Heart rate variability (HRV) is an important marker of an individual’s physiological stress level. Due to recent technological improvements in terms of computation power and accessibility to high-quality wearable technology we are seeing all sorts of applications making use of HRV today, from optimizing performance in sports to monitoring psychological stress in the workplace.
While HRV is a powerful tool and can be very helpful in better understanding physiological responses to both acute and chronic stressors, interpreting HRV data at the individual level is still challenging.
This post focuses on practical ways to acquire and interpret HRV data in the context of monitoring training load and optimizing performance. In particular, using HRV4Training as an example, I will go over the following:
- What’s HRV, why it matters and how to start collecting data for you and your athletes
- All you need to know about your HRV measurements
- Getting started with systematically analyzing HRV and training data
What’s HRV, why it matters and how to start collecting data for you and your athletes?
Practically speaking, our heart does not beat at a constant frequency. So even if we measure our pulse, and get a 60 beats per minute reading, it doesn’t mean we have a beat every second. The time differences between beats are slightly different, they can be 0.9 seconds, 1.2 seconds, and so on. When we talk about HRV, we talk about ways to quantify this variation between heart beats.
This explains also why HRV, is not a single number, and there is sometimes a bit of confusion on different metrics to measure HRV since we can quantify these beat to beat differences in different ways. Most commonly, we use either time or frequency domain methods, which means we collect a certain amount of data, typically 60 seconds or in clinical practice more likely 5 minutes, and then we use the data collected over this time window to do a bit of math and extract what we call HRV features.
There are many time and frequency domain features. However, especially in the context of using HRV to monitor physiological stress, like training load and recovery, the community settled on one specific feature which is called rMSSD. It’s a time domain feature, easy to compute. So most commercial tools or apps will provide you with either rMSSD or with a transformation of rMSSD to make the value a bit easier to interpret, for example scaling it between approximately 1 and 10 or 1 and 100. This is also what HRV4Training does when providing what I called Recovery Points.
Now that we know what HRV is and how to compute it, the second question is, what does HRV represent?
Here we need to take a step back and talk a bit about the autonomic nervous system. The autonomic nervous system regulates many body functions, mainly unconsciously, such as respiration, the heart beating and so on, and consists of two branches, the sympathetic and parasympathetic branches.
The sympathetic branch, is in charge of the fight or flight response, while the parasympathetic branch promotes a rest and recovery. Making a few simplifications, since the autonomic nervous system maintains an adaptive state of balance in our body, we can understand how we react to stressors, by analyzing autonomic function.
This means we would expect higher parasympathetic activity under conditions of rest when we are well recovered. Since the autonomic system regulates the heart beating, we can use HRV as a proxy to autonomic function, and therefore use HRV as a way to measure how we react to stressors like a workout for example. This is where collecting HRV data can become very interesting, because we can, for example, better understand how much time our body needs to get back to normal after an intense workout.
How to get started with collecting your HRV data and monitoring your athletes
Up to a few years ago, HRV was used mainly by academic researchers working at the intersection of sport, health, and medicine. Many of these experts were able to show links between HRV and performance as well as recovery or training load.
However, in the recent past many new, affordable and user-friendly tools have been developed. These tools typically rely on commercially available heart rate monitors (e.g. a Polar chest strap) to analyze data, compute HRV and provide guidance to the user (see for example ithlete, a clinically validated tool, or Elite HRV). Most of these apps rely on spot measurements of about 1 minute.
The latest developments go even a step further in terms of usability and accessibility. With apps like HRV4Training, HRV can be computed accurately without the need for any external sensor or device. The main advantage is the increased compliance and reduced cost.
HRV4Training uses the phone camera to extract photoplethysmography (PPG, basically blood flow from the finger) and then determine markers of the autonomous nervous system activity, in particular, parasympathetic activity. PPG has been used for a long time in clinical settings and has been validated multiple times, proving to be a reliable measurement of HRV, as good as standard electrocardiograms with sticky gel electrodes [1, 2, 3]. Other apps also rely on PPG but using external sensors like ithlete’s finger sensor, which has been recently clinically validated by Flatt and Esco .
