With the recent interest in using the DFA a1 HRV index to determine aerobic thresholds, follow exercise intensity and the use of real time DFA a1 output from HRV Logger/Fatmaxxer, I've decided to put together a "frequently asked questions" list. This is by no means totally inclusive and will be updated on a regular basis as new questions (and answers) come up. Here we go...
First, a quick look at how far this tech has moved in the past year. From an obscure concept, to a real time smartphone app that even runs multi window in Android:
I have an hour to listen to something but can't read the blog right now.
- Here is a Podcast I did with Michael Liberzon of x3training.com
What is DFA a1?
- Simply put, it's an index of heart rate beat to beat, fractal related self similarity. Although your heart rate may be 60 bpm, the beats are not occurring exactly every 1.000 seconds. The pattern of self similarity changes as exercise intensity rises, from values well above 1, moving down to .75 near the aerobic threshold and dropping even further above this exercise intensity. See the articles below for details.
- Uses of a1 observation include first and second ventilatory/lactate threshold estimation as well as monitoring for fatigue effects.
- Yes. In fact we have several articles describing this process.
- My original case report/hypothesis
- Frontiers Perspective Review
- Validation Study in Runners for aerobic threshold (HRVT)
- Effects of Artifact and HRM Device bias
- The HRVT2 for anaerobic threshold detection
- Initial idea for real time use
- Full report on Real time use of DFA a1
- DFA a1 as a marker of endurance exercise fatigue
- Below is a YouTube video I did for a conference going over the advantages of DFA a1 over other indexes
How accurate is it for threshold identification?
- Before answering, we need to think about how accurate the comparison "gold standards" methods are. As discussed in the articles above, there are real issues in both lactate and gas exchange tests, making them subject to various errors and inconsistencies. Some gas exchange results are so confusing that they are not interpretable. Machine based gas exchange results are not always accurate. From the limited study data so far, it seems the DFA a1 is a reasonable surrogate for the AT. Below is the Bland Altman analysis and regression plot from our validation study:
- As you can see, some folks had more or less agreement with the gas exchange AT, but for the most part the differences were small (several bpm)
What sports can this be applied to?
- This is a good question. So far, only running and cycling have been well explored. Other activities such as those using upper and lower extremities (xc skiing, kayaking, rowing) may not follow the same relationship with the AT.
What can affect the numbers I get?
- A very wide range of factors. Stress, heat, caffeine, caffeine withdrawal, food, fasting and over-training are some of the factors before we even process the data. Preprocessing algorithms, software settings are also critical. Kubios may give different results from a python based method. We will need to do formal comparison testing between Kubios and python methods eventually.
- Should I use a fan indoors - YES -
Another look at indoor exercise without a fan
Do I need clean, artifact free data?
- A very important item that will affect the DFA a1 is artifact in the RR series. Missed beat artifact is the most common, and if above 3% could, but if above 6% will affect the values you get. A single APC may also dramatically drop the DFA a1 for that window of measurement. Correction methods help with this but are not perfect. One of the strengths of our Frontiers study was that we used ECG data with almost no artifact. YMMV using a chest belt with artifact.
- We recently had an article published in the journal "Sensors". Below is the abstract from that study:
Recent study points to the value of a non-linear heart rate variability (HRV) biomarker using detrended fluctuation analysis (DFA a1) for aerobic threshold determination (HRVT). Significance of recording artefact, correction methods and device bias on DFA a1 during exercise and HRVT is unclear. Gas exchange and HRV data were obtained from 17 participants during an incremental treadmill run using both ECG and Polar H7 as recording devices. First, artefacts were randomly placed in the ECG time series to equal 1, 3 and 6% missed beats with correction by Kubios software’s automatic and medium threshold method. Based on linear regression, Bland Altman analysis and Wilcoxon paired testing, there was bias present with increasing artefact quantity. Regardless of artefact correction method, 1 to 3% missed beat artefact introduced small but discernible bias in raw DFA a1 measurements. At 6% artefact using medium correction, proportional bias was found (maximum 19%). Despite this bias, the mean HRVT determination was within 1 bpm across all artefact levels and correction modalities. Second, the HRVT ascertained from synchronous ECG vs. Polar H7 recordings did show an average bias of minus 4 bpm. Polar H7 results suggest that device related bias is possible but in the reverse direction as artefact related bias.
