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

Digital Biomarkers and Digital Phenotyping (226) Wearable Devices and Sensors (988) Machine Learning (1719) mHealth for Data Collection and Research (1077) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1564)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45405, first published .
Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study

Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study

Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study

Authors of this article:

Gerard Anmella1, 2, 3, 4, 5 Author Orcid Image ;   Filippo Corponi6 Author Orcid Image ;   Bryan M Li6 Author Orcid Image ;   Ariadna Mas1, 2, 3, 4, 5 Author Orcid Image ;   Miriam Sanabra1, 2, 3, 4, 5 Author Orcid Image ;   Isabella Pacchiarotti1, 2, 3, 4, 5 Author Orcid Image ;   Marc Valentí1, 2, 3, 4, 5 Author Orcid Image ;   Iria Grande1, 2, 3, 4, 5 Author Orcid Image ;   Antoni Benabarre1, 2, 3, 4, 5 Author Orcid Image ;   Anna Giménez-Palomo1, 2, 3, 4, 5 Author Orcid Image ;   Marina Garriga1, 2, 3, 4, 5 Author Orcid Image ;   Isabel Agasi1 Author Orcid Image ;   Anna Bastidas1, 4, 5 Author Orcid Image ;   Myriam Cavero1, 2, 3, 4, 5 Author Orcid Image ;   Tabatha Fernández-Plaza1 Author Orcid Image ;   Néstor Arbelo1, 3, 4, 5, 7 Author Orcid Image ;   Miquel Bioque1, 3, 4, 5, 7 Author Orcid Image ;   Clemente García-Rizo1, 3, 4, 5, 7 Author Orcid Image ;   Norma Verdolini1, 2, 3, 4, 5 Author Orcid Image ;   Santiago Madero1, 3, 4, 5, 7 Author Orcid Image ;   Andrea Murru1, 2, 3, 4, 5 Author Orcid Image ;   Silvia Amoretti1, 2, 3, 4, 5 Author Orcid Image ;   Anabel Martínez-Aran1, 2, 3, 4, 5 Author Orcid Image ;   Victoria Ruiz1 Author Orcid Image ;   Giovanna Fico1, 2, 3, 4, 5 Author Orcid Image ;   Michele De Prisco1, 2, 3, 4, 5 Author Orcid Image ;   Vincenzo Oliva1, 2, 3, 4, 5 Author Orcid Image ;   Aleix Solanes2, 4, 5, 8 Author Orcid Image ;   Joaquim Radua2, 4, 5, 8, 9, 10 Author Orcid Image ;   Ludovic Samalin11, 12 Author Orcid Image ;   Allan H Young13 Author Orcid Image ;   Eduard Vieta1, 2, 3, 4, 5 Author Orcid Image ;   Antonio Vergari6 Author Orcid Image ;   Diego Hidalgo-Mazzei1, 2, 3, 4, 5 Author Orcid Image
Article Authors Cited by (15) Tweetations (5) Metrics

Journals

  1. Anmella G, Mas A, Sanabra M, Valenzuela-Pascual C, Valentí M, Pacchiarotti I, Benabarre A, Grande I, De Prisco M, Oliva V, Fico G, Giménez-Palomo A, Bastidas A, Agasi I, Young A, Garriga M, Corponi F, Li B, de Looff P, Vieta E, Hidalgo-Mazzei D. Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting. Journal of Affective Disorders 2024;345:43 View
  2. Corponi F, Li B, Anmella G, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie S, Whalley H, Hidalgo-Mazzei D, Vergari A. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Translational Psychiatry 2024;14(1) View
  3. Valenzuela‐Pascual C, Mas A, Borràs R, Anmella G, Sanabra M, González‐Campos M, Valentí M, Pacchiarotti I, Benabarre A, Grande I, De Prisco M, Oliva V, Bastidas A, Agasi I, Young A, Garriga M, Murru A, Corponi F, Li B, de Looff P, Vieta E, Hidalgo‐Mazzei D. Sleep–wake variations of electrodermal activity in bipolar disorder. Acta Psychiatrica Scandinavica 2025;151(3):412 View
  4. Stolfi F, Abreu H, Sinella R, Nembrini S, Centonze S, Landra V, Brasso C, Cappellano G, Rocca P, Chiocchetti A. Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine. Frontiers in Psychiatry 2024;15 View
  5. Corponi F, Li B, Anmella G, Valenzuela-Pascual C, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Young A, Lawrie S, Whalley H, Hidalgo-Mazzei D, Vergari A. Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study. JMIR mHealth and uHealth 2024;12:e55094 View
  6. Romão J, Melo A, André R, Novais F. Machine Learning as a Tool to Find New Pharmacological Targets in Mood Disorders: A Systematic Review. Current Treatment Options in Psychiatry 2024;11(3):241 View
  7. Rykov Y, Ng K, Patterson M, Gangwar B, Kandiah N. Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning. Computers in Biology and Medicine 2024;180:108959 View
  8. Anmella G, Corponi F, Li B, Mas A, Garriga M, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Agasi I, Bastidas A, Cavero M, Bioque M, García-Rizo C, Madero S, Arbelo N, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Rivas Y, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young A, Vergari A, Vieta E, Hidalgo-Mazzei D. Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study. BJPsych Open 2024;10(5) View
  9. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  10. Corponi F, Li B, Anmella G, Valenzuela-Pascual C, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie S, Whalley H, Hidalgo-Mazzei D, Vergari A. A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder. npj Mental Health Research 2024;3(1) View
  11. Lipschitz J, Lin S, Saghafian S, Pike C, Burdick K. Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology. Acta Psychiatrica Scandinavica 2025;151(3):434 View
  12. Zhong R, Wu X, Chen J, Fang Y. Utilizing Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: A Systematic Review (Preprint). Journal of Medical Internet Research 2025 View
  13. De Prisco M, Oliva V, Fico G, Kjærstad H, Miskowiak K, Anmella G, Hidalgo-Mazzei D, Murru A, Vieta E, Radua J. Eye-tracking metrics during image viewing as possible biomarkers of cognitive alterations: A systematic review and meta-analysis in people with bipolar disorder. Journal of Affective Disorders 2025;384:69 View

Conference Proceedings

  1. Yokotani K, Takano M, Abe N. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining. Abnormal behavior of following peers in an online game indicates bipolar disorder and manic/hypomanic episodes View
  2. Kim B, Chae M, Kim Y, Kong S, Kim Y, Jung T, Jeong J, Park S, Cho C, Yeom J, Lee T, Lee H, Lee H. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning View