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Fasting is an age-old practice that is gaining speed in our modern-day world.

From intermittent fasting that can take place every few days or once in a while, to something that is a lifestyle, such as the one-meal-a-day, or OMAD, diet, fasting can take many forms.

Many formerly obese individual credit fasting for extreme weight loss. Others have said it improves overall health and wellness. And while the research backing up fasting regularly is mixed on all sides of the vein, the fact remains that when you don’t eat, things start to happen inside your body that affects your autonomic nervous system, and in turn, your heart rate variability.

Here is what happens to your body when you fast over a long period of time, and as a result, what role those changes play on your heart rate variability.

Your body will break down glycogen

In the beginning of your fast, your body will convert glycogen (sugar) into energy. This is entirely normal following a meal because it’s basically digestion (and your autonomic nervous system at its finest).  However, after about six hours, when you have “officially” begun you fast, your glycogen stores will begin to deplete, and you will become hungry.

Effect on HRV

Because of HRV levels being highly dependent on stress levels, in these beginning stages, your HRV could go high or low depending on your approach to the fast. If you are feeling stressed about being hungry, your HRV will likely be low. However, if you are feeling confident about the results of the fast, and even have the desired outcome, you are likely going to find that your HRV is high, indicating that you are handling the stress on your body quite well.

Your blood glucose level will rise

This may seem wrong because wouldn’t your body lose sugar if it doesn’t have the stores to break it down? And wouldn’t that mean that your blood sugar would go down?

You’d think so, but what actually happens when you fast, is that insulin levels start to drop, triggering a surge of hormones like including noradrenaline and growth hormone to fight against low blood sugar. This, in turn, concentrates the blood with sugar that it pulled from stored sugar that is usually in the liver.

Effect on HRV

According to research, high blood glucose concentration is associated with higher parasympathetic, but lower sympathetic CAM. This means that your body is under more stress to perform its normal functions of the nervous system. If you were to measure these using biometrics, you would likely find your HRV to be on the lower level.

Ketosis will begin

When your body doesn’t have the energy sources to break down new glycogen, it starts to starve and begin the hunt for other things to convert into energy. It will start breaking down fat into fatty acids in order to use them for energy rather than carbs. This is when those looking to use fasting for weight loss begin to see results.

However, due to the fact that the brain cannot use broken down fat for fuel, it turns to ketone bodies for energy. This works for a small time because ketone bodies can’t replace glucose. But after a few days, the ketone bodies build up and a volatile substance called acetone begins to form, lowering the pH of the blood. When this happens, a condition called acidosis develop and lead to coma or even death.

Effect on HRV

At this point — usually around the 48-hour mark —  your body is under stress as it searches for energy sources to survive.  Due to this, your HRV will lower. In fact, a study that took 16 young healthy female volunteers, and had them fast for 48 hours, found that parasympathetic withdrawal was induced with simultaneous sympathetic activation. These findings lead researchers to conclude that the changes in the women’s nervous systems appeared to reflect stress.

However, if your body is used to fasting, or if you have prepared yourself mentally and physically for the fast, the change may not be as significant as it could be otherwise.

If you do notice a significant drop in HRV and begin to feel considerably physical and mental stress, it might be best to abandon the fast at this point.

You’ll have cognitive function impairment

If you continue your fast, your body will be in the process of ketosis and quite possibly acidosis. During these stages, the body starts to break down protein to release amino acids that can convert into glucose. This is done to fuel your brain and suppress hunger.

For those who use fasting as a weight-loss measure, this is the next step that the body takes, and many experts — specifically as it relates to the keto diet —  say that ketosis is not entirely harmful. However, due to the strain on your brain, you may lose some simple brain functions that help you remember things, and carry out simple tasks.

Effect on HRV

The strain on many of your cognitive functions, and the continuing decline in your HRV levels will make it more difficult for your autonomic nervous system to work the way it needs to. You will be less alert and therefore unable to respond well to stressful situations

Fasting isn’t all bad …

The above may seem quite terrible and can be if taken to an extreme level. However, if you use fasting intermittently, your body will likely not have many or any of the negative side effects including those related to HRV.

Do your research on the right fasting approach for your health goals. And as always, check with a medical professional to make sure your body is able to handle the effects — whatever they may be — of a fasting regimen.

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. While much is still unknown about the mechanism of action causing variability changes, 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.

In general, a higher HRV is considered better, as high stress and poorer health outcomes have been associated with low values of HRV. 

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 that has yet to be understood clinically, and therefore is more 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, but the inter-subject variability tends to be too high to suggest proper normative ranges.

However, Biostrap has collected HRV on over 3,800 regular users, and can make some general observations. 

The distribution of HRV tends to follow a log-normal pattern with a median of 40.1 and a mean of 45.4 milliseconds, using the rMSSD method. In general, the average coefficient of variability in each individual is 25.8 % , suggesting high day-to-day variability. This demonstrates a need to track HRV over time to understand the ‘profile’ of an individual (mean, standard deviation, and coefficient of variability).

Interpreting 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 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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.
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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.
  24. Sandercock G. Normative values, reliability and sample size estimates in heart rate variability. Clinical Science. 2007;113(3):129–130. doi:10.1042/CS20070137

 

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. 

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.

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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.