b*y Stefano Andriolo*. Building on previous work, we refine a method to accurately determine the relationship between DFA alpha 1 and power. This method can be used to track fitness and thresholds of an athlete. We find in some cases ramp detection tends to overestimate thresholds, a finding mirrored in recent physiological papers. On the other hand, thresholds based on clustering of DFA alpha 1 values tend to agree well with this new method. We propose a hybrid lab and everyday workout experiment to further study the relationship.

The main content and results of this posts are:

- We review the
**challenges in establishing a DFA alpha 1 (a**: cardiac lag, stabilization times, fatigue onset and varying workout structures._{1}) relationship - We present
**shortcomings of the "old" representative method**presented in our previous blog post: it does not account for cardiac lag and stabilization times under all conditions. - We define a "new" grid based representative method that alleviates these issues. In
**58% of cases the grid method does better than the representative method**. In 22% it does worse. In 20% it does equally well. - We look at implications for fitness and threshold tracking:
**ramp(-like) threshold detection can overestimate thresholds**while clustering detection that relies on sufficient temporal coverage where the power-a_{1}relationship stabilizes, tends to do very well. - We
**propose a hybrid lab and everyday workout data experiment**where HRV clustering thresholds are gauged against gas exchange.

Ever since the latest findings, we’ve been on a quest to pinpoint the most accurate way to map out the power -a_{1} relationship using everyday workout data. The pioneering method developed did a fantastic job in uncovering the link between cycling power and a_{1}. However, it left us craving a technique that didn’t just unveil the correlation but could nail down the power-a_{1} relationship with precision. We're here to dive into why the original method didn’t fully satisfy this itch, propose a new method that builds upon it and share some points on its validation.

For those seeking a refresher on a_{1} or wishing to deeply understand the new method, we recommend revisiting our previous blog post.

In essence, we've crafted a novel method that offers greater consistency and reduces the likelihood of biases and errors when determining the power-a_{1} relationship, particularly in the dynamic range (a_{1}<1), which is a critical area of study. This method builds on the previous one but **introduces** **new ways of defining and fitting representatives to establish the power-a _{1} law.**

We continue to analyze workout groups for their numerous benefits, as previously discussed. The data remains the same, focusing on the initial 30 minutes of a workout—when freshness is at its peak—and ensuring a sufficient duration within the dynamic range. For details on data collection, please see our last blog post. Our current discussion will solely focus on the new method.

But let us go in order. We will first highlight the shortcomings of the previous method, proceed with explaining how the new method addresses them and how it yields a better estimate of p(a_{1}) law. Then we compare this result. Let's proceed step by step. We'll first pinpoint the drawbacks of the prior method, then explain how the new method overcomes these challenges and improves the estimation of the p(a_{1}) law. We'll compare these findings to ramp- and clustering-derived threshold powers and conclude with a simplified version of the new method that allows for effective monitoring of power profile development at various intensities over time.

The "old" representative method efficiently uses the concept of condensing the information provided by a number of points in the a_{1}-power plane into fewer 'representatives', which can be used to study the power to a_{1} correlation and ultimately find the relationship among these variables.

The reason we introduced this representative structure is that the power to a_{1} relationship suffers from a variety of temporal lags/cardiac delay and fatigue effects that destroy a straight-forward power to a_{1} correlation in everyday workout data:

**Response lag**: this is the time it takes the a_{1}signal to adapt to a change in power input. It depends on the athlete, athlete's state, size of change of power input, direction of change (from low to high or from low to high). For example, when jumping to high power from an extended period of time at warm up intensity there might be a larger lag than from jumping to a high power output from only a short break after a previous high power output (a_{1}might even still be low when starting the next effort). This effect is always present when power changes significantly.**Stabilization time**: represents the time interval that is required for the a_{1}signal to settle down around a stable value given a constant power output. This effect is only present if power stabilizes for a long enough period of time.**Fatigue onset**: even at a stable power output, a_{1}may further decline a while after the stabilization time is reached due to the onset of fatigue, see e.g. [8].**Variety of workout structures**in every day workouts: an athlete may pursue a variety of workout structures including indoor rides with/without using ERG mode, rides on a variety of terrain, group riding dynamics, etc that all come with their unique power profile and hence different characteristics of the above.

Ideally, one would have extended periods where the athlete performs at stable power and a_{1}, i.e. the effects of various lags are negligible and don't hinder determining the power-a_{1} relationship. Unfortunately, in real life workout data this is rarely the case.

