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Romaniszyn-Kania P, Pollak A, Kania D, Mitas AW. Longitudinal observation of psychophysiological data as a novel approach to personalised postural defect rehabilitation. Sci Rep 2025; 15:8382. [PMID: 40069355 PMCID: PMC11897178 DOI: 10.1038/s41598-025-92368-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Accepted: 02/27/2025] [Indexed: 03/15/2025] Open
Abstract
Postural defects are one of the main diseases reported to be at the top of the list of diseases of civilisation. The present study aimed to develop a novel approach to defining a set of measurable physiological biomarkers and psychological characteristics with identifiable information content and data analysis, enabling the determination of the adaptation period and conditioning the effectiveness of the treatment in personalised rehabilitation. During the rehabilitation, multimodal physiological signals (electrodermal activity, blood volume pulse) and psychological data (anxiety as a state and as a trait, temperament) were recorded on a group of 20 subjects over a period of three months (120 measurement sessions). Preprocessing of the physiological signals and psychological data was performed. A stepwise forward regression method was used to determine a set of successive statistically significant predictors of the model. For each group, a matrix of coefficients for fitting a linear regression of changes in the value of a given predictor in subsequent measurement was determined. Adaptive Boosting was chosen to develop a mathematical model of the patient. The analysis of the results of the psychological tests enabled the participants to be divided into five new, previously undefined subgroups, which were both labels for the classifier. Using the dimensionality reduction method, 8 significant, statistically important features were extracted. AdaBoost classifier allowed the creation of a prediction model for therapy parameters with 84% accuracy, and the Pseudo-Random Number Generator was used to check the validity of it. The AdaBoost algorithm was used again to check the dynamics of changes in regression coefficients for individual groups-a set of psychophysiological characteristics identified as the basis for personalised therapeutic interventions. Each individual requires time to adapt to a new situation, conditioned by their characteristics. An appropriate interdisciplinary approach to professional rehabilitation influences the therapeutic process's quality, duration, and effectiveness. Physiological features determine the patient's involvement in the rehabilitation process, allowing robust personalisation of therapy in a closed feedback loop. The fusion of psychophysiological data and multimodal measurements enables the development of a unique behavioral-physiological profile of the patient undergoing rehabilitation.
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Affiliation(s)
- Patrycja Romaniszyn-Kania
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland.
| | - Anita Pollak
- Institute of Psychology, University of Silesia in Katowice, Grażyńskiego 53, 40-126, Katowice, Poland
| | - Damian Kania
- Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education in Katowice, Mikołowska 72A, 40-065, Katowice, Poland
| | - Andrzej W Mitas
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland
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Pagès EG, Kontaxis S, Siddi S, Miguel MP, de la Cámara C, Bernal ML, Ribeiro TC, Laguna P, Badiella L, Bailón R, Haro JM, Aguiló J. Contribution of physiological dynamics in predicting major depressive disorder severity. Psychophysiology 2025; 62:e14729. [PMID: 39552159 PMCID: PMC11870817 DOI: 10.1111/psyp.14729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 10/01/2024] [Accepted: 11/04/2024] [Indexed: 11/19/2024]
Abstract
This study aimed to explore the physiological dynamics of cognitive stress in patients with Major Depressive Disorder (MDD) and design a multiparametric model for objectively measuring severity of depression. Physiological signal recordings from 40 MDD patients and 40 healthy controls were collected in a baseline stage, in a stress-inducing stage using two cognitive tests, and in the recovery period. Several features were extracted from electrocardiography, photoplethysmography, electrodermal activity, respiration, and temperature. Differences between values of these features under different conditions were used as indexes of autonomic reactivity and recovery. Finally, a linear model was designed to assess MDD severity, using the Beck Depression Inventory scores as the outcome variable. The performance of this model was assessed using the MDD condition as the response variable. General physiological hyporeactivity and poor recovery from stress predict depression severity across all physiological signals except for respiration. The model to predict depression severity included gender, body mass index, cognitive scores, and mean heart rate recovery, and achieved an accuracy of 78%, a sensitivity of 97% and a specificity of 59%. There is an observed correlation between the behavior of the autonomic nervous system, assessed through physiological signals analysis, and depression severity. Our findings demonstrated that decreased autonomic reactivity and recovery are linked with an increased level of depression. Quantifying the stress response together with a cognitive evaluation and personalization variables may facilitate a more precise diagnosis and monitoring of depression, enabling the tailoring of therapeutic interventions to individual patient needs.
