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Citing this Article

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Published on 29.03.16 in Vol 18, No 3 (2016): March

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

Works citing "Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study"

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.5505):

(note that this is only a small subset of citations)

  1. Sened H, Lazarus G, Gleason ME, Rafaeli E, Fleeson W, Mõttus R. The Use of Intensive Longitudinal Methods in Explanatory Personality Research. European Journal of Personality 2018;32(3):269
    CrossRef
  2. Mikus A, Hoogendoorn M, Rocha A, Gama J, Ruwaard J, Riper H. Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data. Internet Interventions 2018;12:105
    CrossRef
  3. Barrigón ML, Baca-García E. Current challenges in research on suicide. Revista de Psiquiatría y Salud Mental (English Edition) 2018;11(1):1
    CrossRef
  4. Boukhechba M, Daros AR, Fua K, Chow PI, Teachman BA, Barnes LE. DemonicSalmon: Monitoring Mental Health and Social Interactions of College Students Using Smartphones. Smart Health 2018;
    CrossRef
  5. Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker SA, McInnis M, Ajilore O, Nelson PC, 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
    CrossRef
  6. Di Matteo D, Fine A, Fotinos K, Rose J, Katzman M. Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire. JMIR Mental Health 2018;5(3):e56
    CrossRef
  7. Mulvaney SA, Vaala S, Hood KK, Lybarger C, Carroll R, Williams L, Schmidt DC, Johnson K, Dietrich MS, Laffel L. Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns. Diabetes Technology & Therapeutics 2018;20(7):465
    CrossRef
  8. Berrouiguet S, Ramirez D, Barrigon Estevez ML, Moreno-Munoz P, Carmona Camacho R, Baca-Garcia E, Artes-Rodriguez A. Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: The feasibility study of the Evidence Based Behavior (eB2) platform (Preprint). JMIR mHealth and uHealth 2018;
    CrossRef
  9. Becker D, van Breda W, Funk B, Hoogendoorn M, Ruwaard J, Riper H. Predictive modeling in e-mental health: A common language framework. Internet Interventions 2018;12:57
    CrossRef
  10. Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120
    CrossRef
  11. Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. 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
    CrossRef
  12. May M, Junghaenel DU, Ono M, Stone AA, Schneider S. Ecological Momentary Assessment Methodology in Chronic Pain Research: A Systematic Review. The Journal of Pain 2018;19(7):699
    CrossRef
  13. Boettcher J, Magnusson K, Marklund A, Berglund E, Blomdahl R, Braun U, Delin L, Lundén C, Sjöblom K, Sommer D, von Weber K, Andersson G, Carlbring P. Adding a smartphone app to internet-based self-help for social anxiety: A randomized controlled trial. Computers in Human Behavior 2018;87:98
    CrossRef
  14. Zhang Y, Olenick J, Chang C, Kozlowski SWJ, Hung H. TeamSense. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(3):1
    CrossRef
  15. Barrigón ML, Baca-García E. Retos actuales en la investigación en suicidio. Revista de Psiquiatría y Salud Mental 2018;11(1):1
    CrossRef
  16. Bertz JW, Epstein DH, Preston KL. Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research. Addictive Behaviors 2018;83:5
    CrossRef
  17. DeMasi O, Feygin S, Dembo A, Aguilera A, Recht B. Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study. JMIR mHealth and uHealth 2017;5(10):e137
    CrossRef
  18. van de Ven P, O’Brien H, Henriques R, Klein M, Msetfi R, Nelson J, Rocha A, Ruwaard J, O’Sullivan D, Riper H. ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions. Internet Interventions 2017;9:74
    CrossRef
  19. Kruger DJ, Duan A, Juhasz D, Phaneuf CV, Sreenivasa V, Saunders CM, Heyblom AM, Sonnega PA, Day ML, Misevich SL. Cell Phone Use Latency in a Midwestern USA University Population. Journal of Technology in Behavioral Science 2017;2(1):56
    CrossRef
  20. Attwood S, Parke H, Larsen J, Morton KL. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC Public Health 2017;17(1)
    CrossRef
  21. Van Ameringen M, Turna J, Khalesi Z, Pullia K, Patterson B. There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and Anxiety 2017;34(6):526
    CrossRef
  22. Luhmann M. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28
    CrossRef
  23. Hallgren KA, Bauer AM, Atkins DC. Digital technology and clinical decision making in depression treatment: Current findings and future opportunities. Depression and Anxiety 2017;34(6):494
    CrossRef
  24. Saeb S, Lattie EG, Kording KP, Mohr DC. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112
    CrossRef
  25. Mohr DC, Zhang M, Schueller SM. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annual Review of Clinical Psychology 2017;13(1):23
    CrossRef
  26. DeMasi O, Kording K, Recht B, Jan Y. Meaningless comparisons lead to false optimism in medical machine learning. PLOS ONE 2017;12(9):e0184604
    CrossRef
  27. Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 2016;4:e2537
    CrossRef
  28. Gao Y, Li A, Zhu T, Liu X, Liu X. How smartphone usage correlates with social anxiety and loneliness. PeerJ 2016;4:e2197
    CrossRef
  29. Meinlschmidt G, Lee J, Stalujanis E, Belardi A, Oh M, Jung EK, Kim H, Alfano J, Yoo S, Tegethoff M. Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting. Frontiers in Psychology 2016;7
    CrossRef
  30. 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
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.5505)

:
  1. Provoost S, Ruwaard J, Neijenhuijs K, Bosse T, Riper H. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. 2018. Chapter 3:24
    CrossRef
  2. Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. 2018. Chapter 212:1332
    CrossRef
  3. Khazaal Y. Traité de Réhabilitation Psychosociale. 2018. :237
    CrossRef
  4. Castro LA, Rodríguez MD, Martínez F, Rodríguez L, Andrade G, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. 2018. :3
    CrossRef