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.
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).
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.
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.
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).
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’s an October 2021 update on my quest to reduce arterial stiffness. For the original story and context, see previous post here.
After a year of >15 000 steps/day, continued adherence to improved nutrition, and a modified training approach, I saw another nice reduction in carotid-femoral pulse wave velocity (cf-PWV) along with an increase in RMSSD (see figure below).
RMSSD values (post-waking, standing position) from this October were the highest I’ve recorded to date. It seems that the more I try to reduce cf-PWV and improve my cardiovascular health, the more my HRV increases, despite no change in RHR. This strengthens my view that HRV can be an effective behavior-modification tool for health.
An important training modification that I implemented this October was changing my 10-min post-lift steady state air bike ride to intervals. The protocol is simply to pedal hard (though sub-maximally) for the first 10 s of every min for 10 min (excluding the first minute). Over ~4 weeks my “sprint” intensity naturally increased from ~450-500 to ~500-550 watts and recovery intensity from ~150 to ~180 watts. I’m pretty confident that this change accounts for the further increase in RMSSD. I performed a very similar protocol back in 2014 (post-lift intervals, 2014 figure re-posted below) for 2 weeks and saw an immediate increase and stabilization in my RMSSD values (middle of trend, LnRMSSDx20), which reverted to “normal” after cessation. My October 2021 PWV assessment occurred 2 weeks after starting this protocol, so it’s hard to say how much this may have had an effect.
Post-RT interval training may be a more time-efficient method to counteract the intense RT-induced arterial stiffening. Studies have shown that 30 min steady state, or 10 min of intervals on a bike attenuate post-RT increases in cf-PWV. Only 10 min of steady state riding attenuates RT-induced endothelial dysfunction (which is what originally inspired me to include this after my lifts). In terms of practicality, most lifters will unlikely perform 30 min of aerobic work post-RT. Ten min may be an easier sell. The intervals I’ve been experimenting with may be intense enough to provoke the desired effects, though short and submaximal enough for long-term use. Spearheaded by my incredibly intelligent and competent GA, Joe Vondrasek, we plan to investigate this further next year. We need to determine how such a protocol impacts both cardiovascular health markers and RT adaptations (i.e., day-to-day recovery, interference effect, etc).
I’m happy with the progress I’ve made thus far in reducing arterial stiffness (8.6 to 7.2 m/s). For context, below are norms for cf-PWV from this study. My values are now much closer to norms for my age group. Moving forward, my goal is to resume heavier powerlifting training while maintaining step-count, post-lift interval training, and aerobic work on non-lifting days to see if I can keep PWV under control while building my strength back up to respectable levels.
Some questions I aimed to address in analyzing >10 years of daily HRV:
Is it possible to substantially increase HRV for an extended period of time (>1 year)?
Can a reasonably healthy individual make long-term increases in HRV through aging? (limited to 10 years in my case)
Are changes in self-recorded HRV associated with changes in other health markers?
What are some likely factors that contributed to changes in HRV and health markers?
People with lower HRV are more likely to die of any cause than people with higher HRV. In addition, nearly every known risk factor for cardiovascular disease is also associated with HRV, including:
Lifestyle factors (diet, nutrition, sleep)
Bolded risk factors are modifiable with lifestyle intervention. Generally, improvement in one or more of these will often also improve HRV. Though HRV decreases as we get older, individuals with a greater number of healthy lifestyle behaviors tend to maintain higher HRV through aging and live longer.
The amount of day-to-day fluctuation in HRV also seems to be relevant, independent of absolute HRV values. We’ve found the coefficient of variation (CV) of RMSSD to be a very sensitive marker to training adaptations in athletes of a variety of ages and skill-levels (lower CV values generally better). In clinical settings, high visit-to-visit (to the clinic) variability in HR and blood pressure measures are independent predictors of cardiovascular morbidity and mortality. Similar results have been obtained from self-recorded (home-based) measures.
I started tracking my HRV in 2011 to determine if it was a useful training tool for powerlifting. I also wanted to know if it would be worth tracking in the team-sport athletes that I was coaching. There was evidence that HRV-guided training was superior to pre-planned approaches for endurance exercise and that it could help avoid overtraining. Over the short term (day-to-day, week-to-week), my HRV decreased with stress, when training got excessive, when I got sick, from too much alcohol, eating poorly, etc. It increased with good sleep, reduced training load, visiting family, more aerobic exercise, etc. Seeing how various behaviors and events impacted my numbers was educational. However, my long-term HRV (year-to-year) didn’t seem to change. I had a very clear average range that my body always reverted, for nearly 8 years!
I became somewhat doubtful that I could make a long-term improvement in values. Perhaps maintaining them was good enough, I thought. However, in the last few years, along with some fairly substantial changes in my lifestyle and life circumstances, my values seemed to have dramatically improved. To be sure, I dumped all of my data into some software and had a look. I also compiled relevant health markers that I’ve had measured over the years (blood work and arterial stiffness) to see how they tracked with the changes in HRV. Below is my story and the data.
Relevant Personal History
My training history and anthropometric characteristics will be relevant to some of the health markers I’ll present. I grew up playing contact sports, including hockey (age 4-16), rugby (age 12-15), and American football (D-line, age 16-21). After undergrad, I started coaching (football and S&C) and competing in powerlifting. During my football days, my weight got as high as 270 lb. For powerlifting, I competed in the 242 lb weight class (age 21-24, pic below). At age 24 (mid-2011, when I started tracking HRV), I relocated to do my Masters in Exercise Science and work as a GA with S&C. Subsequently (2013), I was a visiting researcher and adjunct professor at Auburn Montgomery where I formally began researching HRV with Dr. Mike Esco (eventually my PhD advisor). After a few projects under my belt, I pursued a PhD in Human Performance at Alabama (2014-2017). In mid-2017, I took (and remain) in a faculty position at Georgia Southern University (Armstrong campus in Savannah). From 2011-2018 I did not compete in powerlifting, but trained as if I might do so at any time.
My priorities before 2018 were:
1. Performance (strength and size)
For reasons I’ll elaborate on later, my priorities around 2018 shifted to:
2. Performance (strength and size)
RHR and LnRMSSD (a parasympathetic HRV index) values (n = 3598 measures) were derived from 1-min recordings performed in the standing position after waking and urinating. We’ve extensively investigated the validity of 1 min recordings for RMSSD. From 2011–2020 I used the ithlete app. We’ve previously compared ithlete vs. ECG and found good agreement for both the chest strap and finger sensor. Mid-August 2020, I upgraded my phone and it was no longer compatible with the finger sensor (no headphone slot). Thus, I began using the HRV4Training app with finger PPG enabled by the phones camera and flash. To be sure that values weren’t excessively different between tools, I performed several simultaneous recordings (finger sensor on left middle finger, HRV4training PPG on left index finger). Results below show decent agreement between apps. The magnitude of change in my HRV substantially exceeds the mean bias. Thus, I’m confident that changes were not an effect of inter-device error. Moreover, my values increased prior to the change in apps.
Tables: Comparison of RHR and LnRMSSD between ithlete finger sensor and HRV4Training camera PPG.
95% LOA RHR
95% LOA RMSSD
I’ve assessed month-to-month and year-to-year values so we can focus on long-term changes. Below are a variety of different figures representing my RHR and LnRMSSD values over the last decade (I’ve converted Ln to raw values for some figures). Note that 2011 values only include 5 months (Aug–Dec) of data and 2021 only includes 7 (Jan–July). Thus, values should be compared to full-year data (2012–2020) with this in mind.
Click on figures for greater clarity and to zoom.
Figure 1. Month-to-month mean and SD of LnRMSSD and RHR values.
I’ll mention only a few notable observations here. There was a clear and sustained reduction in RHR in spring 2016. This corresponds with completion of my comprehensive final exams and HRV tracking with football through spring camp. Stress levels were very high. I was terrified of failing comps. For several months, I studied harder than you could possibly imagine. I knew that if I could pass comps, I would complete the PhD. Having published several projects previously, I was less concerned about the subsequent dissertation process. After acing comps and successfully completing spring camp data collection (first project with football), I felt tremendous relief. Everything I’d been working towards since ~2011 was coming together. In my head, I had essentially secured my future. Although I thoroughly enjoyed the process, I was liking the prospect of not living like a poor grad student for much longer. RHR remains relatively stable thereafter whereas LnRMSSD decreases in response to season-long HRV data collection with football (late 2016, early 2017), defending my dissertation, job search/interview, and relocation (all spring 2017). LnRMSSD starts to trend up in 2019 (more on this later). Note increased values in 2020 before changing apps.
Figure 2. Violin plots of daily RHR and LnRMSSD by year. Each dot represents a single day. Generally, outlier dots (low LnRMSSD, high RHR) correspond with being sick, which tends to occur once or twice per year, usually during/after travelling to visit family.
Figure 3. Bar graph of values by year. Here, I’ve included LnRMSSD values relative to HR (green). This provides some indication that HRV increased in recent years, independent of changes in HR. It’s not ideal to calculate this ratio with mixed units, but it should sufficiently represent the general trend.
Figure 4. LnRMSSD values by year with age norms (horizontal lines) and notes of key changes in lifestyle and life circumstances likely attributable to the increase in HRV
This figure puts the magnitude of recent HRV changes into perspective. With a small LnRMSSD increase in 2019, I dropped an age-class. In 2020-2021, my HRV has become comparable to that of teenaged boys. Notes on the figure are fairly self-explanatory. HRV is influenced by a variety of factors and I believe that each item on the list contributed in some way to the increased values. Will elaborate on explaining changes below.
Although I will always be passionate about HRV in sport, I have recently taken greater interest in HRV as a behavior-modification tool for health. This topic is something I’ve been peripherally interested in since 2011. In ~2019, my self-education (via textbooks, journal articles, podcasts, colleagues, etc.) and research activity shifted to HRV in health and disease. My training also shifted to prioritize health. I still lift heavy, but I’m less concerned with being as big and strong as humanly possible (more on this below). It was becoming clear to me that such goals were not conducive to my cardiovascular health. A few key events triggered this dramatic change:
1) My bloodwork in 2015 revealed an unfavorable lipid profile (LDL-C and Total-C were high), which planted a seed.
2) In tracking HRV in college football players, we observed that linemen demonstrated sustained reductions in LnRMSSD throughout the season. In some cases, RHR’s were ~100 bpm. To explain these findings, I went down a rabbit hole on cardiovascular health in linemen. The research was grim. What effect had years of football and powerlifting training (high volumes of static hemodynamic stress), in addition to a fairly high body mass, have on my cardiovascular health? *Because it’s relevant in the current context, I’ll mention that I have never used anabolic steroids. Thus, these should not be considered as potential factors that affected the health markers I’ll share below.
3) Occasional episodes of obstructive sleep apnea (not formally diagnosed by a physician), mostly following a day where I’d overeat. I had no idea until my wife would describe what my breathing (or lack thereof) sounded like in the middle of the night. My roommate at a conference also pointed it out. This condition is common in larger individuals (e.g., linemen, powerlifters) and is associated with an increased risk of cardiovascular disease.
4) With a new lab toy in 2018, I learned that I had fairly stiff arteries for my age. I have a very limited family history of cardiovascular disease (despite most of us having high cholesterol), but I had to consider that no previous family member has had my body mass or lifestyle. I’ve included a pic below from my football days (~270 lb, ~2006) so you can see how I compared to some family members. Collectively, these events (among others) prompted me to prioritize health over performance.
To clarify, it’s not like I was living a terribly unhealthy lifestyle from 2011-2018. Health was still a priority, just not number one. I ate primarily whole foods (ample fruits and vegetables), lifted 4 days per week, and performed some form of aerobic exercise ~90 min per week. But there was certainly room for improvement. Let’s be honest, it’s hard to maintain 250 lb eating spinach and chicken breasts. Binge eating once or twice per week was pretty standard. The BBQ and mac n cheese in Alabama were the real deal, and there was a Cheesecake Factory nearby. Stress was high. I took on a lot of work and there was a lot of uncertainty (I relocated 5 times during this period). Thus, the type of changes I needed to make were easy to identify.
The impact of reduced work-related obligations (i.e., stress) due to the pandemic cannot be overstated. I say this while acknowledging that the pandemic brought substantial stress and hardship for others. We were fortunate that this was not the case for us. Working from home gave me 100% control over my schedule and allowed me to maintain a strict routine (I thrive with routine). I’m a very happy person when I can eat, train, work, and sleep at consistent times. I’m also mostly an introvert, so I didn’t mind less social activity. Moreover, I was able to dedicate more time to analyzing data and writing papers. I love teaching and interacting with students, but I find the research component to be the most challenging and gratifying part of my job. These psychological factors undoubtedly contributed to the improved HRV, likely via less reductions.
Not being able to go to restaurants during the lockdown further improved my diet. We’d typically go downtown for dinner ~1x/week and I’d overeat every time. I couldn’t lift for weeks. This was the first time I did not perform heavy barbell movements for an extended period of time (>1 week) since I was 16 years old. With facility closures, I resorted to body weight circuits, burpees, and a ton of jump-rope. It was around this time that I really started to notice that my HRV was increasing and becoming more stable (screen shot below). It was also the first time I seriously considered that my powerlifting training was preventing my values from improving. But it was hard to rule out the effects of other factors (reduced work stress, improved diet, etc).
Training has changed slightly with my shift in priorities, but is still taken very seriously (put a gym in the garage, pic of beautiful rack below). Although, I’ll admit that the first two pieces of equipment that I bought were a treadmill and an air bike. I continue to lift 4x/week and perform relatively heavy singles and triples, etc. But I don’t use a belt and I leave a minimum of 1 or 2 reps in the tank (minimal grind). I do more bodyweight movements for assistance exercises and maintain a higher tempo (less rest, supersets, etc.) for the cardiovascular stimulus. My overall volume is lower. I’ve eliminated overuse issues (and associated joint inflammation). I start and finish each training session with 10 min steady-state on the air bike. I do another 30 min on the air bike on non-lifting days (1 day continuous, 1 day tempo, 1 day HIIT). Under these conditions, I still aim to progressively increase strength and muscle mass, but I’m less bothered if my numbers are static for a while. I enjoy the training nonetheless.
Screen-shots below show how my body mass changed (-10 lb) in 2019 with my shift in interest and priorities, and how my step-count skyrocketed after Jen (my wife) and I adopted a puppy (summer 2020). We named her Penny Lane and she’s also shown below. My body composition is better and I’ve pretty much eliminated episodes of sleep apnea.
My body mass has held steady at ~235 lb since Dec. 2019. I’ve averaged ~15000 steps per day (training sessions excluded) for nearly a year now (daily range: ~12000 – 28000 steps). This is largely attributable to getting a dog and having to do my lawn twice/week most of the year. The massive bump in physical activity and time spent outdoors in Savannah’s parks and trails (i.e., nature) likely had some impact on my numbers. Moreover, timing of the walks (post-waking, and post-meals) directly improves metabolic responses to feeding and may indirectly contribute to improved numbers long-term.
I’ve been eating twice/day (~noon and 6 pm) since shortly before the pandemic. I rarely binge and eat minimal processed foods. Plenty of meat, seafood, nuts, fruits and vegetables. Our combined income doubled in the last couple years. This has reduced financial stress and enabled us eat higher quality foods. Living on the coast, we eat fresh seafood 1-3x per week. I upgraded my fish oil from Costco-brand to Carlson’s liquid fish oil and take a higher daily dose.
Figure 5. LnRMSSD coefficient of variation (CV) by year. CV = (SD/Mean)*100
Greater fluctuation in values are generally associated with poorer health and lower fitness (there are exceptions). For example, unhealthy individuals tend to show the highest day-to-day variation in HRV, whereas highly fit athletes show the least day-to-day variation. My HRV fluctuations peaked during the PhD (not surprising) and have since progressively improved. Recall that 2011 only included 5 months, so that value (5.66%) is likely an inaccurate representation of the full year. I believe most app companies are drastically underestimating the value of this parameter. Many apps don’t even report it. That will likely change.
