Chris Beardsley (@SandCResearch) interviews Andrew Flatt (@andrew_flatt), a researcher working on Heart Rate Variability (HRV) at the University of Alabama.
Chris: Thanks for the interview, Andrew, we really appreciate your time. Can you tell us, what exactly are we measuring when we record HRV? What are the main measurements? Do each of these measurements of HRV tell us different things?
Andrew: My pleasure, Chris. This site has been a tremendous resource for me since day one and I am happy to contribute to the great content you provide.
In order to assess heart rate variability (HRV), a recording of beat to beat or “R-R” intervals is required. Subtle changes in heart rate regularly occur in response to respiration where HR tends to speed up during inspiration and slow down during expiration. With various mathematical and statistical procedures, we can quantify and assess heart rate variability.
The human heart is equipped with an intrinsic pacemaker called the sinoatrial (SA) node that when left alone sets heart rate at approximately 75 beats per minute, give or take. However, a component of our central nervous system called the autonomic nervous system largely influences SA node activity and thus heart rate.
Parasympathetic and sympathetic nerves extend from the brain stem and directly innervate the heart. Parasympathetic stimulation (via the vagus nerves) tends to inhibit SA node activity via the release of acetylcholine and therefore reduces heart rate and increases variability. Sympathetic stimulation has the opposite effect where SA node activity is increased via the release of norepinephrine which will speed up heart rate and reduce variability.
Therefore, assessing HRV provides a non-invasive measure of centrally mediated cardiovascular-autonomic control. Though numerous HRV parameters exist, the most commonly assessed for athlete monitoring include:
- The log transformed root mean square of successive R-R interval differences (lnRMSSD),
- High frequency spectral power (HF)
- Low frequency spectral power (LF)
- The LF to HF ratio (LF:HF)
LnRMSSD and HF are both representative of parasympathetic activity and tend to correlate quite well with each other. What LF reflects is less clear. It was initially thought that LF reflects sympathetic activity and thus LF:HF provides a nice indication of the balance between sympathetic (LF) and parasympathetic (HF) activity. However, this doesn’t appear to be the case. LF is influenced by both parasympathetic and sympathetic activity and particularly by the baroreflex, which functions to help regulate blood pressure. Therefore, what exactly the LF and LF:HF means is not entirely clear.
For athlete monitoring purposes, parasympathetic activity, assessed via RMSSD or HF, is of primary interest for assessing recovery status and physiological adaptation to training.
Chris: That’s an amazingly comprehensive introduction to HRV, Andrew, thank you. Many reviewers of HRV have recommended using 5 – 10 minutes of resting time for accurate measurements. Do you think shorter measurement durations of HRV can be performed and still tell us something?
Andrew: The topic of HRV recording methodology, specifically as it pertains to measurement duration, has been an area that my colleague Dr. Michael Esco and I have been investigating. Standardized guidelines recommend that a recording period of 5 minutes preceded by a 5-minute stabilization period be used to establish short-term HRV.
These guidelines were primarily developed for clinical/laboratory purposes and assume that a variety of HRV indexes will be included for analysis as some indexes require several minutes for determination (e.g, LF). This may not be a big issue if HRV was assessed only periodically. However, it’s quite clear that daily HRV measures are preferred over weekly, or less frequent measures to be meaningful for athletes. It would therefore be unreasonable for coaches to expect athletes to spend 10 minutes each morning performing an HRV measurement. In light of this, we wanted to determine: a) how short of an HRV recording we get away with that is still valid, and b) how long it takes HRV to stabilize before we should start recording a measure.
To try and answer some of these questions we directed our focus on lnRMSSD. lnRMSSD has been suggested to be the preferred HRV index for athlete monitoring for a variety of reasons. Compared to HF, lnRMSSD is less influenced by breathing rate and provides a lower coefficient of variation across measures, thus making it a more reliable marker. Remember that we have to rely on our athletes to take proper HRV recordings for the data to be useful, so eliminating potential issues such as breathing rate may be helpful.
Since lnRMSSD is a statistical measure, it can be easily calculated in Excel if the R-R interval data is available. In addition, the lnRMSSD is easily interpretable for the end user, particularly when it is modified on a ~100 point scale (done in popular smart phone apps) by simply multiplying the lnRMSSD value by 20. Therefore, when individuals are performing self-measures of HRV at home with a field tool (e.g., smart phone application), the lnRMSSD appears to be the most practical and appropriate.
