Ariadna Mas
A5074747800- Mental Health Research Topics
- Digital Mental Health Interventions
- Functional Brain Connectivity Studies
- Bipolar Disorder and Treatment
- Mental Health Treatment and Access
- Emotion and Mood Recognition
- Psychosomatic Disorders and Their Treatments
- Electroconvulsive Therapy Studies
- EEG and Brain-Computer Interfaces
- COVID-19 and Mental Health
- Tryptophan and brain disorders
- Health disparities and outcomes
- Eating Disorders and Behaviors
- Child and Adolescent Psychosocial and Emotional Development
- Employment and Welfare Studies
- Suicide and Self-Harm Studies
- Heart Rate Variability and Autonomic Control
Hospital Clínic de Barcelona
2022-2024
Universitat de Barcelona
2022-2024
Consorci Institut D'Investigacions Biomediques August Pi I Sunyer
2022-2024
Instituto de Salud Carlos III
2022-2024
Centro de Investigación Biomédica en Red de Salud Mental
2022-2024
University of Edinburgh
2022-2023
Many people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, digital tools been proposed as solution.
Depressive and manic episodes within bipolar disorder (BD) major depressive (MDD) involve altered mood, sleep, activity, alongside physiological alterations wearables can capture. Firstly, we explored whether wearable data could predict (aim 1) the severity of an acute affective episode at intra-individual level 2) polarity euthymia among different individuals. Secondarily, which were related to prior predictions, generalization across patients, associations between symptoms data. We...
Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable ecological physiological recordings thanks to recent advances wearable technology. Therefore, near-continuous passive collection data from wearables daily life, analyzable machine learning (ML), could mitigate this problem, bringing...
Abstract Mood disorders are among the leading causes of disease burden worldwide. They manifest with changes in mood, sleep, and motor-activity, observable physiological data. Despite effective treatments being available, limited specialized care availability is a major bottleneck, hindering preemptive interventions. Nearcontinuous passive collection data from wearables daily life, analyzable machine learning, could mitigate this problem, bringing mood monitoring outside doctor’s office....
Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain real-world settings due to confounders like physical and temperature. We analysed separately during sleep wakefulness varying potential differences mood state discrimination capacities. monitored from 102 participants BD including 35 manic, 29 depressive, 38 euthymic patients, healthy controls (HC), for 48 h....
Background Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes mania depression, which translate into altered mood, sleep activity alongside their physiological expressions. Aims The IdenTifying dIgital bioMarkers illnEss treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers illness bipolar disorder. Method We designed longitudinal observational study including 84 individuals. Group A...
Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), major determinant of the worldwide disease burden. However, collecting annotating wearable resource intensive. Studies this kind can thus typically afford recruit only few dozen patients. This constitutes one obstacles applying modern supervised machine learning techniques MD detection.
Abstract Background The prevalence of mental health disorders has significantly increased in recent years, posing substantial challenges to healthcare systems worldwide, particularly primary care (PC) settings. This study examines trends diagnoses PC settings Catalonia from 2010 2019 and identifies associated sociodemographic, clinical characteristics, psychopharmacological treatments, resource utilization patterns. Methods Data 947,698 individuals without prior severe illness, derived the...
A bstract Mood disorders are severe and chronic mental conditions exacting high costs from society. The lack of reliable biomarkers to aid clinicians in tailoring pharmacotherapy based on distinguishable patient-specific traits means that the current prescribing paradigm is largely one trial error. Previous studies showed different biological signatures, such as patterns heart rate variability or electro-dermal reactivity, associated with clinically meaningful outcomes. Against this...
<sec> <title>BACKGROUND</title> Many people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, digital tools been proposed as solution. </sec> <title>OBJECTIVE</title> We aimed present the development, feasibility, potential effectiveness Vickybot, chatbot at screening, monitoring, reducing burnout, detecting suicide risk in patients from PC health...
<title>Abstract</title> Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable ecological physiological recordings thanks to recent advances wearable technology. Therefore, near-continuous passive collection data from wearables daily life, analyzable machine learning (ML), could mitigate...
Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), major determinant of worldwide disease burden. However, collecting annotating wearable very resource-intensive. Studies this kind can thus typically afford recruit only couple dozens patients. This constitutes one the obstacles applying modern supervised machine learning techniques MDs detection. In...
<sec> <title>BACKGROUND</title> Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), major determinant of the worldwide disease burden. However, collecting annotating wearable resource intensive. Studies this kind can thus typically afford recruit only few dozen patients. This constitutes one obstacles applying modern supervised machine learning...
<sec> <title>BACKGROUND</title> Depressive and manic episodes within bipolar disorder (BD) major depressive (MDD) involve altered mood, sleep, activity, alongside physiological alterations wearables can capture. </sec> <title>OBJECTIVE</title> Firstly, we explored whether wearable data could predict (aim 1) the severity of an acute affective episode at intra-individual level 2) polarity euthymia among different individuals. Secondarily, which were related to prior predictions, generalization...