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Pirinen V, Eggers K, Dindar K, Helminen T, Kotila A, Kuusikko-Gauffin S, Mäkinen L, Ebeling H, Hurtig T, Mäntymaa M, Loukusa S. Associations between social anxiety, physiological reactivity, and speech disfluencies in autistic young adults and controls. J Commun Disord 2024; 109:106425. [PMID: 38593561 DOI: 10.1016/j.jcomdis.2024.106425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 02/02/2024] [Accepted: 04/01/2024] [Indexed: 04/11/2024]
Abstract
INTRODUCTION The aim of this study was to examine possible associations of social anxiety (SA) and speaking-related physiological reactivity with the frequencies of a) total disfluencies, b) typical disfluencies, and c) stuttering-like disfluencies, as well as d) stuttering-severity in autistic young adults and controls. METHODS Thirty-two autistic young adults and 35 controls participated in this study. Participants were presented with video clips (viewing condition) and were then asked to talk about the videos (narrating condition). SA was measured by the self-report Social Phobia and Anxiety Inventory (SPAI). Speaking-related physiological reactivity was measured by the electrodermal activity (EDA), an index of emotional arousal. The speech samples from the narrating condition were analyzed for type and frequency of speech disfluencies and used for determining the stuttering severity. SA and speaking-related physiological reactivity were compared between the groups. Correlation between SA, physiological reactivity, disfluency frequencies, and stuttering severity were tested separately for both groups. RESULTS No between-group differences were found in the overall SA, yet differences were found in SPAI subscales of social interaction, group interaction, and avoidance, as well as in agoraphobia. Both groups had higher physiological arousal in narrating condition in comparison to the video viewing condition, yet there was no between-group difference in the reactivity. No associations were found between SPAI measures, physiological reactivity, disfluency frequencies, and stuttering severity in the autistic group. In the control group, a negative association was found between physiological reactivity and total and typical disfluency frequencies. CONCLUSIONS SA or speaking-related physiological reactivity were not associated with disfluency frequencies or stuttering severity in autistic persons. Negative association between physiological reactivity and disfluency frequencies found in the control group may indicate that the physiological arousal may impact the speech production process by reducing the overt disfluencies.
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Affiliation(s)
- Veera Pirinen
- Research Unit of Logopedics, University of Oulu, Finland.
| | - Kurt Eggers
- Dept. of Rehabilitation Sciences, Ghent University, Belgium; Dept. of Speech-Language Pathology, Thomas More University College, Antwerp, Belgium; Dept. of Psychology and Speech-Language Pathology, University of Turku, Finland
| | - Katja Dindar
- Research Unit of Logopedics, University of Oulu, Finland
| | - Terhi Helminen
- Faculty of Social Sciences, Psychology, Tampere University, Finland
| | - Aija Kotila
- Research Unit of Logopedics, University of Oulu, Finland
| | | | - Leena Mäkinen
- Research Unit of Logopedics, University of Oulu, Finland
| | - Hanna Ebeling
- Research Unit of Clinical Medicine, Child Psychiatry, University of Oulu, Finland; Child Psychiatry, Oulu University Hospital, Finland
| | - Tuula Hurtig
- Child Psychiatry, Oulu University Hospital, Finland; Research Unit of Clinical Medicine, Psychiatry, Child Psychiatry, University of Oulu, Finland
| | - Mirjami Mäntymaa
- Research Unit of Clinical Medicine, Child Psychiatry, University of Oulu, Finland; Child Psychiatry, Oulu University Hospital, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, University of Oulu, Finland
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2
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Corponi F, Li BM, Anmella G, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie SM, Whalley HC, Hidalgo-Mazzei D, Vergari A. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Transl Psychiatry 2024; 14:161. [PMID: 38531865 DOI: 10.1038/s41398-024-02876-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/09/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.
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Affiliation(s)
- Filippo Corponi
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Bryan M Li
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Gerard Anmella
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Ariadna Mas
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Isabella Pacchiarotti
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Marc Valentí
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Iria Grande
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Antoni Benabarre
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Marina Garriga
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Antonio Vergari
- School of Informatics, University of Edinburgh, Edinburgh, UK
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Hu X, Sgherza TR, Nothrup JB, Fresco DM, Naragon-Gainey K, Bylsma LM. From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings. Behav Res Methods 2024:10.3758/s13428-024-02387-3. [PMID: 38528248 DOI: 10.3758/s13428-024-02387-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2024] [Indexed: 03/27/2024]
Abstract
Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.
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Affiliation(s)
- Xin Hu
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Tanika R Sgherza
- School of Psychological Science, University of Western Australia, Perth, Australia
| | - Jessie B Nothrup
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David M Fresco
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | | | - Lauren M Bylsma
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
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Bilucaglia M, Zito M, Fici A, Casiraghi C, Rivetti F, Bellati M, Russo V. I DARE: IULM Dataset of Affective Responses. Front Hum Neurosci 2024; 18:1347327. [PMID: 38571521 PMCID: PMC10987697 DOI: 10.3389/fnhum.2024.1347327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/28/2024] [Indexed: 04/05/2024] Open
Affiliation(s)
- Marco Bilucaglia
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, Milan, Italy
| | - Margherita Zito
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, Milan, Italy
| | - Alessandro Fici
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, Milan, Italy
| | - Chiara Casiraghi
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, Milan, Italy
| | - Fiamma Rivetti
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
| | - Mara Bellati
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
| | - Vincenzo Russo
- Behaviour and Brain Lab, Neuromarketing Research Center, Università IULM, Milan, Italy
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, Milan, Italy
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McVeigh K, Kleckner IR, Quigley KS, Satpute AB. Fear-related psychophysiological patterns are situation and individual dependent: A Bayesian model comparison approach. Emotion 2024; 24:506-521. [PMID: 37603002 PMCID: PMC10882564 DOI: 10.1037/emo0001265] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Is there a universal mapping of physiology to emotion, or do these mappings vary substantially by person or situation? Psychologists, philosophers, and neuroscientists have debated this question for decades. Most previous studies have focused on differentiating emotions on the basis of accompanying autonomic responses using analytical approaches that often assume within-category homogeneity. In the present study, we took an alternative approach to this question. We determined the extent to which the relationship between subjective experience and autonomic reactivity generalizes across, or depends upon, the individual and situation for instances of a single emotion category, specifically, fear. Electrodermal activity and cardiac activity-two autonomic measures that are often assumed to show robust relationships with instances of fear-were recorded while participants reported fear experience in response to dozens of fear-evoking videos related to three distinct situations: spiders, heights, and social encounters. We formally translated assumptions from diverse theoretical models into a common framework for model comparison analyses. Results exceedingly favored a model that assumed situation-dependency in the relationship between fear experience and autonomic reactivity, with subject variance also significant but constrained by situation. Models that assumed generalization across situations and/or individuals performed much worse by comparison. These results call into question the assumption of generalizability of autonomic-subjective mappings across instances of fear, as required in translational research from nonhuman animals to humans, and advance a situated approach to understanding the autonomic correlates of fear experience. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Kieran McVeigh
- Department of Psychology, Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02115
| | | | - Karen S. Quigley
- Department of Psychology, Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02115
| | - Ajay B. Satpute
- Department of Psychology, Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02115
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Yu H, Xu M, Xiao X, Xu F, Ming D. Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students. Cogn Neurodyn 2024; 18:173-184. [PMID: 38406194 PMCID: PMC10881450 DOI: 10.1007/s11571-023-09930-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/02/2022] [Accepted: 01/09/2023] [Indexed: 02/20/2023] Open
Abstract
It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.