Figure 1: *An example of a workout's power vs a _{1}. While the general inverse correlation is obvious there are visible lags such as the rather high a_{1} ~ 1.0 while already power > 200W at ~ 300 seconds or a_{1} < 1.0 while power already reduced to ~ 150W at ~ 700 seconds.*

A method that would overcome these challenges would need to meet several criteria to be effective:

- be available in every workout;
- be affected by the various lags as little as possible;
- be obtained with simple means directly from data, without any additional wrangling that may spoil the data structure (as for example taking moving averages in place of the actual signals would do as we have experimented with).

This is how we developed the ‘**representative method’**. While this was meant to be the first experiment in exploring the possible ways of representing (power, a_{1}) data, it gave surprisingly good correlations and we decided to publish it. The idea is quite simple and relies on:

- dividing the a
_{1}interval into smaller windows; - assigning a representative points to each window;
- study the correlation among the representatives and use them to model the power-a
_{1}law by fitting representative points.

Figure 2: *The representative method for an example workout. The blue points are all (a _{1}, power) points of the workout. The red points are the representatives.*

In particular, while the generic a_{1} values span the interval from 0.4 to 1.6 for most athletes, we are especially interested in the so-called *dynamic range* of a_{1} values, a_{1} < 1, because this is where the most useful thresholds are found. For each window, if it contains enough (a_{1}, power) points, meaning the athlete spent a minimum amount of time in that window (at least one minute), we take as representative the point defined by the averages of a_{1} and power values, (avg_a_{1}, avg_p). This is surely a simple enough procedure that meets requirements (1) and (3). As far as requirement (2) goes, the hope was that taking averages should make this issue weaker — more on this in the following.

The only open question in this method regarded how to choose the window size. We started with windows of 0.1 in the a_{1} range and stuck with it, though we think tailoring this to each athlete could potentially sharpen the method. The idea here is to consider the spread and density of an athlete's data points within the dynamic range to decide on the window size, but we didn't dive too deep into this aspect, thinking it wouldn't shake things up too much.

The method effectively uncovers correlations hidden within both unstructured and structured ERG mode workouts, showcasing its remarkable capability. However, its application to individual sessions revealed significant variability in outcomes, undermining reliability due to the diverse nature of workouts. Conversely, employing this method on aggregated data from multiple sessions within a week or ten-day span significantly enhances reliability and accuracy. This approach mitigates the variance seen in single workouts by leveraging a larger dataset, thereby improving the robustness of findings, reducing the influence of outliers, enabling better identification of non-responders, and more accurately modeling individual profiles. In fact, **the novelty of this method lies not only in the use of representatives, but also in its strategic grouping of workouts to achieve a statistically sound representation of individual physical profiles.**

**The main issue with this method is that representatives thus defined are not always reliable.** For a representative to truly reflect the data within its window, the data points need to be relatively close together, ideally clustering around a central spot. However, when data points are spread out or form more complicated patterns, it's hard to say whether these averages hold any real value. This is clearly visible in Figure 3. It is unclear whether we should use these points for the linear regression to find the power-a_{1} law.

Figure 3: *An ERG mode workout with a large spread in each window.*

The initial solution we thought of was to give each representative a weight, leading us to adopt a weighted linear regression approach. Here, the weight of each representative is inversely related to the standard deviation of data points in its window. Essentially, this method prioritizes representatives from tightly clustered data, while those from windows with scattered points have less influence on the model.

The downside to this is pretty clear: if most of our representatives don't accurately capture their windows, then the overall regression model we get might not tell us much. Imagine a scenario where we have just 3 'good' representatives with a_{1}> 0.7, while all representatives for a_{1}<0.7 are 'bad'. In this case, the weighted regression would essentially ignore the less reliable points, creating a model heavily skewed by the few 'good' ones. Such a fit cannot be consistently trusted in the deep a_{1}<0.7 region.

The reason the "old" representative method may fail resides in the way we understood and dealt with the cardiac lag.

