Heart Rate Variability Podcast

Can heart rate variability help clinicians better understand—and eventually anticipate—the challenges people face during recovery from substance use disorder?

In this episode of the Heart Rate Variability Podcast, host Matt Bennett speaks with Dr. J. Gregory Hobelmann, Dr. Wendy Insalaco, and Dr. Andrew Huhn about their research on physiological and mental health changes during residential substance-use disorder treatment.

Their study paired wearable measurements of heart rate variability (HRV) and resting heart rate with self-reported assessments of stress, anxiety, and depression. Although participants generally demonstrated favorable changes during treatment, their individual trajectories varied widely. For some people, physiological and self-reported improvements aligned; for others, the two told different stories.

The conversation explores why this variability matters, how objective biomarkers may complement—not replace—traditional clinical assessments, and how wearable data could eventually support more individualized care, timely check-ins, and relapse-prevention efforts.

Topics discussed include:

What a typical 28-day residential treatment experience involves

Why substance-use treatment outcomes have traditionally relied heavily on self-report

How WHOOP wearables were used to monitor HRV and resting heart rate

The relationship between HRV, stress, anxiety, and depression

Why physiological recovery and perceived wellness do not always move together

State-versus-trait differences in anxiety and autonomic functioning

The importance of sleep and healthy cortisol patterns during recovery

Using biometric changes as prompts for supportive clinical conversations

The risks of overconfidence and overreliance on wearable data

The potential for personalized treatment plans and just-in-time interventions

How academic and residential-treatment partnerships can advance precision medicine

About the guests:

Dr. J. Gregory Hobelmann, MD, MPH, is an addiction psychiatrist and Co-CEO and President of Ashley Addiction Treatment in Havre de Grace, Maryland.

Wendy Insalaco, PhD, LCADC, LCPC, is an addiction psychologist and researcher who serves as Director of Quality Outcomes and Model of Care at Ashley Addiction Treatment.

Andrew S. Huhn, MBA, PhD, is an Associate Professor of Psychiatry and Behavioral Sciences at the Johns Hopkins University School of Medicine. His research examines opioid use disorder, relapse risk, sleep, stress, mood, wearable technology, and treatment outcomes.

Resources:

Read the study:
https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1755153/full

Learn about Ashley Addiction Treatment:
https://www.ashleytreatment.org/

Learn more about Dr. Andrew Huhn:
https://profiles.hopkinsmedicine.org/provider/andrew-stephen-huhn/2777487

Explore Optimal HRV:
https://www.optimalhrv.com/

The Heart Rate Variability Podcast is produced by Optimal LLC and Optimal HRV.

This podcast is provided for informational purposes only and is not medical advice. Consult a qualified healthcare professional before applying any strategies discussed in this episode.

MEDICAL DISCLAIMER

The information in this podcast is for educational and informational purposes only and is not intended to diagnose, treat, cure, or prevent any medical condition. Please consult with a qualified healthcare provider before making any changes to your health regimen, training program, or clinical practice. Heart rate variability is a powerful and scientifically validated tool for understanding autonomic nervous system function, but it is one piece of a much larger picture, and context always matters. Nothing shared in this podcast should be taken as personalized medical advice, and individual responses to interventions vary enormously. Always work with qualified professionals when making decisions about your health.

This week on This Week in HRV, we explore the relationship between HRV and human performance across four distinct domains — golf putting, sustained cognitive work, drowsy driving, and competitive wrestling. Together, these studies reveal how the autonomic nervous system shapes and reflects our capacity to perform under pressure, sustain attention, stay alert, and adapt to training stress.

1. Exploring Attentional Mechanisms of Strategic Self-Talk Through Heart-Rate Variability in a Golf-Putting Task Among Novices

PUBLICATION: Behavioral Sciences

AUTHORS: Emmanouil Tzormpatzakis, Theodoros Proskinitopoulos, Orestis Panoulas, Evangelos Galanis, Evgenia Nikolakopoulou, Nikos Comoutos, Yannis Theodorakis, Antonis Hatzigeorgiadis

KEY FINDING: Novice golfers who underwent personalized self-talk training showed progressively higher parasympathetic activation (RMSSD) in later stages of a putting task at the final assessment — a pattern not seen at baseline or in the control group — alongside significantly better putting performance.

SIGNIFICANCE: This study provides preliminary physiological evidence that psychological skills training can shift autonomic regulation toward more efficient, automatic processing during motor performance, opening a new direction for understanding how mental skills work at the level of the nervous system.

Read the full study: https://doi.org/10.3390/bs16081347

2. Exploratory Temporal Dynamics of Heart-Rate Variability During a Prolonged Two-Back Task Under Nap-Deprivation Conditions

PUBLICATION: Sensors 

AUTHORS: Di Meng, Luyao Yu, Tianjiao Min, Jing Zhang, Liufeng Zhu, Hengcong Gong, Yifei Feng, Ying He

KEY FINDING: A fiber-optic seat cushion (ballistocardiogram) captured HRV continuously during 60 minutes of demanding cognitive work under nap-deprivation conditions. While mental fatigue was large and unambiguous by self-report and reaction time, no HRV feature survived false discovery rate correction at the group level — and individual HRV trajectories varied dramatically across participants.

SIGNIFICANCE: The null group-level result and striking individual heterogeneity together suggest that HRV-based cognitive fatigue monitoring may require personalized calibration rather than universal group-level algorithms, a critical insight for anyone building real-world fatigue detection systems.

Read the full study: https://doi.org/10.3390/s26196099

3. Tracking Vigilance While Driving: Pilot Study of Heart Rate Variability Classification Under a Controlled Sleep-Deprivation Protocol

PUBLICATION: Sensors

AUTHORS: James Elber Duverger, Eli Moser, Philippe Boudreau, Marie Claude Ouimet, Diane B. Boivin, Alireza Saidi

KEY FINDING: Twelve healthy adults underwent a 30-hour sleep-deprivation protocol with driving simulations every 2 hours. Machine learning classifiers trained on HRV features achieved greater than 85% accuracy in distinguishing rested from sleep-deprived states using leave-one-subject-out cross-validation, confirming that the HRV signature of severe sleep deprivation generalizes across individuals.

SIGNIFICANCE: This participant-independent classification accuracy provides an important benchmark for HRV-based physiological state monitoring in safety-critical contexts such as commercial driving and demonstrates that the drowsiness-related autonomic signature is consistent enough across people to support generalizable detection.

Read the full study: https://doi.org/10.3390/s26196104

4. Identification and Longitudinal Monitoring of Physiological Response Profiles in Wrestlers Using Multivariate Heart Rate Variability Analysis

PUBLICATION: Applied Sciences — Identification and Longitudinal Monitoring of Physiological Response Profiles in Wrestlers Using Multivariate Heart Rate Variability Analysis

AUTHORS: Galya Georgieva-Tsaneva

KEY FINDING: Among 65 competitive wrestlers monitored across five time points over four months, principal component analysis of multi-metric HRV response vectors (capturing RMSSD, SDNN, LF/HF ratio, SD1, SD2, and sample entropy before and after training) revealed two reproducible response profiles: Profile A (55 wrestlers, classical sympathetically-driven suppression of HRV with training) and Profile B (10 wrestlers, maintained or elevated HRV following identical training sessions).

SIGNIFICANCE: The stability of these profiles across four months and the finding that 80.53% of total HRV response variance is captured in just two principal components demonstrate that multivariate HRV profiling can reliably characterize individual differences in training response — differences that group-average monitoring would completely miss.

Read the full study: https://doi.org/10.3390/app16189166

SPONSORED BY OPTIMAL HRV

If you are serious about understanding your heart rate variability and want to track it with tools that are both scientifically rigorous and genuinely accessible, visit OptimalHRV.com. The Optimal HRV app makes it straightforward to measure your HRV consistently, track how it changes over time, understand what those changes mean in context, and use that information to guide your training, recovery, and stress management.

KEY THEMES

MEDICAL DISCLAIMER

The information in this podcast is for educational and informational purposes only and is not intended to diagnose, treat, cure, or prevent any medical condition. Please consult with a qualified healthcare provider before making any changes to your health regimen, training program, or clinical practice. Heart rate variability is a powerful and scientifically validated tool for understanding autonomic nervous system function, but it is one piece of a much larger picture, and context always matters. Nothing shared in this podcast should be taken as personalized medical advice, and individual responses to interventions vary enormously. Always work with qualified professionals when making decisions about your health.

Medical Disclaimer: The information shared on This Week in HRV is for educational purposes only and is not intended as medical advice. Always consult a qualified healthcare professional before making changes to your health routine.

This week on This Week in Heart Rate Variability, we cover six studies that together sketch the future of HRV science. Each one points in a distinct direction: a new metric for measuring rhythm organization across a large psychiatric population, a systematic content audit of consumer HRV apps, a rigorous benchmark of five noninvasive sensor technologies measured simultaneously in the same subjects, a new mathematical framework that challenges the stationarity assumption underlying conventional HRV analysis, a nonlinear dynamical study of group physiological coordination using attractor reconstruction and neural network clustering, and a deep learning system that extracts HRV from facial video alone to detect driver drowsiness at nearly ninety-five percent accuracy. What unites these studies is not a single topic but a shared direction: the field is expanding its metrics, scrutinizing its tools, rethinking its assumptions, and reaching toward applications that would have seemed impractical not long ago.

1. Heart rate fragmentation in psychiatry: across age stages and association with conventional heart rate variability

PUBLICATION: Psychiatry Research: Neuroimaging
AUTHORS: XinFan Zhang, AiMei Ye, Min Su, Hao Chai, YanYan Wei, YiYi Yang, YuXuan Xiong, Yin Cui, Dan Zhang, Xiong Jiao, HuiRu Cui, LiHua Xu, XiaoChen Tang, HaiChun Liu, MingLiang Ju, LingYun Zeng, ChunBo Li, LiYing Huang, Jin Gao, JiJun Wang, and TianHong Zhang

KEY FINDING: Researchers analyzed resting three-minute electrocardiograms from 3,813 patients with schizophrenia, depressive disorder, anxiety disorder, or sleep disorder, ranging in age from 10 to 80 years, treated at the Shanghai Mental Health Center. The study derived both heart rate fragmentation indices—specifically the percentage of inflection points (PIP) and the related percentage of alternating segments (PAS)—alongside conventional HRV measures including RMSSD, SDNN, low-frequency power, and high-frequency power.

Heart rate fragmentation increased systematically across age groups, while every conventional HRV index declined. That divergence is the central finding: aging appears to produce both less variability and less organized variability, and these are not the same thing. Partial Spearman correlations and multivariable linear regression confirmed that PIP retained independent associations with all conventional HRV metrics, even after adjusting for available covariates, with the strongest association observed for high-frequency power. Sex differences in fragmentation were confined to adolescence and early adulthood, with males showing higher PIP than females in those life stages, but these differences disappeared by middle and late adulthood. Diagnostic category had a statistically significant main effect on PIP, but the differences between diagnostic groups were modest and did not meaningfully interact with age.

SIGNIFICANCE: Heart rate fragmentation asks a different question than conventional HRV metrics. While RMSSD and SDNN measure how much the intervals between heartbeats vary, fragmentation measures how organized that variability is. The consistent independent relationship between PIP and conventional HRV metrics suggests that fragmentation captures something about rhythm organization that amplitude-based measures do not fully account for. Because fragmentation can be computed from the same R-R interval data used in conventional HRV analysis, there is no additional measurement burden when adding it to existing protocols.

Read the full study

2. Mobile Apps for Heart Rate Variability: App Store Search and Content Analysis

PUBLICATION: JMIR Cardio
AUTHORS: Eline de Jager, Brian Caulfield, Evgenia Angelidi, and Sinead Holden

KEY FINDING: Researchers conducted a systematic search of both the Google Play Store and the Apple iTunes Store for apps that could record, analyze, or provide feedback on HRV and were available in English. From an initial pool of 746 apps, 206 met the eligibility criteria. Full data extraction was possible for only 93 of the 206 eligible apps—45.1% of the sample—because more than half did not publicly disclose sufficient information about their methods or data practices.

Among the 93 transparent apps, the most common sensing modality was photoplethysmography (56.8%), followed by support for multiple sensors (29.1%). RMSSD appeared in 51 apps and SDNN in 48. Most apps—76 of 93—presented data as personalized trends or individualized ranges. Eighty-six percent offered readiness, recovery, or similar interpretive feedback scores. The majority of those scores were generated by proprietary algorithms not transparently described in publicly available materials.

SIGNIFICANCE: The commercial HRV app market is expanding rapidly, but transparency and methodological rigor are not keeping pace with market growth. For practitioners deciding which apps to recommend, the practical standard is clear: look for explicit disclosure of what metric is being measured, how the signal is collected, what the measurement protocol requires, and how interpretive scores are derived. Downloads and ratings reflect marketing success, not methodological quality.

Read the full study

3. Time and frequency characteristics of various noninvasive heartbeat sensors

PUBLICATION: PLOS ONE
AUTHORS: Pierre Charlier, Mathieu Jeanne, Maxence Hureau, and Julien De Jonckheere

KEY FINDING: Researchers simultaneously recorded five different physiological signals from 20 healthy adult volunteers under standardized resting conditions: the electrocardiogram (ECG), the phonocardiogram (acoustic heart sounds), the seismocardiogram (chest accelerometer), the standard photoplethysmogram (optical fingertip sensor), and the piezoplethysmogram (piezoelectric fingertip sensor). All five signals were captured with the same acquisition hardware.

In the time domain, the ECG, photoplethysmogram, and piezoplethysmogram all showed high inter-individual morphological consistency, with Pearson correlation coefficients above 0.9. The phonocardiogram and seismocardiogram showed substantially more variability (correlations of roughly 0.67 to 0.83). In the frequency domain, the ECG's QRS complex has a dominant frequency of approximately 11 Hz. The photoplethysmogram and piezoplethysmogram have peak frequencies of approximately 1.3 and 2.4 Hz, respectively—reflecting the pulse wave at the fingertip rather than rapid electrical or mechanical transients.

SIGNIFICANCE: The sensors used to measure HRV are not interchangeable. The ECG remains the gold standard because of its temporal precision, high inter-individual reproducibility, and broad spectral content. Any alternative sensor technology should be benchmarked against it within the specific population and context of use before being considered equivalent. The pulse arrival time latency of photoplethysmographic sensors introduces a systematic distortion in timing-sensitive HRV metrics relative to ECG-based values—a distinction that matters when comparing absolute values across individuals or against normative ranges.

Read the full study

4. BAND: A Probabilistic Framework for Modeling Non-Stationary Heart Rate Variability in Rest-Stress-Rest Dynamics

PUBLICATION: Technologies
AUTHORS: Matías Castillo-Aguilar, David Medina-Ortiz, Ruby Méndez Muñoz, Diego Mabe-Castro, Noah Beelders, Atenea Uribe-Ojeda, Marcelo A. Navarrete, and Cristían Núñez-Espinosa

KEY FINDING: This study challenges the foundational assumption of stationarity that underlies almost every standard HRV analytical method. When a conventional HRV metric such as RMSSD is computed from a recording taken during a stress test or exercise bout, the calculation assumes that the signal's statistical properties do not change during that window. In genuinely dynamic contexts, that assumption is false.

The BAND framework—Biphasic Autonomic Non-Stationary Decomposition—models the entire time course of the R-R interval series as a continuous stochastic process governed by a double-logistic function representing two-phase perturbation-and-recovery dynamics. All parameters are estimated within a Bayesian framework that produces full probability distributions rather than point estimates. Applied to a real recording during a two-minute exercise test, BAND identified dissonant autonomic recovery: the baseline R-R interval returned toward resting values relatively quickly after exercise cessation, while total HRV amplitude recovered more slowly and less completely.

SIGNIFICANCE: The concept of dissonant autonomic recovery is immediately relevant to practitioners who monitor HRV during training. Heart rate can appear recovered while HRV measures continue to signal suppression—BAND suggests this reflects genuinely different biological timescales of parasympathetic reactivation versus full sympatho-vagal rebalancing. BAND is a proof of concept at this stage and requires prospective validation, but the underlying critique of windowed averaging applies broadly to how most HRV data is currently analyzed.

Read the full study

5. State-Space Attractor Morphology as a Marker of Complexity Matching in Group Heart Rate Variability Dynamics

PUBLICATION: Applied Sciences
AUTHORS: Naseha Wafa Qammar, Kristina Poškuvienė, Mantas Landauskas, Kiran Shahzadi, Minvydas Ragulskis, Alfonsas Vainoras, Nachum Plonka, Mike Atkinson, and Rollin McCraty

KEY FINDING: Twenty adult participants were led through a structured group session lasting approximately 26 minutes, with six sequential intervals progressing from baseline video-watching to heart-focused breathing, appreciation practices, and, finally, a period of unconditional love and compassion. HRV was recorded simultaneously from all twenty participants using a custom multichannel photoplethysmography system.

Researchers embedded each participant's R-R interval series into a two-dimensional delay-coordinate state space, reconstructed the geometric structure of HRV behavior, and processed the resulting 120 attractor images through a convolutional neural network autoencoder and K-means clustering. The prevalence of Type 1 (coherent, circular) attractor morphology increased from 10% of participants in the first interval to 60% in the sixth interval (Cochran's Q, Holm-adjusted p = 0.0047).

SIGNIFICANCE: The primary methodological contribution is demonstrating that attractor reconstruction, combined with neural network-based clustering, can reveal structured, statistically significant patterns in group-level HRV data that are invisible to conventional metrics. Whether these patterns reflect genuine interpersonal physiological coordination or simply the passage of time and increasing familiarity with the session cannot be determined from this fixed-order design. Future research with randomized order conditions, respiratory monitoring, and larger samples is needed.

Read the full study

6. Multimodal Drowsiness Detection: Fusing Facial Visual Indicators with Heart Rate Variability Indices Extracted via a Novel Deep Learning-Based Remote Photoplethysmography Estimator

PUBLICATION: SSRN 
AUTHORS: Alireza Ganjkarimi, Mohaddeseh Vafaiee, and Farzad Towhidkhah

KEY FINDING: Remote photoplethysmography (rPPG) extracts pulse waveforms from subtle optical changes in facial skin during video recording, enabling heart rate and HRV estimation without physical contact. The researchers developed a hybrid deep learning architecture combining one-dimensional convolutional neural networks, spatio-temporal attention mechanisms, and bidirectional long short-term memory networks to dynamically separate the cardiovascular signal from noise sources, including head movement, lighting changes, and skin tone variation.

Evaluated on benchmark data, the rPPG extraction model achieved a mean absolute error of 2.00 beats per minute for heart rate estimation. The combined system—fusing HRV features with eye aspect ratio and head posture—achieved 94.93% accuracy and an AUC of 0.9753 on the UTARD drowsiness detection dataset. In real-vehicle nighttime testing, accuracy dropped to 83.33%, approximately 11 percentage points below benchmark performance.

SIGNIFICANCE: This preprint demonstrates that contactless HRV monitoring using existing cameras has achieved near-practical accuracy in controlled settings and maintains useful performance under real-world nighttime driving conditions. The fusion of HRV with behavioral signals (eye closure, head droop) captures complementary aspects of drowsiness that neither modality alone reflects as effectively. The broader trajectory—HRV without contact, from ambient cameras, fused with behavioral signals—points toward applications in pediatric monitoring, occupational safety, elder care, and team-level sports physiology. Performance claims should be interpreted with caution pending peer review.

Read the full study

KEY THEMES

SPONSORED BY OPTIMAL HRV

This episode is brought to you by Optimal HRV, which provides HRV biofeedback tools and training programs designed for behavioral health, education, coaching, and organizational settings.

Whether you are a therapist, school counselor, wellness professional, researcher, or someone working to build a more resilient nervous system, Optimal HRV helps turn the science of autonomic regulation into practical daily action.

Visit optimalhrv.com to explore the platform, training programs, and research behind Optimal HRV.

Medical Disclaimer: The research summaries in this episode are provided for informational and educational purposes only. They do not constitute medical advice, diagnosis, or treatment recommendations. Heart rate variability should not be used as the sole basis for clinical decisions. Always consult a qualified healthcare professional before making changes to your health or treatment plan. Individual results may vary.

In this episode of the Heart Rate Variability Podcast, Matt Bennett talks with Tony Crescenzo, founder and CEO of Peak Neuro and a former U.S. Marine, about heart rate variability, trauma, resilience, neuroscience, and human performance.

Tony explores how prolonged exposure to high-stress environments can affect the autonomic nervous system and why resilience requires a highly individualized approach. He also explains why HRV should be understood in context rather than viewed simply as a score where higher is always better.

Matt and Tony discuss the neurological challenges faced by veterans and active-duty service members, the difference between resilience and recovery, somatic approaches to trauma, neuromodulation, and Peak Neuro's approach to combining HRV with brain activity and other physiological measurements.

Tony also shares experiences from his Marine Corps career and discusses how they have shaped his current work in neuroscience and human performance.

The conversation concludes with a look at the growing role of AI, neuroscience, and cognitive-performance technology in military resilience and the future of human optimization.

Learn more:
Optimal HRV — https://www.optimalhrv.com
Peak Neuro — https://peakneuro.com

For educational purposes only. This podcast is not intended as medical advice.

Medical Disclaimer: The information shared on This Week in HRV is for educational purposes only and is not intended as medical advice. Always consult a qualified healthcare professional before making changes to your health routine.

This week on This Week in Heart Rate Variability, we explore how deeply HRV is woven into the biology of mental health. Five studies examine this connection from very different perspectives: structural changes in the brain, biofeedback training for medical students, resting HRV and psychological distress, early prediction of post-stroke depression, and an AI model designed to anticipate depressive episodes in bipolar disorder.

PUBLICATION: International Journal of Psychophysiology
AUTHORS: Kalekirstos Alemu, Hyun Joo Yoo, Kaoru Nashiro, Jungwon Min, Padideh Nasseri, Christine Cho, Paul Choi, Shelby L. Bachman, Shai Porat, Shubir Dutt, Julian F. Thayer, and Mara Mather

KEY FINDING: Researchers analyzed brain scans from younger and older adults who completed five weeks of daily biofeedback practice. Participants were assigned either to increase heart rate oscillations through slow, resonant breathing or to keep their heart rate relatively steady. In the HRV-enhancing condition, changes in left amygdala volume showed an inverse relationship with changes in the right orbitofrontal cortex—a pattern absent in the comparison condition. Coordinated structural changes were also identified across several prefrontal, motor, sensory, and parietal regions.

Both younger and older adults demonstrated similar patterns of structural covariance, although the study’s smaller older-adult sample means the age-related findings should be considered exploratory.

SIGNIFICANCE: Previous research has linked higher HRV to stronger functional communication between the amygdala and the prefrontal cortex. This study adds a structural layer to that evidence, suggesting HRV biofeedback may influence the coordinated physical architecture of brain regions responsible for emotional regulation.

The findings support the neurovisceral integration model, in which the prefrontal cortex helps regulate amygdala-driven emotional and threat responses. They also provide a possible biological mechanism for the use of HRV biofeedback alongside established treatments for anxiety, depression, and post-traumatic stress disorder.

Read the full study

PUBLICATION: Physiological Reports
AUTHORS: Gabriela Panayotova and Margarita Velikova

KEY FINDING: Forty-seven international medical students were followed for approximately three months. Students with elevated stress or affective symptoms entered an HRV biofeedback program consisting of two supervised 30-minute sessions per week, while a separate group received no intervention.

