Corrigendum: A Practical Guide to Resonance Frequency Assessment for Heart Rate Variability Biofeedback

Shaffer, Fred; Meehan, Zachary M.

doi:10.3389/fnins.2020.627512

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…Unlike normal breathing at rest, slow-paced breathing evokes complex interactions between multiple homeostatic systems, such as respiratory sinus arrhythmia, attentional control, and the baroreflex (Shaffer & Meehan, 2020). The baroreflex loop is a feedbackregulated cardiac-brain control system that regulates short-term blood pressure changes (Man et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

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Real‐time heart rate variability biofeedback amplitude during a large‐scale digital mental health intervention differed by age, gender, and mental and physical health

Aschbacher,

Mather,

Lehrer

et al. 2024

Psychophysiology

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Heart rate variability biofeedback (HRVB) is an efficacious treatment for depression and anxiety. However, translation to digital mental health interventions (DMHI) requires computing and providing real‐time HRVB metrics in a personalized and user‐friendly fashion. To address these gaps, this study validates a real‐time HRVB feedback algorithm and characterizes the association of the main algorithmic summary metric—HRVB amplitude—with demographic, psychological, and health factors. We analyzed HRVB data from 5158 participants in a therapist‐supported DMHI incorporating slow‐paced breathing to treat depression or anxiety symptoms. A real‐time feedback metric of HRVB amplitude and a gold‐standard research metric of low‐frequency (LF) power were computed for each session and then averaged within‐participants over 2 weeks. We provide HRVB amplitude values, stratified by age and gender, and we characterize the multivariate associations of HRVB amplitude with demographic, psychological, and health factors. Real‐time HRVB amplitude correlated strongly (r = .93, p < .001) with the LF power around the respiratory frequency (~0.1 Hz). Age was associated with a significant decline in HRVB (β = −0.46, p < .001), which was steeper among men than women, adjusting for demographic, psychological, and health factors. Resting high‐ and low‐frequency power, body mass index, hypertension, Asian race, depression symptoms, and trauma history were significantly associated with HRVB amplitude in multivariate analyses (p's < .01). Real‐time HRVB amplitude correlates highly with a research gold‐standard spectral metric, enabling automated biofeedback delivery as a potential treatment component of DMHIs. Moreover, we identify demographic, psychological, and health factors relevant to building an equitable, accurate, and personalized biofeedback user experience.

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Section: Discussionmentioning

confidence: 99%

“…Higher HRV is considered a marker of psychobiological resilience that predicts lower morbidity and mortality (Fang et al, 2020;Shaffer & Meehan, 2020). Breathing at a slow pace close to 0.1 Hz, or six breaths per minute, has been shown to maximize respiration-induced HRV (Cooke et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Real‐time heart rate variability biofeedback amplitude during a large‐scale digital mental health intervention differed by age, gender, and mental and physical health

Aschbacher,

Mather,

Lehrer

et al. 2024

Psychophysiology

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“…HRV biofeedback is often implemented by encouraging slow, paced breathing at the resonance frequency, resulting in heart rate oscillations that are much larger than baseline values [Shaffer and Meehan (2020)]. HRV biofeedback has been shown to be a promising intervention for reducing anxiety and stress [Goessl et al (2017)].…”

Section: Real Time Biofeedbackmentioning

confidence: 99%

Heart rate variability during mindful breathing meditation

Natarajan

2023

Front. Physiol.

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We discuss Heart Rate Variability (HRV) measured during mindful breathing meditation. We provide a pedagogical computation of two commonly used heart rate variability metrics, i.e. the root mean square of successive differences (RMSSD) and the standard deviation of RR intervals (SDRR), in terms of Fourier components. It is shown that the root mean square of successive differences preferentially weights higher frequency Fourier modes, making it unsuitable as a biosignal for mindful breathing meditation which encourages slow breathing. We propose a new metric called the autonomic balance index (ABI) which uses Respiratory Sinus Arrhythmia to quantify the fraction of heart rate variability contributed by the parasympathetic nervous system. We apply this metric to heart rate variability data collected during two different meditation techniques, and show that the autonomic balance index is significantly elevated during mindful breathing, making it a good signal for biofeedback during meditation sessions.

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“…HRV is maximum during slow breathing at resonance frequency, as documented in HRV biofeedback research. [ 1 , 2 , 3 ] The HRV biofeedback practice increases the acceleration and deceleration of heart rate across the breathing cycle to enhance the autonomic homeostatic capacity. The involvement of RSA enables respiration-driven changes in heart rate mediated by the vagus nerve, which integrates baroreceptor inputs with the brain and changes heart rate.…”

Section: Introductionmentioning

confidence: 99%

“…The involvement of RSA enables respiration-driven changes in heart rate mediated by the vagus nerve, which integrates baroreceptor inputs with the brain and changes heart rate. [ 2 ]…”

Section: Introductionmentioning

confidence: 99%

Effect of Various Lengths of Respiration on Heart Rate Variability during Simple Bhramari (Humming)

Trivedi,

Kathirvel,

Sharma

et al. 2023

International Journal of Yoga

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Background: Heart rate varies during breathing and the heart rate variability (HRV) facilitates the autonomic homeostatic capacity. The maximum HRV was observed at around 10 s of prolonged respiration as per HRV biofeedback literature. However, there is a gap in understanding the variations in HRV by different respiration lengths during simple Bhramari practice. Objective: To assess the effect of various respiration lengths (8, 10, 12, and 14 s) on HRV during the simple Bhramari (humming) practice. Methodology: A total of 118 individuals (67 males, 51 females) signed up for the study based on convenience sampling at a wellness center. A randomized (simple permutation) within-subject crossover design with repeated measures was used to measure their HRV during the simple Bhramari practice. The results were analyzed with one-way ANOVA and paired samples t-test. Results: The results indicate that, unlike the 10 s of respiration during HRV biofeedback breathing, maximum HRV during humming happens between 12 and 14 s of long breathing. Conclusions: The unique findings of the study demonstrate the maximization of HRV between the respiration lengths of 12 and 14 s. Future work should explore expanding the research to a broader group of participants, including individuals with chronic conditions and other demographic variables and mantra chanting.

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Corrigendum: A Practical Guide to Resonance Frequency Assessment for Heart Rate Variability Biofeedback

Cited by 8 publications

References 11 publications

Real‐time heart rate variability biofeedback amplitude during a large‐scale digital mental health intervention differed by age, gender, and mental and physical health

Real‐time heart rate variability biofeedback amplitude during a large‐scale digital mental health intervention differed by age, gender, and mental and physical health

Heart rate variability during mindful breathing meditation

Effect of Various Lengths of Respiration on Heart Rate Variability during Simple Bhramari (Humming)

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