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Affiliation(s)
- Esther García Pagès
- Department de Microelectrònica i Sistemes electrònicsUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y NanomedicinaMadridSpain
| | | | - Sara Siddi
- Parc Sanitari Sant Joan de DéuInstitut de Recerca Sant Joan de DéuSant Boi de LlobregatSpain
- Departament de MatemàtiquesUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
| | | | | | | | - Thais Castro Ribeiro
- Department de Microelectrònica i Sistemes electrònicsUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y NanomedicinaMadridSpain
| | - Pablo Laguna
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y NanomedicinaMadridSpain
- Universidad de ZaragozaZaragozaSpain
| | - Llorenç Badiella
- Departament de MatemàtiquesUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
| | - Raquel Bailón
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y NanomedicinaMadridSpain
- Universidad de ZaragozaZaragozaSpain
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de DéuInstitut de Recerca Sant Joan de DéuSant Boi de LlobregatSpain
- Centro de Investigación Biomédica en Red de Salud MentalMadridSpain
- Universitat de BarcelonaBarcelonaSpain
| | - Jordi Aguiló
- Department de Microelectrònica i Sistemes electrònicsUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y NanomedicinaMadridSpain
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Castillo-Navarrete JL, Guzmán-Castillo A, Bustos C. Longitudinal analysis of academic stress and its effects on salivary cortisol, alpha-amylase, and academic outcomes: Study protocol. PLoS One 2024; 19:e0315650. [PMID: 39705290 DOI: 10.1371/journal.pone.0315650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Accepted: 11/27/2024] [Indexed: 12/22/2024] Open
Abstract
INTRODUCTION Academic stress is a prevalent problem among university students, affecting both their psychological well-being and academic performance. This study aims to investigate the mediating roles of biological and psycho-behavioural variables in the relationship between academic stress and academic performance over the course of a semester. Through a longitudinal approach and using accessible data collection technologies, the results will enable the design of effective interventions to mitigate the impact of academic stress. HYPOTHESES (i) Biological variables related to academic performance will mediate the relationship between academic stress and students' academic performance. (ii) Psycho-behavioural variables will also act as mediators in this relationship, impacting academic performance differently. GENERAL OBJECTIVE To explore the mediating roles of biological and psycho-behavioural variables in the relationship between academic stress and academic performance over the course of a university semester. DESIGN A longitudinal non-experimental observational design will be applied. Data will be collected in three assessment cycles, each consisting of three consecutive weeks during the academic semester. PARTICIPANTS A sample of 160 undergraduate students from the Faculty of Medicine of the University of Concepción will be included. Students will be recruited on a voluntary basis through social networks and student associations. Students under psychological or pharmacological treatment will also be included to more representatively reflect the student reality and to ensure the ecological validity of the study. BIOLOGICAL AND PSYCHO-BEHAVIOURAL DATA COLLECTION Participants will answer electronic questionnaires on academic stress and psycho-behavioural variables three times a week via the REDCap platform. In addition, smart devices will be used to continuously collect biological data such as heart rate, oxygen saturation, and sleep patterns. Students will also collect saliva samples three times a week to measure cortisol levels, and alpha-amylase enzyme activity. STATISTICAL ANALYSIS (i) Descriptive analysis of variables will be performed using measures of central tendency and dispersion for continuous variables and frequencies and percentages for categorical variables. (ii) Bivariate and multivariate analyses will be conducted to compare groups. (iii) Random intercept cross-lagged models will be used to assess the direction and reciprocal effects between variables over time. To analyze mediations, structural models (SEM) will be applied, considering biological and psycho-behavioural variables as mediators. EXPECTED RESULTS It is anticipated that (i) biological variables, such as cortisol and salivary alpha-amylase, will play a significant mediating role in the relationship between academic stress and academic performance, particularly towards the end of the semester. (ii) psycho-behavioural variables will also have a mediating effect, with different impacts on academic performance depending on the level of stress experienced. The use of accessible technologies and non-invasive methods such as saliva sample collection will provide a replicable model for future research.
