The time course of autonomic parameters and muscle tension during recovery following a moderate cognitive stressor: Dependency on trait anxiety level

Willmann, Magali; Langlet, Cécile; Hainaut, Jean-Philippe; Bolmont, Benoı̂t

doi:10.1016/j.ijpsycho.2012.01.009

Cited by 24 publications

(14 citation statements)

References 48 publications

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“…It should be noted that trait anxiety and experience with height-related activities were not identified as significant independent predictors of changes in quiet standing. Although trait anxious individuals have been shown to perceive greater state anxiety in response to different stressors (Arena & Hobbs, 1995;Baggett et al, 1996;Gonzalez-Bono et al, 2002;Noteboom et al, 2001;Steptoe & Vogele, 1992;Willmann et al, 2012), exposure to a height-induced threat may not be relevant to this trait. As a consequence, trait anxious individuals may perceive this situation to be equally threatening in comparison to less anxious individuals.…”

Section: Individual Characteristics Predict Changes In Quiet Standingmentioning

confidence: 99%

“…Thus, this trait may predispose individuals to experience greater perceptions of anxiety when presented with a postural threat. For example, studies have shown that despite low and high trait anxious individuals often showing similar changes in physiological arousal in response to physical (e.g., threat of electrical stimulation), cognitive (e.g., mental arithmetic), and social (e.g., public speaking) stressors, high trait anxious individuals consistently self-report greater increases in state anxiety (Arena & Hobbs, 1995;Baggett, Saab, & Carver, 1996;Gonzalez-Bono et al, 2002;Noteboom, Barnholt, & Enoka, 2001;Steptoe & Vogele, 1992;Willmann, Langlet, Hainaut, & Bolmont, 2012). Furthermore, changes in postural control following exposure to an anxiety-inducing cognitive task have been shown to be dependent on an individual's level of trait anxiety (Hainaut, Caillet, Lestienne, & Bolmont, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Personality traits and individual differences predict threat-induced changes in postural control

Zaback

Cleworth

Carpenter

et al. 2015

Human Movement Science

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Section: Individual Characteristics Predict Changes In Quiet Standingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personality traits and individual differences predict threat-induced changes in postural control

Zaback

Cleworth

Carpenter

et al. 2015

Human Movement Science

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“…In this study, we used several physiological measures to understand individual responses to a stressor. GSR has been extensively used to measure stress response (22)(23)(24)(25)(26)(27)(28)(29) and, even in this study, GSR increased with stress induction. However, GSR as a measure was not as sensitive as heart rate or EEG-based measurements in identifying stress states.…”

Section: Identifying Stressful States and Non-stressful Statesmentioning

confidence: 57%

“…While biochemical assays are invasive, expensive, not amenable to continuous measurements, and vulnerable to diurnal cycles (18)(19)(20)(21), electrophysiological measures are non-invasive and more suitable for continuous monitoring. Measures like galvanic skin response (GSR) (22)(23)(24)(25)(26)(27)(28)(29), heart rate (HR) (24)(25)(26)28,30), heart rate variability (HRV) (27,31,32), and electroencephalography (EEG) (33)(34)(35)(36) are increasingly being used to assess stress response. The relationship between these physiological measures and stress response, and both state and trait anxiety, however, remains poorly understood (37).…”

Section: Introductionmentioning

confidence: 99%

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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“…This hypothesis maybe also related to the fact that physiological muscle recovery depends on trait anxiety levels. [44] Since absence or delayed muscle relaxation is a potential mechanism for TrP activation, [45] it is possible that higher trait anxiety levels represent a risk factor for active TrPs. Finally, we do not currently know the effect of TrP management in mood disorders; future studies should investigate these hypotheses.…”

Section: Discussionmentioning

confidence: 99%

Relationship of active trigger points with related disability and anxiety in people with tension-type headache

Palacios‐Ceña

Castaldo²,

Wang³

et al. 2017

Medicine

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To investigate the differences in the presence of trigger points (TrPs) and their association with headache-related disability and mood disorders in people with frequent episodic tension-type headache (TTH) (FETTH) and chronic TTH (CTTH). One hundred twenty-two individuals with TTH participated. Clinical features of headache (i.e., intensity, duration, and frequency) were recorded on a headache diary. Headache-related disability was assessed with the Headache Disability Inventory, trait and state anxiety levels with State-Trait Anxiety Inventory, and depression with the Hospital Anxiety and Depression Scale. TrPs were bilaterally explored in the temporalis, masseter, suboccipital, upper trapezius, splenius capitis, and sternocleidomastoid muscles. Sixty-two (51%) patients were classified as FETTH, whereas 60 (49%) were classified as CTTH. Individuals with CTTH showed higher burden of headache and depression than FETTH (P < 0.001). Subjects with FETTH showed similar number of TrPs (total number: 5.9 ± 3.1, active TrPs: 4.7 ± 2.5, and latent TrPs: 1.2 ± 1.9) than those with CTTH (total number: 5.7 ± 3.2, active TrPs: 4.2 ± 3.0, and latent TrPs: 1.5 ± 1.8). The number of active TrPs was significantly associated with the burden of headache (r = 0.189; P = 0.037) and trait anxiety (r = 0.273; P = 0.005): the higher the number of active TrPs, the greater the physical burden of headache or the more the trait anxiety level. No association with the depression was observed. The presence of active TrPs in head and neck/shoulder muscles was similar between individuals with FETTH and CTTH and associated with the physical burden of headache and trait anxiety levels independently of the subgroup of TTH.

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The time course of autonomic parameters and muscle tension during recovery following a moderate cognitive stressor: Dependency on trait anxiety level

Cited by 24 publications

References 48 publications

Personality traits and individual differences predict threat-induced changes in postural control

Personality traits and individual differences predict threat-induced changes in postural control

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

Relationship of active trigger points with related disability and anxiety in people with tension-type headache

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