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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28735, 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

Journals

  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) 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
  16. Marciano L, Vocaj E, Bekalu M, La Tona A, Rocchi G, Viswanath K. The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review. Journal of Medical Internet Research 2023;25:e45540 View
  17. Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. Journal of Medical Internet Research 2023;25:e44502 View
  18. Tani N, Fujihara H, Ishii K, Kamakura Y, Tsunemi M, Yamaguchi C, Eguchi H, Imamura K, Kanamori S, Kojimahara N, Ebara T. What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies. Journal of Occupational Health 2024;66(1) View
  19. Fuhr D, Wolf-Ostermann K, Hoel V, Zeeb H. Digitale Technologien zur Verbesserung der psychischen Gesundheit. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 2024;67(3):332 View
  20. De La Fabián R. Digital Therapeutic Cultures and Their New Regime of Psychological Truth. Sociological Research Online 2024;29(2):299 View
  21. Schmitter-Edgecombe M, Luna C, Dai S, Cook D. Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment. The Clinical Neuropsychologist 2024:1 View
  22. Conrad J. Digitization and its Discontents: The Promise and Limitations of Digital Mental Health Interventions. Journal of Contemporary Psychotherapy 2024;54(3):209 View
  23. Dong T, Yu C, Mao Q, Han F, Yang Z, Yang Z, Pires N, Wei X, Jing W, Lin Q, Hu F, Hu X, Zhao L, Jiang Z. Advances in biosensors for major depressive disorder diagnostic biomarkers. Biosensors and Bioelectronics 2024;258:116291 View
  24. Adhibai R, Kosiyaporn H, Markchang K, Nasueb S, Waleewong O, Suphanchaimat R, Sadek I. Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review. PLOS ONE 2024;19(6):e0304845 View
  25. 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
  26. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  27. Annajigowda H, Chaturvedi S. Digital Mental Health: A Way Forward for Public Mental Health. World Social Psychiatry 2023;5(2):153 View
  28. Leijse M, van Dam L, Jambroes T, Timmerman A, Popma A. Using Active and Passive Smartphone Data to Enhance Adolescents’ Emotional Awareness in Forensic Outpatient Setting: A Qualitative Feasibility and Usability Study. JMIR Formative Research 2024;8:e53613 View
  29. Sonig A, Deeney C, Hurley M, Storch E, Herrington J, Lázaro-Muñoz G, Zampella C, Tunc B, Parish-Morris J, Blumenthal-Barby J, Kostick-Quenet K. What patients and caregivers want to know when consenting to the use of digital behavioral markers. NPP—Digital Psychiatry and Neuroscience 2024;2(1) View
  30. Turner G, Ferguson A, Katiyar T, Palminteri S, Orben A. Old Strategies, New Environments: Reinforcement Learning on Social Media. Biological Psychiatry 2025;97(10):989 View
  31. van Genugten C, Thong M, van Ballegooijen W, Kleiboer A, Spruijt-Metz D, Smit A, Sprangers M, Terhorst Y, Riper H. Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review. Frontiers in Digital Health 2025;7 View
  32. Alt A, Klein C, Pascher A, Conzelmann A, Kosel F, Kühnhausen J, Hollmann K, Renner T. Acceptance of a sensor-based online psychotherapy for adolescents with obsessive-compulsive disorder (SSTeP-KiZ). DIGITAL HEALTH 2025;11 View
  33. Putica A, Yurtbasi M, Khanna R. Integrating digital health technologies for ecological validity in computational psychiatry: challenges and solutions. AI & SOCIETY 2025;40(7):5509 View
  34. Zhang Y, Wang J, Zong H, Singla R, Ullah A, Liu X, Wu R, Ren S, Shen B. The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact. npj Digital Medicine 2025;8(1) View
  35. de Oliveira A, Azevedo J, Ruback L, Moreira R, Teixeira S, Teles A. Effect of Explainable Artificial Intelligence on Trust of Mental Health Professionals in an AI-Based System for Suicide Prevention. IEEE Access 2025;13:60987 View
  36. Botes M. Regulatory challenges of digital health: the case of mental health applications and personal data in South Africa. Frontiers in Pharmacology 2025;16 View
  37. 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. The feasibility of collecting and linking digital phenotyping, clinical, and genetics data for mental health research: A pilot observational study (Preprint). JMIR Formative Research 2025 View
  38. Byun A, Lane E, Langholm C, Flathers M, Hall M, Torous J. Towards clinical subtypes in schizophrenia: integrating cognitive, functional, and digital phenotyping assessments. Molecular Psychiatry 2025;30(10):4641 View
  39. Kim Y, Basu S, Banerjee S. A Co‐Segmentation Algorithm to Predict Emotional Stress From Passively Sensed mHealth Data. Statistics in Medicine 2025;44(10-12) View
  40. Deng H, Kandt J, Signorelli V, Shelton N. Daily mobility, greenspace exposure and affective states: A systematic review of studies that use mobile methods. Landscape and Urban Planning 2025;262:105407 View
  41. Qu A, Wen F, Li B, Li P, Zhang B, Yang X, Yao X, Li B, Lao X, Zhang L. The effects of long-term ambient air pollutant mixture exposure on incident diabetes: A prospective cohort study in China. Ecotoxicology and Environmental Safety 2025;302:118652 View
  42. Hocherman M, Mizrachi Y, Chalutz‐BenGal H. Personality Constructs Predictions Beyond FFM/Big5: A Digital Phenotyping‐Based Exploration. Journal of Personality 2025 View
  43. Zakai J, Alharthi S. Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress. Healthcare 2025;13(16):2008 View
  44. Tan C, Koh J, Ang W, Tan X, Koh S, Lin W, Lee J, Chew H. State-of-the-art digital phenotyping methods for cardiometabolic risk prevention and management: a scoping review. International Journal of Medical Informatics 2026;206:106133 View
  45. Hamitouche D, Zamorano T, Barkat Y, Parekh D, Palaniyappan L, Jalali S, Benrimoh D. Sleep and Activity Patterns as Transdiagnostic Behavioral Biomarkers in Psychiatry: Longitudinal Observational Study From the DeeP-DD Study. JMIR Formative Research 2025;9:e81107 View
  46. Tlachac M, Heinz M, Bryan A, LaPreay A, Dimas G, Zhao T, Jacobson N, Ogden S. Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review. IEEE Transactions on Affective Computing 2025;16(4):2599 View

