Published on in Vol 5, No 8 (2017): August

Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety

Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety

Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety

Journals

  1. Berman A, Carter G. Technological Advances and the Future of Suicide Prevention: Ethical, Legal, and Empirical Challenges. Suicide and Life-Threatening Behavior 2020;50(3):643 View
  2. Thong M, Chan R, van den Hurk C, Fessele K, Tan W, Poprawski D, Fernández-Ortega P, Paterson C, Fitch M. Going beyond (electronic) patient-reported outcomes: harnessing the benefits of smart technology and ecological momentary assessment in cancer survivorship research. Supportive Care in Cancer 2021;29(1):7 View
  3. Jones M, Johnson M, Shervey M, Dudley J, Zimmerman N. Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept. Journal of Medical Internet Research 2019;21(8):e13600 View
  4. Yim S, Lui L, Lee Y, Rosenblat J, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho R, Coles A, Majeed A, Wong E, Phan L, Nasri F, McIntyre R. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602 View
  5. Montag C, Sindermann C, Baumeister H. Digital phenotyping in psychological and medical sciences: a reflection about necessary prerequisites to reduce harm and increase benefits. Current Opinion in Psychology 2020;36:19 View
  6. 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
  7. 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
  8. Jim H, Hoogland A, Brownstein N, Barata A, Dicker A, Knoop H, Gonzalez B, Perkins R, Rollison D, Gilbert S, Nanda R, Berglund A, Mitchell R, Johnstone P. Innovations in research and clinical care using patient‐generated health data. CA: A Cancer Journal for Clinicians 2020;70(3):182 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. Boukhechba M, Chow P, Fua K, Teachman B, Barnes L. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Mental Health 2018;5(3):e10101 View
  11. 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
  12. Pisco Almeida A, Almeida H, Figueiredo-Braga M. Mobile solutions in depression: enhancing communication with patients using an SMS-based intervention. Procedia Computer Science 2018;138:89 View
  13. Liu T, Nicholas J, Theilig M, Guntuku S, Kording K, Mohr D, Ungar L. Machine Learning for Phone-Based Relationship Estimation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(4):1 View
  14. Nepal S, Mirjafari S, Martinez G, Audia P, Striegel A, Campbell A. Detecting Job Promotion in Information Workers Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1 View
  15. Pratap A, Atkins D, Renn B, Tanana M, Mooney S, Anguera J, Areán P. The accuracy of passive phone sensors in predicting daily mood. Depression and Anxiety 2019;36(1):72 View
  16. Burgess E, Ringland K, Nicholas J, Knapp A, Eschler J, Mohr D, Reddy M. "I think people are powerful". Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1 View
  17. Miralles I, Granell C. Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions. International Journal of Environmental Research and Public Health 2019;16(7):1197 View
  18. 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
  19. Pulantara I, Parmanto B, Germain A. Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study. JMIR Human Factors 2018;5(2):e21 View
  20. Pulantara I, Parmanto B, Germain A. Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study. Journal of Medical Internet Research 2018;20(12):e10124 View
  21. Luo X, Chen Z. English text quality analysis based on recurrent neural network and semantic segmentation. Future Generation Computer Systems 2020;112:507 View
  22. dos Santos Paula L, Barbosa J, Dias L. A model for assisting in the treatment of anxiety disorder. Universal Access in the Information Society 2022;21(2):533 View
  23. Sheu Y. Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research. Frontiers in Psychiatry 2020;11 View
  24. 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
  25. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  26. 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
  27. Montag C, Rumpf H. The Potential of Digital Phenotyping and Mobile Sensing for Psycho-Diagnostics of Internet Use Disorders. Current Addiction Reports 2021;8(3):422 View
  28. Teepe G, Da Fonseca A, Kleim B, Jacobson N, Salamanca Sanabria A, Tudor Car L, Fleisch E, Kowatsch T. Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review. Journal of Medical Internet Research 2021;23(9):e29412 View