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Affiliation(s)
- Haiqing Yu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xiaolin Xiao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Fangzhou Xu
- Department of Physics, School of Electronic and Information Engineering, Qilu University of Technology, Jinan, Shandong China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Kleckner IR, Wormwood JB, Jones RM, Culakova E, Barrett LF, Lord C, Quigley KS, Goodwin MS. Adaptive thresholding increases sensitivity to detect changes in the rate of skin conductance responses to psychologically arousing stimuli in both laboratory and ambulatory settings. Int J Psychophysiol 2024; 196:112280. [PMID: 38104772 PMCID: PMC10872538 DOI: 10.1016/j.ijpsycho.2023.112280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/03/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Psychophysiologists recording electrodermal activity (EDA) often derive measures of slow, tonic activity-skin conductance level (SCL)-and faster, more punctate changes-skin conductance responses (SCRs). A SCR is conventionally considered to have occurred when the local amplitude of the EDA signal exceeds a researcher-determined threshold (e.g., 0.05 μS), typically fixed across study participants and conditions. However, fixed SCR thresholds can preferentially exclude data from individuals with low SCL because their SCRs are smaller on average, thereby reducing statistical power for group-level analyses. Thus, we developed a fixed plus adaptive (FA) thresholding method that adjusts identification of SCRs based on an individual's SC at the onset of the SCR to increase statistical power and include data from more participants. We assess the utility of applying FA thresholding across two independent samples and explore age and race-related associations with EDA outcomes. Study 1 uses wired EDA measurements from 254 healthy adults responding to evocative images and sounds in a laboratory setting. Study 2 uses wireless EDA measurements from 20 children with autism in a clinical environment while they completed behavioral tasks. Compared to a 0.01, 0.03, and 0.05 μS fixed threshold, FA thresholding at 1.9% modestly increases statistical power to detect a difference in SCR rate between tasks with higher vs. lower subjective arousal and reduces exclusion of participants by up to 5% across both samples. This novel method expands the EDA analytical toolbox and may be useful in populations with highly variable basal SCL or when comparing groups with different basal SCL. Future research should test for reproducibility and generalizability in other tasks, samples, and contexts. IMPACT STATEMENTS: This article is important because it introduces a novel method to enhance sensitivity and statistical power in analyses of skin conductance responses from electrodermal data.
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Affiliation(s)
| | | | - Rebecca M Jones
- Weill Cornell Medicine, The Center for Autism and the Developing Brain, White Plains, NY, USA
| | - Eva Culakova
- University of Rochester Medical Center, Rochester, NY, USA
| | - Lisa Feldman Barrett
- Northeastern University, Boston, MA, USA; Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Catherine Lord
- Weill Cornell Medicine, The Center for Autism and the Developing Brain, White Plains, NY, USA; Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
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Greenlee JL, Lorang E, Olson RH, Rodriquez G, Yoon DM, Hartley S. Comparative analysis of electrodermal activity metrics and their association with child behavior in autism spectrum disorder. Dev Psychobiol 2024; 66:e22461. [PMID: 38388193 PMCID: PMC10901449 DOI: 10.1002/dev.22461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/07/2023] [Accepted: 01/05/2024] [Indexed: 02/24/2024]
Abstract
Researchers are increasingly utilizing physiological data like electrodermal activity (EDA) to understand how stress "gets under the skin." Results of EDA studies in autistic children are mixed, with some suggesting autistic hyperarousal, others finding hypoarousal, and yet others detecting no difference compared to non-autistics. Some of this variability likely stems from the different techniques used to assess EDA. Therefore, the purpose of this study is to investigate and compare commonly used metrics of EDA (frequency of peaks, average amplitude of peaks, and standard deviation of skin conductance level) using two data processing programs (NeuroKit2 and Ledalab) and their link to observed child behavior. EDA data were collected using Empatica E4 wristbands from 60 autistic children and adolescents (5-18 years old) during a 7-min play interaction with their primary caregiver. The play interaction was coded for a range of child behaviors including mood, social responsiveness, dysregulation, and cooperation. Results indicate a strong correlation between NeuroKit2 and Ledalab and a weak correlation between metrics within each program. Furthermore, the frequency of peaks was associated with more positive child social behaviors, and the magnitude of peaks was associated with less adaptive child behaviors. Recommendations for replication and the need for generalizability of this research are given.
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Affiliation(s)
| | - Emily Lorang
- Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan, USA
| | - Robert H Olson
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Geovanna Rodriquez
- Department of Special Education and Clinical Sciences, University of Oregon, Eugene, Oregon, USA
| | - Dasoo Milton Yoon
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- School of Human Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Sigan Hartley
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- School of Human Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Grasser LR, Erjo T, Goodwin MS, Naim R, German RE, White J, Cullins L, Tseng WL, Stoddard J, Brotman MA. Can peripheral psychophysiological markers predict response to exposure-based cognitive behavioral therapy in youth with severely impairing irritability? A study protocol. BMC Psychiatry 2023; 23:926. [PMID: 38082431 PMCID: PMC10712194 DOI: 10.1186/s12888-023-05421-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Irritability, an increased proneness to anger, is a primary reason youth present for psychiatric care. While initial evidence supports the efficacy of exposure-based cognitive behavioral therapy (CBT) for youth with clinically impairing irritability, treatment mechanisms remain unclear. Here, we propose to measure peripheral psychophysiological indicators of arousal-heart rate (HR)/electrodermal activity (EDA)-and regulation-heart rate variability (HRV)-during exposures to anger-inducing stimuli as potential predictors of treatment efficacy. The objective of this study is to evaluate whether in-situ biosensing data provides peripheral physiological indicators of in-session response to exposures. METHODS Blood volume pulse (BVP; from which HR and HRV canl be derived) and EDA will be collected ambulatorily using the Empatica EmbracePlus from 40 youth (all genders; ages 8-17) undergoing six in-person exposure treatment sessions, as part of a multiple-baseline trial of exposure-based CBT for clinically impairing irritability. Clinical ratings of irritability will be conducted at baseline, weekly throughout treatment, and at 3-month and 6-month follow-ups via the Clinical Global Impressions Scale (CGI) and the Affective Reactivity Index (ARI; clinician-, parent-, and child-report). Multilevel modeling will be used to assess within- and between-person changes in physiological arousal and regulation throughout exposure-based CBT and to determine whether individual differences are predictive of treatment response. DISCUSSION This study protocol leverages a wearable biosensor (Empatica) to continuously record HR/HRV (derived from BVP) and EDA during in-person exposure sessions for youth with clinically impairing irritability. Here, the goal is to identify changes in physiological arousal (EDA, HR) and regulation (HRV) over the course of treatment in tandem with changes in clinical symptoms. TRIAL REGISTRATION The participants in this study come from an overarching clinical trial (trial registration numbers: NCT02531893 first registered on 8/25/2015; last updated on 8/25/2023). The research project and all related materials were submitted and approved by the appropriate Institutional Review Board of the National Institute of Mental Health (NIMH).
