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Frontal Alpha Asymmetry, Heart Rate Variability, and Positive Resources in Bereaved Family Members with Suicidal Ideation after the Sewol Ferry Disaster

  • Jang, Kuk-In (Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea) ;
  • Lee, Sangmin (Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea) ;
  • Lee, Seung-Hwan (Clinical Emotion and Cognition Research Laboratory, Inje University) ;
  • Chae, Jeong-Ho (Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea)
  • 투고 : 2018.07.30
  • 심사 : 2018.09.16
  • 발행 : 2018.12.31

초록

Objective After the Sewol ferry disaster, bereavement with suicidal ideation was a critical mental health problem that was accompanied by various neuropsychological symptoms. This study examined the frontal alpha asymmetry (FAA), heart rate variability (HRV), and several psychological symptoms in bereaved family members (BFM) after the Sewol ferry disaster. Methods Eighty-three BFM after the Sewol ferry disaster were recruited. We assessed FAA, HRV, and psychological symptoms, including depression, post-traumatic stress, post-traumatic growth factor, anxiety, grief, and positive resources, between BFM with the presence and absence of current suicidal ideation. Results Compared to BFM without suicidal ideation, BFM with suicidal ideation showed a higher FAA with right dominance. Significant differences in psychological symptoms were observed between the groups. In BFM with suicidal ideation, the low: high frequency (LF:HF) ratio correlated with social resources and support. Conclusion The FAA and LF:HF ratio may be biomarkers that represent the pathological conditions of BFM with suicidal ideation. If researched further, they may shed light on the interaction between bereavement with suicidal ideation and social resources for therapeutic intervention.

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과제정보

연구 과제 주관 기관 : Korea Health Industry Development Institute

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피인용 문헌

  1. Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network vol.20, pp.16, 2018, https://doi.org/10.3390/s20164400