特质焦虑对急性心理性应激反应的预测          &amp;lt;sup&amp;gt;*&amp;lt;/sup&amp;gt;

Peng, Huini; Wu, Jianhui; Sun, Xiufa; Guan, Qing; Luo, Yuejia

doi:10.3724/sp.j.1041.2018.00997

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…They exhibit low baseline HPA axis response but display a high sympathetic and adrenomedullary responses to acute threats (66)(67)(68). In our study, a blunted response to acute stressors in high trait anxiety participants is consistent with a "dove-like" phenotype (68,69).…”

Section: Recognizing Individuals With High Trait Anxietysupporting

confidence: 70%

A machine learning approach to identifying objective biomarkers of anxiety and stress

Ramakrishnan

Pardes²,

Lynch³

et al. 2019

Preprint

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Anxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state.Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety -a vulnerability foreshadowing development of anxiety and mood disorders -remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

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Section: Recognizing Individuals With High Trait Anxietysupporting

confidence: 70%

A machine learning approach to identifying objective biomarkers of anxiety and stress

Ramakrishnan

Pardes²,

Lynch³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Theoretically, both arousal and attention can result in time distortion. Previous studies have found that anxiety would massively increase arousal when negative stimuli appear, such as increased heart rate (Shalom et al, 2015;Peng et al, 2018), increased systolic blood pressure (James et al, 1986;Matthews et al, 1986), reduced heart rate variability (Chalmers et al, 2014), excessive blushing (Gerlach et al, 2001), and increased self-report arousal (Martin, 1961;McLeod et al, 1986). Anxiety has also been found to show an abnormally large attentional bias toward negative stimuli (Bar-Haim et al, 2007), makes individual attracted by negative stimuli and difficult to distract attention from them (Yiend and Mathews, 2001;Koster et al, 2004;Pergamin-Hight et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Automatic Suppression Reduces Anxiety-Related Overestimation of Time Perception

Yuan

Tian

2020

Front. Physiol.

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Anxiety has been found to lengthen time perception, especially the time perception of negative stimuli. This anxiety-related time overestimation is thought to be mainly associated with massively increased arousal. Suppression, which can be achieved either deliberately or automatically, has been demonstrated to be effective in reducing arousal. Consequently, the present study explored the effectiveness of both deliberate suppression (Experiment 1) and automatic suppression (Experiment 2) in reducing the time distortion in anxiety. A temporal bisection task (TBT), featuring negative and neutral pictures, was used to measure time perception, while the self-reported arousal was used to assess arousal. The deliberate suppression was manipulated by asking participants to suppress their emotional expressions; while automatic suppression was manipulated through a sentence-unscrambling task featuring suppression-related words, which can unconsciously prime suppression. The results of Experiment 1 showed that deliberate suppression did not reduce the anxiety-related time overestimation and arousal. However, Experiment 2 showed that automatic suppression significantly reduced the anxiety-related time overestimation, with significant arousal reduction being observed. In conclusion, automatic suppression, but not deliberate suppression, is effective for reducing the effect of anxiety on time perception.

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“…In addition, Pearson correlation analysis indicated that the lower the level of trait anxiety, the higher the level of mental resilience. Previous research also suggested that special factors, such as anxiety, can promote mental well-being (Johnson et al, 2017; Peng et al, 2018; Zhang, 2000). Thus, trait anxiety plays a pivotal role in determining whether athletes can deliver their peak athletic performance in the environment of competitions (Hosseini and Besharat, 2010).…”

Section: Discussionmentioning

confidence: 98%

A Survey of the Relationship Between Trait Anxiety and Mental Resilience of Marathoners in China

et al. 2022

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This study aims to investigate the relationship between trait anxiety and mental resilience in marathoners and the general population. The study recruited 135 marathoners and 140 members of the general population. Trait anxiety and mental resilience were measured by the State-Trait Anxiety Scale and the Chinese version of the Connor-Davidson Resilience Scale. The amount of weekly exercise resulted in significant differences in the trait anxiety (F = 6.358, p < 0.01) and mental resilience (F = 5.996, p < 0.05) levels of the general population and marathoners. Age led to a significant difference in the trait anxiety of marathoners (F = 10.890, p < 0.01). The half marathoners' daily exercise program is most suitable for the general population for reducing trait anxiety level and increasing mental resilience. Furthermore, marathoners showed lower trait anxiety and higher mental resilience. Therefore, long-term marathon adherence can help improve mental health.

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特质焦虑对急性心理性应激反应的预测 <sup>*</sup>

Cited by 10 publications

References 48 publications

A machine learning approach to identifying objective biomarkers of anxiety and stress

A machine learning approach to identifying objective biomarkers of anxiety and stress

Automatic Suppression Reduces Anxiety-Related Overestimation of Time Perception

A Survey of the Relationship Between Trait Anxiety and Mental Resilience of Marathoners in China

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