Episode Transcript
[00:00:00] Welcome friends to the Heart Rate Variability Podcast this week in Heart Rate Variability Edition. Each week we explore the latest research and news from the world of hrv. Please consider the information in this podcast for informational purposes only and not as medical advice. Always consult your healthcare provider before applying any strategies we discuss. It was a slow week in HRV research. Since our main article examines HRV and insomnia, it gives us an excellent opportunity to explore this topic in more detail. Let's start by reviewing the new research and then delve into this topic in more detail. I was surprised that the relationship is more complex than most people would expect. We'll start with the new paper by Dhanushri Rajalakshmi, Prakash Bharadwaj and Har Chandrakumar. Insomnia and its impact on Psychomotor Reactivity, Autonomic Function and psychological well being among medical students A cross sectional analytical study published in Koreas which asks a practical question for anyone tracking HRV when sleep starts to slip, which signal moves first? The investigators recruited a cohort of students. They placed several dials on the same dashboard. Insomnia severity via a standard screener Reaction time for both auditory and visual cues psychological distress via brief inventory and resting HRV analyzed using conventional time and frequency domain metrics over five minutes. Here's the core pattern simplified first, insomnia symptoms were common enough to matter. Second, as insomnia severity increased, reaction times slowed in response to both sound and color cues with a clear dose like relationship. Third, psychological distress tracked alongside insomnia. Higher sleep problem scores aligned with more stress, anxiety and low mood. And then comes the nuance that many HRV users need to hear. In this young, otherwise healthy sample, short term resting HRV didn't separate the groups as effectively as reaction time and distress did. In plain terms, cognition and mood blinked red before 5 minute parasympathetic indices budged. Why might that be? Think of resting HRV in healthy young adults as a sturdy suspension system that can absorb a fair amount of day to day rumble before the ride feels rough at idle. But ask the system to handle a task such as sustained attention under poor sleep conditions, for example, and the microlapses appear quickly. That's precisely what reaction time tests reveal. Meanwhile, mood systems tightly intertwined with sleep architecture start to wobble as REM continuity and overnight emotional processing get choppy. The autonomic resting snapshot may need either more substantial sleep loss, longer accumulation or comorbidity before differences are evident at five minutes in supine rest. The study reminds us that early insomnia impairment is functional and often felt before it becomes autonomically obvious at rest.
[00:02:20] Support the behavior change consistent sleep window, pre bed, downshift, brief daytime breathwork and let the autonomic system catch up as recovery consolidates. Picking up from where we left off, the broader literature doesn't always show a clean autonomic signature for insomnia, and a systematic review reinforces that caution. Zhao and Jiang Heart rate variability in patients with insomnia disorder A systematic review and meta analysis in Sleep and Breathing the authors pulled data from studies comparing people with insomnia to controls. They found that while the direction of the effect often pointed toward lower vagal activity or overall variability in insomnia, the aggregate signal was small and not reliably significant once synthesized across methods and samples. Translation for practice if you're expecting a robust across the board drop in short term resting HRV as a hallmark of insomnia, this paper argues, you'll be disappointed. Differences may be subtle, method dependent and easily masked in younger or healthier cohorts. What do we do with that? Two things. First, match the measurement to the question. If your question is is this person's sleep complaint already affecting daytime function? Use a simple psychomotor probe and a brief mood screener alongside hrv. Second, if your question is is there nocturnal, hyperarousal or fragmented autonomic regulation overnight? Consider sleep period, HRV and staging context Rather than relying solely on a five minute daytime reading, Zhao and Jiang synthesis nudges us toward multimodal assessment and away from one metric thinking.
[00:03:36] Adding to the picture, Jiang, Nu, Ma, Wei, Jiang, and Du published Effects of Sleep Deprivation on Heart Rate Variability A Systematic Review and Meta Analysis in Frontiers in Neurology. This review steps back from insomnia itself and asks a related question, what happens to autonomic balance when we deliberately cut sleep short? Pooling data from randomized controlled trials, the authors found that sleep deprivation consistently tipped HRV toward sympathetic dominance across 11 studies. The time domain measure RMSSD, our best short term window on vagal activity, dropped significantly after a night or more without sleep. In contrast, sdnn, a broader measure of total variability, didn't change much, echoing the pattern seen in milder insomnia studies. On the frequency side, low frequency power and the LF HF ratio rose meaningfully. In contrast, high frequency power, another marker of parasympathetic tone, showed a downward trend but did not reach statistical significance. The overall pattern suggests a measurable loss of vagal modulation and a stronger sympathetic response. The physiological signature of being wired but tired Interestingly, the most potent effects were observed in high pressure groups such as resident physicians and SHIFT workers, suggesting that baseline stress may amplify autonomic vulnerability.