Books/Policy Documents

  1. Marchionatti L, Mastella N, Bouvier V, Passos I. Digital Mental Health. View
  2. Ahmed M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
  3. Laurindo L, de Moura I, Coutinho L, da Silva e Silva F. Pervasive Computing Technologies for Healthcare. View
  4. Tovino S. Legal Medicine. View
  5. . Metaverse‐Based Digital Twins. View
  6. Bahadure N, Khomane R, Mahadule S, Kalbande S, Shrirao V, Potey S, Dhande S, Khandal S. Proceedings of Data Analytics and Management. View

Conference Proceedings

  1. Mendes J, Silva F, Cardoso A, Moura I, Coutinho L, Viana D, Endler M, Teles A. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). OpenDPMH: A Framework for Developing Mobile Sensing Applications of Digital Phenotyping View
  2. Shrirao V, Parihar B, Rakesh N, Gulhane M, Choudhary S, Agrawal P. 2024 International Conference on Information Science and Communications Technologies (ICISCT). The Impact of Digitalization on Psychological Treatment: Exploring Emerging Technologies in Mental Health Care View
  3. Azevedo J, de Oliveira A, Silva F, Coutinho L, Teles A. 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). Harnessing Generative Llms to Detect and Explain Suicidal Ideation in Brazilian Portuguese Texts View