Within the biofeedback group, perceived stress, anxiety, and depression declined significantly, with large effect sizes ranging from approximately 1.28 to 1.86. Clinically meaningful improvement—defined as a reduction of at least 20%—was reported by:

87.5% of the biofeedback group on the Beck Anxiety Inventory, compared with 34.8% of controls

79.2% on the depression inventory, compared with 34.8% of controls

70.8% on a separate anxiety scale, compared with 8.7% of controls

62.5% on perceived stress, compared with 39.1% of controls

During individual sessions, heart rate decreased by an average of 3.6 beats per minute, interbeat intervals lengthened by 17 milliseconds, and heart-rhythm coherence improved. RMSSD increased over the first approximately 14 supervised sessions before plateauing around the seventh week.

SIGNIFICANCE: The results suggest that HRV biofeedback may function like a trainable self-regulation skill: participants show rapid early autonomic improvements before reaching a period of consolidation. This provides practical guidance for schools and other high-stress institutions considering structured biofeedback programs.

However, the students were not randomly assigned, the groups differed substantially at baseline, and there was no sham intervention. The psychological improvements, therefore, cannot be attributed definitively to biofeedback alone. The session-by-session physiological changes nevertheless provide an important foundation for future randomized trials.

Read the full study

PUBLICATION: Revista Psicologia: Teoria e Prática
AUTHORS: Sophie Selleny Trentin Sodré, Daiane Rocha-Oliveira, and Murilo Ricardo Zibetti

KEY FINDING: Researchers recruited 60 adults aged 18 to 40. Half reported having received a mental health diagnosis, while the remaining participants had no diagnostic history. Each participant completed a five-minute resting HRV recording using a chest-strap monitor and a questionnaire measuring depression, anxiety, and stress.

Across the complete sample, lower RMSSD was significantly associated with higher total psychological distress and with higher depression, anxiety, and stress scores. The strongest relationships were observed for depression and total symptom burden.

When participants were divided by diagnostic status, HRV remained significantly associated with total symptoms, depression, and stress in the diagnosed group. None of these relationships reached statistical significance among participants without a diagnosis.

The diagnosed group also had substantially lower resting RMSSD—22.67 milliseconds compared with 42.85 milliseconds in the group without a diagnosis—and considerably higher total symptom scores.

SIGNIFICANCE: These findings support the view of HRV as a potential transdiagnostic biomarker: a physiological signal that may reflect psychological distress across multiple diagnostic categories rather than being limited to one condition.

The results also suggest that the relationship between subjective distress and autonomic regulation may become stronger once symptoms reach clinically significant levels. Because this was a small correlational study, it cannot determine whether reduced HRV causes psychological symptoms, results from them, or reflects another shared biological process.

Read the full study

PUBLICATION: Frontiers in Neuroscience
AUTHORS: Lan Chen, Bin Wang, Dan Kuang, and Lei Chen

KEY FINDING: Researchers enrolled 437 patients following an acute ischemic stroke. Within seven days of the stroke, participants completed an overnight sleep study that included continuous electrocardiogram recording for HRV analysis. At the three-month follow-up, 390 patients completed a depression assessment, and 139—or 35.6%—had developed clinically significant post-stroke depression.

Longer sleep latency, more frequent sleep disruptions, lower sleep efficiency, and lower overnight RMSSD independently predicted a greater risk of depression. Reduced RMSSD remained predictive even after accounting for stroke severity, previous strokes, and depressive symptoms measured at baseline.

A gradient-boosting prediction model achieved an area under the curve of 0.763, which remained approximately 0.738 after bootstrap correction. Depression developed in:

14.3% of patients in the lowest-risk category

23.6% in the moderate-risk category

57.1% in the high-risk category

70.3% in the highest-risk category

The researchers also proposed practical warning thresholds: sleep latency above 25 minutes, more than 20 arousals per hour, sleep efficiency below 75%, and RMSSD below 34 milliseconds.

SIGNIFICANCE: Post-stroke depression can interfere with recovery, reduce quality of life, and increase mortality risk, yet it is often difficult to identify early. This study suggests that sleep and HRV data collected shortly after a stroke could help clinicians recognize high-risk patients before depression becomes established.

Because comprehensive stroke units already collect overnight sleep data for many patients, adding HRV analysis could provide a relatively low-cost way to guide closer psychological monitoring and earlier support. External validation across additional stroke centers is still required.

Read the full study

PUBLICATION: Open Access Library Journal
AUTHORS: Rocco de Filippis and Abdullah Al Foysal

KEY FINDING: Researchers created 30 days of synthetic data for 700 hypothetical patients with bipolar disorder. The simulated data combined three streams of information:

Movement and sleep patterns measured through actigraphy

HRV captured through a wearable heart-rate sensor

Smartphone behavior, including screen time, typing speed, app use, mobility, calls, and messages

For approximately 28.6% of the simulated patients, the researchers introduced a gradually worsening pattern beginning around day 16. This included declining physical activity and HRV, increasingly disrupted sleep, greater screen time, reduced mobility, and less social communication.

A transformer-based model combining all three data streams achieved an area under the curve of 0.981, an F1 score of 0.901, sensitivity of 93.3%, and specificity of 94.7%. The combined model outperformed actigraphy alone, HRV alone, smartphone behavior alone, and the other machine-learning approaches tested.

SIGNIFICANCE: The results demonstrate the technical possibility of combining HRV, movement, sleep, and smartphone behavior to identify patterns that may precede a depressive episode.

However, this was entirely a synthetic proof-of-concept study. No real patients participated, and the model may simply have learned the mathematical patterns intentionally built into the simulation. The findings do not establish clinical validity and should not be interpreted as evidence that the model is ready for clinical use.

Prospective research involving real patients, missing data, differences between devices, privacy protections, and informed consent will be essential before this type of early-warning system can be considered for practice.

Read the full study

HRV connects brain structure with emotional regulation: The neuroimaging study provides structural evidence that HRV biofeedback influences the coordinated relationship between the amygdala and prefrontal regions involved in emotional control.

Autonomic regulation appears trainable: Medical students showed session-by-session improvements in HRV alongside meaningful reductions in stress, anxiety, and depression. The physiological gains followed a skill-acquisition pattern, increasing over approximately 14 sessions before plateauing.

Lower HRV accompanies greater psychological distress: Resting RMSSD was associated with anxiety, depression, and stress across the Brazilian sample, with the clearest relationships appearing among participants with a mental health diagnosis.

HRV may contribute to earlier mental health intervention: Overnight HRV helped predict depression following stroke, while the simulated bipolar-disorder study explored how HRV could eventually contribute to continuous early-warning systems.

Promising prediction models still require careful validation: HRV becomes more informative when combined with sleep, movement, and behavioral data, but strong performance—especially in synthetic research—does not automatically translate into clinical reliability.

This episode is brought to you by Optimal HRV, which provides HRV biofeedback tools and training programs designed for behavioral health, education, coaching, and organizational settings.

Whether you are a therapist, school counselor, wellness professional, researcher, or someone working to build a more resilient nervous system, Optimal HRV helps turn the science of autonomic regulation into practical daily action.

Visit optimalhrv.com to explore the platform, training programs, and research behind Optimal HRV.

Medical Disclaimer: The research summaries in this episode are provided for informational and educational purposes only. They do not constitute medical advice, diagnosis, or treatment recommendations. Heart rate variability should not be used as the sole basis for clinical decisions. Always consult a qualified healthcare professional before making changes to your health or treatment plan. Individual results may vary.

Medical Disclaimer: The information shared on This Week in HRV is for educational purposes only and is not intended as medical advice. Always consult a qualified healthcare professional before making changes to your health routine.

This week on This Week in Heart Rate Variability, we explore the theme of Medical — studying HRV across populations dealing with identifiable disease states and clinical questions. From noise exposure in everyday life to smoking, cardiac surgery rehabilitation, rare autoimmune disease, AI-powered sleep staging, and physical activity in chronic lung disease, today's research spans the full clinical spectrum of what HRV can tell us.