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Affiliation(s)
- Lana Ruvolo Grasser
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Trinity Erjo
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Matthew S Goodwin
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
| | - Reut Naim
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Ramaris E German
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Jamell White
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Lisa Cullins
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Wan-Ling Tseng
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Joel Stoddard
- Department of Psychiatry and Biomedical Informatics, University of Colorado, School of Medicine, Aurora, CO, USA
| | - Melissa A Brotman
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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10
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Pattyn E, Thammasan N, Lutin E, Tourolle D, Van Kraaij A, Kosunen I, De Raedt W, Van Hoof C. Simulation of ambulatory electrodermal activity and the handling of low-quality segments. Comput Methods Programs Biomed 2023; 242:107859. [PMID: 37863009 DOI: 10.1016/j.cmpb.2023.107859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND AND OBJECTIVES Monitoring electrodermal activity (EDA) in daily life requires effective handling of low-quality segments, which are common in ambulatory EDA data. Although several low-quality handling methods have been implemented, systematic comparison of these methods, which requires a large annotated dataset, is lacking. METHODS Therefore, we proposed the simulation of realistic ambulatory EDA data starting from high-quality EDA signals, which were subsequently contaminated with varying concentrations of artifacts. Subsequently, three approaches for handling low-quality data were evaluated regarding the preservation of several EDA-derived features: removing all artifacts, interpolating over removed artifacts, and retaining all artifacts. Specifically, multiple EDA features were assessed, derived from response detection (evaluated using F1, precision, recall) as well as EDA, phasic, and tonic features (assessed using absolute error), by comparing the simulated EDA data with and without the inserted artifacts, using the latter as ground truth. RESULTS For response detection, retaining artifacts resulted in the highest F1-scores, while interpolating over removed artifacts achieved the highest F1-scores for the phasic signal. The approaches did significantly differ in the mean error for the phasic but not for the tonic component and raw EDA. CONCLUSION This work generated ambulatory EDA datasets of 200 h, containing 0.125 to 3 artifacts per minute, and showed that interpolation over removed artifacts was an effective approach to reconstruct phasic-derived features up to 2 artifacts per minute. The proposed simulation and evaluation methodology, which are easily customizable, offer opportunities for future research to develop and systematically compare signal quality indicators, decomposition methods, and response detectors for processing ambulatory EDA.
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Affiliation(s)
- E Pattyn
- Department of Electrical Engineering, Elektronische Circuits en Systemen (ECS), KU Leuven, Leuven (Arenberg), Kasteelpark Arenberg 10 - bus 2443, Heverlee, Leuven 3001, Belgium; OnePlanet Research Center, Wageningen, The Netherlands.
| | | | - E Lutin
- Department of Electrical Engineering, Elektronische Circuits en Systemen (ECS), KU Leuven, Leuven (Arenberg), Kasteelpark Arenberg 10 - bus 2443, Heverlee, Leuven 3001, Belgium; OnePlanet Research Center, Wageningen, The Netherlands
| | | | | | | | - W De Raedt
- OnePlanet Research Center, Wageningen, The Netherlands
| | - C Van Hoof
- Department of Electrical Engineering, Elektronische Circuits en Systemen (ECS), KU Leuven, Leuven (Arenberg), Kasteelpark Arenberg 10 - bus 2443, Heverlee, Leuven 3001, Belgium; Imec Leuven, Leuven, Belgium; OnePlanet Research Center, Wageningen, The Netherlands
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11
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Sanchez-Perez JA, Gazi AH, Rahman FN, Seith A, Saks G, Sundararaj S, Erbrick R, Harrison AB, Nichols CJ, Modak M, Chalumuri YR, Snow TK, Hahn JO, Inan OT. Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study. Front Neurosci 2023; 17:1213982. [PMID: 37746156 PMCID: PMC10512834 DOI: 10.3389/fnins.2023.1213982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/18/2023] [Indexed: 09/26/2023] Open
Abstract
Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life. This study aimed to evaluate the effectiveness of three well-established transcutaneous PNS modalities in reducing physiological manifestations of stress compared to a sham: auricular and cervical Vagus Nerve Stimulation (taVNS and tcVNS), and Median Nerve Stimulation (tMNS). Using a single-blind sham-controlled crossover study with four visits, we compared the stress mitigation effectiveness of taVNS, tcVNS, and tMNS, quantified through physiological markers derived from five physiological signals peripherally measured on 19 young healthy volunteers. Participants underwent three acute mental and physiological stressors while receiving stimulation. Blinding effectiveness was assessed via subjective survey. taVNS and tMNS relative to sham resulted in significant changes that suggest a reduction in sympathetic outflow following the acute stressors: Left Ventricular Ejection Time Index (LVETI) shortening (tMNS: p = 0.007, taVNS: p = 0.015) and Pre-Ejection Period (PEP)-to-LVET ratio (PEP/LVET) increase (tMNS: p = 0.044, taVNS: p = 0.029). tMNS relative to sham also reduced Pulse Pressure (PP; p = 0.032) and tonic EDA activity (tonicMean; p = 0.025). The nonsignificant blinding survey results suggest these effects were not influenced by placebo. taVNS and tMNS effectively reduced stress-induced sympathetic arousal in wearable-compatible physiological signals, motivating their future use in novel personalized stress therapies to improve quality of life.
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Affiliation(s)
| | - Asim H. Gazi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Farhan N. Rahman
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Alexis Seith
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Georgia Saks
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | | | - Rachel Erbrick
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Anna B. Harrison
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Christopher J. Nichols
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Mihir Modak
- Department of Bioengineering, University of Maryland, College Park, MD, United States
| | - Yekanth R. Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD, United States
| | - Teresa K. Snow
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD, United States
| | - Omer T. Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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12
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Valesi R, Gabrielli G, Zito M, Bellati M, Bilucaglia M, Caponetto A, Fici A, Galanto A, Falcone MG, Russo V. From Coaching to Neurocoaching: A Neuroscientific Approach during a Coaching Session to Assess the Relational Dynamics between Coach and Coachee-A Pilot Study. Behav Sci (Basel) 2023; 13:596. [PMID: 37504044 PMCID: PMC10376351 DOI: 10.3390/bs13070596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023] Open
Abstract
Life transitions represent moments characterized by changes that can profoundly influence individual life trajectories and subjective well-being. Recently, career coaching has become an important method of helping people expand their self-awareness, facilitate personal development, and increase their performance in the school-to-work transition. Although previous studies have confirmed that one of the most important keys to the success of a coaching program is the quality of the relationship between coach and coachee, there is a lack of knowledge regarding how to objectively measure it. In this pilot study, we adopted a neuroscientific approach to introduce objective measures of the relationship between coach and coachee through the phases of a coaching session. A sample of 14 university students and a professional coach participated in career-coaching sessions while their affective states were measured by recording brain (EEG) and physiological (Skin conductance) activity. Electroencephalographic indicators of valence, arousal, and engagement showed differences between session phases, highlighting the possibility of a neurophysiological measurement of relational dynamics. Our results provide initial evidence that neurophysiological activity can be considered a way to understand differences in the coach-coachee relationship, thereby providing information on the effectiveness of coaching interventions and facilitating a better life transition from school to work.
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Affiliation(s)
- Riccardo Valesi
- Department of Management, University of Bergamo, 24129 Bergamo, Italy
| | - Giorgio Gabrielli
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Margherita Zito
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Mara Bellati
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Marco Bilucaglia
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Alessia Caponetto
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Alessandro Fici
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Annarita Galanto
- Skillmatch-Insubria Group, Università Carlo Cattaneo-LIUC, 21053 Castellanza, Italy
| | | | - Vincenzo Russo
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM-Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
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13
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Amidei A, Spinsante S, Iadarola G, Benatti S, Tramarin F, Pavan P, Rovati L. Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance. Sensors (Basel) 2023; 23:4004. [PMID: 37112345 PMCID: PMC10143251 DOI: 10.3390/s23084004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 06/19/2023]
Abstract
The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver's physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness.
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Affiliation(s)
- Andrea Amidei
- Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, Italy; (A.A.)
| | - Susanna Spinsante
- Department of Information Engineering, Polytechnic University of Marche, 60131 Ancona, Italy
| | - Grazia Iadarola
- Department of Information Engineering, Polytechnic University of Marche, 60131 Ancona, Italy
| | - Simone Benatti
- Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, Italy; (A.A.)
| | - Federico Tramarin
- Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, Italy; (A.A.)
| | - Paolo Pavan
- Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, Italy; (A.A.)
| | - Luigi Rovati
- Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, Italy; (A.A.)
