TY  - JOUR
AU  - Anmella, Gerard
AU  - Corponi, Filippo
AU  - Li, Bryan M
AU  - Mas, Ariadna
AU  - Sanabra, Miriam
AU  - Pacchiarotti, Isabella
AU  - Valentí, Marc
AU  - Grande, Iria
AU  - Benabarre, Antoni
AU  - Giménez-Palomo, Anna
AU  - Garriga, Marina
AU  - Agasi, Isabel
AU  - Bastidas, Anna
AU  - Cavero, Myriam
AU  - Fernández-Plaza, Tabatha
AU  - Arbelo, Néstor
AU  - Bioque, Miquel
AU  - García-Rizo, Clemente
AU  - Verdolini, Norma
AU  - Madero, Santiago
AU  - Murru, Andrea
AU  - Amoretti, Silvia
AU  - Martínez-Aran, Anabel
AU  - Ruiz, Victoria
AU  - Fico, Giovanna
AU  - De Prisco, Michele
AU  - Oliva, Vincenzo
AU  - Solanes, Aleix
AU  - Radua, Joaquim
AU  - Samalin, Ludovic
AU  - Young, Allan H
AU  - Vieta, Eduard
AU  - Vergari, Antonio
AU  - Hidalgo-Mazzei, Diego
PY  - 2023
DA  - 2023/5/4
TI  - Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study
JO  - JMIR Mhealth Uhealth
SP  - e45405
VL  - 11
KW  - depression
KW  - mania
KW  - bipolar disorder
KW  - major depressive disorder
KW  - machine learning
KW  - deep learning
KW  - physiological data
KW  - digital biomarker
KW  - wearable
KW  - Empatica E4
AB  - Background: Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. Objective: Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. Methods: We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. Results: Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52). Conclusions: Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2023/1/e45405
UR  - https://doi.org/10.2196/45405
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36939345
DO  - 10.2196/45405
ID  - info:doi/10.2196/45405
ER  -