Our recent study in national-level wrestlers shows that post‑match foam rolling, and to a lesser extent static stretching, may modestly improve acute post-match HRV recovery vs. passive rest, with neutral effects on CMJ performance. More research needed. Full text below.
Acute Effects of Traditional Versus Cluster Set Upper Body Resistance Training on Heart Rate Variability and Blood Pressure in Trained Men
Our recent paper evaluating acute HRV and blood pressure responses to two different styles of upper-body resistance exercise in trained men shows that:
– For greater hypotensive effects, traditional sets > cluster sets
– For faster cardiac-parasympathetic reactivation, cluster sets > traditional sets
Full text below:
New Study: Effects of Short-Duration Cycling After Resistance Exercise on Aortic Stiffness and Next-Day Recovery in Strength-Trained Men
Highlights from our latest study in JSCR. Full-text available here.
We aimed to determine how 10 min of post-resistance exercise cycling affects aortic stiffness responses and next day recovery markers in well-trained men.
– A 10-min bout of air bike cycling was ineffective at countering acute RE-induced increases in cfPWV (aortic stiffness), likely because of the rapid and unanticipated cfPWV return to baseline by Post-RE 15min in both conditions (intervention & control).
– Accelerated post-RE cfPWV normalization may be an adaptation to habitual RE, as acute RE-induced aortic stiffness typically persists for >60 min in less experienced lifters.
– Thus, targeting the attenuation of acute post-RE increases in cfPWV is likely unnecessary, but whether the intervention exerts chronic effects, such as limiting long-term RT-induced increases in resting cfPWV, remains TBD.
– Despite no effect of the intervention on cfPWV at the group level, it altered changes at the individual level, such that those with a lower relative cycling power output at the target HR exhibited greater reductions in cfPWV.
– This may indicate that lifters with lower aerobic fitness may derive greater AE-induced destiffening effects after acute RE. – Finally, the AE intervention neither enhanced nor impaired recovery indicators (HRV, subjective, barbell velocity), alleviating concerns about short-term AE interfering with next-day recovery status or performance.

Associations between morning heart rate variability and ambulatory blood pressure characteristics in young adults
Our new paper examining associations between morning HRV and ambulatory blood pressure characteristics in young adults.
In men, higher HRV was associated with lower nocturnal blood pressure and greater nocturnal blood pressure dipping.
In women, higher HRV was associated with lower daytime and overall diastolic blood pressure.
Full text can be download below.
ABSTRACT
We aimed to quantify associations between resting heart rate variability (HRV) and ambulatory blood pressure (BP) characteristics in young adults. Thirty-two apparently healthy young adults (50% male) were included in the study. Short-term HRV was obtained via electrocardiography in the laboratory following an overnight fast to determine the mean RR interval, standard deviation of normal RR intervals (SDNN), and root-mean square of successive differences (RMSSD). Participants left the laboratory wearing an ambulatory BP monitor for 24 h to determine awake, asleep, and overall systolic and diastolic BP, and asleep BP dipping ratios. In males, higher SDNN and RMSSD were associated with lower asleep systolic and diastolic BP, and greater systolic BP dipping, with SDNN also associated with diastolic BP dipping (Ps <0.05). In females, higher mean RR, RMSSD, and SDNN were associated with lower awake diastolic BP, and RMSSD with lower overall diastolic BP (Ps <0.05). Our findings indicate potential sex differences in how cardiac-autonomic function associates with BP regulation throughout the day. In males, HRV showed stronger associations with nocturnal BP characteristics, whereas in females, HRV associations were more pronounced with daytime BP.


