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.

Fig 1. RMSSD and HR responses to HFMD virus.

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.

Fig 2. Nocturnal and Standing HR and HRV trends pre-, during, and post-COVID symptoms

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.

Fig. 3. Sleep duration and daily Oura Readiness scores.

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).

Fig. 4. Association between nocturnal RMSSD and HR
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.

Fig. 5. Association between standing RMSSD and sleep duration during and post-symptoms.
Vertical line at 401 min represents mean sleep duration from previous month.


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).

Fig. 6. Association between HR and RMSSD for nocturnal and standing values.

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.

Fig. 7. Nocturnal and standing RMSSD/HR

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).

Illness, recovery time, travel stress, monitoring, etc.

I think many would agree that the biggest obstacle in making continued training progress is experiencing illness or injury. This assumes of course that the programming is appropriate and progressive in nature for the individual. Therefore, monitoring training status is essential to appropriately manipulate training loads in effort to; a) maximize progress and b) avoid set backs. This gives you much more control over the process of training and in many cases can potentially allow you to avoid illness, injury, overtraining etc.

Unfortunately sometimes, illness or injury happens despite careful monitoring. However, it’s how you handle these unfortunate situations with proper training loads that can make a huge difference in continuing where you left off before the incident, or seeing massive performance decrements that take much longer to recover from. I have experienced both situations. I’ve fallen ill and seen my strength plummet for quite some time after the illness. This was most likely from insufficient recovery from before I resumed intense training again, lifting too heavy, too soon. More recently however, I handled illness much more appropriately and have been able to continue from where I left off without suffering significant performance decrements.


My nephew Kevin and I at the park

When I was visiting some family in Cincinnati this spring I was very excited to see my twin nephews. I hadn’t seen them in over a year since they were born. A few days before they arrived in Cincinnati (coming from New Hampshire) they contracted hand, foot and mouth disease. My sister warned us that it was contagious for anyone who has never had it before. I wasn’t too concerend and we all wanted to see the twins even if it meant getting a little sick. Well, long story short I picked up the virus and it destroyed me. If you’ve ever had this as an adult you know how awful this can be.

My nephew Ethan and I on the back porch

In my chart below you can see a distinct disruption in my HRV trend occuring when I experienced the first symptoms of the illness. On June 9th I woke up with a resting heart rate of 108bpm and an HRV score of 42.9! I had a terrible sleep that night and had a high fever that morning. The fever persisted for about 72 hours at which point I assumed the worst was over. I saw my HRV start to climb back up a bit, however at this point some new symptoms appeared and my HRV again dropped. As you can see in the chart, I didn’t train (the vertical purple bars represent training load). Once all of my symptoms subsided and HRV returned to previous baseline levels I resumed training at very moderate loads (session RPE of 7).

You’ll notice that these moderate loads were apparently very stressful on my body reflected by large HRV fluctuations. Typically a workout rated as a 7 is a deload workout for me. Being able to see my body’s responsiveness to these moderate loads showed me that although my symptoms were gone, my body was still trying to overcome the illness. In the past I likely would’ve resumed intense training once symptoms subsided, however by monitoring HRV, I was able to hold off on more intense loading until my body was capable of handling it sufficiently. You can see that it was nearly 3 weeks until I performed a more intense workout (sRPE 8). I can happily say that althought there was some minor strength loss (bound to happen after nearly 3 weeks of 0-moderate training loads), I was able to gain it all back very quickly unlike previous instances.

Purple Vertical Bars = Training Load
Horizontal Blue Wavy Line = HRV Baseline
Horizontal White Line = Day to Day HRV Fluctuations

Travel/Moving Stress

In the image above on the right hand side of the chart, you will see about a week’s worth of low HRV scores indicated by red and amber deflections. This was the week that I moved from grad school (I completed my Masters) back to Toronto. Clearly this was a very stressful week settling into a new place and dealing with all of the typical issues associated with a move. After appropriately manipulating my training loads (reducing them) I was able to maintain strength and see a return to baseline once I felt settled in. In the past after my first day of being back I likely would’ve continued with intense training. As you can see, this likely would’ve been detrimental to my progress.