  29. D’Mello R, Melcher J, Torous J. Similarity matrix-based anomaly detection for clinical intervention. Scientific Reports 2022;12(1) View
  30. Hart A, Reis D, Prestele E, Jacobson N. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment. Journal of Medical Internet Research 2022;24(4):e34015 View
  31. van Berkel N, D’Alfonso S, Kurnia Susanto R, Ferreira D, Kostakos V. AWARE-Light: a smartphone tool for experience sampling and digital phenotyping. Personal and Ubiquitous Computing 2023;27(2):435 View
  32. 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
  33. Baglione A, Cai L, Bahrini A, Posey I, Boukhechba M, Chow P. Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study. JMIR Medical Informatics 2022;10(6):e30712 View
  34. Montag C, Elhai J, Dagum P. On Blurry Boundaries When Defining Digital Biomarkers: How Much Biology Needs to Be in a Digital Biomarker?. Frontiers in Psychiatry 2021;12 View
  35. Li T, Zhang M, Li Y, Lagerspetz E, Tarkoma S, Hui P. The Impact of Covid-19 on Smartphone Usage. IEEE Internet of Things Journal 2021;8(23):16723 View
  36. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042 View
  37. Jacobson N, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry 2022;12(1) View
  38. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  39. 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
  40. Montag C, Dagum P, Hall B, Elhai J. How the study of digital footprints can supplement research in behavioral genetics and molecular psychology. Molecular Psychology: Brain, Behavior, and Society 2022;1:2 View
  41. Highland D, Zhou G. A review of detection techniques for depression and bipolar disorder. Smart Health 2022;24:100282 View
  42. Paula L, Pfeiffer Salomão Dias L, Francisco R, Barbosa J. Analysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy. International Journal of Human–Computer Interaction 2022:1 View
  43. Hong J, Kim J, Kim S, Oh J, Lee D, Lee S, Uh J, Yoon J, Choi Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare 2022;10(7):1189 View
  44. Asani F, Patel B, Srikanthan S, Agu E. BioscoreNet: Traumatic Brain Injury (TBI) detection using a multimodal self-attention fusion neural network and a passive bioscore monitoring framework from smartphone sensor data. Smart Health 2023;27:100352 View
  45. Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Formative Research 2023;7:e43296 View
  46. Meyerhoff J, Liu T, Kording K, Ungar L, Kaiser S, Karr C, Mohr D. Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(9):e22844 View
  47. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  48. Memon A, Kilby J, Breñosa J, Espinosa J, Ashraf I. Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix. Sensors 2022;22(24):9898 View
  49. 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
  50. Zou B, Zhang X, Xiao L, Bai R, Li X, Liang H, Ma H, Wang G. Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:1786 View
  51. Matz S, Beck E, Atherton O, White M, Rauthmann J, Mroczek D, Kim M, Bogg T. Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale. Perspectives on Psychological Science 2023 View
  52. Shin J, Bae S. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984 View
  53. Zhuparris A, Maleki G, van Londen L, Koopmans I, Aalten V, Yocarini I, Exadaktylos V, van Hemert A, Cohen A, Gal P, Doll R, Groeneveld G, Jacobs G, Kraaij W. A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity. Scientific Reports 2023;13(1) View
  54. Panlilio L, Burgess-Hull A, Feldman J, Rogers J, Tyburski M, Smith K, Epstein D. Activity space during treatment with medication for opioid use disorder: Relationships with personality, mood, and drug use. Journal of Substance Use and Addiction Treatment 2024;157:209219 View
  55. Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. npj Mental Health Research 2023;2(1) View
  56. Alamoudi D, Nabney I, Crawley E. Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances. Sensors 2024;24(3):722 View

Books/Policy Documents

  1. Iyawa G, Ondiek C, Osakwe J. Smart Medical Data Sensing and IoT Systems Design in Healthcare. View
  2. Teles A, Barros F, Rodrigues I, Barbosa A, Silva F, Coutinho L, Teixeira S. IoT and ICT for Healthcare Applications. View
  3. Heinz M, Price G, Song S, Bhattacharya S, Jacobson N. Digital Mental Health. View