This paper is in the following e-collection/theme issue:

Early Reports (28) e-Mental Health and Cyberpsychology (1402) mHealth for Data Collection and Research (914) Depression and Mood Disorders; Suicide Prevention (1282) Ecological Momentary Assessment (EMA) (295)

Published on in Vol 5, No 3 (2016): Jul-Sept

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Authors of this article:

Galen Chin-Lun Hung1, 2 Author Orcid Image ;   Pei-Ching Yang3 Author Orcid Image ;   Chia-Chi Chang3 Author Orcid Image ;   Jung-Hsien Chiang3 Author Orcid Image ;   Ying-Yeh Chen1, 4 Author Orcid Image
Article Authors Cited by (32) Tweetations (7) Metrics

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  3. 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
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  7. 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
  8. Balaskas A, Schueller S, Cox A, Doherty G, Myers B. Ecological momentary interventions for mental health: A scoping review. PLOS ONE 2021;16(3):e0248152 View
  9. Basantani A, Kesarwani Y, Bhatia S, Jain S. EmoCure: Utilising Social Media Data and Smartphones to Predict and Cure Depression. IOP Conference Series: Materials Science and Engineering 2021;1110(1):012010 View
  10. Sadeghian A, Kaedi M. Happiness recognition from smartphone usage data considering users’ estimated personality traits. Pervasive and Mobile Computing 2021;73:101389 View
  11. 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
  12. Bocu R, Bocu D, Iavich M. An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors. Sensors 2022;23(1):294 View
  13. Tikhonova O, Antonov A, Bogomolov Y, Khulbe D, Sobolevsky S. Detecting a citizens' activity profile of an urban territory through natural language processing of social media data. Procedia Computer Science 2022;212:11 View
  14. Jin H, Nath S, Schneider S, Junghaenel D, Wu S, Kaplan C. An informatics approach to examine decision-making impairments in the daily life of individuals with depression. Journal of Biomedical Informatics 2021;122:103913 View
  15. Delgado-Santos P, Stragapede G, Tolosana R, Guest R, Deravi F, Vera-Rodriguez R. A Survey of Privacy Vulnerabilities of Mobile Device Sensors. ACM Computing Surveys 2022;54(11s):1 View

Books/Policy Documents

  1. Nenko A, Petrova M. Digital Transformation and Global Society. View
  2. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  3. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
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