Nocturnal versus standing HRV for reflecting changes in fitness
Background:
Following a ~2-week family trip, I was relatively detrained. As expected, my standing HRV trend worsened while away with lower and less stable values, reflecting reduced fitness, travel effects, routine change, etc. For my latest n=1 experiment, I decided to track my fitness improvement as I progressively resumed training when I returned home. With increases in aerobic fitness, HRV typically improves by increasing and becoming more stable. I track both nocturnal HRV and post-waking HRV in the standing position. Which of these two is more sensitive to my change in fitness?
What did I do?
I compared associations between my submaximal exercise heart rate at a fixed intensity (HRex, an indicator of aerobic fitness) and both nocturnal and standing RHR and HRV over a 4-week period.
Note: All tools used for this self-experiment were wearable devices that I validated on myself (examples below). However, the accuracy of a device can vary from person to person depending on various factors. For example, many HRV apps show greater error with higher HRV values. Additionally, HRex from a wearable is often less accurate at higher intensities, in less stable conditions, in cold weather, etc. Thus, I encourage everyone to test the accuracy of your own device if you’re able to do so. Otherwise, refer to published agreement studies.
Why did I do it?
My nocturnal and standing HRV often show divergent patterns. If I were to guide training with HRV, which should I focus on? I’ve written about this issue extensively. Here’s a previous example comparing my nocturnal HRV vs. standing HRV during and after COVID-19 infection.
How did I do it?
HRex: I measured HRex during a 20-min treadmill walk (3.7 mph, 1.5% grade, ~50-60% HRmax) every morning at ~7 AM. HRex was measured with the Polar Vantage V3. The mean HR from the last 3 min of each session was used to determine HRex via Polar Flow. I previously compared the Vantage V3 vs. Polar H10 during various exercise sessions and found near identical average HRex values (within 1 bpm, example from treadmill walk below).

Nocturnal Values: Nocturnal HR and HRV (RMSSD) were measured with Oura Ring 4. I previously compared Oura to ECG during a night of sleep and found good agreement (2 ms difference, image below). One outlier sleep HRV value was removed (very high RMSSD, 106 ms spike vs ~70 ms average) which occurred with frequent breathing disturbances reported in the app (i.e., episode of sleep apnea, which happens when I overeat with a later dinner time).

Standing Values: Standing HR/HRV were measured after waking, peeing, and logging body mass. I used the Kubios HRV app paired with a Polar Verity Sense worn around the forearm. The sample duration was 1 min. I compared simultaneous HRV measurements performed with the Vertiy Sense and H10 via the Kubios app and found good agreement. Here is an example comparison below using Kubios software.

What did I find regarding trends over time?
HRex: Consistent with a progressive increase in aerobic fitness, HRex tended to decrease over time.
Nocturnal HRV: Consistent with a progressive increase in aerobic fitness, sleep HR tended to decrease over time. However, inconsistent with fitness improvement, sleep HRV tended to decrease over time.
Standing HRV: Standing HR tended to decrease over time and standing HRV tended to increase over time. Each as expected with an increase in fitness. Figures below showing trends for all parameters across time.

What did I find regarding associations between HRex and HR/HRV values?
Nocturnal Values: Sleep HR was associated with HRex (r = 0.58) in the expected direction, such that when sleep HR was lower, HRex also tended to be lower. Sleep HRV was not strongly associated with HRex and the slope was directionally opposite of what one would expect (r = 0.25). This means that when sleep RMSSD was higher, HRex tended to be higher. Figure below for nocturnal values.

Standing Values: The association between standing HR and HRex was directionally as expected (lower HR associated with lower HRex) but not very strong (r = 0.32). Standing HRV was associated with HRex in the expected direction (r = -0.64), indicating that higher standing HRV was associated with lower HRex. Standing HRV provided the strongest correlation coefficient with HRex. Figure below for standing values.

A strong correlation between my standing RMSSD and HRex is consistent with a previous case study we published in a pro soccer player who showed a similar association with seated upright RMSSD and the heart rate-running speed index (effectively, HRex). See scatter plot below. It didn’t matter if we used a 1 min or a 5 min HRV measure, the associations were the same.

What’s going on with sleep HRV?
My sleep HR was consistently <55 bpm. When HR drops below this threshold, HRV often diminishes. This is because cholinergic receptors on the heart become saturated at very high levels of cardiac parasympathetic activity (i.e. high parasympathetic activity = high vagal discharge of acetylcholine). As a result, phasic modulation decreases, leading to reduced beat-to-beat variability. In the figure below, you can see that the association between sleep HR and RMSSD is counterintuitively positive rather than negative. As my sleep HR decreased, RMSSD decreased as well. Contrastingly, as standing HR decreased, RMSSD increased.