Comparison between RR intervals extracted from a chest-worn Polar H7 sensor and the iPhone Camera using the signal processing techniques implemented in HRV4Training. The data was collected during a paced breathing exercise. Increases and decreases in RR interval values due to inhaling and exhaling are clearly visible in both traces
Regardless of the tool you decide to use, make sure the technology is reliable. For example, there are many optical watches out there, but almost all of them as of 2016 can only provide reliable HR, and not HRV, due to much averaging performed to stabilize the signal (check out this post for details).
If you don’t have an iPhone, I’d suggest ithlete and Elite HRV as solid tools. No fuss.
What about your athletes?
At HRV4Training we recently launched a Coach platform to allow advanced users, personal trainers, and coaches to start monitoring their clients & athletes with minimal effort. The Coach platform provides plenty of advanced features (automatically receive and sync your athletes’ data, daily advice based on individualized trends, analysis of acute HRV changes, correlations between physiological data and other annotations, experimental long-term trends analysis, etc. ) and you can find out more at this link: http://www.hrv4training.com/hrv4training-coach.html or directly reach out to me if you have further questions.
HRV4Training Coach runs on iPad and iPhone, to allow for local storage of all data and to make sure you can access and analyze your athlete’s data even when offline, on the go.
HRV4Training Coach screenshot. All data is automatically retrieved from your athletes and stored locally. You can also export all measurements and analyse them separately.
All you need to know about your HRV measurements
By quantifying parasympathetic activity, HRV4Training and other apps are able to translate HRV information into an assessment of training load and provide actionable insights on physical condition, helping users to better understand how their body responds to trainings and other important factors in life (e.g. sleep, stress, etc.) [5, 6, 7, 8].
However, taking advantage of the data is not that simple. We need to take care of a few extra steps to make sure the information we are collecting is reliable.
Clinical studies were carried out under very specific, strict, laboratory conditions. Typically, this meant showing up at a lab early in the morning, measuring 2 hours after a light breakfast and at rest, a pre-measurement period including up to 30 minutes lying down in the lab, and so on. Even with the right tools, HRV can be difficult to gather under similar conditions when measured in the wild, and these difficulties can make interpretation harder.
Additionally, research showed that many factors influence HRV, from body posture to respiration, age, genetics, gender, physical exercise, chronic health conditions and more. So how can we get reliable insights from HRV data, if it is affected by so many factors?
Interpret your data always with respect to yourself
First, we need to look only at measurements with respect to ourselves, which means we need to collect a baseline, or a series of recordings so that the effect of different stressors is always evaluated with respect to what are our normal values, without looking much at the general population.
Secondly, we also need to control for as many of the previous factors as we can, and then evaluate the effect of what we care about. Apps like HRV4Training provide a set of simple rules (or best practices) so that the measurements your take at home are as close as possible to supervised laboratory recordings, and your data is more reliable.
Here are a few best practices for short HRV measurements that are very important to follow to get consistent results that can be interpreted correctly:
Measurement time & context
Take the measurement first thing after waking up, possibly while still in bed. This way you have a consistent time of the day, and you are not affected by other stressors. A good exception is if you need to empty the bladder, in that case, do it, then go back, rest 1 or 2 minutes to make sure your body is not affected by physical activity, and then take the measurement. When you take the measurement, it’s very important to try to relax, and not to think about other factors that might be stressing you out, like what’s coming up during the day. Never read your email before the measurement.
Breathing is key: paced breathing can be a simple way to stay focused on the measurement and limit other stressors. Measurement to measurement repeatability at constant breathing rates is good, even though there will always be differences between consecutive HRV measurements. As a suggestion for your measurements, I would advise using paced breathing at a breathing rate that is comfortable for you, i.e. not too fast or too slow, and stick to that for all your measurements. In general, it doesn’t really matter what breathing frequency you pick, but be consistent, use the same every time.