[00:04:46] In university students, variability was lower, implying some resilience or a narrower response range. What this synthesis reveals is that when sleep deprivation is explicit and acute, HRV does change, but primarily through decreases in RMSSD and increases in LF over HF rather than through significant shifts in total variability. It reinforces the notion that RMSSD is the more sensitive marker for short term stress on the parasympathetic system. Taken together, the sequence across our first three studies, Dhanushtri and colleagues in Koreas, Zhao and Jiang in Sleep and Breathing and Jiang and co authors in Frontiers in Neurology sketches that continuum insomnia begins with cognitive slowing and mood disruption while HRV still looks steady. Meta analytic evidence shows only mild inconsistent HRV suppression in chronic insomnia and under complete sleep deprivation, sympathetic dominance becomes clear. That progression is exactly what clinicians observe. First comes fatigue, then emotional volatility, and then, if stress and sleeplessness persist, a measurable decline in autonomic flexibility. And then just this spring, Martin, Montero, Vacarizo, Villar, Garcia, Vicente, Gutierrez, Tabal, Penzel, and Hornero published heart rate variability analysis in Comorbid Insomnia and Sleep Apnea in Scientific Reports. This article is one of the first large scale looks at what happens when the two most common sleep disorders, insomnia and obstructive sleep apnea, collide in the same person. Drawing on more than 5,000 overnight ECGs from the Sleepheart Health Study, the authors categorized participants into four those with insomnia, only those with apnea, only those with those with both conditions, the COMMISSA group and controls. They then employ detailed time and frequency domain HRV analysis to map the behavior of the autonomic nervous system during wakefulness and sleep. The headline Finding Komissa produces a distinct hybrid form of autonomic dysfunction. During wakefulness, parasympathetic activity is suppressed, RMSSD is reduced, suggesting that even before sleep, the vagal break is already off. During sleep, sympathetic activity increases, accompanied by a corresponding rise in mean heart rate and a shift in frequency domain patterns toward the low frequency bands associated with apnea related arousals. The team even identified a new spectral signature dubbed BW COMISA that appears uniquely blunted in these patients, a kind of frequency fingerprint for the comorbidity itself. Taken together, the results suggest that when insomnia's hyperarousal meets apnea's repeated hypoxic stress, the heart's nightly rhythm loses both flexibility and rest. Mean heart rate stays elevated when it should dip, while parasympathetic modulation fails to recover across non REM sleep. In practical terms, that means Kamaya's patients experience continuous autonomic strain both awake and asleep, which potentially explains the elevated cardiovascular risk observed in this population.
[00:07:20] For clinicians, the takeaway is straightforward but urgent. If a patient presents with persistent insomnia and is also snoring or showing apneic events, screen for both. Treating one without addressing the other leaves the autonomic system trapped in a 24 hour tug of war. With that, we've covered four interlocking windows into the insomnia sleep deprivation HRV axis From lab studies of students to meta analyses and now to a massive cohort analysis linking insomnia and apnea, it paints a continuum from subjective sleep loss to measurable autonomic imbalance to the compounded risk when sleep disorders coexist. Now let's take a short pause and thank our sponsor. This podcast is sponsored by Optimal hrv, the app built to make heart rate variability tracking practical. Their newest update elevates the experience Full multilingual support in 17 languages, a a smoother onboarding process that gets you from download to measurement in minutes, and a guided device testing feature that confirms your hardware is connected and reading accurately day to day. You'll feel the speed, faster login times, cleaner translations and improved audio playback for your mindfulness sessions. They've also refined the interface and resolved several bugs to make everything feel more responsive and reliable. If you've been curious about HRV or you're already tracking, now's a great time to try it. Search optimal HRV in your app store and install the latest version. Lets take a quick look at a new piece that brings HRV research into the world of mental health, specifically depression among university students.
[00:08:41] This report, written by Widget P and published on Compassion in November 2025, is titled Diagnosis of Depression based on SDNN Indicator of Heart Rate Variability in Students.
[00:08:50] The article describes a cross sectional study of 120 students aged 18 to 25 who completed the Patient Health Questionnaire 9PHQ 9 while undergoing electrocardiogram based HRV recording. The focus was on the sdnn, the standard deviation of normal to normal intervals, a measure of overall autonomic flexibility. The findings were clear and statistically significant. Higher depression scores on the PHQ9 were associated with lower SDNN values with a correlation coefficient of minus 0.48 and a p value of less than 0.001. In practical terms, that means students with more severe depressive symptoms tended to have lower heart rate variability. The authors proposed a threshold SDNN value of 50 milliseconds, which yielded approximately 82% sensitivity and 77% specificity for identifying moderate to severe depression. While an early stage exploratory study, the pattern echoes what has been reported in peer reviewed literature that reduced HRV notably lower. SDNN often accompanies depressive symptom severity. It doesn't diagnose depression by itself, but it suggests that hrv, especially sdnn, can act as an objective physiological complement to traditional self report tools.