PUBLICATION: Journal of Exposure Science and Environmental Epidemiology (Nature Portfolio)
AUTHORS: Xin Zhang, Sung Kyun Park, Lauren M. Smith, and Richard L. Neitzel

KEY FINDING: Using wearable-derived SDNN and sound-exposure data from roughly 980–1,180 participants per analysis in the Apple Hearing Study, hierarchical Bayesian distributed-lag models found that each 10 dB increase in environmental noise was associated with an overall SDNN reduction of 6.8% over a short-term (20-minute) window and 16.0% over a long-term (160-minute) window. Headphone sound produced smaller, more consistent reductions of 7.1–7.4% regardless of window length. Reductions were greater in older adults and those with tinnitus.

SIGNIFICANCE: This elevates noise — including overnight traffic noise and headphone volume — to the same level of concern as sleep quality, training load, and nutrition in shaping HRV. The data also provides context for unexpectedly low morning HRV readings after noisy nights.

Read the full study

PUBLICATION: Hypertension Research (Nature Portfolio)
AUTHORS: Yuya Akagi, Kimika Arakawa, Atsushi Sakima, Mai Kabayama, Ken Sugimoto, Yuichi Akasaki, Ako Fukami, Satoko Sakata, Hirochika Ryuno, Hiroyuki Kadoya, Toshiya Yamamoto, Hiroko Yoshida, Yoichi Nozato, Taisuke Ueno, Makiko Abe, Hisatomi Arima, Mitsuru Ohishi, Nobuhito Hirawa, Chisa Matsumoto, Shin-Ichiro Miura, Masaki Mogi, Akira Nishiyama, Akihiro Nomura, Takayoshi Ohkubo, Yusuke Ohya, Shigeru Shibata, Yasuharu Tabara, Koichi Yamamoto, and Kazuomi Kario

KEY FINDING: This systematic review and meta-analysis of four non-randomized crossover comparative studies (104 participants total) compared ambulatory blood pressure and heart rate within the same current smokers during active smoking versus abstinence periods (1 day to 1 week). Twenty-four-hour diastolic blood pressure was 2.67 mmHg higher during smoking (95% CI 0.72–4.62), and daytime diastolic was 3.17 mmHg higher. Twenty-four-hour heart rate was 6.47 bpm higher during smoking (95% CI 3.98–8.96); daytime 7.19 bpm higher; nighttime 3.58 bpm higher. Systolic blood pressure showed no significant difference between periods.

SIGNIFICANCE: This helps explain the long-standing office blood pressure paradox in smokers: they appear similar to non-smokers in clinical readings because they abstain before appointments and are at rest. Ambulatory monitoring reveals the true picture — persistent sympathetic arousal manifesting as chronically elevated heart rate around the clock. For HRV practitioners, this means a smoking patient's measured baseline is not their true autonomic baseline, and it positions HRV tracking as a motivational tool in cessation programs.

Read the full study

PUBLICATION: Journal of Vascular Diseases (MDPI)
AUTHORS: Samuel Ramos Souza, Davi Nascimento Cônsoli, Luana Elisa Weber, Maria Eduarda Squinca Dias Campos, Célia Cristina Diogo Ferreira, and Gustavo Vieira de Oliveira

KEY FINDING: Seventeen resistance-trained adults (8 women, 9 men) completed two sessions in a crossover design: theacrine (200 mg, 90 minutes pre-exercise) versus placebo, separated by a washout. RMSSD and HFnu were measured pre-supplement, 90 minutes post-supplement/pre-exercise, and 30 minutes post-exercise. In the theacrine condition, RMSSD was significantly higher 90 minutes post-supplement versus baseline (p = 0.01), and post-exercise RMSSD did not differ significantly from pre-supplement levels. In the placebo condition, post-exercise RMSSD dropped significantly versus both prior time points. The time-by-condition interaction was not statistically significant; blood pressure and heart rate did not differ between conditions.

SIGNIFICANCE: Theacrine — a purine alkaloid related to caffeine — elevated vagal tone 90 minutes after ingestion and appeared to buffer the expected post-resistance-exercise HRV drop. Because the interaction test didn't reach significance, this pilot can't conclusively confirm the effect, and a larger trial is needed. Still, the direction of the findings is novel and biologically plausible — an open question worth tracking for athletes and practitioners managing post-exercise autonomic recovery.

Read the full study

PUBLICATION: Cureus
AUTHORS: Pranoti R. Patil and Poovishnu T. Devi

KEY FINDING: Forty post-coronary artery bypass graft (CABG) patients (ages 40–60) completed an 8-week program combining the Otago Exercise Program (three times a week) with resonance frequency breathing at 6 breaths per minute (daily, 15–20 minutes). Five-minute resting RMSSD increased from 18.03 ms to 21.96 ms — a 21.8% increase (p<0.001). Timed Up and Go improved from 17.95 to 14.68 seconds, and the 30-second Chair Stand improved from 13.03 to 15.78 repetitions. Balance measures also improved significantly. The design was single-group pre/post without a control arm.

SIGNIFICANCE: CABG produces profound and persistent autonomic dysfunction — reduced HRV, elevated resting heart rate, slower heart rate recovery — that independently predicts worse outcomes. Resonance frequency breathing deliberately exploits the coupling between respiration and the baroreflex to train the autonomic reflex arc; with repeated daily practice, baroreflex sensitivity increases and RMSSD improves. A randomized controlled trial is still needed, but the feasibility, low cost, and strong directional signal make this a compelling addition to cardiac rehabilitation protocol design.

Read the full study

PUBLICATION: Revue Neurologique (Elsevier)
AUTHORS: Chandregowda Nandan, Ganagarajan Inbaraj, Krishnamurthy Arjun, Kaviraja Udupa, Krishna Prasad B S, Harry W.M. Steinbusch, Atchayaram Nalini, and Talakad N. Sathyaprabha

KEY FINDING: This comparative cross-sectional study assessed cardiovascular autonomic function in 19 patients with Morvan syndrome — a rare autoimmune encephalopathy characterized by antibodies against voltage-gated potassium channel complex proteins (CASPR2, LGI1), resulting in neuromyotonia, encephalopathy, and dysautonomia — versus 24 healthy controls. Researchers applied both HRV analysis (resting autonomic baseline) and Ewing's battery of cardiovascular autonomic reflex tests (deep breathing, Valsalva, orthostatic, and handgrip responses) to systematically characterize the extent and distribution of autonomic damage. HRV indices showed marked parasympathetic loss and reduced overall autonomic tone; Ewing's battery revealed significant impairments in reflex autonomic responses; illness duration correlated with progressive declines in key HRV parameters; and CASPR2-positive patients showed more severe autonomic dysfunction.

SIGNIFICANCE: Morvan syndrome damages the autonomic system at multiple levels — peripheral nerves, autonomic ganglia, and central control. HRV captures resting baseline dysfunction; Ewing's battery captures reserve capacity and reflex responsiveness. Together, they provide a comprehensive autonomic profile that neither tool alone can provide, potentially sharpening diagnostic specificity for a disease that is frequently misdiagnosed for months to years. More broadly, this reinforces HRV's role as a frontline tool for detecting and tracking autonomic damage in neurological and autoimmune diseases.

Read the full study

PUBLICATION: The National Medical Journal of India
AUTHORS: Suvradeep Chakraborty, Manish Goyal, Paritosh Goyal, and Priyadarshini Mishra

KEY FINDING: Using 645 subjects from the PhysioNet Computing in Cardiology Challenge 2018 database, researchers extracted time-domain, frequency-domain, and nonlinear HRV features from overnight ECG. A random forest classifier (RFC) achieved 79.6% validation accuracy; a bidirectional LSTM achieved 74.7%. Adding the sleep epoch index — the temporal position within the night — as a feature meaningfully improved accuracy. External validation on the independent Haaglanden dataset (43 subjects) yielded 78.9% accuracy, a Cohen's kappa of 0.70, and a macro F1 score of 0.789.

