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Bae JH, Lee SH, Hong JH. Changes in the choice motive and emotional perception of chocolates in response to stress. Food Res Int 2024; 187:114378. [PMID: 38763650 DOI: 10.1016/j.foodres.2024.114378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/03/2024] [Accepted: 04/17/2024] [Indexed: 05/21/2024]
Abstract
Although chocolates are often chosen for sensory pleasure, they are also selected to enhance mood and relieve emotional stress, or potentially chosen for its perceived health benefits if stress adversely affects physical well-being. This study aimed to investigate whether emotional stress influenced the motivations behind chocolate selection, subsequent liking, and emotional response. Participants were divided into a control group (n = 76) and a group with induced acute stress (n = 74). Stimuli were presented as dark chocolate packaging, each evoking sensory appeal, health, and emotional stress relief. Participants chose one stimulus from three options that they were most inclined to consume and evaluated the overall liking and emotional attributes of the stimuli. They also rated the overall liking and emotional attributes of three types of chocolates, each identical but paired with distinct stimuli. Their food attitudes were also assessed. Stress did not change the choice of stimuli, indicating that stress did not influence the motivation for chocolate selection. Instead, the choice of stimuli aligned with participants' food attitudes; those favoring sensory appeal and emotional stress relief prioritized pleasure in their usual food choices. Stress tended to increase liking and chocolate-associated positive emotions with sensory appeal, as opposed to others, to immediately alleviate negative emotions. The most robust motivation to consume chocolates was sensory pleasure, irrespective of stress, because of a preestablished association between sensory pleasure and mood enhancement.
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Affiliation(s)
- Jeong-Hyun Bae
- Department of Food and Nutrition, Seoul National University, Seoul 08826, Republic of Korea
| | - Soo-Hyun Lee
- Department of Food and Nutrition, Seoul National University, Seoul 08826, Republic of Korea
| | - Jae-Hee Hong
- Department of Food and Nutrition, Seoul National University, Seoul 08826, Republic of Korea; Research Institute of Human Ecology, Seoul National University, Seoul 08826, Republic of Korea.
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Lazarou E, Exarchos TP. Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices. AIMS Neurosci 2024; 11:76-102. [PMID: 38988886 PMCID: PMC11230864 DOI: 10.3934/neuroscience.2024006] [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: 12/27/2023] [Revised: 03/22/2024] [Accepted: 04/08/2024] [Indexed: 07/12/2024] Open
Abstract
Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.
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Affiliation(s)
| | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Dept of Informatics, Ionian University, GR49132, Corfu, Greece
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Corvin J, Hoskinson Z, Mozolic-Staunton B, Hattingh L, Plumbridge-Jones R. The effects of virtual reality interventions on occupational participation and distress from symptoms in palliative care patients: A pilot study. Palliat Support Care 2024:1-8. [PMID: 38605653 DOI: 10.1017/s1478951524000245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
BACKGROUND Virtual reality (VR) offers the prospect of a safe and effective adjunct therapeutic modality to promote mental health and reduce distress from symptoms in palliative care patients. Common physiological and psychological symptoms experienced at the end of life may impact the person's participation in day-to-day activities that bring them meaning. The purpose of this study was to examine the effect of VR interventions on occupational participation and distress from symptoms. OBJECTIVES To describe the stimulus, results, and learnings from a single-site pilot study of virtual reality therapy in a specialist palliative care setting. METHODS Participants engaged in a VR session lasting from 9 to 30 minutes related to coping with pain, inner peace and mindfulness, adventure, and bucket list. METHODS MEASURES The pilot prospective quantitative observational cohort study was conducted from November 2021 through March 2022 using a pre-post VR intervention research design. Quantitative data was collected using patient-rated assessments and a wireless pulse oximeter. Occupational performance, satisfaction, and distress symptoms were measured using the Canadian Occupational Performance Measure and the Palliative Care Outcomes Collaboration Symptom Assessment Scale (PCOC SAS). The intervention and study design adhered to international guidelines. RESULTS Ten participants engaged in the VR interventions. Data showed significantly improved occupational performance and satisfaction scores (p < .001), decreases in PCOC SAS distress from pain (p = .01), fatigue (p < .001), and heart rate (p = .018). No adverse side effects were observed. SIGNIFICANCE OF RESULTS Outcomes included an analysis of virtual reality's effectiveness to alleviate symptom burden and increase occupational participation for palliative care patients. Of specific interest to the research team was the application of virtual reality in a community-based and inpatient palliative care context to supplement allied health services and its feasibility of integration into standard palliative care. CONCLUSION VR therapy showed positive improvements in the participants' occupational performance, satisfaction, and distress from pain and fatigue.
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Affiliation(s)
- Julian Corvin
- Faculty of Health Science and Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Zara Hoskinson
- Supportive and Specialist Community Palliative Care Service, Gold Coast Health, Robina, Queensland, Australia
| | - Beth Mozolic-Staunton
- Faculty of Health Science and Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Laetitia Hattingh
- Department of Allied Health Research, Allied Health & Rehabilitation Services, Gold Coast Health, Southport, Queensland, Australia
| | - Russell Plumbridge-Jones
- Supportive and Specialist Community Palliative Care Service, Gold Coast Health, Robina, Queensland, Australia
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Dilrukshi EAC, Ogino T, Ishikawa M, Kuroda H, Nomura S. Assessing the usability of aromatic mouthwashes in alleviating physiological stress responses. FRONTIERS IN ORAL HEALTH 2024; 5:1343937. [PMID: 38638174 PMCID: PMC11024226 DOI: 10.3389/froh.2024.1343937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Background Mouthwashes play a pivotal role in oral care, and their efficacy has been explored extensively across various dimensions. As a contribution to the development of novel oral care products, this study aims to investigate the psychophysiological effects of aromatic mouthwashes during the resilience period from a short-term cognitive stressor utilizing biological signals and subjective evaluations. Methods A within-participant experimental design with 22 healthy females was conducted with four mouthwashes; peppermint (Mint), peppermint + bergamot (MB), peppermint + sweet orange (MO), and peppermint + lavender (ML), and water as the control (Ctl), after a 20-min calculation task. Subjective evaluations and physiological responses including skin conductance level and electrocardiogram were recorded throughout the experiment. Results Citrus mouthwashes (MO and MB) showed a greater decrease in heart rate and a significant increase in the high-frequency component of heart rate variability. The participants indicated a significant effect in terms of "flavor preference" and "refreshing sensation" for mouthwash use compared to the Ctl. Conclusion The results suggest that rinsing with citrus-flavored mouthwashes has a positive impact in alleviating the physiological stress response (in terms of cardiac activity). These findings may have implications for the development of innovative, novel oral care products that promote stress reduction and improve oral health.
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Affiliation(s)
- E. A. Chayani Dilrukshi
- Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan
- Department of Industrial Management, Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri Lanka
| | - Tatsuki Ogino
- Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan
| | | | - Hiroki Kuroda
- Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan
| | - Shusaku Nomura
- Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan
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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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Victor TS, Jacquet B, El Massioui F. Exploring stress response's role in executive function impairments among adults with early adverse childhood experiences. Sci Rep 2024; 14:4081. [PMID: 38374227 PMCID: PMC10876952 DOI: 10.1038/s41598-024-53819-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 02/05/2024] [Indexed: 02/21/2024] Open
Abstract
Adverse childhood experiences (ACEs) are recognised as precursors to numerous physical and mental health challenges. However, research on their impact on inhibitory control and working memory, particularly among healthy young adults, remains limited. The role played by the stress response as a moderator in these effects is likewise underexplored. Our study addresses this gap by examining cognitive impairments in non-clinical adults with early childhood trauma, specifically trauma before the age of 13 years, and by assessing the influence of the stress response on these effects. A total of 15 participants with early ACEs were compared with a control group (n = 18) using the Corsi Block Tapping Test (CBTT) and Stroop Word Colour Test (SCWT). Results showed that participants with early ACEs exhibited lower scores on the SCWT but not the CBTT. The stress response emerged as a potential factor in the relationship between early ACEs and cognitive performance. The implications of these findings are then discussed in relation to the existing literature.
