Shownotes

Bruce's Background

03:21 -

• I am a double voided medical doctor in endocrinology and internal medicine. I became interested in exercise physiology a few decades ago. I teamed up with Thomas Gronwald after publishing exciting research on DFA alpha-1, a non-linear hear variability index, a fractal correlation property used in the intensity distribution.
• Essentially, I am a clinician teacher of other physicians, which became a second career for me.

What is DFA alpha-1

04:51 -

• We already have some information on HRV (variability of the beat to beat heart rate).
• Alpha-1 goes beyond that. It is variability but also looks at the correlation of the beat to beat sequence. (looks for patterns)
• For example, if you are walking and choose to walk left or right. We call that correlated behaviour if that choice depended on predictions based on the last 3/4 steps.
•  If your next step has nothing to do with your previous steps, it is a DFA alpha uncorrelated value of 0.5.
• Therefore, we have correlated patterns with an order to the heart rate pattern. We also have an uncorrelated pattern that generally occurs around FTP/second/anaerobic threshold. Beyond that heart rate, it becomes uncorrelated.
• It means those patterns tend to return to the mid-line. If you have a step pattern gradually moving from the midline, you move back to the midline once you pass below the 0.5 value. (self-regulation pattern)
• These patterns appear to be predictable at specific intensities.
• So, a pattern of 0.75 for someone represents the same intensity for me or someone else that is 20 years older than you and ten years younger than me.
• These patterns are dimensionless numbers. And it is that reason they are so attractive, as you do not need to do any comparative ramps.
• We do ramps, but we do not need to compare them to gas exchange or lactate unless we validate it.
• One attractive aspect of DFA-alpha 1 is its dynamic range. If we look at conventional HRV and simple indexes like RMSSD, those values are mathematical equations. (standard deviation, root mean square)
• They hit a minimum value around the aerobic threshold. If you go above the aerobic threshold, we can obtain no more information by looking at conventional indexes.
• However, with alpha-1, the middle of the dynamic range is at the aerobic threshold. (0.75 points)
• Once you pass the aerobic threshold, we still have "plenty of meat" in that HRV alpha-1 index to give us information on your intensity.

Previous work on HRV to obtain aerobic threshold and evaluate physiology

10:09 -

• At the beginning of the XXI century, people used SDNN, where the variability's values became minimal. (it would be the aerobic threshold)
• However, that approach lost traction because you did not have information once over that point. Moreover, you needed to do a ramp test to failure because you needed to obtain a minimum value.
• Therefore, you could not do a half ramp where SDNN was dropping, and it looked to flatten out. You could not stop there because you did not know if it would drop any lower.
• Many studies used conventional indexes, but you needed a ramp to failure.

How is DFA alpha-1 different from past research

11:24 -

• We were not the first to look at this parameter and its correlation with intensity.
• Many reviews presented information that as the intensity rose, the alpha-1 would drop. If you look back at those old studies, there were clues that around the aerobic threshold, the value was at around 0.75.
• Researchers did not validate if that was a threshold marker. And that was our unique idea of using it, with the added benefit of being a dimensionless number.
• You do not need to calibrate the values to other validated methodologies.
• If you want to go on the bike and do a ramp or constant efforts, with an alpha-1 of 0.75, you will be close to your aerobic threshold.

Populations and sports where Bruce applied alpha-1

12:59 -

• In the first validation test, we used ECG data with few artefacts in runners.
• We found excellent agreement with 0.75, and we used the same population for obtaining the anaerobic threshold. 
• The second validation test we did was in a total opposite population. (People with cardiac disease and heart failure)
• It was also an ECG study, and we found good agreement with 0.75, but we did not look to the anaerobic threshold in that population.
• We did the third validation study on elite triathletes in a training camp. We took their data and did not dictate a protocol to them.
• They did their lactate threshold tests and ramp protocols, and we found good agreement with the point 0.75 and lactate threshold obtained with a Log-Log method.
• One problem with the lactate threshold is that there are many different concepts to the first and second thresholds.
• It is a problem with lactate and how we should interpret the numbers.
• There was another study with 5-min intervals at VT1 and VT2, and they wanted to see the alpha-1 of a group of runners.
• They found 0.75 and 0.49 for the VT1 and VT2.
• Another group from Spain came with another validation study on blood markets.

