Humming Breathing as a Device-Free Method for Inducing Resonance Frequency: A Preliminary Investigation into Autonomic Regulatory Mechanisms

Humming Breathing as a Device-Free Method for Inducing Resonance Frequency: A Preliminary Investigation into Autonomic Regulatory Mechanisms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Humming Breathing as a Device-Free Method for Inducing Resonance Frequency: A Preliminary Investigation into Autonomic Regulatory Mechanisms Teri Kim, Sujin Lee, Minjung Woo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7063841/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to determine whether the effects of humming breathing (Bhramari Pranayama) on the autonomic nervous system were caused by vibratory sounds or specific respiratory patterns. Methods : Eleven healthy adults participated in four randomized sessions (rest, deep breathing, humming breathing, and calm humming breathing) over five consecutive days. Heart rate variability and respiratory measures were analyzed, along with visual analog scale responses for stress, anxiety, confidence, and relaxation. Results : Humming and calm humming breathing resulted in lower respiration rates and less variability than those of rest and deep breathing, with a target resonance frequency of 0.1 Hz. All breathing conditions showed higher root mean square of successive differences, standard deviation of normal-to-normal intervals, total power, and low-frequency power values, along with lower high-frequency power peaks, compared with those of rest. Humming breathing exhibited higher standard deviation of normal-to-normal intervals, total power, and low-frequency power values than those of deep breathing. Visual analog scale analysis revealed no significant differences across the breathing conditions. No significant differences were observed between humming and calm humming breathing for any of the measures. Conclusion : The effects of humming breathing appear to be associated with the 0.1 Hz resonance frequency induced by extended exhalation with an “mm” sound rather than the vibratory sound itself. Humming breathing, which does not require a biofeedback device, is a practical alternative to cyclical breathing. Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Physiology humming breathing Bhramari Pranayama heart rate variability resonance frequency yogic breathing 1. Introduction Chronic stress is a major contributor to various health disorders due to its disruptive impact on autonomic nervous system (ANS) balance (Humiston & Lansing, 2022). The ANS, comprising the sympathetic and parasympathetic branches, orchestrates the body’s responses to stress and recovery, and its dysregulation—often stemming from prolonged sympathetic activation—can lead to allostatic overload and compromised physiological homeostasis (Charmandari et al., 2005; Ernst, 2017; Logan & Barksdale, 2008; McCorry, 2007). This imbalance is associated with a heightened risk of metabolic, cardiovascular, and mental health disorders, underscoring the urgent need for effective interventions to restore ANS balance (Sterling, 2014; Thayer et al., 2010). Breathing training has emerged as a simple, accessible, and effective approach for reducing sympathetic dominance and enhancing parasympathetic activity. Slow, controlled breathing—particularly with prolonged exhalation—can induce respiratory sinus arrhythmia (RSA) and cardiorespiratory synchronization, optimizing oxygen delivery and promoting physiological stability (Lotrič & Stefanovska, 2000). Such synchronization is linked to reduced stress, improved psychological well-being, enhanced athletic performance, and the maintenance of cardiovascular health (Giardino et al., 2003; Lehrer et al., 2020; Lehrer, Vaschillo, & Vaschillo, 2000; Vaschillo, Vaschillo, & Lehrer, 2006). Consequently, research has focused on identifying breathing techniques that most effectively promote these benefits. A well-established method is resonance frequency breathing at 0.1 Hz (5 s inhalation, 5 s exhalation), which maximizes the resonance between cardiac rhythm and baroreflex activity (Lehrer, 2013). This pattern enhances vagal afferent signaling, self-regulation, and resilience to stress, and has led to the development of various biofeedback devices and applications that guide users to maintain a 5:5 breathing cycle (Steffen et al., 2017). Evidence supports that such slow-paced breathing increases heart rate variability (HRV), improves emotional regulation, and aids in stress recovery (Kim, Shin, & Woo, 2023; Lehrer, Vaschillo, & Vaschillo, 2000; Park, Shin, & Woo, 2023). However, some individuals—especially beginners—report dizziness or discomfort with strict 5:5 cycles, and reliance on external devices may limit accessibility and adherence (Saeed & Masters, 2021; Yadav & Yadav, 2015). To address these limitations, Woo and Kim introduced Bhramari (humming) breathing, which emphasizes internal proprioception over external pacing cues (Woo & Kim, 2025). Their research demonstrated that both slow-paced breathing and humming breathing significantly increased HRV compared to rest, with no significant differences between the two techniques. This suggests that humming breathing, which involves sustained “mm” sound production during exhalation, may offer similar autonomic benefits without the need for external devices. While the mechanisms of slow-paced breathing are relatively well-studied, the physiological basis for humming breathing’s effects remains less clear. Two main hypotheses have been proposed: (1) humming breathing may naturally guide participants toward a resonance frequency similar to 5:5 paced breathing, or (2) vibratory sound and resonance may independently stimulate vagal activity (Woo & Kim, 2025). However, previous studies did not directly measure breathing rates or frequencies during humming breathing, leaving it uncertain whether the autonomic effects are primarily due to induced resonance frequency or vibrational stimulation (Brown & Gerbarg, 2005; Jerath et al., 2006; Weitzberg & Lundberg, 2002). Traditional Bhramari breathing also involves blocking auditory and visual stimuli, but Woo and Kim omitted auditory occlusion to avoid confounding muscle tension (Woo & Kim, 2025). This raises the question of whether vibrational effects were maximized. The present study addresses this limitation by comparing standard humming breathing (without auditory occlusion) and “calm humming” (with noise-canceling earphones to block external sounds and enhance vibratory resonance). By quantifying breathing cycles and frequencies, this study aims to clarify whether the autonomic benefits of humming breathing are primarily attributable to breathing rhythm (frequency) or vibrational resonance. In summary, this study seeks to advance understanding of the mechanisms underlying humming breathing’s impact on autonomic balance, with the goal of informing practical, device-free interventions for stress reduction and health promotion. 2. Methods 2.1 Participants This preliminary investigation involved adult participants who voluntarily took part in five consecutive breathing training sessions conducted at a fixed morning time. Individuals with a history of heart-related conditions (e.g., hypertension, arrhythmia, angina, or myocardial infarction) or musculoskeletal disorders that could affect heart rate variability (HRV) were excluded. The final sample comprised 11 healthy adults (mean age = 34.36 ± 14.26 years), including six men (mean age = 29.83 ± 15.92 years) and five women (mean age = 39.80 ± 11.12 years). All participants provided written informed consent prior to participation. This study was approved by the university’s Institutional Review Board (DGU IRB 20250010) and conducted in accordance with the principles of the Declaration of Helsinki. 2.2 Measures 2.2.1 Visual Analog Scale (VAS) We used the VAS, originally developed by Cline et al. and modified by Rietschel et al., to assess stress, anxiety, confidence, and relaxation (Cline et al., 1992; Rietschel et al., 2011). The scale required participants to mark their feelings on a 0–10 cm line, where zero represented "not at all" and 10 represented "at maximum." Participants indicated their affective states by drawing a vertical line on the scale, and the distance from the zero point to the marked point was measured using a ruler. 2.2.2 Heart Rate Variability (HRV) We measured HRV using the Polar H10 (Polar Electro Oy, Kempele, Finland) and EliteHRV app (EliteHRV, Asheville, USA). The HRV values measured by the Polar H10 with the EliteHRV app are highly correlated with stationary ECG devices, confirming their reliability for research-based HRV measurements (Im & Woo, 2024; Woo & Kim, 2025). The Polar H10 is a chest-worn, ECG-based wireless heart rate monitor with a sampling rate of 130 Hz, meaning it collects data every 7.69 ms (1/130 s). This meets the minimum sampling rate requirement for HRV analysis (<250 ms) and is particularly suitable for detecting short-term HRV changes (Giles et al., 2016). The EliteHRV app was installed on each participant’s smartphone and connected to the Polar H10 via Bluetooth. The OPEN HRV READING option in the EliteHRV app was selected, with the time set to 5 min and position set to sitting. After verifying signal stability via the live preview feature, the measurement was initiated by pressing the start button, and upon completion, the data were automatically saved. HRV data were reviewed by selecting all data from the previous log section of the EliteHRV app, which provided the heart rate graph (tachogram), signal quality, time-domain analysis, and frequency-domain analysis results. Only data with an artifact score of zero, indicating high signal quality, were used for analysis. The HRV variables analyzed in this study included time-domain measures, such as the root mean square of successive differences between normal heartbeats (RMSSD) and the standard deviation of all R-R intervals (SDNN), and frequency-domain measures including total power (TP), low-frequency power (LF), high-frequency power (HF), LF