Tuesday, June 22, 2021

Apologies to Those Who Comment





Probably none of you are still here, probably none of you will ever see this, and yet I must apologize. I am sorry the comment you so generously wrote in response to one of my blog posts never appeared below that post, that it seemed to vanish into the blogosphere. That was not intentional, I promise you! The good news, such as it is, is that your comment has finally made it onto my blog.

When I started blogging in 2012, when someone would make a comment in response to one of my posts, I would get an email. I could decide to approve the comment which would then appear on my blog or not approve it and it would not. Mostly I approved comments, rejecting only those that clearly were advertisements having nothing to do with my post. I never got very many comments so when, sometime in 2018, I stopped getting them at all, I didn't notice.

Recently, I had an unrelated issue with Blogger. A draft post that I had been working on for months simply vanished one day and nothing I could do would bring it back. I searched the Internet for a way to get my content back and found that this was a known, replicable bug that Google (who maintains Blogger) is apparently ignoring. All of a sudden I became very concerned about protecting my content. In the past, there had been a way of backing up an entire blog with one click and and went looking for it and found that, apparently, that got silently removed. What I ended up doing to backup my blog is to go through post by post, all 200+ posts, and print them as PDF files. (I had many fewer drafts and saved those by cutting and pasting them into a file on my desktop computer.) However, before I gave up and took that approach, I went through every option and setting in Blogger looking for that missing backup capability, and in so doing, came across an option I had never seen before, an option called "Comments." Clicking on it, I found nine or ten comments on various posts, sitting in limbo, waiting for my approval. With no notification whatsoever, Google had stopped sending me emails so I never knew to look. I immediately approved them all, but for some of you, that was three years late. Sorry!

I have a love/hate relationship with Google (more love than hate.) When, in terror of losing my blog's content, to where did I paste my draft blog posts? To Google Docs, that's where. More than that, my entire digital history is backed up onto Google Drive. The biggest problem with Google software is that it is free. At that price, what right to I have to complain about anything? (I do pay for my Google Drive backup space.) Several weeks ago, I was fuming in response to another Blogger problem and ran across a post advising all future bloggers to avoid Google's Blogger software in favor of WordPress, a for-money product that the poster claimed was more reliable, feature-rich, and credible. A few years ago I had the opportunity of working with WordPress and think, if I could set my Wayback Machine to 2012 when I started my blog, I might advise myself to pay the money and take that approach. (Switching now would be much more problematic.) Realizing the absurdity of pasting my draft content into Google Docs to protect myself from the flaws in Google Blogger, I looked to see what it would cost me to go back to Microsoft Word and I have started to wonder if I should be taking another look at Apple Cloud for backup. But what assurance do I have that Apple or Microsoft or WordPress wouldn't let me down as well?

This post is meant to be an apology to those kind folks to commented on my blog only to have their comments (accidentally) ignored, not an assault on Google. There is no such thing as perfection in this world and certainly not in computerverse. I have been involved with computers since 1980 and for most of that time have felt that too much attention was being paid to feature novelty and not enough to stability and reliability. About twenty years ago, the IT staff at the university where I worked begged me to move all my emails onto their system, promising me they would preserve them forever. A few years later, at the advice of university attorneys, they deleted all but the last three years of my emails. When I complained bitterly, they merely shrugged. So Google is far from unique in struggling with these issues. Nonetheless, I continue to be frustrated with problems which, though not unique to Google, are problems to which Google is not immune. But mostly I wanted to explain to you, kind commenter, what went wrong.



Monday, June 7, 2021

What Is Truth?




Ride speed on my New Alpine and New Alpine Cañada routes since my move to Emerald Hills. Ride speeds are in blue. The curve in red is a moving average of 8 rides centered around each time point.



A recurring theme on this blog is the impact of long term fatigue on my cycling. Back in 2012, 2013, and 2014, I was attempting to be a randonneur, a long distance cyclist who does rides of 200 to 1200 kilometers (km). During those years, I only managed two such rides, both at the shortest 200 km distance, and the biggest factor in my failure to complete more was long term fatigue. When I moved to California three and a half years ago, I decided to switch to shorter rides, 100 km rides known as metric centuries. My hope was that, unlike with the longer 200 km rides, I could do the 100 km rides more frequently, that as compared to the long term fatigue generated by training for and riding the 200 km rides, fatigue would be less of an issue with the 100 km rides. That has definitely been the case. However, less of an issue and not an issue are not the same thing. Although reduced, I believe that long term fatigue is still an issue for my 100 km rides. Or is it? Was fatigue ever the issue I thought it was? As a scientist, I always question my assumptions, there is a lot to question, and as much as I have discussed long term fatigue I still have a lot of uncertainty about it, an uncertainty that recently manifest itself yet again.

