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

  1. 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
  2. Jacobson N, Chung Y. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020;20(12):3572 View
  3. Ware S, Yue C, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Predicting depressive symptoms using smartphone data. Smart Health 2020;15:100093 View
  4. Paolillo E, Tang B, Depp C, Rooney A, Vaida F, Kaufmann C, Mausbach B, Moore D, Moore R. Temporal Associations Between Social Activity and Mood, Fatigue, and Pain in Older Adults With HIV: An Ecological Momentary Assessment Study. JMIR Mental Health 2018;5(2):e38 View
  5. . Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Advances in Geriatric Medicine and Research 2020 View
  6. Wang R, Wang W, daSilva A, Huckins J, Kelley W, Heatherton T, Campbell A. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  7. Ike K, de Boer S, Buwalda B, Kas M. Social withdrawal: An initially adaptive behavior that becomes maladaptive when expressed excessively. Neuroscience & Biobehavioral Reviews 2020;116:251 View
  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
  9. Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819 View
  10. Calvo R, Dinakar K, Picard R, Christensen H, Torous J. Toward Impactful Collaborations on Computing and Mental Health. Journal of Medical Internet Research 2018;20(2):e49 View
  11. Friedmann F, Santangelo P, Ebner-Priemer U, Hill H, Neubauer A, Rausch S, Steil R, Müller-Engelmann M, Kleindienst N, Bohus M, Fydrich T, Priebe K, Matsumura K. Life within a limited radius: Investigating activity space in women with a history of child abuse using global positioning system tracking. PLOS ONE 2020;15(5):e0232666 View
  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
  13. Chow P, Drago F, Kennedy E, Cohn W. A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women’s Cancer: Pre-Post Pilot Study. JMIR Cancer 2020;6(1):e15750 View
  14. Agarwal R, Dugas M, Gao G, Kannan P. Emerging technologies and analytics for a new era of value-centered marketing in healthcare. Journal of the Academy of Marketing Science 2020;48(1):9 View
  15. Dejonckheere E, Mestdagh M, Houben M, Rutten I, Sels L, Kuppens P, Tuerlinckx F. Complex affect dynamics add limited information to the prediction of psychological well-being. Nature Human Behaviour 2019;3(5):478 View
  16. Cai L, Boukhechba M, Gerber M, Barnes L, Showalter S, Cohn W, Chow P. An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients. Smart Health 2020;15:100086 View
  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
  19. Armstrong C, Ciulla R, Williams S, Micheel L. An Applied Test of Knowledge Translation Methods Using a Mobile Health Solution. Military Medicine 2020;185(Supplement_1):526 View
  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
  25. Peleh T, Ike K, Frentz I, Buwalda B, de Boer S, Hengerer B, Kas M. Cross-site Reproducibility of Social Deficits in Group-housed BTBR Mice Using Automated Longitudinal Behavioural Monitoring. Neuroscience 2020;445:95 View
  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
  27. Watson R, Christensen J. Big data and student engagement among vulnerable youth: A review. Current Opinion in Behavioral Sciences 2017;18:23 View
  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
  29. McQuoid J, Thrul J, Ling P. A geographically explicit ecological momentary assessment (GEMA) mixed method for understanding substance use. Social Science & Medicine 2018;202:89 View
  30. Sano A, Taylor S, McHill A, Phillips A, Barger L, Klerman E, Picard R. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study. Journal of Medical Internet Research 2018;20(6):e210 View
  31. Chan S, Li L, Torous J, Gratzer D, Yellowlees P. Review of Use of Asynchronous Technologies Incorporated in Mental Health Care. Current Psychiatry Reports 2018;20(10) View
  32. Poudyal A, van Heerden A, Hagaman A, Maharjan S, Byanjankar P, Subba P, Kohrt B. Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal. JMIR Research Protocols 2019;8(8):e14734 View
  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
  34. Hur J, DeYoung K, Islam S, Anderson A, Barstead M, Shackman A. Social context and the real-world consequences of social anxiety. Psychological Medicine 2020;50(12):1989 View
  35. Weingarden H, Matic A, Calleja R, Greenberg J, Harrison O, Wilhelm S. Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial. JMIR mHealth and uHealth 2020;8(6):e16350 View
  36. Garcia-Ceja E, Riegler M, Nordgreen T, Jakobsen P, Oedegaard K, Tørresen J. Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing 2018;51:1 View
  37. 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
  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
  40. dos Santos Paula L, Barbosa J, Dias L. A model for assisting in the treatment of anxiety disorder. Universal Access in the Information Society 2021 View
  41. Page-Reeves J, Murray-Krezan C, Regino L, Perez J, Bleecker M, Perez D, Wagner B, Tigert S, Bearer E, Willging C. A randomized control trial to test a peer support group approach for reducing social isolation and depression among female Mexican immigrants. BMC Public Health 2021;21(1) View
  42. 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
  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 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
  47. Melcher J, Lavoie J, Hays R, D'Mello R, Rauseo-Ricupero N, Camacho E, Rodriguez-Villa E, Wisniewski H, Lagan S, Vaidyam A, Torous J. Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students. Journal of American College Health 2021:1 View

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