Parasympathetic saturation is a well documented effect. In fact, the very first studies to compare HRV-guided versus pre-planned training by Kiviniemi et al. (2007, 2010) intentionally used standing HRV measures to counteract potential saturation effects that commonly occur when lying down. See screenshots below from the Methods sections.


Therefore, If your sleep HR is <55 bpm, I would strongly encourage you to create a scatter plot of your HR and HRV to determine if this applies to you. It’s critically important to understand that in cases of saturation, HRV does not reflect parasympathetic activity.
I believe there are also other reasons to opt for standing HRV over nocturnal HRV (discussed here).
Conclusion
There was a tendency for greater cardiac efficiency during exercise when standing HRV was higher. Conversely, there was a tendency for greater internal load in response to a fixed stimulus when standing HRV was reduced.
Considering that I cannot use sleep HRV to reliably reflect cardiac-parasympathetic activity due to saturation, and that standing HRV provided the strongest association with HRex, I will continue to use standing HRV as my primary metric.
Effects of Position and Injury Status on Associations Between Preseason Workload and Heart-Rate Variability Profiles in American College Football Players
New study: “Effects of Position and Injury Status on Associations Between Preseason Workload and Heart-Rate Variability Profiles in American College Football Players”
Main findings:
- The effect of very high preseason training loads on HRV in college football players varies by position group.
- Skill group players who consistently performed the highest total workloads had the most stable HRV, which typically reflects high/increasing fitness.
- Conversely, mid-skill group players who performed the highest total workloads had the least stable HRV (often reflects fatigue), along with greater daily variation in high intensity outputs.
- Thus, skill players tolerated high loads with stable HRV while mid-skill players better maintained high intensity movement and stable HRV at more moderate workloads.
- HRV tended to be lower in those who were playing hurt (“go as can” status), and we suspect that the association between HRV and injury is bidirectional (low HRV precedes injury, injury causes reduced HRV).
Read the full text here: Effects of Position and Injury Status on Associations Between Preseason Workload and Heart-Rate Variability Profiles in American College Football Players


Biostrap Kairos Wristband Versus Electrocardiography for Resting Heart Rate Variability Assessment
New study of ours comparing the Biostrap Kairos wristband to ECG for HRV assessment.
The Kairos wristband offers on-demand heart rate variability (HRV) assessment
through its “Spot Check” feature, enabling standardized recordings for clinical, research,
or self-tracking purposes, but its validity is untested. Therefore, we compared the Kairos
wristband to electrocardiography (ECG) for resting HRV assessment in young adults,
and investigated the influence of skin pigmentation (M-index) on measurement accuracy.
Keep reading the full text here: Biostrap Kairos Wristband vs ECG for HRV

Self-recorded heart rate variability profiles are associated with health and lifestyle markers in young adults
Here’s a new study from our lab entitled “Self-recorded heart rate variability profiles are associated with health and lifestyle markers in young adults”. The full text can be accessed for free through this link: https://rdcu.be/cUd9T. A practical summary is provided below.