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Affiliation(s)
- Taïna Steevine Victor
- Université Paris 8, UFR Psychologie, 93200, Saint-Denis, France.
- Laboratoire Cognition Humaine et Artificielle (CHArt, RNSR 200515259U), 93322, Aubervilliers, France.
| | - Baptiste Jacquet
- Université Paris 8, UFR Psychologie, 93200, Saint-Denis, France
- Laboratoire Cognition Humaine et Artificielle (CHArt, RNSR 200515259U), 93322, Aubervilliers, France
| | - Farid El Massioui
- Université Paris 8, UFR Psychologie, 93200, Saint-Denis, France
- Laboratoire Cognition Humaine et Artificielle (CHArt, RNSR 200515259U), 93322, Aubervilliers, France
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del Rosario K, West TV, Mendes WB, Kunduzi B, Mamode N, Gogalniceanu P. How Does Surgeons' Autonomic Physiology Vary Intraoperatively?: A Real-time Study of Cardiac Reactivity. Ann Surg 2024; 279:258-266. [PMID: 38197241 PMCID: PMC10782823 DOI: 10.1097/sla.0000000000006007] [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] [Indexed: 01/11/2024]
Abstract
OBJECTIVE To measure the physiological responses of surgical team members under varying levels of intraoperative risk. BACKGROUND Measurement of intraoperative physiological responses provides insight into how operation complexity, phase of surgery, and surgeon seniority impact stress. METHODS Autonomic nervous system responses (interbeat intervals, IBIs) were measured continuously during different surgical operations of various complexity. The study investigated whether professional role (eg attending surgeon), operative risk (high vs. low), and type of primary operator (attending surgeon vs. resident) impacted IBI reactivity. Physiological synchrony captured the degree of correspondence between individuals' physiological responses at any given time point. RESULTS A total of 10,005 observations of IBI reactivity were recorded in 26 participants during 16 high-risk (renal transplant and laparoscopic donor nephrectomy) and low-risk (arteriovenous fistula formation) operations. Attending surgeons showed greater IBI reactivity (faster heart rate) than residents and nurses during high-risk operations and while actively operating (Ps<0.001). Residents showed lower reactivity during high-risk (relative to low-risk) operations (P<0.001) and similar reactivity regardless of whether they or the attending surgeon was operating (P=0.10). Nurses responded similarly during low-risk and high-risk operations (P=0.102) but were more reactive when the resident was operating compared to when the attending surgeon was the primary operator (P<0.001). In high-risk operations, attending surgeons had negative physiological covariation with residents and nurses (P<0.001). In low-risk operations, only attending surgeons and nurses were synchronized (P<0.001). CONCLUSION Attending surgeons' physiological responses were well-calibrated to operative demands. Residents' and nurses' responses were not callibrated to the same extent. This suggests that risk sensitivity is an adaptive response to stress that surgeons acquire.
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Sourkatti H, Pettersson K, van der Sanden B, Lindholm M, Plomp J, Määttänen I, Henttonen P, Närväinen J. Investigation of different ML approaches in classification of emotions induced by acute stress. Heliyon 2024; 10:e23611. [PMID: 38173518 PMCID: PMC10761802 DOI: 10.1016/j.heliyon.2023.e23611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 11/02/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
Background Machine learning is becoming a common tool in monitoring emotion. However, methodological studies of the processing pipeline are scarce, especially ones using subjective appraisals as ground truth. New method A novel protocol was used to induce cognitive load and physical discomfort, and emotional dimensions (arousal, valence, and dominance) were reported after each task. The performance of five common ML models with a versatile set of features (physiological features, task performance data, and personality trait) was compared in binary classification of subjectively assessed emotions. Results The psychophysiological responses proved the protocol was successful in changing the mental state from baseline, also the cognitive and physical tasks were different. The optimization and performance of ML models used for emotion detection were evaluated. Additionally, methods to account for imbalanced classes were applied and shown to improve the classification performance. Comparison with existing methods Classification of human emotional states often assumes the states are determined by the stimuli. However, individual appraisals vary. None of the past studies have classified subjective emotional dimensions with a set of features including biosignals, personality and behavior. Conclusion Our data represent a typical setup in affective computing utilizing psychophysiological monitoring: N is low compared to number of features, inter-individual variability is high, and class imbalance cannot be avoided. Our observations are a) if possible, include features representing physiology, behavior and personality, b) use simple models and limited number of features to improve interpretability, c) address the possible imbalance, d) if the data size allows, use nested cross-validation.
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Affiliation(s)
- Heba Sourkatti
- VTT Technical Research Center of Finland, Tekniikantie 1, 02150 Espoo, Finland
| | - Kati Pettersson
- VTT Technical Research Center of Finland, Tekniikantie 1, 02150 Espoo, Finland
| | | | - Mikko Lindholm
- VTT Technical Research Center of Finland, Tekniikantie 1, 02150 Espoo, Finland
| | - Johan Plomp
- VTT Technical Research Center of Finland, Tekniikantie 1, 02150 Espoo, Finland
| | - Ilmari Määttänen
- University of Helsinki, Department of Psychology and Logopedics, Faculty of Medicine, P.O. Box 63, 00014 University of Helsinki, Finland
| | - Pentti Henttonen
- University of Helsinki, Department of Psychology and Logopedics, Faculty of Medicine, P.O. Box 63, 00014 University of Helsinki, Finland
| | - Johanna Närväinen
- VTT Technical Research Center of Finland, Tekniikantie 1, 02150 Espoo, Finland
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Fernández J, Albayay J, Gálvez-García G, Iborra O, Huertas C, Gómez-Milán E, Caballo VE. Facial infrared thermography as an index of social anxiety. ANXIETY, STRESS, AND COPING 2024; 37:114-126. [PMID: 37029987 DOI: 10.1080/10615806.2023.2199209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 03/29/2023] [Indexed: 04/09/2023]
Abstract
Previous research on physiological indices of social anxiety has offered unclear results. In this study, participants with low and high social anxiety performed five social interaction tasks while being recorded with a thermal camera. Each task was associated with a dimension assessed by the Social Anxiety Questionnaire for Adults (1 = Interactions with strangers. 2 = Speaking in public/Talking with people in authority, 3 = Criticism and embarrassment, 4 = Assertive expression of annoyance, disgust or displeasure, 5 = Interactions with the opposite sex). Mixed-effects models revealed that the temperature of the tip of the nose decreased significantly in participants with low (vs. high) social anxiety (p < 0.001), while no significant differences were found in other facial regions of interest: forehead (p = 0.999) and cheeks (p = 0.999). Furthermore, task 1 was the most effective at discriminating between the thermal change of the nose tip and social anxiety, with a trend for a higher nose temperature in participants with high social anxiety and a lower nose temperature for the low social anxiety group. We emphasize the importance of corroborating thermography with specific tasks as an ecological method, and tip of the nose thermal change as a psychophysiological index associated with social anxiety.
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Affiliation(s)
- Jesús Fernández
- Centro de Investigación Mente, Cerebro y Comportamiento, Universidad de Granada, Granada, Spain
| | - Javier Albayay
- Centro Interdipartimentale Mente/Cervello, Università degli Studi di Trento, Rovereto, Italy
| | - Germán Gálvez-García
- Departamento de Psicología, Universidad de La Frontera, Temuco, Chile
- Departamento de Psicología Básica, Psicobiología y Metodología de las Ciencias del Comportamiento, Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain
| | - Oscar Iborra
- Centro de Investigación Mente, Cerebro y Comportamiento, Universidad de Granada, Granada, Spain
| | - Carmen Huertas
- Centro de Investigación Mente, Cerebro y Comportamiento, Universidad de Granada, Granada, Spain
| | - Emilio Gómez-Milán
- Centro de Investigación Mente, Cerebro y Comportamiento, Universidad de Granada, Granada, Spain
| | - Vicente E Caballo
- Centro de Investigación Mente, Cerebro y Comportamiento, Universidad de Granada, Granada, Spain
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Roos LG, Slavich GM. Wearable technologies for health research: Opportunities, limitations, and practical and conceptual considerations. Brain Behav Immun 2023; 113:444-452. [PMID: 37557962 PMCID: PMC11233111 DOI: 10.1016/j.bbi.2023.08.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/31/2023] [Accepted: 08/06/2023] [Indexed: 08/11/2023] Open
Abstract
One of the most notable limitations of laboratory-based health research is its inability to continuously monitor health-relevant physiological processes as individuals go about their daily lives. As a result, we have generated large amounts of data with unknown generalizability to real-world situations and also created a schism between where data are collected (i.e., in the lab) and where we need to intervene to prevent disease (i.e., in the field). Devices using noninvasive wearable technology are changing all of this, however, with their ability to provide high-frequency assessments of peoples' ever-changing physiological states in daily life in a manner that is relatively noninvasive, affordable, and scalable. Here, we discuss critical points that every researcher should keep in mind when using these wearables in research, spanning device and metric decisions, hardware and software selection, and data quality and sampling rate issues, using research on stress and health as an example throughout. We also address usability and participant acceptability issues, and how wearable "digital biomarker" and behavioral data can be integrated to enhance basic science and intervention studies. Finally, we summarize 10 key questions that should be addressed to make every wearable study as strong as possible. Collectively, keeping these points in mind can improve our ability to study the psychobiology of human health, and to intervene, precisely where it matters most: in peoples' daily lives.
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Affiliation(s)
- Lydia G Roos
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
| | - George M Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
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Nguyen B, Torres A, Espinola CW, Sim W, Kenny D, Campbell DM, Lou W, Kapralos B, Beavers L, Peter E, Dubrowski A, Krishnan S, Bhat V. Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107645. [PMID: 37352806 PMCID: PMC10258128 DOI: 10.1016/j.cmpb.2023.107645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 05/19/2023] [Accepted: 06/04/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND AND OBJECTIVE Due to the constraints of the COVID-19 pandemic, healthcare workers have reported acting in ways that are contrary to their moral values, and this may result in moral distress. This paper proposes the novel digital phenotype profile (DPP) tool, developed specifically to evaluate stress experiences within participants. The DPP tool was evaluated using the COVID-19 VR Healthcare Simulation of Stress Experience (HSSE) dataset (NCT05001542), which is composed of passive physiological signals and active mental health questionnaires. The DPP tool focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with moral injury outcome scale (Brief MIOS). METHODS Data-driven techniques are encompassed to develop a tool for robust evaluation of distress among participants. To accomplish this, we applied pre-processing techniques which involved normalization, data sanitation, segmentation, and windowing. During feature analysis, we extracted domain-specific features, followed by feature selection techniques to rank the importance of the feature set. Prior to classification, we employed k-means clustering to group the Brief MIOS scores to low, moderate, and high moral distress as the Brief MIOS lacks established severity cut-off scores. Support vector machine and decision tree models were used to create machine learning models to predict moral distress severities. RESULTS Weighted support vector machine with leave-one-subject-out-cross-validation evaluated the separation of the Brief MIOS scores and achieved an average accuracy, precision, sensitivity, and F1 of 98.67%, 98.83%, 99.44%, and 99.13%, respectively. Various machine learning ablation tests were performed to support our results and further enhance the understanding of the predictive model. CONCLUSION Our findings demonstrate the feasibility to develop a DPP tool to predict distress experiences using a combination of mental health questionnaires and passive signals. The DPP tool is the first of its kind developed from the analysis of the HSSE dataset. Additional validation is needed for the DPP tool through replication in larger sample sizes.
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Affiliation(s)
- Binh Nguyen
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Andrei Torres
- maxSIMhealth, Ontario Tech University, Oshawa, ON L1H 7K4, Canada
| | - Caroline W Espinola
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Interventional Psychiatry Program, St. Michael's Hospital, Toronto M5B 1W8, Canada
| | - Walter Sim
- Interventional Psychiatry Program, St. Michael's Hospital, Toronto M5B 1W8, Canada
| | - Deborah Kenny
- College of Nursing, University of Colorado Anschutz Medical Campus, Aurora 80045, United States
| | - Douglas M Campbell
- Neonatal Intensive Care Unit, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto M5T 1P8, Canada; Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada; Allan Waters Family Simulation Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Bill Kapralos
- maxSIMhealth, Ontario Tech University, Oshawa, ON L1H 7K4, Canada
| | - Lindsay Beavers
- Allan Waters Family Simulation Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto M5T 1P8, Canada
| | - Elizabeth Peter
- Faculty of Nursing, University of Toronto, Toronto M5T 1P8, Canada
| | - Adam Dubrowski
- maxSIMhealth, Ontario Tech University, Oshawa, ON L1H 7K4, Canada
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Venkat Bhat
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Interventional Psychiatry Program, St. Michael's Hospital, Toronto M5B 1W8, Canada.
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Almadhor A, Sampedro GA, Abisado M, Abbas S. Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity. SENSORS (BASEL, SWITZERLAND) 2023; 23:6664. [PMID: 37571448 PMCID: PMC10422546 DOI: 10.3390/s23156664] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 08/13/2023]
Abstract
Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.
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Affiliation(s)
- Ahmad Almadhor
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines;
- Center for Computational Imaging and Visual Innovations, De La Salle University, Manila 1004, Philippines
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila 1008, Philippines;
| | - Sidra Abbas
- Department of Computer Science, COMSATS University, Islamabad 22060, Pakistan
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13
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Nguyen B, Torres A, Rueda A, Sim W, Campbell DM, Lou W, Kapralos B, Beavers L, Dubrowski A, Bhat V, Krishnan S. Digital Interventions to Reduce Distress Among Frontline Health Care Providers: Analysis of Self-Perceived Stress. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083372 DOI: 10.1109/embc40787.2023.10340958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Due to the constraints of the COVID-19 pandemic, healthcare workers have reported behaving in ways that are contrary to their values, which may result in distress and injury. This work is the first of its kind to evaluate the presence of stress in the COVID-19 VR Healthcare Simulation for Distress dataset. The dataset collected passive physiological signals and active mental health questionnaires. This paper focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with the Perceived Stress Scale (PSS)-10 questionnaire. The analysis involved data-driven techniques for a robust evaluation of stress among participants. Low-complexity pre-processing and feature extraction techniques were applied and support vector machine and decision tree models were created to predict the PSS-10 scores of users. Imbalanced data classification techniques were used to further enhance our understanding of the results. Decision tree with oversampling through Synthetic Minority Oversampling Technique achieved an accuracy, precision, recall, and F1 of 93.50%, 93.41%, 93.31%, and 93.35%, respectively. Our findings offer novel results and clinically valuable insights for stress detection and potential for translation to edge computing applications to enhance privacy, longitudinal monitoring, and simplify device requirements.
