Tag: variability during sleep

Not Getting Enough Sleep? How to Repay Your Sleep Debt

Reading time: 4 min

Not getting enough sleep? You’re not alone. According to the CDC, more than one-third of adults don’t get the recommended seven hours of sleep they need to feel well-rested and energized the following day. When this occurs, we fall into what’s known as 6sleep debt.

Sleep debt, or sleep deprivation, occurs when you aren’t getting the sleep you need to feel awake, alert, and ready to go. And while one night of interrupted sleep may be a nuisance the following day, prolonged periods of sleep loss can lead to daytime sleepiness, emotional instability, weight gain, and several other health problems.

Why We Sleep

As human beings, our bodies require prolonged periods of rest not only to feel rejuvenated and refreshed but also to repair tissue, grow muscles, and synthesize hormones. We spend one-third of our lives asleep, and going without sleep can lead to psychosis or even death.

We can break down the stages of sleep into two primary categories: non-rapid eye movement and rapid eye movement (REM) sleep. Non-REM slow-wave deep sleep is characterized by slow brain waves and the release of growth hormones as our brain and many physiological systems enter a state of repair. REM sleep is similar to how our mind operates during the day, with one caveat — the brain is active and working, but our muscles are in a state of paralysis.

Beyond these realities, scientists don’t fully understand why we sleep. Some propose that sleep restores the brain’s energy while others hypothesize that sleep plays a major role in the connectivity and plasticity of the brain. The latter theory explains why individuals who are sleep-deprived suffer from memory loss and the inability to pay attention.

Regardless of the underlying reasons behind our need for sleep, we ultimately know that sleep is an extremely important aspect of our well-being. Without it, we suffer.

What Is Sleep Debt?

Sleep debt is the act of not getting enough sleep. You can often gauge whether or not you’re receiving enough sleep by monitoring how you feel the following day. If you’re tired, drowsy, and inattentive, chances are you’re suffering from short-term sleep debt. And if symptoms such as blood pressure changes, weight gain, or other serious health problems take shape over time, you may be suffering from the cumulative effects of chronic sleep debt.

The Symptoms of Sleep Debt

The primary short-term symptom of sleep debt is excessive daytime sleepiness. Other symptoms may include the following:

Irritability
Depressed mood
Forgetfulness
Clumsiness
Lack of motivation
Increased appetite
Carbohydrate cravings
Reduced sex drive
Inability to concentrate
Fatigue

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The Effects of Sleep Debt

Sleep loss in any form can come with serious side effects that will impact both your short-term and long-term health. Here’s a look at some of these effects.

Weight Gain

The hormones leptin and ghrelin control feelings of hunger and fullness. When you suffer from lack of sleep, leptin will decrease and lead to the constant feeling of hunger alongside a general slowdown of your metabolism, which may cause weight gain over time. Ghrelin will increase with lack of sleep increasing hunger levels. Also, keep in mind that getting plenty of sleep can burn calories.

Blood Pressure & Heart Disease

During normal sleep, your blood pressure will naturally decrease. If you’re suffering from a sleep deficit, your blood pressure will stay higher for a longer period of time, just as it does during the day. Over time, this may lead to an increased risk of heart disease, thus illustrating the need for a normal sleep schedule.

Type 2 Diabetes

Diabetes is a disease that causes sugar to build up in your blood, which will damage your blood vessels over time. According to the National Sleep Foundation, when your sleep patterns are negatively impacted, less insulin is released into the bloodstream after you eat.

Meanwhile, your body may release other stress hormones to help you stay awake. These stress hormones impact the ability of insulin to do its job effectively. As a result, glucose will remain in your bloodstream and increase your risk of type 2 diabetes.

Sleep Debt Treatments

Treating sleep debt in any form is only required if you physically can’t go to sleep or suffer from a sleep disorder, such as sleep apnea. Oftentimes you can improve sleep debt by simply increasing the amount of time you’re asleep or by altering your sleep habits to further encourage healthy amounts of sleep.

If you physically can’t go to sleep or you suffer from a sleep disorder, two primary avenues exist that can treat your sleep deprivation: cognitive treatments and medications.

Cognitive Treatment

Cognitive treatments that seek to repay your sleep debt are available in abundance. For instance, relaxation and meditation techniques utilize guided breathing and mindfulness approaches that encourage your body and mind to fall asleep naturally.

Other cognitive treatments include controlling pre-bedtime activities and optimizing your sleep environment to increase your sleep duration. This may include limiting social media usage before bed and removing other distractions like bright lights or screens.