SIGNIFICANCE: Polysomnography requires a lab, a technologist for scoring, and a single artificial night. HRV-based staging from a chest strap worn at home enables longitudinal sleep assessment at scale for cardiac, metabolic, and chronic disease populations. Five-stage accuracy of 79%, with a kappa of 0.70 ("substantial" agreement), is clinically meaningful. For consumer HRV tracker users, this benchmarks your device's sleep-score performance — roughly 1 in 5 epochs may be misclassified, with more errors in patients with atypical sleep architecture.

Read the full study

PUBLICATION: PLOS One
AUTHORS: Fien Hermans, Eva Arents, Astrid Blondeel, Wim Janssens, Nina Cardinaels, Patrick Calders, Thierry Troosters, Eric Derom, and Heleen Demeyer

KEY FINDING: 36 COPD patients (mean age 69; 28% on beta-blockers) wore Polar H10 chest sensors for 4 consecutive nights at baseline and at 6-month follow-up during a physical activity coaching intervention. HRV metrics — SDNN, RMSSD (time domain), LF/HF ratio (frequency domain), and SD2/SD1 from Poincaré plot analysis (nonlinear domain) — were computed via Kubios. Group-level changes were not statistically significant. However, individual-level correlation analyses showed that change in walking intensity was inversely associated with change in the SD2/SD1 ratio (rs = −0.37 in the full sample; rs = −0.51 excluding beta-blocker users). In the beta-blocker-excluded subgroup, increases in daily step count correlated with lower resting heart rate (rs = −0.48) and lower LF/HF ratio (rs = −0.35). The nonlinear Poincaré metrics showed the clearest signal.

SIGNIFICANCE: COPD produces a vicious-cycle autonomic dysfunction — disease limits activity, reduced activity worsens autonomic tone, and worsened tone accelerates progression. The key finding here is that walking intensity, not step count, shifts autonomic balance: SD2/SD1 — the elongation ratio of the Poincaré ellipse, a sympathovagal balance marker — was more sensitive than RMSSD or SDNN. The implications extend beyond COPD: "walk with purpose and speed" may be a more powerful autonomic intervention than "walk more."

Read the full study

This episode is brought to you by Optimal HRV — the most comprehensive HRV platform built for practitioners and serious self-quantifiers.

Optimal HRV provides morning readiness scores, full frequency-domain analysis, and nonlinear metrics — SD1, SD2, and Poincaré plots — that, as this week's research shows, are often more sensitive than RMSSD alone. Whether you're tracking post-surgical recovery, monitoring a COPD patient's exercise response, or simply optimizing your own autonomic health, Optimal HRV provides clinical-grade tools to do it right.

Visit optimalhrv.com to learn more and start your free trial.

Medical Disclaimer: The research summaries in this episode are provided for informational and educational purposes only. They do not constitute medical advice, diagnosis, or treatment recommendations. Heart rate variability metrics discussed here are research tools and should not be used as the sole basis for clinical decisions. Always consult a qualified healthcare professional before making changes to your health regimen. Individual results may vary.

In this episode of the Heart Rate Variability Podcast, Matt Bennett sits down with Dr. Patrick K. Porter, PhD, founder of BrainTap®, to explore the connection between brainwave activity, heart rate variability, breathwork, and nervous system regulation.

Dr. Porter shares how his interest in brainwave training began at an early age and how decades of work in neuroscience-based performance ultimately led to the development of BrainTap. His background includes extensive work in brainwave entrainment and non-invasive approaches to supporting brain health and human performance.

In this conversation, Matt and Dr. Porter discuss how HRV can be used as a practical measure of nervous system activity, why chronic high-stress states may affect cognitive performance and recovery, and how intentional practices can help shift the body toward more regulated states.

They also explore:
- Brainwave states including beta, alpha, theta, gamma, delta, and SMR
- The relationship between HRV, brain activity, and autonomic regulation
- Brainwave entrainment compared with neurofeedback
- Breathwork practices for different times of the day
- Vagal stimulation and simple nervous-system regulation techniques
- BrainTap research related to pain, stress, cognitive performance, and recovery
- Dr. Porter’s personal routine for supporting brain and nervous system health
- How AI may influence the future of personalized brain fitness

Dr. Porter also shares his perspective on where brain optimization is headed—from clinics and universities to schools, workplaces, and personalized digital coaching.

Whether you’re interested in HRV, neuroscience, biofeedback, breathwork, stress regulation, or improving human performance, this conversation offers a fascinating look at how the brain and autonomic nervous system may work together.

Learn more about Optimal HRV: https://www.optimalhrv.com
Learn more about BrainTap: https://braintap.com

This podcast is for educational purposes only and is not intended to provide medical advice, diagnosis, or treatment.

Medical disclaimer: The information shared on this podcast is for educational and informational purposes only. It is not intended as medical advice and should not be used as a substitute for professional guidance from a qualified healthcare provider. If you have questions about your health or a medical condition, please consult a licensed clinician who knows you and your history.

This week on This Week in Heart Rate Variability, we explore the theme of Performance. Seven studies this week cover urban air pollution, shift work, weighted vests, functional clothing, ergogenic supplementation, elite athlete aging, and real-time exercise intensity classification with DFA-alpha-1.

1. Combined mask and headphone use significantly improves HRV during traffic exposure: a randomized crossover RCT

PUBLICATION: Journal of Hazardous Materials
AUTHORS: Meng, Qi, Wang, and Duan

KEY FINDING: In a randomized crossover study, 51 healthy young adults (mean age 20, ~40% female) were exposed to four 2-hour conditions at a high-traffic site in Beijing: no intervention, KN95 mask alone, noise-canceling headphones alone, and both combined. Single interventions did not yield a significant HRV benefit. The combined intervention significantly elevated SDNN (+20.3%), RMSSD (+56.8%), SDSD (+62.0%), pNN50 (+61.1%), LF (+128.6%), and HF (+73.8%) relative to control. It also reduced the myocardial injury marker CK-MB by 27.6% and homocysteine by 79.5%. Females showed greater autonomic benefits than males.

SIGNIFICANCE: This finding has real implications for urban athletes and anyone training in polluted environments. Wearing just one intervention (mask or headphones) alone did not provide significant protection, but the combined approach demonstrated robust cardiovascular protection during traffic exposure.

Read the full study

2. Night shift workers show elevated heart rate fragmentation independent of traditional HRV changes: MESA Sleep cohort

PUBLICATION: Chronobiology International
AUTHORS: Multi-Ethnic Study of Atherosclerosis Sleep study cohort analysis

KEY FINDING: A cross-sectional analysis of 845 participants (mean age 64, 51% women) compared heart rate variability and heart rate fragmentation (HRF) across shift types. Night and rotating shift workers had significantly higher PAS - the percentage of normal-to-normal intervals in alternation segments - compared to day workers (+11.9%, 95% CI 1.8-22.0). Traditional HRV metrics showed minimal and inconsistent differences. Sleep duration modified the effect: the PAS difference was most pronounced in short sleepers (+20.4%) and reversed in long sleepers (-10.8%, interaction p=0.06).

SIGNIFICANCE: The surprising finding is that traditional HRV showed almost no difference between night-shift and day-shift workers. But a newer metric called heart rate fragmentation - specifically PAS, the percentage of intervals in alternation segments - was significantly elevated in night/rotating shift workers. Sleep duration modulated the severity of that fragmentation, suggesting that this metric captures autonomic stress that standard HRV indices miss.

Read the full study

3. Weighted vest use increases cardiovascular workload and transiently suppresses HRV during active wearing, with more pronounced effects in females: ATLAS RCT sub-study

PUBLICATION: Physiological Reports
AUTHORS: Bellman, PA Jansson, JO Jansson, Ohlsson, and Bergfeldt

KEY FINDING: In a proper RCT sub-study, 51 adults with class I obesity (BMI 30-35) wore either a high-load vest (11% of body weight) or a low-load vest (1% of body weight) for 8+ hours/day for 15 days. During the afternoon active-wearing period, the high-load group showed significantly higher HR (+7%), higher %HRR (+6%), lower SDNN (p=0.01), and lower RMSSD (p=0.002). In females specifically, high-load wearing significantly reduced RMSSD, high-frequency power, and elevated the LF/HF ratio. Resting morning and nighttime measurements were unchanged, suggesting no sustained autonomic adaptation over 15 days.