So what does this mean on a practical basis? Anything with >6% artifact in the area of interest should not be trusted. Since both the Kubios threshold correction method, Runalyze, Fatmaxxer and HRV logger use similar techniques, 3% or less artifact containing data will provide reasonable HRVT accuracy. There is also a chance that 3-5% artifact containing data series will be fine, but you may want to re test yourself. The effect of missed beat artifact on DFA a1 is to artificially raise the computed value at low DFA a1 ranges (not high ranges). For example, if the DFA a1 was .5 with no artifact, after adding 6% missed beats with correction (by Kubios), the software will output .65 +-.
Here is a look at how that works out on a Bland Altman assessment
The solid line is the "average" difference between methods, notice how this process is dependent on what DFA a1 actually is. There is minimal "bias" between DFA a1 of 1 and .5 which is important for the HRVT. However, values below .5 are very much altered.
- Also see below under recording devices.
- It appears the greatest source of missed beat artifact is the use of AnT+ data transmission.
What artifact correction settings do you recommend?
- If you are using the Kubios paid premium version, use the "auto" method. Free version Kubios uses the threshold method (similar to HRV logger, Fatmaxxer and Runalyze). The medium correction setting is the default and should work well (similar to the 20% setting in FM, Runalyze and Logger). The exception is with an APC where a sudden drop is seen. Using the extra strong filter setting (or the "work out mode" in Logger, 5% or auto in Fatmaxxer) will filter out the APC but can also filter some physiologic beat to beat variation. Get a feel if you exhibit frequent APC activity, and if so, use the more aggressive settings. IMO, Fatmaxxer has the best method - automatic alteration of threshold mode as HR rises.
Does recording device matter?
- This is something else we are looking at. The above validation study was done with a research grade ECG. It is very possible that a chest belt device will detect R peaks differently as well as be affected by preprocessing issues. Interference with either chest wall or diaphragm related activity can change the ECG waveform. Disturbance of the pattern of self similarity would then occur after the introduction of this type of distortion. However, the Polar H10 results appear very close to accurate waveform ECG derived values.
- Also see this: DFA a1, Sample rates and Device quirks
In the Sensors study, we found that the Polar H7 "measures" DFA a1 as slightly lower values. This is in the opposite direction as what missed beat correction induces, which is actually quite convenient! The end result of a Polar H7 recording with 3-5% missed beat correction may yield values that are very close to those of an ECG. Below is a figure from our article that shows this very nicely. The Polar reads lower than the ECG, but the 6% artifact recording reads high - making for a "self correcting" effect. If you had a Polar RR series with no artifact, yes, you might have bias. We are continuing to look into this.Time-varying analysis (window width: 120s, grid interval: 5s), DFA a1 for matched time series containing no artefact in one representative participant, ECG (solid triangle), Polar H7 (open circle), ECG 6% MC (open triangle).
I have the option of recording HRV either using ANT+ or bluetooth - is there a difference?
- Although I initially couldn't believe this should make a difference, it apparently does! Here is an experiment I ran. The first tracing shows a recording using a Polar H10 to my Garmin watch using Ant+. The second is a recording of the same power/duration/conditions on another day using bluetooth (same H10, same watch).
- There are clear differences - the bluetooth tracing has zero missed beats (one APC noted), but the ANT+ recording has many (the vertical lines). This has been replicated many times and is reproducable.
- Bottom line - if you are seeing many artifacts using ANT+, try switching to bluetooth. Thanks to Marco Altini for the initial anecdotal observation.
If I shouldn't use ANT+ then how can I get the RR data to both my Garmin watch/head-unit and HRV logger during an exercise session?
- The Polar H10 has a nice feature that enables two different devices to simultaneously receive RR packets over bluetooth. It is not enabled by default so you will need to do so. The instructions are here.
- Once enabled you can have your Garmin watch and the HRV logger (or other bluetooth device) receive data at the same time. But remember, other nearby receivers may be able to pick up your data and see your stats. This applies to ANT+ as well. Using the Polar Beats app, you can turn off multi device bluetooth and/or ANT+ at will.
- If the H10 is already added as an Ant device in the Garmin unit we need to get rid of it - first - delete the Ant device from "Sensors", Go to - add new external HR, but don't add the Ant, the Garmin will then ask to search bluetooth, say yes and add the bluetooth HRM.
- Note - on H10 battery change (or pull) these settings will be lost! You will need to reapply with the Polar app.
I don't want to deal with Kubios, is there a low cost, easy option for DFA a1 tracking?
- Absolutely! Although minor differences in data values are present, they are certainly good enough for most purposes.