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14
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Lukenga MP, Billonnet L, Gaugue J, Denis J. Exploring female students' perceptions of the use of digital technologies in managing academic stress. Front Psychol 2023; 14:1199038. [PMID: 37333588 PMCID: PMC10274148 DOI: 10.3389/fpsyg.2023.1199038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Objective The purpose of this research is to explore the perceptions of female students regarding the implementation of digital technologies for academic stress management. We aim to determine if the contribution of these technologies could offer to female students a better management of the stress related to their studies and thus, a better deployment of strategies to cope with academic difficulties. Method A qualitative study using the focus group methodology was conducted. Our inductive and exploratory approach allowed us to focus on the experience and perception of eleven female students from the University of Mons. The cohort was divided into two groups according to their score on the Perceived Stress Scale-10. Results The data collected was analyzed using the thematic analysis of which allowed us to identify fourteen sub-themes divided into three axes: coping strategies used to manage academic stress, students' needs to improve their management of academic stress, and the implementation of technology for managing academic stress. Conclusion Our results show that the issues present in the academic context lead students to use various coping strategies, some of which are harmful to their physical and mental health. The implementation of digital technologies and biofeedback seems to be an approach that could help students adopt more functional coping strategies and alleviate their daily difficulties in managing academic stress.
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Affiliation(s)
| | | | - Justine Gaugue
- Department of Clinical Psychology, University of Mons, Mons, Belgium
| | - Jennifer Denis
- Department of Clinical Psychology, University of Mons, Mons, Belgium
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15
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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: 0] [Impact Index Per Article: 0] [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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16
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Almadhor A, Sampedro GA, Abisado M, Abbas S, Kim YJ, Khan MA, Baili J, Cha JH. Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:3984. [PMID: 37112323 PMCID: PMC10146352 DOI: 10.3390/s23083984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient's data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.
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Affiliation(s)
- Ahmad Almadhor
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia;
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines;
- Center for Computational Imaging and Visual Innovations, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila 1008, Philippines;
| | - Sidra Abbas
- Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan
| | - Ye-Jin Kim
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea; (Y.-J.K.); (J.-H.C.)
| | | | - Jamel Baili
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
- Higher Institute of Applied Science and Technology of Sousse (ISSATS), Cité Taffala (Ibn Khaldoun) 4003 Sousse, University of Sousse, Sousse 4000, Tunisia
| | - Jae-Hyuk Cha
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea; (Y.-J.K.); (J.-H.C.)
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17
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Hamidi Shishavan H, Garza J, Henning R, Cherniack M, Hirabayashi L, Scott E, Kim I. Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence. APPLIED ERGONOMICS 2023; 108:103937. [PMID: 36462453 DOI: 10.1016/j.apergo.2022.103937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.
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Affiliation(s)
- Hossein Hamidi Shishavan
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
| | - Jennifer Garza
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA.
| | - Robert Henning
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, 06269, USA.
| | - Martin Cherniack
- Center for the Promotion of Health in the New England Workplace, University of Connecticut, USA.
| | - Liane Hirabayashi
- Northeast Center for Occupational Health and Safety in Agriculture, Forestry, and Fishing, Bassett Medical Center, NY, 13326, USA.
| | - Erika Scott
- Northeast Center for Occupational Health and Safety in Agriculture, Forestry, and Fishing, Bassett Medical Center, NY, 13326, USA.
| | - Insoo Kim
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
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18
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Bouchal SM, Meyer JH, Bendok BR. Commentary: Physiological Responses and Training Satisfaction During National Rollout of a Neurosurgical Intraoperative Catastrophe Simulator for Resident Training. Oper Neurosurg (Hagerstown) 2023; 24:e139-e141. [PMID: 36637327 DOI: 10.1227/ons.0000000000000548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 09/27/2022] [Indexed: 01/14/2023] Open
Affiliation(s)
| | - Jenna H Meyer
- Neurosurgery Simulation and Innovation Lab, Department of Neurologic Surgery, Mayo Clinic, Phoenix, Arizona, USA
| | - Bernard R Bendok
- Neurosurgery Simulation and Innovation Lab, Department of Neurologic Surgery, Mayo Clinic, Phoenix, Arizona, USA
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19
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García Pagès E, Arza A, Lazaro J, Puig C, Castro T, Ottaviano M, Arredondo MT, Bernal ML, López-Antón R, Cámara CDL, Gil E, Laguna P, Bailón R, Aguiló J, Garzón-Rey JM. Psychosomatic response to acute emotional stress in healthy students. Front Physiol 2023; 13:960118. [PMID: 36699693 PMCID: PMC9870289 DOI: 10.3389/fphys.2022.960118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/29/2022] [Indexed: 01/11/2023] Open
Abstract
The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress.
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Affiliation(s)
- Esther García Pagès
- Universitat Autònoma de Barcelona, UAB, Barcelona, Spain,*Correspondence: Esther García Pagès,
| | - Adriana Arza
- École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | | | - Carlos Puig
- Universitat Autònoma de Barcelona, UAB, Barcelona, Spain
| | - Thais Castro
- Universitat Autònoma de Barcelona, UAB, Barcelona, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
| | - Manuel Ottaviano
- Life Supporting Technologies, Universidad Politécnica de Madrid, UPM, Madrid, Spain
| | | | | | | | | | - Eduardo Gil
- Universidad de Zaragoza, UZ, Zaragoza, Spain
| | - Pablo Laguna
- Universidad de Zaragoza, UZ, Zaragoza, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
| | - Raquel Bailón
- Universidad de Zaragoza, UZ, Zaragoza, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
| | - Jordi Aguiló
- Universidad de Zaragoza, UZ, Zaragoza, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
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20
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Li Z, Xing Y, Pi Y, Jiang M, Zhang L. A novel physiological feature selection method for emotional stress assessment based on emotional state transition. Front Neurosci 2023; 17:1138091. [PMID: 37034171 PMCID: PMC10073504 DOI: 10.3389/fnins.2023.1138091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/20/2023] [Indexed: 04/11/2023] Open
Abstract
The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.
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Affiliation(s)
- Zhen Li
- The School of Electronic and Information Engineering, Tongji University, Shanghai, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yun Xing
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yao Pi
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mingzhe Jiang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, China
- Research and Development Center for E-Learning, Ministry of Education, Beijing, China
- College of Information Engineering, Yangzhou University, Yangzhou, China
- *Correspondence: Lejun Zhang
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21
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Iqbal T, Simpkin AJ, Roshan D, Glynn N, Killilea J, Walsh J, Molloy G, Ganly S, Ryman H, Coen E, Elahi A, Wijns W, Shahzad A. Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218135. [PMID: 36365837 PMCID: PMC9654418 DOI: 10.3390/s22218135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/15/2022] [Accepted: 10/20/2022] [Indexed: 05/14/2023]
Abstract
With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.
