Power Duration – Xert Breakthrough Training

The Making of a UCI World Champion – An In-Depth Xert Analysis of Loes Adegeest

Armando Mastracci — Sun, 06 Mar 2022 22:19:53 +0000

Loes Adegeest Wins the 2022 UCI eSports Women’s World Championships

On February 26th, 2022, athletes from around the world competed in the UCI eSports World Championships. It was the second edition of the event and in the women’s race, former World Champion and professional rider Ashleigh Moolman Pasio (who also just finished 3rd place in this year’s Strade Bianche) was the overwhelming favourite. This year’s race was two and a half laps of the NYC Knickerbocker circuit with the finish line atop the NYC KOM. The circuit is relative flat aside from the NYC KOM climb giving the entire race nearly 1000m of elevation gain over the 57km. It’s a tough finish with a middle 500m section at 10.6% for 400m and then 100m at 15.7%. The final 500m of the race features a flat 300m followed by 100m climb at 10.8% and a flat 100m to the finish.

Courtesy Zwift Insider

Loes Adegeest is an accomplished speed skater, having started racing as a teenager and races for the Dutch National Team. Speed skaters make great cyclists and vice versa and Loes is no different. She had always enjoyed cycling and quickly saw success on the Zwift racing scene. She joined the Aeonian Zwift cycling team and she has been a consistent and strong performer, racing in the Zwift Racing League’s Premier Division. She qualified for the UCI World Championship Women’s event in December 2021.

Over the 11 weeks leading into the event, she trained, strategized, focused and prepared herself for the final race on February 26th. On that day, she raced a perfect race and crossed the line to become the 2022 UCI eSports World Champion. Many saw this as an upset. But one look at her Xert breakthroughs and training leading up to the race, it wasn’t. She was destined to win.

The Race

The race was a tough race from the beginning with two hard efforts up the NYC KOM that wittled down the field. To see the story of the race, we can look at her power and elevation data and as well as her Maximum Power Available (MPA). MPA is Xert’s unique metric the depicts how much power an athlete has any given moment in a race. It is the purple line on the chart above and is an indicator of fatigue. The value goes further than just showing fatigue as it also depicts how this fatigue is affecting an athlete’s ability to produce power – their maximum power. When an athlete’s power reaches their MPA value, it’s a maximal effort and often results in a breakthrough in Xert.

As you can see, Loes’ MPA sees some relatively minor dips during the first two climbs. Her power data is mostly blue (low intensity) with only short yellow and red efforts during the NYC KOM climbs on each lap. She had conserved energy very effectively, never being pushed or tested at any point in the race. On the final lap, a few women remained, including defending champion, Ashleigh Moolman-Pasio. Team USA launched attack after attack with Katheryn Curi making a strong move leading into the final NYC KOM climb. She was caught passed by “AMP” and three women managed to bridge and hang on: Cecilia Hansen, Zoe Langham and Loes Adegeest. Loes had been preparing for this moment and knew exactly what to do. She took a moment on the flat second section before the final pitch to the finish to gather just a bit of needed energy and at the base of the climb she deployed her feather power-up and launched an all-out maximal effort with everything she had for 20s. The power-up and effort were perfectly timed, something she had practiced over and over with breakthrough after breakthrough efforts in her training. With her fitness at a level it had never been before, everything had come together at the moment: the perfect strategy, perfect execution, perfect planning and perfect training. Loes wins the race.

Examining the Winning Effort

Looking the final winning effort, Loes gets the hallmark power and MPA representation of a win – a breakthrough-on-the-line. This happens when an athlete reaches and sustains a breakthrough effort as they cross the finish line. It’s the definition of a perfect ending to a race.

The system evaluated her signature on this day to be 853W, 19.3kJ and 309W for her Peak Power, High Intensity Energy and Threshold Power, respectively. These three numbers are called a fitness signature in Xert. Her Threshold Power was 309W and at just 57kg is an impressive 5.4W/kg. This saw an increase from her previous high of 302W just 5 days earlier. But it wasn’t just her Threshold Power that made this win happen. It was her High Intensity Energy too. It’s as important as Threshold Power, if not more, to achieve this race-winning, all-out effort.

Is it all about FTP?

It was just recently that her High Intensity Energy was as low as 15.3kJ, in fact. This was at the end of a phase where she had focused on raising her Threshold Power (more on this below). What if she had continued to focus on her Threshold Power and not on her High Intensity Energy? What would it have taken to achieve the same result? To find out, we use the Advanced capabilities of Xert to examine how another athlete’s signature fits the same power data:

Here we have an athlete with a High Intensity Energy of 15kJ. In order to perform the same effort Loes performed that day, this athlete would need to have a Threshold Power of 327W! (5.7W/kg!) For female cyclists, this would be reserved for a very, very few at the top of their game. Had Loes been focused on her Threshold Power, reaching 327W would have been highly improbable, perhaps even impossible.

Others may have examined race details and thought that focusing on sprint power would be a good way to prepare. The final effort is only 20s so could be for sprinters? What if her Peak Power had risen to 1000W?

Doesn’t seem like it would have helped much at all. Threshold Power would still need to be in rarified territory (324W). That final 20s effort isn’t performed in a relatively rested state hence Peak Power doesn’t have a lot of influence on the athlete’s ability to push this power under this much fatigue. Pure sprinters really don’t have a chance here.

Focus Focus Focus

Loes had intentionally chosen Puncheur (4 minute W/kg Focus Athlete Type) in Xert to help guide her toward the right training in preparation for the event. She had done the NYC KOM climb many times before and knew that it was a 4 minute effort. She also knew that it wasn’t a paced as a 4 minute constant power effort. There was a short flat in the middle where there was some recovery. Her 1 minute power was also important. Indeed, the final sprint saw her reach a breakthrough at 500W, far above her 4 minute power. If we examine the power curve for the race, we get some indication of this. Rarely do power curves reveal this unfortunately, since efforts paced from fresh to failure do not occur often in racing power data. However in this case, there are some interesting things to observe:

On the top/left we have the power curve for Loes and on the bottom/right we have the power curve for the athlete with Threshold Power of 327W. You can see that the effort was nearly and all out 3 minute and 4 minute effort. This is due to the rest section before the final 200m sprint to the finish. Both sets of signatures have nearly the same values for these durations so, in effect, both athletes have the same capabilities at 3-4 minute power. But the effort isn’t a 3 or 4 minute effort. It ends with sprint ending at 500W which well above 3 and 4 minute power. Practicing this finish and being able to reach up to MPA and follow it down under fatigue will be key to winning.