- We have three options, HRV logger by Marco Altini, Runalyze and Fatmaxxer (see below). Each has their pros and cons. HRV logger needs a smartphone to record and display (in real time) the HRM belt output - It only calculates a value every 2 minutes and may not have the accuracy of the other options. Runalyze is able to automatically transfer your Garmin fit file recording and display DFA a1 over time and also compare it to HR and power. It will automatically calculate a aerobic threshold as well. See this post for further details - Best practices for Runalyze and DFA a1 thresholds
- Fatmaxxer, is an app designed as a dedicated DFA a1 monitoring tool for android. This is my choice as the option for real time a1 tracking. Reasons include the ability to track DFA a1 at an every 5 second refresh rate and ECG strip recording of artifacts. What does this mean? We can get a fine/granular plot of DFA a1 over time with points every 5 seconds. If an artifact is detected, a separate file is saved with that data to graph and inspect. It also seems to have the best a1 accuracy compared to Kubios software at this time (using the same detrending method as Kubios). See below
How do I set up an aerobic threshold test scenario with HRV Logger?
- I've devoted many posts on doing this in Kubios but lets look at a simple method in HRV Logger. Warm up 15 to 20 minutes then do 6 minute constant load efforts. Make sure you line up the first effort with an even time number in the Logger. Since the Logger spits out a value every 2 minutes, it's helpful to have the time under effort match up. As an example, start the Logger, warm up 20 minutes then at 20 min exactly (on the Logger), start your first interval at a very easy level. Throw out the first value after the interval start (it's not at steady state yet) but the values at 24 and 26 minutes will be valid. At 26 minutes, boost your power or speed by a notch (still easy) and measure at 30 and 32 minutes (remember that 28 was non steady state). Keep this progression up until you pass through .7 to .8 DFA a1. That would have been your AT related intensity. Do one more effort at the next stage higher to confirm the DFA a1 is indeed below .7.
- Possibly, although most ramps are done with a 5 to 10w/min rise, you may be able to use the 30w/min protocol - see DFA a1 HRVT and Ramp slope
- Just perform a typical ramp of 5 or 10 watts per minute in Zwift. Try not to include the warmup, or post ramp data. Here is a guide for Runalyze ramping.
- Sure - just plot the DFA a1 and HR in the "Features" file.
- Over a series of many "agreement" comparsions, Fatmaxxer appears to track most closely with Kubios HRV software.
How do I reproduce your published study protocol?
- Here it is:
- The following procedure was used to indicate at what level of running intensity (as VO2 or HR) the DFA a1 would cross a value of .75: DFA a1 was calculated from the incremental exercise test RR series using 2 minute time windows with a recalculation every 5 seconds throughout the test. Two minute time windowing was chosen based on the reasoning of Chen et al. (2002). The rolling time window measurement was used to better delineate rapid changes in the DFA a1 index over the course of the test. Each DFA a1 value is based on the RR series 1 minute pre and 1 minute post the designated time stamp. For example, at a time of 10 minutes into the testing, the DFA a1 is calculated from the 2 minute window starting from minute 9 and ending at minute 11 and labeled as the DFA a1 at 10 minutes. Based on a rolling time recalculation every 5 seconds, the next data point would occur at 10:05 minutes (start 9:05 minutes and end 11:05 minutes).
Plotting of DFA a1 vs time was then performed. Inspection of the DFA a1 relationship with time generally showed a reverse sigmoidal curve with a stable area above 1.0 at low work rates, a rapid, near linear drop reaching below .5 at higher intensity, then flattening without major change. A linear regression was done on the subset of data consisting of the rapid near linear decline from values near 1.0 (correlated) to approximately .5 (uncorrelated). The time of DFA a1 reaching .75 was calculated based on the linear regression equation from that straight section (Figure 1b). The time of DFA a1 reaching .75 was then converted to VO2 using the VO2 vs time relation, resulting in the VO2 at which DFA a1 equaled .75 (HRVT). A similar analysis was done for the HR reached at a DFA a1 of .75. First, ECG data from each 2 minute rolling window was used to plot the average HR and DFA a1. The HR at which DFA a1 equaled .75 was found using the same technique as above, a linear regression through the rapid change section of DFA a1 values of 1.0 to below .5, with a subsequent equation for HR and DFA a1 (Figure 1c). Using a fixed variable of DFA a1 equals .75, the resulting HR was obtained. The HR at DFA a1 .75 (based on ECG data) was then compared to the HR at VT1 GAS obtained from the metabolic cart data (based on the Polar H7).
- Note should be made of the difference between Kubios "time varying" timestamps and the Logger. In Kubios, a given time varying window timestamp is centered in the middle of the window (bold print above) whereas in the Logger the timestamp is at the end of the window.
- Runalyze will do this automatically for you.
- Fatmaxxer will have the data (every 5s) and can be manually plotted.
How do I make sure I'm really doing a recovery ride on my rest day?