Medications

If the cognitive or non-medical intervention proves to be ineffective, sleep medicines are available that can help induce sleep. Some of these medications are available over-the-counter while others require a prescription.

Some individuals may form a dependence on sleeping medications, meaning they can’t go to sleep without taking medication. For this reason, it’s important to speak with your healthcare provider and review all your options before determining if sleep medication is right for you.

Habits for Healthy Sleep

Getting a good night’s sleep is dependent upon your sleeping habits and nightly routines. Also known as sleep hygiene, healthy sleep habits will leave you feeling rested and refreshed each morning.

Some good sleep habits include:

Going to bed when you feel tired
Not eating 2-3 hours before bed
Engaging in regular, daily exercise
Keeping the bedroom quiet and cool
Turning off electronic devices
Using an alarm clock to regulate when you wake up

Paying off sleep debt

If you fail to get your recommended amount of sleep, you’ll begin accumulating a sleep debt. For instance, if you need eight hours of sleep but only get five, you’ll have a sleep debt of three hours. If this pattern continues throughout the week, your sleep debt will climb, and the effects of sleep deprivation will quickly take hold.

The only way to pay off your sleep debt is to start getting the sleep you need, along with some extra time each night, or with naps, until the debt is fully ”paid off”. Once you’ve paid off the sleep debt, you can resume your normal sleeping schedule.

Even if paying off your sleep debt seems impossible, remember that it can be done with conscious effort. While repaying tens or even hundreds of hours of sleep debt may seem out of reach, it can be accomplished by reflecting on your current sleep habits and making adjustments whenever necessary.

Consider using a sleep tracker to fully understand your sleeping habits. Once you’ve finally woken up feeling refreshed and recovered, you’ll have paid off your sleep debt in full.

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Academy > Biometrics 101 > whitepaper

Heart Rate Variability: how to measure it, why it’s relevant to many aspects of health

Reading time: 5 min

What is it?

Heart rate variability (HRV) is a measure of differences in the time intervals between heart beats. Heart rate by itself is the expression of how many contractions of the heart there are in a given unit of time; however, the rate itself is not constant. There is normal fluctuation of time between heartbeats, in a manner that speeds up and slows down heart rate. Therefore, HRV is a quantifiable measure that assesses these differences.

This variation in the time between heartbeats is thought to be a composite measure of parasympathetic and sympathetic neural inputs and hormonal inputs as regulated by the autonomic nervous system. Much is still unknown about the mechanism of action causing variability changes. However, many studies have shown correlations between HRV and diseased states, such as heart disease, Parkinson disease, and cardiovascular disease; emotional stress, such as depression; physical/mechanical stress, such as high-intensity or resistance training; sleep in the context of both acute stress and chronic stress; and meditation whether it’s “inward- attention” or Vipassana meditation. Therefore, HRV is becoming a more common non-invasive measure to examine the physiological state and responses.

How it’s measured

HRV can be measured by use of an electrocardiogram (ECG) or photoplethysmography (PPG). By referencing a common point in the ECG or PPG waveform, the time between each heart beat can be recorded in milliseconds (ms). Collecting each beat-to-beat interval in ms allows us to compute HRV, most commonly reported as rMSSD (root mean square of successive differences).

The rMSSD method of calculation takes each interval, squares the interval, takes the overall mean, and then the square root of that mean is taken. Biostrap computes the rMSSD using this method and remains the standard computational method for HRV.

More complex measures of HRV, including frequency domain analysis can be performed to get further information out of heart rate patterns, which will be covered in another review.

Correlation with health conditions

HRV is most notably correlated with stress conditions, such as anxiety disorders, depression, PTSD, and other psychological states, with lower HRV indicating higher-stressed states. The suggested mechanism is an increased sympathetic arousal, which affects HRV; HRV alone does not cause these states, but reflects and provides insight into the heightened stress on the physiological systems, which in turn have effects on other bodily systems, particularly the cardiovascular and endocrine systems.

Because of the chronic effects of stress, as previously mentioned, HRV has been noted to be a predictor of all-cause mortality and correlated with obesity, cardiovascular disease, cancer, and neurodegenerative diseases, among other health conditions.

What is a “normal” range?

Heart rate variability has a large individual component and is often used to assess changes in health over time (see “Interpreting Trends” below).

Heart rate variability can fluctuate day-to-day based on exposure to stress, sleep quality, diet, and exercise. This leads to low repeatability, and therefore makes normative data difficult to collect.