Pulse wave velocity is a marker of arterial stiffness. Stiff arteries are bad news in terms of cardiovascular risk. Football linemen experience arterial stiffening following one or more seasons at the collegiate level. Chronic lifters (i.e., powerlifters and strength athletes) have stiffer arteries than healthy controls, and stiffness seems to be associated with length of training history (greater history of lifting = stiffer arteries). This does not bode well for me. A couple years ago, we acquired a tool in our lab that measures pulse wave velocity via carotid-femoral applanation tonometry. This is when I learned that my arteries were much stiffer than norms for my age (values in the range of 50-59 year olds!). With the aforementioned changes to my lifestyle, I was able to see nice improvement in this value (dropped an age group) with the increase in my HRV (see below).
Figure 6. Pulse wave velocity and daily averages for steps, RMSSD, and RHR from the full month of Oct. 2019 and Oct 2020.
RMSSD improved despite a similar RHR. This is relevant because there is great debate among researchers that HRV is explained almost entirely by RHR. I strongly disagree with this. They are certainly correlated in cross-sectional studies, but with self-tracking over time, dissociation is common. Note that a daily change from 60-85 ms is nothing to write home about, but a monthly average change of this magnitude is substantial.
Stiff arteries are problematic for a variety of reasons. For one, arterial baroreceptors (embedded within the arterial wall of the carotid bodies and aortic arch) are sensitive to stretch/deformation, thereby relaying information about blood pressure changes to the brain. They cannot detect changes in pressure very well if the wall they reside in becomes stiff. Without important information from baroreceptors about pressure changes, blood pressure can become dysregulated. Vagal modulation of HR is one of the primary ways that the brain regulates blood pressure. Thus, reducing arterial stiffness may enhance cardio-vagal baroreflex sensitivity, which may improve regulation of blood pressure and improve HRV.
Thanks to Carl Valle and Inside Tracker, I’ve had some bloodwork done (“Ultimate” panel https://www.insidetracker.com/ultimate/) a few times over the past several years. Importantly, I have bloodwork from mid-PhD in fall 2015 when my HRV was lower and from late summer 2021, when my HRV was substantially higher. Although the gap between measures is lengthier than ideal, the changes are still worth examining. The figure below highlights key changes in relevant biomarkers and HRV.
Figure 7. Absolute changes in blood biomarkers and HRV parameters (month average).
Virtually all biomarkers improved (to varying degrees) along with the increase in HRV, as to be expected with the noted lifestyle changes. My triglyceride/HDL-C ratio (a predictor of cardiovascular disease risk) improved from 2.2 to 1.5. There is research linking hsCRP (systemic inflammation), vitamin D, lipid profiles, and cortisol (stress hormone) to HRV. I will not attempt to make any association here given the length of time between measures and overall sensitivity of HRV to a variety of factors. But the directionality of changes support the idea that improving HRV through lifestyle changes is likely associated with improvements in some blood biomarkers.
Based on my analysis and interpretation of my data, it seems that:
A “reasonably” healthy individual can make long-term increases in HRV, despite aging 10 years
Changes in self-recorded HRV seem to be associated (in some form) with changes in other health markers
Changes in lifestyle factors unquestionably contributed to improved HRV, and in turn, a likely reduction in cardiovascular risk. However, the extent to which any given change in lifestyle (walking, diet, weight loss, stress reduction, etc.) contributed to the change in HRV is unclear. I suspect there is synergy among the various factors.
It feels strange to publicly share a lot of these personal details, but I think there is value in this n=1 case study. I’m hopeful this story encourages others to take greater interest in their cardiovascular health. A great place to start would be to find out your numbers.
As part of my PhD work at Alabama, we tracked HRV in football players from day 1 of preseason training through to the national championship. A practical summary of some key findings follow the full-text link below.
Fluctuations in HRV are expected throughout a season. However, chronically suppressed values are cause for concern. Sustained parasympathetic hypoactivity is associated with various pathological conditions and is a hallmark of stress and impaired recovery in athletes.
We learned from spring camp that day-to-day HRV recovery was delayed in linemen vs. the smaller and more aerobically fit skill players. Thus, we anticipated that linemen would be more susceptible to attenuated HRV throughout the season.
HRV started to decline by week 6 of the competitive period for linemen. A couple notable events occurred here: 1) the first of 5 consecutive SEC match-ups vs Top 25 nationally-ranked opponents and 2) the week of mid-term exams for many players.
Although significant group-level reductions for linemen weren’t observed until later, key players showed descending HRV by mid-season, in the absence of changes in PlayerLoad. Suppressed HRV preceded illness and injury in 2 starters. Temporary rest restored HRV.
Group-level reductions occurred during an intensive camp-style preparation period for the college football playoffs following the SEC championship. Most players took a hit to their HRV, but linemen were hit the hardest. Note magnitudes of the effect sizes in the table below.
HRV remain suppressed for linemen through prep weeks for the national semi-final and the national championship. Smaller decrements (non-significant) were observed for skill players. In addition to accumulating physical stress, psycho-emotional factors (pre-competitive anxiety, pressure to perform, media attention, etc) likely contributed.
Although we emphasize the toll of a season on linemen, some skill players also showed suppressed values. The table below shows the rate of change in HRV for all players. 25% of skill and 63% of linemen showed sig. descending HRV patterns throughout the season.
Linemen experience hypertension, arterial stiffening, and pathologic LV hypertrophy following 1 or more seasons. These maladaptations are possibly preceded by ANS imbalance. We hypothesize that larger players showing the worst HRV profiles suffer the greatest decrement in cardiovascular health markers.
If so, intervening when a decreasing HRV pattern is observed may not only be relevant to performance (limiting fatigue, injury-, and infection-risk), it may also help mitigate the cardiovascular toll of playing football at such a high level. Seeking funding to explore this in the future.
The findings highlight potential deficiencies in or greater taxation to the coping capacity of linemen vs. smaller players. Factors hypothesized to contribute to more prevalent ANS imbalance in linemen and potential implications for health and performance are summarized below.
Linemen need careful attention and monitoring. We need strategies to prevent ANS imbalance from occurring (load management, aerobic capacity, treatment of health conditions like sleep apnea, etc) and we need restorative methods to implement if it occurs.
Tracking HRV with a mobile app was inexpensive and easy. Time-demand from players was ~3 min/week while waiting to get taped. Though sub-optimal relative to post-waking measures, this approach enabled timely detection of descending patterns, which may be useful for guiding interventions relevant to player health and wellbeing.
Though a better understanding of the health and performance ramifications of suppressed HRV in football players is needed, a descending pattern may serve as an easily identifiable red flag requiring attention from performance and medical staff.
Context: we previously resorted to standardized HRV measures performed in the athletic training room with college football players to overcome non-compliance with post-waking tests.
Problem: pre-training hydration practices confound HRV measures. Players typically opt for cold bottles of water or Gatorade. Thus, we needed to determine how much and for how long these drinks impacted HRV.
Findings: Gatorade had small effects that lasted about 45 min. Effects of water were larger and persisted for 60 min.
If measuring HRV in a lab/clinic/training facility, be mindful of recent fluid ingestion. HRV measures obtained within 60 min of 591 ml water or 45 min of an equal volume of Gatorade will be capturing their physiology effects and result in falsely elevated values. This would result in misinterpretation of autonomic status.
When first getting started with tracking HRV in athletes, the inter-individual variation in trend characteristics can be confusing. Some athletes will display very high values and others will show lower values. Likewise, some will show quite stable values while others display substantial day-to-day variation. Naturally, the following question arises: why do some athletes have higher and more stable values than others?
Collegiate swim rosters typically include a mixed roster of athletes (males and females with a broad range of experience and skill). In this investigation we compared HRV trend characteristics between the national-level (including 6 Olympians) and conference-level sprint-swimmers throughout 4 weeks of standardized preparatory training. We also obtained details of individual training history.
The main findings were that national-level swimmers had higher and more stable HRV (higher mean LnRMSSD, lower LnRMSSD coefficient of variation) than their conference-level teammates. Differences in trend characteristics were attributable to a greater history of training and competing among the national-level swimmers (i.e., greater training age).
Whether these findings can be explained by greater aerobic fitness (we don’t think so), greater familiarity with training (possibly), or chronic physiological adaptations (possibly) among the higher-level swimmers is unclear.
