Published on in Vol 5, No 3 (2018): Jul-Sept

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10101, first published .
Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Journals

  1. Obuchi M, Huckins J, Wang W, daSilva A, Rogers C, Murphy E, Hedlund E, Holtzheimer P, Mirjafari S, Campbell A. Predicting Brain Functional Connectivity Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
  2. Bickman L. Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. Administration and Policy in Mental Health and Mental Health Services Research 2020;47(5):795 View
  3. 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
  4. Boukhechba M, Cai L, Wu C, Barnes L. ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors. Smart Health 2019;14:100082 View
  5. Pastor N, Khalilian E, Caballeria E, Morrison D, Sanchez Luque U, Matrai S, Gual A, López-Pelayo H. Remote Monitoring Telemedicine (REMOTE) Platform for Patients With Anxiety Symptoms and Alcohol Use Disorder: Protocol for a Case-Control Study. JMIR Research Protocols 2020;9(6):e16964 View
  6. 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
  7. 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
  8. 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
  9. 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
  10. Baglione A, Gong J, Boukhechba M, Wells K, Barnes L. Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence. IEEE Pervasive Computing 2020;19(3):24 View
  11. 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
  12. 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
  13. Aydemir T, Şahin M, Aydemir O. A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor. Journal of Medical and Biological Engineering 2020;40(6):934 View
  14. Parrish E, Depp C, Moore R, Harvey P, Mikhael T, Holden J, Swendsen J, Granholm E. Emotional determinants of life-space through GPS and ecological momentary assessment in schizophrenia: What gets people out of the house?. Schizophrenia Research 2020;224:67 View
  15. Ueafuea K, Boonnag C, Sudhawiyangkul T, Leelaarporn P, Gulistan A, Chen W, Mukhopadhyay S, Wilaiprasitporn T, Piyayotai S. Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review. IEEE Sensors Journal 2021;21(6):7162 View
  16. Mansoor H, Gerych W, Alajaji A, Buquicchio L, Chandrasekaran K, Agu E, Rundensteiner E. ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms. Visual Informatics 2021;5(3):39 View
  17. Alessandrini M, Biagetti G, Crippa P, Falaschetti L, Turchetti C. Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data. Electronics 2021;10(14):1715 View
  18. Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman M, Rose J. Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study. Journal of Medical Internet Research 2021;23(8):e28918 View
  19. Mansoor H, Gerych W, Alajaji A, Buquicchio L, Chandrasekaran K, Agu E, Rundensteiner E, Rodriguez A. INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping. Visual Informatics 2023;7(2):13 View
  20. Hnoohom N, Mekruksavanich S, Jitpattanakul A. Physical Activity Recognition Based on Deep Learning Using Photoplethysmography and Wearable Inertial Sensors. Electronics 2023;12(3):693 View
  21. 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
  22. Nowrouzi-Kia B, Stier J, Ayyoub L, Hutchinson L, Laframboise J, Mihailidis A. The Characteristics of Canadian University Students’ Mental Health, Engagement in Activities and Use of Smartphones: A descriptive pilot study. Health Psychology Open 2021;8(2) View
  23. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  24. Chen C, Lou S, Kaewkitipong L. Online social anxiety and mobile instant messaging adoption and continuance usage intention: How does it relate to social, technical, and mobility factors?. Cogent Business & Management 2022;9(1) View
  25. 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
  26. Ibrahim A, Zhang H, Clinch S, Harper S. From GPS to semantic data: how and why—a framework for enriching smartphone trajectories. Computing 2021;103(12):2763 View
  27. Jacobson N, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy 2022;149:104013 View
