Abstract
Auditioning is at the very center of educational and professional life in music and is associated with significant psychophysical demands. Knowledge of how these demands affect cardiovascular responses to psychosocial pressure is essential for developing strategies to both manage stress and understand optimal performance states. To this end, we recorded the electrocardiograms (ECGs) of 16 musicians (11 violinists and 5 flutists) before and during performances in both low- and high-stress conditions: with no audience and in front of an audition panel, respectively. The analysis consisted of the detection of R-peaks in the ECGs to extract heart rate variability (HRV) from the notoriously noisy real-world ECGs. Our data analysis approach spanned both standard (temporal and spectral) and advanced (structural complexity) techniques. The complexity science approaches—namely, multiscale sample entropy and multiscale fuzzy entropy—indicated a statistically significant decrease in structural complexity in HRV from the low- to the high-stress condition and an increase in structural complexity from the pre-performance to performance period, thus confirming the complexity loss theory and a loss in degrees of freedom due to stress. Results from the spectral analyses also suggest that the stress responses in the female participants were more parasympathetically driven than those of the male participants. In conclusion, our findings suggest that interventions to manage stress are best targeted at the sensitive pre-performance period, before an audition begins.
Highlights
The first attempt to introduce a taxonomy of stress dates back to Hans Selye in 1936, who defined stress as a “non-specific endocrine response” [1]
Multiscale sample entropy (MSE) [26] and multiscale fuzzy entropy (MFE) [33] approaches were introduced in order to examine entropy values over increasing time scales, producing so-called complexity profiles
We have examined the cardiovascular reactivity of musicians experiencing low- and highstress performance conditions within the framework of complexity loss theory, quantified using the multiscale entropy (MSE) and multiscale fuzzy entropy (MFE) algorithms
Summary
The first attempt to introduce a taxonomy of stress dates back to Hans Selye in 1936, who defined stress as a “non-specific endocrine response” [1]. While research into physiological stress in musicians has usefully examined the degree of cardiovascular reactivity at different points in the performance cycle (e.g. pre-, during-, and post-performance), existing studies have relied mainly on HR [4, 6,7,8] rather than the dynamically more informative heart rate variability (HRV). Multiscale sample entropy (MSE) [26] and multiscale fuzzy entropy (MFE) [33] approaches were introduced in order to examine entropy values over increasing time scales, producing so-called complexity profiles (the MSE and MFE algorithms are described in S1 File) In this way, do we account quantitatively for objective aspects of performance stress, but we are able to identify the critical timing of stress reactivity and estimate the most appropriate period during which to intervene using stress management strategies
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