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

Formative Evaluation of Digital Health Interventions (3500) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2291) Natural Language Processing (971) mHealth for Screening (311) Bipolar Disorders (79)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65555, first published .
Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Authors of this article:

Cristina Crocamo1 Author Orcid Image ;   Riccardo Matteo Cioni1 Author Orcid Image ;   Aurelia Canestro1 Author Orcid Image ;   Christian Nasti1 Author Orcid Image ;   Dario Palpella1 Author Orcid Image ;   Susanna Piacenti1 Author Orcid Image ;   Alessandra Bartoccetti1 Author Orcid Image ;   Martina Re1 Author Orcid Image ;   Valentina Simonetti2 Author Orcid Image ;   Chiara Barattieri di San Pietro2, 3 Author Orcid Image ;   Maria Bulgheroni2 Author Orcid Image ;   Francesco Bartoli1 Author Orcid Image ;   Giuseppe Carrà1 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Crocamo C, Palpella D, Cavaleri D, Nasti C, Piacenti S, Morello P, Lauria G, Villa O, Riboldi I, Bartoli F, Torous J, Carrà G. Digital health interventions for mental health disorders: an umbrella review of meta-analyses of randomised controlled trials. The Lancet Digital Health 2025;7(8):100878 View
  2. Lewin G, Abakasanga E, Titcombe I, Cosma G, Gangadharan S. Artificial intelligence-enabled predictive modelling in psychiatry: overview of machine learning applications in mental health research. BJPsych Advances 2026;32(3):169 View
  3. Li A, Yang K. Digital Phenotyping of Sensation Seeking: A Machine Learning Approach Using Gait Analysis. Behavioral Sciences 2025;15(9):1222 View
  4. Bartoli F, Cavaleri D, Crocamo C. Artificial Intelligence and Bipolar Disorder: Applications of Machine Learning Models for Diagnosis, Treatment, and Outcome Prediction. Alpha Psychiatry 2025;26(5) View
  5. Zidi I. Two-Stage self-labeling and multi-objective optimization for bipolar mood-state classification. Journal of King Saud University Computer and Information Sciences 2026 View
  6. Jacobson S, Carling H, Sarraf L, Draper E, Jacobson S, St-James M, Misiasz C, Williamson S, Thibaudeau E, Sauvé G, Lavigne K, Raucher-Chéné D. Digital Tools for Assessing Bipolar Disorder: A Scoping Review of the Current Landscape. Neuroscience Applied 2026:107004 View