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

Bipolar Disorders (64) Viewpoints and Perspectives (566)

Published on in Vol 7, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14267, first published .
Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Authors of this article:

Henning Daus1, 2 Author Orcid Image ;   Timon Bloecher3 Author Orcid Image ;   Ronny Egeler4 Author Orcid Image ;   Richard De Klerk5 Author Orcid Image ;   Wilhelm Stork6 Author Orcid Image ;   Matthias Backenstrass1, 7 Author Orcid Image
Article Authors Cited by (5) Tweetations (1) Metrics

Journals

  1. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  2. DAUS H, FINK-LAMOTTE J, BACKENSTRASS M. Mobile-based, emotion-sensitive video diaries and ambulatory third-party assessments as indicators of mood states in bipolar disorder. Minerva Psychiatry 2022;63(4) View
  3. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  4. Crocamo C, Cioni R, Canestro A, Nasti C, Palpella D, Piacenti S, Bartoccetti A, Re M, Simonetti V, Barattieri di San Pietro C, Bulgheroni M, Bartoli F, Carrà G. Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study. JMIR Formative Research 2025;9:e65555 View

Conference Proceedings

  1. Mondéjar A, Silva-Calpa G, Raposo A, Mograbi D. 2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH). Redesign of an m-Health Application for Individuals with Bipolar Disorder: A Strategy for User Adherence and Effective Data Collection View