The Effect of Bipolar Disorder Illness Course on Physiological Digital Signals: Observational Study
13 affiliations
- 1Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, Villarroel 170, Barcelona, Catalonia, 08036, Spain, 34 932275400 ext 1470
- 2Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- 3Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- 4Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Barcelona, Catalonia, Spain
- 5Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, Gelderland, The Netherlands
- 6Fivoor, Science and Treatment Innovation, Rotterdam, The Netherlands
- 7Department of Developmental Psychology, Tilburg University, Tilburg, The Netherlands
- 8Specialized Forensic Care, De Borg National Expert Center, Den Dolder, The Netherlands
- 9Department of Psychology, University of Cádiz, Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cadiz, Andalusia, Spain
- 10Department of Neuroscience, Biobizkaia Health Research Institute, Basque Country University, Basurto University Hospital, OSI Bilbao-Basurto, Barakaldo, Bizkaia, Spain
- 11Hospital Psiquiàtric Universitari Institut Pere Mata, Reus, Spain
- 12Rovira i Virgili University, Reus, Spain
- 13Institut Investigació Sanitària Pere Virgili, CERCA, Reus, Spain
Abstract
Background: Although bipolar disorder (BD) lacks objective biomarkers of illness progression, wearable devices enable continuous, real-world collection of physiological digital biosignals, offering a promising approach to address this gap. Measures such as electrodermal activity (EDA) and heart rate variability (HRV) fluctuate with mood state and symptom severity. Previous studies have reported reduced EDA during depressive episodes and increased HRV following symptom resolution after acute mood episodes. Furthermore, greater illness burden in BD has been associated with lower HRV, suggesting that digital biosignals may capture not only state-related changes but also aspects of illness course.
Objective: This study investigated whether naturalistic EDA and heart rate (HR) profiles differ according to illness course variables in BD—including diagnosis, predominant polarity, years since onset, and lifetime number of episodes—in euthymic patients, to evaluate their potential as illness course biomarkers.
Methods: This cross-sectional post hoc analysis used data from 80 euthymic patients on the BD spectrum from the INTREPIBD-TIMEBASE cohort. Participants were recruited at Hospital Clínic de Barcelona (Spain) between 2021 and 2024 and wore an Empatica E4 wristband for 48 hours in naturalistic settings. Inclusion criteria comprised a current or past DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) diagnosis of BD confirmed by structured interview and sustained euthymia. We examined the effects of 4 illness course variables (diagnosis, predominant polarity, years since onset, and number of lifetime episodes) on 3 biosignals (mean EDA, EDA peaks per minute, and HR) using generalized additive models. Interactions between illness course variables and circadian patterns were also assessed. All models were adjusted for age, sex, and current antipsychotic use and included a participant-specific random effect.
Results: Of the 80 participants, 49 (61.3%) were female; participants’ mean age was 46.9 (SD 14.1) years. After adjustment for covariates and multiple comparisons, no significant associations were observed between illness course variables and EDA or HR. In sensitivity analyses excluding patients with schizoaffective disorder (n=75), years since illness onset and predominant polarity were associated with EDA peaks per minute after multiple comparison correction. Specifically, patients with more than 22 years since illness onset had fewer EDA peaks compared with other groups (β=−1.06, 95% CI −1.71 to −0.41; P=.001). No significant interactions were found between illness course variables and circadian patterns of EDA or HR.
Conclusions: To our knowledge, this is the first study to examine the association between illness course variables and EDA and HR recorded in naturalistic settings in BD. In contrast to prior findings linking illness burden with HRV, our results suggest that years since illness onset may show modest associations with phasic EDA activity. These findings indicate that digital biosignals may be more sensitive to state-related changes than to illness course characteristics, limiting their use as longitudinal biomarkers of disease progression.
