A 15-Minute Abdominal Breathing Exercise Promotes Nap in Undergraduates: Instrumental Study Findings

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Abstract

The study purpose – to validate by polysomnography (PSG) tools the efficacy of deep abdominal breathing (AB) as a technique improving daytime nap in healthy subjects. Materials and methods: 43 healthy subjects participated in the study, of whom 22 were included into intervention group and 21 into control group. In the intervention group, nap PSGs were recorded for 30 min after performing AB for 15 minutes. In the control group, a similar PSGs were recorded after 15 min of wakefulness. To assess the nap quality, standard sleep characteristics (latency, etc.) were determined from the subjects' hypnograms. In the intervention group total sleep time was significantly longer and activation index was significantly lower than in control group, while sleep latency did not differ significantly. In addition, the electroencephalogram (EEG) spectrum power ratio in alpha (8–13 Hz) and theta (4–8 Hz) frequency bands was analyzed. Linear regression model of alpha/theta power ratio time series was constructed within the framework of statistical analysis. It was concluded based on comparison of coefficients of this model along with the time domain sleep characteristics, that AB exercise preceding daytime nap activates physiological mechanisms accelerating fall-asleep process and making sleep more stable. This finding may be useful in the development of non-invasive approaches to insomnia treatment.

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About the authors

A. E. Khuurak

RUDN University

Email: shumov_de@pfur.ru
Russian Federation, Moscow

D. E. Shumov

Institute of Higher Nervous Activity and Neurophysiology of the RAS; RUDN University

Author for correspondence.
Email: shumov_de@pfur.ru
Russian Federation, Moscow; Moscow

D. S. Sveshnikov

RUDN University

Email: shumov_de@pfur.ru
Russian Federation, Moscow

Z. V. Bakaeva

RUDN University

Email: shumov_de@pfur.ru
Russian Federation, Moscow

E. B. Yakunina

RUDN University

Email: shumov_de@pfur.ru
Russian Federation, Moscow

V. I. Torshin

RUDN University

Email: shumov_de@pfur.ru
Russian Federation, Moscow

V. V. Dementienko

Neurocom JSC

Email: shumov_de@pfur.ru
Russian Federation, Moscow

V. B. Dorokhov

Institute of Higher Nervous Activity and Neurophysiology of the RAS

Email: shumov_de@pfur.ru
Russian Federation, Moscow

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2. Fig. 1. Example of the PSG screen. Registration channels are visible, including the SOUND channel, with which the abdominal respiration signal (PNEUMO_ABD) is synchronised. A close look shows the effect of respiratory sinus arrhythmia on the ECG channel (regular changes in R-R intervals on inhalation and exhalation) and involuntary eye movements on exhalation.

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3. Fig. 2. Comparison of the RF determined based on three different algorithms. It can be seen that the group mean values of the frequency determined on the basis of the standard deviation (SDNN) and integral power of the HRV spectrum (TP) are approximately the same, but the RF determined on the basis of the peak-to-trough algorithm (RSA_P2T) is significantly lower (p < 0.05).

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4. Fig. 3. Results of regression analysis of the time series of the ratio of spectral powers in the alpha (8-13 Hz) and theta (4-8 Hz) bands for the experimental and control groups. Curves - time series with 1 s interval averaged by groups of ln(Palpha / Ptheta) values, where Palpha is the power of the EEG spectrum in the 8-13 Hz band; Ptheta is the power of the EEG spectrum in the 4-8 Hz band. The boundaries of the 95% confidence intervals are shown in lighter colour. Vertical dotted lines divide the recording time into 3 sections: decline (0-300 s, deepening sleep), plateau (300-1500 s), and recovery (1500-1800 s). The solid line indicates the results of model fitting in the first two sections for the experimental group, and the dashed line for the control group. Estimates of regression coefficients are given as "mean ± standard error". The estimation results are analysed in the Discussion of Results section.

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