Protocols for obtaining aerobic threshold with non-invasive tests

18:28 -

• First, we must understand how we modulate our intensity to achieve the best ramp or obtain the most accurate thresholds. Then, we have to know if our data is precise to what we achieve in a lab.
•  The second part is harder to answer.
• Can you go out and do a mixed run and judge your pace based on alpha 1? We examine that through old data from soccer players. Even though they did not do a ramp during a game, we could plot out their Alpha-1 and obtain a relationship.
• We will obtain a rough estimation of your aerobic threshold.
• If you want a precise threshold that you can compare over time, you should do a ramp with steady 3-min intensity increases on the bike/treadmill.
• If you have a linear rise in HR, your VO2 with also rise linearly.
• You do not want your heart rate to go up and remain steady for a while.
• If you have a way to measure alpha-1 live and want to know your aerobic threshold for 20 minutes, you will check the intensity where your alpha-1 is around 0.75.
• You need a ramp if you want a 203 vs 210 W precision. However, the steps should not be longer than three minutes. If you go longer than that, HR starts to plateau.
• We have the same amount of information with HR monitors like Polar H10. You can measure RR and other parameters, but what you do with that matters.
• People who publish data use a program called Kubios, the gold standard.
• Kubios preprocesses the data but presents some slow correlations that do not exist in the actual data. And we need to take them out.
• There are various filters we can use to do that. 
• Kubios uses a specific protocol, and if you do not use the same protocol or use one that is not validated, we will compare different pieces of data.
• Therefore, to understand the data you are obtaining, you have to agree with Kubios.
• FatMax is an app for Android that gives us Alpha-1. It also records artefacts as ECG traces, which is excellent for a person like me who is a physician.
• FatMax, in the initial launch phases, did not use the proper detrending method, and results were not precise with Kubios.
• The developer took my suggestions and rewrote the software, and we obtained a good agreement with Kubios.
• The same thing happened with Runalyse and "AIendurance", both web-based apps.
•  Now, to do this on a Garmin watch is like handicapping yourself with a lack of processing power and memory constraints. Nevertheless, they developed an app that calculates Alpha-1, and during the last few weeks, we have been tuning in these preprocessing methods to align with the Kubios software. And luckily, we are getting an excellent agreement.
• However, some apps have more agreement than others. Some apps have more of these stationaries. For example, I have many stationaries, so apps like HRVlogger do not work for me.
• The others I mentioned seem reasonable, at least in my hands.

Addressing the validation of the apps measuring Alpha-1

30:10 -

• It is not that one app is right and the other is wrong. It is what developers used before that will affect the results.
• Kubios team has published the algorithms, and FatMax is open-source so that anyone can check them.
• Moreover, this is like a power meter. If you do not have an accurate way to measure your power, you will not train properly.
• Therefore, having apps calibrated through validation studies is crucial.

Supportive evidence needed to validate alpha-1 fully

31:47 -

• We need studies on women, but we hope to publish something in this area soon.
• There is not much data on women elite athletes. I mentioned that it is not guaranteed to work on elite athletes in my articles.
• Elite athletes have a lot of vagal tones, and we use this to obtain our parasympathetic and sympathetic balance.
• Elite athletes will have a high vagal balance with low resting heart rates, so there was no guarantee it would work.
• However, it seems that it does.

Errors and tolerances in these measurements

33:26 -

• It is a complicated question to answer because it will depend on the quality of the data.
• It will also depend on what you are comparing against in your work.
• For example, a study compared the VT1 with the LT1. The regression obtained is what we see with our data. (alpha-1 vs VT1)
• Therefore, none of these things is definitive. The results will also vary depending on the person looking at the data.
• In our running study, we were only within a few beats away from the VT1. And with our cardiac disease study, we were within 10 W to their VT1.
•  But, of course, there will be individual variations. If I do the Alpha-1 test on myself five times, I might get five different results. They are all clustered around the correct value.
• It happens the same thing with the FTP. You go out and do your 20 min best power on one day, but it will vary between efforts.
• Therefore, I would not attach to the values obtained because they give you valuable information.