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Affiliation(s)
- Talha Iqbal
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- Correspondence:
| | - Andrew J. Simpkin
- School of Mathematical and Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Davood Roshan
- School of Mathematical and Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
- CÚRAM Center for Research in Medical Devices, University of Galway, H91 W2TY Galway, Ireland
| | - Nicola Glynn
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - John Killilea
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Jane Walsh
- School of Psychology, University of Galway, H91 TK33 Galway, Ireland
| | - Gerard Molloy
- School of Psychology, University of Galway, H91 TK33 Galway, Ireland
| | - Sandra Ganly
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Hannah Ryman
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Eileen Coen
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Adnan Elahi
- Electrical and Electronic Engineering, University of Galway, H91 TK33 Galway, Ireland
| | - William Wijns
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- CÚRAM Center for Research in Medical Devices, University of Galway, H91 W2TY Galway, Ireland
| | - Atif Shahzad
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- Centre for Systems Modelling and Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham B15 2TT, UK
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22
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Kantor J, Vilímek Z, Vítězník M, Smrčka P, Campbell EA, Bucharová M, Grohmannová J, Špinarová G, Janíčková K, Du J, Li J, Janátová M, Regec V, Krahulcová K, Kantorová L. Effect of low frequency sound vibration on acute stress response in university students-Pilot randomized controlled trial. Front Psychol 2022; 13:980756. [PMID: 36312194 PMCID: PMC9606670 DOI: 10.3389/fpsyg.2022.980756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/20/2022] [Indexed: 12/01/2022] Open
Abstract
Background Low frequency sound (LFS, combined with music listening) is applied by practitioners in vibroacoustic therapy who report a positive effect of this intervention on acute stress response. However, there is a lack of research on this topic and studies with mainly objective measurements are scarce. Materials and methods In this pilot double-blinded Randomized Controlled Trial we used a multimodal approach to measurement of acute stress response in 54 international university students attending a university summer school in Olomouc, the Czech Republic who were individually randomized into a group receiving LFS vibration and a control group. In both groups, the acute stress response was measured by heart rate variability (HRV), visual analogue scales (VAS) for stress and muscle relaxation. Results Differences were found in pre-test post-test measures, however, between groups differences occurred only for HRV, with statistically significant improvement in the experimental group (parameter LF/HF and pNN50). Conclusion Vibroacoustic therapy has the potential to contribute to the stress management of university students. Further research is needed to explore the effect of LFS on stress response, especially when applied without additional music listening.
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Affiliation(s)
- Jiří Kantor
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Zdeněk Vilímek
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Martin Vítězník
- Department of Information and Communication Technologies in Medicine, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Pavel Smrčka
- Department of Information and Communication Technologies in Medicine, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Elsa A. Campbell
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- VIBRAC Skille-Lehikoinen Centre for Vibroacoustic Therapy and Research, Eino Roiha Institute, Jyväskylä, Finland
| | - Monika Bucharová
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Jana Grohmannová
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Gabriela Špinarová
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Kateřina Janíčková
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
| | - Jian Du
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Jiaoli Li
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Markéta Janátová
- Department of Information and Communication Technologies in Medicine, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Vojtěch Regec
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Kristýna Krahulcová
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- Faculty of Education, Institute of Special Education Studies, Palacký University Olomouc, Olomouc, Czechia
| | - Lucia Kantorová
- Center of Evidence-Based Education and Arts Therapies: A JBI Affiliated Group, Faculty of Education, Palacký University Olomouc, Olomouc, Czechia
- The Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech CEBHC: JBI Centre of Excellence, Masaryk University GRADE Centre), Faculty of Medicine, Institute of Biostatistics and Analyses, Masaryk University, Brno, Czechia
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Kryklywy JH, Lu A, Roberts KH, Rowan M, Todd RM. Lateralization of autonomic output in response to limb-specific threat. eNeuro 2022; 9:ENEURO.0011-22.2022. [PMID: 36028330 PMCID: PMC9463978 DOI: 10.1523/eneuro.0011-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/23/2022] [Accepted: 08/11/2022] [Indexed: 11/21/2022] Open
Abstract
In times of stress or danger, the autonomic nervous system (ANS) signals the fight or flight response. A canonical function of ANS activity is to globally mobilize metabolic resources, preparing the organism to respond to threat. Yet a body of research has demonstrated that, rather than displaying a homogenous pattern across the body, autonomic responses to arousing events - as measured through changes in electrodermal activity (EDA) - can differ between right and left body locations. Surprisingly, an attempt to identify a function of ANS asymmetry consistent with its metabolic role has not been investigated. In the current study, we investigated whether asymmetric autonomic responses could be induced through limb-specific aversive stimulation. Participants were given mild electric stimulation to either the left or right arm while EDA was monitored bilaterally. In a group-level analyses, an ipsilateral EDA response bias was observed, with increased EDA response in the hand adjacent to the stimulation. This effect was observable in ∼50% of individual particpants. These results demonstrate that autonomic output is more complex than canonical interpretations suggest. We suggest that, in stressful situations, autonomic outputs can prepare either the whole-body fight or flight response, or a simply a limb-localized flick, which can effectively neutralize the threat while minimizing global resource consumption. These findings are consistent with recent theories proposing evolutionary leveraging of neural structures organized to mediate sensory responses for processing of cognitive emotional cues.Significance statementThe present study constitutes novel evidence for an autonomic nervous response specific to the side of the body exposed to direct threat. We identify a robust pattern of electrodermal response at the body location that directly receives aversive tactile stimulation. Thus, we demonstrate for the first time in contemporary research that the ANS is capable of location-specific outputs within single effector organs in response to small scale threat. This extends the canonical view of the role of ANS responses in stressful or dangerous stresses - that of provoking a 'fight or flight' response - suggesting a further role of this system: preparation of targeted limb-specific action, i.e., a flick.
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Affiliation(s)
| | - Amy Lu
- Department of Psychology, University of British Columbia
| | | | - Matt Rowan
- Peter A. Allard School of Law, University of British Columbia
| | - Rebecca M Todd
- Department of Psychology, University of British Columbia
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia
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24
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Williamson S, Daniel-Watanabe L, Finnemann J, Powell C, Teed A, Allen M, Paulus M, Khalsa SS, Fletcher PC. The Hybrid Excess and Decay (HED) model: an automated approach to characterising changes in the photoplethysmography pulse waveform. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17855.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Photoplethysmography offers a widely used, convenient and non-invasive approach to monitoring basic indices of cardiovascular function, such as heart rate and blood oxygenation. Systematic analysis of the shape of the waveform generated by photoplethysmography might be useful to extract estimates of several physiological and psychological factors influencing the waveform. Here, we developed a robust and automated method for such a systematic analysis across individuals and across different physiological and psychological contexts. We describe a psychophysiologically-relevant model, the Hybrid Excess and Decay (HED) model, which characterises pulse wave morphology in terms of three underlying pressure waves and a decay function. We present the theoretical and practical basis for the model and demonstrate its performance when applied to a pharmacological dataset of 105 participants receiving intravenous administrations of the sympathomimetic drug isoproterenol (isoprenaline). We show that these parameters capture photoplethysmography data with a high degree of precision and, moreover, are sensitive to experimentally-induced changes in interoceptive arousal within individuals. We conclude by discussing the possible value in using the HED model as a complement to standard measures of photoplethysmography signals.
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25
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Elia R, Plastiras G, Pettemeridou E, Savva A, Theocharides T. A real‐world data collection framework for a fused dataset creation for joint human and remotely operated vehicle monitoring and anomalous command detection. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Rafaella Elia
- Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus
- KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus
| | - George Plastiras
- Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus
| | - Eva Pettemeridou
- KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus
- Center for Applied Neuroscience (CAN) University of Cyprus Nicosia Cyprus
| | - Antonis Savva
- KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus
| | - Theocharis Theocharides
- Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus
- KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus
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26
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DellrAgnola F, Jao PK, Arza A, Chavarriaga R, Millan JDR, Floreano D, Atienza D. Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones. IEEE J Biomed Health Inform 2022; 26:4751-4762. [PMID: 35759604 DOI: 10.1109/jbhi.2022.3186625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.
