Published on in Vol 7, No 1 (2019): January

Preprints (earlier versions) of this paper are available at, first published .
Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study

Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study

Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study


  1. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  2. Radhakrishnan K, Kim M, Burgermaster M, Brown R, Xie B, Bray M, Fournier C. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;68(5):548 View
  3. Kaonga N, Morgan J. Common themes and emerging trends for the use of technology to support mental health and psychosocial well-being in limited resource settings: A review of the literature. Psychiatry Research 2019;281:112594 View
  4. Howard D, Folkersen L, Coleman J, Adams M, Glanville K, Werge T, Hagenaars S, Han B, Porteous D, Campbell A, Clarke T, Breen G, Sullivan P, Wray N, Lewis C, McIntosh A. Genetic stratification of depression in UK Biobank. Translational Psychiatry 2020;10(1) View
  5. Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454 View
  6. Rodriguez-León C, Villalonga C, Munoz-Torres M, Ruiz J, Banos O. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR mHealth and uHealth 2021;9(6):e25138 View
  7. de Moura I, Teles A, Endler M, Coutinho L, da Silva e Silva F. Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals. Sensors 2020;21(1):86 View
  8. Yu J, Chiu C, Wang Y, Dzubur E, Lu W, Hoffman J. A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis. Journal of Medical Internet Research 2021;23(8):e27709 View
  9. Vlisides-Henry R, Gao M, Thomas L, Kaliush P, Conradt E, Crowell S. Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk. Frontiers in Psychiatry 2021;12 View
  10. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668 View
  11. Xu X, Mankoff J, Dey A. Understanding practices and needs of researchers in human state modeling by passive mobile sensing. CCF Transactions on Pervasive Computing and Interaction 2021;3(4):344 View
  12. Mendes J, Moura I, Van de Ven P, Viana D, Silva F, Coutinho L, Teixeira S, Rodrigues J, Teles A. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735 View
  13. Choudhary S, Thomas N, Ellenberger J, Srinivasan G, Cohen R. A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study. JMIR Formative Research 2022;6(5):e37736 View
  14. MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638 View
  15. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  16. Roca S, Lozano M, García J, Alesanco Á. Validation of a Virtual Assistant for Improving Medication Adherence in Patients with Comorbid Type 2 Diabetes Mellitus and Depressive Disorder. International Journal of Environmental Research and Public Health 2021;18(22):12056 View
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  18. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  19. Nagpal M, Barbaric A, Sherifali D, Morita P, Cafazzo J. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021;6(4):e29027 View
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  21. Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
  22. Cao X, Liu X. Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges. World Journal of Psychiatry 2022;12(10):1287 View
  23. Harvey P, Depp C, Rizzo A, Strauss G, Spelber D, Carpenter L, Kalin N, Krystal J, McDonald W, Nemeroff C, Rodriguez C, Widge A, Torous J. Technology and Mental Health: State of the Art for Assessment and Treatment. American Journal of Psychiatry 2022;179(12):897 View
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Books/Policy Documents

  1. Ahmed M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
  2. Bourkhime H, Qarmiche N, Bahra N, Omari M, Chakri I, Berraho M, Tachfouti N, Fakir S, Otmani N. Artificial Intelligence, Data Science and Applications. View