Preparing to Win

Loes had been using Xert for just over 6 months and decided to use the platform in preparation for the world championships. Having examined the route, she recognized from the beginning that this was going to be a puncheur’s race. She set her Athlete Type to Puncheur and Target Event Date to Feb 26th and monitored her training. Most of her training would be by choosing both indoor Zwift rides and outdoor riding that are in-line with the advice being offered by Xert’s Adaptive Training Advisor. She also knew that she’s need to be creative. Having been selected in December, it’d didn’t give her much time to perform a thorough Base-Build-Peak Training Program. She had less than 80 days to prepare and also had to fit in a Training Camp with her team in early February:

She had been doing a lot of Zwift training and racing prior to the start her program and had a well developed High Intensity Energy but had to switch to building some base. But she also had to be mindful of the intensities needed to win and practicing her race winning efforts throughout her training. In the 10 weeks of her focused training, starting in mid December and ending on race day, she had 18 breakthroughs – nearly 2 a week – many of them similar in intensity and duration like she’d have to perform on race day. Even with all those breakthroughs, she held steady on building her Low Intensity Training Load over that period, increasing it from 55 to 76:

This phase saw her doing more lower intensity training than before, including more outdoor longer rides:

In that process she had to let her High Intensity Training Load slide in order to keep the right focus on your Low Intensity Training Load / Threshold Power to get it as high as possible. She also knew she was going to get a huge dose of High Intensity training during her Training Camp and was going to use that as her Peak phase to get her back on Focus and top up her High Intensity Energy to where it started:

The training camp ended up being a great opportunity to overreach. She loaded up on both Low Intensity Training Load with some very long rides:

.. but also worked hard to get her High Intensity system ready by practicing race winning efforts for breakthroughs during training camp:

Following the training camp, all that remained was to trust the taper to allow her systems to rebound from the time in deep yellow and red form (this is Xert’s Training Status) and bring it back to blue for the race. To ensure everything was ready, she tossed a few breakthrough efforts during taper week, mimicking the race event:

Even just a few days before the race to confirm she was ready, she pushed for a breakthrough, seeing her signature reach a level it had never reached before:

Then on race day, she went in with the confidence that she had fully prepared herself, she had practiced the race winning move over and over for breakthroughs and that she knew she had an opportunity to win the race. And she did.

The Demands of Cyclocross and How to Train For Them

Armando Mastracci — Mon, 10 Sep 2018 16:25:00 +0000

The mere mention of cyclocross can make some rider’s heart race with excitement. Usually riders who love a little mud. Or to suffer. Or ideally both! Because CX differs from what many of us do – or train to do – the rest of the year, questions about the specific demands of cyclocross, and how to train for them, often arise.

At Xert we have excellent cyclocross power files to dig into, a whole new way to look at – and use – power data, and some passionate CX’ers on staff to share with you the implications to your training!

Let’s begin by looking at one race that’s like many CX races, followed by more macro set of observations seen across a much broader dataset – with the implications to your training throughout under the ‘Train for It” headline.

CX Demands – Micro Level

SubstanX Cyclocross Summary

“SubstanX” was a 2017 CX race done by our Chief Sport Scientist, Dr. Stephen Cheung. It was a Masters 2 / Elite 3 category race of 50 minutes (ended up 54 minutes for Stephen), completing 7 laps. The course was generally flat with one punchy hill and two barriers per lap. This was a great race to analyze because Stephen ended up in a back-and-forth battle with another racer for almost the entire race, so there was no slacking or just trying to “time-trial” the race.

First lap with start-line power output near your 30s power

Off the start line of this race we see an effort of a little over 10 seconds with a 5 second average of 571W, which can approach your 30 second power. This is the effort – on ‘cold’ legs’ – needed to get a good position for the first corner, a critical time in CX. Over a longer start straight, or start loop, this kind of effort often lasts up to 30 seconds before a corner appears that’s sharp enough to let off the gas.

Train for It
Train your starts! Do workouts of 6-10 all-out efforts off the line in a big gear, with full recovery between each. This is also excellent practice for clipping in and fine-tuning gear selection. In the Xert library of Workouts, search “Cheung CX – Hot Laps” for a workout that replicates these starts quite well. This workout includes 6 start-line sprint efforts…

Over the course of the event there is an incredible amount of work done above threshold (i.e. heavily reliant on HIE). This is only possible because of the many – but very brief – rests leading into more than a dozen corners each lap.

Train for It
This is the time of year to really focus on developing your HIE. That means doing very focused work well above your threshold. Remember, physiologically you adapt to this high intensity work quickly, but it can be very taxing; don’t do more than two of these per week. If you’re racing on the weekend you’ll need even more rest, so do one of these per week to leave your race as the other hard effort.

First lap with power up the climb, and impact on MPA.

This race also included a short but steep climb typical of CX. The huge effort required to get up the hill results in a drain on his MPA; it’s at its lowest just after the climb each lap. The climb is short though, so a power output similar to the start of the race (about 200% of threshold) is delivered on every one of the seven laps.

Train for It
It’s rarely necessary to train on long climbs during CX season. Rather, the focus should be on short punchy climbs, especially seated and in a relatively low cadence to replicate the types of courses common in CX. You don’t always need a structured workout to build this capability. If you’re on a club ride with short punchy hills for example, hit the hills hard, seated, and in a big gear to build this kind of power.

At the end of the race we see another big surge in power. CX is often ‘shoulder-to-shoulder’ with other riders, whether for a podium place or not, so a sprint close to or at the line is not uncommon. In this race, Stephen had the highest XEP of the entire race (206 W) with two laps to go – when he attacked the other rider and put in a huge effort to establish a demoralizing gap. His last lap XEP was still hard, but lower at 195W as he concentrated on being smooth and not risking his placing.

Train for It
Your ability to handle slow-recovery fatigue remains important despite the relatively short duration of CX races. Consider workouts where you back-load your intervals towards the latter portions of your ride. Even if the absolute power you can generate may not be as high as early on during the ride, you are training your durability and stamina. This is a great time to take advantage the Xert Workout Player option to start the workout later if you like.

SubstanX Heart Rate

Heart Rate

Heart Rate doesn’t provide the same depth of insight into the demands of CX, or cycling in general, compared to some other sports, but a couple of observations are apparent.

There is a relatively narrow range during the event, which means that though there may be many moments of recovery (the MPA line is ‘bumpy’), the recoveries are so short that heart rate rarely comes down much.
If you were to draw a straight trendline through the HR data over the course of the event, it would clearly decline. Here, the athlete is fatiguing over the course of the event, though towards the end he clearly produces some very hard work which pushes his heart rate back up.

Train for It
If you have a heart rate strap, wear it. Heart rate can provide valuable insight how you are generating power and handling the stress of the event (including heat, hydration, etc.)

CX Demands – Macro Level

Where Xert really comes into its own is in its ability to parse out the demands of a race on the macro level, allowing you to tailor your training to suit the unique requirements of your race. As we dig into the data we begin to get a unique picture of a CX race by observing the event’s Focus, Specificity, and Strain.