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27
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Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:355-381. [PMID: 35356509 PMCID: PMC7612541 DOI: 10.1109/jproc.2022.3149785] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 05/29/2023]
Abstract
Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Panicos A. Kyriacou
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
| | - Jonathan Mant
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Vaidotas Marozas
- Department of Electronics Engineering and the Biomedical Engineering Institute, Kaunas University of Technology44249KaunasLithuania
| | - Phil Chowienczyk
- Department of Clinical PharmacologyKing’s College LondonLondonSE1 7EHU.K.
| | - Jordi Alastruey
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
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28
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Gioia F, Greco A, Callara AL, Scilingo EP. Towards a Contactless Stress Classification Using Thermal Imaging. SENSORS 2022; 22:s22030976. [PMID: 35161722 PMCID: PMC8839779 DOI: 10.3390/s22030976] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 11/16/2022]
Abstract
Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner.
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Affiliation(s)
- Federica Gioia
- Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, Italy; (A.G.); (A.L.C.); (E.P.S.)
- Research Center “E. Piaggio”, University of Pisa, 56122 Pisa, Italy
- Correspondence:
| | - Alberto Greco
- Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, Italy; (A.G.); (A.L.C.); (E.P.S.)
- Research Center “E. Piaggio”, University of Pisa, 56122 Pisa, Italy
| | - Alejandro Luis Callara
- Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, Italy; (A.G.); (A.L.C.); (E.P.S.)
- Research Center “E. Piaggio”, University of Pisa, 56122 Pisa, Italy
| | - Enzo Pasquale Scilingo
- Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, Italy; (A.G.); (A.L.C.); (E.P.S.)
- Research Center “E. Piaggio”, University of Pisa, 56122 Pisa, Italy
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29
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Salivary Cortisol Values and Personality Features of Atopic Dermatitis Patients: A Prospective Study. Dermatitis 2022; 33:341-348. [PMID: 35089897 DOI: 10.1097/der.0000000000000834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Atopic dermatitis (AD) patients commonly experience psychological stress and impaired psychosocial functioning. OBJECTIVE The aim of this study was to compare patients' salivary cortisol levels with AD severity and other associated stress-related psychological measures/parameters. METHODS This prospective study analyzed salivary cortisol levels (enzyme-linked immunosorbent assay) in 84 AD patients (42 symptomatic patients and 42 asymptomatic patients). Each subject filled out the Perceived Stress Scale (PSS), Brief Illness Perception Questionnaire, and the Crown-Crisp Experiential Index, which concerns personality features. RESULTS Increased cortisol values were found in both groups and were not dependent on disease severity (Scoring Atopic Dermatitis [SCORAD]) and PSS. Patients with severe AD had significantly lower cortisol levels than those with moderate and mild AD (P = 0.042). The PSS levels were not dependent on SCORAD but correlated with the perceived effect of AD on emotional states (Illness Perception Questionnaire 8), personality traits, anxiety, and depression (P < 0.001). CONCLUSIONS The severity of perceived stress in AD patients is not adequately measured by salivary cortisol levels nor SCORAD; it does, however, correlate with the impact of AD on patients' emotional states and personality features (anxiety, depression). All AD patients, regardless of disease severity, should be assessed for impacts of stress, and a multidisciplinary approach should address mental wellness.
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Ćosić K, Popović S, Šarlija M, Kesedžić I, Gambiraža M, Dropuljić B, Mijić I, Henigsberg N, Jovanovic T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Front Psychol 2021; 12:782866. [PMID: 35027902 PMCID: PMC8751545 DOI: 10.3389/fpsyg.2021.782866] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/02/2021] [Indexed: 12/30/2022] Open
Abstract
The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.
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Affiliation(s)
- Krešimir Ćosić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Siniša Popović
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Marko Šarlija
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Ivan Kesedžić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Mate Gambiraža
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Branimir Dropuljić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Igor Mijić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Neven Henigsberg
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
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Iranfar A, Arza A, Atienza D. ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:535-541. [PMID: 34891350 DOI: 10.1109/embc46164.2021.9630040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.
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DellrAgnola F, Pale U, Marino R, Arza A, Atienza D. MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:994-1007. [PMID: 34495839 DOI: 10.1109/tbcas.2021.3110317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58 h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.
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Momeni N, Valdes AA, Rodrigues J, Sandi C, Atienza D. CAFS: Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices. IEEE Trans Biomed Eng 2021; 69:1072-1084. [PMID: 34543185 DOI: 10.1109/tbme.2021.3113593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Today, stress monitoring on wearable devices is challenged by the tension between high-detection accuracy and battery lifetime driven by multimodal data acquisition and processing. Limited research has addressed the classification cost on multimodal wearable sensors, particularly when the features are cost-dependent. Thus, we design a Cost-Aware Feature Selection (CAFS) methodology that trades-off between prediction-power and energy-cost for multimodal stress monitoring. METHODS CAFS selects the most important features under different energy-constraints, which allows us to obtain energy-scalable stress monitoring models. We further propose a self-aware stress monitoring method that intelligently switches among the energy-scalable models, reducing energy consumption. RESULTS Using CAFS methodology on experimental data and simulation, we reduce the energy-cost of the stress model designed without energy constrains up to 94.37%. We obtain 90.98% and 95.74% as the best accuracy and confidence values, respectively, on unseen data, outperforming state-of-the-art studies. Analyzing our interpretable and energy-scalable models, we showed that simple models that use only heart rate (HR) or skin conductance level (SCL), confidently predict stress for HR >93.30 BPM and non-stress for SCL <6.42S, but, outside these values, a multimodal model using respiration and pulse waves features is needed for confident stress classification. Our self-aware stress monitoring proposal saves10x energy and provides 88.72% of ac-curacy on unseen data. CONCLUSION We propose a comprehensive solution for the design of cost-aware stress monitoring addressing the problem of selecting an optimal feature subset considering their cost-dependency and cost-constrains. Significant: Our design framework enables long-term, confident, and accurate stress monitoring on wearable devices.
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de Pedro-Carracedo J, Fuentes-Jimenez D, Ugena AM, Gonzalez-Marcos AP. Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry. SENSORS (BASEL, SWITZERLAND) 2021; 21:5661. [PMID: 34451105 PMCID: PMC8402390 DOI: 10.3390/s21165661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/11/2021] [Accepted: 08/18/2021] [Indexed: 11/30/2022]
Abstract
This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0-1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed's biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.
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Affiliation(s)
- Javier de Pedro-Carracedo
- Departamento de Tecnología Fotónica y Bioingeniería, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), E-28040 Madrid, Spain
| | - David Fuentes-Jimenez
- Departamento de Electrónica, Universidad de Alcalá (UAH), Escuela Politécnica Superior, Alcalá de Henares (Madrid), E-28871 Alcalá de Henares, Spain
| | - Ana María Ugena
- Departamento de Matemática Aplicada a las Tecnologías de la Información, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), E-28040 Madrid, Spain
| | - Ana Pilar Gonzalez-Marcos
- Departamento de Tecnología Fotónica y Bioingeniería, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), E-28040 Madrid, Spain
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Pourmohammadi S, Maleki A. Continuous mental stress level assessment using electrocardiogram and electromyogram signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Human stress classification during public speaking using physiological signals. Comput Biol Med 2021; 133:104377. [PMID: 33866254 DOI: 10.1016/j.compbiomed.2021.104377] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/31/2021] [Accepted: 04/01/2021] [Indexed: 11/24/2022]
Abstract
Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity.