To illustrate, consider an athlete with anaerobic threshold around 280W. Imagine them increasing their power output from 150W (easy domain, a_{1}=1.2) to 250W (hard domain, a_{1}=0.7) and then maintaining that level. At the onset of the increase, the a_{1} might remain elevated, say at 1.2, because of response lag—it doesn't immediately begin to decrease. The lapse from the onset of the power increase to the onset of the a_{1} decline represents the response lag. Subsequently, a_{1} decreases until it stabilizes at a consistent value, 0.7 in our case. This demonstrates stabilization time, defined as the interval from the first moment power hits 250W to the moment a_{1} stabilizes at 0.7, and it highlights that the a_{1} signal is more 'inertial' compared to the power signal, which responds instantly. It takes a notably longer period to settle into a new steady state after the power output has changed. This dynamic is applicable to both increases and decreases in power output, with a_{1} adjusting accordingly.

These effects are deeply entangled and tricky to identify during a typical workout. While response lag might be considered a unique physiological trait inherent to each individual, observing and analyzing stabilization times require a_{1} to actually stabilize, which depends on the intensity of power changes and time spent at given power. If there are no periods where a_{1} stabilizes, it implies that power is fluctuating (and thus inducing a_{1} fluctuations) within timeframes shorter than what's needed for a_{1} to adjust.

So, we can't just look at lag as the time between a power spike (local maximum) and when a_{1} hits its lowest point (local minimum), or the opposite. This interpretation doesn't give us the full picture because it misses out on how often someone changes their exercise intensity. Consider the previous example, where power jumps from 150W to 250W, but over different time frames. If they only keep up each effort for a minute, the a_{1} doesn't change much. The amount a_{1} drops in response to the power increase grows the longer the interval duration (2 minutes or more), until the duration is greater-equal to the stabilization time necessary for a_{1} to stabilize. For durations that are shorter, only local a_{1} minima are formed and they get deeper the longer someone stays at the new effort level. Thus the lag value increases accordingly (since the starting point, the moment power hits 250W, is always the same).

A real world example example for this might be a fast group ride in a small group with each athlete taking short turns at the front: the short time (seconds to maybe a minute) the athlete spends at high power output is not enough for stabilization at high intensity while the short break before the next turn at the front may be too short for a_{1} to increase even though the power output is significantly lower.

How is this *'combined lag'* affecting data points in the a_{1}-power plane, where any temporal reference is lost? And why didn't our old approach always effectively tackle this issue?

We can look at it using the same example. At 150W, which is relatively light for this athlete, a_{1} might hover around 1.2, creating a dense cluster of points near (1.2, 150W) on our graph. Let us consider long interval duration, longer than both stabilization times needed at 150W and 250W. The shift to 250W leads to a vertical leap on the graph: a_{1} stays the same initially. After a bit, as a_{1} begins to drop, we see a horizontal movement towards the higher intensity area (constant 250W), eventually settling at 0.7 as a_{1} stabilizes, forming another dense cluster around (0.7, 250W). If the power then drops back to 150W, the points initially shoot down vertically to lower power and then shift horizontally back to higher a_{1} values, gathering around (1.2, 150W) once more. Shorter intervals between power changes create a pattern that's more vertical. Obviously, this is a simplified view—the real trajectory is smoother and varies with the power levels and time spent.

This example sheds light on the limitations of our previous method, which assumed that within each window (or segment of similar a_{1}), power changes caused by lag would be roughly equal and opposite, so averaging them out would negate any spikes or drops. However, for certain workout structures the described scenario and actual workout data show that **lag influences different a _{1} regions uniquely**, illustrating why our old approach can fall short.

Specifically, when there's a sudden drop in power at low a_{1} levels following an intense effort, the average power in that period gets noticeably lower. As highlighted from Figure 3, this effect becomes more stark with fewer data points in a window or when a window contains a similar number of points at widely varying power levels within the same a_{1} range. This scenario is typical in workouts with very short-term structure. Both situations distort the average power values for different reasons. While mixing a variety of workout types may lessen this distortion, the issue doesn't completely disappear.

Given the scarcity of stable points as mentioned, solely relying on them for representative values isn't viable. The next best approach is to identify representatives through data clusters. Although the majority of clusters do not correspond to stable a_{1} windows (since the temporal reference is lost, particularly when grouping together workouts), they surely contain them. Furthermore, defining representatives using clusters from a mix of workouts is theoretically appealing. It's based on the straightforward idea that:

**If a pattern in the data consistently shows up short-term across various days and session types, it likely follows an underlying rule.**

Machine learning algorithms, especially those focusing on point density, often fall short in identifying data clusters within datasets rich in noise. This challenge led to the development of a nuanced, classical algorithm known for its clarity and adaptability—a **grid-based** approach, outlined as follows:

**Grid definition.**For a dataset composed of various workouts, we establish a grid by calculating the total averages of power and a_{1}(avg_p, avg_a_{1}). Each cell within the grid measures 0.1*avg_a_{1}in the a_{1}direction and 0.12*avg_p wide in the power direction. This differentiation accounts for the generally wider spread of power values at a given a_{1}. While basic, this setup can be fine-tuned further using additional metrics like standard deviations to optimize cell sizing. Typically, a cell is about 0.1 units wide in a_{1}and ranges from 12W to 36W in power.**Representative definition.**Within each grid cell, we count the points. If the count exceeds a predefined threshold—indicating a meaningful duration spent at those levels—a representative for that cell is determined using the average a_{1}and power values. This threshold is initially set at 60 seconds, though adjustments may be made to better reflect the dataset's size and diversity.**Refined regression**. Utilizing all the cell representatives, we conduct a refined linear regression to explore the power-a_{1}relationship. Linear regression is chosen for its straightforward nature, with refinements including weighting each representative by its data point count and excluding outliers with residuals beyond two standard deviations from the regression.

The grid method aims to highlight areas of higher point density effectively. Cell size is critical—too large, and we risk overlooking details; too small, and cells may lack data. Through experimentation, certain cell dimensions and thresholds have been identified that efficiently pinpoint cluster representatives across varied datasets.

As part of our validation process, we also examine what we refer to as 'best averages'. These are specific points (best_avg_a_{1}, best_avg_p) obtained as top moving power averages across various time spans, ranging from 3 to 20 minutes—the same used for calculating Critical Power. While these points aren't ideal representatives for the entire dataset since they only shine during maximal effort, and thus reflect the high-intensity zone, they may serve the role of testing the accuracy of the relationship uncovered by our method. Visual inspection confirms that best averages tend to align closer to the new method's rather than the old method's regression. In particular, this is the case in 58% of the workout groups evaluated where both methods yield a result (the old method almost always finds representatives, while the new method, being stricter, does not always find clusters). The new method performs clearly worse than the old one in 22% of cases. Finally, in 20% of situations, they are either equally good (usually the case when raw data points already exhibit a clear linear relationship) or equally bad (when there is no clear correlation between a_{1} and power aka athlete is a non-responder). An example is provided in Figure 4.

Figure 4: *Comparing the new grid method (black triangles) with the old representative method (red dots). The grid based regression (black dashed line) aligns much better with the best efforts (green squares) than the representative based regression (red dashed line).*

Now that we have found a better way to uncover the power-a_{1} relationship in the entire dynamic range, we can use the linear law from each workout group to extrapolate power values at given a_{1} values, as a_{1}=0.5, 0.75, 1, or similar. For consistency reasons, we do so in the a_{1} region covered by the fit.

We can use these three (or more) power values to track fitness over time. In particular, this allows to track how fitness at different intensity changes over time, permitting to track the athlete adaptations to different training regimes and intensities. This is extremely valuable not only in qualitatively understanding whether one athlete is more of a volume/intensity responder, but also in quantifying how much the powers at low, moderate and high intensity are changing with training as implemented in models of AI Endurance.

We can now check whether the grid method inferred power values at a_{1}=0.5 and 0.75 are different or not with respect to power values obtained with a a_{1} ramp test or a_{1} cluster threshold detection in AI Endurance.

Figure 5: *Comparing the grid method (black triangles) and the representative method (red dots) with ramp and clustering detection in AI Endurance. Grid method better accounts for the a _{1} data between 0.75 and 1.0 than the representative method. Ramps (blue diamonds) overestimate thresholds relative to both the grid and the representative method in this example whereas average of cluster thresholds (orange diamonds) fit both models rather well.*

Figure 6:*Comparing the grid method (black triangles) and the representative method (red dots) with ramp and clustering detection in AI Endurance. Grid and representative method lead to almost identical results in this example. Again, ramps (blue diamonds) overestimate thresholds relative to both the grid and the representative method in this whereas cluster thresholds (orange diamonds) fit both models rather well.*

Figure 7: *Example of a detection of a 0.75 ramp like-effort in AI Endurance: starting at around 8 minutes there is a steady increase in power and steady decrease in a _{1} values. The a_{1} values used to estimate the 0.75 ramp threshold are the solid blue dots. Muted blue (yellow) dots show clustering threshold detection at 0.75 (0.5).*

Since AI Endurance automatically detects ramp-like efforts around a_{1}=0.5 and 0.75, we can check if these ramp-derived (first and second) threshold powers correspond to the grid method inferred powers. We found the following results out of 50 workout groups with detected ramp efforts:

**out of 80 detected 0.75 ramps, 29 have higher thresholds**, 12 lower and 39 are the same as grid inferred p(0.75);- out of 36 detected 0.5 ramps, 10 have higher thresholds, 0 lower and 26 are the same as grid inferred p(0.5).