The findings may be of some practical use for coaches when interpreted with previous work (see links below). For example, preliminary expectations with HRV monitoring should be that higher-level swimmers will display higher and more stable values throughout training and vice-versa for lower-level athletes. This may be interpreted to mean that the higher-level athletes could tolerate greater loads or that the lower-level athletes may need reduced loads. However, it is unclear if these training modifications would offer any performance/adaptation advantage. In addition, a higher-level athlete showing lower and less-stable values may be cause for concern (fatigue, stress, detraining, etc. depending on context). Whereas a lower-level athlete displaying higher and more stable values is likely adapting well to the training.
We’ve previously assessed how overload and tapering impact HRV in sprint-swimmers here.
We’ve previously assessed associations between subjective indicators of recovery and daily HRV in sprint-swimmers here.
Our new meta-analysis determined that parasympathetic hyperactivity in overreached endurance athletes is best detected using weekly averaged versus isolated HRV values and in the standing versus supine position.
Thanks to Agustín Manresa-Rocamora, Antonio Casanova-Lizón, Juan A. Ballester-Ferrer, José M. Sarabia, Francisco J. Vera-Garcia, and Manuel Moya-Ramón for inviting my collaboration.
Here’s our latest study comparing 1 min vs 5 min HRV throughout a 4-week camp in international-level girls field hockey players. Values were highly correlated, showed similar responses to load, & similar associations with fitness. Practically same insight, 80% less time. Thanks to Drs. Gonzalez-Fimbres and Hernandez-Cruz for the collaboration.
Intensive training periods tend to increase RHR and decrease HRV, reflecting stress and fatigue. However, adaptations to heat exposure (e.g., plasma volume expansion) tend to have the opposite effects. So we wanted to see what happens when players were exposed to both intense training and intense heat stress during preseason camp.
Despite increases in perceived fatigue throughout the 2-week period, RHR and HRV reflected responses consistent with heat acclimation.
HRV initially decreased in linemen, then peaked after a day of rest. Non-linemen faired a little better with smaller decrements in perceived fatigue and more frequent day-to-day improvements in RHR and HRV.
These results indicate that heart rate parameters and perceived fatigue are independent markers of training status, and that desirable cardiovascular adaptations can occur in the presence of soreness and fatigue.
This is especially important for tech companies who try to infer recovery status from HRV alone. As HRV improved throughout camp, an app’s algorithm would report to coaches that players are well-recovered. Given that no player feels well-recovered during preseason camp in the heat, the technology suddenly loses credibility for being wrong and will likely be dismissed.
This is unfortunate because the heart rate parameters are likely reflecting important adaptations that may indicate better tolerance to training in the heat, a reduced exercising heart rate, and improved fitness. Thus, I encourage users to ignore “recovery” scores and interpret the data in appropriate context.
We aimed to characterize cardiac-autonomic responses to a 13-day preseason camp in the heat among an American college football team. Players were categorized as linemen (n=10) and non-linemen (n=18). RHR, natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD), and subjective wellbeing (LnWellness) were acquired daily. Effect sizes±90% confidence interval showed that for linemen, LnRMSSD decreased (moderate) on day 2 (71.2±10.4) and increased (moderate) on day 12 (87.1±11.2) relative to day 1 (77.9±11.2) while RHR decreased (small–moderate) on days 6, 7, and 12 (67.7±9.3–70.4±5.5 b∙min-1) relative to day 1 (77.1±10.1 b∙min-1). For non-linemen, LnRMSSD increased (small–large) on days 3–5, 7, 12, and 13 (83.4±6.8–87.6±8.5) relative to day 1 (80.0±6.5) while RHR decreased (small–large) on days 3–9, 12, and 13 (62.1±5.2–67.9±8.1 b∙min-1) relative to day 1 (70.8±6.2 b∙min-1). Decrements in LnWellness were observed on days 4–10 and 13 for linemen (moderate) and on days 6–9, 12, and 13 for non-linemen (small–moderate). Despite reductions in LnWellness, cardiac-autonomic parameters demonstrated responses consistent with heat-acclimation, which possibly attenuated fatigue-related decrements.