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14
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Anmella G, Corponi F, Li BM, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young AH, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study. JMIR Mhealth Uhealth 2023; 11:e45405. [PMID: 36939345 DOI: 10.2196/45405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture. OBJECTIVE We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data. METHODS We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses. RESULTS 35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52). CONCLUSIONS Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes. CLINICALTRIAL
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Affiliation(s)
- Gerard Anmella
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Filippo Corponi
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Bryan M Li
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Ariadna Mas
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Miriam Sanabra
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Isabella Pacchiarotti
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Marc Valentí
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Iria Grande
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Antoni Benabarre
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anna Giménez-Palomo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Marina Garriga
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Isabel Agasi
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anna Bastidas
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Myriam Cavero
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | | | - Néstor Arbelo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Miquel Bioque
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Clemente García-Rizo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Norma Verdolini
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Santiago Madero
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Andrea Murru
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Silvia Amoretti
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anabel Martínez-Aran
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Victoria Ruiz
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Giovanna Fico
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Michele De Prisco
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Vincenzo Oliva
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Aleix Solanes
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Barcelona, ES
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Barcelona, ES
| | - Ludovic Samalin
- Department of Psychiatry, CHU Clermont-Ferrand, University of Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal (UMR 6602), Clermont-Ferrand, France., Clermont-Ferrand, FR
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom., London, GB
| | - Eduard Vieta
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Antonio Vergari
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Diego Hidalgo-Mazzei
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
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15
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Böttcher S, Vieluf S, Bruno E, Joseph B, Epitashvili N, Biondi A, Zabler N, Glasstetter M, Dümpelmann M, Van Laerhoven K, Nasseri M, Brinkman BH, Richardson MP, Schulze-Bonhage A, Loddenkemper T. Data quality evaluation in wearable monitoring. Sci Rep 2022; 12:21412. [PMID: 36496546 PMCID: PMC9741649 DOI: 10.1038/s41598-022-25949-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.
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Affiliation(s)
- Sebastian Böttcher
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany ,grid.5836.80000 0001 2242 8751Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Solveig Vieluf
- grid.38142.3c000000041936754XDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MS USA
| | - Elisa Bruno
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Boney Joseph
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Nino Epitashvili
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Andrea Biondi
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Nicolas Zabler
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Martin Glasstetter
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany ,grid.5963.9Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Kristof Van Laerhoven
- grid.5836.80000 0001 2242 8751Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Mona Nasseri
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA ,grid.266865.90000 0001 2109 4358School of Engineering, University of North Florida, Jacksonville, FL USA
| | - Benjamin H. Brinkman
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Mark P. Richardson
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Andreas Schulze-Bonhage
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Tobias Loddenkemper
- grid.38142.3c000000041936754XDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MS USA
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16
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Sánchez-Reolid R, López de la Rosa F, Sánchez-Reolid D, López MT, Fernández-Caballero A. Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review. Sensors (Basel) 2022; 22:s22228886. [PMID: 36433482 PMCID: PMC9695360 DOI: 10.3390/s22228886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 05/14/2023]
Abstract
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.
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Affiliation(s)
- Roberto Sánchez-Reolid
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | | | - Daniel Sánchez-Reolid
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - María T. López
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - Antonio Fernández-Caballero
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
- CIBERSAM-ISCIII (Biomedical Research Networking Center in Mental Health, Instituto de Salud Carlos III), 28016 Madrid, Spain
- Correspondence:
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17
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Loveys K, Antoni M, Donkin L, Sagar M, Xu W, Broadbent E. Effects of Cognitive Behavioral Stress Management Delivered by a Virtual Human, Teletherapy, and an E-Manual on Psychological and Physiological Outcomes in Adult Women: An Experimental Test. MTI 2022; 6:99. [DOI: 10.3390/mti6110099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Technology may expand the reach of stress management to broader populations. However, issues with engagement can reduce intervention effectiveness. Technologies with highly social interfaces, such as virtual humans (VH), may offer advantages in this space. However, it is unclear how VH compare to telehealth and e-manuals at delivering psychological interventions. This experiment compared the effects of single laboratory session of Cognitive Behavioral Stress Management (CBSM) delivered by a VH (VH-CBSM), human telehealth (T-CBSM), and an e-manual (E-CBSM) on psychological and physiological outcomes in a community sample of stressed adult women. A pilot randomized controlled trial (RCT) with a parallel, mixed design was conducted. Adult women (M age =43.21, SD = 10.70) who self-identified as stressed were randomly allocated to VH-CBSM, T-CBSM, or E-CBSM involving one 90 min session and homework. Perceived stress, stress management skills, negative affect, optimism, relaxation, and physiological stress were measured. Mixed factorial ANOVAs and pairwise comparisons with Bonferroni correction investigated main and interaction effects of time and condition. Participants’ data (N = 38) were analysed (12 = VH-CBSM; 12 = T-CBSM; 14 = E-CBSM). Each condition significantly improved stress, negative affect, optimism, relaxation, and physiological stress over time with large effect sizes. No significant differences were found between conditions on outcomes. Overall, all three technologies showed promise for remotely delivering CBSM in a controlled setting. The findings suggest feasibility of the VH-CBSM delivery approach and support conducting a fully powered RCT to examine its effectiveness when delivering a full 10-week CBSM intervention.
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18
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Subramanian S, Tseng B, Barbieri R, Brown EN. An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting. Physiol Meas 2022; 43. [PMID: 36113446 DOI: 10.1088/1361-6579/ac92bd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/16/2022] [Indexed: 02/07/2023]
Abstract
Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible.Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it.Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances.Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring.
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Affiliation(s)
- Sandya Subramanian
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
| | - Bryan Tseng
- Picower Institute for Learning and Memory, Cambridge, MA, United States of America
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Engineering, Politecnico di Milano, Milano, Italy.,Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Emery N Brown
- Picower Institute for Learning and Memory, Cambridge, MA, United States of America.,Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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19
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Zhang Z, Amegbor PM, Sigsgaard T, Sabel CE. Assessing the association between urban features and human physiological stress response using wearable sensors in different urban contexts. Health Place 2022; 78:102924. [DOI: 10.1016/j.healthplace.2022.102924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/15/2022] [Accepted: 09/22/2022] [Indexed: 11/05/2022]
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20
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Russo V, Bilucaglia M, Circi R, Bellati M, Valesi R, Laureanti R, Licitra G, Zito M. The Role of the Emotional Sequence in the Communication of the Territorial Cheeses: A Neuromarketing Approach. Foods 2022; 11:foods11152349. [PMID: 35954114 PMCID: PMC9368719 DOI: 10.3390/foods11152349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022] Open
Abstract
Over the past few years, many studies have shown how territoriality can be considered a driver for purchasing agri-food products. Products with certification of origin are perceived as more sustainable, safer and of better quality. At the same time, producers of traditional products often belong to small entities that struggle to compete with large multinational food corporations, having less budget to allocate to product promotion. In this study, we propose a neuromarketing approach, showing how the use of these techniques can help in choosing the most effective commercial in terms of likeability and ability to activate mnemonic processes. Two commercials were filmed for the purpose of this study. They differed from each other in terms of emotional sequence. The first aimed primarily at eliciting positive emotions derived from the product description. The second aimed to generate negative emotions during the early stages, highlighting the negative consequences of humans' loss of contact with nature and tradition and then eliciting positive emotions by presenting cheese production using traditional techniques as a solution to the problem. Based on the literature on the emotional sequences in social advertising, we hypothesised that the second commercial would generate an overall better emotional reaction and activate mnemonic processes to a greater extent. Our results partially support the research hypotheses, providing useful insights both to marketers and for future research on the topic.
