Published on in Vol 17, No 7 (2015): July

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Journals

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  44. Dogrucu A, Perucic A, Isaro A, Ball D, Toto E, Rundensteiner E, Agu E, Davis-Martin R, Boudreaux E. Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health 2020;17:100118 View
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  47. Suffoletto B, Aguilera A. Expanding Adolescent Depression Prevention Through Simple Communication Technologies. Journal of Adolescent Health 2016;59(4):373 View
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  134. Adler D, Ben-Zeev D, Tseng V, Kane J, Brian R, Campbell A, Hauser M, Scherer E, Choudhury T. Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks. JMIR mHealth and uHealth 2020;8(8):e19962 View
  135. Lu J, Shang C, Yue C, Morillo R, Ware S, Kamath J, Bamis A, Russell A, Wang B, Bi J. Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  136. Saeb S, Cybulski T, Kording K, Mohr D. Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles. Journal of Medical Internet Research 2017;19(4):e118 View
  137. 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
  138. Johansen B, Petersen M, Korzepa M, Larsen J, Pontoppidan N, Larsen J. Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data. Computers 2017;7(1):1 View
  139. Miloff A, Marklund A, Carlbring P. The challenger app for social anxiety disorder: New advances in mobile psychological treatment. Internet Interventions 2015;2(4):382 View
  140. Malhi G, Hamilton A, Morris G, Mannie Z, Das P, Outhred T. The promise of digital mood tracking technologies: are we heading on the right track?. Evidence Based Mental Health 2017;20(4):102 View
  141. Mohr D, Zhang M, Schueller S. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annual Review of Clinical Psychology 2017;13(1):23 View
  142. 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
  143. Frank E, Pong J, Asher Y, Soares C. Smart phone technologies and ecological momentary data. Current Opinion in Psychiatry 2018;31(1):3 View
  144. Goodspeed R, Yan X, Hardy J, Vydiswaran V, Berrocal V, Clarke P, Romero D, Gomez-Lopez I, Veinot T. Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study. JMIR mHealth and uHealth 2018;6(8):e168 View
  145. Aledavood T, Lehmann S, Saramäki J. Digital daily cycles of individuals. Frontiers in Physics 2015;3 View
  146. Craske M. Honoring the Past, Envisioning the Future: ABCT’s 50th Anniversary Presidential Address. Behavior Therapy 2018;49(2):151 View
  147. 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
  148. Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker S, McInnis M, Ajilore O, Nelson P, Ryan K, Leow A. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research 2018;20(7):e241 View
  149. Palmius N, Tsanas A, Saunders K, Bilderbeck A, Geddes J, Goodwin G, De Vos M. Detecting Bipolar Depression From Geographic Location Data. IEEE Transactions on Biomedical Engineering 2017;64(8):1761 View
  150. Place S, Blanch-Hartigan D, Rubin C, Gorrostieta C, Mead C, Kane J, Marx B, Feast J, Deckersbach T, Pentland A, Nierenberg A, Azarbayejani A. Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. Journal of Medical Internet Research 2017;19(3):e75 View
  151. Saeb S, Lattie E, Kording K, Mohr D. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112 View
  152. Spaiser V, Luzzatti D, Gregoriou A, Ferrara E, Chadefaux T. Advancing sustainability: Using smartphones to study environmental behavior in a field-experimental setup. Data Science 2019;2(1-2):277 View
  153. Leonard N, Silverman M, Sherpa D, Naegle M, Kim H, Coffman D, Ferdschneider M. Mobile Health Technology Using a Wearable Sensorband for Female College Students With Problem Drinking: An Acceptability and Feasibility Study. JMIR mHealth and uHealth 2017;5(7):e90 View
  154. Harari G, Lane N, Wang R, Crosier B, Campbell A, Gosling S. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 2016;11(6):838 View
  155. Torous J, Rodriguez J, Powell A. The New Digital Divide For Digital Biomarkers. Digital Biomarkers 2017 View
  156. Roberts L, Chan S, Torous J. New tests, new tools: mobile and connected technologies in advancing psychiatric diagnosis. npj Digital Medicine 2018;1(1) View
  157. 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
  158. 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
  159. Mastoras R, Iakovakis D, Hadjidimitriou S, Charisis V, Kassie S, Alsaadi T, Khandoker A, Hadjileontiadis L. Touchscreen typing pattern analysis for remote detection of the depressive tendency. Scientific Reports 2019;9(1) View