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SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. SENSORS 2021; 21:s21082725. [PMID: 33924351 PMCID: PMC8070644 DOI: 10.3390/s21082725] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/04/2021] [Accepted: 04/09/2021] [Indexed: 11/17/2022]
Abstract
The photoplethysmographic (PPG) signal is an unobtrusive blood pulsewave measure that has recently gained popularity in the context of the Internet of Things. Even though it is commonly used for heart rate detection, it has been lately employed on multimodal health and wellness monitoring applications. Unfortunately, this signal is prone to motion artifacts, making it almost useless in all situations where a person is not entirely at rest. To overcome this issue, we propose SPARE, a spectral peak recovery algorithm for PPG signals pulsewave reconstruction. Our solution exploits the local semiperiodicity of the pulsewave signal, together with the information about the cardiac rhythm provided by an available simultaneous ECG, to reconstruct its full waveform, even when affected by strong artifacts. The developed algorithm builds on state-of-the-art signal decomposition methods, and integrates novel techniques for signal reconstruction. Experimental results are reported both in the case of PPG signals acquired during physical activity and at rest, but corrupted in a systematic way by synthetic noise. The full PPG waveform reconstruction enables the identification of several health-related features from the signal, showing an improvement of up to 65% in the detection of different biomarkers from PPG signals affected by noise.
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A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare. Diagnostics (Basel) 2021; 11:diagnostics11030556. [PMID: 33808914 PMCID: PMC8003811 DOI: 10.3390/diagnostics11030556] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/14/2021] [Accepted: 03/17/2021] [Indexed: 12/05/2022] Open
Abstract
Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.
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Morais P, Quaresma C, Vigário R, Quintão C. Electrophysiological effects of mindfulness meditation in a concentration test. Med Biol Eng Comput 2021; 59:759-773. [PMID: 33728595 DOI: 10.1007/s11517-021-02332-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 02/03/2021] [Indexed: 11/26/2022]
Abstract
In this paper, we evaluate the effects of mindfulness meditation training in electrophysiological signals, recorded during a concentration task. Longitudinal experiments have been limited to the analysis of psychological scores through depression, anxiety, and stress state (DASS) surveys. Here, we present a longitudinal study, confronting DASS survey data with electrocardiography (ECG), electroencephalography (EEG), and electrodermal activity (EDA) signals. Twenty-five university student volunteers (mean age = 26, SD = 7, 9 male) attended a 25-h mindfulness-based stress reduction (MBSR) course, over a period of 8 weeks. There were four evaluation periods: pre/peri/post-course and a fourth follow-up, after 2 months. All three recorded biosignals presented congruent results, in line with the expected benefits of regular meditation practice. In average, EDA activity decreased throughout the course, -64.5%, whereas the mean heart rate displayed a small reduction, -5.8%, possibly as a result of an increase in parasympathetic nervous system activity. Prefrontal (AF3) cortical alpha activity, often associated with calm conditions, saw a very significant increase, 148.1%. Also, the number of stressed and anxious subjects showed a significant decrease, -92.9% and -85.7%, respectively. Easy to practice and within everyone's reach, this mindfulness meditation can be used proactively to prevent or enhance better quality of life. 25 volunteers attended a Mindfulness-Based Stress Reduction (MBSR) course in 4 evaluation periods: Pre/Peri/Post-course and a fourth follow-up after two months. A Depression, Anxiety and Stress State (DASS) survey is completed in each period. Electrodermal Activity (EDA), Electrocardiography (ECG) and Electroencephalography (EEG) are also recorded and processed. By integrating self-reported surveys and electrophysiological recordings there is strong evidence of evolution in wellbeing. Mindfulness meditation can be used proactively to prevent or enhance better quality of life.
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Affiliation(s)
- Pedro Morais
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics - Department of Physics, NOVA School of Science and Technology - NOVA University of Lisbon, Lisbon, Portugal.
| | - Claúdia Quaresma
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics - Department of Physics, NOVA School of Science and Technology - NOVA University of Lisbon, Lisbon, Portugal
| | - Ricardo Vigário
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics - Department of Physics, NOVA School of Science and Technology - NOVA University of Lisbon, Lisbon, Portugal
| | - Carla Quintão
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics - Department of Physics, NOVA School of Science and Technology - NOVA University of Lisbon, Lisbon, Portugal
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de la Vega R, Jiménez-Castuera R, Leyton-Román M. Impact of Weekly Physical Activity on Stress Response: An Experimental Study. Front Psychol 2021; 11:608217. [PMID: 33510685 PMCID: PMC7835705 DOI: 10.3389/fpsyg.2020.608217] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 12/04/2020] [Indexed: 11/15/2022] Open
Abstract
The aim of this research is focused on analyzing the alteration of the psychophysiological and cognitive response to an objective computerized stress test (Determination Test - DT-, Vienna test System®), when the behavioral response is controlled. The sample used was sports science students (N = 22), with a mean age of 22.82 (Mage = 22.82; SDyears = 3.67; MPhysicalActivity hours/Week = 7.77; SDhours/week = 3.32) A quasi-experimental design was used in which the response of each participant to the DT test was evaluated. The variable “number of hours of physical activity per week” and the variable “level of behavioral response to stress” were controlled. Before and after this test, the following parameters were measured: activation and central fatigue (Critical Flicker Fusion Threshold (CFF Critical flicker fusion ascending and Critical flicker fusion descending; DC potential), and perceived exertion (Central Rating of Perceived Exertion and Peripheral Rating of Perceived Exertion). Significant differences were found in all of the measures indicated. The usefulness of this protocol and the measures used to analyze the stress response capacity of the study subjects are discussed.
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Affiliation(s)
- Ricardo de la Vega
- Department of Physical Education, Sport and Human Movement, Autonomous University of Madrid, Madrid, Spain
| | - Ruth Jiménez-Castuera
- Didactic and Behavioral Analysis in Sport Research Group, Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain
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Iqbal T, Redon-Lurbe P, Simpkin AJ, Elahi A, Ganly S, Wijns W, Shahzad A. A Sensitivity Analysis of Biophysiological Responses of Stress for Wearable Sensors in Connected Health. IEEE ACCESS 2021; 9:93567-93579. [DOI: 10.1109/access.2021.3082423] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Kontaxis S, Gil E, Marozas V, Lazaro J, Garcia E, Posadas-de Miguel M, Siddi S, Bernal ML, Aguilo J, Haro JM, de la Camara C, Laguna P, Bailon R. Photoplethysmographic Waveform Analysis for Autonomic Reactivity Assessment in Depression. IEEE Trans Biomed Eng 2020; 68:1273-1281. [PMID: 32960759 DOI: 10.1109/tbme.2020.3025908] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE In the present study, a photoplethysmographic (PPG) waveform analysis for assessing differences in autonomic reactivity to mental stress between patients with Major Depressive Disorder (MDD) and healthy control (HC) subjects is presented. METHODS PPG recordings of 40 MDD and 40 HC subjects were acquired at basal conditions, during the execution of cognitive tasks, and at the post-task relaxation period. PPG pulses are decomposed into three waves (a main wave and two reflected waves) using a pulse decomposition analysis. Pulse waveform characteristics such as the time delay between the position of the main wave and reflected waves, the percentage of amplitude loss in the reflected waves, and the heart rate (HR) are calculated among others. The intra-subject difference of a feature value between two conditions is used as an index of autonomic reactivity. RESULTS Statistically significant individual differences from stress to recovery were found for HR and the percentage of amplitude loss in the second reflected wave ( A13) in both HC and MDD group. However, autonomic reactivity indices related to A13 reached higher values in HC than in MDD subjects (Cohen's [Formula: see text]), implying that the stress response in depressed patients is reduced. A statistically significant ( ) negative correlation ( r=-0.5) between depression severity scores and A13 was found. CONCLUSION A decreased autonomic reactivity is associated with higher degree of depression. SIGNIFICANCE Stress response quantification by dynamic changes in PPG waveform morphology can be an aid for the diagnosis and monitoring of depression.