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Affiliation(s)
- Vincenzo Russo
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM—Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Marco Bilucaglia
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM—Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Riccardo Circi
- Behavior and Brain Lab IULM—Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
| | - Mara Bellati
- Institute of Agricultural Biology and Biotechnology (IBBA), National Research Council of Italy (CNR), 20133 Milan, Italy
- Correspondence:
| | - Riccardo Valesi
- Department of Management, Università degli Studi di Bergamo, 24129 Bergamo, Italy
| | - Rita Laureanti
- Departments of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy
| | - Giuseppe Licitra
- Departmentf of Agricolture, Food and Enviroment (Di3A), Università di Catania, 95123 Catania, Italy
| | - Margherita Zito
- Department of Business, Law, Economics and Consumer Behaviour “Carlo A. Ricciardi”, Università IULM, 20143 Milan, Italy
- Behavior and Brain Lab IULM—Neuromarketing Research Center, Università IULM, 20143 Milan, Italy
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21
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de Looff P, Duursma R, Noordzij M, Taylor S, Jaques N, Scheepers F, de Schepper K, Koldijk S. Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers. Front Behav Neurosci 2022; 16:856544. [PMID: 35813597 PMCID: PMC9262092 DOI: 10.3389/fnbeh.2022.856544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022] Open
Abstract
Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.
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Affiliation(s)
- Peter de Looff
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
- De Borg, Den Dolder, Netherlands
- Fivoor Science and Treatment Innovation, Den Dolder, Netherlands
- *Correspondence: Peter de Looff,
| | | | - Matthijs Noordzij
- Department of Psychology, Health and Technology, University of Twente, Enschede, Netherlands
| | - Sara Taylor
- Affective Computing Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Natasha Jaques
- Affective Computing Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Floortje Scheepers
- PsyData Group, Department of Psychiatry, UMC Utrecht, Utrecht, Netherlands
| | - Kees de Schepper
- PsyData Group, Department of Psychiatry, UMC Utrecht, Utrecht, Netherlands
| | - Saskia Koldijk
- PsyData Group, Department of Psychiatry, UMC Utrecht, Utrecht, Netherlands
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22
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Hossain MB, Posada-Quintero HF, Kong Y, McNaboe R, Chon KH. Automatic motion artifact detection in electrodermal activity data using machine learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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23
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Tronstad C, Amini M, Bach DR, Martinsen OG. Current trends and opportunities in the methodology of electrodermal activity measurement. Physiol Meas 2022; 43. [PMID: 35090148 DOI: 10.1088/1361-6579/ac5007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022]
Abstract
Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.
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Affiliation(s)
- Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, Sognsvannsveien 20, Oslo, 0372, NORWAY
| | - Maryam Amini
- Physics, University of Oslo Faculty of Mathematics and Natural Sciences, Sem Sælands vei 24, Oslo, 0371, NORWAY
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, London, WC1N 3AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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24
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Posada-Quintero HF, Landon CS, Stavitzski NM, Dean JB, Chon KH. Seizures Caused by Exposure to Hyperbaric Oxygen in Rats Can Be Predicted by Early Changes in Electrodermal Activity. Front Physiol 2022; 12:767386. [PMID: 35069238 PMCID: PMC8767060 DOI: 10.3389/fphys.2021.767386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Hyperbaric oxygen (HBO2) is breathed during undersea operations and in hyperbaric medicine. However, breathing HBO2 by divers and patients increases the risk of central nervous system oxygen toxicity (CNS-OT), which ultimately manifests as sympathetic stimulation producing tachycardia and hypertension, hyperventilation, and ultimately generalized seizures and cardiogenic pulmonary edema. In this study, we have tested the hypothesis that changes in electrodermal activity (EDA), a measure of sympathetic nervous system activation, precedes seizures in rats breathing 5 atmospheres absolute (ATA) HBO2. Radio telemetry and a rodent tether apparatus were adapted for use inside a sealed hyperbaric chamber. The tethered rat was free to move inside a ventilated animal chamber that was flushed with air or 100% O2. The animal chamber and hyperbaric chamber (air) were pressurized in parallel at ~1 atmosphere/min. EDA activity was recorded simultaneously with cortical electroencephalogram (EEG) activity, core body temperature, and ambient pressure. We have captured the dynamics of EDA using time-varying spectral analysis of raw EDA (TVSymp), previously developed as a tool for sympathetic tone assessment in humans, adjusted to detect the dynamic changes of EDA in rats that occur prior to onset of CNS-OT seizures. The results show that a significant increase in the amplitude of TVSymp values derived from EDA recordings occurs on average (±SD) 1.9 ± 1.6 min before HBO2-induced seizures. These results, if corroborated in humans, support the use of changes in TVSymp activity as an early "physio-marker" of impending and potentially fatal seizures in divers and patients.
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Affiliation(s)
- Hugo F Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Carol S Landon
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Nicole M Stavitzski
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Jay B Dean
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
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Posada-Quintero HF, Derrick BJ, Winstead-Derlega C, Gonzalez SI, Claire Ellis M, Freiberger JJ, Chon KH. Time-varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary results. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:1242-1245. [PMID: 34891512 DOI: 10.1109/embc46164.2021.9629924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The most effective method to mitigate decompression sickness in divers is hyperbaric oxygen (HBO2) pre-breathing. However, divers breathing HBO2 are at risk for developing central nervous system oxygen toxicity (CNS-OT), which can manifest as symptoms that might impair a diver's performance, or cause more serious symptoms like seizures. In this study, we have collected electrodermal activity (EDA) signals in fifteen subjects at elevated oxygen partial pressures (2.06 ATA, 35 FSW) in the "foxtrot" chamber pool at the Duke University Hyperbaric Center, while performing a cognitive stress test for up to 120 minutes. Specifically, we have computed the time-varying spectral analysis of EDA (TVSymp) as a tool for sympathetic tone assessment and evaluated its feasibility for the prediction of symptoms of CNS-OT in divers. The preliminary results show large increase in the amplitude TVSymp values derived from EDA recordings ~2 minutes prior to expert human adjudication of symptoms related to oxygen toxicity. An early detection based on TVSymp might allow the diver to take countermeasures against the dire consequences of CNS-OT which can lead to drowning.Clinical Relevance-This study provides a sensitive analysis method which indicates a significant increase in the electrodermal activity prior to human expert adjudication of symptoms related to CNS-OT.
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26
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Hossain MB, Posada-Quintero HF, Kong Y, McNaboe R, Chon KH. A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6920-6923. [PMID: 34892695 DOI: 10.1109/embc46164.2021.9629513] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.
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Föll S, Maritsch M, Spinola F, Mishra V, Barata F, Kowatsch T, Fleisch E, Wortmann F. FLIRT: A feature generation toolkit for wearable data. Comput Methods Programs Biomed 2021; 212:106461. [PMID: 34736174 DOI: 10.1016/j.cmpb.2021.106461] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 10/06/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction. METHODS FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions - a basis for a wide variety of ML algorithms. RESULTS We evaluated FLIRT on the publicly available WESAD dataset, which focuses on stress detection with an Empatica E4 wearable. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered. In the classification task, FLIRT outperforms the preprocessing baseline of the original WESAD paper. CONCLUSION FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs.
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Affiliation(s)
- Simon Föll
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Martin Maritsch
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Federica Spinola
- Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
| | - Varun Mishra
- Department of Computer Science, Dartmouth College, Hanover, NH, USA.
| | - Filipe Barata
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Tobias Kowatsch
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland; Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.
| | - Elgar Fleisch
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland; Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.
| | - Felix Wortmann
- Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.