  160. Webb C, Rosso I, Rauch S. Internet-Based Cognitive-Behavioral Therapy for Depression: Current Progress and Future Directions. Harvard Review of Psychiatry 2017;25(3):114 View
  161. Kleiman E, Nock M. Real-time assessment of suicidal thoughts and behaviors. Current Opinion in Psychology 2018;22:33 View
  162. Berrouiguet S, Perez-Rodriguez M, Larsen M, Baca-García E, Courtet P, Oquendo M. From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health. Journal of Medical Internet Research 2018;20(1):e2 View
  163. Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild. JMIR mHealth and uHealth 2016;4(3):e111 View
  164. Bhugra D, Tasman A, Pathare S, Priebe S, Smith S, Torous J, Arbuckle M, Langford A, Alarcón R, Chiu H, First M, Kay J, Sunkel C, Thapar A, Udomratn P, Baingana F, Kestel D, Ng R, Patel A, Picker L, McKenzie K, Moussaoui D, Muijen M, Bartlett P, Davison S, Exworthy T, Loza N, Rose D, Torales J, Brown M, Christensen H, Firth J, Keshavan M, Li A, Onnela J, Wykes T, Elkholy H, Kalra G, Lovett K, Travis M, Ventriglio A. The WPA- Lancet Psychiatry Commission on the Future of Psychiatry. The Lancet Psychiatry 2017;4(10):775 View
  165. DeMasi O, Kording K, Recht B, Jan Y. Meaningless comparisons lead to false optimism in medical machine learning. PLOS ONE 2017;12(9):e0184604 View
  166. Šimon M, Vašát P, Poláková M, Gibas P, Daňková H. Activity spaces of homeless men and women measured by GPS tracking data: A comparative analysis of Prague and Pilsen. Cities 2019;86:145 View
  167. Singh V, Goyal R, Wu S. Riskalyzer. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  168. Eichstaedt J, Smith R, Merchant R, Ungar L, Crutchley P, Preoţiuc-Pietro D, Asch D, Schwartz H. Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences 2018;115(44):11203 View
  169. Piau A, Rumeau P, Nourhashemi F, Martin M. Information and Communication Technologies, a Promising Way to Support Pharmacotherapy for the Behavioral and Psychological Symptoms of Dementia. Frontiers in Pharmacology 2019;10 View
  170. Li B, Sano A. Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(2):1 View
  171. Bourla A, Mouchabac S, El Hage W, Ferreri F. e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD). European Journal of Psychotraumatology 2018;9(sup1):1424448 View
  172. Bidargaddi N, Musiat P, Makinen V, Ermes M, Schrader G, Licinio J. Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Molecular Psychiatry 2017;22(2):164 View
  173. Kennedy S, Ceniti A. Unpacking Major Depressive Disorder: From Classification to Treatment Selection. The Canadian Journal of Psychiatry 2018;63(5):308 View
  174. Bourla A, Ferreri F, Ogorzelec L, Guinchard C, Mouchabac S. Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre. L'Encéphale 2018;44(2):168 View
  175. Bader C, Skurla M, Vahia I. Technology in the Assessment, Treatment, and Management of Depression. Harvard Review of Psychiatry 2020;28(1):60 View
  176. Harari G. A process-oriented approach to respecting privacy in the context of mobile phone tracking. Current Opinion in Psychology 2020;31:141 View
  177. Arean P, Hallgren K, Jordan J, Gazzaley A, Atkins D, Heagerty P, Anguera J. The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial. Journal of Medical Internet Research 2016;18(12):e330 View
  178. Sarda A, Munuswamy S, Sarda S, Subramanian V. Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study. JMIR mHealth and uHealth 2019;7(1):e11041 View
  179. Doryab A, Villalba D, Chikersal P, Dutcher J, Tumminia M, Liu X, Cohen S, Creswell K, Mankoff J, Creswell J, Dey A. Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data. JMIR mHealth and uHealth 2019;7(7):e13209 View
  180. 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
  181. Singh V, Ghosh I. Inferring Individual Social Capital Automatically via Phone Logs. Proceedings of the ACM on Human-Computer Interaction 2017;1(CSCW):1 View
  182. Jongs N, Jagesar R, van Haren N, Penninx B, Reus L, Visser P, van der Wee N, Koning I, Arango C, Sommer I, Eijkemans M, Vorstman J, Kas M. A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data. Translational Psychiatry 2020;10(1) View
  183. 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
  184. Sarikaya R. The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components. IEEE Signal Processing Magazine 2017;34(1):67 View
  185. 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
  186. Ha Q, Chen J, Uy H, Capistrano E. Exploring the Privacy Concerns in Using Intelligent Virtual Assistants under Perspectives of Information Sensitivity and Anthropomorphism. International Journal of Human–Computer Interaction 2021;37(6):512 View