[00:09:55] Now a quick note of celebration. OptimalHRV, the sponsor of this podcast, has been nominated for the 2026 Edison Awards, a recognition that honors meaningful innovation. Their app continues to make HRV tracking faster, more global and more reliable, with multilingual Support in over 17 languages and smoother onboarding that helps you start measuring in minutes. It's fitting, really. As we enter an era where wearables and wellness tools help bridge the gap between psychology, physiology and real world outcomes, it's encouraging to see a platform like Optimal HRV earn recognition for making that bridge more accessible to everyone.
[00:10:27] Now we're delving into an ambitious study that sits squarely at the intersection of cardiology, wearable technology and machine learning. The paper is by Kim Sangi Kim and it's titled Multimodal Inflammatory Risk Modeling in Post PCI Patients using Behavioral and Physiologic Data. It's Open Access in PLOS One. Published November 10, 2025.
[00:10:48] Here's the problem the study takes on After a stent procedure, some patients stay inflamed for months, and that low grade inflammation is tied to worse outcomes. Clinicians routinely check laboratory markers such as high sensitivity C reactive protein.
[00:11:02] Still, those snapshots don't always capture what's happening in real life. Between clinic visits, Kim asks a deceptively simple if we combine those lab markers with the rhythms of daily life captured by wearables how much we move, how well we sleep, how, how our HRV fluctuates, can we better predict who will actually calm their inflammation over time? The team followed three hundred and twelve adults who underwent pci. They extracted clinical details, collected blood samples at baseline and again approximately six months later, and streamed wearable data. The wearable variables were the hits you'd expect daily step count, sleep efficiency, heart rate variability, and oxygen saturation. They asked a yes or no outcome question. Did HS CRP drop by at least 1 milligram per liter.
[00:11:41] Before we examine the models, it's worth considering the human signal in the numbers, approximately 59% of patients improved compared to non responders. The improvers walked more approximately 8050 steps per day versus 6140, slept more efficiently, and showed higher HRV 65 milliseconds versus 51 milliseconds. All those differences were statistically robust, and they point in one direction, behavior and nightly restoration matter. On the modeling side, Kim compared four approaches, and the transformer model emerged as the top performer with an area under the curve of 0.8.
[00:12:12] Importantly, the study didn't stop at accuracy it tackled interpretability. The model consistently assigned substantial weight to modifiable behavioral features, with step count and HRV ranking near the top. In plain language, the way patients lived between visits was not just background noise, it was a predictive signal. So where does this leave us? Kim's plos ONE paper points toward a future in which post PCI care is not just a calendar of appointments, but a feedback loop. The clinic orders the labs, the patient lives the week, the wearable records the rhythms, the and a model helps forecast who's likely to quiet their inflammation and who might need a nudge. We have covered a tremendous amount of ground today, from the university lab to the post cardiac care unit. What does this all mean for us? Let's distill it into our actionable insights. First, for individuals, this week's message focuses on context and gradients. Trust your subjective experience. The CURA study shows that if you're feeling fatigued, your reaction time is slowing and your mood is dropping. That's a real signal. Don't wait for your resting HRV to drop before taking your sleep hygiene seriously.
[00:13:08] Your brain and mood are often the first things to show the strain. Second, for clinicians, this week's research provides a masterclass in matching the tool to the question. If you suspect chronic mild insomnia, the sleep and breathing meta analysis warns that a 5 minute resting HRV test isn't a reliable diagnostic. However, if you're screening for depression, the compassiona report suggests SDNN is a useful objective correlate to a PHQ9. And if a patient has both insomnia and apnea, Scientific Reports study urges you to treat both as their nervous system is under 24. Seven strain that treating one condition alone won't fix. Third, for researchers, the path is clearly multimodal. The PLAS1 paper by Sanghee Kim serves as a blueprint for the future, fusing clinical labs with daily wearable data steps sleep HRV using modern machine learning, which provides a much stronger predictive signal than either data stream alone. The sleep studies also show a clear gradient the HRV signal becomes stronger as the sleep insult worsens, from mild to chronic to acute deprivation.
[00:14:06] Finally, for businesses and health tech innovators, the plos ONE study is your roi. It provides a direct link between modifiable behaviors tracked by wearables and hard billable clinical outcomes, such as inflammatory markers in a high risk cardiac population. The Edison Award nomination for optimal HRV confirms that the market is recognizing and rewarding innovation in this space, particularly in terms of accessibility, reliability and user experience. And that is our week. From the subtle dip in a student's reaction time to a predictive signal for inflammatory recovery, HRV remains the common thread that ties our daily behaviors to our long term health. Thank you for joining us on the Heart Rate Variability podcast.