Let's start with some definitions: Form = Fitness - Fatigue. Form is what determines how fast I can potentially complete a ride, how long a ride I can complete, etc. If I haven't been training, my Fitness will be low and I will not be able to ride very quickly or very far. On the other hand, I might be very fit but completely exhausted from the training required to develop that Fitness, as a result my Fatigue is high, and so again, my Form and thus speed on a ride will be low. Also, factors other than cycling can cause Fatigue, other kinds of exercise (e.g. yard work), emotional stress, lack of sleep, illness, etc. Finally, this is not a quantitative equation. The biological mechanisms underlying Form, Fitness, and Fatigue are far from completely understood and there are no generally agreed-upon ways of measuring them and thus no units of Form, Fitness, or Fatigue. Thus, Form = Fitness - Fatigue is a conceptual equation. This post is about Form. To what extent my Form at any moment in time is due to Fitness or due to Fatigue has been and will be discussed elsewhere.

What inspired this post? As the COVID-19 pandemic has started to wane, I had hoped to restart my metric century group rides last month by riding the Art of Survival on May 29. However, I was not able to do that. I tried to replicate the very successful training plan I had developed for the 2019 Golden Hills metric century which includes a weekly 33 mile ride in the months before the event and then a 44 mile ride 4 weeks before and a 54 mile ride 2 weeks before. I had been successful at doing the 33 mile ride almost every week and completed the 44 mile ride on schedule, but there were warning signs. The two observations I have been able to use to measure my long term fatigue are how fast I ride and how I feel. By "how I feel" I mean do my legs feel sore and tired? Do I feel generally lethargic? Am I more grumpy than usual, do little things bother me more than they should? Am I unenthusiastic about starting a ride and do I find it an unpleasant slog once I go? As I attempted to train for the Art of Survival, that is how I felt. However, when I looked back at my subjective fatigue data, I found it unconvincing. I think I am just a pessimist, and it seems that my evaluation "how I felt" during the vast majority of my rides can be summarized as "I felt bad." Maybe I'm just old. Bill Clinton famously said that, after age 50, if you wake up in the morning and nothing hurts, you know you died during the night.

The second way I evaluate my readiness for a challenging ride is how fast I am riding. Currently, there are two routes I ride regularly enough that I can use them to assess my speed, named "New Alpine" and "New Alpine-Cañada". I felt like my speed on these rides were slow. The central question of this post is, is this feeling of slowness because the rides were really slow, or was it a subjective illusion?

The New Alpine and New Alpine-Cañada routes are similar to each other and similar to routes I rode back in the Fall of 2019 (routes named "Alpine" and "Alpine-Cañada"), a time when I was riding well. and thus I might be able to compare speeds between now and then. But is not the speed of a ride dependent on how fast I choose to ride as much as my fatigue level? To some extent, yes. (I have blogged about that.) That said, I firmly believe that the speed at which I complete these rides is governed much more by my Form than by a conscious decision; I tend to ride them at a relatively constant subjective feel, not holding back but at a speed I feel I could maintain indefinitely. Thus, when I notice that, over several rides, the speed of these rides is lower than average, I take note. Unfortunately, this is still a subjective assessment. Yes, the speed of a ride is objective but what I consider fast and what I consider slow is subjective as is how many slow rides it takes before I conclude I am suffering from fatigue. I wanted to use statistical analysis to make this assessment less subjective and more quantitative, and that is the subject of this post.