Heart rate monitors that are good to measure alpha-1

36:13 -

• I would get a Polar H10.
• It is because we get two Bluetooth connections. You will always want to record through Bluetooth because ant+ has limited bandwidth. If you have an HR of 140 bpm, you will have a lot of missed beats.
• You observed this when we started working on alpha-1, so you want Bluetooth to have measurements with free artefacts.
• I regularly use a Polar H10, where one channel goes to FatMax, and the other goes to my Garmin watch.
• I have been testing the Garmin Alpha HRV app for Garmin, and it has limitations on Bluetooth connections.
• Garmin devices use the default for ant+. If you pair your watch with a Polar, it will immediately pick up the ant+channel. You will have to manually not accept that channel and look for Bluetooth.
• If you have a Bluetooth channel coming to your watch, you cannot have a Bluetooth channel to the Garmin Alpha app.
• Thus, either you use two HR monitors, or one of the channels will connect with ant+.
• If you use Alpha-1, use the Bluetooth transmission for that.
• Garmin Alpha-1 is part of your Garmin watch, but it records HRV separately.

Garmin compatibility with Polar

39:35 –

• I had an old Fenix 5, and it worked well. I have an Epics 2 now, and it is excellent.
• The Garmin Forerunner 35 is equivalent to the Fenix 5.

Different software options available on the market

40:35 –

• All of the apps mentioned use the same trending, except the Garmin Alpha HRV.
• Even though I believe it will be close, it will not be as good as the others.
• All of them are fine to use, and I do use them interchangeably, and I validated each.
• Moreover, all apps are currently free to download.
• I do not know if Garmin Alpha HRV will be free for an extended period. However, the others are open source.

Application in training

42:19 –

• We thought the only use for this would be obtaining thresholds a year ago.
• Since then, we have published a fascinating study of what Alpha-1 looks like before and after an ultra-marathon. We found a substantial suppression in Alpha-1 after a six-hour trial run.
• This fact opens many applications where we can use this every day. We can use it to evaluate the readiness to train and durability. (fatigue)
• If you do a constant interval at 10 W below the aerobic threshold and look at the Alpha-1, you notice the steady values throughout 40-60 minutes.
• However, after 60-75 min, the alpha-1 starts to drop. It tells you that you are getting autonomic system fatigue, indicating decreased durability.
• And that is an application to this metric. We can evaluate your fatigue resistance over time.
• The other thing is the readiness to train, and this is where I use it every day that I train.
• I am 65, and I do not have the recovery I had when I was younger.
• Therefore, it is helpful for me to know if I am tired or not.
• Sometimes, I feel terrific, but the Alpha-1 is starting to drop already at the warm-up. So, it is a good metric for me to access if I am ready to train each day.
• We can evaluate my fatigue level and overtraining risk. When you are young and invincible, you are much more resistant to that.
• However, even the younger guys will hit overreaching if they train too hard for too long.
• In this aspect, I see the real potential for evaluating this metric.
• Suppose you look at the Alpha-1 after an ultra-marathon was well below 0.5. It means athletes were at the high-intensity domain, even at low speeds. (Above the FTP type of value, you tend to expect from training)
• Another exciting mark is that there was no heart rate drift. Their HR and VO2 on the treadmill at the low speed we evaluated were the same. The only thing that changed was the alpha-1.
• It turns out that after 5-6h of exercise, your heart rate drift can occur in the opposite direction.