SubstanX full race MPA Chart

Focus

Different CX races have different Focus types depending on the type of course. The SubstanX race produced a Focus Duration of 4m:15s, so it was categorized as a Puncheur focus. Stephen reports that, over his past few years of CX files, they consistently end up in either the Puncheur or Breakaway Specialist (5 min) focus range.

Train for It
When setting up your Xert Adaptive Training Advisor (XATA) goals, set your desired Athlete Type to Puncheur or Breakaway Specialist. This will direct the system to progress you through the training phases to produce your max power over 4 to 5 minutes by the end of your Peak phase. You can also look through your Activities list and sort by Focus to find rides you’ve done with a Puncheur or Breakaway Specialist profile. ‘Favourite’ these rides and drag them into your Fitness Planner/calendar to remind you what route or group to ride with!

Specificity

Xert considers the relative allocation of strain along the Power Duration curve from a particular activity. There are three categories:

Pure: all the strain matches a specific power on the Power Duration Curve.

Polar: the strain of the activity was either at Peak Power or below Threshold.

Mixed: a rating between Pure and Polar, and most common in cycling.

From SubstanX and many other CX races, we see that the Specificity is Mixed. Power is produced at points all along the Power Duration curve, from Peak Power efforts (coming out of corners), near Threshold Power (along straightaways), and all the way down to much lower power efforts (beginning to approach a corner).

Train for It
CX is not just about sprinting or training maximal efforts. Make sure you maintain your aerobic base and Threshold Power. This is why many of the suggested workouts in XATA will still be at a Focus of GC Specialist (8 min) or Climber (10 min). Remember, the higher your Threshold Power, the more recovery you can collect when the pace slows.

Strain

At 60 minutes or less, the total strain of a CX race may be much less than a long road race or hard club ride. Don’t be fooled though, because the Difficulty Score or XSS/hour can be extremely high. Over the 54 minutes of the SubstanX CX race, Stephen accumulated 75XSS. Stephen has accumulated as much as 101XSS over 62 min. These Difficulty Scores can often place CX races into the 4+ star (Tough) category.

Train for It
Use your Form (colour of stars) as a guide and always listen to your body. Short does NOT mean easy. You need to manage recovery extremely well during CX season. Especially if you do a club CX session during the week and race on the weekend, the rest of your week will likely find you in Tired (yellow) or Very Tired (red) form, and most of your other training may be endurance or recovery rides. This is especially true for Masters racers with many off-bike demands, and those with a Training Status of 3 stars or less.

Last but not least, the library of Workouts in Xert has many workouts relevant to, and specific for, cyclocross. They will either appear in your Recommended Workout list depending on your training phase, Training Load, Form, and other factors, or you can add them to you Fitness Planner (calendar). These are some of our favourites; feel free to search for these by keyword and favourite them by clicking on the star so they’re easy to find later:

Our Favourite CX Workouts
Name	Duration	Focus	Rating	XSS
Cheung CX – 2×20 min Over-Unders	00:55:00	Climber	3 – Difficult	86
CX – Draining the Swamp	01:03:23	GC Specialist	4 – Tough	137
Cheung CX – Ronnestad	00:58:15	GC Specialist	5 – Hard	157
Cheung CX – Hot Laps	01:14:18	Rouleur	4 – Tough	139
CX Twenty-Tens	01:02:30	Puncheur	4 – Tough	109

Hunting Segments with Power

Armando Mastracci — Wed, 25 Jul 2018 12:02:54 +0000

Everyone can agree that achieving new personal records out on the road is one of the things that makes cycling so much fun. Seeing all the hard work you’ve put into improving and then to see it come together when you get your best time on your favourite stretch of road is just so satisfying.

Your friends at Xert know how great these achievements make you feel. To help you get more of them, we decided to put some of our mathematical magic to task and came up with just the right app to satisfy your urges: The Xert Segment Hunter.

The Xert Segment Hunter is the first ever dynamic power pacing app for your Garmin. The app can be used with Strava segments or on time-trial courses ranging anywhere from 10s or over an hour. It is based on Xert’s advanced, athlete specific algorithm that uses your unique Fitness Signature and determines the best power you should target, second-by-second from start to finish. Additionally, the algorithm dynamically adjusts power targets to help you optimize your results as you complete the segment. The app is available on Garmin’s Connect IQ App Store and can be used with a variety of wearables and bike computers including the Fenix 5, Fenix 5s, Edge 1030, 1000, 820, 520 as well as the new Edge 520 Plus and the Edge 130.

The Xert Segment Hunter runs as a Garmin datafield allowing it to be part of every activity you record. During activity recording, as you approach the start line, the app counts down and then provides power targets using helpful colors, to assist you in performing it with your highest paced power. During execution, the algorithm adjusts power targets using a feedback process, with the goal to get a breakthrough-on-the-line – a new concept in cycling, which means that a maximal effort is perfectly timed to occur as you cross the finish line.

What You Need

A power meter. Your power meter will tell you how hard you’re working. It must be connected to your Garmin Edge computer.
An Xert Premium Trial or Subscription. Your unique Fitness Signature from your Xert profile is used to calculate your fatigue and real-time power targets. In your Xert account, link your Strava account (under the ‘Sync’ menu)
A Strava account. You will need an account as well as ‘starred’ segments you’d like to have synced with the Segment Hunter app.
A compatible Connect IQ Garmin unit. Click for Compatible Devices.

If you have all of the above, watch this video that shows how to get everything up and running:

Also, checkout our Quick Start Guide.

Important Things to Know about the Xert Segment Hunter

Full Screen View vs. Field View

The app can run in one of two view modes:

Full Screen View* – This mode shows the Target Power, Colour Band, Current Power, Distance Elapsed Bar, Time Remaining and Distance Remaining.
Field View – This view mode shows only the Target Power, Colour Band and Distance Elapsed Bar. It is well-suited to be included on an Activity Display Screen with multiple fields.

* While we have tried to provide full support for all Edge units, the Edge 520, the new Edge 130 and the wearables have restricted functionality due to their limited memory. They currently do NOT fully-support Full Screen View. They do however support Field View and can be used together with other activity data on your device.

What is “Breakthrough-on-the-Line”?

Breakthrough-on-the-line is something all coaches and advanced athletes have intuitively understood for many years. It means that you reach exhaustion as you cross the finish line, ensuring you gave it everything you had and left nothing in the tank. A breakthrough-on-the-line means you “left it all out on the course” and is a sign that you couldn’t have gone any harder.

When this option is set for your segment, the Segment Hunter will pace you evenly such that you reach a breakthrough just as you cross the finish line. The ability to time this to occur on the finish line and to make it happen with evenly paced power leading up to it, is what makes the Segment Hunter such an impressive little app.

How should I set Target Time? What if it’s too short or too long?

The Segment Hunter doesn’t pace you to complete the segment in the set Target Time. Rather it uses this in order to determine your optimal power pacing. As you execute the segment, based on how much time is left and how much fatigue you have accumulated, the app adjusts your power target to reflect your highest power for the remaining time.

For example, if you set your Target Time to be time that may not be physically possible for you, this will still be ok since what will happen is that the segment hunter will start you out at a target power that is too high and as you execute, you’ll become more fatigued but with less of the segment completed. The algorithm detects this and adjusts your Target Power lower to allow you to complete the remainder of the segment. The app does this this second-by-second, making sure you reach your limit just as you cross the finish line. In effect, setting a Target Time that’s too short, will cause your pacing to be positively-split. This will still work well in pacing you to a great time.

On the other hand, if you set your Target Time to be much longer than what you are capable of, the algorithm will still pace you to get the most out of the segment. It will starti you off at a lower Target Power but, as time progresses, since you don’t fatigue as much, the app will begin to increase your Target Power such that, in the end, you still reach a breakthrough-on-the-line. You’ll have a time that is much less than your original Target Time. In effect, setting a Target Time that’s too long, will cause your pacing to be negatively-split but will still likely get you a great finishing time.

Getting the Target Time precisely right isn’t critical. This is what makes the Segment Hunter so adaptable and useful.

Pacing on Short Segments

On very short segments, it will be difficult to watch the screen to know your exact power and if you’re on target or not. Shorter segments will likely take a bit of practice to get the feel for how hard you’ll need to push to hit the power target shown. Generally speaking, look down at your target during the countdown before the segment starts and try and get a feel for how you can produce that power for the whole segment. Use 1s or 3s average power in order to get more timely feedback.

Before you hit the beginning of a short segment, it is important to go into it carrying a lot of momentum. Often this means going pretty hard before you reach the start of the segment, perhaps having some accumulated fatigue even before the segment has started. The Segment Hunter will recognize this fatigue as you start your segment and adjust the power target for you. Remember, that some of your available power should be used to make sure you hit the start with sufficient pace.

Using the Segment Hunter for Time Trials

Experienced time-trialists often say that a successfully executed time trial isn’t purely about maintaining a fixed power over the entire course. The goal is to complete the course as fast as possible and this can mean varying power when it is needed.

The Segment Hunter can help even the most experienced time-trialist recognize just how much power they should be able to hold for the remainder of the race and can use this knowledge to give them feedback, avoiding periods when they failed to push just a bit harder and to recognize if they may have gone a bit too hard over a previous section. Ultimately, the goal is to complete the course with a breakthrough-on-the-line and the Segment Hunter will help them achieve that.

For inexperienced time-trialists, the Segment Hunter acts like your real-time feedback coach, helping you learn how to pace your power on courses and get a feel for how to modulate your power and manage your fatigue.

How wind and hills will affect your power targets

The Xert Segment Hunter’s 5 colour screen together with it’s dynamic power target algorithm helps you better manage situations where the conditions may dictate that you should go harder/easier in order to get a better time.

For example, if the course/segment is entirely into the wind or up a climb, it will take longer to complete. If you didn’t increase your Target Time, it will be set to be too short. As described above, this is okay as the algorithm will adjust your power by decreasing it as you execute the course.

Similarly, if the course/segment is entirely downwind, it will take less time to complete. Your pacing will be increased by the algorithm as you execute this course.

As described in the video, when performing into the wind/climbing, you should aim to have your power higher than the green band (purple) and if you are going downhill/downwind, aiming for blue is more appropriate. An experienced time-trialist will have a good feel for how they can sustain their momentum over shorter hills and can often even ride in the red band for short durations. With the Segment Hunter, the time spent in the red means that they will need to take it easier and recover a bit more at some point later on. Often times, this comes on the subsequent downhill section that follows the previous hill which then brings them back to their original target power.

Doing Segments at the end of a Long Ride

You will find that if you plan on using the Segment Hunter at the end of a long ride or after a hard workout, the pacing may be too hard for you to sustain. You will have likely accumulated slow-recovery fatigue that will prevent you from doing your absolute best. The Segment Hunter expects you to be ready to do your best, especially if you are looking for a breakthrough-on-the-line on the segment. Hunting segments within the first 1 or 2 hours of your ride and when you are fresh, will give you best results.

Training Using Xert’s Adaptive Training Advisor – PEZ Cycling News

Armando Mastracci — Sat, 19 May 2018 14:31:40 +0000

The Xert Adaptive Training Advisor (XATA) uses advanced analytics to not just model your current fitness, but to individually prescribe workouts guiding you towards a desired fitness and time. I relied on it for >90% of my training this winter leading up to April’s gravel season.

Read the whole story below…

Periodized & Progressive Training

Training needs to be progressive, steadily building upwards from a solid aerobic foundation to become more and more specialized towards your target event and its specific demands.

With Xert, I set my target event date to April 29 for the 25^th running of the Paris-Ancaster gravel race. XATA then sets up a template of a 45 day “Base” phase, 45 day “Build” phase, and a final 30 day “Peak” phase.

Here is overall fitness progression from January 1 to Paris-Ancaster on April 29th. Yellow dashes = Threshold Power; Black line = Training Load; Purple bars = daily Xert Strain Score; Coloured Circles = Breakthrough Activities.

I started systematic training on the bike again in mid-December, following a 6 week break (3 weeks totally off and 3 weeks cross-training). As you see from the progression chart, my Training Load at the start had dropped down to 50 (2 out of 5 stars), while my starting fitness signature was 1010W/17.5kJ/228W for Peak Power (PP), High-Intensity Energy (HIE) and Threshold Power. Lower Threshold Power (LTP), an indication of sustained endurance power, was 184 W.

Note also the pretty close matching between Training Load and Threshold Power. That is one reason why Training Load is often used as a surrogate of overall fitness.

During the Base phase, XATA focuses on building up aerobic capacity, prescribing a variety of endurance workouts that aim to raise both LTP and TP. By the end of January and Base phase, with every ride done on the trainer due to the harsh Canadian winter, my LTP and TP rose to 199 W and 242 W, respectively, while my Training Load increased to 77 (3 out of 5 stars).

Graph of my Peak Power (green), High Intensity Energy (blue), Threshold Power (red), and Lower Threshold Power (grey) from January 1 to April 29. TP generally changes gradually, whereas PP and HIE can spike up and down more.

