Published on in Vol 19, No 3 (2017): March

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Journals

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  8. Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Automatic depression prediction using Internet traffic characteristics on smartphones. Smart Health 2020;18:100137 View
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  12. Rohani D, Faurholt-Jepsen M, Kessing L, Bardram J. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR mHealth and uHealth 2018;6(8):e165 View
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  17. Xu X, Chikersal P, Doryab A, Villalba D, Dutcher J, Tumminia M, Althoff T, Cohen S, Creswell K, Creswell J, Mankoff J, Dey A. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(3):1 View
  18. Jacobson N, Summers B, Wilhelm S. Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors. Journal of Medical Internet Research 2020;22(5):e16875 View
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  20. Chow P, Showalter S, Gerber M, Kennedy E, Brenin D, Schroen A, Mohr D, Lattie E, Cohn W. Use of Mental Health Apps by Breast Cancer Patients and Their Caregivers in the United States: Protocol for a Pilot Pre-Post Study. JMIR Research Protocols 2019;8(1):e11452 View
  21. Rashid H, Mendu S, Daniel K, Beltzer M, Teachman B, Boukhechba M, Barnes L. Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1 View
  22. Chan S, Godwin H, Gonzalez A, Yellowlees P, Hilty D. Review of Use and Integration of Mobile Apps Into Psychiatric Treatments. Current Psychiatry Reports 2017;19(12) View
  23. Nicholas J, Shilton K, Schueller S, Gray E, Kwasny M, Mohr D. The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2019;7(4):e12578 View
  24. Montag C, Baumeister H, Kannen C, Sariyska R, Meßner E, Brand M. Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology. J 2019;2(2):102 View
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  26. Harari G, Müller S, Aung M, Rentfrow P. Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences 2017;18:83 View
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  28. Ware S, Yue C, Morillo R, Lu J, Shang C, Kamath J, Bamis A, Bi J, Russell A, Wang B. Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(4):1 View
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  33. Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman M, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751 View
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  38. Chikersal P, Doryab A, Tumminia M, Villalba D, Dutcher J, Liu X, Cohen S, Creswell K, Mankoff J, Creswell J, Goel M, Dey A. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing. ACM Transactions on Computer-Human Interaction 2021;28(1):1 View
  39. 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
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  43. Mei S, Hu Y, Sun M, Fei J, Li C, Liang L, Hu Y. Association between Bullying Victimization and Symptoms of Depression among Adolescents: A Moderated Mediation Analysis. International Journal of Environmental Research and Public Health 2021;18(6):3316 View
  44. 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
  45. 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
  46. Maharjan S, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt B, Hagaman A. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Medical Informatics and Decision Making 2021;21(1) View
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  59. Chikersal P, Venkatesh S, Masown K, Walker E, Quraishi D, Dey A, Goel M, Xia Z. Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping. JMIR Mental Health 2022;9(8):e38495 View
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Books/Policy Documents

  1. Lee J, Lam M, Chiu C. Pervasive Computing Paradigms for Mental Health. View
  2. Hur J, Stockbridge M, Fox A, Shackman A. Emotion and Cognition. View
  3. Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  4. Seidl D. The Geographies of COVID-19. View
  5. Chemagosi M, Barongo S. Student Stress in Higher Education. View
  6. Zafeiridi E, Qirtas M, Bantry White E, Pesch D. Bridging the Gap Between AI and Reality. View