Assessment of Cardiac Autonomic Tone Following Long Sudarshan Kriya Yoga in Art of Living Practitioners

Background: Yoga therapy is widely applied to the maintenance of health and to treatment of various illnesses. Previous researches indicate the involvement of autonomic control in its effects, although the general agreement has not been reached regarding the acute modulation of autonomic function. Aim: The present study aimed at revealing the acute effect of yoga on the autonomic activity using heart rate variability (HRV) measurement. Methods: Twenty-seven healthy controls participated in the present study. Fifteen of them (39.5 ± 8.5 years old) were naïve and 12 (45.1 ± 7.0 years old) were experienced in yoga. Yoga skills included breath awareness, two types of asana, and two types of pranayama. HRV was measured at the baseline, during yoga, and at the resting state after yoga. Results: In both yoga-naïve and experienced participants, the changes in low-frequency (LF) component of HRV and its ratio to high-frequency (HF) component (LF/HF) after yoga were found to be correlated negatively with the baseline data. The changes in LF after yoga were also correlated with LF during yoga. The changes in HF as well as the raw HRV data after yoga were not related to the baseline HRV or the HRV during yoga. Conclusion: The results indicate that yoga leads to an increase in LF when LF is low and leads to a decrease in LF when it is high at the baseline. This normalization of LF is dependent on the autonomic modulation during yoga and may underlie the clinical effectiveness of yoga therapy both in yoga-naïve and experienced subjects.

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“…Following long Sudarshan Kriya Yoga, Bhaskar et al . [ 23 ] reported that LF decreased and HF increased. On the other hand, Telles et al .…”

Section: Introductionmentioning

confidence: 99%

Changes in heart rate variability after yoga are dependent on heart rate variability at baseline and during yoga: a study showing autonomic normalization effect in yoga-naïve and experienced subjects

et al. 2020

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“…Such autonomic benefits were seen for all subjects, independent of age [32,33], even though the magnitude of such effects appeared to be higher for older than for younger adults, according to Pal and colleagues [34].…”

Section: Discussionmentioning

confidence: 86%

Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

et al. 2020

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Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.

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“…A SKY program involves slow Ujjayi breathing (diaphragmatic breathing) with inhalatory and exhalatory pauses, followed by fast-paced Bhastrika pranayama (rapid breathing) and cyclic rhythmic breathings. The breathings in a SKY program occur in slow, medium, and fast frequencies (Bhaskar, Kharya, Deepak, & Kochupillai, 2017).…”

Section: Discussionmentioning

confidence: 99%

Heart Rate Variability Changes in the Integrated Yoga Program: Results and Implications

Bernotienė¹,

Razón²,

Daunoravičienė³

et al. 2020

BJSHS

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Background. Different methods are used to study cardiovascular parameters. Of these, heart rate variability (HRV) is a relevant marker for both athletes and physically active people. With regards to physical activity, yoga has become a popular form of activity in the recent years. In fact, research has indicated that yoga is a useful modality for both the management and prevention of several cardiovascular conditions. The purpose of the current study was to observe HRV dynamics in young women during a 6-month yoga intervention.Methods. We recruited 13 women from multiple yoga studios. All participants were practicing Hatha yoga 2 times a week for 6 months. Data were collected: a) at baseline, b) 2 months after the yoga intervention, c) 4 months after the intervention and d) 6 months after the intervention. We used wireless Bluetooth electrocardiogram (ECG) recording device. ECG HVR parameters and heart rate (HR) were used for data analysis.Results. HR dynamics showed no differences during the 6-month yoga intervention. Meanwhile, vLF parameter after relaxation and LF/HF ratio after body scan meditation significantly changed after yoga intervention. No changes were observed in LF or HF parameter dynamics.Conclusion. Our results suggest that 6 months twice per week yoga practice influences vLF and LF/HF ratio, especially during relaxation and body scan meditation. We also suggest that healthy women could benefit from practicing body scan meditation. In conclusion, through modification of emotion reactivity, regular practice of yoga can help improve stress response and offset negative consequences associated with the stress process.Keywords: yoga, heart rate variability, women’s health, ECG, stress.

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Assessment of Cardiac Autonomic Tone Following Long Sudarshan Kriya Yoga in Art of Living Practitioners

Cited by 33 publications

References 25 publications

Changes in heart rate variability after yoga are dependent on heart rate variability at baseline and during yoga: a study showing autonomic normalization effect in yoga-naïve and experienced subjects

Changes in heart rate variability after yoga are dependent on heart rate variability at baseline and during yoga: a study showing autonomic normalization effect in yoga-naïve and experienced subjects

Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

Heart Rate Variability Changes in the Integrated Yoga Program: Results and Implications

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