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28
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Laureanti R, Bilucaglia M, Zito M, Circi R, Fici A, Rivetti F, Valesi R, Wahl S, Mainardi LT, Russo V. Yellow (Lens) Better: Bioelectrical and Biometrical Measures to Assess Arousing and Focusing Effects. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6163-6166. [PMID: 34892523 DOI: 10.1109/embc46164.2021.9630201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Colours can induce several psychological effects, conditioning perceptions, cognitive/emotional states and human performances. In this exploratory study we investigated the effect of a yellow light exposure, obtained filtering the ambient light with coloured glasses, on the human's psychological functioning. In particular we wanted to assess if people are more able to focus when exposed to a yellow light. We recorded EEG, SC, HR and gaze-related data from 16 subjects (50% split in experimental and control group) during the execution of a reactivity test (the Hazard Perception Test, HPT). Compared with the control group, the experimental group showed increases in concentration, focus, visual attention and arousal, as measured by increases of first fixation duration and Beta over-Alpha ratio (BAR) as well as by decreases of distraction, workload, and number of gaze revisits.
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Susam B, Riek N, Akcakaya M, Xu X, de Sa V, Nezamfar H, Diaz D, Craig K, Goodwin M, Huang J. Automated Pain Assessment in Children using Electrodermal Activity and Video Data Fusion via Machine Learning. IEEE Trans Biomed Eng 2021; 69:422-431. [PMID: 34242161 DOI: 10.1109/tbme.2021.3096137] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of childrens self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable of discriminating clinically significant pain from clinically nonsignificant pain in children.
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Hoyniak CP, McQuillan MM, Bates JE, Staples AD, Schwichtenberg A, Honaker SM. Presleep Arousal and Sleep in Early Childhood. J Genet Psychol 2021; 182:236-251. [PMID: 33870880 PMCID: PMC8684049 DOI: 10.1080/00221325.2021.1905596] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/26/2021] [Indexed: 10/21/2022]
Abstract
Research suggests that arousal during the transition to sleep-presleep arousal-is associated with sleep disturbances. Although a robust literature has examined the role of presleep arousal in conferring risk for sleep disturbances in adults, substantially less research has examined the developmental origins of presleep arousal in early childhood. The authors examined presleep arousal using parent report and psychophysiological measures in a sample of preschoolers to explore the association between different measures of presleep arousal, and to examine how nightly presleep arousal is associated with sleep. Participants included 29 children assessed at 54 months of age. Presleep arousal was measured using parent reports of child arousal each night at bedtime and using a wearable device that took minute-by-minute recordings of heart rate, peripheral skin temperature, and electrodermal activity each night during the child's bedtime routine. This yielded a dataset with 4,550 min of ambulatory recordings across an average of 3.52 nights per child (SD = 1.84 nights per child; range = 1-8 nights). Sleep was estimated using actigraphy. Findings demonstrated an association between parent-reported and psychophysiological arousal, including heart rate, peripheral skin temperature, and skin conductance responses during the child's bedtime routine. Both the parent report and psychophysiological measures of presleep arousal showed some associations with poorer sleep, with the most robust associations occurring between presleep arousal and sleep onset latency. Behavioral and biological measures of hyperarousal at bedtime are associated with poorer sleep in young children. Findings provide early evidence of the utility of wearable devices for assessing individual differences in presleep arousal in early childhood.
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Affiliation(s)
- Caroline P. Hoyniak
- Department of Psychiatry, Washington University in St. Louis School of Medicine, USA
| | | | - John E. Bates
- Department of Psychological and Brain Sciences, Indiana University – Bloomington, USA
| | | | | | - Sarah M. Honaker
- Department of Pediatrics, Indiana University School of Medicine, USA
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Chong PLH, Abel E, Pao R, McCormick CEB, Schwichtenberg AJ. Sleep Dysregulation and Daytime Electrodermal Patterns in Children With Autism: A Descriptive Study. J Genet Psychol 2021; 182:335-347. [PMID: 33860740 DOI: 10.1080/00221325.2021.1911919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Sleep deficiency influences emotion and behavior regulation but the mechanisms of influence are poorly understood. Emotion, behavioral, and sleep theories highlight differences in autonomic function as a potential pathway of influence and research in typical populations draw links between sleep deficiency and autonomic dysregulation (e.g., elevated reactivity within the sympathetic nervous system). In populations at elevated risk for sleep deficiency/problems (i.e., individuals with autism), greater variability in sleep and autonomic/arousal profiles may be particularly informative. Using electrodermal activity (EDA) as an indicator of sympathetic nervous system activation, this descriptive pilot study aimed to document daytime EDA patterns in children with autism and to explore their relations with sleep dysregulation/deficiency. EDA and sleep were measured using ankle and wrist worn sensors in 13 children (Meanage 6.11 years). EDA indices included nonspecific skin conductance responses (NSSCR) and tonic skin conductance levels (SCL). Descriptively, children in the dysregulated sleep group had fewer NSSCRs and lower SCL in the afternoon. This blunted physiological arousal profile/pattern is consistent with previous research, but this is the first study to explore how sleep may be linked. Notably, this pattern may not reflect sleep but an overall dysregulation profile which in this sample included: dysregulated sleep, a blunted afternoon arousal profile, and elevated ASD symptom severity. Replication with larger, more diverse samples is needed to disentangle the complex relations among sleep, arousal, and ASD behavioral features. However, this study represents an important first step in documenting extended daytime arousal patterns.
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Affiliation(s)
- Pearlynne Li Hui Chong
- Department of Human Development and Family Studies, Purdue University, West Lafayette, USA
| | - Emily Abel
- Department of Human Development and Family Studies, Purdue University, West Lafayette, USA
| | - Ryan Pao
- Department of Human Development and Family Studies, Purdue University, West Lafayette, USA
| | - Carolyn E B McCormick
- Department of Human Development and Family Studies, Purdue University, West Lafayette, USA
| | - A J Schwichtenberg
- Department of Human Development and Family Studies, Purdue University, West Lafayette, USA
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Ueafuea K, Boonnag C, Sudhawiyangkul T, Leelaarporn P, Gulistan A, Chen W, Mukhopadhyay SC, Wilaiprasitporn T, Piyayotai S. Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review. IEEE Sens J 2021; 21:7162-7178. [PMID: 37974630 PMCID: PMC8768987 DOI: 10.1109/jsen.2020.3046259] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/12/2020] [Accepted: 12/17/2020] [Indexed: 11/14/2023]
Abstract
The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.
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Affiliation(s)
- Kawisara Ueafuea
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | | | - Thapanun Sudhawiyangkul
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Pitshaporn Leelaarporn
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Ameen Gulistan
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Wei Chen
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
- Human Phenome Institute, Fudan UniversityShanghai200433China
| | | | - Theerawit Wilaiprasitporn
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Supanida Piyayotai
- Learning Institute, King Mongkut’s University of Technology ThonburiBangkok10140Thailand
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Davila-Montero S, Dana-Le JA, Bente G, Hall AT, Mason AJ. Review and Challenges of Technologies for Real-Time Human Behavior Monitoring. IEEE Trans Biomed Circuits Syst 2021; 15:2-28. [PMID: 33606635 DOI: 10.1109/tbcas.2021.3060617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A person's behavior significantly influences their health and well-being. It also contributes to the social environment in which humans interact, with cascading impacts to the health and behaviors of others. During social interactions, our understanding and awareness of vital nonverbal messages expressing beliefs, emotions, and intentions can be obstructed by a variety of factors including greatly flawed self-awareness. For these reasons, human behavior is a very important topic to study using the most advanced technology. Moreover, technology offers a breakthrough opportunity to improve people's social awareness and self-awareness through machine-enhanced recognition and interpretation of human behaviors. This paper reviews (1) the social psychology theories that have established the framework to study human behaviors and their manifestations during social interactions and (2) the technologies that have contributed to the monitoring of human behaviors. State-of-the-art in sensors, signal features, and computational models are categorized, summarized, and evaluated from a comprehensive transdisciplinary perspective. This review focuses on assessing technologies most suitable for real-time monitoring while highlighting their challenges and opportunities in near-future applications. Although social behavior monitoring has been highly reported in psychology and engineering literature, this paper uniquely aims to serve as a disciplinary convergence bridge and a guide for engineers capable of bringing new technologies to bear against the current challenges in real-time human behavior monitoring.