I think I have a pretty good understanding of the theory of statistics but I am not a statistician; I lack the years of experience that made the statisticians I have worked with over the years so valuable. Also, the ride data I want to analyze is quite different in structure than the experimental data for which standard statistical tools were developed. Statistics is always as much of an art as it is a science and even the best of statistical approaches will not give informative results if the underlying data is not what it is assumed to be. With all those pitfalls in mind, I have taken a very redundant, conservative approach to my analysis, an approach using different tools to cross check I wasn't making a silly mistake and which I kept as close to first principles as I could so as to avoid the oh so common errors resulting from plugging data into the wrong formula or algorithm. Finally, I would mention the importance of thinking carefully about exactly what question I am answering with any specific analysis.

I noted above that I thought I rode four of my routes at speeds that were, on average, the same. Here is the data on those four routes:



Back when I first started riding the Alpine and Alpine-Cañada routes, I predicted that, because the Alpine-Cañada route was longer it would, on average, be slower, but that has not been my experience. Subjectively, I noted that my average speed on the two routes tended to be similar. On the other hand, looking at the small changes in the routes caused by my move from San Carlos to Emerald Hills, I predicted that, in the long run, the average speeds on the new routes and the old would be very close to the same. However, what might obscure that similarity in the short term is that my Fatigue seems to have been high for a lot of the time since the move. That illustrates a general complication of the analysis I am trying to do. Even assuming that ride speed is a simple reflection of my Form, they will not be randomly distributed over time, fast rides and slow rides will cluster, something I needed to keep firmly in mind as I did my analysis.

I could try to assess my Form using just the data from one of the four above datasets but there would be significant advantages if I could use them interchangeably, and thus my first statistical task was an analysis of variance to determine if I am formally justified in doing so. To perform Analysis of Variance (ANOVA), I used the formulas provided in "Primer of Biostatistics, Sixth Edition" by Stanton A. Glantz. (In general, that book was my guide for most of the statistics in this post. Hereafter, I will refer to this book as "Primer of Biostatistics.") Rather than use the table of critical values in that book, I used the Google Sheets FDIST function to calculate the P-value that the rides from the four routes were from the same "group", e.g. have the same average speeds. The equations I used required that the four groups to be compared have the same number of samples. The Alpine-Cañada route I have ridden the fewest number of times, 29, so I had to take subsets of the other three so that I had four sets of 29 rides to compare. The normal way of doing that is to pick random samples. However, because of the data is clustered with respect to time, that is, the speed I ride on Monday tends to be correlated to the speed I ride on Wednesday much more than it is to the ride I did six months ago, I used matching instead. Since the set needing the most subsampling, the Alpine ride, is interleaved with the smallest set, the Alpine-Cañada ride, I picked subsamples from the Alpine ride one week before or after an Alpine-Cañada ride when possible, and when that didn't give enough subsamples, scattered the remainder as evenly over time as I could. In the case of the New Alpine and New Alpine-Cañada datasets, they were only slightly too large (30 and 33 samples) and I had reason to believe that the most recent rides were outliers. In addition, I figured there was some virtue in having the rides I selected as close in time to the rides on the other two routes as possible. For those reasons, I picked the oldest 29 rides for each of these two routes. 

When I then did an ANOVA analysis on these four sets of 29 rides I determined that there was an approximately 60% chance they were all the same and a 40% chance that at least one was different. If I had been trying to prove that one of these rides was different (the most common use for this kind of analysis) I would have been disappointed that I failed the P < 0.05 test. Since I am hoping for the opposite, I should be happy, right? Well, as happy as I can be. In my opinion, there is no way to "prove" that rides on all four routes have the same average speed, I can only say I failed to prove otherwise. What my analysis does say is that there is no good reason, based on this data, to separate rides on these four routes from each other and thus provides justification for me to treat my ride speed on any of these four routes the same.

Does common sense agree with the above analysis? Common sense says that my average speed on these four rides cannot be exactly the same; after all, they are all different routes and those differences are almost certain to affect average speed. The question I really care about is this: "How big is the impact of route selection on average speed compared to the impact of Form?" Fortunately, statistics has a way to estimate the answer to that question, calculating confidence intervals. 