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Affiliation(s)
- Juan Luis Castillo-Navarrete
- Facultad de Medicina, Departamento de Tecnología Médica, Universidad de Concepción, Concepción, Chile
- Programa de Neurociencia, Psiquiatría y Salud Mental, NEPSAM, Universidad de Concepción, Concepción, Chile
- Facultad de Medicina, Programa Doctorado en Salud Mental, Universidad de Concepción, Concepción, Chile
| | - Alejandra Guzmán-Castillo
- Programa de Neurociencia, Psiquiatría y Salud Mental, NEPSAM, Universidad de Concepción, Concepción, Chile
- Facultad de Medicina, Programa Doctorado en Salud Mental, Universidad de Concepción, Concepción, Chile
- Facultad de Medicina, Departamento de Ciencias Básicas y Morfología, Universidad Católica de la Santísima Concepción, Concepción, Chile
| | - Claudio Bustos
- Programa de Neurociencia, Psiquiatría y Salud Mental, NEPSAM, Universidad de Concepción, Concepción, Chile
- Facultad de Medicina, Programa Doctorado en Salud Mental, Universidad de Concepción, Concepción, Chile
- Facultad de Ciencias Sociales, Departamento de Psicología, Universidad de Concepción, Concepción, Chile
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Castro Ribeiro T, García Pagès E, Huguet A, Alda JA, Badiella L, Aguiló J. Physiological parameters to support attention deficit hyperactivity disorder diagnosis in children: a multiparametric approach. Front Psychiatry 2024; 15:1430797. [PMID: 39575190 PMCID: PMC11578978 DOI: 10.3389/fpsyt.2024.1430797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 10/21/2024] [Indexed: 11/24/2024] Open
Abstract
Introduction Attention deficit hyperactivity disorder (ADHD) is a high-prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity, frequently co-occurring with other psychiatric and medical conditions. Current diagnosis is time-consuming and often delays effective treatment; to date, no valid biomarker has been identified to facilitate this process. Research has linked the core symptoms of ADHD to autonomic dysfunction resulting from impaired arousal modulation, which contributes to physiological abnormalities that may serve as useful biomarkers for the disorder. While recent research has explored alternative objective assessment tools, few have specifically focused on studying ADHD autonomic dysregulation through physiological parameters. This study aimed to design a multiparametric physiological model to support ADHD diagnosis. Methods In this observational study we non-invasively analyzed heart rate variability (HRV), electrodermal activity (EDA), respiration, and skin temperature parameters of 69 treatment-naïve ADHD children and 29 typically developing (TD) controls (7-12 years old). To identify the most relevant parameters to discriminate ADHD children from controls, we explored the physiological behavior at baseline and during a sustained attention task and applied a logistic regression procedure. Results ADHD children showed increased HRV and lower EDA at baseline. The stress-inducing task elicits higher reactivity for EDA, pulse arrival time (PAT), and respiratory frequency in the ADHD group. The final classification model included 4 physiological parameters and was adjusted by gender and age. A good capacity to discriminate between ADHD children and TD controls was obtained, with an accuracy rate of 85.5% and an AUC of 0.95. Discussion Our findings suggest that a multiparametric physiological model constitutes an accurate tool that can be easily employed to support ADHD diagnosis in clinical practice. The discrimination capacity of the model may be analyzed in larger samples to confirm the possibility of generalization.