Now in Build phase from February to mid-March, XATA began prescribing workouts of some intensity 1-2x/week. In keeping with my desired focus of 6-min power and my fitness level of 3 stars, these intensity workouts were primarily 3-3.5 stars in difficulty, and with the focus of 8-10 min power to build me gradually towards my 6-min power focus. By the end of Build phase, my Training Load had increased to 92, while my LTP moved up to 208 W and my Fitness Signature to 1031W/18.6kJ/254 W.

The ‘smart’ Closer 140 workout that was a staple of my Build phase. It’s ‘smart’ because the wattage constantly changes to keep you fatiguing at a set rate, recognizing that your ability to generate wattage decreases as you fatigue.

Over the final Peak phase, XATA’s prescribed intense workouts were mostly in the direct focus of Rouleur or 6-min power itself. My overall fitness did not really change much over this phase but, as with the whole idea of a Peak phase itself, the plan worked towards sharpening my fitness to match the specific requirements of the racing I would be doing. My final LTP and Fitness Signatures on P2A race day were 208 W and 1046W/18.4kJ/254W, with a Training Load of 97.

This Positive Split Micro workout was a staple of my Peak phase, being right in my target focus of ‘Rouler’ or 6-min power, and also featuring lots of repeated surges just like in racing.

Racing Results

I did three gravel races in April, each one drastically different despite being of similar 60-70 km length.

Paris-Ancaster was a peak physical & mental performance for me. Times are impossible to compare due to the conditions always changing, but my dream target was top 200^th overall in the >2,500 field. This would be a pretty challenging goal, as my previous best-ever placing was 263^rd overall. But I did it, moving all the way up to 177^th!

What other things about training and about using XATA shone through this experiment?

Appropriate Difficulty Levels

One lesson learned is that, the fitter you become, the harder the workouts that you can tolerate and complete. Xert ranks each workout in its database, and each of your activities, according to a Difficulty Score based on the rate at which you accumulate strain. These are ranked 1-5 stars, matching the 1-5 stars fitness ranking you have based on your Training Load.

One of my favourite workouts that fit my desired focus of Breakaway Specialist is “Let the Sparks Fly.” Typically a workout with a maximum difficulty score of just under 100, it’s ranked as a 3.5/5 star workout in terms of difficulty.

Throughout summer of 2017 and during my Peak training phase in March & April 2018, I could easily complete this workout despite its difficulty.

However, early in training after 3+ weeks off the bike and travelling, I tried the workout December 30, 2017, two weeks after restarting training and with a Training Load of 60 (2 star fitness).

As you can see, despite the workout wattages being much easier due to my lower fitness signature, I simply found the workout far too intense even in the very first set, and ended up bailing and just riding easy.

Lesson Learned: Xert does a nice job of only recommending workouts that are appropriate for you in terms of difficulty. If you’re at a 2 star fitness, it’s not going to recommend workouts in the >3.5 difficulty ranking.

Frequent Fitness Testing

The old axiom of “garbage in, garbage out” absolutely applies to proper training planning, either with or without power. When using power, one important keystone is to accurately and frequently set your fitness signature. Without this accurate fitness signature, you will either be doing workouts that are too easy – slowing your fitness development. Probably even worse is doing workouts that are too hard for your current abilities, leading to repeated failed workouts, non-functional overreaching, or injury.

Traditionally, this involves doing 20-min threshold tests every 4 weeks or so. But I’ve always hated these, and I’m actually not really the type of rider who can do a good sustained 20 min effort. I know from past history that I’m much more of a TTT-type, thriving with hard pacelines featuring short & hard efforts with brief recovery bouts.

A strength of Xert is that it makes frequent testing much easier, both indoors and out. Rather than requiring standard, steady-state efforts of different durations, Xert will automatically recalculate your Peak Power (PP), High Intensity Energy (HIE) and Threshold Power whenever you do a ride of sufficient intensity, indoors or outdoors.

So whether you’re doing a hard Zwift race, a fast club ride/race, a set workout on Xert or other training platforms, all you have to do is ride hard once a week or so, and Xert will automatically adjust all future workouts based on your new fitness signature.

For me, I have a favourite Tabata-esque workout that is guaranteed to reset my fitness signature. Each set consists of 13 reps of 30-s all out, with 15-s recovery at about 125 W. I find these easy to do both indoors and out, and it fits with both my preferred style of riding and my main focus on cyclocross racing. During summer 2017, my average watts can hit 270-282 W. In contrast, my average watts during our flat 15 km club TT is only about 250-255 W.

Here is an indoor effort that I did in mid-February. My Fitness Signature prior to the workout was 1003W/17.1kJ/243W. You see how my Maximal Power Available (MPA) with the blue line repeatedly fell below my actual power (red line). This tells Xert that the current fitness signature cannot explain the power profile.

As a result, Xert automatically recalculates to determine a Fitness Signature that fits the power profile, potentially changing 1-3 of the PP, HIE, or TP parameters. In this case, PP remained at 1003W, but HIE rose to 17.3kJ and TP to 245W.

To see what happens without an accurate fitness signature, if I did the exact same workout and power profile with my current fitness signature of 1043W/18.3kJ/253W, that same workout wouldn’t be nearly as intense, nor would it provide the same training stimulus.

Lesson Learned: No matter what system you use, accurate and frequent updating of your fitness parameters is essential to ensure that your workouts are optimized to your abilities. Xert makes this frequent assessment super-simple and automated.

Summary

I hope that you see the power and adaptability of Xert’s Training Advisor. Day to day, it will adapt to your training history and your desired target, recommending training that will put you on that ideal path to a peak performance.

MPA™ – Maximal Power Available

Armando Mastracci — Fri, 04 Dec 2015 01:00:56 +0000

What is Maximal Power Available?

Maximal Power Available (MPA) is a measurement of the maximal power that can be produced by an athlete for a given exercise, factoring in fatigue. Simply, MPA establishes at every given instant, exactly how much power you have. It is a concept that is well understood by sport scientists and coaches, since it establishes the upper limits for activities like running, swimming, cycling, rowing, etc. In the simplest of terms, it is the upper limit of athletic performance for a given activity, at a given time. MPA forms the foundation of performance and training, albeit indirectly at the moment since before Xert, there have not been convenient ways to measure it. Interval training, for example, can be thought of as exercise that reduces and recovers MPA over a series of repetitions. Coaches will often prescribe high-intensity interval workouts with specific intensity, durations, and recovery periods to use as part of a training plan. Knowing what workouts are suitable for the athlete that will enable them to achieve their training goals has been considered part of the art of coaching as it requires experience and a deep understanding of the athlete in order to do well. However, by determining MPA during bouts of exercise where fatigue has been accumulated, Xert has introduced a revolutionary way of viewing performance and training. It provides coaches and athletes with direct, real-time feedback on the how close an athlete is to their limit. This provides numerous benefits as will be explored here.