Using the same four subset of rides as I used for ANOVA, there are six possible pairwise comparisons I could make; Alpine vs Alpine-Cañada, Alpine vs New Alpine, Alpine vs New Alpine-Cañada, Alpine-Cañada vs New Alpine-Cañada, Alpine-Cañada vs New Alpine, and New Alpine vs New Alpine-Cañada. However, it would be a mistake to blindly make all six comparisons. If comparisons are done with the usual P < 0.05 criterion, there is a one in twenty chance that any difference declared significant will, in fact, be due to chance. With two comparisons, because there are now two chances to get unlucky, there is an almost 10% chance at least one of the two will be positive and by the time all six comparisons were made, that 5% risk has risen to about 26%. There are ways of correcting for that, but these corrections reduces the sensitivity of the analysis and so the right thing to do is to make as few comparisons as is necessary to answer the question at hand. I decided to make only one comparison, New Alpine vs New Alpine-Cañada. The reason I picked that one is that I probably will never ride the Alpine and Alpine-Cañada routes again (because my rides are door to door and the location of my door has permanently changed with my move from San Carlos to Emerald Hills) and so going forward, what I really want to know is if I am justified in pooling my rides on those two different routes. The 29 rides I used from the set of rides taken on the New Alpine route have an average speed of 12.06 mph, compared to those taken on the longer New Alpine-Cañada route which have an average speed of 12.24 mph. The difference in those average speeds is 0.18 mph. When I revisit that a year from now when I have 50 or 60 rides on each route, is that difference likely to stay the same, get larger, or get smaller? How close is 12.06 mph to the true average speed I would see on the New Alpine route if I rode it many more times? Using the approach outlined in Chapter 6 of Primer of Biostatistics, I calculated that it is 95% certain that the real difference in average speed over these two routes is between -0.01 and +0.47 mph. That is, the New Alpine route may even be a bit faster than the New Alpine-Cañada route, but is not likely to be more than 0.47 mph slower. Without belaboring the point, this suggests that any difference in speed between these two routes is unlikely to confound my attempts to determine my current Form using a random mixture of rides on them. Going forward, I will use rides on all four routes interchangeably and refer to them as the Alpine-Like routes and rides on those routes as Alpine-Like rides.

There is one more approach I would like to introduce before asking the question that inspired this blog post. Given the assumption (tested above) that speeds on all four of the Alpine-related routes can be used interchangeably, I have a total of 230 rides ridden over more than 40 months. This number is so large that I am going to consider it a statistical universe which allows me to use a different kind of test to ask questions like "Have my slow speeds over the last two months been slower than expected by chance?" That test is the one sample T-test, which determines if a set of measurements matches a known value. For example, given the above analysis, I am now claiming that I know that my average speed on any mixture of the Alpine routes is 12.26 mph. Using this approach, I don't have to take a small subset of those rides to compare to a small number of recent rides, I can compare those recent rides to the mean determined from the entire dataset. Unfortunately, Primer of Biostatistics does not include this version of the t-test, so I found an online calculator to do that for me:
I then recalculated using tools provided by Google Sheets as outlined in the following website:
Good news, the two approaches gave the same answer. The only thing left to do is decide which of my recent rides I should compare to that known average.

Visualization is always a good place to start an analysis, and so finally the graph at the top of this post becomes relevant. It displays the speed I rode an Alpine-Like ride for all the rides since moving to my new home in Emerald Hills. The actual ride speeds are in blue. The line in red is a running average of 8 of those ride speeds centered on each of those data points. My hypothesis based on that graph is as follows: During September and October of 2020 my speeds were increasing due to improved Fitness, that during November and December my speeds were decreasing due to a buildup of Fatigue, and since then my speeds have remained low because I failed to alter my training to allow me to recover from that Fatigue. In this post I will not attempt to determine if it is actually a buildup of Fatigue that caused my rides to be slow rather than lack of Fitness, I will simply ask: Are my recent rides truly slow or did I just have a few slow rides due to chance? There are statistical approaches for analyzing the rate at which ride speeds are increasing or decreasing, but I will not attempt to develop those for this post. That being the case, the easiest (and most relevant) rides to test are those rides that I am claiming were ridden at a unchanging low speed due to Fatigue, the rides between the beginning of February of 2021 and the middle of May. There are 24 rides between those dates with an average speed of 11.88±.46 mph. Using the one sample T-test, the chance that this is the same as the 12.26 mph average speed of all 230 Alpine-Like rides is ~0.004%, virtually non-existent. My recent rides have truly been slower than average.

