Your heart rate variability recovery model

Your heart rate variability recovery model


Before every workout you should know if you're actually ready for it. Everyone responds differently to stress, bad sleep and exercise fatigue - our new recovery model makes data driven decisions about when you should train and when you shouldn't - based on heart rate variability (HRV).

HRV studies [1, 2] show evidence that HRV guided training reduces the number of non-responders in a training population. While athletes who adapt their training based on HRV might not necessarily improve more than those who don't, a smaller fraction shows no improvement at all.

At AI Endurance we want to give you the best of both worlds:

  • a personalized and optimized AI training plan based on the learnings from your historical data
  • adaptiveness when things become too much so you don't become a 'non-responder'

In this context, we are introducing your personalized recovery model to AI Endurance.

Measuring recovery via HRV

There are various indicators that measure readiness to train. AI Endurance uses the following three measurements to assess your recovery:

1. RMSSD

Root mean square of successive differences between normal heartbeats (RMSSD) measures the variation in the time between heart beats. Generally, low RMSSD correlates with high stress/low recovery although the relationship can be more complex depending on the circumstances.

Trends in RMSSD correlate with recovery as shown in the sport science literature, e.g. [3, 4]. You can measure RMSSD 'at rest' during the night or upon waking up. This requires one of the following options:

AI Endurance uses a 60 day normal vs 7 day baseline to asses your recovery from RMSSD. For example, if your 7 day baseline/average drops below the band of values defined by your 60 day normal, your recovery is indicated to be low. The normal band is defined via the 60 day average plus/minus the 60 day standard deviation.

normal vs baseline heart rate variability

2. Resting heart rate

Resting heart rate is either your minimum heart rate measured during the night or your heart rate upon waking up, consistently measured at the same time during your morning routine (e.g. laying down vs standing up). Once you measure HRV, resting heart rate can be easily calculated as a by-product. Otherwise it can simply be taken by hand or with a heart rate sensor. Generally, the higher your resting heart rate the less recovered you are [4].

AI Endurance uses a 30 day normal vs a 3 day baseline to asses your recovery from your resting heart rate. For example, if your 3 day average is above the band defined by your 30 day normal, your recovery might be low.

3. DFA alpha 1 HRV readiness

DFA alpha 1 (a1) measures how much stress your cardiovascular system is under during exercise. The lower a1, the more stress you are putting on the system. It can be used to assess readiness to train, durability and thresholds.

AI Endurance introduced pa, the power/pace it takes to perform at fixed a1 - think power multiplied by a1. If on a given day your pa is lower than usual, i.e. you're pushing less power for the same internal effort quantified via a1, your a1 readiness is low. See also [5].

In order to asses your recovery from a1, AI Endurance uses a 30 day normal vs a 3 day baseline. For example, if your a1 baseline is lower than the band defined by your normal, this suggests that you might not be well recovered.

AI Endurance's recovery model

AI Endurance's recovery model operates in 4 steps:

1. Translate measurements to recovery

A measurement of your current recovery state via the above metrics (RMSSD, resting heart rate, a1) translates into a recovery state between 0 and 100%. A particular deviation of the baseline relative to the normal might describe different absolute recovery values for an individual. For example, athlete A might still be 20% recovered when the baseline crosses the normal while athlete B might only be 10% recovered.

Finding your individual HRV metric to recovery relationship is part of our personalized recovery model.

2. Fit personalized recovery model

Apart from measured recovery via HRV metrics, your training stress is key in creating your personalized recovery model. AI Endurance measures training stress via external stress score (ESS). It grows with intensity and duration of your workouts - think one hour at threshold equals ESS of 100.

Our recovery model relates your training stress ESS to your recovery R via the following recursive formula:

recovery formula
  • k represents how much recovery is reduced by training stress. The larger k, the more 'damage' a training impulse does to your system from which you have to recover.
  • τ represents how fast you recover. The larger τ, the longer it takes for you to recover from training stress.

It is natural to model recovery as an exponential decay process, and this is an approach used frequently in the scientific literature following the original model of Banister et al. [6, 7].

AI Endurance determines your personalized recovery model by finding a best fit of the recovery model to your historical data:

  • all historical values of training stress ESS
  • all measured HRV recovery values (RMSSD, resting heart rate, a1)

The model finds the k and τ that best fits your personal data and hence learns

  • how much training stress affects your recovery (k)
  • how long you need to recover from training stress (τ)
  • the relationship between baseline vs normal to your recovery in every HRV metric

We weigh the different metrics according to their evidence and accuracy. For instance, RMSSD is weighted twice as much as resting heart rate when fitting the recovery model.