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Bizzego A, Gabrieli G, Furlanello C, Esposito G. Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals. Sensors (Basel) 2020; 20:E6778. [PMID: 33260880 PMCID: PMC7730565 DOI: 10.3390/s20236778] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/20/2020] [Accepted: 11/25/2020] [Indexed: 12/29/2022]
Abstract
A key access point to the functioning of the autonomic nervous system is the investigation of peripheral signals. Wearable devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. However, the achievable data quality can be lower, and data are subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study, we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and electrodermal activity signals is validated with a standard set of signal quality indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.
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Affiliation(s)
- Andrea Bizzego
- Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, Italy;
| | - Giulio Gabrieli
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;
| | | | - Gianluca Esposito
- Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, Italy;
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
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Laureanti R, Bilucaglia M, Zito M, Circi R, Fici A, Rivetti F, Valesi R, Oldrini C, Mainardi LT, Russo V. Emotion assessment using Machine Learning and low-cost wearable devices. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:576-579. [PMID: 33018054 DOI: 10.1109/embc44109.2020.9175221] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The advancement in bioelectrical measurement technologies and the push towards a higher impact of the Brain Computer Interfaces and Affective Computing in the daily life have made non-invasive and low-priced devices available to the large population to record physiological states. The aim of this study is the assessment of the abilities of the MUSE headband, together with the Shimmer GSR+ device, to assess the emotional state of people during stimuli exposure. Twenty-four pictures from the IAPS database were showed to 54 subjects and were evaluated in their emotional values by means of the Self-Assessment Manikin (SAM). Using a Machine Learning approach, fifty-two scalar features were extracted from the signals and used to train 6 binary classifiers to predict the valence and arousal elicited by each stimulus. In all classifiers we obtained accuracies ranging from 53.6% to 69.9%, confirming that these devices are able to give information about the emotional state.
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Thammasan N, Stuldreher IV, Schreuders E, Giletta M, Brouwer AM. A Usability Study of Physiological Measurement in School Using Wearable Sensors. Sensors (Basel) 2020; 20:E5380. [PMID: 32962191 PMCID: PMC7570846 DOI: 10.3390/s20185380] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 12/13/2022]
Abstract
Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.
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Affiliation(s)
- Nattapong Thammasan
- Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands;
| | - Ivo V. Stuldreher
- Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands;
- Perceptual and Cognitive Systems, The Netherlands Organisation for Applied Scientific Research (TNO), 3769 DE Soesterberg, The Netherlands;
| | - Elisabeth Schreuders
- Department Developmental Psychology, Institute of Psychology, Tilburg University, 5000 LE Tilburg, The Netherlands; (E.S.); (M.G.)
| | - Matteo Giletta
- Department Developmental Psychology, Institute of Psychology, Tilburg University, 5000 LE Tilburg, The Netherlands; (E.S.); (M.G.)
- Department of Developmental, Personality and Social Psychology, Faculty of Psychology and Educational Sciences, Ghent University, 9000 Ghent, Belgium
| | - Anne-Marie Brouwer
- Perceptual and Cognitive Systems, The Netherlands Organisation for Applied Scientific Research (TNO), 3769 DE Soesterberg, The Netherlands;
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Bilucaglia M, Laureanti R, Zito M, Circi R, Fici A, Rivetti F, Valesi R, Wahl S, Russo V. Looking through blue glasses: bioelectrical measures to assess the awakening after a calm situation .. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:526-529. [PMID: 31945953 DOI: 10.1109/embc.2019.8856486] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Colors can elicit cognitive and emotional states. In particular, blue colour is associated to "refresh" and "restart" effects and is suggested to enhance a wake-up after a calm situation. In this exploratory study, these claims are investigated using Electroencephalographic (EEG), Skin Conductance (SC) and pupil diameter data. The results confirmed the "wake-up effect" for subjects wearing the lenses, as measured by Global Field Power (GFP) in Theta Band, Skin Conductance Response (SCR) and pupil diameter data.
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Posada-Quintero HF, Chon KH. Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review. Sensors (Basel) 2020; 20:E479. [PMID: 31952141 PMCID: PMC7014446 DOI: 10.3390/s20020479] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/06/2020] [Accepted: 01/11/2020] [Indexed: 02/05/2023]
Abstract
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.
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Affiliation(s)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA;
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Abstract
Background: Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. Objective: This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. Methods: We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. Results: We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. Conclusions: The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.
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Affiliation(s)
- Donna L Coffman
- Department of Epidemiology and Biostatistics, Temple University, Philadelphia, PA, United States
| | - Xizhen Cai
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, United States
| | - Runze Li
- Department of Statistics, Pennsylvania State University, State College, PA, United States
| | - Noelle R Leonard
- Rory Meyers College of Nursing, New York University, New York City, NY, United States
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Pfeiffer B, Stein Duker L, Murphy A, Shui C. Effectiveness of Noise-Attenuating Headphones on Physiological Responses for Children With Autism Spectrum Disorders. Front Integr Neurosci 2019; 13:65. [PMID: 31798424 PMCID: PMC6863142 DOI: 10.3389/fnint.2019.00065] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 10/21/2019] [Indexed: 12/11/2022] Open
Abstract
Objective: The purpose of this study was to evaluate the proof of concept of an intervention to decrease sympathetic activation as measured by skin conductivity (electrodermal activity, EDA) in children with an autism spectrum disorder (ASD) and auditory hypersensitivity (hyperacusis). In addition, researchers examined if the intervention provided protection against the negative effects of decibel level of environmental noises on electrodermal measures between interventions. The feasibility of implementation and outcome measures within natural environments were evaluated. Method: A single-subject multi-treatment design was used with six children, aged 8–16 years, with a form of Autism (i.e., Autism, PDD-NOS). Participants used in-ear (IE) and over-ear (OE) headphones for two randomly sequenced treatment phases. Each child completed four phases: (1) a week of baseline data collection; (2) a week of an intervention; (3) a week of no intervention; and (4) a week of the other intervention. Empatica E4 wristbands collected EDA data. Data was collected on 16–20 occasions per participant, with five measurements per phase. Results: Separated tests for paired study phases suggested that regardless of intervention type, noise attenuating headphones led to a significance difference in both skin conductance levels (SCL) and frequency of non-specific conductance responses (NS-SCRs) between the baseline measurement and subsequent phases. Overall, SCL and NS-SCR frequency significantly decreased between baseline and the first intervention phase. A protective effect of the intervention was tested by collapsing intervention results into three phases. Slope correlation suggested constant SCL and NS-SCR frequency after initial use of the headphones regardless of the increase in environmental noises. A subsequent analysis of the quality of EDA data identified that later phases of data collection were associated with better data quality. Conclusion: Many children with ASD have hypersensitivities to sound resulting in high levels of sympathetic nervous system reactivity, which is associated with problematic behaviors and distress. The findings of this study suggest that the use of noise attenuating headphones for individuals with ASD and hyperacusis may reduce sympathetic activation. Additionally, results suggest that the use of wearable sensors to collect physiological data in natural environments is feasible with established protocols and training procedures.