Different modalities where we can apply Alpha-1

47:40 -

• Technically, cycling and running should not be different, but there is some artefact, the low "a walk running syndrome", where someone starts with a walk and then starts jogging, and even before the heart rate stabilises, alpha-1 drops.
• The reason may be up and down vertical motion when running, but I cannot explain why this happens.
• Some people see that the moment they start bouncing up and down, their alpha-1 drops. Alpha-1 is looking for patterns, and some vibration or change on the heart position related to the chest interferes with recognising the pattern and alpha-1 drops.
• I recommend that people who have that type of problem will not use alpha-1 when running. They can use a very steep climb or stair steps, where you are not bouncing. Or even do a cycling test, and translate that HR to a running heart rate will give you an approximation.

Different preprocessing methods

50:19 –

• If you have many artefacts, these will increase the alpha-1. For example, if you are doing a run or a bike session and are training hard, you will miss a lot of RR intervals because of all the noise from the motoring activity.
• It is why it is essential to look at the artefacts. If they are above 4 %, you cannot trust that data. 
• One of the common problems that people have when interpreting the results is there were many artefacts on the data, and they did not consider them.

Additional topics to consider

52:13 –

• When you are doing thresholds and want to measure them accurately, make those measurements while fresh. If you are not fresh, you will get fatigue effects.
• This aspect is a blessing and a curse. It is a blessing because it will tell you you are not ready to train and tired if you have an accurate threshold beforehand. So, it is better if you back off.
• However, if you base your training on a fatigued value, that is a mistake because that value will be lower than what it should be. 
• So, if this is your introduction and you took a couple of days off training, and you feel good, do a threshold.
• Another point is that we have some data on cross country skiers and other sports. It seems to work there as well.
• If those individuals have questions, they can always contact me on Twitter or something like that.

Three take-home messages for endurances athletes about DFA Alpha-1

54:13 –

• First, I would use the proper measuring equipment and software to get the correct values. The suggested measuring equipment would be a Polar H10. The software would be one of the mentioned above.
• However, Alpha-1 for Garmin will be a live metric where you train and have direct feedback on the data.
• But to measure the threshold, I would use the other software.
• Second, if you are fresh and in great shape, feel free to do a test. But if you are tired, do not do it.
• The other point would be to use Alpha-1 during warm-up as a guide to understanding if you should train hard or not on that day.
• We have already a study on that in a manuscript, and it looks very promising.
• Another way of using Alpha-1 is by comparing it with the resting HRV. We would look at the autonomic nervous system statically and with stress and understand the different body responses to training.
• I have never had much variability in my HRV, so it has never worked for me.
• I also want to add that when doing blood lactate measurements in hot and humid environments, your strip will be with a lot of humidity, and data will not be good. But with Alpha-1, you can do this type of testing in any circumstance.

Rapid fire questions

57:20 -

What is your favourite book, blog or resource?

I do not have a favourite book. But the primary resource is PubMed because it has everything you wish either in abstract or in other libraries like ResearchGate.

What is an important habit that benefited athletically, professionally or personally?

When I was taking my degree in Medicine, I learnt that we should not believe in anyone. We should always question the standard practices and the results we obtain.

Who is someone you have looked up to or who has inspired you?

Nowadays, most people specialise in only one area. So, while they might be relevant in a topic, they do not know the rest. Therefore, Leonardo Da Vinci is someone that did it all.

LINKS AND RESOURCES:

• Bruce profiles on Twitter and Research Gate
• A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability - Rogers et al. 2021
• An Index of Non-Linear HRV as a Proxy of the Aerobic Threshold Based on Blood Lactate Concentration in Elite Triathletes - Rogers et al. 2022
• Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability - Rogers et al. 2021
• Influence of Artefact Correction and Recording Device Type on the Practical Application of a Non-Linear Heart Rate Variability Biomarker for Aerobic Threshold Determination - Rogers et al. 2021
• Fractal correlation properties of heart rate variability as a biomarker of endurance exercise fatigue in ultramarathon runners - Rogers et al. 2021
• Correlation Properties of Heart Rate Variability during a Marathon Race in Recreational Runners: Potential Biomarker of Complex Regulation during Endurance Exercise - Rogers et al. 2021
• Heart Rate Variability – New perspectives and insights with Marco Altini, PhD | EP#325