We’ve been tracking ANS status in athletes via daily ultra-short HRV for nearly 10 years now. In general, we (and others) have found that higher and more stable values are often observed in athletes who are more aerobically fit and who are adapting well to training. Contrastingly, lower and less stable values are commonly observed when athletes are stressed, fatigued from training, and not adapting favorably.
There is also a sizable body of research showing that isolated HRV derived from clinical and laboratory assessment is associated with a variety of health and lifestyle markers in general and clinical populations. Healthier individuals tend to have higher vagal-mediated HRV, are less likely to develop chronic diseases, and often live longer. There is also research showing that less stable HR parameters (i.e., greater day-to-day fluctuation) are independently associated with an increased risk of cardiovascular events in older adults. Importantly, HRV is modifiable. With lifestyle improvement, one can make their HRV higher and more stable. Here’s a case example showing substantial improvements in HRV and other healthy markers with improvements in various lifestyle factors: https://hrvtraining.com/2021/08/10/increasing-hrv-and-cardiovascular-health-10-year-case-study/.
Thus, similar to how we track HRV in athletes to guide training and monitor adaptations, we hypothesize that regular folks can track their HRV to guide lifestyle behaviors towards those that increase cardiac-parasympathetic function, thereby supporting health and longevity. However, no previous investigations have examined the association between self-recorded HRV and health/lifestyle metrics in young adults using accessible HRV tools and ultra-short (60-s) daily recordings. Therefore, that’s what we set out to do.
We had subjects perform 60-s post-waking HRV recordings in the supine and standing position with a cost-free smartphone application and Bluetooth chest strap for 7 days. They also wore an Actigraph on their wrist to measure activity levels and sleep profiles. Following the observation period, we obtained a variety of cardiovascular, metabolic, and psychoemotional health markers in the laboratory.
As anticipated, higher and/or more stable HRV parameters were generally associated with more favorable cardiovascular (higher VO2max, lower systolic and diastolic blood pressure, and lower aortic stiffness), metabolic (lower body fat percentage, fasting glucose, and LDL-C), and psychoemotional (lower perceived stress) health markers. Some variation between sexes and recording positions were noted. Additionally, most, but not all, associations weakened after adjusting for VO2max, supporting previous work indicating that increasing fitness is one of the most effective ways to increase HRV and derive health benefits associated with increased parasympathetic (and reduced sympathetic) modulation. For more details and conclusions, see the full text here: https://rdcu.be/cUd9T
Sleep duration and Nocturnal vs. Standing HRV recovery from COVID
Many of us have learned the hard way to stop training when we’re sick, and to ease back into it as we recover. In 2012, I contracted the hand/foot/mouth (HFMD) virus from my nephews. It was awful. After symptoms resolved, my training resumed with a deload week. Taking it easy, it took ~9 days after symptoms cleared for my 7-day rolling HR and LnRMSSD averages to return to within 1 standard deviation (SD) of pre-infection values (14-d baseline) (Fig. 1). These HRV values were obtained in the standing position after waking.

I’ve observed the same general standing HRV pattern in every subsequent illness, with the time-course of HRV recovery proportional to the severity of illness. I recently had COVID. Predictably, I observed a very similar HRV response. It took ~10 days post-symptom resolution for my standing values to normalize. Training was even more conservative than post-HFMD since I’m getting older (and somewhat wiser, hopefully).
However, unlike in previous cases of illness, this time I also had nocturnal HRV and sleep data from my Oura ring. Without careful interpretation, it would seem that HRV responses (standing vs. nocturnal) reveal largely contradictory responses. This is problematic if one were trying to use HRV, or “Readiness” scores based largely on HR or HRV, to guide training decisions post-illness (or any time for that matter). Which should you follow? The data and my interpretations are shared below.
Supine vs. Standing HRV
First, I’ll address why nocturnal and standing HRV are different. Nocturnal HRV is acquired during sleep, representing parasympathetic function under passive conditions, largely undisturbed by our wakeful thoughts and emotions. HRV should peak during sleep, reflecting healthy circadian variation in ANS activity, which is associated with nocturnal blood pressure dipping and a lower risk of cardiovascular diseases. Contrastingly, standing HRV captures the ANS response to a mild challenge. Blood wants to pool in the legs when standing, which would cause hypotension, limit blood supply to the brain, and cause dizziness or fainting if not for properly functioning counteractive mechanisms. Baroreceptors detect reduced blood pressure following postural change, resulting in a reflexive increase in HR and vasoconstriction to maintain blood pressure. In healthy people, there is a sudden HR response (↓parasympathetic activity) followed by a quick recovery. However, when sick or stressed (mental, physical, etc.), the HR response may be exaggerated and slower to recover.
By measuring HR/HRV ~1 min after standing, we can observe how efficiently the ANS is adapting to a minor challenge. In fact, ANS testing in clinical settings typically involves a series of reflex tests. These include HR or HRV responses to deep breathing, orthostatic stress (standing), isometric handgrip, etc. This is because abnormalities are more likely to be revealed when the ANS is challenged. It always seemed intuitive to me that if we wanted to use HRV as an indicator of how we may adapt to physical stress (i.e. training), we should measure HRV in response to a little bit of physical stress.
COVID
Four hours before my flight to San Diego for the ACSM Annual meeting, I was walking the dog when my wife texted me a picture of her positive COVID test. Thus, I cancelled my flights, cared for my ill wife, and waited to get sick. Two days later, I woke up with a sinus headache and both my nocturnal and standing HRV were substantially reduced (good agreement between responses). A sore throat came on day 2 and stuck around for 5-6 days. My average ambulatory HR measured continuously throughout the first few days by Oura was ~10 bpm higher than usual, despite being more sedentary. My appetite increased dramatically to compensate. I attempted some HRV biofeedback sessions with limited success (screenshot comparison of sessions with and without COVID below). Coughing up phlegm was my last symptom to clear.