Our research indicates that lnRMSSD can be accurately assessed in athletes in only 60-seconds and that lnRMSSD stabilization appears to occur within about one minute. This research was done with collegiate athletes and involved ECG measures in the supine position. We are continuing to explore this area with a fellow colleague, Dr. Fabio Nakamura where we are assessing the agreement between 60-second lnRMSSD measures with traditional 5-minute recordings in addition to the time-course for lnRMSSD stabilization.
This work in collaboration with Dr. Nakamura involves elite team sport athletes who self-recorded HRV with a field tool in the seated position. This is an important next step because field tools require less subject preparation for HRV measurement compared to ECG and the seated position may be preferred over supine measures, particularly in highly fit individuals. Based on our recent findings and preliminary analysis from our more current project, I am confident that meaningful HRV data can be collected in much shorter than 10 minutes.
In a recent case study, we monitored HRV with a smart phone app using a 55-second HRV recording after waking in a seated position in a collegiate endurance athlete. We found that the weekly CV correlated almost perfectly with weekly 8 km race times.
In another case study currently being written up, we found that weekly mean HRV related well to training load during competition preparation in a high level powerlifter with cerebral palsy. HRV was recorded with the same app under the same conditions (i.e., waking, seated). Taken with all of the other data I’ve collected on myself and other athletes I’ve worked with, I’m quite confident that meaningful HRV data can be collected with ultra-short measures and minimal stabilization periods for lnRMSSD.
Chris: Great insights into HRV, again, thank you Andrew. Interpretation of HRV measurements seems very complex. Do you have any general rules of thumb for what HRV metrics to measure and what different movements mean?
Andrew: The problem with providing general guidelines for HRV interpretation is that this would assume a homogenous group of individuals who do not differ by training level (elite, amateur), age, fitness level, race, gender, sport, exercise modality (resistance training, intervals, steady state) and so forth. HRV responses are largely individual which increases the complexity of interpretation, but at the same time, provides a unique physiological marker to consider when assessing training status and responses.
Some very important review papers on this topic (see references) have been written by researchers who are both athletes and coaches. I would encourage people to read these. Based on the available research and my own experimentation, there are 3 main values that I use for HRV (all of which use lnRMSSD) interpretation with athletes:
- Acute or daily HRV change
- Weekly mean HRV change
- Weekly coefficient of variation (CV) change
#1. Acute changes
Once a baseline is established (a one-week mean works well for this), it is easy to determine when a daily change is well above or below baseline. Intense training sessions or novel training stimuli (new exercises, set/rep schemes, conditioning, etc.) will generally result in an acute decrease in HRV that can take between 48-72 hours to return to baseline.
Over the course of a training cycle, acute changes will generally become smaller (smaller decrease in HRV, faster return to baseline), which I interpret to mean positive adaptation to the training (more on this below with discussion of CV).
Moderate aerobic exercise tends to have a stimulatory effect on parasympathetic activity and therefore it is common to see increases in HRV 24 hours after this type of exercise and thus has been suggested as an effective active recovery tool.
It’s important to understand however that HRV is sensitive to a wide variety of physical, chemical and psychological stimuli, and therefore an acute HRV measure can be obscured by non-training related stressors. For example, alcohol, poor sleep, nutrition, pharmaceuticals and so forth can all impact HRV.
Therefore, though the acute changes in HRV are meaningful, I would suggest that coaches use caution when trying to determine training prescription solely based on an acute change. Another prime example of this is the anxiety/excitement experienced by athletes on the day of competition which often results in a low HRV score. This certainly does not mean however that they are fatigued or not prepared to perform. Context is very important when interpreting acute changes.
HRV has primarily been researched in endurance athletes. Adjusting training on a daily basis according HRV changes is likely most effective in that population. There has yet to be any research that evaluates HRV guided training for strength/power athletes. An acute increase or decrease in HRV likely will not differentiate strength power/performance except for in obvious situations, like when HRV is low due to heavy drinking the night before, or because of very intense training. In this case, the low HRV score will likely relate to reduced performance.
HRV may still be useful for strength/power athletes, though serving more as a global marker. For example, during overload weeks (high volume resistance training) there will definitely be some HRV changes compared to lower load weeks. The question really is whether HRV data provides any additional useful information that other training load and performance data does not provide. This is an area my colleague and I will explore in the future.
#2. Weekly mean changes
The weekly mean provides the coach with a simple value that may provide a good indication of the weekly load experienced by the athlete. An increase in the weekly mean for the most part is reflective of positive adaptation or quality recovery.
