9. Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
  10. Bavaresco R, Barbosa J. Ubiquitous computing in light of human phenotypes: foundations, challenges, and opportunities. Journal of Ambient Intelligence and Humanized Computing 2023;14(3):2341 View
  11. Schmidt S, D'Alfonso S. Clinician perspectives on how digital phenotyping can inform client treatment. Acta Psychologica 2023;235:103886 View
  12. Ford T, Buchanan D, Azeez A, Benrimoh D, Kaloiani I, Bandeira I, Hunegnaw S, Lan L, Gholmieh M, Buch V, Williams N. Taking modern psychiatry into the metaverse: Integrating augmented, virtual, and mixed reality technologies into psychiatric care. Frontiers in Digital Health 2023;5 View
  13. Jabir A, Martinengo L, Lin X, Torous J, Subramaniam M, Tudor Car L. Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments. Journal of Medical Internet Research 2023;25:e44548 View
  14. Yeo G, Loo G, Oon M, Pang R, Ho D. A Digital Peer Support Platform to Translate Online Peer Support for Emerging Adult Mental Well-being: Randomized Controlled Trial. JMIR Mental Health 2023;10:e43956 View
  15. Galatzer-Levy I, Onnela J. Machine Learning and the Digital Measurement of Psychological Health. Annual Review of Clinical Psychology 2023;19(1):133 View

Books/Policy Documents

  1. Marchionatti L, Mastella N, Bouvier V, Passos I. Digital Mental Health. View