In general, younger individuals, males, and more active individuals tend to have higher heart rate variability. However, the inter-subject variability tends to be too high to suggest proper normative ranges. This demonstrates a need to track HRV over time to understand the ‘profile’ of an individual.

When considering a normal range, there is not a normal scale of 0-100. HRV scale is 0-255. Many factors influence where your HRV sits on this scale, including; genetics, lifestyle, and age. Once you track HRV over a period of time you will have a baseline HRV. Once a baseline is established you will be able to see how day-to-day internal and external stressors influence your HRV, upward or downward.

Watching your HRV deviate positively or negatively from your baseline is the most important factor to observe. The actual HRV number matters less than how much it has varied from your “normal” baseline.

Interpreting trends

As previously mentioned, HRV is difficult to interpret and generally a nonspecific data point from a single spot check. However, since it is a dynamic measure that responds to various lifestyle factors, tracking HRV over time allows for non-invasive insight into changes in health status or efficacy of certain interventions.

In general, since higher HRV is preferable, a greater ability to manage stress results in an increased HRV. The results of the studies demonstrating the relationship between stress and HRV suggest that interventions aimed at reducing mental and physical stress could increase HRV and minimize day-to-day fluctuations. The increase in HRV itself will not reduce risk and improve health over the long term, but rather, it reflects positive changes in an individual’s physiology.

Biostrap

In a 2018 study, the Biostrap sensor as a wrist-worn device was shown to produce high-quality signals which are useful for the estimation of heart rate variability.