Related
- Machine Learning Classification of Bipolar Disorder Across Mood States Using Natural Language Processing and Speech Analysis: Observational Study
- Health, diet and exercise needs of people living with bipolar disorder in Australia
- Lamotrigine-Associated Visual and Tactile Hallucinations in a Patient With Bipolar Disorder: A Case Report
- Overestimated Volatility Underpins Aberrant Decision Making in Bipolar I Disorder
- The Prevalence of Schizophrenia and Bipolar Disorder in Multiple Sclerosis: A Systematic Review and Meta-Analysis
BibTeX
@article{valenzuelapascual2026effect,
title = {{The Effect of Bipolar Disorder Illness Course on Physiological Digital Signals: Observational Study}},
author = {Clàudia Valenzuela-Pascual and Marina Garriga and Roger Borràs and Ruth Voltà-Solerdelcoll and Marta Korniyenko and Ariadna Mas and Clara Escala and Peter De Looff and Gerard Anmella and Antonio Benabarre and Isabella Pacchiarotti and Anna Bastidas and Isabel Agasi and Meritxell González-Campos and Iria Grande and Cristina Romero-López-Alberca and Cecilia Muñoz-Doña and Ana Catalan and Esteban Sepulveda and Eduard Vieta and Marc Valentí and Diego Hidalgo-Mazzei},
journal = {JMIR Mental Health},
year = {2026},
month = nov,
doi = {10.2196/94737},
url = {https://doi.org/10.2196/94737},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The Effect of Bipolar Disorder Illness Course on Physiological Digital Signals: Observational Study
AU - Clàudia Valenzuela-Pascual
AU - Marina Garriga
AU - Roger Borràs
AU - Ruth Voltà-Solerdelcoll
AU - Marta Korniyenko
AU - Ariadna Mas
AU - Clara Escala
AU - Peter De Looff
AU - Gerard Anmella
AU - Antonio Benabarre
AU - Isabella Pacchiarotti
AU - Anna Bastidas
AU - Isabel Agasi
AU - Meritxell González-Campos
AU - Iria Grande
AU - Cristina Romero-López-Alberca
AU - Cecilia Muñoz-Doña
AU - Ana Catalan
AU - Esteban Sepulveda
AU - Eduard Vieta
AU - Marc Valentí
AU - Diego Hidalgo-Mazzei
PY - 2026
DA - 2026/11/04
JO - JMIR Mental Health
DO - 10.2196/94737
UR - https://doi.org/10.2196/94737
AB - Background: Although bipolar disorder (BD) lacks objective biomarkers of illness progression, wearable devices enable continuous, real-world collection of physiological digital biosignals, offering a promising approach to address this gap. Measures such as electrodermal activity (EDA) and heart rate variability (HRV) fluctuate with mood state and symptom severity. Previous studies have reported reduced EDA during depressive episodes and increased HRV following symptom resolution after acute mood episodes. Furthermore, greater illness burden in BD has been associated with lower HRV, suggesting that digital biosignals may capture not only state-related changes but also aspects of illness course. Objective: This study investigated whether naturalistic EDA and heart rate (HR) profiles differ according to illness course variables in BD—including diagnosis, predominant polarity, years since onset, and lifetime number of episodes—in euthymic patients, to evaluate their potential as illness course biomarkers. Methods: This cross-sectional post hoc analysis used data from 80 euthymic patients on the BD spectrum from the INTREPIBD-TIMEBASE cohort. Participants were recruited at Hospital Clínic de Barcelona (Spain) between 2021 and 2024 and wore an Empatica E4 wristband for 48 hours in naturalistic settings. Inclusion criteria comprised a current or past DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) diagnosis of BD confirmed by structured interview and sustained euthymia. We examined the effects of 4 illness course variables (diagnosis, predominant polarity, years since onset, and number of lifetime episodes) on 3 biosignals (mean EDA, EDA peaks per minute, and HR) using generalized additive models. Interactions between illness course variables and circadian patterns were also assessed. All models were adjusted for age, sex, and current antipsychotic use and included a participant-specific random effect. Results: Of the 80 participants, 49 (61.3%) were female; participants’ mean age was 46.9 (SD 14.1) years. After adjustment for covariates and multiple comparisons, no significant associations were observed between illness course variables and EDA or HR. In sensitivity analyses excluding patients with schizoaffective disorder (n=75), years since illness onset and predominant polarity were associated with EDA peaks per minute after multiple comparison correction. Specifically, patients with more than 22 years since illness onset had fewer EDA peaks compared with other groups (β=−1.06, 95% CI −1.71 to −0.41; P=.001). No significant interactions were found between illness course variables and circadian patterns of EDA or HR. Conclusions: To our knowledge, this is the first study to examine the association between illness course variables and EDA and HR recorded in naturalistic settings in BD. In contrast to prior findings linking illness burden with HRV, our results suggest that years since illness onset may show modest associations with phasic EDA activity. These findings indicate that digital biosignals may be more sensitive to state-related changes than to illness course characteristics, limiting their use as longitudinal biomarkers of disease progression.
SN - 2368-7959
ER -