Body position: lying, sitting or standing are all good positions, again what matters is consistency. For elite athletes, typically sitting or standing is preferable, to avoid saturation effects that can happen while lying down in individuals with extremely low heart rates (<40). However, if you decide to stand, it’s very important to be patient, and wait a minute or two before starting the measurement, since your body needs to be at complete rest and measuring too early after standing up might cause the measurement to be affected.
Most apps make following best practices easy for you by providing 50 to 60 seconds measurements with paced breathing and suggesting a morning measurement as part of your routine, as shown below:
Measurement screen in HRV4Training
Getting started with systematically analyzing HRV and training data
One of the most practical ways to use HRV is to look at acute HRV changes. Simply put, monitoring parasympathetic activity via HRV can provide insights on physiological stress, with a higher level of stress resulting in lower HRV. For example, heavy training is responsible for shifting the cardiac autonomic balance toward a predominance of the sympathetic over the parasympathetic drive. This means that heavy training will typically reduce HRV as measured by common HRV tools. Acute HRV changes refer to interpretations relying on this principle, i.e. on a day following intense training HRV is expected to drop.
Clearly, it’s very easy today to start collecting HRV data. It’s also rather easy to start gathering some anecdotal evidence of the usefulness of HRV data in the context of acute HRV changes, e.g. seeing a drop after an intense training.
However, it can be helpful in order to gain additional confidence in the measurement and better understand complex relations between physiological parameters, lifestyle and training, to start looking at things a bit more systematically.
HRV4Training provides new features that do this analysis for you, when enough data is acquired (ideally 60 to 90 days):
Example acute HRV changes analysis in HRV4Training
If you are a coach using HRV4Training Coach, you can run the same analysis for all your athletes and see how each individual responds to training of different intensities:
Example acute HRV changes analysis in HRV4Training Coach
Day to day HRV guidance based on acute HRV changes is very useful to make small changes to our overall training plan. If today we are down, we can easily adapt and move our intense training by a day, or make some other small adjustments.
By looking at acute HRV changes longitudinally over weeks or months, we can get some better understanding of how we cope with training of different intensities.
HRV analysis is a great tool to monitor training load and adaptations to a training program. However, it’s very important to keep in mind that data should be collected under consistent conditions, in order to be reliable and meaningful during interpretation.
Analysis of acute HRV changes can be particularly useful in making day to day adaptations to a training plan, and understand how we are responding to different types of training. Looking at this changes over longer periods of time, as in the HRV4Training acute HRV changes analysis, can help in getting a better grasp of how each athlete recovers from training of different intensities.
 Russoniello, C. V., et al. “A measurement of electrocardiography and photoplethesmography in obese children.” Applied psychophysiology and biofeedback 35.3 (2010): 257-259.
 Lu, Sheng, et al. “Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information?.” Journal of clinical monitoring and computing 22.1 (2008): 23-29.
 HRV4Training Blog: http://www.hrv4training.com/blog/heart-rate-variability-using-the-phones-camera
 Flatt, Andrew A., and Michael R. Esco. “Validity of the ithleteTM smart phone application for determining ultra-short-term heart rate variability.” Journal of human kinetics 39, no. 1 (2013): 85-92.
 Garet, Martin, et al. “Individual interdependence between nocturnal ANS activity and performance in swimmers.” Medicine and science in sports and exercise 36 (2004): 2112-2118.
 Pichot, Vincent, et al. “Relation between heart rate variability and training load in middle-distance runners.” Medicine and science in sports and exercise 32.10 (2000): 1729-1736.
 Kiviniemi, Antti M., et al. “Endurance training guided individually by daily heart rate variability measurements.” European journal of applied physiology 101.6 (2007): 743-751
 Myllymäki, Tero, et al. “Effects of exercise intensity and duration on nocturnal heart rate variability and sleep quality.” European journal of applied physiology112.3 (2012): 801-809