References

Mccraty R, Shaffer F. Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health Risk. Global Advances in Health and Medicine. 2015;4(1):46–61. doi:10.7453/gahmj.2014.073
Silva LEV, Silva CAA, Salgado HC, Fazan R. The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics. American Journal of Physiology-Heart and Circulatory Physiology. 2016;312(3):H469–H477. doi:10.1152/ajpheart.00507.2016
Dobrek Ł, Skowron B, Baranowska A, Malska-Woźniak A, Ciesielczyk K, Thor PJ. Spectral heart rate variability and selected biochemical markers for autonomic activity in rats under pentobarbital anesthesia. Polish Annals of Medicine. 2017;24(2):180–187. doi:10.1016/j.poamed.2017.01.001
Huikuri HV, Mäkikallio TH. Heart rate variability in ischemic heart disease. Autonomic Neuroscience. 2001;90(1):95–101. (Neural Regulation of Cardiovascular Function Explored in the Frequency Domain). doi:10.1016/S1566-0702(01)00273-9
Alonso A, Huang X, Mosley TH, Heiss G, Chen H. Heart rate variability and the risk of Parkinson disease: The Atherosclerosis Risk in Communities study. Annals of Neurology. 2015;77(5):877–883. doi:https://doi.org/10.1002/ana.24393
Thayer JF, Yamamoto SS, Brosschot JF. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology. 2010;141(2):122–131. doi:10.1016/j.ijcard.2009.09.543
McCraty R, Atkinson M, Tiller WA, Rein G, Watkins AD. The effects of emotions on short-term power spectrum analysis of heart rate variability. The American Journal of Cardiology. 1995;76(14):1089–1093. doi:10.1016/S0002-9149(99)80309-9
CARNEY RM, FREEDLAND KE. Depression and heart rate variability in patients with coronary heart disease. Cleveland Clinic journal of medicine. 2009;76(Suppl 2):S13–S17. doi:10.3949/ccjm.76.s2.03
Sarmiento S, García-Manso JM, Martín-González JM, Vaamonde D, Calderón J, Da Silva-Grigoletto ME. Heart rate variability during high-intensity exercise. Journal of Systems Science and Complexity. 2013;26(1):104–116. doi:10.1007/s11424-013-2287-y
Kingsley JD, Figueroa A. Acute and training effects of resistance exercise on heart rate variability. Clinical Physiology and Functional Imaging. 2016;36(3):179–187. doi:https://doi.org/10.1111/cpf.12223
Hall M, Vasko R, Buysse D, Ombao H, Chen Q, Cashmere JD, Kupfer D, Thayer JF. Acute Stress Affects Heart Rate Variability During Sleep. Psychosomatic Medicine. 2004;66(1):56–62. doi:10.1097/01.PSY.0000106884.58744.09
da Estrela C, McGrath J, Booij L, Gouin J-P. Heart Rate Variability, Sleep Quality, and Depression in the Context of Chronic Stress. Annals of Behavioral Medicine. 2021;55(2):155–164. doi:10.1093/abm/kaaa039
Busek P, Vanková J, Opavsky J, Salinger J, Nevsimalova S. Spectral analysis of heart rate variability in sleep. Physiological research / Academia Scientiarum Bohemoslovaca. 2005;54:369–76.
Krygier JR, Heathers JAJ, Shahrestani S, Abbott M, Gross JJ, Kemp AH. Mindfulness meditation, well-being, and heart rate variability: A preliminary investigation into the impact of intensive Vipassana meditation. International Journal of Psychophysiology. 2013;89(3):305–313. (Psychophysiology in Australasia – ASP conference – November 28-30 2012). doi:10.1016/j.ijpsycho.2013.06.017
Wu S-D, Lo P-C. Inward-attention meditation increases parasympathetic activity: a study based on heart rate variability. Biomedical Research. 2008;29(5):245–250. doi:10.2220/biomedres.29.245
Jarchi D, Salvi D, Velardo C, Mahdi A, Tarassenko L, Clifton DA. Estimation of HRV and SpO2 from wrist-worn commercial sensors for clinical settings. In: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 2018. p. 144–147. doi:10.1109/BSN.2018.8329679
Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health. 2017 [accessed 2021 Apr 14];5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624990/. doi:10.3389/fpubh.2017.00258
Chalmers JA, Quintana DS, Abbott MJ-A, Kemp AH. Anxiety Disorders are Associated with Reduced Heart Rate Variability: A Meta-Analysis. Frontiers in Psychiatry. 2014 [accessed 2021 Apr 21];5. https://www.frontiersin.org/articles/10.3389/fpsyt.2014.00080/full. doi:10.3389/fpsyt.2014.00080
Hauschildt M, Peters MJV, Moritz S, Jelinek L. Heart rate variability in response to affective scenes in posttraumatic stress disorder. Biological Psychology. 2011;88(2):215–222. doi:10.1016/j.biopsycho.2011.08.004
Cohen H, Kotler M, Matar MA, Kaplan Z, Miodownik H, Cassuto Y. Power spectral analysis of heart rate variability in posttraumatic stress disorder patients. Biological Psychiatry. 1997;41(5):627–629. doi:10.1016/S0006-3223(96)00525-2
Tsuji H, Venditti F J, Manders E S, Evans J C, Larson M G, Feldman C L, Levy D. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation. 1994;90(2):878–883. doi:10.1161/01.CIR.90.2.878
Karason K, Mølgaard H, Wikstrand J, Sjöström L. Heart rate variability in obesity and the effect of weight loss. The American Journal of Cardiology. 1999;83(8):1242–1247. doi:10.1016/S0002-9149(99)00066-1
Stein PK, Reddy A. Non-Linear Heart Rate Variability and Risk Stratification in Cardiovascular Disease. Indian Pacing and Electrophysiology Journal. 2005;5(3):210–220.
Sandercock G. Normative values, reliability and sample size estimates in heart rate variability. Clinical Science. 2007;113(3):129–130. doi:10.1042/CS20070137

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Academy > Personal Performance

Heart Rate Variability: A Comprehensive Guide to HRV

Reading time: 3 min

There are a number of metrics we can use to get a snapshot of our health and well-being. From blood pressure to heart rate, doctors and researchers are more interested in our physiological data than ever before.

There is one marker for resilience and well-being that researchers have just begun to utilize over the past two decades. It’s called heart rate variability, or HRV. This metric, once measured primarily in athletes and those with abnormal heart rhythms, has since become a key piece of data for individuals wanting insight into the state of their physiology and nervous system.

So what exactly is heart rate variability? How do we measure it? And what can it tell us about our overall health? Let’s break down the intricacies of this emerging physiological measurement.

What Is Heart Rate Variability?

Heart rate variability, or HRV for short, is a measure of the time between each heartbeat. Heart rate by itself is the expression of how many contractions of the heart there are in a given unit of time; however, the rate itself is not constant. There is normal fluctuation of time between heartbeats, in a manner that speeds up and slows down heart rate. Therefore, HRV is a quantifiable measure that assesses these differences.

Regulated by a fundamental part of our nervous system called the autonomic nervous system (ANS), HRV is one of many functions that occurs without us even having to think about it. HRV has been shown to correlate with emotional and physical stress, sleep, and disease making it a common method for assessing the overall physiological state and the rate of adaptation to stressors.

Generally, the higher the HRV the better, as high stress and poorer health outcomes have been associated with low values of HRV.

