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Citing this Article

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Published on 13.08.18 in Vol 6, No 8 (2018): August

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

Works citing "Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review"

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.9691):

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

  1. Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing LV, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow PC. Smartphones in mental health: a critical review of background issues, current status and future concerns. International Journal of Bipolar Disorders 2020;8(1)
    CrossRef
  2. Tazawa Y, Liang K, Yoshimura M, Kitazawa M, Kaise Y, Takamiya A, Kishi A, Horigome T, Mitsukura Y, Mimura M, Kishimoto T. Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning. Heliyon 2020;6(2):e03274
    CrossRef
  3. Zulueta J, Leow AD, Ajilore O. Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. FOCUS 2020;18(2):175
    CrossRef
  4. Busk J, Faurholt-Jepsen M, Frost M, Bardram JE, Vedel Kessing L, Winther O. Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach. JMIR mHealth and uHealth 2020;8(4):e15028
    CrossRef
  5. . Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. Journal of Psychiatry and Brain Science 2020;
    CrossRef
  6. Henson P, Barnett I, Keshavan M, Torous J. Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophrenia 2020;6(1)
    CrossRef
  7. Til K, McInnis MG, Cochran A. A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder. Bipolar Disorders 2020;22(2):182
    CrossRef
  8. Chung MH, Leung SF, Välimäki M. Use of tracking technology to examine life-space mobility among people with depression: a systematic review protocol. BMJ Open 2020;10(1):e034208
    CrossRef
  9. Bardram JE, Matic A. A Decade of Ubiquitous Computing Research in Mental Health. IEEE Pervasive Computing 2020;19(1):62
    CrossRef
  10. Wilhelm S, Weingarden H, Ladis I, Braddick V, Shin J, Jacobson NC. Cognitive-Behavioral Therapy in the Digital Age: Presidential Address. Behavior Therapy 2020;51(1):1
    CrossRef
  11. Radhakrishnan K, Kim MT, Burgermaster M, Brown RA, Xie B, Bray MS, Fournier CA. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;
    CrossRef
  12. Goodday SM. The unique utility of digital technology for bipolar disorder. Bipolar Disorders 2020;22(2):197
    CrossRef
  13. Aubourg T, Demongeot J, Provost H, Vuillerme N. Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study. JMIR mHealth and uHealth 2020;8(2):e12452
    CrossRef
  14. Jacobson NC, Weingarden H, Wilhelm S. Using Digital Phenotyping to Accurately Detect Depression Severity. The Journal of Nervous and Mental Disease 2019;207(10):893
    CrossRef
  15. Torous J, Wisniewski H, Bird B, Carpenter E, David G, Elejalde E, Fulford D, Guimond S, Hays R, Henson P, Hoffman L, Lim C, Menon M, Noel V, Pearson J, Peterson R, Susheela A, Troy H, Vaidyam A, Weizenbaum E, Naslund JA, Keshavan M. Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach. Journal of Technology in Behavioral Science 2019;4(2):73
    CrossRef
  16. Spinazze P, Rykov Y, Bottle A, Car J. Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol. BMJ Open 2019;9(12):e032255
    CrossRef
  17. Lorenz N, Spada J, Sander C, Riedel-Heller SG, Hegerl U. Circadian skin temperature rhythms, circadian activity rhythms and sleep in individuals with self-reported depressive symptoms. Journal of Psychiatric Research 2019;117:38
    CrossRef
  18. Aubourg T, Demongeot J, Renard F, Provost H, Vuillerme N. Association between social asymmetry and depression in older adults: A phone Call Detail Records analysis. Scientific Reports 2019;9(1)
    CrossRef
  19. Torous J, Gershon A, Hays R, Onnela J, Baker JT. Digital Phenotyping for the Busy Psychiatrist: Clinical Implications and Relevance. Psychiatric Annals 2019;49(5):196
    CrossRef
  20. 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
    CrossRef
  21. Fillekes MP, Giannouli E, Kim E, Zijlstra W, Weibel R. Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research. International Journal of Health Geographics 2019;18(1)
    CrossRef