In some cases however, increases in the weekly mean can be indicative of high fatigue, although this is generally in response to very high volumes of endurance training. Taken into context of the weekly training load and other markers of training status (e.g., wellness, performance), it should be easy to determine if the mean HRV change indicates positive or maladaptive responses.
The weekly mean is influenced by the content of aerobic exercise performed in that week. Moderate to high levels of aerobic work will generally increase mean values (up to a point) since this type of work has that stimulatory effect on parasympathetic activity. Therefore, decreases in a weekly mean value may be reflective of reductions in aerobic activity. Higher intensity exercise can result in greater acute HRV responses decreases and thus effect the weekly mean. Therefore, coaches should use caution when trying to asses fitness based on weekly mean HRV. Again, context is key.
#3. Weekly CV changes
The CV reflects the variance in HRV scores across the week that is not captured in the weekly mean value. The CV is easily calculated as the standard deviation divided by the mean and expressed as a percentage. Higher CV values indicate higher variance in scores across the week, and lower CV values indicate less variation in scores across the week.
High variation in day to day scores may indicate the fatigue (low scores) and recovery (return to or above baseline) process from a week of training. A higher CV likely reflects a higher training load, or a more stressful week (perhaps due to travel schedules, etc.).
In my experience, a gradual reduction in the CV throughout training is indicative of positive adaptation. In our case study of the collegiate runner, lower CV values were almost perfectly related to his 8km run times, where his worst performances occurred on weeks with the higher CV and his best performances occurred on the weeks with the lowest CV.
In a female collegiate soccer team, we are seeing that CV changes are relating to training load and performance changes. It should be noted that a reduced CV was related to the development of overtraining in an elite female triathlete in a case comparison study by Daniel Plews and colleagues. Therefore, as with each of the other values, the CV must be taken into context.
Chris: That’s a really helpful how-to guide for HRV measurements, thank you Andrew. Is there any other practical guidance would you offer a coach who was looking to start implementing monitoring HRV measurements with a team of athletes?
Andrew: Here are some final suggestions to coaches who are interested in using HRV with their athletes.
Experiment with a handful of athletes before you try and attempt to implement HRV monitoring with an entire team. This will be much more manageable in terms of data collection and analysis. Consider this a trial run to determine if HRV will be practical in your situation. This includes assessing a) if you think your athletes can reliably perform self-measures at home, and b) if the data you are collecting is actually meaningful.
Don’t start using HRV if you currently do not monitor any other training status markers. For one, HRV is much less meaningful when taken alone. It would be difficult to put an HRV score into context if you do not know what training load (sRPE, tonnage, distance, etc.) was or how wellness scores are evolving. Start with the basics first.
When assessing team HRV data, use the team mean to assess the general responses of the team as a whole. But understand that it is the individual responses that are more important. Some athletes will be responding favorably while others will not. HRV can be useful for helping determine which athletes fall into which category and thus may influence decision making for intervention.
Although smartphone apps conveniently display a nice visual of the HRV trend, you will likely need to also perform some further analysis in Excel, specifically for assessing mean and CV. Most apps have an “export” function that allows you to download a spreadsheet of the data. Specific statistical procedures for determining meaningful changes in HRV from such downloaded data can be found in Martin Buchheit’s paper (see references).
Also, if you want to compare your data from a smartphone download to published lnRMSSD values, you will need to divide your score by 20 (if using the ithlete or BioForce apps). For example, An HRV score of 83 with ithlete or Bioforce is actually an lnRMSSD value of 4.15 (83/20 = 4.15). These values are multiplied by 20 in the smartphone apps to transform the lnRMSSD value to fit onto an approximately 100-point scale for more intuitive interpretation by the casual end user. Also, be sure to note what position HRV is measured in when comparing to published data as supine values will be different than seated or standing values.
Chris: Thanks for your time, Andrew!
If you are interested in learning more about HRV and would like to contact or follow Andrew Flatt, please follow him on Twitter or check out his blog.
If you would like to do graduate work exploring HRV, the University of Alabama has a dedicated laboratory with the latest equipment, making it the place to go. Contact Andrew on hrvtraining(at)gmail.com for more details.
- Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol (2014); 5.
- Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: Opening the door to effective monitoring. Sports Med (2013); 43; 773-781.
- Stanley, J., Peake, J. M., & Buchheit, M. Cardiac parasympathetic reactivation following exercise: implications for training prescription. Sports Med, 43:1259-1277, 2013.
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