- This is an ideal scenario for the the real time apps (FM, Logger). Just watch the live read out and keep DFA a1 above .8 or even .9 (yes .75 is the cutoff, but there is individual variation and a small buffer is advised). A single value that falls below .75 then normalizes where it started again was probably due to an APC.
- To train hard or not, that's the question
Are my values going to be the same day to day?
- Probably not. Although they may be close, it's normal and expected to have some shifting in heart rate or power on a day to day basis. This would be the case with gas exchange or lactate as well. As stated above, other factors will change the index result, especially heat, skin temp and humidity.
Can the intensity of exercise where DFA a1 = .75 be used as a way of tracking fitness changes after training?
- we are currently looking at this and I will update when I am able.
I'm on beta blocker therapy, will this change the DFA a1 to intensity relation?
How do I match up timestamps in Kubios and HRV Logger?
The time stamping is tricky.
The times are all different in each "method".
Logger - time is at the end of a 2 min window - so a timestamp of 2 minutes is from 0 to 2 min elapsed.
Kubios free - the time is from the beginning of a 2 min window - so a timestamp of 2 min is from 2 min to 4 min elapsed
Kubios premium time varying download (enclosed) - the timestamp is in the window center - so a timestamp of 1 min is from zero to 2 min elapsed.
Therefore the logger will be different from Kubios either way.
Make sure you shift Logger 1 minute forward (the Logger at T=2 min equals the time varying Kubios at 1 min)
Why are my DFA a1 values too high for the level of effort I am doing?
- The most common reason would be the effects of high rates of missed beats in the RR sequence. Although programs like HRV logger auto correct for artifacts, they don't tell you how many. Kubios will give you artifact rates - you should not trust rates beyond 5% as per our Sensors study.
- Different detrending method used. This is possibly why Fatmaxxer is the most accurate (in my hands) option aside from Kubios.
Can I use the DFA a1 as a way of checking my respiratory compensation point, MLSS, VT1, LT2?
- However, you are going to need very clean, artifact free data with an accurate device. We have not validated the Polar H10, but that's what I have used personally with reasonable results.
- Please read the above article from JFMK for details.
Where does the DFA a1 value of .75 actually come from? Why doesn't it vary person to person?
- Initially, the .75 value was "guesstimated" from data showing that DFA a1 runs about 1 during very light exercise (representing very correlated/self similar patterns) but drops to .5 (corresponding to random beat patterns) at very high intensity. Therefore an in-between point of .75, could represent a moderate effort. Looking at previously published data by Gronwald, Hautala and Blasco-Laforga shows a DFA a1 of about .75 lying in the area where the AeT should be. We went on to show that in recreational runners, an a1 of .75 is a valid surrogate (on average) for the AeT.
- A key advantage of the a1 is it's dynamic range - whereas other HRV indexes hit a nadir at the AeT, the a1 value is at it's midpoint and will continue to fall past the AeT.
- The other important consideration is that no calibration is needed. These are dimensionless values, so my value of .75 represents a similar physiologic state as yours (partiality correlated) . Of course since it represents net "organismic demand" and status of the autonomic nervous system, there can be day to day fluctuations and effects from fatigue, stress, temp etc.
- Heart rate and power, although great metrics, can't be used for accurate zone assessment, unless one calibrated them to a lactate or gas exchange test. The closest parallel example to a1 would be lactate (a measure of internal metabolic status), but even that has very wide variation in levels at the MLSS.
- All these factors result in our ability to use the index for intensity assessment while exercising.
I've noticed that I can't drop my a1 below .5 using the HRV logger, any ideas.
- Yes - see this
DFA a1 agreement using Polar H10, ECG, HRV logger
- And the difference detrending makes
- Use a more precise app such as AI Endurance, Fatmaxxer or Runalyze
Can DFA a1 be used as a marker of fatigue?
- Yes according to out recent publication - DFA a1 as a marker of endurance exercise fatigue
- In short, the usual pattern of DFA a1 behavior will be shifted after a session of fatiguing exercise. This can also be used as an indicator of "training readiness" in lieu of resting HRV. For instance if you see that your a1 is running lower than it should in the warm up period of your session, that may indicate that you are still recovering from a previous stressful set of session.
- To train hard or not, that's the question
Does the HRVT (a1 derived AeT) change if I'm sick?
- It probably depends on how ill you are - anecdotal reports have appeared showing substantial a1 suppression at low exercise intensities.
- Given the known effects seen with fatigue, it would make perfect sense for the a1 to be lower than normal during or immediately after an illness.