How much slower have my recent rides been? The Standard Deviation for the 24 recent rides is 0.46 from which I can calculate that the Standard Error of the Mean (SEM) of those rides is 0.093 and so I can be 95% sure my true average speed between those dates was between 11.8 and 12.0 mph. Although statistically different, that is not very different in magnitude from my all time average of 12.26 mph. Sure, my ride speeds were really slower, but were they enough slower to matter? That is not a question of statistics, it is a question of biology and exercise science.

The biological rather than statistical significance of my slow rides is not a question for statistician but for an exercise physiologist (scientist) or a coach. I am unaware of any guidance from any scientist or coach on this precise question, and besides, every athlete is different and I am very different than the athletes scientists and coaches usually discuss, so I will attempt to answer this question myself. Let me start by asking what was the actual event that finally decided me I should not attempt to ride the Art of Survival this year? It was that I did not complete the training plan I had developed to get ready for this ride. Specifically, I failed to complete the last, 55 mile long training ride. This is similar to the reasons I failed to complete 200K brevets back between 2012 and 2014, it was not that I attempted the brevet and gave up along the way, it was as I approached the end of my training plan, I did not complete the longest rides in those plans. I have previously discussed ad nauseum why that might have happened and won't repeat that here, I will just take it as a fact. Back then, I noted that relatively small changes in my MAF test rides seemed to predict success or failure in preparing for a brevet. What I have accomplished in this post is to develop a California replacement for the MAF test. It is not that a 0.4 mph decrease in the speed at which I would have ridden the Art of Survival would have been the difference between success and failure, that would have only made a 5 hour ride less than 5 minutes longer, a matter of no consequence. It was that this decrease in speed on my standard rides is an indicator of the level of my Form. Attempting a physically challenging ride with such poor Form would, in my opinion, have been unwise. So yes, I believe that the relatively small decrease in average speed I have been riding recently is important, not in its own right, but as an indicator of my overall wellbeing.

Am I guilty of attacking a gnat with a sledge hammer? Have I belabored an obvious point? I don't think so. I have been eyeballing ride speed as an indicator of Form since I restarted cycling back in 2008 even though I knew that I might be deceiving myself. My tracking got even shakier when I moved to California, lost access to the Rice Track and MAF tests, and stopped using a heart rate monitor. Coaches recommend riding a test ride every month or so to access Form. The problem with that is that it is a single ride. Both intuition ("it was just a bad day") and statistics council us on the folly of basing a conclusion on one of anything. Back in Houston, I liked that the many MAF tests I rode for training gave me a statistically robust indicator of Form and by combining four of my most common rides I can now replicate that to some extent here in California. And yet, will all that, I was still eyeballing. This post is one of my iceberg posts where only a tiny fraction of the effort I put into it shows above the surface. I recently posted how I had made a copy of my training log in a relational computer database. I could not have done the analysis for this post without that database. I had to relearn (and in some cases learn) the statistics I needed to do this analysis. I tried many different approaches to analyzing the data as my originally fuzzy thinking about the questions I was asking became clearer. And finally, in the process of asking one specific question about my recent rides, I assembled statistical tools that will make it easier to use objective statistical analysis in place of eyeballing going forward. I may never figure out why I felt fatigued in the runup to Art of Survival 2021 or know if skipping it was the right decision, but at least now, one piece of that puzzle is real and not imaginary. As I have said many times before, I blog because it is fun, I do not deceive myself that it is any substitute for time on the bike, and it never is. I have never forgone even a single ride to work on this blog. And yet, I take satisfaction in knowing I am one small step closer to understanding why my cycling doesn't always go the way I'd like it to.































Tuesday, May 4, 2021

Training Zones, Calories, Oxygen, and Power



From "Intensity Training 2016" by John Hughes, modified from Allen, Hunger and Andrew Coggan, Ph. D. (2006) "Training and Racing with a Power Meter." VeloPress, Boulder, CO.

Some comments on Coach Hughes' training Zones:
1) Sweet Spot is not an independent training zone, but rather an alternative to parts of Zones 3 and 4.
2) This is a practical, not theoretical set of zones. In that context, this is a 7 zone system with "Sprints" being Zone 7. Because it is difficult and unnecessary to measure heart rate (and to a lesser extent power) during an all out sprint, Hughes does not give metrics for this zone.