Take Home Messages

First and foremost, have an effective monitoring strategy with yourself/athletes. Without one, it’s nearly impossible to make critical manipulations in training load to avoid running into problems. I’m obviously a proponent of HRV and recommend you track yours. Once you have your monitoring in place, have the discipline to reduce loads when you know you should. Sometimes you may not even perceive yourself as being under significant stress, however this is often how people end up hitting a wall with their training. You can’t necessarily ‘feel’ if your adaptive capacity is high or low. In previous posts I showed what happens when you train hard with low HRV. You simply delay recovery and potentially hurt progress.

Think outside the box a little. Training hard for 3 weeks and deloading on the 4th week is pretty standard and for the most part effective. However, just because your program tells you it’s week 3 and therefore you need to train heavy, doesn’t actually meant you HAVE to. I used to do this and thought that if I missed a workout or didn’t hit my goals that day, that I wouldn’t make progress. I’ve learned that the opposite is actually the case.

Lastly, have a plan in place for when certain events occur such as moving or illness. Have a strategy for how you will deal with it (hopefully in response to your monitoring data). This should help you maintain training progress better by allowing your body the appropriate time to recover while imposing loads that remain within your body’s ability to adapt.

If you’re not assessing (the ANS), you’re guessing

“If you’re not assessing, you’re guessing” is a phrase often used by strength and conditioning professionals to explain the importance of movement assessment prior to exercise prescription. Prescribing a program that doesn’t consider the athlete’s movement ability (or lack thereof) can end up causing problems.Essentially, you would be guessing that your exercise prescription is helpful when in fact it could be exacerbating a problem. I wholeheartedly agree with this. However this article has nothing to do with movement assessment. This was just my way of illustrating what my next point is.

I am going to apply the same logic we use for why we assess movement (to influence program design) with monitoring the function of the autonomic nervous system (ANS); if you’re not assessing the ANS, you’re guessing.

If you’re unfamiliar with what the ANS is and why it’s important I suggest you read this. In a nutshell the ANS governs “rest and digest” and “fight or flight” responses in the body. This is done without our conscious control. The two components of the ANS are the parasympathetic branch and sympathetic branch. Sympathetic activity is elevated in response to stress be it physical, or mental. Adrenaline is secreted and catabolic activity (the breakdown of structures) ensues. Parasympathetic activity is elevated in the absence of stress and functions to heal and repair the body.

We can monitor our ANS status non-invasively and inexpensively through heart rate variability (HRV). I explain how you can do this here.

HRV as an indicator of autonomic function can tell you a tremendous amount about your athlete’s responsiveness to training. I shared plenty of research in this post that lends support to HRV as an effective tool for; reflecting recovery status, showing better adaptation to training and even predicting performance. In a separate post I shared my thoughts on HRV as a predictor for injury.

Let me summarize what I shared in my initial research review post;

HRV reflects recovery status in elite Olympic weightlifters (Chen et al 2011), national level rowers (Iellamo et al 2004) and untrained athletes (Pichot et al 2002).

Cipryan et al (2007) showed that hockey players performed better when HRV was high while performance was rated lower when HRV was low.

Endurance athletes who improved vo2 max had consistently high HRV while athletes who did not improve vo2 max had low HRV (Hedelin et al 2001).

Endurance athletes who trained using HRV to determine their training loads had a significantly higher maximum running velocity compared to athletes in a pre planned training group (Kiviniemi et al 2007, Kiviniemi et al 2010).

Female athletes who used HRV to guide their training increased their fitness levels to the same level as females in a pre planned training group but the HRV group had fewer high intensity training days (Kiviniemi et al 2010).

(references for the above articles can be found in my original post here.

I’d now like to show some more research that lends support to the usefulness of HRV in monitoring athletes.

Mourot, L (2004) saw decreased HRV in overtrained aerobic athletes. Uusitalo et al (2000) also saw decreased HRV in overtrained female aerobic athletes.

Huovinen et al (2009) measured HRV and testosterone to cortisol (T-C) ratio in army recruits during their first week of basic training. The training was class room based (not physical) and therefore all stress can be considered mental. The authors found that HRV declined in several soldiers, though not all. This demonstrates that, what can be interpreted as stress is highly variable and dependent on the individual. The authors used the terms “high responders” and “low responders” to describe the differences among soldiers. Immediately I thought about the differences among athletes and how their bodies perceive stress. You can’t assume everyone is responding in kind to a training program. What is stressful for one athlete may not be as stressful to another.