- We really don't know. But a recent observation I made might shed some light. I did a 20 minute Zwift ramp (130 to 230w) the morning of my second Moderna Covid vaccine (Pre) and another the next morning afterward (Post). Yes, I had the typical post vaccine sore arm, nausea, fatigue, muscle pain and was really "spaced out". Like the flu but no sore throat or congestion. Did the HRVT change? Very surprisingly it did not:
The time/power at crossing a1=.75 was just as it usually is 210-215 watts. N=1 data certainly, but intriguing that a1 seems fairly well linked to exercise load, at least in the short time frame - I wasn't up to going on for another couple of hours to see what would happen.
Why does my DFA a1 seem lower (for a given HR) running vs cycling?
Several potential reasons:
- Random differences and day to day variation - try to repeat the tests on a regular basis to see if it is real.
- Potential loss of R peak precision. What I have found is that in certain people (a minority) some electro-mechanical factor creates some distortion of the R peak. This may be diaphragm related but more likely trunk musculature that is firing more strongly while running. If you wore the Movesense ECG you may see this:
- Since the DFA a1 is related to "correlation" of beat patterns, having a loss of precision of those patterns by distortion of the R peak will reduce the value seen. This was nicely demonstrated by Dr Mourot's study
- This will not affect the HR since the same beat count per time is present. It also is not noticeable at rest since those offending muscles are not firing.
- If you see a large discrepancy and don't have the ECG to conform why, trust the bike data over the run until we get more information on this problem - again, it occurs but not in everyone.
- This is a more detailed discussion
Does the position of the HRM belt matter?
Can the index be used for monitoring endurance and HIT fatigue
- It seems possible - see my N=1 data
- As noted, don't look for the HRVT after a HIT interval or post a long exercise session.
- See also - DFA a1 stability over longer exercise times - yes it appears to be "stable for long durations (1-2 hrs) as long as pace is below the aerobic threshold.
Can I get a single lead ECG from a Polar H10 sensor?
- Here is a guide
- Even better, just the artifact sections - ECG artifact strips from Fatmaxxer - a guide
Is the HRVT concept valid in non athletes or those with cardiac disease and beta blocker therapy?
- We think so - abstract - ACSM - HRVT validation in a cardiac disease population
- Here is the full version article - DFA a1 threshold in a cardiac population
Heart rate variability during dynamic exercise
- Firstbeat VO2 estimation - valid or voodoo?
- Heart rate variability during exercise - threshold testing
- Exercise in the heat and VO2 max estimation
- DFA alpha1, HRV complexity and polarized training
- HRV artifact avoidance vs correction, getting it right the first time
- VT1 correlation to HRV indexes - revisited
- DFA a1 and Zone 1 limits - the effect of Kubios artifact correction
- HRV artifact effects on DFA a1 using alternate software
- First article on DFA a1 and Zone 1 demarcation
- DFA a1 vs intensity metrics via ramp vs constant power intervals
- DFA a1 decline with intensity, effect of elevated skin temperature
- Fractal Correlation Properties of Heart Rate Variability (DFA a1): A New Biomarker for Intensity Distribution in Endurance Exercise
- Movesense Medical ECG V2.0 Firmware brief review
- Movesense Medical ECG - improving the waveform and HRV accuracy
- DFA a1 and the aerobic threshold, video conference presentation
- DFA a1 - running ramp and sample rate observations with the Movesense ECG
- DFA a1 calculation - Kubios vs Python mini validation
- Frontiers in Physiology - Validation of DFA a1 as a marker of VT1
- Real time Aerobic thresholds and polarized training with HRV Logger
- Active Recovery with HRV Logger
- DFA a1 and exercise intensity FAQ
- DFA a1 agreement using Polar H10, ECG, HRV logger
- DFA a1 post HIT, and as marker of fatigue
- DFA a1 stability over longer exercise times
- DFA a1, Sample rates and Device quirks
- DFA a1 and the HRVT2 - VT2/LT2
- Low DFA a1 while running - a possible fix?
- Runalyze vs Kubios DFA a1 agreement
- DFA a1 - Runalyze vs Kubios vs Logger results in a cyclist
- Best practices for Runalyze and DFA a1 thresholds
- ACSM - HRVT validation in a cardiac disease population
- FatMaxxer - a new app for real time a1
- Another look at indoor exercise without a fan
- ECG artifact strips from Fatmaxxer - a guide
- DFA a1 as a marker of endurance exercise fatigue
- To train hard or not, that's the question
- DFA a1 HRVT and Ramp slope
- DFA a1 and optimal HRM belt position
- DFA a1 threshold in a cardiac population
- Atrial fibrillation - warning signs from chest belt recordings