The main purpose of training zones is to help coaches communicate levels of intensity to their athletes. The coach has a training plan that they believe will maximally benefit their athlete given that athlete's strengths, weaknesses, schedule, and goals, and that it is just a matter of getting their athlete to execute that plan. In this context, all that is required is that the zones are sufficiently fine grained so that specification of a zone adequately limits the variability in the athlete's intensity, that from ride to ride a zone specifies the same level of intensity, and that there is some practical way for the athlete to measure what zone they are in during their training. Perhaps the two most common ways of accomplishing this last goal are by using heart rate (HR) or by using power output (measured by a power meter attached to the bicycle.) Relative Perceived Exertion (RPI) is a third metric than can be used as well.

The value of the metrics defining training zones are not the same from athlete to athlete. Zone 2 might run from an HR of 130 to 140 for one athlete and from 100 to 120 for another. It is the job of the coach to locate those boundaries for each of their athletes. To do that, coaches sometimes use metrics in addition to heart rate, power, and RPI: VO2 (sometimes including VCO2) and blood lactate are two common examples. (I will be explaining these metrics below.) The idea is that the coaches are looking for specific physiological boundaries that can occur at different relative heart rates or power outputs in different athletes, and these other metrics can help them find those boundaries. Once found, such boundaries can be associated with specific values of HR or power which then can be used by their athletes to track the intensity at which they are riding.

I have no complaint with any of the above. Where I depart from common practice is that sometimes coaches try to use training zones to monitor training load (accumulated fatigue) of their athletes. For example, some training books suggest that training load = length of ride x training zone number. For example, a 30 minute ride in Zone 4 would be assumed to produce the same amount of fatigue as a 60 minute ride in Zone 2. I have previously blogged a critique of this way of estimating training load and today's post is background for a followup to that critique. One reason coaches and athletes might be misled to use training zones in this way because the common metrics used to define these zones track each other pretty closely. In theory, coaches could set training zones to anything they like, but in fact most common training zones are set up so that each zone represents a similarly-sized range of heart rates and power levels and so the zones themselves seem to fit this same pattern. Thus, it is easy to believe that this pattern is more universal than it actually is. The question I will address in today's post is why do these metrics track each other so closely?

To attempt to explain why the standard metrics used to track training zones tend to be similarly spaced, I will be explaining in some detail the physiological meaning of these metrics. This explanation is quite long and fairly technical, I needed to make it that way to provide the foundation for future posts. However, if all you want is the bottom line, here it is:
The metrics used to delineate training zones are all related to power and energy, either the watts measured by a bicycle power meter or the calories and oxygen consumed by an athlete to generate those watts. That is why they track each other so closely. In my future post, I will argue that it does not necessarily follow that other outputs like training benefit or fatigue will do the same.

Power

The simplest of the intensity metrics to understand is Power. It is how much energy an athlete's muscles apply to the pedals each second. Uphill, downhill, windy, none of it matters, power is power. Of course, it might be much harder for one athlete to generate 300 watts of power than for another, so training zone boundaries measured by power will vary from athlete to athlete. One way coaches try to correct for that is to not measure power in watts, but rather relative to power at some defined level of intensity, for example, the power than can be maintained for 30 minutes, known as Functional Threshold Power (FTP.) Thus, many tables of training zones use %FTP to specify training zone boundaries.

Calories to Power


It is commonly known that the power an athlete's muscles apply to their bicycle's pedals comes from the food they eat. The amount of energy in food is measured in calories. Energy and power are not the same thing but they are closely related: power is the energy is used per unit time. Bicycle power meters measure power in units of watts. 100 watts of power is more or less what is needed to move a bicycle forward on a level road at about 15 miles per hour. If the conversion of food calories to watts was 100% efficient (which it is not) generating those 100 watts would require eating 86 calories per hour. In fact, the efficiency of the conversion of food energy to bicycle motion is more in the ballpark of 20% so you might expect that to ride a bicycle at 15 miles per hour would burn approximately 400 calories per hour. If you go onto one of the many websites that estimates calories burned while bicycling, you will find they vary a lot from site to site but that they give numbers which are in that ballpark^.