Fortunately, work stress and other obligations were minimal throughout this period. Light exercise (zone 2 air bike) and desk work resumed on 06/09, corresponding with my first “normal” standing daily RMSSD value. Thereafter is where nocturnal and standing HRV patterns diverge (Fig. 2). Nocturnal RMSSD increases above baseline post-symptoms, whereas standing RMSSD remains mostly suppressed.

Thus, Oura is telling me that my “readiness” is high (some of the highest scores I’ve recorded to date) (Fig. 3). Contrastingly, standing RMSSD values are telling me to gear down, that things aren’t quite normal yet.

Why the discrepancy?
Although I was feeling pretty much back to normal post-symptoms, when I resumed work and light exercise, I was exhausted by the end of the day (but not during the day). I was falling asleep on the couch by ~9 pm (normal bed time 10-10:30 pm). I was also sleeping longer than usual (Fig. 3). My average total sleep duration for May was 401 min, whereas for the first 10 days post-symptoms, I averaged 429 min. Clearly, I had increased sleep needs. Interestingly, my total sleep time was the strongest correlate (vs. sleep stages, efficiency, etc.) of my nocturnal RMSSD (Fig 4).

Grey dots represent baseline. Vertical line at 401 min represents mean sleep duration from previous month
However, the association between sleep duration and standing HRV reverses post-symptoms (Fig. 5). You can see in Fig. 2 and Fig 5. that my standing RMSSD trends to baseline as my total sleep duration trends to baseline. Thus, my standing HRV remained suppressed apparently until my sleep duration returned towards normal. Reduced sleep duration in this instance likely reflects that my body no longer needed the extra rest, and by returning to baseline at this time, my standing HRV suggests the same thing. I also stopped falling asleep on the couch and returned to my habitual sleep/wake time.

Vertical line at 401 min represents mean sleep duration from previous month.
Saturation?
My first instinct was that my increase in nocturnal RMSSD was probably a result of reduced saturation effects. This is a poorly understood concept and creates a lot of confused wearable users. Essentially, there is an inverse linear relationship between HR and RMSSD (as HR decreases, RMSSD increases) until ~50-55 bpm, at which point RMSSD starts to decrease, reflecting a quadratic relationship. This phenomenon is well documented in numerous studies, yet is poorly conveyed to users by wearable companies. There’s a strong possibility that if your HR is <50 bpm (i.e., fit individuals), your nocturnal RMSSD is reduced due to saturation. The mechanism seems to be that very high parasympathetic activity saturates cholinergic receptors in the myocardium, resulting in sustained inhibitory effects on the SA node, causing a slow HR with reduced respiratory sinus arrhythmia (thus, reduced RMSSD).
Practically, this means that if you are typically experiencing saturation due to a very low HR (as I often do with nocturnal value), increased stress can increase your HR out of the saturation zone and thus result in increased RMSSD. This is entirely counterintuitive because you’d expect more stress = increased HR and reduced RMSSD. I receive more emails from wearable users over this issue than any other.
Fig. 6 below clearly shows a quadratic association between nocturnal RMSSD and HR, suggesting that reduced saturation effects may be contributing to the increased nocturnal RMSSD post-symptoms. This is unlike the expected linear association observed in my standing values, where HR is mostly > 55 bpm (Fig).