peak, and HF peak. 2.2.3 Respiratory Measures We analyzed four respiratory variables over a 5 min period: respiratory rate, respiratory variability across trials, respiratory cycle, and respiratory frequency. Respiratory Rate Respiratory rate was calculated using RSA patterns identified from heart rate peaks (increases during inhalation) and troughs (decreases during exhalation). Each RSA cycle marked by consecutive peaks was counted as one full breath (inhalation + exhalation) (Schäfer, & Kratky, 2008). For example, if 30 RSA cycles were detected in 300 s (5 min), then the respiratory rate would be 30 breaths per 5 min. Respiratory Variability Across Trials The participants performed breathing exercises under four conditions (rest, deep breathing, humming breathing, and calm humming breathing) over 5 d, repeating each condition five times. The respiratory rate for each trial was recorded, and the respiratory variability across trials was calculated as the standard deviation of the respiratory rates within each condition using Microsoft Excel. Higher variability indicates greater fluctuations in breathing rates across the five trials, whereas lower variability reflects more consistent breathing patterns. Respiratory Cycle ( seconds ) The respiratory cycle represented the duration of a single breath (inhalation or exhalation) in seconds. This was calculated by dividing the total measurement time (300 s) by the total number of breaths. For example, if a participant took 30 breaths in 300 s, the respiratory cycle was 300 / 30 = 10 s/breath. Respiratory Cycle (s) = Measurement Time (s) / Total Number of Breaths Respiratory Frequency (Hz) The respiratory frequency, or the number of breaths per second, was calculated as the inverse of the respiratory cycle. For example, if the respiratory cycle time was 10 s, the respiratory frequency was 1/10 = 0.1 Hz. Respiratory Frequency (Hz) = 1 / Respiratory Cycle (s) 2.3 Breathing Conditions 2.3.1 Rest The participants sat comfortably on a chair with their eyes closed, and their heart rates were measured for 5 min. The participants were instructed to remain still throughout the measurements. 2.3.2 Deep Breathing The participants sat comfortably on a chair with their eyes closed and were instructed to perform deep breathing for 5 min—inhaling deeply and exhaling fully. No specific feedback was provided regarding inhalation or exhalation processes. 2.3.3 Humming Breathing Humming breathing is derived from the yogic practices of Bhramari Pranayama. Participants inhaled through the nose with their lips gently closed and exhaled while producing a humming sound (“mmm”). Unlike paced breathing, this method did not follow a predetermined inhalation-exhalation cycle; instead, participants were instructed to breathe naturally according to their lung capacity without any guidance on breath timing. During exhalation, they maintained a light closure of their lips while producing a humming sound, ensuring complete exhalation with subtle vibrations. Deep, slow inhalation was performed through the nose. The breathing exercise was conducted for 5 min with participants keeping their eyes closed. 2.3.4 Calm Humming Breathing Traditional Bhramari breathing eliminates visual and auditory stimuli, typically by covering the ears with fingers. We used wireless earphones with noise-canceling features (DX-200, Daximen (Shenzhen)Technology Co., Ltd., China) to avoid activation of the shoulder and upper arm muscles while maximizing the vibratory effects. This modified method, termed calm humming breathing, was identical to humming breathing except for the use of earphones. The earphones blocked external sounds and enhanced the vibratory sensation of the humming sound during exhalation, promoting sinus resonance and amplifying its effects. 2.4 Procedure After signing the consent form, the participants installed the EliteHRV app on their personal smartphones and wore a Polar H10 chest strap, which was then connected to the EliteHRV app via Bluetooth. The participants received instructions on the measurement protocol and breathing techniques (rest, deep breathing, humming breathing, and calm humming breathing). After practicing the breathing techniques to ensure correct execution, participants randomly determined the order in which they would perform the four types of breathing over 5 d (randomized orders for all 5 d). During the 5 d of the experiment, the participants were instructed to refrain from alcohol consumption. Upon waking in the morning, they were instructed to complete measurements for each condition (5 min each) before consuming food or engaging in other activities. The participants wore the Polar H10, sat comfortably, and connected it to the EliteHRV app. After ensuring stable heart rate data, they performed the four breathing conditions in a predetermined order, with each breathing condition lasting 5 min. After each condition, the participants completed the VAS to assess their affective state and then proceeded to the next condition. After completing all four conditions, the participants sent their data to the experimenter. 2.5 Statistical Analysis To examine differences in respiratory metrics (respiratory rate, respiratory variability across trials, respiratory cycle, and respiratory frequency) across breathing conditions (rest, deep breathing, humming breathing, and calm humming breathing), a repeated-measures one-way analysis of variance (ANOVA) was conducted, with breathing conditions as the independent variable and respiratory metrics as the dependent variable.To examine differences in HRV metrics (HR, RMSSD, SDNN, TP, LF, HF, LF peak, and HF peak) across breathing conditions, a repeated-measures one-way ANOVA was conducted, with breathing conditions as the independent variable and HRV metrics as the dependent variable.To investigate differences in affective states (anxiety, stress, relaxation, and confidence) across breathing conditions, a repeated-measures one-way ANOVA was performed, with breathing conditions as the independent variable and VAS scores as the dependent variable. The effect sizes for all ANOVA results were calculated using eta-squared values. Mauchly’s test of sphericity was performed for all dependent variables. Where the assumption of sphericity was violated, the Greenhouse–Geisser correction was applied to adjust the degrees of freedom for the ANOVA. All statistical analyses were conducted using PASW Statistics 18 (IBM Corporation, USA), with the significance level set at .05. 3. Results 3.1 Respiratory Measures Significant differences were found across breathing conditions for respiratory rate (F(3,30) = 47.42, p = .000, η² = .826), respiratory variability across trials (F(3,30) = 8.51, p = .000, η² = .460), respiratory cycle (F(3,30) = 54.514, p = .000, η² = .845), and respiratory frequency (F(3,30) = 47.42, p = .000, η² = .826). The respiratory rate significantly decreased in all conditions (deep breathing, humming breathing, and calm humming breathing) compared to that of rest. In addition, the respiratory rate was lower during humming breathing than that during deep breathing. The respiratory variability across trials was lower in all conditions compared to that of rest and lower in humming breathing and calm humming breathing than in deep breathing. The respiratory cycle was longer in all conditions than at rest and was longer in humming breathing and calm humming breathing than in deep breathing. The respiratory frequency was lower in all conditions than at rest and lower during humming breathing than during deep breathing. Interestingly, the respiratory frequency, which was 0.2 Hz at rest, decreased to the resonance frequency (0.1 Hz) during both humming and calm humming breathing. However, no significant differences were found between humming and calm humming breathing for any variable (Table 1). Table 1. Differences in respiratory measures across breathing conditions (mean ± standard deviation) Variable Rest Deep Breathing Humming Breathing Calm Humming Breathing F p Post hoc Respiratory rate 65.59± 16.27 34.51±8.42 30.6±7.05 30.12±7.45 47.42 .000*** a<b,c,d*** b<c* Respiratory variation across trials 7.79±4.40 5.08±2.43 3.78±2.69 3.29±1.48 8.51 .000*** a<b,c*,d** b<d* Respiratory cycle 4.88±1.419 9.19±2.29 10.49±2.44 10.46±2.27 54.51 .000*** a<b,c,d*** b<c,d* Respiratory frequency (Hz) .219±.054 .115±.028 .100±.023 .100±.025 47.42 .000*** a<b,c,d*** b<c* * p < .05 ** p < .01; *** p < .001 a=rest, b=deep breathing, c=humming breathing, d=calm humming breathing 3.2 HRV Measures Significant differences were observed across breathing conditions for RMSSD (F(3,30) = 7.428, p = .001, η² = .426), SDNN (F(3,30) = 19.44, p = .000, η² = .660), TP (F(3,30) = 12.947, p = .000, η² = .564), LF (F(3,30) = 13.109, p = .000, η² = .567), and HF peak (F(3,30) = 9.579, p = .000, η² = .489). The RMSSD was higher under all breathing conditions than at rest. SDNN and LF were higher in all breathing conditions than at rest, with humming breathing exhibiting higher SDNN and LF than those of deep breathing. The TP was higher in all breathing conditions than at rest, with higher TP observed during humming breathing and calm humming breathing than during deep breathing. The HF peak was lower in all breathing conditions than at rest. However, no differences were observed between humming breathing and calm humming breathing for any of the HRV variables (Table 2). Table 2. Differences in heart rate variability measures across breathing conditions (mean ± standard deviation) Variable Rest Deep Breathing Humming Breathing Calm Humming Breathing F p Post hoc Heart rate 73.25±10.89 72.20±10.67 73.32±11.39 73.45±11.39 1.426 .255 RMSSD 29.62±10.09 36.77±15.41 38.80±17.38 40.06±.19.20 7.428 .001 ** a<b,c,d * SDNN 47.82±13.00 65.82±20.21 70.75±23.10 71.06±25.08 19.44 .000 *** a<b,c,d ** b<c * TP 1017.43±760.66 3498.88±2619.29 4605.87±3187.780 4922.96±3731.04 12.947 .000 *** a<b,c,d ** b<c,d * LF 566.87±392.57 2856.87±2086.62 3852.85±2751.53 3841.87±3173.15 13.109 .000 *** a<b,c,d ** b<c * LF peak .0902±.0208 .0975±.0150 .0922±.0227 .0928±.0229 .483 .697 HF 460.75±415.47 606.30±667.58 834.53±1081.65 1114.98±1517.53 1.743 .179 HF peak .2208±.0344 .1923±.0263 .1804±.0158 .1818±.0197 9.579 .000 a<b * ,c,d ** * p < .05 ** p < .01; *** p < .001 a=rest, b=deep breathing, c=humming breathing, d=calm humming breathing. root mean square of successive differences (RMSSD); standard deviation of all R-R intervals (SDNN); total power (TP); low-frequency power (LF); high-frequency power (HF) 3.3 VAS Measures As shown in Table 3, no significant differences were observed across the breathing conditions in any of the VAS measures, including anxiety, stress, relaxation, and confidence (p > .05). Table 3. Differences in visual analog scale measures across breathing conditions (mean ± standard deviation) Variable Rest Deep Breathing Humming Breathing Calm Humming Breathing F p Post hoc Anxiety 1.76±1.30 1.65±1.21 1.71±1.09 1.79±1.26 .440 .726 Stress 2.13±1.65 2.02±1.82 2.26±1.67 2.16±1.81 .669 .578 Relaxation 6.50±1.07 6.58±.90 6.51±.99 6.87±.62 2.110 .120 Confidence 6.33±1.25 6.57±1.22 6.43±1.28 6.62±1.41 1.541 .224 * p < .05 ** p < .01; *** p < .001 a=rest, b=deep breathing, c=humming breathing, d=calm humming breathing 4. Discussion This study examined whether vibratory sounds or respiratory characteristics are key factors in the effect of humming breathing on the ANS. The results indicated that the respiration rate was lower during all breathing conditions (i.e., deep breathing, humming breathing, and calm humming breathing) than at rest, with humming and calm humming breathing showing lower rates than those of deep breathing, suggesting that these conscious breathing practices are effective in promoting longer breaths (Jerath et al., 2006; Vieira et al., 2014; Zaccaro et al., 2018). Furthermore, the respiratory variability across trials was lower in all conditions than at rest and lower in humming and calm humming breathing than in deep breathing. The reduced variability suggests a more consistent breathing pattern over the 5-d measurement period, likely due to the consistent lung capacity maintained during the humming exhalation process. The respiratory cycle, defined as the time taken for one full inhalation and exhalation, increased in all breathing conditions compared to that at rest (4.9 s), with humming (10.5 s) and calm humming breathing (10.5 s) showing longer cycles than those of deep breathing (9.2 s). This suggests that, regardless of auditory occlusion, humming breathing—with its prolonged exhalation accompanied by the "mm" sound—induces a longer breath compared to that during deep breathing, which emphasizes both inhalation and exhalation. Consequently, the respiratory frequency decreased from 0.2 Hz at rest to 0.12 Hz during deep breathing and further to 0.1 Hz during both humming and calm humming breathing. Without following a fixed breathing cycle, both humming and calm humming breathing were able to achieve the target resonance frequency of 0.1 Hz, similar to paced breathing, which involves a 5 s inhale and 5 s exhale. These findings suggest that humming breathing can achieve the desired resonance frequency without the use of devices or applications. Moreover, it maintained consistent breathing patterns with lower respiratory variability, indicating more stable and controlled breathing. This makes humming breathing a practical and effective alternative to cyclical breathing that is suitable for real-world applications. Analysis of HRV revealed higher RMSSD, SDNN, TP, and LF and a lower HF peak in all breathing conditions than at rest. Furthermore, SDNN and LF were higher during humming breathing than during deep breathing, whereas TP was higher during humming and calm humming breathing than during deep breathing. However, no differences were observed between humming breathing and calm humming breathing for any of the HRV variables. These results are consistent with those of previous studies, confirming that breathing training generally increases HRV parameters (Edmonds et al., 2009; Kim, Shin, & Woo, 2023; Park & Park, 2012; Woo & Kim, 2025). A higher RMSSD suggests enhanced parasympathetic activity, and higher SDNN and TP indicate heightened autonomic regulation and physiological resilience (Kim, Shin, & Woo, 2023; Laborde et al., 2022; McCraty & Zayas, 2014; Pal et al., 2014; Wu & Lo, 2008; Ursino & Magosso, 2003; Shaffer & Ginsberg, 2017). Furthermore, the substantial increase in LF (0.05–0.15 Hz) power density indicates that as the respiratory cycle of humming breathing extends to 10 s, the power density aligns with the resonance frequency of 0.1 Hz, leading to an increase in LF power. This suggests that humming breathing is an effective method for inducing resonance frequency. Compared to that at rest, deep breathing showed a lower HF peak, and both humming and calm humming breathing exhibited even lower HF peaks. Specifically, the frequency bands with the highest power density in the high-frequency range (0.15–0.4 Hz) were 0.22 Hz during rest, 0.19 Hz during deep breathing, and 0.18 Hz for both humming and calm humming breathing. HF peak is closely related to the respiratory cycle, with a lower HF peak frequency indicating slower and deeper breathing (Ursino, & Magosso, 2002; Wang et al., 2022). In this study, the respiratory cycles for rest, deep breathing, humming breathing, and calm humming breathing were 4.9 s, 9.2 s, 10.5 s, and 10.5 s, respectively. This suggests that breathing training led to slower respiratory cycles than at rest. Collectively, humming breathing proved to be more effective than deep breathing in improving HRV. The VAS analysis of affective states showed no significant differences in anxiety, stress, relaxation, or confidence across the breathing conditions. This may be attributed to several factors. First, the conditions of rest, deep breathing, humming breathing, and calm humming breathing may not have been strong enough to induce significant affective changes in the short term. Second, because the measurements were taken shortly after the participants woke up, they were likely in a stable affective state, which could have limited the potential for further affective changes. Finally, the VAS used in the study may not have had sufficient sensitivity to capture subtle affective shifts, as evidenced by the changes detected in HRV, a more sensitive psychophysiological measure. Future research should explore whether the VAS can detect long-term affective changes resulting from sustained training. The present study aimed to determine whether the effects of humming breathing on HRV are driven by vibratory sounds or the induction of resonance frequency. Our findings illustrated no significant differences between humming breathing and calm humming breathing performed with auditory blocking in any respiratory or HRV parameters. Although the extent to which noise-canceling earphones increased resonance was not quantitatively measured, all participants reported perceiving stronger vibrations when wearing earphones. This suggests that the use of earphones effectively amplified vibratory sounds. However, the lack of significant differences in respiratory and HRV parameters between the two conditions indicates that vibratory sounds alone may not be the primary mechanism underlying the effects of humming breathing. Rather than the vibratory sound itself directly influencing the ANS, the effects appear to be associated with the 0.1 Hz resonance frequency induced by extended exhalation with an "mmm" sound. This has practical implications for sports and clinical settings, as it suggests that humming breathing alone, without the need for special biofeedback devices, may be sufficient to reduce excessive sympathetic nervous system activation and promote autonomic balance. However, given the exploratory nature of this study and small sample size, replication in larger populations is necessary. Future studies should also examine whether regular practice of humming breathing induces lasting physiological changes over time and how different breathing techniques uniquely affect psychological and neurophysiological markers, such as stress, depression, and anxiety. Declarations Funding Information This research was supported by the University of Ulsan in 2025. 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American Journal of Pharmaceutical Education, 71 (4), 78. https://doi.org/10.5688/aj710478 McCraty, R., & Zayas, M. A. (2014). Cardiac coherence, self-regulation, autonomic stability, and psychosocial well-being. Frontiers in Psychology, 5 , 1090. https://doi.org/10.3389/fpsyg.2014.01090 Pal, G. K., Agarwal, A., Karthik, S., Pal, P., & Nanda, N. (2014). Slow yogic breathing through right and left nostril influences sympathovagal balance, heart rate variability, and cardiovascular risks in young adults. North American Journal of Medical Sciences, 6 (3), 145–151. https://doi.org/10.4103/1947-2714.128477 Park, S., Shin, M., & Woo, M. (2023). The effects of cognitive stress and ventilatory training according to the stress response level on mental health and heart rate variability. Journal of Korean Society of Physical Education, 91 , 247–258. https://doi.org/10.51979/KSSLS.2023.01.91.247 Park, Y. J., & Park, Y. B. (2012). Clinical utility of paced breathing as a concentration meditation practice. Complementary Therapies in Medicine, 20 (6), 393–399. https://doi.org/10.1016/j.ctim.2012.07.008 Rietschel, J. C., Goodman, R. N., King, B. R., Lo, L. C., Contreras‐Vidal, J. L., & Hatfield, B. D. (2011). Cerebral cortical dynamics and the quality of motor behavior during social evaluative challenge. Psychophysiology, 48 (4), 479–487. https://doi.org/10.1111/j.1469-8986.2010.01120.x Saeed, S. A., & Masters, R. M. (2021). Disparities in health care and the digital divide. Current Psychiatry Reports, 23 , 1–6. https://pmc.ncbi.nlm.nih.gov/articles/PMC8300069/ Schäfer, A., & Kratky, K. W. (2008). Estimation of breathing rate from respiratory sinus arrhythmia: Comparison of various methods. Annals of Biomedical Engineering, 36 , 476–485. https://doi.org/10.1007/s10439-007-9428-1 Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5 , 258. https://doi.org/10.3389/fpubh.2017.00258 Steffen, P. R., Austin, T., DeBarros, A., & Brown, T. (2017). The impact of resonance frequency breathing on measures of heart rate variability, blood pressure, and mood. Frontiers in Public Health, 5 , 288289. https://doi.org/10.3389/fpubh.2017.00222 Sterling, P. (2014). Homeostasis vs allostasis: Implications for brain function and mental disorders. JAMA Psychiatry, 71 (10), 1192–1193. https://doi.org/10.1001/jamapsychiatry.2014.1043 Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology, 141 (2), 122–131. https://doi.org/10.1016/j.ijcard.2009.09.543 Ursino, M., & Magosso, E. (2003). Role of short-term cardiovascular regulation in heart period variability: A modeling study. American Journal of Physiology-Heart and Circulatory Physiology, 284 (4), H1479–H1493. https://doi.org/10.1152/ajpheart.00850.2002 Vaschillo, E. G., Vaschillo, B., & Lehrer, P. M. (2006). Characteristics of resonance in heart rate variability stimulated by biofeedback. Applied Psychophysiology and Biofeedback, 31 (2), 129–142. https://doi.org/10.1007/s10484-006-9009-3 Vieira, D. S., Mendes, L. P., Elmiro, N. S., Velloso, M., Britto, R. R., & Parreira, V. F. (2014). Breathing exercises: Influence on breathing patterns and thoracoabdominal motion in healthy subjects. Brazilian Journal of Physical Therapy, 18 (6), 544–552. https://doi.org/10.1590/bjpt-rbf.2014.0048 Wang, Y., van Gelderen, P., de Zwart, J. A., Özbay, P. S., Mandelkow, H., Picchioni, D., & Duyn, J. H. (2022). Cerebrovascular activity is a major factor in the cerebrospinal fluid flow dynamics. NeuroImage, 258 , 119362. https://doi.org/10.1016/j.neuroimage.2022.119362 Weitzberg, E., & Lundberg, J. O. (2002). Humming greatly increases nasal nitric oxide. American Journal of Respiratory and Critical Care Medicine, 166 (2), 144–145. https://doi.org/10.1164/rccm.2203002 Woo, M., & Kim, T. (2025). Effects of slow-paced breathing and humming breathing on heart rate variability and affect: A pilot investigation. Physiology & Behavior , 114972. https://doi.org/10.1016/j.physbeh.2025.114972 Wu, S. D., & Lo, P. C. (2008). Inward-attention meditation increases parasympathetic activity: A study based on heart rate variability. Biomedical Research, 29 (5), 245–250. https://doi.org/10.2220/biomedres.29.245 Yadav, A. S., & Yadav, S. K. (2015). Breathing techniques—A review. International Journal of Physical Education, Sports and Health, 2 , 362–365. https://www.kheljournal.com/archives/2015/vol2issue2/PartE/2-2-13.pdf Zaccaro, A., Piarulli, A., Laurino, M., Garbella, E., Menicucci, D., Neri, B., & Gemignani, A. (2018). How breath-control can change your life: A systematic review on psycho-physiological correlates of slow breathing. Frontiers in Human Neuroscience, 12 , 353. https://doi.org/10.3389/fnhum.2018.00353 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7063841","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483244314,"identity":"e46f3a0a-7146-4a99-8ab3-79113aa81494","order_by":0,"name":"Teri Kim","email":"","orcid":"","institution":"Dongguk University-WISE","correspondingAuthor":false,"prefix":"","firstName":"Teri","middleName":"","lastName":"Kim","suffix":""},{"id":483244316,"identity":"b32b0f7f-c84e-49dd-98a1-604fc7b4d033","order_by":1,"name":"Sujin Lee","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"Sujin","middleName":"","lastName":"Lee","suffix":""},{"id":483244318,"identity":"81054e74-ce64-4431-89d3-66ad65063d18","order_by":2,"name":"Minjung Woo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFCCA0BcwSAD4xoQqeUMAw8pWoCAsY0ULbqNZwwffJx3mEe+/ewBhh81DMbmDQS0mB04Y2w4c9thHoMzeQmMPccYzGQOENRydps0L0gLQ44BA28Dg40EIYdBtMwBOqz/jQHjX+K1NBzmYbiRY8AMtMWMCC3nPxvOOJbOY3DjjcFhmWMSxoS13DiW+OBDjbWcfH+O4cM3NTaGMwhpYZA4gGADmQTtAAL+BiIUjYJRMApGwcgGAKNpPlKmJ6VPAAAAAElFTkSuQmCC","orcid":"","institution":"University of Ulsan","correspondingAuthor":true,"prefix":"","firstName":"Minjung","middleName":"","lastName":"Woo","suffix":""}],"badges":[],"createdAt":"2025-07-07 09:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7063841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7063841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88074465,"identity":"b8401094-630d-48e1-877e-7906e744ccb8","added_by":"auto","created_at":"2025-08-01 06:46:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508819,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7063841/v1/5b29332a-deb9-40f5-883e-ca6266051b73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Humming Breathing as a Device-Free Method for Inducing Resonance Frequency: A Preliminary Investigation into Autonomic Regulatory Mechanisms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic stress is a major contributor to various health disorders due to its disruptive impact on autonomic nervous system (ANS) balance (Humiston \u0026amp; Lansing, 2022). The ANS, comprising the sympathetic and parasympathetic branches, orchestrates the body’s responses to stress and recovery, and its dysregulation—often stemming from prolonged sympathetic activation—can lead to allostatic overload and compromised physiological homeostasis (Charmandari et al., 2005; Ernst, 2017; Logan \u0026amp; Barksdale, 2008; McCorry, 2007). This imbalance is associated with a heightened risk of metabolic, cardiovascular, and mental health disorders, underscoring the urgent need for effective interventions to restore ANS balance (Sterling, 2014; Thayer et al., 2010).\u003c/p\u003e\n\u003cp\u003eBreathing training has emerged as a simple, accessible, and effective approach for reducing sympathetic dominance and enhancing parasympathetic activity. Slow, controlled breathing—particularly with prolonged exhalation—can induce respiratory sinus arrhythmia (RSA) and cardiorespiratory synchronization, optimizing oxygen delivery and promoting physiological stability\u0026nbsp;(Lotrič \u0026amp; Stefanovska, 2000). Such synchronization is linked to reduced stress, improved psychological well-being, enhanced athletic performance, and the maintenance of cardiovascular health (Giardino et al., 2003; Lehrer et al., 2020; Lehrer, Vaschillo, \u0026amp; Vaschillo, 2000; Vaschillo, Vaschillo, \u0026amp; Lehrer, 2006). Consequently, research has focused on identifying breathing techniques that most effectively promote these benefits.\u003c/p\u003e\n\u003cp\u003eA well-established method is resonance frequency breathing at 0.1 Hz (5 s inhalation, 5 s exhalation), which maximizes the resonance between cardiac rhythm and baroreflex activity (Lehrer,\u0026nbsp;2013). This pattern enhances vagal afferent signaling, self-regulation, and resilience to stress, and has led to the development of various biofeedback devices and applications that guide users to maintain a 5:5 breathing cycle (Steffen et al., 2017). Evidence supports that such slow-paced breathing increases heart rate variability (HRV), improves emotional regulation, and aids in stress recovery (Kim, Shin, \u0026amp; Woo, 2023; Lehrer, Vaschillo, \u0026amp; Vaschillo, 2000; Park, Shin, \u0026amp; Woo, 2023). However, some individuals—especially beginners—report dizziness or discomfort with strict 5:5 cycles, and reliance on external devices may limit accessibility and adherence\u0026nbsp;(Saeed \u0026amp; Masters, 2021; Yadav \u0026amp; Yadav, 2015).\u003c/p\u003e\n\u003cp\u003eTo address these limitations, Woo and Kim introduced Bhramari (humming) breathing, which emphasizes internal proprioception over external pacing cues (Woo \u0026amp; Kim, 2025). Their research demonstrated that both slow-paced breathing and humming breathing significantly increased HRV compared to rest, with no significant differences between the two techniques. This suggests that humming breathing, which involves sustained “mm” sound production during exhalation, may offer similar autonomic benefits without the need for external devices.\u003c/p\u003e\n\u003cp\u003eWhile the mechanisms of slow-paced breathing are relatively well-studied, the physiological basis for humming breathing’s effects remains less clear. Two main hypotheses have been proposed: (1) humming breathing may naturally guide participants toward a resonance frequency similar to 5:5 paced breathing, or (2) vibratory sound and resonance may independently stimulate vagal activity (Woo \u0026amp; Kim, 2025). However, previous studies did not directly measure breathing rates or frequencies during humming breathing, leaving it uncertain whether the autonomic effects are primarily due to induced resonance frequency or vibrational stimulation (Brown \u0026amp; Gerbarg, 2005; Jerath et al., 2006; Weitzberg \u0026amp; Lundberg, 2002).\u003c/p\u003e\n\u003cp\u003eTraditional Bhramari breathing also involves blocking auditory and visual stimuli, but Woo and Kim omitted auditory occlusion to avoid confounding muscle tension (Woo \u0026amp; Kim, 2025). This raises the question of whether vibrational effects were maximized. The present study addresses this limitation by comparing standard humming breathing (without auditory occlusion) and “calm humming” (with noise-canceling earphones to block external sounds and enhance vibratory resonance). By quantifying breathing cycles and frequencies, this study aims to clarify whether the autonomic benefits of humming breathing are primarily attributable to breathing rhythm (frequency) or vibrational resonance.\u003c/p\u003e\n\u003cp\u003eIn summary, this study seeks to advance understanding of the mechanisms underlying humming breathing’s impact on autonomic balance, with the goal of informing practical, device-free interventions for stress reduction and health promotion.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Participants\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This preliminary investigation involved adult participants who voluntarily took part in five consecutive breathing training sessions conducted at a fixed morning time. Individuals with a history of heart-related conditions (e.g., hypertension, arrhythmia, angina, or myocardial infarction) or musculoskeletal disorders that could affect heart rate variability (HRV) were excluded. The final sample comprised 11 healthy adults (mean age = 34.36 ± 14.26 years), including six men (mean age = 29.83 ± 15.92 years) and five women (mean age = 39.80 ± 11.12 years). All participants provided written informed consent prior to participation. This study was approved by the university’s Institutional Review Board (DGU IRB 20250010) and conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e2.2 Measures\u003c/p\u003e\n\u003cp\u003e2.2.1 Visual Analog Scale (VAS)\u003c/p\u003e\n\u003cp\u003eWe used the VAS, originally developed by Cline et al. and modified by Rietschel et al., to assess stress, anxiety, confidence, and relaxation (Cline et al., 1992; Rietschel et al., 2011). The scale required participants to mark their feelings on a 0–10 cm line, where zero represented \"not at all\" and 10 represented \"at maximum.\" Participants indicated their affective states by drawing a vertical line on the scale, and the distance from the zero point to the marked point was measured using a ruler.\u003c/p\u003e\n\u003cp\u003e2.2.2 Heart Rate Variability (HRV)\u003c/p\u003e\n\u003cp\u003eWe measured HRV using the Polar H10 (Polar Electro Oy, Kempele, Finland) and EliteHRV app (EliteHRV, Asheville, USA). The HRV values measured by the Polar H10 with the EliteHRV app are highly correlated with stationary ECG devices, confirming their reliability for research-based HRV measurements (Im \u0026amp; Woo, 2024; Woo \u0026amp; Kim, 2025). The Polar H10 is a chest-worn, ECG-based wireless heart rate monitor with a sampling rate of 130 Hz, meaning it collects data every 7.69 ms (1/130 s). This meets the minimum sampling rate requirement for HRV analysis (\u0026lt;250 ms) and is particularly suitable for detecting short-term HRV changes (Giles et al., 2016). The EliteHRV app was installed on each participant’s smartphone and connected to the Polar H10 via Bluetooth. The OPEN HRV READING option in the EliteHRV app was selected, with the time set to 5 min and position set to sitting. After verifying signal stability via the live preview feature, the measurement was initiated by pressing the start button, and upon completion, the data were automatically saved. HRV data were reviewed by selecting all data from the previous log section of the EliteHRV app, which provided the heart rate graph (tachogram), signal quality, time-domain analysis, and frequency-domain analysis results. Only data with an artifact score of zero, indicating high signal quality, were used for analysis. The HRV variables analyzed in this study included time-domain measures, such as the root mean square of successive differences between normal heartbeats (RMSSD) and the standard deviation of all R-R intervals (SDNN), and frequency-domain measures including total power (TP), low-frequency power (LF), high-frequency power (HF), LF peak, and HF peak.\u003c/p\u003e\n\u003cp\u003e2.2.3 Respiratory Measures\u003c/p\u003e\n\u003cp\u003eWe analyzed four respiratory variables over a 5 min period: respiratory rate, respiratory variability across trials, respiratory cycle, and respiratory frequency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespiratory Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRespiratory rate was calculated using RSA patterns identified from heart rate peaks (increases during inhalation) and troughs (decreases during exhalation). Each RSA cycle marked by consecutive peaks was counted as one full breath (inhalation + exhalation) (Schäfer, \u0026amp; Kratky, 2008).\u0026nbsp;For example, if 30 RSA cycles were detected in 300\u0026nbsp;s\u0026nbsp;(5\u0026nbsp;min), then\u0026nbsp;the respiratory rate\u0026nbsp;would be\u0026nbsp;30 breaths per 5\u0026nbsp;min.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespiratory Variability Across Trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants performed breathing exercises under four conditions (rest, deep breathing, humming breathing, and calm humming breathing) over 5 d, repeating each condition five times. The respiratory rate for each trial was recorded, and the respiratory variability across trials was calculated as the standard deviation of the respiratory rates within each condition using Microsoft Excel. Higher variability indicates greater fluctuations in breathing rates across the five trials, whereas lower variability reflects more consistent breathing patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespiratory Cycle (\u003c/strong\u003e\u003cstrong\u003eseconds\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe respiratory cycle represented the duration of a single breath (inhalation or exhalation) in seconds. This was calculated by dividing the total measurement time (300 s) by the total number of breaths. For example, if a participant took 30 breaths in 300 s, the respiratory cycle was 300 / 30 = 10 s/breath.\u003c/p\u003e\n\u003cp\u003eRespiratory Cycle (s) = Measurement Time (s) / Total Number of Breaths\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespiratory Frequency (Hz)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe respiratory frequency, or the number of breaths per second, was calculated as the inverse of the respiratory cycle. For example, if the respiratory cycle time was 10 s, the respiratory frequency was 1/10 = 0.1 Hz.\u003c/p\u003e\n\u003cp\u003eRespiratory Frequency (Hz) = 1 / Respiratory Cycle (s)\u003c/p\u003e\n\u003cp\u003e2.3 Breathing Conditions\u003c/p\u003e\n\u003cp\u003e2.3.1 Rest\u003c/p\u003e\n\u003cp\u003eThe participants sat comfortably on a chair with their eyes closed, and their heart rates were measured for 5 min. The participants were instructed to remain still throughout the measurements.\u003c/p\u003e\n\u003cp\u003e2.3.2 Deep Breathing\u003c/p\u003e\n\u003cp\u003eThe participants sat comfortably on a chair with their eyes closed and were instructed to perform deep breathing for 5 min—inhaling deeply and exhaling fully. No specific feedback was provided regarding inhalation or exhalation processes.\u003c/p\u003e\n\u003cp\u003e2.3.3 Humming Breathing\u003c/p\u003e\n\u003cp\u003eHumming breathing is derived from the yogic practices of Bhramari Pranayama. Participants inhaled through the nose with their lips gently closed and exhaled while producing a humming sound (“mmm”). Unlike paced breathing, this method did not follow a predetermined inhalation-exhalation cycle; instead, participants were instructed to breathe naturally according to their lung capacity without any guidance on breath timing. During exhalation, they maintained a light closure of their lips while producing a humming sound, ensuring complete exhalation with subtle vibrations. Deep, slow inhalation was performed through the nose. The breathing exercise was conducted for 5 min with participants keeping their eyes closed.\u003c/p\u003e\n\u003cp\u003e2.3.4 Calm Humming Breathing\u003c/p\u003e\n\u003cp\u003eTraditional Bhramari breathing eliminates visual and auditory stimuli, typically by covering the ears with fingers. We used wireless earphones with noise-canceling features (DX-200, Daximen (Shenzhen)Technology Co., Ltd., China) to avoid activation of the shoulder and upper arm muscles while maximizing the vibratory effects. This modified method, termed calm humming breathing, was identical to humming breathing except for the use of earphones. The earphones blocked external sounds and enhanced the vibratory sensation of the humming sound during exhalation, promoting sinus resonance and amplifying its effects.\u003c/p\u003e\n\u003cp\u003e2.4 Procedure\u003c/p\u003e\n\u003cp\u003eAfter signing the consent form, the participants installed the EliteHRV app on their personal smartphones and wore a Polar H10 chest strap, which was then connected to the EliteHRV app via Bluetooth. The participants received instructions on the measurement protocol and breathing techniques (rest, deep breathing, humming breathing, and calm humming breathing). After practicing the breathing techniques to ensure correct execution, participants randomly determined the order in which they would perform the four types of breathing over 5 d (randomized orders for all 5 d). During the 5 d of the experiment, the participants were instructed to refrain from alcohol consumption. Upon waking in the morning, they were instructed to complete measurements for each condition (5 min each) before consuming food or engaging in other activities. The participants wore the Polar H10, sat comfortably, and connected it to the EliteHRV app. After ensuring stable heart rate data, they performed the four breathing conditions in a predetermined order, with each breathing condition lasting 5 min. After each condition, the participants completed the VAS to assess their affective state and then proceeded to the next condition. After completing all four conditions, the participants sent their data to the experimenter.\u003c/p\u003e\n\u003cp\u003e2.5 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eTo examine differences in respiratory metrics (respiratory rate, respiratory variability across trials, respiratory cycle, and respiratory frequency) across breathing conditions (rest, deep breathing, humming breathing, and calm humming breathing), a repeated-measures one-way analysis of variance (ANOVA) was conducted, with breathing conditions as the independent variable and respiratory metrics as the dependent variable.To examine differences in HRV metrics (HR, RMSSD, SDNN, TP, LF, HF, LF peak, and HF peak) across breathing conditions, a repeated-measures one-way ANOVA was conducted, with breathing conditions as the independent variable and HRV metrics as the dependent variable.To investigate differences in affective states (anxiety, stress, relaxation, and confidence) across breathing conditions, a repeated-measures one-way ANOVA was performed, with breathing conditions as the independent variable and VAS scores as the dependent variable. The effect sizes for all ANOVA results were calculated using eta-squared values. Mauchly’s test of sphericity was performed for all dependent variables. Where the assumption of sphericity was violated, the Greenhouse–Geisser correction was applied to adjust the degrees of freedom for the ANOVA. All statistical analyses were conducted using PASW Statistics 18 (IBM Corporation, USA), with the significance level set at .05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Respiratory Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences were found across breathing conditions for respiratory rate (F(3,30) = 47.42, p = .000, \u0026eta;\u0026sup2; = .826), respiratory variability across trials (F(3,30) = 8.51, p = .000, \u0026eta;\u0026sup2; = .460), respiratory cycle (F(3,30) = 54.514, p = .000, \u0026eta;\u0026sup2; = .845), and respiratory frequency (F(3,30) = 47.42, p = .000, \u0026eta;\u0026sup2; = .826). The respiratory rate significantly decreased in all conditions (deep breathing, humming breathing, and calm humming breathing) compared to that of rest. In addition, the respiratory rate was lower during humming breathing than that during deep breathing. The respiratory variability across trials was lower in all conditions compared to that of rest and lower in humming breathing and calm humming breathing than in deep breathing. The respiratory cycle was longer in all conditions than at rest and was longer in humming breathing and calm humming breathing than in deep breathing. The respiratory frequency was lower in all conditions than at rest and lower during humming breathing than during deep breathing. Interestingly, the respiratory frequency, which was 0.2 Hz at rest, decreased to the resonance frequency (0.1 Hz) during both humming and calm humming breathing. However, no significant differences were found between humming and calm humming breathing for any variable (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1. Differences in respiratory measures across breathing conditions (mean \u0026plusmn; standard deviation)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eDeep Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eHumming Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eCalm Humming Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ePost hoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRespiratory rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e65.59\u0026plusmn;\u0026nbsp;16.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e34.51\u0026plusmn;8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e30.6\u0026plusmn;7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e30.12\u0026plusmn;7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e47.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d***\u003c/p\u003e\n \u003cp\u003eb\u0026lt;c*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRespiratory variation across trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e7.79\u0026plusmn;4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e5.08\u0026plusmn;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e3.78\u0026plusmn;2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e3.29\u0026plusmn;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c*,d**\u003c/p\u003e\n \u003cp\u003eb\u0026lt;d*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRespiratory cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e4.88\u0026plusmn;1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e9.19\u0026plusmn;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e10.49\u0026plusmn;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e10.46\u0026plusmn;2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e54.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d***\u003c/p\u003e\n \u003cp\u003eb\u0026lt;c,d*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRespiratory frequency\u003c/p\u003e\n \u003cp\u003e(Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e.219\u0026plusmn;.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e.115\u0026plusmn;.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e.100\u0026plusmn;.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e.100\u0026plusmn;.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e47.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d***\u003c/p\u003e\n \u003cp\u003eb\u0026lt;c*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 565px;\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .05 \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001 a=rest, b=deep breathing, c=humming breathing, d=calm humming breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2 HRV Measures\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed across breathing conditions for RMSSD (F(3,30) = 7.428, p = .001, \u0026eta;\u0026sup2; = .426), SDNN (F(3,30) = 19.44, p = .000, \u0026eta;\u0026sup2; = .660), TP (F(3,30) = 12.947, p = .000, \u0026eta;\u0026sup2; = .564), LF (F(3,30) = 13.109, p = .000, \u0026eta;\u0026sup2; = .567), and HF peak (F(3,30) = 9.579, p = .000, \u0026eta;\u0026sup2; = .489). The RMSSD was higher under all breathing conditions than at rest. SDNN and LF were higher in all breathing conditions than at rest, with humming breathing exhibiting higher SDNN and LF than those of deep breathing. The TP was higher in all breathing conditions than at rest, with higher TP observed during humming breathing and calm humming breathing than during deep breathing. The HF peak was lower in all breathing conditions than at rest. However, no differences were observed between humming breathing and calm humming breathing for any of the HRV variables (Table 2).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Differences in heart rate variability measures across breathing conditions (mean \u0026plusmn; standard deviation)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eDeep Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eHumming Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eCalm Humming Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ePost hoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e73.25\u0026plusmn;10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e72.20\u0026plusmn;10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e73.32\u0026plusmn;11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e73.45\u0026plusmn;11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eRMSSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e29.62\u0026plusmn;10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e36.77\u0026plusmn;15.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e38.80\u0026plusmn;17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e40.06\u0026plusmn;.19.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e7.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eSDNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e47.82\u0026plusmn;13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e65.82\u0026plusmn;20.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e70.75\u0026plusmn;23.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e71.06\u0026plusmn;25.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e19.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eb\u0026lt;c\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1017.43\u0026plusmn;760.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3498.88\u0026plusmn;2619.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4605.87\u0026plusmn;3187.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4922.96\u0026plusmn;3731.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e12.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eb\u0026lt;c,d\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e566.87\u0026plusmn;392.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2856.87\u0026plusmn;2086.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3852.85\u0026plusmn;2751.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3841.87\u0026plusmn;3173.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e13.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ea\u0026lt;b,c,d\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eb\u0026lt;c\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLF peak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.0902\u0026plusmn;.0208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e.0975\u0026plusmn;.0150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e.0922\u0026plusmn;.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e.0928\u0026plusmn;.0229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e460.75\u0026plusmn;415.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e606.30\u0026plusmn;667.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e834.53\u0026plusmn;1081.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1114.98\u0026plusmn;1517.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eHF peak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.2208\u0026plusmn;.0344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e.1923\u0026plusmn;.0263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e.1804\u0026plusmn;.0158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e.1818\u0026plusmn;.0197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e9.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ea\u0026lt;b\u003csup\u003e*\u003c/sup\u003e,c,d\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .05 \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001 a=rest, b=deep breathing, c=humming breathing, d=calm humming breathing. root mean square of successive differences (RMSSD); standard deviation of all R-R intervals (SDNN); total power (TP); low-frequency power (LF); high-frequency power (HF)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.3 VAS Measures\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3, no significant differences were observed across the breathing conditions in any of the VAS measures, including anxiety, stress, relaxation, and confidence (p \u0026gt; .05).\u003c/p\u003e\n\u003cp\u003eTable 3. Differences in visual analog scale measures across breathing conditions (mean \u0026plusmn; standard deviation)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eDeep Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eHumming Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eCalm Humming Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePost hoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.76\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.65\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.71\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.79\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.13\u0026plusmn;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.02\u0026plusmn;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2.26\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2.16\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRelaxation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.50\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.58\u0026plusmn;.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e6.51\u0026plusmn;.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e6.87\u0026plusmn;.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eConfidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.33\u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.57\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e6.43\u0026plusmn;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e6.62\u0026plusmn;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 565px;\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .05 \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001 a=rest, b=deep breathing, c=humming breathing, d=calm humming breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study examined whether vibratory sounds or respiratory characteristics are key factors in the effect of humming breathing on the ANS. The results indicated that the respiration rate was lower during all breathing conditions (i.e., deep breathing, humming breathing, and calm humming breathing) than at rest, with humming and calm humming breathing showing lower rates than those of deep breathing, suggesting that these conscious breathing practices are effective in promoting longer breaths (Jerath et al., 2006; Vieira et al., 2014; Zaccaro et al., 2018). Furthermore, the respiratory variability across trials was lower in all conditions than at rest and lower in humming and calm humming breathing than in deep breathing. The reduced variability suggests a more consistent breathing pattern over the 5-d measurement period, likely due to the consistent lung capacity maintained during the humming exhalation process. The respiratory cycle, defined as the time taken for one full inhalation and exhalation, increased in all breathing conditions compared to that at rest (4.9 s), with humming (10.5 s) and calm humming breathing (10.5 s) showing longer cycles than those of deep breathing (9.2 s). This suggests that, regardless of auditory occlusion, humming breathing—with its prolonged exhalation accompanied by the \"mm\" sound—induces a longer breath compared to that during deep breathing, which emphasizes both inhalation and exhalation. Consequently, the respiratory frequency decreased from 0.2 Hz at rest to 0.12 Hz during deep breathing and further to 0.1 Hz during both humming and calm humming breathing. Without following a fixed breathing cycle, both humming and calm humming breathing were able to achieve the target resonance frequency of 0.1 Hz, similar to paced breathing, which involves a 5 s inhale and 5 s exhale. These findings suggest that humming breathing can achieve the desired resonance frequency without the use of devices or applications. Moreover, it maintained consistent breathing patterns with lower respiratory variability, indicating more stable and controlled breathing. This makes humming breathing a practical and effective alternative to cyclical breathing that is suitable for real-world applications.\u003c/p\u003e\n\u003cp\u003eAnalysis of HRV revealed higher RMSSD, SDNN, TP, and LF and a lower HF peak in all breathing conditions than at rest. Furthermore, SDNN and LF were higher during humming breathing than during deep breathing, whereas TP was higher during humming and calm humming breathing than during deep breathing. However, no differences were observed between humming breathing and calm humming breathing for any of the HRV variables. These results are consistent with those of previous studies, confirming that breathing training generally increases HRV parameters (Edmonds et al., 2009; Kim, Shin, \u0026amp; Woo, 2023; Park \u0026amp; Park, 2012; Woo \u0026amp; Kim, 2025). A higher RMSSD suggests enhanced parasympathetic activity, and higher SDNN and TP indicate heightened autonomic regulation and physiological resilience (Kim, Shin, \u0026amp; Woo, 2023; Laborde et al., 2022; McCraty \u0026amp; Zayas, 2014; Pal et al., 2014; Wu \u0026amp; Lo, 2008; Ursino \u0026amp; Magosso, 2003; Shaffer \u0026amp; Ginsberg, 2017). Furthermore, the substantial increase in LF (0.05–0.15 Hz) power density indicates that as the respiratory cycle of humming breathing extends to 10 s, the power density aligns with the resonance frequency of 0.1 Hz, leading to an increase in LF power. This suggests that humming breathing is an effective method for inducing resonance frequency.\u003c/p\u003e\n\u003cp\u003eCompared to that at rest, deep breathing showed a lower HF peak, and both humming and calm humming breathing exhibited even lower HF peaks. Specifically, the frequency bands with the highest power density in the high-frequency range (0.15–0.4 Hz) were 0.22 Hz during rest, 0.19 Hz during deep breathing, and 0.18 Hz for both humming and calm humming breathing. HF peak is closely related to the respiratory cycle, with a lower HF peak frequency indicating slower and deeper breathing (Ursino, \u0026amp; Magosso, 2002; Wang et al., 2022). In this study, the respiratory cycles for rest, deep breathing, humming breathing, and calm humming breathing were 4.9 s, 9.2 s, 10.5 s, and 10.5 s, respectively. This suggests that breathing training led to slower respiratory cycles than at rest. Collectively, humming breathing proved to be more effective than deep breathing in improving HRV.\u003c/p\u003e\n\u003cp\u003eThe VAS analysis of affective states showed no significant differences in anxiety, stress, relaxation, or confidence across the breathing conditions. This may be attributed to several factors. First, the conditions of rest, deep breathing, humming breathing, and calm humming breathing may not have been strong enough to induce significant affective changes in the short term. Second, because the measurements were taken shortly after the participants woke up, they were likely in a stable affective state, which could have limited the potential for further affective changes. Finally, the VAS used in the study may not have had sufficient sensitivity to capture subtle affective shifts, as evidenced by the changes detected in HRV, a more sensitive psychophysiological measure. Future research should explore whether the VAS can detect long-term affective changes resulting from sustained training.\u003c/p\u003e\n\u003cp\u003eThe present study aimed to determine whether the effects of humming breathing on HRV are driven by vibratory sounds or the induction of resonance frequency. Our findings illustrated no significant differences between humming breathing and calm humming breathing performed with auditory blocking in any respiratory or HRV parameters. Although the extent to which noise-canceling earphones increased resonance was not quantitatively measured, all participants reported perceiving stronger vibrations when wearing earphones. This suggests that the use of earphones effectively amplified vibratory sounds. However, the lack of significant differences in respiratory and HRV parameters between the two conditions indicates that vibratory sounds alone may not be the primary mechanism underlying the effects of humming breathing. Rather than the vibratory sound itself directly influencing the ANS, the effects appear to be associated with the 0.1 Hz resonance frequency induced by extended exhalation with an \"mmm\" sound. This has practical implications for sports and clinical settings, as it suggests that humming breathing alone, without the need for special biofeedback devices, may be sufficient to reduce excessive sympathetic nervous system activation and promote autonomic balance.\u003c/p\u003e\n\u003cp\u003eHowever, given the exploratory nature of this study and small sample size, replication in larger populations is necessary. Future studies should also examine whether regular practice of humming breathing induces lasting physiological changes over time and how different breathing techniques uniquely affect psychological and neurophysiological markers, such as stress, depression, and anxiety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the University of Ulsan in 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrown, R. P., \u0026amp; Gerbarg, P. L. (2005). Sudarshan Kriya yogic breathing in the treatment of stress, anxiety, and depression: Part I—Neurophysiologic model. \u003cem\u003eJournal of Alternative and Complementary Medicine, 11\u003c/em\u003e(3), 189–201.\u0026nbsp;https://doi.org/10.1089/acm.2005.11.189\u003c/li\u003e\n \u003cli\u003eCharmandari, E., Tsigos, C., \u0026amp; Chrousos, G. (2005). Endocrinology of the stress response. \u003cem\u003eAnnual Review of Physiology, 67\u003c/em\u003e, 259–284.\u0026nbsp;https://doi.org/10.1146/annurev.physiol.67.040403.120816\u003c/li\u003e\n \u003cli\u003eCline, M. E., Herman, J., Shaw, E. R., \u0026amp; Morton, R. D. (1992). Standardization of the visual analogue scale. \u003cem\u003eNursing Research, 41\u003c/em\u003e, 378–380.\u003c/li\u003e\n \u003cli\u003eEdmonds, W. A., Kennedy, T. D., Hughes, P. A., \u0026amp; Calzada, P. J. (2009). A single-participant’s investigation of the effects of various biofeedback-assisted breathing patterns on heart rate variability: A practitioner’s approach. \u003cem\u003eBiofeedback, 37\u003c/em\u003e(4), 141–146.\u0026nbsp;https://doi.org/10.5298/1081-5937-37.4.141\u003c/li\u003e\n \u003cli\u003eErnst, G. (2017). Heart-rate variability—More than heart beats? \u003cem\u003eFrontiers in Public Health, 5\u003c/em\u003e, 240.