Published on in Vol 7, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13209, first published .
Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Journals

  1. . Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Advances in Geriatric Medicine and Research 2020 View
  2. Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, Zheng X, Wang T, Lei J. Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed. Journal of Medical Internet Research 2020;22(1):e16816 View
  3. Potier R. The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective. Frontiers in Psychology 2020;11 View
  4. Badal V, Graham S, Depp C, Shinkawa K, Yamada Y, Palinkas L, Kim H, Jeste D, Lee E. Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech. The American Journal of Geriatric Psychiatry 2021;29(8):853 View
  5. H. Birk R, Samuel G. Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’. Sociology of Health & Illness 2020;42(8):1873 View
  6. Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health 2020;23(4):161 View
  7. Jayakumar P, Lin E, Galea V, Mathew A, Panda N, Vetter I, Haynes A. Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review. Journal of Personalized Medicine 2020;10(4):282 View
  8. He-Yueya J, Buck B, Campbell A, Choudhury T, Kane J, Ben-Zeev D, Althoff T. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 2020;6(1) View
  9. Manea V, Wac K. Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method. Journal of Personalized Medicine 2020;10(4):203 View
  10. Kelly J, Campbell K, Gong E, Scuffham P. The Internet of Things: Impact and Implications for Health Care Delivery. Journal of Medical Internet Research 2020;22(11):e20135 View
  11. Mendu S, Baglione A, Baee S, Wu C, Ng B, Shaked A, Clore G, Boukhechba M, Barnes L. A Framework for Understanding the Relationship between Social Media Discourse and Mental Health. Proceedings of the ACM on Human-Computer Interaction 2020;4(CSCW2):1 View
  12. Lee E, Torous J, De Choudhury M, Depp C, Graham S, Kim H, Paulus M, Krystal J, Jeste D. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2021 View
  13. Wu C, Barczyk A, Craddock R, Harari G, Thomaz E, Shumake J, Beevers C, Gosling S, Schnyer D. Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health 2021;20:100180 View
  14. Hilty D, Armstrong C, Luxton D, Gentry M, Krupinski E. A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions. Journal of Technology in Behavioral Science 2021;6(2):278 View
  15. Xu X, Chikersal P, Dutcher J, Sefidgar Y, Seo W, Tumminia M, Villalba D, Cohen S, Creswell K, Creswell J, Doryab A, Nurius P, Riskin E, Dey A, Mankoff J. Leveraging Collaborative-Filtering for Personalized Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1 View
  16. Lekkas D, Jacobson N. Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Scientific Reports 2021;11(1) View
  17. Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840 View
  18. Wetzel B, Pryss R, Baumeister H, Edler J, Gonçalves A, Cohrdes C. “How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(12):6212 View
  19. Escobar-Viera C, Cernuzzi L, Miller R, Rodríguez-Marín H, Vieta E, González Toñánez M, Marsch L, Hidalgo-Mazzei D. Feasibility of mHealth interventions for depressive symptoms in Latin America: a systematic review. International Review of Psychiatry 2021;33(3):300 View
  20. Alshdadi A. Cyber-physical system with IoT-based smart vehicles. Soft Computing 2021 View
  21. 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 View

Books/Policy Documents

  1. Nishiyama Y, Ferreira D, Eigen Y, Sasaki W, Okoshi T, Nakazawa J, Dey A, Sezaki K. Distributed, Ambient and Pervasive Interactions. View
  2. Carretero P, Campana-Montes J, Artes-Rodriguez A. Behavioral Neurobiology of Suicide and Self Harm. View