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Affiliation(s)
- Thais Castro Ribeiro
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Esther García Pagès
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Anna Huguet
- Child and Adolescent Mental Health Service, Sant Joan de Déu Terres de Lleida, Lleida, Spain
- Children and Adolescent Mental Health Research Group, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Jose A. Alda
- Children and Adolescent Mental Health Research Group, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
- Child and Adolescent Psychiatry and Psychology Department, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Llorenç Badiella
- Applied Statistics Service, Autonomous University of Barcelona, Barcelona, Spain
| | - Jordi Aguiló
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
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Castro Ribeiro T, García Pagès E, Ballester L, Vilagut G, García Mieres H, Suárez Aragonès V, Amigo F, Bailón R, Mortier P, Pérez Sola V, Serrano-Blanco A, Alonso J, Aguiló J. Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study. JMIR Res Protoc 2024; 13:e51298. [PMID: 38551647 PMCID: PMC11015365 DOI: 10.2196/51298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/22/2023] [Accepted: 01/11/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual's well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. OBJECTIVE This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. METHODS This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. RESULTS Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. CONCLUSIONS This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. TRIAL REGISTRATION OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/51298.
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Affiliation(s)
- Thais Castro Ribeiro
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| | - Esther García Pagès
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| | - Laura Ballester
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Gemma Vilagut
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Helena García Mieres
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Víctor Suárez Aragonès
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
| | - Franco Amigo
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Raquel Bailón
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
| | - Philippe Mortier
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Víctor Pérez Sola
- CIBER en Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Institute of Neuropsychiatry and Addictions (INAD), Parc de Salut Mar (PSMAR), Barcelona, Spain
- Neurosciences Research Group, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Antoni Serrano-Blanco
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, Barcelona, Spain
| | - Jordi Alonso
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jordi Aguiló
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
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Castro Ribeiro T, Sobregrau Sangrà P, García Pagès E, Badiella L, López-Barbeito B, Aguiló S, Aguiló J. Assessing effectiveness of heart rate variability biofeedback to mitigate mental health symptoms: a pilot study. Front Physiol 2023; 14:1147260. [PMID: 37234414 PMCID: PMC10206049 DOI: 10.3389/fphys.2023.1147260] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction: The increasing burden on mental health has become a worldwide concern especially due to its substantial negative social and economic impact. The implementation of prevention actions and psychological interventions is crucial to mitigate these consequences, and evidence supporting its effectiveness would facilitate a more assertive response. Heart rate variability biofeedback (HRV-BF) has been proposed as a potential intervention to improve mental wellbeing through mechanisms in autonomic functioning. The aim of this study is to propose and evaluate the validity of an objective procedure to assess the effectiveness of a HRV-BF protocol in mitigating mental health symptoms in a sample of frontline HCWs (healthcare workers) who worked in the COVID-19 pandemic. Methods: A prospective experimental study applying a HRV-BF protocol was conducted with 21 frontline healthcare workers in 5 weekly sessions. For PRE-POST intervention comparisons, two different approaches were used to evaluate mental health status: applying (a) gold-standard psychometric questionnaires and (b) electrophysiological multiparametric models for chronic and acute stress assessment. Results: After HRV-BF intervention, psychometric questionnaires showed a reduction in mental health symptoms and stress perception. The electrophysiological multiparametric also showed a reduction in chronic stress levels, while the acute stress levels were similar in PRE and POST conditions. A significant reduction in respiratory rate and an increase in some heart rate variability parameters, such as SDNN, LFn, and LF/HF ratio, were also observed after intervention. Conclusion: Our findings suggest that a 5-session HRV-BF protocol is an effective intervention for reducing stress and other mental health symptoms among frontline HCWs who worked during the COVID-19 pandemic. The electrophysiological multiparametric models provide relevant information about the current mental health state, being useful for objectively evaluating the effectiveness of stress-reducing interventions. Further research could replicate the proposed procedure to confirm its feasibility for different samples and specific interventions.
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Affiliation(s)
- Thais Castro Ribeiro
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Pau Sobregrau Sangrà
- Clínic Foundation for Biomedical Research, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Esther García Pagès
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Llorenç Badiella
- Applied Statistics Service, Autonomous University of Barcelona, Barcelona, Spain
| | | | - Sira Aguiló
- Emergency Department, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Jordi Aguiló
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
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