  187. Thakur S, Roy R. Predicting mental health using smart-phone usage and sensor data. Journal of Ambient Intelligence and Humanized Computing 2020 View
  188. Bertoa M, Moreno N, Perez-Vereda A, Bandera D, Álvarez-Palomo J, Canal C, Linaje M. Digital Avatars: Promoting Independent Living for Older Adults. Wireless Communications and Mobile Computing 2020;2020:1 View
  189. Wang Y, Mao H. Intelligent soccer system based on biosensor network technology. Measurement 2021;173:108564 View
  190. Fischer F, Kleen S. Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review. Journal of Medical Internet Research 2021;23(1):e17691 View
  191. Taeger J, Bischoff S, Hagen R, Rak K. Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study. JMIR mHealth and uHealth 2021;9(1):e19346 View
  192. Fulford D, Mote J, Gonzalez R, Abplanalp S, Zhang Y, Luckenbaugh J, Onnela J, Busso C, Gard D. Smartphone sensing of social interactions in people with and without schizophrenia. Journal of Psychiatric Research 2021;137:613 View
  193. Moshe I, Terhorst Y, Opoku Asare K, Sander L, Ferreira D, Baumeister H, Mohr D, Pulkki-Råback L. Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data. Frontiers in Psychiatry 2021;12 View
  194. Aubourg T, Demongeot J, Vuillerme N. Novel statistical approach for assessing the persistence of the circadian rhythms of social activity from telephone call detail records in older adults. Scientific Reports 2020;10(1) View
  195. Zulueta J, Ajilore O. Beyond non-inferior: how telepsychiatry technologies can lead to superior care. International Review of Psychiatry 2020:1 View
  196. Kumar D, Jeuris S, Bardram J, Dragoni N. Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications. ACM Transactions on Computing for Healthcare 2021;2(1):1 View
  197. Thongnopakun S, Visanuyothin S, Manwong M, Rodjarkpai Y, Patipat P. <p>Promoting Health Literacy to Prevent Depression Among Workers in Industrial Factories in the Eastern Economic Corridor of Thailand</p>. Journal of Multidisciplinary Healthcare 2020;Volume 13:1443 View
  198. Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu D, Bhathena D, Fisher L, Cusin C, Nyer M, Yeung A, Sangermano L, Mischoulon D, Alpert J, Picard R. Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors. Frontiers in Psychiatry 2020;11 View
  199. 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
  200. 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
  201. Wang Y, Ren X, Liu X, Zhu T. Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study. JMIR mHealth and uHealth 2021;9(1):e19046 View
  202. Wen H, Sobolev M, Vitale R, Kizer J, Pollak J, Muench F, Estrin D. mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study. JMIR Mental Health 2021;8(1):e25019 View
  203. Aubourg T, Demongeot J, Provost H, Vuillerme N. Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study. JMIR mHealth and uHealth 2020;8(11):e13535 View
  204. 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
  205. Low C. Harnessing consumer smartphone and wearable sensors for clinical cancer research. npj Digital Medicine 2020;3(1) View
  206. Asuzu K, Rosenthal M. Mobile device use among inpatients on a psychiatric unit: A preliminary study. Psychiatry Research 2021;297:113720 View
  207. Hafiz P, Miskowiak K, Maxhuni A, Kessing L, Bardram J. Wearable Computing Technology for Assessment of Cognitive Functioning of Bipolar Patients and Healthy Controls. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(4):1 View
  208. Martinez-Martin N, Dasgupta I, Carter A, Chandler J, Kellmeyer P, Kreitmair K, Weiss A, Cabrera L. Ethics of Digital Mental Health During COVID-19: Crisis and Opportunities. JMIR Mental Health 2020;7(12):e23776 View
  209. Gutierrez L, Rabbani K, Ajayi O, Gebresilassie S, Rafferty J, Castro L, Banos O. Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management. International Journal of Environmental Research and Public Health 2021;18(3):1327 View
  210. Elhai J, Sapci O, Yang H, Amialchuk A, Rozgonjuk D, Montag C. Objectively‐measured and self‐reported smartphone use in relation to surface learning, procrastination, academic productivity, and psychopathology symptoms in college students. Human Behavior and Emerging Technologies 2021 View
  211. Klein A, Clucas J, Krishnakumar A, Ghosh S, Van Auken W, Thonet B, Sabram I, Acuna N, Keshavan A, Rossiter H, Xiao Y, Semenuta S, Badioli A, Konishcheva K, Abraham S, Alexander L, Merikangas K, Swendsen J, Lindner A, Milham M. Remote Digital Psychiatry: MindLogger for Mobile Mental Health Assessment and Therapy (Preprint). Journal of Medical Internet Research 2020 View