Review of Power-Duration

In our last blog , we explored the relationship of power-duration and how one’s fitness signature is represented by a power-duration curve. Each point on the curve represents a point-of-failure – in other word, the athlete was unable to generate the target power, because of the fatigue accumulated by the fatigue. At precisely the point-of-failure, the target power is exactly equal to the athlete’s MPA and any power output beyond this duration is below their MPA, by definition of the power-duration curve. If the athlete wishes to return to riding at that steady-state target power, the athlete first requires recovery. During a steady-state, fixed power effort above threshold, from fresh to failure, an athlete starts with an MPA that is equal to their peak power and MPA reduces until it eventually becomes equal to the fixed, steady-state power value. MPA represents their limit at any given moment and at the point of failure, their power equals their limit. Thus a point of failure can also be viewed as a point of maximal effort. The duration at a given point in time to the point of failure, is often called time-to-exhaustion (TTE) and it represents the time it will take the athlete to reach failure. (Note that the use of the word exhaustion may cause confusion with efforts of short duration since failure will be achieved before the feeling of exhaustion. Exhaustion in this context means failure to produce the target wattage). The affect on MPA is a function of the total amount of energy consumed. (It is also a function of cadence and this will be explored in a future blog. For the moment, we’ll assume that cadence is optimal during the effort). As the athlete fatigues, MPA reduces. As the athlete rests, MPA rises. At any point in time, MPA can be determined. Plotting MPA and power vs. time, we can compare power output with MPA to see how the athlete is performing relative to their MPA: We can now visualize the affect on MPA due to accumulated fatigue and recovery. We can also compare power output with MPA at a given moment. When MPA equals the athlete’s power output, we have a point of maximal effort / point-of-failure, by definition. Also similarly, if the power output crosses the MPA line, the athlete’s signature used to derive the MPA is too low and the athlete has achieved a fitness breakthrough.

Intermittent Power-Duration

An an athlete’s power-duration curve provides a representation of steady-state, fixed power, fresh-to-failure efforts where MPA reduces from peak power to the fixed power. When partially fatigued, a intermittent power-duration curve can be determined from MPA since it represents the athlete’s fatigue-adjusted peak power at that given instant: These intermittent curves show the power-duration of an athlete in a fatigued state. One can consider these as fatigue-adjusted power-duration curves. What this means is that we can not only determine an athlete’s MPA at a given moment but also establish their TTE for any given power output above threshold from the partially-fatigued state. Similarly, we are also able establish the highest power that the athlete can sustain for a given duration while in a fatigued state. We’ll explore these concepts in a future blog on real-time applications of MPA. When the athlete recovers and MPA rises, the time to recover can be determined. Generally this means full recovery, but importantly, the time to recover to a reach a given target MPA can also be determined. Establishing the target MPA recovery duration is a key component of workout design and we’ll be covering this topic in our next blog: Training Applications and MPA.

Analyzing MPA Data

Reviewing data from an MPA Analysis provides the following information:

Shows where an athlete was at their limit during a race, ride or workout,
Shows how well they were able to manage fatigue in preparation for the final sprint,
Shows how well they were able to manage fatigue during a time-trial,
Shows how a winning/failing attack was executed,
Determines if they “emptied the tank” at the appropriate time,
Shows how difficult an activity was to perform for the athlete,
Shows wattages in context such that efforts near threshold for example, can be assessed in terms of their difficulty,
Exposes other limiters should the athlete have given up or wasn’t able to output the needed power. Examples are heat/dehydration, feeding and others,
Highlights fitness breakthroughs,
Exposes potential PED use, where fitness breakthroughs that are outliers may suggest potential infractions.

Similarly, an athlete’s power output and ability to perform fluctuates due to many reasons:

The athlete may be subject to temporary, day-to-day reduction in fitness as the result of previous training or illness. This long-term fatigue may impact the ability for the athlete to achieve the same performance level as compared to a recent activity,
The athlete may be subject to other limitations to performance such as overheating, poor nutrition, dehydration, altitude, etc. that may also play a role in reduced performance in comparison to recent activities,
The athlete may have had a fitness breakthrough where their fitness has improved over previous activities,
The athlete may be tapering such that fitness is increasing in preparation for an important event,
The athlete may have benefited from either legal or illegal substance use before or during the activity,
The athlete may have detrained and have reduced fitness due to lack of / improper training,
The athlete may have never had points of maximal effort. Many rides, particularly lower intensity or recreational rides, do not have the athlete ever reaching a point-of-failure. These activities will not provide an accurate measure of an athlete’s fitness signature at the time of the activity even though one may obtain it from the data.

When a fitness breakthrough is achieved, it generally means that the athlete’s signature can be adjusted upwards such that training workout targets and MPA analysis will now use the updated signature. Providing feedback on fitness gains and breakthroughs is a great motivator and one could argue that, outside of competitive athletic pursuit, is the main reason why recreational cyclists track and manage their performance data.

Signature Extraction

Just as a fitness signature can be obtained with three best-effort power-duration pairs, using regression, the athlete’s fitness signature can be extracted from power data containing points of maximal effort. This signature represents the athlete’s fitness at the time of the activity. Obtaining a fitness signature from an activity in this fashion provides numerous benefits:

Athletes no longer need to perform specific fresh-to-failure tests, sometimes multiple tests, to determine their fitness.
Measuring fitness from races, rides or workouts enables faster feedback on progress.
Detraining and fitness losses can be discovered and addressed.
Other limiters can be discovered.

Signature extraction can also be performed intra-ride to determine how one’s signature has been affected by other slow-recovery fatigue factors, fatigue that can’t be recovered during the activity. Based on these types of time-variant analyses, patterns in the variation of an athlete’s signature can be seen and measures to address the decline put in place:

MPA Analysis with Xert Online

Xert^TM enables athletes to perform MPA Analysis on activity data. Power data from an uploaded activity file can be analyzed and information about the athlete or the performance can be assessed. An example MPA Analysis from Xert Online is shown below: In this activity, the athlete performed a series of intervals including sprints and short hill climbs. Highlighted on the chart are yellow markers indicating points of maximal effort based on the fitness signature: TP = 315W, HIE = 30kJ and PP = 1210W. As a coach reviewing this analysis, the athlete’s workout was successful as they had intended to reach points-of-failure based on their current signature. Zooming into the area of the activity where the greatest difference between power and MPA was achieved (indicated with green triangles), we can further examine specific occurrences and locations associated with the breakthrough: Using the Extract Signature button, the signature for the activity can be determined: This establishes the athlete’s fitness at the time of the activity. The new signature can be saved to the activity for future reporting or saved to the athlete’s profile such that all future activities will use this signature as their default. In our next blog, we’ll examine how we can train with MPA.