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Affiliation(s)
- Beth Pfeiffer
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
| | - Leah Stein Duker
- USC Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - AnnMarie Murphy
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
| | - Chengshi Shui
- School of Nursing, University of California, Los Angeles, Los Angeles, CA, United States
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Smith KE, Mason TB, Juarascio A, Schaefer LM, Crosby RD, Engel SG, Wonderlich SA. Moving beyond self-report data collection in the natural environment: A review of the past and future directions for ambulatory assessment in eating disorders. Int J Eat Disord 2019; 52:1157-1175. [PMID: 31313348 PMCID: PMC6942694 DOI: 10.1002/eat.23124] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 12/14/2022]
Abstract
OBJECTIVE In recent years, ecological momentary assessment (EMA) has been used to repeatedly assess eating disorder (ED) symptoms in naturalistic settings, which has allowed for increased understanding of temporal processes that potentiate ED behaviors. However, there remain notable limitations of self-report EMA, and with the rapid proliferation of technology there are ever-increasing possibilities to improve ambulatory assessment methods to further the understanding and treatment of EDs. Therefore, the purpose of this review was to (a) systematically review the studies in EDs that have utilized ambulatory assessment methods other than self-report, and (b) provide directions for future research and clinical applications. METHOD A systematic literature search of electronic databases was conducted, and data regarding study characteristics and methodological quality were extracted. RESULTS The search identified 17 studies that used ambulatory assessment methods to gather objective data, and focused primarily on autonomic functioning, physical activity, and cognitive processes in ED and control groups. DISCUSSION Together the literature demonstrates the promise of using a range of ecologically valid ambulatory assessment approaches in EDs, though there remains limited research that has utilized methods other than self-report (e.g., wearable sensors), particularly in recent years. Going forward, there are several technology-enhanced momentary assessment methods that have potential to improve the understanding and treatment of EDs.
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Affiliation(s)
- Kathryn E. Smith
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota,Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
| | - Tyler B. Mason
- Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | | | - Lauren M. Schaefer
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota
| | - Ross D. Crosby
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota,Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
| | - Scott G. Engel
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota,Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
| | - Stephen A. Wonderlich
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota,Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
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de Looff P, Noordzij ML, Moerbeek M, Nijman H, Didden R, Embregts P. Changes in heart rate and skin conductance in the 30 min preceding aggressive behavior. Psychophysiology 2019; 56:e13420. [PMID: 31184379 DOI: 10.1111/psyp.13420] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 05/10/2019] [Accepted: 05/12/2019] [Indexed: 11/28/2022]
Abstract
Aggressive behavior of inpatients threatens the safety and well-being of both mental health staff members and fellow patients. It was investigated whether heart rate and electrodermal activity can be used to signal imminent aggression. A naturalistic study was conducted in which 100 inpatients wore sensor wristbands during 5 days to monitor their heart rate and electrodermal activity while staff members recorded patients' aggressive incidents on the ward. Of the 100 patients, 36 displayed at least one aggressive incident. Longitudinal multilevel models indicated that heart rate, skin conductance level, and the number of nonspecific skin conductance responses per minute rose significantly in the 20 min preceding aggressive incidents. Although psychopathy was modestly correlated with displaying aggression, it was not a significant predictor of heart rate and skin conductance preceding aggression. The current findings may provide opportunities for the development of individual prediction models to aid acute risk assessment and to predict aggressive incidents in an earlier stage. The current results on the physiological indicators of aggression are promising for reducing aggression and improving both staff as well as patient safety in psychiatric mental health institutions.
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Affiliation(s)
- Peter de Looff
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands.,Wier, Specialized and Forensic Care, Den Dolder, The Netherlands.,De Borg, National Expertcentre Specialized and Forensic Care, Den Dolder, The Netherlands
| | - Matthijs L Noordzij
- Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands
| | - Mirjam Moerbeek
- Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands
| | - Henk Nijman
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands.,Wier, Specialized and Forensic Care, Den Dolder, The Netherlands.,De Borg, National Expertcentre Specialized and Forensic Care, Den Dolder, The Netherlands
| | - Robert Didden
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands.,De Borg, National Expertcentre Specialized and Forensic Care, Den Dolder, The Netherlands.,Trajectum, Specialized and Forensic Care, Zwolle, The Netherlands
| | - Petri Embregts
- Department of Tranzo, Tilburg School of Social and Behavioural Sciences, Tilburg University, Tilburg, The Netherlands
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Ness SL, Bangerter A, Manyakov NV, Lewin D, Boice M, Skalkin A, Jagannatha S, Chatterjee M, Dawson G, Goodwin MS, Hendren R, Leventhal B, Shic F, Frazier JA, Janvier Y, King BH, Miller JS, Smith CJ, Tobe RH, Pandina G. An Observational Study With the Janssen Autism Knowledge Engine (JAKE ®) in Individuals With Autism Spectrum Disorder. Front Neurosci 2019; 13:111. [PMID: 30872988 PMCID: PMC6402449 DOI: 10.3389/fnins.2019.00111] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 01/30/2019] [Indexed: 11/13/2022] Open
Abstract
Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was "easy" (69%, 74/108) or "very easy" (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991.
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Affiliation(s)
- Seth L. Ness
- Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, FL, United States
| | - Abigail Bangerter
- Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, FL, United States
| | - Nikolay V. Manyakov
- Computational Biology, Discovery Sciences, Janssen Research & Development, Beerse, Belgium
| | - David Lewin
- Statistically Speaking Consulting, LLC, Chicago, IL, United States
| | - Matthew Boice
- Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, FL, United States
| | - Andrew Skalkin
- Informatics, Janssen Research & Development, Spring House, PA, United States
| | - Shyla Jagannatha
- Statistical Decision Sciences, Janssen Research & Development, Titusville, NJ, United States
| | - Meenakshi Chatterjee
- Computational Biology, Discovery Sciences, Janssen Research & Development, Spring House, PA, United States
| | - Geraldine Dawson
- Departments of Psychiatry and Behavioral Sciences, Duke Center for Autism and Brain Development, Duke University School of Medicine, Durham, NC, United States
| | - Matthew S. Goodwin
- Department of Health Sciences, Northeastern University, Boston, MA, United States
| | - Robert Hendren
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Bennett Leventhal
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Frederick Shic
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, United States
- Department of Pediatrics, University of Washington, Seattle, WA, United States
| | - Jean A. Frazier
- Eunice Kennedy Shriver Center and Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, United States
| | - Yvette Janvier
- Department of Developmental-Behavioral Pediatrics, Children's Specialized Hospital, Toms River, NJ, United States
| | - Bryan H. King
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Judith S. Miller
- Center for Autism Research, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | | | - Russell H. Tobe
- Department of Outpatient Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Gahan Pandina
- Neuroscience Therapeutic Area, Janssen Research & Development, Pennington, NJ, United States
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44
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Nabian M, Yin Y, Wormwood J, Quigley KS, Barrett LF, Ostadabbas S. An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data. IEEE J Transl Eng Health Med 2018; 6:2800711. [PMID: 30443441 PMCID: PMC6231905 DOI: 10.1109/jtehm.2018.2878000] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 09/05/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.
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Affiliation(s)
- Mohsen Nabian
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
- Harvard Medical SchoolBostonMA02115USA
| | - Yu Yin
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| | | | | | - Lisa F. Barrett
- Department of PsychologyNortheastern UniversityBostonMA02115USA
| | - Sarah Ostadabbas
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
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