  212. Labus A, Radenković B, Rodić B, Barać D, Malešević A. Enhancing smart healthcare in dentistry: an approach to managing patients’ stress. Informatics for Health and Social Care 2021:1 View
  213. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  214. Sadeghian A, Kaedi M. Happiness recognition from smartphone usage data considering users’ estimated personality traits. Pervasive and Mobile Computing 2021;73:101389 View
  215. Wang X, Vouk N, Heaukulani C, Buddhika T, Martanto W, Lee J, Morris R. HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning. Journal of Medical Internet Research 2021;23(3):e23984 View
  216. Bai R, Xiao L, Guo Y, Zhu X, Li N, Wang Y, Chen Q, Feng L, Wang Y, Yu X, Xie H, Wang G. Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study. JMIR mHealth and uHealth 2021;9(3):e24365 View
  217. 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
  218. 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
  219. Gloster A, Meyer A, Klotsche J, Villanueva J, Block V, Benoy C, Rinner M, Walter M, Lang U, Karekla M. The spatiotemporal movement of patients in and out of a psychiatric hospital: an observational GPS study. BMC Psychiatry 2021;21(1) View
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  221. Ríssola E, Losada D, Crestani F. A Survey of Computational Methods for Online Mental State Assessment on Social Media. ACM Transactions on Computing for Healthcare 2021;2(2):1 View
  222. Poudyal A, van Heerden A, Hagaman A, Islam C, Thapa A, Maharjan S, Byanjankar P, Kohrt B. What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment. Frontiers in Public Health 2021;9 View
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  224. Baglione A, Clemens M, Maestre J, Min A, Dahl L, Shih P. Understanding the Technological Practices and Needs of Music Therapists. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW1):1 View

Books/Policy Documents

  1. Dagum P, Montag C. Digital Phenotyping and Mobile Sensing. View
  2. Derksen J. Preventie psychische aandoeningen. View
  3. Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  4. Vayena E, Gasser U. The Ethics of Biomedical Big Data. View
  5. Lee H, Jo Y, Kim H, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  6. . The Cambridge Handbook of Research Methods in Clinical Psychology. View
  7. Losada D, Crestani F. Experimental IR Meets Multilinguality, Multimodality, and Interaction. View
  8. Ferguson S, Jahnel T, Elliston K, Shiffman S. The Cambridge Handbook of Research Methods in Clinical Psychology. View
  9. Chanchaichujit J, Tan A, Meng F, Eaimkhong S. Healthcare 4.0. View
  10. Fang Y, Mao R. Depressive Disorders: Mechanisms, Measurement and Management. View
  11. Maglogiannis I, Zlatintsi A, Menychtas A, Papadimatos D, Filntisis P, Efthymiou N, Retsinas G, Tsanakas P, Maragos P. Artificial Intelligence Applications and Innovations. View
  12. Cho A, Lee H, Hwang H, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
  13. Klaas V, Calatroni A, Hardegger M, Guckenberger M, Theile G, Tröster G. Wireless Mobile Communication and Healthcare. View
  14. Thakur S, Roy R. Computational Intelligence: Theories, Applications and Future Directions - Volume I. View
  15. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
  16. Rabbi M. Encyclopedia of Behavioral Medicine. View
  17. Cummins N, Matcham F, Klapper J, Schuller B. Artificial Intelligence in Precision Health. View
  18. Duke É, Montag C. Internet Addiction. View
  19. Pérez-Vereda A, Flores-Martín D, Canal C, Murillo J. Gerontechnology. View
  20. Theilig M, Blankenhagel K, Zarnekow R. Information Systems and Neuroscience. View
  21. Wolfer J. Online Engineering & Internet of Things. View
  22. Rabbi M, Hane Aung M, Choudhury T. Mobile Health. View
  23. Singh V, Ghosh I. Encyclopedia of Behavioral Medicine. View
  24. Rustagi A, Manchanda C, Sharma N, Kaushik I. International Conference on Innovative Computing and Communications. View
  25. Castro L, Rodríguez M, Martínez F, Rodríguez L, Andrade Á, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. View
  26. Singh V, Ghosh I. Encyclopedia of Behavioral Medicine. View
  27. Rabbi M. Encyclopedia of Behavioral Medicine. View
  28. Harari G, Stachl C, Müller S, Gosling S. The Handbook of Personality Dynamics and Processes. View
  29. Tushar A, Kabir M, Ahmed S. Signal Processing Techniques for Computational Health Informatics. View
  30. Beierle F. Integrating Psychoinformatics with Ubiquitous Social Networking. View
  31. Beierle F. Integrating Psychoinformatics with Ubiquitous Social Networking. View
  32. Flores-Martin D, Laso S, Berrocal J, Murillo J. Gerontechnology III. View