How Do We Measure Heart Rate Variability?

HRV can be measured by an electrocardiogram (ECG) or photoplethysmography (PPG). By referencing a common point in the ECG or PPG waveform, the time between each heart beat can be recorded in milliseconds (ms). Collecting each beat-to-beat interval in ms allows us to compute HRV, most commonly reported as rMSSD (root mean square of successive differences). The rMSSD method of calculation takes each interval, squares the interval, takes the overall mean, and then the square root of that mean. More complex measures of HRV, including frequency domain analysis, can be used to get further information out of heart rate patterns and the state of one’s nervous system.

What Is a Normal Heart Rate Variability?

HRV has a large individual component that has yet to be understood clinically, and therefore is more often used to assess changes in health over time. HRV can fluctuate day-to-day based on exposure to stress, sleep quality, diet, exercise, and more. This leads to low repeatability, and therefore makes normative data difficult to collect. In general, younger individuals, males, and more active individuals tend to have higher heart rate variability, but the inter-subject variability tends to be too high to suggest proper normative ranges.

Focusing On Trends

As previously mentioned, HRV is difficult to interpret and generally nonspecific using data from a single spot check. However, since it is a dynamic measure that responds to various lifestyle factors, tracking HRV over time allows for non-invasive insight into changes in health status or efficacy of certain interventions. In general, since higher HRV is preferable, a greater ability to manage stress results in an increased HRV. The results of the studies demonstrating the relationship between stress and HRV suggest that interventions aimed at reducing mental and physical stress could increase HRV and minimize day-to-day fluctuations (coefficient of variation, CV%). The increase in HRV itself will not reduce risk and improve health over the long term, but rather, it reflects positive adaptations in an individual’s physiology.

For example, if we’re incorporating exercise or meditation into our daily routine, HRV should steadily increase. A downward trend, on the other hand, may be indicative of overtraining, poor sleep, illness, bad eating habits, increased exposure to stress, or failure to hydrate.

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What Factors Influence Heart Rate Variability?

Heart rate variability can be influenced by training, lifestyle, and biological factors.

Training factors that influence HRV include the intensity of a workout, exposure to unfamiliar stimuli, training load, and proper balance between rest days and training days.

Lifestyle factors that influence HRV include diet and nutrition, stress, sleep habits, and alcohol consumption. Leading a healthy lifestyle that focuses on proper diet and physical fitness, while paying attention to mental health, is a valuable means of improving HRV.

Finally, biological factors such as age, gender, genetics, and health conditions can influence HRV as well. As we age, our HRV tends to decline, and men often have higher HRV than women. Genetics and health conditions such as cardiovascular disease are additional factors that may influence our heart’s ability to operate normally.

Should We Focus on Heart Rate Variability?

Measuring heart rate variability is a valuable form of analysis to monitor healthy individuals or to identify those who should seek improvement. The amount of information we get from HRV is making it a popular health data to assess physiological state, overall well-being and stress adaptation. You can track your HRV with clinical reliability with the Biostrap wrist-worn device and keep an eye on your nocturnal HRV as well as weekly, monthly and yearly trends.

Academy > article > Personal Performance

Should you trust HRV values your wearable gives you?

Reading time: 5 min

My experience as an athlete

As a health coach and fitness trainer and being 53, I place a huge importance on the optimization of my health. I also love to challenge what I call conventional stupidity approach to health, fitness and life. I do things a bit differently than most Triathletes and Marathoners and Personal Trainers. I fundamentally believe we need to rest more, reduce chronic stress, and connect more with what is going on in our bodies.

I use a wide range of subjective measures in relation to my health and fitness. Subjective measures such as how I feel, my energy levels, my bowel movements, my mood, my ability to think and make decisions, and of course how I feel when I am in the gym, the pool, the track or on the bike. Some people place a lot of importance on Objective metrics and numbers and tend to negate the Subjective measures.

I think it is very important to have a good balance between both.

I recently found this to be important when I started looking at biometrics. I was looking at my RHR, O2 Saturation, Respiration and HRV all from a nocturnal measurement lens. I found there was a trend for my HRV to be quite low and I mean low 32, 41, 35, and it did not vary much regardless of if I had had a 5 hr training day or a rest day. It also did not vary based on my RHR, or how I felt. I was very confused. I was worried, I was starting to think something was wrong. There was a huge disconnect between the subjective rating I would give myself for my state and the objective numbers provided by the HRV tool I was using.