Your Fitness Signature

Armando Mastracci — Mon, 30 Nov 2015 05:00:52 +0000

Measuring Fitness

Each person possesses a unique level of fitness for a given physical activity. Different activities possess different measures of fitness. Fitness for weight-lifting, for example, is measured by the amount of weight one is able to lift. Alternatively, success in sports such running, swimming and cycling are primarily determined by how fast one can complete a given distance. In stark contrast, performance in other activities such as golf or alpine skiing, are highly dependent upon skill, rather than fitness per se,

A fitness measurement is different from a performance measurement as there are often many factors that influence the final end-result, independent of fitness. The fitness measurement is specific to the athlete, whilst the performance measurement is the ultimate outcome – the time to complete a distance, the mean power output, or the finishing position, for example. Cycling is one activity where the measurement of both fitness and performance are possible, using power meter instrumentation. This equipment enables athletes to perform testing in semi-controlled environments, to determine their fitness, whereas previously a laboratory was required. Furthermore, it allows the analysis of actual training and race performance, and from that we can infer an athlete’s fitness.

Fitness measurement has four main applications:

To establish protocols to determine an athlete’s strengths, weaknesses, limiters and areas of improvement.
For the prescription of individualized training, set relative to the fitness level of the athlete.
To determine the success/failure of training program, by monitoring the change in fitness
To establish athlete performance targets to be used in racing that assist the athlete in optimizing the pacing or tactics for an event.

As of today, there are many measures, or correlates of fitness – maximal oxygen update, lactates threshold(s), exercise economy/efficiency etc are all well known from laboratory based research. Within the field utilizing power meter measurements, the main measure of fitness in endurance cycling is Threshold Power (TP). Often referred to as functional threshold power (FTP), and generally used synonymously with the terms critical power, anaerobic threshold power, lactate threshold power , and others. Threshold Power as the highest power that an athlete can sustain without accumulating short-term fatigue is a marker of fitness. Simplistically, the higher the threshold power, the higher the intensity an athlete can sustain without accumulating immediate fatigue.

Recently, other measures have started to enter into the mainstream for the assessment of fitness – terms such as anaerobic work capacity, W-prime (W’) and functional reserve capacity (FRC). These are terms used to describe the amount of High Intensity Energy (HIE) you have available, or the available capacity to perform work above the threshold power.

Another measure of fitness, particularly for cyclists that compete in shorter events or in events that require a final sprint is Peak Power (PP). Although not an often discussed measure of endurance fitness per se, peak power establishes the highest power one can achieve and is measured when the cyclist is in a fresh state.

Together – threshold power, high intensity energey, and peak power – form the cyclist’s fitness signature. The higher any of these measures are, the fitter the cyclist.

The Concept of Power-Duration

When we measure the length of time an athlete can sustain a given power output, and plot the power output vs. the time, we obtain a Power-time (P-t) curve, also known as a Power-Duration (PD) curve, or sometimes simply a power curve. This is commonly known as the power-duration relationship, and is evident across many dynamic sports in humans, in health and disease, and indeed right across the animal kingdom. It is a fundamental cornerstone of exercise physiology, and also of endurance performance.

We can visualize the fitness of an athlete using their power-duration curve, plotted as power output (in watts) vs. time (in seconds) using a logarithmic scale for visual clarity:

On the chart above, the top left represents the athlete’s peak power (PP), the bottom right the threshold power (TP) and the region in between is the capacity available above threshold, i.e. your high intensity energy (HIE). Thus a power-duration curve is a visual representation of a fitness signature.

During a steady-state effort from fresh-to-failure, that is, the athlete starts from a rested state and maintains a given power intensity until they are unable to sustain that intensity, the end-point of the effort rests on the power duration curve:

The points on your power-duration curve can also be considered as points-of-failure for steady-state efforts from rested states. In Xert, points-of-failure are synonymous with maximal efforts. In our definition, a maximal effort is any time an athlete attempts to generate as much power as they have available. Maximal power available (MPA) is the term used that defines the maximal amount of power an athlete has available at any given moment. Maximal efforts are points during an activity where an effort to produce as much power as possible occurred, i.e. both power and MPA are equal. Typical examples of maximal efforts in cycling are as follows:

During an all-out sprint or attack
Failing to bridge
Getting dropped
Failing to sustain a target power during an effort, such as in a time-trial or fitness test

In each of these situations, if more power was available, it would have been used. It is at these exact moments that one’s fitness is expressed. It is also at these moments which define the points of the power-duration relationship.

A Deeper Look

The power-duration relationship notably, is in a constant state of flux. During an activity, power-duration is affected intermittently by various types of fatigue. Fast-recovery fatigue can be recovered during the activity. This type of fatigue reduces one’s power-duration relationship and thus points-of-failure happen sooner:

For longer activities, some fatigue is accumulated which cannot be recovered easily. This type of fatigue takes much longer for the athlete to recover from and, within an activity, no recovery from this type of fatigue is possible:

Fitness Breakthroughs

If an athlete had performed an activity that expressed a fitness signature and then, during a subsequent activity the athlete was able to surpass what would normally have been a point-of-failure, the athlete has demonstrated an improved fitness level. They have achieved a fitness breakthrough. Identifying fitness breakthroughs are critical in the assessment of a given athlete in terms of their fitness but, more importantly, it provides feedback about the success of a given training program or methods.

Determining MPA during an activity or workout and comparing it to the power output is a very powerful way of assessing the athlete’s fitness and performance. We’ll cover the concept of MPA and MPA analysis techniques in our next blog.

Fitness Measures

Although a fitness signature provides a convenient way to define a power duration relationship, often working with three simultaneous measurements can be cumbersome. In fact, for most athletes, improving all three in equal amounts isn’t always the optimal approach for their training goals. One parameter may have more relative importance than another.

To simplify the management and reporting of fitness, Xert enables the ability to select a fitness measure. Your fitness measure is a combination of your fitness signature parameters, and may also include your body mass, that helps you assess whether your fitness for the type of athletic events you are targeting, is increasing or decreasing.

Fitness measures combine all three fitness parameters by evaluating fitness as the maximal steady-state power that could be performed for a given duration, optionally accounting for body mass. For example, the fitness measure well-suited for road sprinting is a 1 minute maximal steady-state power divided by your body mass (60s W/kg). Successful road sprinters tend to possess higher 60s W/kg and tracking your 60s W/kg power as your fitness measure, would enable you to see how your fitness as a road sprinter is progressing.

A Fitness Signature Calculator

Xert Online provides a handy fitness signature calculator which enables the determination of your fitness signature using tested best-efforts. The calculator requires just 3 pairs of values. Typically, the 3 pairs are entered in order of duration, with your highest, peak-power tested effort first and your lowest threshold tested effort last. One other datapoint along your curve is needed to determine your fitness signature.