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Pourmohammadi S, Maleki A. Stress detection using ECG and EMG signals: A comprehensive study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105482. [PMID: 32408236 DOI: 10.1016/j.cmpb.2020.105482] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 03/27/2020] [Accepted: 03/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE In recent years, stress and mental health have been considered as important worldwide concerns. Stress detection using physiological signals such as electrocardiogram (ECG), skin conductance (SC), electromyogram (EMG) and electroencephalogram (EEG) is a traditional approach. However, the effect of stress on the EMG signal of different muscles and the efficacy of combination of the EMG and other biological signals for stress detection have not been taken into account yet. This paper presents a comprehensive review of the EMG signal of the right and left trapezius and right and left erector spinae muscles for multi-level stress recognition. Also, the ECG signal was employed to evaluate the efficacy of EMG signals for stress detection. METHODS Both EMG and ECG signals were acquired simultaneously from 34 healthy students (23 females and 11 males, aged 20-37 years). Mental arithmetic, Stroop color-word test, time pressure, and stressful environment were employed to induce stress in the laboratory. RESULTS The accuracies of stress recognition in two, three and four levels were 100%, 97.6%, and 96.2%, respectively, obtained from the distinct combination of feature selection and machine learning algorithms. CONCLUSIONS The comparison of stress detection accuracies resulted from EMG and ECG indicators demonstrated the strong ability and the effectiveness of EMG signal for multi-level stress detection.
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Affiliation(s)
| | - Ali Maleki
- Biomedical Engineering Department, Semnan University, Semnan, Iran.
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A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers. Sci Rep 2020; 10:8600. [PMID: 32451424 PMCID: PMC7248090 DOI: 10.1038/s41598-020-65610-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/04/2020] [Indexed: 11/08/2022] Open
Abstract
Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the "fight-freeze-flight" response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller's stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator's health. In this regard, sixteen controllers were asked to perform a realistic air traffic management (ATM) simulation during which subjective data (i.e. stress perception) and neurophysiological data (i.e. brain activity, heart rate, and galvanic skin response) were collected with the aim of accurately characterising the controller's stress level experienced in the various experimental conditions. In addition, external supervisors regularly evaluated the controllers in terms of manifested stress, safety, and efficiency throughout the ATM scenario. The results demonstrated 1) how the stressful events caused both supervisors and controllers to underestimate the experienced stress level, 2) the advantage of taking into account both cognitive and hormonal processes in order to define a reliable stress index, and 3) the importance of the points in time at which stress is measured owing to the potential transient effect once the stressful events have ceased.
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Manning JB, Blandford A, Edbrooke-Childs J, Marshall P. How Contextual Constraints Shape Midcareer High School Teachers' Stress Management and Use of Digital Support Tools: Qualitative Study. JMIR Ment Health 2020; 7:e15416. [PMID: 32338623 PMCID: PMC7215497 DOI: 10.2196/15416] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 09/10/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Persistent psychosocial stress is endemic in the modern workplace, including among midcareer high school (secondary comprehensive) teachers in England. Understanding contextual influences on teachers' self-management of stress along with their use of digital health technologies could provide important insights into creating more usable and accessible stress support interventions. OBJECTIVE The aim of this study was to investigate the constraints on stress management and prevention among teachers in the school environment and how this shapes the use of digitally enabled stress management tools. METHODS Semistructured interviews were conducted with 14 teachers from southern England. The interviews were analyzed using thematic analysis. RESULTS Teachers were unanimous in their recognition of workplace stress, describing physical (such as isolation and scheduling) and cultural (such as stigma and individualism) aspects in the workplace context, which influence their ability to manage stress. A total of 12 participants engaged with technology to self-manage their physical or psychological well-being, with more than half of the participants using consumer wearables, but Web-based or smartphone apps were rarely accessed in school. However, digital well-being interventions recommended by school leaders could potentially be trusted and adopted. CONCLUSIONS The findings from this study bring together both the important cultural and physical contextual constraints on the ability of midcareer high school teachers to manage workplace stress. This study highlights correlates of stress and offers initial insight into how digital health interventions are currently being used to help with stress, both within and outside high schools. The findings add another step toward designing tailored digital stress support for teachers.
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Affiliation(s)
- Julia B Manning
- University College London Interaction Centre, London, United Kingdom
| | - Ann Blandford
- Institute of Healthcare Engineering, University College London, London, United Kingdom
| | - Julian Edbrooke-Childs
- Evidence-based Practice Unit, Anna Freud Centre and University College London, London, United Kingdom
| | - Paul Marshall
- Department of Computer Science, University of Bristol, London, United Kingdom
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Phase Space Reconstruction from a Biological Time Series: A Photoplethysmographic Signal Case Study. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10041430] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In the analysis of biological time series, the state space is comprised of a framework for the study of systems with presumably deterministic and stationary properties. However, a physiological experiment typically captures an observable that characterizes the temporal response of the physiological system under study; the dynamic variables that make up the state of the system at any time are not available. Only from the acquired observations should state vectors be reconstructed to emulate the different states of the underlying system. This is what is known as the reconstruction of the state space, called the phase space in real-world signals, in many cases satisfactorily resolved using the method of delays. Each state vector consists of m components, extracted from successive observations delayed a time τ . The morphology of the geometric structure described by the state vectors, as well as their properties depends on the chosen parameters τ and m. The real dynamics of the system under study is subject to the correct determination of the parameters τ and m. Only in this way can be deduced features have true physical meaning, revealing aspects that reliably identify the dynamic complexity of the physiological system. The biological signal presented in this work, as a case study, is the photoplethysmographic (PPG) signal. We find that m is five for all the subjects analyzed and that τ depends on the time interval in which it is evaluated. The Hénon map and the Lorenz flow are used to facilitate a more intuitive understanding of the applied techniques.
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Celka P, Charlton PH, Farukh B, Chowienczyk P, Alastruey J. Influence of mental stress on the pulse wave features of photoplethysmograms. Healthc Technol Lett 2019; 7:7-12. [PMID: 32190335 PMCID: PMC7067056 DOI: 10.1049/htl.2019.0001] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 06/30/2019] [Accepted: 08/19/2019] [Indexed: 01/20/2023] Open
Abstract
Mental stress is a major burden for our society. Invasive and non-invasive methods have been proposed to monitor and quantify it using various sensors on and off body. In this Letter, the authors investigated the use of the arm photoplethysmogram (PPG) to assess mental stress in laboratory conditions. Results were in correspondence with their previous in-silico study which guided the present study. Three wave shape parameters were identified for stress assessment from the PPG signal: (i) the time from dicrotic notch to end diastole; (ii) the time from pulse onset to systolic peak; and (iii) the ratio of diastolic to systolic area. The proposed in-vivo results showed that the two first parameters responded significantly to increased mental stress and to a breathing relaxation procedure, complementing heart rate, heart rate variability, and pulse transit time as indices of stress.
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Affiliation(s)
- Patrick Celka
- Polar Electro Oy, Professorintie 5, 90440 Kempele, Finland
| | - Peter H Charlton
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London SE1 7EH, UK
| | - Bushra Farukh
- King's College London British Heart Foundation Centre, Department of Clinical Pharmacology, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK
| | - Philip Chowienczyk
- King's College London British Heart Foundation Centre, Department of Clinical Pharmacology, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London SE1 7EH, UK.,Institute of Personalized Medicine, Sechenov University, Moscow, Russia
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