3. Evaluating daily recovery

When evaluating your daily recovery, we want to be conservative. We choose the minimum value of

  • model predicted recovery value based on ESS history
  • RMSSD measured recovery
  • resting heart rate measured recovery
  • a1 measured recovery

to represent your daily recovery. This way, we accommodate stresses in your life that are not represented via training stress. The actual measured HRV recovery values always overwrite the model predicted ones. So a night of bad sleep and/or stress at work are taken into account for your training.

4. Adaptive training plan

If your HRV indicated recovery is low, AI Endurance proposes to either move or skip a hard workout that was originally planned for the day in your training plan. We also show your historical recovery trends and measurements on your AI Endurance dashboard.

AI Endurance recovery model

How to get your HRV data into AI Endurance's recovery model

1. Garmin and Suunto DFA a1 data

  • Use a Polar H10 or Suunto Smart Sensor heart rate monitor.
  • For Garmin make sure HRV logging is enabled.
  • For Garmin, make sure you have connected your heart rate monitor via Bluetooth.

2. Garmin HRV at rest data

To get your Garmin HRV at rest data (RMSSD and heart rate at rest) into AI Endurance, simply make sure you are sharing Daily Health Stats from your Garmin Connect with AI Endurance. If you already have an AI Endurance account, make sure to re-connect 'Garmin - Import Data' and check Daily Health Stats. Garmin sends your data automatically to AI Endurance after you wake up in the morning.

Garmin HRV

3. Oura HRV at rest data

To get your Oura HRV at rest data (RMSSD and heart rate at rest) into AI Endurance, simply connect your Oura account to AI Endurance.

Oura HRV data

AI Endurance syncs with Oura multiple times a day. To synchronize your Oura data immediately, hit the sync button on your AI Endurance dashboard.

4. HRV at rest data from other apps

You can get your RMSSD and heart rate at rest measurements into AI Endurance via TrainingPeaks Premium while we are working on more direct integrations for you in the future.

For example, you can connect your HRV4Training account to TrainingPeaks and every HRV measurement you take with HRV4Training will be automatically pushed to TrainingPeaks right away. AI Endurance syncs with TrainingPeaks multiple times a day, or you can hit the sync button on your AI Endurance dashboard for an immediate synchronization.

Which metrics should you collect? Generally the answer is the more the better. However, not all of us have the luxury or patience that allow for a consistent HRV data collection a la HRV4Training.

Our personal favourite is Oura + a1 as it requires virtually no interruptions to your routine/life while still getting the benefit of proactively deciding whether to exercise or not before a workout.

Second favourite is the 'budget option' of a1 only, as it only requires you to buy a Polar H10 heart rate monitor and enable HRV logging on your Garmin or Suunto device. This continually collects data to train your personalized recovery model with the drawback that it does not inform AI Endurance about acute recovery setbacks such as a night of bad sleep as the data is collected during activity only. It still informs you if your recovery is generally low. For example, if you did a workout yesterday that indicated low recovery, your body would not have had enough time to be ready today. With a1, you also get all the benefits of automatic threshold and durability detection.

To help you decide what data to collect, here's an overview:

recovery hrv comparison

Outlook

We have exciting additions planned for you:

  • let you directly connect your favourite HRV app to AI Endurance
  • incorporate your HRV recovery model into your AI optimized training plan to optimize your planned rest periods
  • incorporate additional metrics into your recovery model

Stay tuned and thanks for reading!

References

  1. Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: A systematic review with meta-analysis - Peter Düking, Christoph Zinner, Khaled Trabelsi, Jennifer L. Reed, Hans-Christer Holmberg, Philipp Kunz, Billy Sperlich - Journal of Science and Medicine in Sport 2021
  2. Individualized Endurance Training Based on Recovery and Training Status in Recreational Runners - Olli-Pekka, Nuuttila; Ari, Nummela; Elisa, Korhonen; Keijo, Häkkinen; Heikki, Kyröläinen - Medicine & Science in Sports & Exercise 2022
  3. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring - Daniel J Plews, Paul B Laursen, Jamie Stanley, Andrew E Kilding, Martin Buchheit - Sports Med 2013
  4. Reliability and Sensitivity of Nocturnal Heart Rate and Heart-Rate Variability in Monitoring Individual Responses to Training Load - Olli-Pekka Nuuttila, Santtu Seipäjärvi, Heikki Kyröläinen, Ari Nummela - Int J Sports Physiol Perform 2022
  5. Fractal correlation properties of heart rate variability as a biomarker of endurance exercise fatigue in ultramarathon runners - Bruce Rogers, Laurent Mourot, Gregory Doucende, Thomas Gronwald - Physiol Rep 2021
  6. Modeling human performance in running - R. H. Morton, J. R. Fitz-Clarke, E. W. Banister - J Appl Physiol 1985
  7. Variable dose-response relationship between exercise training and performance - Thierry Busso - Med Sci Sports Exerc 2003
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