For example, say you know the maximal power you’ve been able to produce, 1000W lasted 3 seconds, a 20 minute test you performed recently you did at 300W and you did rested, hard, all-out efforts at 500W where you were able to hold them for 100 seconds.

The calculator establishes your fitness signature based on these three points and determined that your peak power is 1004W, your threshold power is 278W and your high intensity energy is 26.6 kJ. These three values represent your signature based on the 3 power-duration values you provided.

Note that the signature is only an estimate of your fitness based on the three data points provided. Often, the fitness you had during the three individual tests varied so the determination of your fitness signature is subject to variation from different fitness levels and day-to-day changes as a result from training and recovery.

To obtain a signature that is a representation of your fitness for a given point in time, we’ll need to look deeper into the data itself. That will be the topic of our next blog where we will explore the concept of maximal power available in more detail.

Xert and Bioshift – A History

Armando Mastracci — Thu, 12 Nov 2015 14:16:12 +0000

A Brief History of Our Research – Xert and Bioshift

Many of you are likely wondering: “How do Xert and Bioshift relate? They seem so different.”

To answer that question, we turn to another seemingly disparate topic: Heart-rate data and analysis.

About four years ago, our research began by examining power and heart-rate data and their relationship using performance data collected while cycling. We had known that power and heart-rate, or more specifically steady-state heart-rate, are nearly linear in their relationship. We can expect heart-rate to increase at about the same rate as power does as intensity increases. So called “Ramp Tests” were performed that demonstrated this relationship. What was discovered was that steady-state heart-rate and power can be made more linear when you account for cadence. This would appear intuitively true as pedaling at very low cadences at high torque but with low power output can be very hard work for the body, thus heart-rate increases. Similarly, pedaling really-really fast producing little power also is hard-work for the body and heart-rate increases.

A mathematical formula for this was established, that is, the relationship between power, steady-state heart-rate and cadence was formulated. Using simple parameters: resting heart-rate, maximal heart-rate, maximal power and new one for pedaling efficiency, we were able to determine how steady-state heart-rate will increase and decrease with respect to changes in power and cadence.

The linearity of heart-rate and power had its limitations though. For higher intensities, the relationship was less linear, particularly as fatigue rose. It appeared that at higher intensities, it became harder and harder to get heart-rate to increase.

What was postulated then was that heart-rate was being subject to a slow-component and that when work was performed above a threshold power, it would respond differently to power outputs. Several methods were attempted early on with varying degrees of success. Using regression techniques, all the methods seemed to improve the linearity but not always consistently. This all changed when we introduced into our model, a varying maximal power, one that was a function of the work performed above threshold. Our regressions results jumped suggesting that our formula was it. Fatigue manifests as a reduced maximal power and heart-rate response to power decreases as fatigue progresses. The relationship between work performed above threshold and its impact to maximal power was thus established.

Throughout this process of discovery, other factors that affect heart-rate such as heat dissipation were also introduced. All of these were built over a novel heart-rate kinetics model that enabled the ability to establish what an equivalent steady-state heart rate would be, based on the current heart-rate and its rate of change. Given that there was no term for this value, it was decided to use the word “exertion” to represent this equivalent steady-state value. It is a derived value based on heart-rate, its rate of change and individual heart-rate kinetics characteristics.

Bioshift

Having developed an ability to determine how to minimize exertion for a given power by varying cadence, optimal cadence could be determined. This optimal cadence would essentially be the most efficient for the body to perform as it would be the least taxing on the cardiovascular system.

Initially, the thought was to provide cyclists information on when they should change gears. But then we had a better thought: if we could interface to a shifting system, rather than just provide information on shifting, we could shift gears on the cyclist’s behalf. This became the genesis of Bioshift. From there, it was only a matter of creating the interfaces to shifting systems, develop a platform to run the optimal cadence algorithm and calculate the optimal gear in real-time. At first, we chose dedicated hardware and have now moved to wireless Bluetooth and smart phone technology. With our fatigue model having been developed too, we could also account for how optimal cadence will change with fatigue.

Xert

The original fatigue model established how maximal power available (MPA) was affected by fatigue and accounted for both work expended and recovered above and below threshold power. Initially, it was simply used in a regression to establish threshold power from heart-rate, cadence and power data.

It was only after a number of months and upon further inspection and rearrangement of the formula that a new power-duration model was formulated. It was a novel, three-parameter model – peak maximal power, above threshold capacity and threshold power – that closely matched mean-maximal power values across the entire power-duration (PD) spectrum, from 1s all the way to over an hour. It closely matched the two parameter Monod-Scherrer CP/W’ model at longer durations but didn’t suffer from its limitations.

What further distinguished the model is that not only could an entire PD curve be derived for an athlete with just 3 parameters, but a PD curve could also be established when the athlete was in a fatigued state. An intermittent or fatigue-adjusted PD curve could be determined at any point. We could calculate any mean-maximal power and time-to-exhaustion at any point in time. Using this information in real-time applications was envisioned and the first thoughts about a possible mobile application were imagined.

When we examined the meaning of a PD curve, the points on the PD curve have special significance – they represent points-of-failure for steady-state, fresh-to-exhaustion efforts. The points on the curve represent MPA since, by definition, one cannot sustain the target steady-state power beyond this duration. Since we have a way of deriving MPA and if we plot power and MPA against time and inspect where they meet, we discover that these meeting points indicate points-of-failure. These points mean that we just can’t hold the power we want, such as when we’re sprinting, failing to bridge, getting dropped or at the end of a steady-state interval, trying to hang on to the target power. These points express our limits of ability since we can’t go any harder at that instant. MPA that’s derived with these 3 parameters defines the limits of athletic ability and exposes them in plain sight.

But what if we don’t know our 3 parameters but we know we had points-of-failure occur during the activity? Could we use this to determine our 3 parameters? A regression routine was developed and tested and a signature extraction routine was created. Using this extraction process, we were able to establish the 3 parameters that govern our ability to perform at the time of activity was performed. In fact, all we needed was a handful of points-of-failure and we could determine the 3 parameters that define MPA.

With MPA Analysis, Signature Extraction software and the vision of real-time fatigue, the development of Xert Online and Xert Mobile was initiated with the goal to expand them as tools that could be offered to cyclists and athletes to increase their knowledge of their athletic ability and potential.

Future Applications

Bioshift and Xert are both based on the use of power data. But what about heart-rate data and exertion? What happened to all that?

This is will be our third project: taking heart-rate data to new levels of analysis. Our model enables the derivation of power and fatigue from heart-rate data. Every individual has unique heart-rate kinetics characteristics and by determining this, we can obtain valuable information about them and their ability to perform from a standard heart-rate monitor. There will be many benefits to this technology – from sports, to military, to occupational, to clinical.