So I tried several HRV devices/applications and tools and they all seemed to show the same result. I was desperate for a deeper understanding of what was going on.

My experience with Biostrap

So why is this so important? Well I am a serious AG athlete. Last year I raced in the 70.3 Ironman World Championships and I train about 13 hrs a week and I am serious about my sport. This was important to me. I also feel that recovery is one of the key pillars of health and fitness.

The last thing I want to do is cause further stress to my body that would impact my ability to recover, ie doing a solid training session when not fully recovered.

I started looking at a system for biometrics that to me appeared to be more focussed on HRV than simple fitness tracking, it also provided the ability to do a 2 minute biometric scan. I decided to give this a trial. It is called Biostrap.

I had been hearing a lot about the fact that nocturnal HRV reading for elite athletes could be not effective due to a phenomenon called “parasympathetic saturation”

My understanding is that this has been reported in high level ultra-marathoners, triathletes and endurance athletes that are more susceptible to it in the supine position simply because you’re in a more rested or relaxed state where our heart is not being challenged to overcome gravity, to pump blood upwards and so forth. When you already have a very low RHR lying down makes it even worse.

Andrew Flatt PhD, CSCS

Andrew is a highly qualified practitioner in this field and writes fantastic content around biometrics. Flatt explains in more detail:

“Parasympathetic saturation, the results of would be having decreased heart rate variability despite having a very low resting heart rate, which is counterintuitive because typically, the lower your resting heart rate, the higher your heart rate variability is. There tends to be an inverse relationship there. But what’s happening kind of physiologically is that the acetylcholine receptors on the heart that respond to vagal stimulation, the vagus nerve is going to release acetylcholine which will bind to the muscarinic cholinergic receptors on the heart, and that tends to slow heart rate down”

So after reading all of this one morning before training I decided to conduct a Sitting biometric scan.

“Kiviniemi et al. (2007) provides a very thorough explanation of why HRV might be better measured in a standing position as opposed to seated or supine. Essentially, HRV is susceptible to saturation of the parasympathetic nervous system in subjects with low heart rates”

Yes, this is me at 36-41 RHR. I got excited maybe I found the reason why my Nocturnal HRV was so low. He further explains:

“Mourout et al (2004) saw decreased HRV in overtrained athletes compared to not overtrained athletes in the supine position. Similar results were found when HRV was measured after 60 degree tilt. The non-OT group always had higher HRV in the standing position and saw greater reactivity to the postural change. Therefore, pick a position and stick to it 100% of the time for your values to be meaningful. Switching positions from day to day will provide skewed data.”

Endurance athletes and athletes with low resting heart rates (yes that’s me) are probably better off measuring HRV in a standing position. We understand that when an elite athlete has a very low RHR then they are likely to be in a state of parasympathetic saturation. Andrew Flatt Explains this as follows:

“This is when vagal HRV markers (e.g., lnRMSSD) are low despite a low resting heart rate. This has to do with excess acetylcholine within the myocardium that maintains inhibitory actions on the SA node, and thus limits the typical arrhythmia observed from respiration. See below”

“There are several potential explanations for the decrease in HRV with increasing parasympathetic effect. If with increasing blood pressure there is higher-frequency vagal discharge and inspiratory suppression is maintained,18 23 then there must be persistent parasympathetic effect during inspiration despite the suppression of vagal nerve discharge. In in vitro preparations, the dose-response curve to acetylcholine has a rapidly rising portion and at higher concentrations is flat,24 25 displaying a simple saturation relationship. High-intensity vagal nerve discharges during expiration may release enough acetylcholine to result in saturation of the parasympathetic effect during expiration. If acetylcholine concentrations during expiration are high enough, the expected decline in acetylcholine concentrations in the region of the sinus node during inspiration may not be enough to significantly diminish the parasympathetic effect. Alternatively, it is possible that with increasing blood pressure, there is loss of phasic respiratory changes in vagal nerve discharges,26 resulting in a loss of phasic effect and a decrease in HRV. It is unclear which mechanism is operative in humans.”

Goldberger, J. J., Challapalli, S., Tung, R., Parker, M. A., & Kadish, A. H. (2001). Relationship of heart rate variability to parasympathetic effect. Circulation, 103(15), 1977-1983. http://circ.ahajournals.org/content/103/15/1977.full.html

So if you are using an HRV device, and you have a low RHR maybe you should do a self check and consider are your Objective numbers from your HRV app lining up with the Subjective measures and, if not, consider using a device that allows you to do a sitting or standing biometric scan.

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