SIGNIFICANCE: Use of a weighted vest during afternoon active wear led to significant HRV suppression and an elevated heart rate. The autonomic suppression was more pronounced in women. The lack of change in resting HRV (morning and night) suggests the body did not develop sustained adaptation over 15 days of wear, indicating the suppressive effect was transient and activity-dependent.

Read the full study

4. Black-silica clothing shows no significant HRV effects in a small, uncontrolled, industry-funded pilot: a case study in critical appraisal

PUBLICATION: Physiologia
AUTHORS: Tainaka (Niigata University)

KEY FINDING: In an unblinded, uncontrolled, fixed-order pilot study, 10 adults wore comparator garments (Day 1) and then black-silica-containing BS Fine clothing (Day 2). Garment conditions were not compositionally matched. After Holm correction for multiple comparisons, no HRV outcome reached statistical significance (all Holm p >= 0.797). A modest leg surface temperature increase (0.668 degrees C) did not survive correction (Holm p=0.100). A secondary total-power dispersion finding was not confirmed in the n=9 sensitivity analysis.

SIGNIFICANCE: The methodological problems are significant - small sample, no blinding, unmatched conditions, single-order design. However, what makes this study valuable is the author's own honest conclusion: "cannot isolate an effect attributable specifically to black silica or demonstrate autonomic benefit." This serves as a useful template for reading product-sponsored HRV research critically and understanding the limits of poorly controlled pilot studies.

Read the full study

5. Acute theacrine supplementation does not significantly alter post-resistance-exercise HRV versus placebo: a randomized pilot crossover

PUBLICATION: Journal of Vascular Diseases
AUTHORS: Ramos Souza, Nascimento Consoli, Weber, Campos, Diogo Ferreira, and de Oliveira (UFRJ, Macaé)

KEY FINDING: In a double-blind crossover trial, 17 resistance-trained young adults (8 female) received 200 mg of theacrine (TEA) or placebo, taken 90 minutes before performing 5 sets of back squats at 85% of 10RM. The placebo condition showed significant post-exercise drops in RMSSD, HFnu, and SDNN. TEA condition did not show a significant pre-supplementation to post-exercise RMSSD reduction. Critically, the time x condition interaction was not significant (p=0.246, eta-squared=0.043), meaning the between-condition difference was not statistically confirmed.

SIGNIFICANCE: The moderate effect size for post-exercise RMSSD warrants a larger confirmatory trial. While the trend suggests theacrine may attenuate post-exercise HRV suppression, the interaction term remains unconfirmed, so we cannot yet claim a protective effect. This is a rigorous negative finding that illustrates the importance of checking interaction statistics, not just main effect trends.

Read the full study

6. Younger professional soccer players show higher HRmax and a trend toward higher RMSSD despite identical training loads: wrist PPG monitoring study

PUBLICATION: Electronics
AUTHORS: Takai, Morikawa, and Yuda

KEY FINDING: In a wrist PPG monitoring study, 14 Japanese professional soccer players - 7 aged 23 or younger and 7 aged 24 or older - were monitored throughout a training season. Maximum heart rate was robustly higher in the younger group (Hedges' g=2.02, FDR-corrected p<0.05). RMSSD was also higher in younger players, but this finding did not survive FDR correction and is classified as exploratory. Training load was statistically identical across age groups.

SIGNIFICANCE: The HRmax finding is solid and has real implications for age-adjusted monitoring in elite athletic cohorts. The study highlights the importance of age-adjusted HRV interpretation even when players train identically. Limitations include n=14, a single club, a cross-sectional design, and the risk of wrist PPG motion artifacts, but the core finding on HRmax is robust and actionable.

Read the full study

7. DFA-alpha-1 classifies exercise modality and intensity with 88-98% accuracy, but values are unexpectedly elevated during intermittent protocols: implications for real-time monitoring

PUBLICATION: PLoS One
AUTHORS: Sanchez, Favier, Fabre, and Varray

KEY FINDING: In 26 healthy adults completing three cycling protocols (continuous 40% MAP, continuous 60% MAP, and intermittent alternating 75%/25% MAP), combined use of mean RR, SDNN, and DFA-alpha-1 classified exercise type with 88-98% sensitivity and specificity. Critically, DFA-alpha-1 during the intermittent session was unexpectedly higher (~0.8) than during continuous 60% MAP (~0.5), because recovery intervals allowed rapid DFA-alpha-1 recovery.

SIGNIFICANCE: If you're monitoring DFA-alpha-1 during interval sessions, this context is essential. DFA-alpha-1 values during recovery intervals can appear in the "aerobic zone" (~0.8) even when overall session load is high. The relationship between DFA-alpha-1 and intensity holds for continuous exercise; contextualizing readings against workout structure is essential for accurate interpretation during HIIT.

Read the full study

The Optimal HRV app is built around a simple morning measurement, taken at the same time each day, so that consistency turns a single number into a meaningful signal over time. Over weeks and months, you earn your own baseline - not someone else's - so you can see when your body needs rest, when it's ready for training, and when it's simply having an off day. The app also includes biofeedback tools with guided, paced breathing to help you influence your nervous system in real time, and gentle in-app education so you're learning the rhythm your system is measuring.

Learn More

Medical disclaimer: The information shared on this podcast is for educational and informational purposes only. It is not intended as medical advice and should not be used as a substitute for professional guidance from a qualified healthcare provider. If you have questions about your health or a medical condition, please consult a licensed clinician who knows you and your history.

Medical disclaimer: The information shared on this podcast is for educational and informational purposes only. It is not intended as medical advice and should not be used as a substitute for professional guidance from a qualified healthcare provider. If you have questions about your health or a medical condition, please consult a licensed clinician who knows you and your history.

This week on This Week in Heart Rate Variability, we open a new themed month by looking at where heart rate variability is headed next. Six studies map the future of the field — from a unifying theory of cardiovascular instability built on Shannon entropy, to the regulatory and technical realities of validating consumer wearables, to a meta-analysis quantifying how far photoplethysmography still is from true electrocardiography-derived HRV, to a virtual reality study, a forest-based multimodal field study, and a deep dive into the asymmetric structure of the heartbeat itself.

RESEARCH HIGHLIGHTS THIS WEEK

1. Shannon Entropy in Heart Rate Variability: A Unified Indicator of Electrical and Hemodynamic Cardiovascular Instability

PUBLICATION: Medical Research Archives

AUTHORS: Orazio Antonio Barra

KEY FINDING: This systematic review pooled 22 published studies that applied Shannon entropy of RR intervals to atrial fibrillation and tilt-induced syncope. Entropy declined progressively across both conditions — roughly ten percent at baseline instability, fifteen to twenty percent approaching pre-crisis, and twenty-five to thirty percent as crisis became imminent — with entropy showing better specificity than other HRV indices while holding onto its sensitivity.

SIGNIFICANCE: Entropy looks less like a metric tied to one specific condition and more like a general-purpose marker of autonomic complexity loss that shows up consistently whether the underlying instability is electrical or hemodynamic. This is a synthesis of existing studies rather than a new trial, so it should be read as a strong, well-supported hypothesis rather than a validated bedside tool.

Read the full study:

https://esmed.org/MRA/mra/article/view/7606

2. Validity, Reliability, and Regulatory Considerations for Consumer Devices in Clinical Research Data Collection

PUBLICATION: Applied Clinical Trials Online

AUTHORS: Lauren Crooks, Greta Marie van Schoor, Anthony Everhart, Bill Byrom, and Thijs Sondag

KEY FINDING: This article maps the validity, reliability, and regulatory landscape for consumer wearables used in decentralized clinical trials. Heart rate accuracy can approach ECG-level concordance under favorable conditions, but motion artifacts, device-to-device variation, and demographic performance gaps can cause aggregate agreement statistics to mask large individual-level errors. Only an estimated 11% of devices on the market have been rigorously and publicly validated.

SIGNIFICANCE: Regulators are converging on "fit-for-purpose" validation rather than universal accuracy claims — asking whether a specific device is accurate enough for a specific clinical question, not whether it's accurate for everything. That's a useful discipline for anyone using consumer devices outside a formal trial, too.

Read the full study:

https://www.appliedclinicaltrialsonline.com/view/validity-reliability-regulatory-considerations-consumer-devices-clinical-research-data-collection

3. Accuracy of Photoplethysmography-Derived Pulse Rate Variability Compared with Electrocardiography-Derived Heart Rate Variability: A Systematic Review and Meta-Analysis

PUBLICATION: Sensors

AUTHORS: Shiwen Xu, Hao Liu, Zhengliang Liu, Peng Su, and Zhuangzhuang Gu

KEY FINDING: Pooling 43 studies, this meta-analysis found absolute standardized mean differences of about 19% for RMSSD and 13% for SDNN when comparing PPG-derived pulse rate variability with ECG-derived HRV. Results were drawn from resting, controlled conditions only.

SIGNIFICANCE: PPG and true HRV are not interchangeable, and the authors explicitly caution against generalizing these best-case accuracy numbers to sleep, exercise, stress, or free-living conditions — where PPG accuracy is likely to be worse, not better. PPG remains attractive for continuous, low-burden monitoring, but the absolute numbers deserve real caution outside of rest.

Read the full study:

https://www.mdpi.com/1424-8220/26/16/5192

4. Virtual Reality and Mental Stress: Linear, Non-Linear, and Machine Learning Analysis of Heart Rate Variability

PUBLICATION: Technologies

AUTHORS: Penio Lebamovski and Evgeniya Gospodinova

KEY FINDING: In 42 healthy volunteers exposed to a stress-inducing VR game under low-immersion (polarizing glasses) and high-immersion (VR headset) conditions, HRV responses differed meaningfully between immersion technologies. Nonlinear methods (Poincaré and recurrence plots) captured structural changes that linear metrics missed, and a Random Forest model using 17 HRV parameters successfully classified resting versus immersive states.

SIGNIFICANCE: The level of sensory immersion doesn't just intensify the stress response — it appears to shape its character. This is a modest, controlled lab sample, so it's a proof of concept rather than a deployable tool, but it points toward VR-calibrated biofeedback and exposure-therapy applications with objective physiological readouts.

Read the full study:

https://www.mdpi.com/2227-7080/14/8/487

5. Neural Dissociation of Cognitive Effort and Physiological Arousal: Multimodal Single-Channel EEG, Cortisol, and HRV Evidence from an Ecologically Valid Field Study

PUBLICATION: International Journal of Psychophysiology

AUTHORS: Neta B. Maimon, Ganit Baruchin, Itamar Grotto, Nathan Intrator, Lior Molcho, Talya Zeimer, Ofir Chibotero, Gal Levi, Hagit Cohen, Merav Greenstein, and Efrat Danino

KEY FINDING: In a forest-based field study of 101 healthy adults completing an auditory cognitive battery, EEG arousal showed a graded hierarchy (startle, then emotional, then mental load, then rest, in descending order), while a regulatory EEG feature (T2) was selectively suppressed during mental load but not during acute stress. Higher baseline cortisol negatively predicted arousal but positively predicted cognitive recruitment. Neural complexity features predicted reduced HRV three hours later; the T2 feature predicted self-reported anger.

SIGNIFICANCE: Cognitive effort and physiological arousal are dissociable, not the same phenomenon wearing different names. This is a correlational field study, not an experiment establishing causation, and the rich multi-channel design carries a real risk of findings that don't replicate — but it's a compelling case for multimodal, rather than single-signal, stress screening.

Read the full study:

https://www.sciencedirect.com/science/article/abs/pii/S0167876026001042?via%3Dihub

6. The Asymmetry of Deceleration Input into the Heart Rate Transitions - A Study Over Healthy Subjects in Head-Up Tilt

PUBLICATION: Biocybernetics and Biomedical Engineering

AUTHORS: Rafał Pawłowski, Paweł Zalewski, and Katarzyna Buszko

KEY FINDING: In 151 healthy men undergoing head-up tilt testing, 60.3% of resting supine heart rate signals showed measurable heart rate asymmetry. The relationships between asymmetry metrics and standard time/frequency-domain HRV metrics were nonlinear. A newly proposed metric, Deceleration Input (DI), outperformed existing asymmetry indices (Porta, Guzik, Ehlers, Slope) in resistance to outliers.

SIGNIFICANCE: Heart rate asymmetry is a normal feature of a healthy resting rhythm, not a rare anomaly, and it appears to carry information that the standard symmetric-assuming metrics can't see. This was studied only in healthy men using a single provocation (tilt), so generalization to other populations and stressors remains open — but it's a strong argument that nonlinear, geometric analysis is where a meaningful share of future HRV research is headed.

Read the full study:

https://www.sciencedirect.com/science/article/pii/S0208521626000550

KEY THEMES

Taken together, this week's six studies point toward the same conclusion from six different angles: the future of heart rate variability isn't a single new metric; it's better measurement. Shannon entropy offers a unifying way to think about autonomic complexity across different kinds of cardiovascular crises, while the wearable validation and photoplethysmography meta-analysis both push back, with real numbers, on the assumption that any device labeled "HRV" measures the same thing an ECG measures.

Methodology matters as much as the finding itself. Honest cross-validation and disciplined preprocessing are what separate a useful signal from a headline artifact — a theme that shows up explicitly in the wearables and PPG studies, and implicitly in how carefully the VR and forest-field studies had to be designed just to isolate what they were actually measuring.

Increasingly, the interesting information seems to live outside the numbers most of us are used to reading. Nonlinear, geometric methods captured structure in the VR study that linear metrics missed entirely, and the heart rate asymmetry study revealed meaningful information in the simple fact that the heart doesn't speed up and slow down symmetrically. The nervous system is best understood through its flexibility — and capturing that flexibility well is where this field is headed next.

SPONSORED BY OPTIMAL HRV

The Optimal HRV app is built around a simple morning measurement, taken at the same time each day, so that consistency turns a single number into a meaningful signal over time. Over weeks and months, you earn your own baseline — not someone else's — so you can see when your body needs rest, when it's ready for training, and when it's simply having an off day. The app also includes biofeedback tools with guided, paced breathing to help you influence your nervous system in real time, and gentle in-app education so you're learning the rhythm your system is measuring.

Learn More: www.optimalhrv.com

Medical disclaimer: The information shared on this podcast is for educational and informational purposes only. It is not intended as medical advice and should not be used as a substitute for professional guidance from a qualified healthcare provider. If you have questions about your health or a medical condition, please consult a licensed clinician who knows you and your history.

In this follow-up episode of the Heart Rate Variability Podcast, Matt Bennett reconnects with Lieven Van Linden to discuss the newly published English version of his book, Fully Charged. Since their last conversation two years ago, Lieven’s work has continued to evolve at the intersection of leadership, nervous system regulation, sustainable performance, and HRV.

Lieven shares how his engineering background shaped the framework behind Fully Charged, applying process improvement thinking to human performance. Rather than treating energy, resilience, and leadership as vague concepts, his model looks at the body as a system with inputs, processes, outputs, and measurable feedback. HRV plays a central role as a biomarker for nervous system health, recovery, and available energy.

Matt and Lieven also explore what has changed since the book's original publication, including new English-language chapters on artificial intelligence and women's energy levels. Lieven reflects on the slower-than-expected adoption of HRV technology in Europe and the growing conversation around AI health coaches. While AI can support education and insight, Lieven emphasizes that human connection remains essential for lasting behavioral change.

The conversation also moves into Lieven’s personal evolution. After years of extreme endurance racing, he has shifted toward freediving, which challenges him in a very different way. Rather than pushing through suffering, freediving requires relaxation, self-regulation, inner awareness, and trust in the body.