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Comparative Study
. 2016 Jan 25;11(1):e0146131.
doi: 10.1371/journal.pone.0146131. eCollection 2016.

State Anxiety and Nonlinear Dynamics of Heart Rate Variability in Students

Dimitriy A Dimitriev  1 , Elena V Saperova  1 , Aleksey D Dimitriev  1
Affiliations
Comparative Study

State Anxiety and Nonlinear Dynamics of Heart Rate Variability in Students

Dimitriy A Dimitriev et al. PLoS One. .

Abstract

Objectives: Clinical and experimental research studies have demonstrated that the emotional experience of anxiety impairs heart rate variability (HRV) in humans. The present study investigated whether changes in state anxiety (SA) can also modulate nonlinear dynamics of heart rate.

Methods: A group of 96 students volunteered to participate in the study. For each student, two 5-minute recordings of beat intervals (RR) were performed: one during a rest period and one just before a university examination, which was assumed to be a real-life stressor. Nonlinear analysis of HRV was performed. The Spielberger's State-Trait Anxiety Inventory was used to assess the level of SA.

Results: Before adjusting for heart rate, a Wilcoxon matched pairs test showed significant decreases in Poincaré plot measures, entropy, largest Lyapunov exponent (LLE), and pointwise correlation dimension (PD2), and an increase in the short-term fractal-like scaling exponent of detrended fluctuation analysis (α1) during the exam session, compared with the rest period. A Pearson analysis indicated significant negative correlations between the dynamics of SA and Poincaré plot axes ratio (SD1/SD2), and between changes in SA and changes in entropy measures. A strong negative correlation was found between the dynamics of SA and LLE. A significant positive correlation was found between the dynamics of SA and α1. The decreases in Poincaré plot measures (SD1, complex correlation measure), entropy measures, and LLE were still significant after adjusting for heart rate. Corrected α1 was increased during the exam session. As before, the dynamics of adjusted LLE was significantly correlated with the dynamics of SA.

Conclusions: The qualitative increase in SA during academic examination was related to the decrease in the complexity and size of the Poincaré plot through a reduction of both the interbeat interval and its variation.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Individual data for examination-induced changes in state anxiety from rest session to the exam session.
(A) Increasing from low to moderate anxiety levels. (B) Increasing from low to high anxiety levels. (C) Increasing from moderate to high anxiety levels. (D) Absence of qualitative changes in anxiety.
Fig 2
Fig 2. Poincaré plots during the rest (A) and exam sessions (B).
Fig 3
Fig 3. Guzik’s indexes for the rest session (panel A) and exam session (panel B).
Fig 4
Fig 4. Correlation between the dynamics of state anxiety (DYN SA = SA at exam − SA at rest) and Largest Lyapunov exponent (LLE; DIN LLE = LLE at exam − LLE at rest).
See this image and copyright information in PMC

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