However, when I adjust for HR (by dividing RMSSD by HR), it shows that nocturnal RMSSD was increased, independent of changes in HR (Fig. 7). This suggests that the elevated RMSSD was unlikely due to reduced saturation effects, alone. Thus, in this case, it seems that RMSSD was increased due to greater parasympathetic modulation.

While it’s tempting to interpret longer sleep duration (often a good thing) and higher nocturnal RMSSD (also often a good thing) as signs of high readiness to train, that’s obviously incorrect in this context. I interpret these responses to reflect higher recovery demands and processes from lingering effects of the illness, plus the additional stress from resuming work and exercise (even at low intensity and volume). This is supported by the fact that my ANS was not yet able to respond as efficiently as usual to the minor stress of standing within the first several days post-symptoms.
Conclusion
Nocturnal values provided valuable insight regarding my sleep patterns and nightly ANS activity during and after COVID. However, taken alone and out of context, one could easily misinterpret these changes to support resumption of intense training. Thus, standing HRV also provided important insight, showing that my ANS was poorly adaptive to a mild physical challenge post-symptoms. Taken together and in context, the appropriate interpretation is likely that I was still adjusting and recovering from the illness (greater sleep/recovery needs and enhanced nocturnal parasympathetic activity to support them), and that intense training would be poorly tolerated (suppressed HRV in response to standing, a minor physical challenge). Consequently, exercise remained light (air bike, body weight circuits, deload-style lifting) until standing HRV finally normalized at ~10 days post-symptoms, which corresponded fairly well with the return of my sleep duration to normal values. Therefore, nocturnal and standing HRV were both valuable, but different. These are not interchangeable values.
I’ll finish with a brief thought on “Readiness” scores and the misguided idea that HRV is somehow analogous to a % recovery meter. I used to think that despite often being wrong and creating false expectations of what HRV is, that readiness scores were relatively harmless. I am now of the opinion that circumstances exist where readiness scores can be harmful. We should use these tools to identify pattern-changes in the data and interpret them in context, but we should not use these tools for their automated algorithms and training advice (e.g. from a wearable below).

Effects of Long-Haul Travel and the Olympic Games on Heart-Rate Variability in Rugby Sevens Medalists
If success leaves clues, then there was something to learn from what Dan Howells & staff did to prepare GB 7s for the 2016 Olympics where they advanced to the gold medal final with an undefeated record.
After sorting through the data (HRV, wellness, training load) and having several video and email conversations with Dan, we decided to share the story of their Olympic expedition.
Prior to analyzing the data or obtaining specific details from Dan, I anticipated substantial decrements in status markers in response to a full day in transit (travel fatigue/jet lag, etc.), pre-tournament (arousal/anxiety), & throughout the tournament (match fatigue, sleep loss).
However, data showed minimal effects of travel (decrements mostly in non-starters), no evidence of pre-competitive anxiety (values improved pre-match), & intra-tournament decrements (small in magnitude) comparable to a previous domestic tournament.
Essentially, the data suggest that the team travelled across multiple time zones, adjusted to a foreign environment, and competed successfully on the worlds biggest stage with hardly any indication of stress or fatigue. Incredible!
Although we can’t say for sure that the strategies employed by staff can explain the findings (no control group, unfortunately), we felt that the details were worth sharing.
The paper discusses various proactive and reactive interventions that were used to support training adaptation, manage travel and competition related stress/fatigue, and aid recovery in players.
I’m very grateful to Dan and staff for the collaboration and for being open with these details. There is tremendous vulnerability in giving everyone access to how you do things. Thank you, Dan. You shared tremendous insights that many coaches and players can benefit from.
Here is the full text: