Heart Rate Variability Podcast

Optimal HRV
  • 3.5 (10)
  • Health & Fitness
  • Updated Weekly

Welcome to the Heart Rate Variability Podcast where we discuss the research and applications of heart rate variability.

  1. This Week In HRV - Episode 44

    1d ago

    This Week In HRV - Episode 44

    This week's episode spans nine studies — from biofeedback and cognitive performance to chronic parenting stress, leadership in VR, body composition, AI-powered hypertension detection, post-cardiac-procedure monitoring, academic burnout, and the question everyone keeps asking about 5G. Whether you're a practitioner, researcher, or someone tracking your own autonomic health, this episode offers something worth sitting with. RESEARCH HIGHLIGHTS THIS  WEEK 1. Can HRV Biofeedback Sharpen Your Memory? A Systematic Review Weighs In Publication: International Journal of Psychophysiology Authors: Fernando Rosendo da Cunha e Silva, Esther P.F. Wöllner, Carlos Eduardo Norte KEY FINDING: Across ten studies, HRV biofeedback consistently increased HRV — but its effects on working memory were mixed. Clinical populations, particularly veterans with PTSD, showed meaningful cognitive improvements. Healthy young adults and older adults showed less consistent gains. Significance: HRV biofeedback reliably shifts autonomic function, but cognitive benefits appear context-dependent. Who you're training matters as much as how you're training. Read full study: https://www.sciencedirect.com/science/article/pii/S0167876026000644 2. Low HRV Predicts Worse Outcomes in Somatic Symptom Disorder — 12 Months Out Publication: Journal of Psychosomatic Research Authors: Paul Hüsing, Wei-Lieh Huang, Kerstin Maehder, Franz Pauls, Yvonne Nestoriuc, Bernd Löwe, Kristina Blankenburg, Sophie Schmitz, Stefanie Hahn, Anne Toussaint KEY FINDING: In 148 patients with Somatic Symptom Disorder, those with a low HRV pattern showed consistently higher somatic symptom severity, depression, and psychological distress — and these differences held stable across a full 12 months with no significant change over time. Significance: HRV pattern classification at baseline may identify which SSD patients are at risk for persistent, long-term symptom burden — offering a physiological lens for a condition that is otherwise difficult to stratify. Read full study: https://www.sciencedirect.com/science/article/pii/S0022399926003855 3. Chronic Parenting Stress Shows Up in HRV — and in the Blood Publication: Stress and Health Authors: Marija Ljubičić, Ivana Kolčić KEY FINDING: Parents of children with chronic conditions — particularly autism spectrum disorder — showed reduced HRV and elevated Advanced Glycation End Products (AGEs), a marker of oxidative stress. A child's challenging behaviour and parental stress were the key drivers of these physiological changes. Significance: Chronic caregiving stress doesn't just feel hard — it produces measurable autonomic and oxidative consequences. HRV monitoring in caregiving populations may be an underutilized health tool. Read full study: https://onlinelibrary.wiley.com/doi/10.1002/smi.70185 4. Reading the Room in VR: How Physiological Signals Could Help Leaders Facilitate Better Publication: Frontiers in Computer Science Authors: Chenghao Gu, Jiadong Chen, Tianyuan Y...

    1h 17m
  2. This Week In HRV - Episode 43

    Jun 23

    This Week In HRV - Episode 43

    This week on This Week in Heart Rate Variability, we explore four studies that collectively challenge us to think more deeply about what autonomic function tells us — and what it doesn't tell us on its own. From addiction treatment to heart failure, adolescent fitness to chronic pain, this episode traces the threads connecting heart rate variability to some of the most pressing questions in clinical and population health. Whether you're a practitioner, researcher, or someone tracking your own autonomic health, there's something in this episode that will change how you think about what your nervous system is doing. RESEARCH HIGHLIGHTS THIS WEEK 1. HRV and the Recovery Gap: When Physiology and Mental Health Walk Different Paths Publication: Frontiers in Psychiatry Authors: Wendy Insalaco, Charlotte Clapham, Brett Gelino, Jami Mayo Barney, Brianna Billings, Jennifer D. Ellis, J. Gregory Hobelmann, Andrew S. Huhn, Vadim Zipunnikov, Jill A. Rabinowitz KEY FINDING: In fifty-nine individuals undergoing residential substance use disorder treatment, resting heart rate, heart rate variability, and self-reported stress, anxiety, and depression all tended to improve over the first month. However, at the individual level, physiological improvement and mental health improvement did not reliably co-occur — fewer than half of participants with improving physiological metrics showed concurrent improvements across all mental health domains. Significance: This finding challenges the assumption that wearable physiological metrics and subjective mental health assessments are capturing the same recovery signal. For clinicians and treatment providers, it suggests that both dimensions of recovery must be monitored independently, and that physiological improvement should not be interpreted as a proxy for psychological wellbeing in early recovery. → Read full study: https://doi.org/10.3389/fpsyt.2026.1755153 2. The Clock is Broken: Circadian HRV Disruption in Heart Failure Publication: Biomedicines Authors:Natalia Buitrago-Ricaurte, Andre J. Riveros, Rafael González Niño, Liliana Otero, Juan David Meléndez, Alain Riveros-Rivera Key Finding: In eighty-six patients with cardiac remodeling compared to eighty-six controls, twenty-four-hour autonomic monitoring with Cosinor modeling revealed not only reduced overall heart rate variability but blunted circadian amplitude and phase shifts in autonomic modulation — a loss of the normal day-night rhythm of sympathovagal balance. Significance: This study highlights that the timing and rhythm of autonomic dysfunction may matter as much as its average level. Circadian HRV profiling may provide diagnostic and prognostic information in heart failure patients beyond what short-term or snapshot measurements offer, and opens therapeutic avenues targeting circadian autonomic restoration. → Read full study: https://doi.org/10.3390/biomedicines14051054 3. Moving More Matters Most When It's Hardest: Physical Activity and HRV in Young Men Publication: Physical Activity and Health Authors: Jaakko Tornberg, Tiina Ikäheimo, Kaisu Kaikkonen, Riitta Pyky, Marjukka Nurkkala, Arto Hautala, Timo Jämsä, Raija Korpelainen Key Finding: Across three thousand three hundred and eighty-nine adolescent men, higher physical activity was significantly associated with higher RMSSD across all body mass...

    1h 8m

  3. Jun 18

    Cameron Allen talks HRV in Virtual Reality

    In this episode, Matt Bennett talks to Cameron Allen about his work in heart rate variability biofeedback and the benefits of biofeedback in virtual reality.

    1h 1m
  4. This Week In HRV - Episode 42

    Jun 16

    This Week In HRV - Episode 42

    This week's episode covers five studies spanning sleep medicine, transportation safety, signal complexity methodology, cardiac mortality prediction, and autonomic neuroscience in a rare genetic condition. Together, they reveal how much untapped information lives in the heart rate variability signal — and how rapidly the field is developing tools to access it. RESEARCH HIGHLIGHTS THIS WEEK 1. Can an AI Stage Your Sleep From Your Heartbeat Alone? Publication: The National Medical Journal of India Authors: Suvradeep Chakraborty, Manish Goyal, Paritosh Goyal, Priyadarshini Mishra KEY FINDING: A random forest classifier trained on time-domain, frequency-domain, and nonlinear heart rate variability features — with ectopic beat correction and epoch index as a temporal marker — achieved 78.9% accuracy, a Cohen's kappa of 0.70, and a macro F1 score of 0.789 on external validation for five-stage sleep classification using electrocardiogram data alone. SIGNIFICANCE: Heart rate variability-based automated sleep staging is approaching clinical viability as a population-level research and screening tool, though it is not yet a replacement for polysomnography. The study demonstrates that preprocessing quality and temporal context are as important as model architecture — findings with direct implications for any wearable-based sleep monitoring application. Read the full study: https://nmji.in/artificial-intelligence-based-automated-sleep-staging-using-heart-rate-variability-assessment-of-performance-and-clinical-prospects/ 2. A 30-Second Heartbeat Test Before You Drive Publication: IAES International Journal of Artificial Intelligence Authors: Tia Haryanti, Eri Prasetyo Wibowo, Wahyu Kusuma Raharja, Rossi Septy Wahyuni, Ilmiyati Sari KEY FINDING: A subject-independent logistic regression model trained on short-term heart rate variability features from 30-second electrocardiogram recordings achieved an ROC-AUC of 0.687 and 100% sensitivity for detecting pre-driving fatigue (Karolinska Sleepiness Scale score of 7 or above) at the chosen operating threshold, with a proposed three-tier triage scheme to manage the high false positive rate. SIGNIFICANCE: This feasibility study demonstrates that brief, wearable-compatible heart rate variability recordings carry discriminable signal about fatigue state under subject-independent validation — the appropriate test for real-world deployment. Specificity remains very low at the sensitivity-optimized threshold, and replication in larger samples is needed before operational translation. Read the full study: https://ijai.iaescore.com/index.php/IJAI/article/view/30466/15254 3. Bubble Entropy Earns Its Place in the HRV Toolkit Publication: Entropy Authors: Dimitrios Platakis, Roberto Sassi, George Manis KEY FINDING: Bubble entropy consistently outperformed sample entropy, approximate entropy, and permutation entropy in classifying RR interval time series from healthy individuals versus cardiac patients across four machine learning classifiers and multiple feature-importance ranking methods. SIGNIFICANCE: Bubble entropy's freedom from the tolerance parameter that limits cross-study comparability of sample entropy is a genuine methodological advantage. This head-to-head benchmark strengthens the cas...

    1h 13m
  5. This Week In HRV - Episode 41

    Jun 9

    This Week In HRV - Episode 41

    This week's episode covers four peer-reviewed studies spanning machine learning feature selection, clinical epidemiology, wearable device validation, and real-world mobile health observation. Whether you are a clinician, researcher, coach, or practitioner, this episode has direct relevance for how you think about measuring and applying HRV in your work. RESEARCH HIGHLIGHTS THIS WEEK Adaptive Genetic Selection of Heart Rate Variability and Electrocardiographic Morphology Features for Cognitive Stress Detection Using Multi-Classifier Evaluation PUBLICATION: Eng AUTHORS: Salvador Ortiz-Santos, Georgina Mota-Valtierra, Jesús-Norberto Guerrero-Tavares, Xóchitl Siordia-Vásquez, Miguel Rojas-Hernández, Juvenal Rodríguez-Reséndiz KEY FINDING: A binary genetic algorithm with a dimensionality penalty selected eleven features from a pool of over three hundred HRV and electrocardiographic morphology descriptors across twelve leads, achieving a mean area under the receiver operating characteristic curve of 0.830 for cognitive stress classification. This outperformed both the full feature set and principal component analysis when paired with a radial basis function support vector machine classifier. SIGNIFICANCE: Supervised, discriminative feature selection outperforms unsupervised variance-based reduction for cognitive stress detection from multichannel electrocardiogram data. The finding that 11 compact features can achieve meaningful classification performance supports the feasibility of wearable-compatible stress-monitoring systems, though validation in more diverse and clinically representative populations is needed before this approach can inform practice. Read the full study: https://doi.org/10.3390/eng7060273 Association of Severe Obesity, Hypertension, and Physical Activity with 24-h Heart Rate Variability in Adults PUBLICATION: Journal of Cardiovascular Development and Disease AUTHORS: Débora Andrea Castiglioni Alves, Pamela Carvalho da Rosa, Andréa Castiglioni Alves Teixeira e Silva, Joceli Fernandes Alencastro Bettini de Albuquerque Lins, Gisela Arsa, Lucieli Teresa Cambri KEY FINDING: In a retrospective cross-sectional study of 1,048 adults undergoing bariatric surgery evaluation, severe obesity was associated with lower 24-hour HRV and higher odds of hypertension (odds ratio 2.04) and antihypertensive medication use (odds ratio 1.98). Hypertension was associated with lower HRV and higher odds of diabetes (odds ratio 4.20) and dyslipidemia (odds ratio 2.85). Meeting physical activity criteria was associated with higher HRV and lower odds of hypertension (odds ratio 0.64). SIGNIFICANCE: This large cross-sectional study documents the co-occurrence of lower 24-hour HRV with severe obesity, hypertension, and physical inactivity in a bariatric surgery evaluation population. Note that cross-sectional designs identify associations, not causes. The findings reinforce the clinical value of 24-hour HRV assessment for characterizing autonomic impairment in high cardiometabolic risk profiles and highlight physical activity as a meaningful modifier of autonomic health, even in this population. Read the full study: https://doi.org/10.3390/jcdd13060242 Validation of photoplethysmography-derived short-term heart rate variability using a wearable device PUBLICATION: Scientific Reports AUTHORS: Christine S. Zuern, Maximilian Felkel, Florian Tilquin, Yann Le Guillou, Emmanuel Dervie...