Here, "higher" means the ramp value is at least 110% the grid inferred value; "lower" at most 90% the grid inferred value and "same" the ramp and grid inferred value are within a 10% deviation.

It's important to remember that ramp-like efforts are not necessarily following the ramp test protocol but could happen during any workout if the criteria of a ramp are met, for example steadily increasing power with a ramp rate of 10-30W/min, steadily reducing a_{1}, and sufficient coverage in the a_{1} interval to determine the ramps at 0.75 and 0.5. These are essentially efforts that mirror the standard prescribed a_{1} ramp test.

Examples of higher ramp inferred values at both a_{1}=0.75 and 0.5 can be seen in Figure 5 and 6.

Interestingly, there have been a few findings in recent literature of ramp detection (particularly at 0.75) overestimating VT1 [8, 9, 10], however see also e.g. [11] as a recent study finding no overestimation. A potential reason for this overestimation may be that for a certain number of athletes, cardiac lag and stabilization times are too long for the given ramp rate: while the athlete is still lagging behind in a_{1} signal at a given power, i.e. a_{1} hasn't stabilized yet, the ramp protocol already moves on to the next ramp step. This would clearly result in overestimation of thresholds. We definitely see a range in stabilization times and for some athletes in our data set it does appear too long for a ramp rate protocol of 10-30 /min which is somewhat standard.

However, there have been studies [12] to assess threshold assessment as a function of ramp rate that did not find a dependence on ramp rate which seems to contradict this point. One also has to keep in mind that for too slow a ramp rate, fatigue kicks in - especially around VT2 - that would result in reduced a_{1} due to exhaustion and hence an underestimation of thresholds. There is a limited window of ramp rates that are a) not too fast for lag to mess with the results and b) not too slow for fatigue to kick in. The fact that both lag and fatigue onset seem to depend on the individual athlete may make it difficult to prescribe a universal ramp protocol that works for all athletes.

Figure 8: *Example of a 0.75 clustering threshold detection: the muted blue dots and their corresponding power values are used to estimate the 0.75 cluster threshold in this example.*

AI Endurance also estimates a_{1} thresholds via clustering: it collects power values when a_{1} stabilizes around 0.75 and 0.5, and computes the power average. By definition this method thus computes power values related to somewhat stable a_{1}=0.75 and 0.5 values. If the points in the stable window are enough, such that the points affected by the lag are the minority, the power average is in principle a good estimate, and it should closely match p(0.75) and p(0.5) obtained with the new method. Since we are working with workout groups, we can check that this is indeed the case by taking all these power estimates and averaging them across all workouts in the group: the **clustering averages are in much better agreement with the p(0.5) and p(0.75) values inferred by the new grid method**, see Figures 5 and 6.

Thus, we can conclude that, a good way to track fitness that is backed by the new algorithm we developed is:

- Look for and identify in each workout the
**power values corresponding to a given stable a**;_{1} - Track how the trend for
**each power at a given a**using a moving average over 7 to 21 days or so._{1}develops over time

Clustering thresholds are already implemented in AI Endurance so athletes are already benefiting from this detection method that is per definition more reflective of stabilized intensities than ramp efforts. It is very encouraging that clustering thresholds actually match the grid method inferred thresholds at 0.75 and 0.5 quite well. While AI Endurance's models currently give more weight to ramp detected HRV thresholds than clustering detected thresholds, we may re-evaluate this in light of this finding and push more weight onto clustering detection.

A missing link here is to tie the grid inferred/clustering thresholds to VT1 and VT2 as measured via gas exchange. To this end we propose the following hybrid lab and everyday workout experiment:

- Have athletes do a
_{1}**ramp test in the lab**to establish their power at VT1 and VT2 and derive ramp a_{1}thresholds via gas exchange. Additionally, have them**work for extended periods of time at approximately constant ERG mode power**in a separate session around a_{1}~0.75(0.5) while measuring gas exchange to validate cluster thresholds in the lab. Real time a_{1}measuring apps such as alphaHRV and Fatmaxxer can be used to fine-tune the ERG mode power. - Have athletes
**follow their usual outdoor and/or indoor workout routine and collect their power and a**'in the wild' via e.g. Garmin API and calculate cluster thresholds via AI Endurance._{1}values - Compare clustering thresholds to gas exchange VT1 and VT2 as well as ramp thresholds.