  28. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  29. Jiang J, Meng Q, Ji J. Combining Music and Indoor Spatial Factors Helps to Improve College Students’ Emotion During Communication. Frontiers in Psychology 2021;12 View
  30. Dong G, Tang M, Wang Z, Gao J, Guo S, Cai L, Gutierrez R, Campbel B, Barnes L, Boukhechba M. Graph Neural Networks in IoT: A Survey. ACM Transactions on Sensor Networks 2023;19(2):1 View
  31. Lee K, Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas M, Wright A, Demiris G, Ritchie C, Pickering C, Nicholas Dionne-Odom J. Using digital phenotyping to understand health-related outcomes: A scoping review. International Journal of Medical Informatics 2023;174:105061 View
  32. Ramzan M, Hamid M, Alhussan A, AlEisa H, Abdallah H. Accurate Prediction of Anxiety Levels in Asian Countries Using a Fuzzy Expert System. Healthcare 2023;11(11):1594 View
  33. Stamatis C, Liu T, Meyerhoff J, Meng Y, Cho Y, Karr C, Curtis B, Ungar L, Mohr D. Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety. Internet Interventions 2023;34:100683 View
  34. Li C, Xu B, Chen Z, Huang X, He J, Xie X. A Stacking Model-Based Classification Algorithm Is Used to Predict Social Phobia. Applied Sciences 2024;14(1):433 View
  35. Gopalakrishnan A, Gururajan R, Zhou X, Venkataraman R, Chan K, Higgins N. A survey of autonomous monitoring systems in mental health. WIREs Data Mining and Knowledge Discovery 2024;14(3) View
  36. Fernández-Álvarez J, Colombo D, Gómez Penedo J, Pierantonelli M, Baños R, Botella C. Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review. JMIR Mental Health 2024;11:e46593 View
  37. Dev A, Broos H, Llabre M, Saab P, Timpano K. Risk estimation in relation to anxiety and depression for low probability negative events. Behaviour Research and Therapy 2024;176:104500 View
  38. Musella K, DiFonte M, Michel R, Stamates A, Flannery-Schroeder E. Emotion regulation as a mediator in the relationship between childhood maltreatment and symptoms of social anxiety among college students. Journal of American College Health 2024:1 View
  39. Najeh H, Lohr C, Leduc B. Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study. Applied Sciences 2024;14(6):2377 View
  40. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Ibrahim O, Rosenberg J, O’Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12-25 Years): A Scoping Review. SSRN Electronic Journal 2024 View
  41. Choi H, Cho Y, Min C, Kim K, Kim E, Lee S, Kim J. Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning. DIGITAL HEALTH 2024;10 View
  42. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Dabash O, Rosenberg J, O'Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review. Heliyon 2024;10(15):e35472 View
  43. 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
  44. Thahır A, Bulantika S, Wahyuni C, Kamran M. Reduction of Social Anxiety and Increase in Academic Adjustment through Motivational Enhancement Therapy in Freshmen College students in Indonesia. Türk Psikolojik Danışma ve Rehberlik Dergisi 2024;14(74):415 View
  45. Boukhechba M, Baglione A, Barnes L. Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care. Ergonomics in Design: The Quarterly of Human Factors Applications 2020;28(4):18 View
  46. Yi L, Hart J, Straczkiewicz M, Karas M, Wilt G, Hu C, Librett R, Laden F, Chavarro J, Onnela J, James P. Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults. JMIR Public Health and Surveillance 2024;10:e55170 View
  47. Sanjay Suryawanshi N. Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning. International Journal of Innovative Science and Research Technology (IJISRT) 2024:1334 View

Books/Policy Documents

  1. Hur J, Stockbridge M, Fox A, Shackman A. Emotion and Cognition. View
  2. Boukhechba M, Barnes L. Advances in Usability, User Experience, Wearable and Assistive Technology. View
  3. Psathas A, Papaleonidas A, Iliadis L. Computational Collective Intelligence. View
  4. Garatva P, Terhorst Y, Messner E, Karlen W, Pryss R, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  5. Mansoor H, Gerych W, Alajaji A, Buquicchio L, Chandrasekaran K, Agu E, Rundensteiner E. Computer Vision, Imaging and Computer Graphics Theory and Applications. View
  6. Mondragón-González S, Burguière E, N’diaye K. Machine Learning for Brain Disorders. View