Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study
BackgroundSocial anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences....
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-09-01
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| Series: | JMIR Serious Games |
| Online Access: | https://games.jmir.org/2022/3/e38284 |
| _version_ | 1797734758499745792 |
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| author | Joo Young Chun Hyun-Jin Kim Ji-Won Hur Dooyoung Jung Heon-Jeong Lee Seung Pil Pack Sungkil Lee Gerard Kim Chung-Yean Cho Seung-Moo Lee Hyeri Lee Seungmoon Choi Taesu Cheong Chul-Hyun Cho |
| author_facet | Joo Young Chun Hyun-Jin Kim Ji-Won Hur Dooyoung Jung Heon-Jeong Lee Seung Pil Pack Sungkil Lee Gerard Kim Chung-Yean Cho Seung-Moo Lee Hyeri Lee Seungmoon Choi Taesu Cheong Chul-Hyun Cho |
| author_sort | Joo Young Chun |
| collection | DOAJ |
| description |
BackgroundSocial anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences.
ObjectiveThis study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions.
MethodsThis study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data.
ResultsThe severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model.
ConclusionsThis study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias.
Trial RegistrationClinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508 |
| first_indexed | 2024-03-12T12:49:05Z |
| format | Article |
| id | doaj.art-fa40e4ef0cb44ec584c7c83abed9781e |
| institution | Directory Open Access Journal |
| issn | 2291-9279 |
| language | English |
| last_indexed | 2024-03-12T12:49:05Z |
| publishDate | 2022-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Serious Games |
| spelling | doaj.art-fa40e4ef0cb44ec584c7c83abed9781e2023-08-28T23:05:54ZengJMIR PublicationsJMIR Serious Games2291-92792022-09-01103e3828410.2196/38284Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods StudyJoo Young Chunhttps://orcid.org/0000-0003-1032-9849Hyun-Jin Kimhttps://orcid.org/0000-0002-0312-7862Ji-Won Hurhttps://orcid.org/0000-0002-1939-7365Dooyoung Junghttps://orcid.org/0000-0002-5381-4847Heon-Jeong Leehttps://orcid.org/0000-0002-9560-2383Seung Pil Packhttps://orcid.org/0000-0002-4874-0265Sungkil Leehttps://orcid.org/0000-0003-0123-9382Gerard Kimhttps://orcid.org/0000-0001-9880-8021Chung-Yean Chohttps://orcid.org/0000-0003-0879-2535Seung-Moo Leehttps://orcid.org/0000-0002-4958-353XHyeri Leehttps://orcid.org/0000-0002-1386-8349Seungmoon Choihttps://orcid.org/0000-0002-5889-1083Taesu Cheonghttps://orcid.org/0000-0002-5146-7394Chul-Hyun Chohttps://orcid.org/0000-0002-1663-9680 BackgroundSocial anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. ObjectiveThis study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. MethodsThis study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. ResultsThe severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. ConclusionsThis study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias. Trial RegistrationClinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508https://games.jmir.org/2022/3/e38284 |
| spellingShingle | Joo Young Chun Hyun-Jin Kim Ji-Won Hur Dooyoung Jung Heon-Jeong Lee Seung Pil Pack Sungkil Lee Gerard Kim Chung-Yean Cho Seung-Moo Lee Hyeri Lee Seungmoon Choi Taesu Cheong Chul-Hyun Cho Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study JMIR Serious Games |
| title | Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study |
| title_full | Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study |
| title_fullStr | Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study |
| title_full_unstemmed | Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study |
| title_short | Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study |
| title_sort | prediction of specific anxiety symptoms and virtual reality sickness using in situ autonomic physiological signals during virtual reality treatment in patients with social anxiety disorder mixed methods study |
| url | https://games.jmir.org/2022/3/e38284 |
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