    57 min

  6. Jun 4

    Dr. Andrew Seely talk HRV in the ICU and Beyond

    In this episode, Dr. Andrew Seely joins Matt Bennett to discuss his work with HRV in the ICU and health care.

    53 min
  7. This Week In HRV - Episode 40

    Jun 2

    This Week In HRV - Episode 40

    From the reliability of the tools we use to measure it, to a mathematical algorithm built in its image, to machine learning models that read stress from its patterns, to a clinical trial showing it shifts in response to music — this week's four studies reveal HRV science at its most wide-ranging. Whether you're a clinician, researcher, coach, or curious practitioner, this episode offers something worth sitting with. Study 1: A Reproducible Benchmark of QRS Detection Algorithms Across Diverse ECG Datasets and Noise Conditions Publication: Scientific Reports Authors: Simon Maximilian Wolf, Tim Rahlmeier, Stefan Lustfeld, Detlef Schoder KEY FINDING: Seventeen R-peak detection algorithms were benchmarked across five ECG databases in a unified, reproducible framework. Under strict cross-dataset generalization conditions, traditional signal processing methods outperformed machine learning and deep learning approaches in consistency across diverse signal environments. SIGNIFICANCE: The algorithm used to detect R-peaks in an ECG signal is not a neutral technical detail — it directly shapes the accuracy of every HRV metric derived from that signal. Researchers and practitioners selecting HRV tools should ask how the underlying detection algorithm has been validated across diverse populations and noise conditions. Read the full study: https://www.nature.com/articles/s41598-026-53724-9 Study 2: Heart Rate Optimizer: A Novel Bio-Inspired Metaheuristic Algorithm Publication: Scientific Reports Authors: Mosa E. Hosney, Marwa M. Emam, Mohammed R. Saad, Nagwan Abdel Samee, Essam H. Houssein KEY FINDING: A novel bio-inspired optimization algorithm called the Heart Rate Optimizer — modeled on HRV dynamics and autonomic nervous system regulation — outperformed nine competing state-of-the-art algorithms on standard mathematical benchmarking suites and real-world engineering design problems. SIGNIFICANCE: The success of an algorithm explicitly built around HRV dynamics offers an independent, cross-disciplinary argument for why high HRV matters: the adaptive, flexible balance between sympathetic and parasympathetic regulation that high HRV reflects is computationally rich enough to serve as a blueprint for solving complex, high-dimensional problems. Low HRV, by analogy, corresponds to a system locked out of that adaptive range. Read the full study: https://www.nature.com/articles/s41598-026-44516-2 Study 3: Mental Stress Recognition Using Interpretable Machine Learning Models with Heart Rate Variability Among Chinese University Students Publication: World Journal of Psychiatry Authors: Yan-Ge Wei, Lu-Han Yang, Shi-Sen Qin, Yuan-Le Chen, Jin-Nan Yan, Rong-Xun Liu, Yi-Meng Ma, Chao Wang, Zhen-Jie Song, Fei Wang, Guang-Jun Ji KEY FINDING: In a cross-sectional study of 207 Chinese university students, eleven resting-state HRV parameters showed significant differences between stressed and non-stressed groups. A random forest classifier achieved an AUC of 0.733 (95% CI: 0.655–0.811) and 68.9% accuracy. SHAP analysis identified the Diastolic/Systolic Pressure-Time Index (DPTI/SPTI) as the most important classification feature. SIGNIFICANCE: This observational study found that resting HRV parameters are associated with self-reported stress status — it does not establish that stress caused the observed differences. The findings represent a well-structured proof of concept for HRV-based stress monitori...

    53 min
  8. This Week In HRV - Episode 39

    May 26

    This Week In HRV - Episode 39

    This week's lineup takes HRV science somewhere it doesn't always go — into genetics labs, operating theaters, and the physiology of breath control. Five new peer-reviewed studies examine HRV biofeedback combined with mindfulness for long-term workplace stress, a genetic polymorphism that shapes athlete burnout risk, yoga's measurable impact on autonomic function, a novel method for detecting high-intensity thresholds directly from an electrocardiogram signal, and whether a simple preoperative HRV reading can predict dangerous hemodynamic instability in diabetic surgical patients. Each study opens a different window on what HRV can tell us — and what it still can't. Research Highlights This Week 1. Exploring the Long-Term Effects of HRV Biofeedback Interventions Combined with Mindfulness Practices in Alleviating Workplace Stress Among Asian Professionals Publication: International Journal of Innovative Research and Scientific Studies Authors: Adrian Low, Benny Lam KEY FINDING: In a two-group, 8-week trial of 100 Hong Kong professionals, participants who combined HRV biofeedback with structured mindfulness practice showed significantly greater improvements in SDNN, RMSSD, coherence, and perceived stress than those who received biofeedback alone — and crucially, those gains continued to grow at a 6-month follow-up, while the biofeedback-only group showed attrition of benefits. SIGNIFICANCE: The durability gap between the two groups is the central finding here: mindfulness appears to provide a psychological scaffold that sustains the autonomic improvements initiated by biofeedback, even after formal programming ends. Qualitative data also revealed that emotional suppression is a culturally embedded barrier among Asian professionals, and that mindfulness framed around cognitive clarity rather than emotional processing proved more culturally acceptable and sustainable. Read the full study →: https://www.ijirss.com/index.php/ijirss/article/view/11655/2772 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2. The Influence of the COMT Val158Met Polymorphism on Heart Rate Variability Parameters, Psychoemotional Status, and Sports Burnout in Athletes Publication: Research Journal of Pharmacy and Technology Authors: Mavlyanova Z.F., Kim O., Doniyorov B.B., Ibragimova M.S., Khudoykulova F.V., Khalimova F.T. KEY FINDING: Among 100 male athletes, those carrying the AA (Met/Met) genotype of the catechol-O-methyltransferase Val158Met polymorphism showed resting heart rates 9.6% higher and RMSSD values 32.5% lower than GG (Val/Val) athletes, along with 17% higher anxiety scores and significantly greater risk of emotional exhaustion on the Athlete Burnout Questionnaire. The AG heterozygous group fell between both extremes on all measures. SIGNIFICANCE: This observational study suggests that a meaningful portion of the individual variation in athletes' HRV and susceptibility to burnout may be constitutionally determined by catecholamine clearance rate—an enzyme variant that modulates ambient norepinephrine and dopamine levels throughout the autonomic system. For practitioners interpreting chronically suppressed HRV in athletes who appear otherwise well recovered, genotypic baseline differences are a plausible contributor to consider. Read the full study →: https://www.rjptonline.org/HTML_Papers/Research%20Journal%20of%20Pharmacy%20and%20Technology__PID__2026-19-3-25.html ━━━━━━━━━━━━━━...

    54 min
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3.5
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10 Ratings

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Welcome to the Heart Rate Variability Podcast where we discuss the research and applications of heart rate variability.

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