\u0026nbsp;https://doi.org/10.3389/fpubh.2017.00240\u003c/li\u003e\n \u003cli\u003eGiardino, N. D., Glenny, R. W., Borson, S., \u0026amp; Chan, L. (2003). Respiratory sinus arrhythmia is associated with efficiency of pulmonary gas exchange in healthy humans. \u003cem\u003eAmerican Journal of Physiology-Heart and Circulatory Physiology, 284\u003c/em\u003e, H1585–H1591.\u0026nbsp;https://doi.org/10.1152/ajpheart.00893.2002\u003c/li\u003e\n \u003cli\u003eGiles, D., Draper, N., \u0026amp; Neil, W. (2016). Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. \u003cem\u003eEuropean Journal of Applied Physiology, 116\u003c/em\u003e, 563–571.\u003c/li\u003e\n \u003cli\u003eHumiston, T., \u0026amp; Lansing, A. H. (2022). Stress: Historical approaches to allostasis. In \u003cem\u003eBiopsychosocial Factors of Stress, and Mindfulness for Stress Reduction\u003c/em\u003e (pp. 3–16). Springer International Publishing. https://doi.org/10.1007/978-3-030-81245-4_1\u003c/li\u003e\n \u003cli\u003eIm, C., \u0026amp; Woo, M. (2024). Evaluation of the suitability of digital devices for measuring heart rate variability: A comparative analysis with ECG. \u003cem\u003eJournal of Sport Leisure Studies, 98\u003c/em\u003e, 325–336.\u0026nbsp;https://doi.org/10.51979/KSSLS.2024.10.98.325\u003c/li\u003e\n \u003cli\u003eJerath, R., Edry, J. W., Barnes, V. A., \u0026amp; Jerath, V. (2006). Physiology of long pranayamic breathing: Neural respiratory elements may provide a mechanism that explains how slow deep breathing shifts the autonomic nervous system. \u003cem\u003eMedical Hypotheses, 67\u003c/em\u003e(3), 566–571.\u0026nbsp;https://doi.org/10.1016/j.mehy.2006.02.042\u003c/li\u003e\n \u003cli\u003eKim, T., Shin, M., \u0026amp; Woo, M. (2023). Autonomic nervous system activity in response to stress and cardiac coherence breathing exercise depending on body composition differences. \u003cem\u003eInternational Journal of Sport and Exercise Psychology\u003c/em\u003e, 1–17.\u0026nbsp;https://doi.org/10.1080/1612197X.2023.2287505\u003c/li\u003e\n \u003cli\u003eLaborde, S., Allen, M. S., Borges, U., Dosseville, F., Hosang, T. J., Iskra, M., Mosley, E., Salvotti, C., Spolverato, L., Zammit, N., \u0026amp; Javelle, F. (2022). Effects of voluntary slow breathing on heart rate and heart rate variability: A systematic review and a meta-analysis. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews, 138\u003c/em\u003e, 104711.\u0026nbsp;https://doi.org/10.1016/j.neubiorev.2022.104711\u003c/li\u003e\n \u003cli\u003eLehrer, P. (2013). How does heart rate variability biofeedback work? Resonance, the baroreflex, and other mechanisms. \u003cem\u003eBiofeedback, 41\u003c/em\u003e(1), 26–31.\u0026nbsp;https://doi.org/10.5298/1081-5937-41.1.02\u003c/li\u003e\n \u003cli\u003eLehrer, P., Kaur, K., Sharma, A., Shah, K., Huseby, R., Bhavsar, J., Sgobba, P., \u0026amp; Zhang, Y. (2020). Heart rate variability biofeedback improves emotional and physical health and performance: A systematic review and meta-analysis. \u003cem\u003eApplied Psychophysiology and Biofeedback, 45\u003c/em\u003e(3), 109–129.\u0026nbsp;https://doi.org/10.1007/s10484-020-09466-z\u003c/li\u003e\n \u003cli\u003eLehrer, P. 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The effects of cognitive stress and ventilatory training according to the stress response level on mental health and heart rate variability. \u003cem\u003eJournal of Korean Society of Physical Education, 91\u003c/em\u003e, 247–258.\u0026nbsp;https://doi.org/10.51979/KSSLS.2023.01.91.247\u003c/li\u003e\n \u003cli\u003ePark, Y. J., \u0026amp; Park, Y. B. (2012). Clinical utility of paced breathing as a concentration meditation practice. \u003cem\u003eComplementary Therapies in Medicine, 20\u003c/em\u003e(6), 393–399.\u0026nbsp;https://doi.org/10.1016/j.ctim.2012.07.008\u003c/li\u003e\n \u003cli\u003eRietschel, J. C., Goodman, R. N., King, B. R., Lo, L. C., Contreras‐Vidal, J. L., \u0026amp; Hatfield, B. D. (2011). Cerebral cortical dynamics and the quality of motor behavior during social evaluative challenge. \u003cem\u003ePsychophysiology, 48\u003c/em\u003e(4), 479–487.\u0026nbsp;https://doi.org/10.1111/j.1469-8986.2010.01120.x\u003c/li\u003e\n \u003cli\u003eSaeed, S. A., \u0026amp; Masters, R. M. (2021). Disparities in health care and the digital divide. \u003cem\u003eCurrent Psychiatry Reports, 23\u003c/em\u003e, 1–6. https://pmc.ncbi.nlm.nih.gov/articles/PMC8300069/\u003c/li\u003e\n \u003cli\u003eSchäfer, A., \u0026amp; Kratky, K. W. (2008). Estimation of breathing rate from respiratory sinus arrhythmia: Comparison of various methods. \u003cem\u003eAnnals of Biomedical Engineering, 36\u003c/em\u003e, 476–485.\u0026nbsp;https://doi.org/10.1007/s10439-007-9428-1\u003c/li\u003e\n \u003cli\u003eShaffer, F., \u0026amp; Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. \u003cem\u003eFrontiers in Public Health, 5\u003c/em\u003e, 258.\u0026nbsp;https://doi.org/10.3389/fpubh.2017.00258\u003c/li\u003e\n \u003cli\u003eSteffen, P. R., Austin, T., DeBarros, A., \u0026amp; Brown, T. (2017). The impact of resonance frequency breathing on measures of heart rate variability, blood pressure, and mood. \u003cem\u003eFrontiers in Public Health, 5\u003c/em\u003e, 288289.\u0026nbsp;https://doi.org/10.3389/fpubh.2017.00222\u003c/li\u003e\n \u003cli\u003eSterling, P. (2014). Homeostasis vs allostasis: Implications for brain function and mental disorders. \u003cem\u003eJAMA Psychiatry, 71\u003c/em\u003e(10), 1192–1193.\u0026nbsp;https://doi.org/10.1001/jamapsychiatry.2014.1043\u003c/li\u003e\n \u003cli\u003eThayer, J. F., Yamamoto, S. S., \u0026amp; Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. \u003cem\u003eInternational Journal of Cardiology, 141\u003c/em\u003e(2), 122–131.\u0026nbsp;https://doi.org/10.1016/j.ijcard.2009.09.543\u003c/li\u003e\n \u003cli\u003eUrsino, M., \u0026amp; Magosso, E. (2003). 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Effects of slow-paced breathing and humming breathing on heart rate variability and affect: A pilot investigation. \u003cem\u003ePhysiology \u0026amp; Behavior\u003c/em\u003e, 114972.\u0026nbsp;https://doi.org/10.1016/j.physbeh.2025.114972\u003c/li\u003e\n \u003cli\u003eWu, S. D., \u0026amp; Lo, P. C. (2008). Inward-attention meditation increases parasympathetic activity: A study based on heart rate variability. \u003cem\u003eBiomedical Research, 29\u003c/em\u003e(5), 245–250.\u0026nbsp;https://doi.org/10.2220/biomedres.29.245\u003c/li\u003e\n \u003cli\u003eYadav, A. S., \u0026amp; Yadav, S. K. (2015). Breathing techniques—A review. \u003cem\u003eInternational Journal of Physical Education, Sports and Health, 2\u003c/em\u003e,\u0026nbsp;362–365.\u0026nbsp;https://www.kheljournal.com/archives/2015/vol2issue2/PartE/2-2-13.pdf\u003c/li\u003e\n \u003cli\u003eZaccaro, A., Piarulli, A., Laurino, M., Garbella, E., Menicucci, D., Neri, B., \u0026amp; Gemignani, A. (2018). How breath-control can change your life: A systematic review on psycho-physiological correlates of slow breathing. \u003cem\u003eFrontiers in Human Neuroscience, 12\u003c/em\u003e, 353.\u0026nbsp;https://doi.org/10.3389/fnhum.2018.00353\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"humming breathing, Bhramari Pranayama, heart rate variability, resonance frequency, yogic breathing ","lastPublishedDoi":"10.21203/rs.3.rs-7063841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7063841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to determine whether the effects of humming breathing (Bhramari Pranayama) on the autonomic nervous system were caused by vibratory sounds or specific respiratory patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Eleven healthy adults participated in four randomized sessions (rest, deep breathing, humming breathing, and calm humming breathing) over five consecutive days. Heart rate variability and respiratory measures were analyzed, along with visual analog scale responses for stress, anxiety, confidence, and relaxation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Humming and calm humming breathing resulted in lower respiration rates and less variability than those of rest and deep breathing, with a target resonance frequency of 0.1 Hz. All breathing conditions showed higher root mean square of successive differences, standard deviation of normal-to-normal intervals, total power, and low-frequency power values, along with lower high-frequency power peaks, compared with those of rest. Humming breathing exhibited higher standard deviation of normal-to-normal intervals, total power, and low-frequency power values than those of deep breathing. Visual analog scale analysis revealed no significant differences across the breathing conditions. No significant differences were observed between humming and calm humming breathing for any of the measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The effects of humming breathing appear to be associated with the 0.1 Hz resonance frequency induced by extended exhalation with an “mm” sound rather than the vibratory sound itself. Humming breathing, which does not require a biofeedback device, is a practical alternative to cyclical breathing.\u003c/p\u003e","manuscriptTitle":"Humming Breathing as a Device-Free Method for Inducing Resonance Frequency: A Preliminary Investigation into Autonomic Regulatory Mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 03:05:18","doi":"10.21203/rs.3.rs-7063841/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d323e9d-9735-4d30-a783-fa191c7ee77b","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51310625,"name":"Health sciences/Health care"},{"id":51310626,"name":"Biological sciences/Neuroscience"},{"id":51310627,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2025-08-01T06:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 03:05:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7063841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7063841","identity":"rs-7063841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}