All soldiers that showed decreases in HRV also showed lower T-C ratios. In contrast, soldiers with higher HRV had higher T-C ratio’s. Baseline T-C levels were not recorded so we shouldn’t draw any concrete conclusions however it appears that low HRV (increased sympathetic activity with parasympathetic withdrawl) is associated with a reduced T-C ratio.

Hellard et al. (2011) found that in national level swimmers, as HRV dropped (sympathetic predominance) there was an increased risk of illness. The drops in HRV that lead to illness were preceded by a sudden increase in parasympathetic activity the week prior to illness. The authors speculated that the preceding increase in HRV (parasympathetic/vagal activity) was a reflection of the body experiencing the first incubation period and that an increase in vagal activity was a protective response trying to modulate the magnitude of early immune responses to inflammatory stimuli. The subsequent increase in sympathetic activity and decrease in HRV occurs during the symptomatic phase of the illness.

In humans, increased sympathetic activity is generally associated with inflammatory responses while parasympathetic predominance actually inhibits inflammation. At this point in time I will not elaborate on this for the simple fact that I don’t fully understand it. However, we can speculate that if we’re seeing consistently low HRV scores in ourselves or our athletes there is probably an increase in inflammation occurring. Check out Thayer (2009) for more information regarding HRV and inflammation. Simon from iThlete sent me that paper and I’m still processing it.

When dealing with a team or if we train multiple athletes at the same time we need to be aware of how they are adapting and recovering from training. Work by Hautala et al (2001) shows that athletes will recover from exercise at different rates according to fitness levels (obviously). Basically, fit individuals recover faster and show less HRV fluctuation compared to less fit individuals. In a team setting, some individuals who are highly fit may not be getting a sufficient training stimulus while other athletes who are less fit can be overworked.

Kiviniemi et al (2010) found that females take longer to recover from aerobic training than males. This needs to be considered if you are training a mixed gender group.

Buchheit et al (2009) and Manzi et al (2009) both found HRV to be a predictor of aerobic performance.

I’m well aware that the development of athletes has been taking place without the use of HRV monitoring. There are many great coaches and trainers who have their own systems and methods of monitoring recovery in their athletes that work well.

HRV is a tool to use within your own systems. I have thoughts about how I would implement this in a team setting that I will share another time.

To truly autoregulate the training of ourselves or of athletes, we need as much information about present physiologic status as possible. Based on the research and my own personal experience with HRV, this technology takes much of the guesswork out of load/volume manipulation and training prescription. Training hard when HRV is low can be counterproductive and delay recovery. Training hard when HRV is chronically low can lead to illness, injury, overtraining syndrome and suppressed testosterone. Alternatively, increasing load/volume on days when HRV is high can lead to more favourable adaptation. HRV can tell us how stressful the training was for our athletes based on how long it takes HRV to reach baseline in subsequent days. HRV can indicate how much stress your athlete is experiencing outside of training. There are several indications one can take from a simple HRV measurement. Further research will reveal more correlation between HRV and sports performance.

I believe that to train an athlete optimally, we need to be assessing the state of the autonomic nervous system… otherwise we’re guessing.


Buchheit, M. et al (2009) Monitoring endurance running performance using cardiac parasympathetic function. European Journal of Applied Physiology, DOI 10.1007/s00421-009-1317-x

Hellard, P., et al. (2011) Modeling the Association between HR Variability and Illness in Elite Swimmers. Medicine & Science in Sports & Exercise, 43(6): 1063-1070

Huovinen, J. et al. (2009) Relationship between heart rate variability and the serum testosterone-to-cortisol ratio during military service. European Journal of Sports Science, 9(5): 277-284

Kiviniemi, A.M., Hautala A.J., Kinnunen, H., Nissila, J., Virtanen, P., Karjalainen, J., & Tulppo, M.P. (2010) Daily exercise prescription on the basis of HR variability among men and women. Medicine & Science in Sport & Exercise, 42(7): 1355-1363.

Manzi, V. et al (2009) Dose-response relationship of autonomic nervous system responses to individualized training impulse in marathon runners. American Journal of Physiology, 296(6): 1733-40

Mourot, L. et al (2004) Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis. Clinical Physiology & Functional Imaging, 24(1):10-8.

Thayer, J. (2009) Vagal tone and the inflammatory reflex. Cleveland Clinic Journal of Medicine, 76(2): 523-526

Uusitalo, A.L.T., et al (2000) Heart rate and blood pressure variability during heavy training and overtraining in the female athlete. International Journal of Sports Medicine, 21(1): 45-53