Tying to gas exchange thresholds would put the clustering method on solid footing and would potentially help to overcome the above listed lag induced challenges that complicate the power-a_{1} relationship and impair the detection of ramp thresholds.

Finally the clustering method promises a lot more convenience and ease of use for the athlete as they don't have to follow a specific test protocol such as a ramp and can just go about their planned workout routine without testing interruptions.

- Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription? - Thomas Gronwald, Bruce Rogers, Olaf Hoos - Front. Physiol. 2020
- Correlation properties of heart rate variability during endurance exercise: A systematic review - Thomas Gronwald, Olaf Hoos - Ann. Noninvasive Electrocardiology 2019
- Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists - Manuel Mateo-March, Manuel Moya-Ramón, Alejandro Javaloyes, Cristóbal Sánchez-Muñoz, Vicente J. Clemente-Suárez - Eur. J. Sport Sci. 2023
- A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability - Bruce Rogers, David Giles, Nick Draper, Olaf Hoos, Thomas Gronwald - Front. Physiol. 2021
- Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability - Bruce Rogers, David Giles, Nick Draper, Laurent Mourot, Thomas Gronwald - J. Funct. Morphol. Kinesiol. 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 - Bruce Rogers, David Giles, Nick Draper, Laurent Mourot, Thomas Gronwald - Sensors 2021
- Establishing the VO2 versus constant-work-rate relationship from ramp-incremental exercise: simple strategies for an unsolved problem - Danilo Iannetta, Rafael de Almeida Azevedo, Daniel A. Keir, Juan M. Murias - J Appl Physiol 2019
- Fractal correlation properties of heart rate variability as a marker of exercise intensity during incremental and constant-speed treadmill running - C. R. van Rassel, O. O. Ajayi, K. M. Sales, A. C. Clermont, M. Rummel, M.J. MacInnis - https://doi.org/10.1101/2023.12.19.23300234
- Heart Rate Variability Thresholds: Agreement with Established Approaches and Reproducibility in Trained Females and Males - Fleitas-Paniagua, Pablo R.; Marinari, Gabriele; Rasica, Letizia; Rogers, Bruce; Murias, Juan M. - Medicine & Science in Sports & Exercise 2024
- Reliability and validity of a non-linear index of heart rate variability to determine intensity thresholds - Noemí Sempere-Ruiz, Jos Manuel Sarabia, José Manuel Sarabia, Sabina Baladzhaeva, Sabina Baladzhaeva, Manuel Moya-Ramon, Manuel Moya-Ramón - https://doi.org/10.3389/fphys.2024.1329360
- Correlation properties of heart rate variability to assess the first ventilatory threshold and fatigue in runners - Bas Van Hooren, Bram Mennen, Thomas Gronwald, Bart C Bongers, Bruce Rogers - Journal of Sports Sciences 2023
- Effect of ramp slope on intensity thresholds based on correlation properties of heart rate variability during cycling - Pablo R. Fleitas-Paniagua, Rafael de Almeida Azevedo, Mackenzie Trpcic, Juan M. Murias, Bruce Rogers - https://doi.org/10.14814/phy2.15782

Paris to Ancaster is the biggest gravel grinder bike race in Canada. It’s in 8 weeks and I need to get in shape. AI Endurance can predict race performance and create a training plan which is optimized to my training responses. It predicts that I can increase my FTP by 14% to 293 Watts on race day with just 3.5 hours of training a week.

b*y Grant Paling*. In the first of three blog posts, Grant shares his experience of qualifying for Great Britain's Age Group team in the upcoming European Triathlon Championships.

Stay on top of your goals and support our local businesses at the same time. A virtual running challenge that comes as close to a race as possible now that social distancing is crucial in slowing down the spread of COVID-19.

b*y Grant Paling*. In his last blog post in a series of three, "I don’t really know how I’m doing," Grant reflects on self-assessment and personal growth, emphasizing the role of AI Endurance in setting realistic goals and predicting performance in triathlons.