VO2, VCO2, and VO2max


How can the number of calories being consumed by exercise be measured? The calories in food are turned into energy the body can use by reaction with oxygen to generate carbon dioxide (CO2) and water (H2O). Oxygen consumed is much easier to measure than calories. The units used to measure oxygen consumption is VO2. VO2 is the volume of oxygen absorbed by the body, the amount breathed in minus the amount breathed out (not all the oxygen breathed in is absorbed.) The same equipment can often be used to measure CO2 at the same time, the volume of CO2 expelled, a metric named VCO2. It is useful to measure both VO2 and VCO2 because the ratio of the two depends on how much fat and how much carbohydrate the athlete is burning. As the intensity of a ride increases, more carbohydrate is burned and the ratio of fat to carbohydrate and thus the ratio of CO2 expelled and O2 absorbed go down. This ratio is one of the physiological states coaches can use to determine training zones. The ever popular metric, VO2max, is just the highest level of VO2 an athlete can achieve when they are cycling as hard as they can. Although it is possible to purchase a user grade device for measuring VO2, it costs as much as a high end bike so VO2 is usually measured in a gym or medical lab. 

METs and %VO2max


METs are a unit that is used much more by the medical community than by the exercise community. Fundamentally, it measures exactly the same thing as VO2. One issue with both these measures is that a larger person will use more calories and oxygen than a smaller person. To correct for that, METs are almost always expressed per kilogram (kg) of body weight, and VO2 often is as well. This is far from a perfect correction, however. How much of the body weight is fat vs muscle matters and size has different impacts for different kinds and intensities of exercise. As a result, there is a lot of talk in the exercise community about reconsidering this correction, but as of today, dividing by body weight is usually what is done.

Another problem with both METs and VO2 is that they ignore levels of fitness. Riding at 15 mph might be very fast for a completely untrained person but very slow for a professional cyclist. Even though METs and VO2 are the same thing, the medical community, who are the main users of METs, almost always ignore this. The exercise community correct for this by not using VO2 itself, but by using the ratio of the VO2 at various intensities to VO2max, a number known as %VO2max.

A final practical difference between METs and VO2 is that the medical community is interested in even low levels of exercise whereas the exercise community is not, so the values reported for %VO2 max do not extend into these low intensities of exercise.

Another way to think about the low level of intensity problem is to ask, what is the level of intensity at the bottom of Zone 1? Theoretically, it is the cyclist sitting on the couch, watching TV. In practice, it is considerably higher than that. If that were not true, then Zone 1 would be much larger than the other training zones. In fact, if Zone 1 were to be made the same size as the other zones, there would be room for two more zones below Zone 1 (as I discuss below in Putting It All Together.)

Heart Rate


The oxygen the athlete breathes in is transported from their lungs to their muscles by their blood which is pumped by their heart. The amount of blood which is pumped by each beat of the heart can be increased by training, but on any given day, each beat of the heart pumps the same volume of blood, so that (in some cases, to some degree of accuracy) heart rate is determined by the oxygen consumed by an athlete's muscles and thus watts measured by their power meter.

There is enough truth in the above story to be very useful, but it is a simplification. Anyone who has ever used heart rate to determine training intensity zones knows that heart rate can vary enormously at a constant level of effort (intensity.) For example, emotional stress affects heart rate dramatically. Similarly, at constant level of power output, an athlete's heart rate will be higher on a hot day than on a more temperate one. Training has a huge impact on heart rate. One of the more useful statistics for monitoring training is the decrease in heart rate that occurs at a constant level of power output. Finally, there is the phenomenon of decoupling. On a long bike ride at a constant effort, heart rate will be steady at first, but as the ride goes on will start increasing. When used to define ride intensity, heart rate has to be used with caution and with awareness of all these factors, but despite that can be extremely useful. The point is, heart rate turns out to be just another way of looking at this same energy in/energy out system.

Baseline Metabolic Rate

An athlete's calorie consumption does not start at zero. Even when they are sleeping or sitting on the couch watching TV their heart is beating, they are breathing, and they are burning calories. The other extreme depends very much on the fitness and genetics of the specific athlete. An average untrained person can burn calories about 10 times as fast when they are working as hard as they can as they do when watching TV.  The best professional athletes can do much better, burning calories 25 times faster when working at their maximum rate than when at rest. The point is, when comparing calories in and power out, it is necessary to subtract this baseline. To further complicate things, different metrics of metabolic rate (intensity) have different baselines. Heart rate, for example, has a relatively high baseline compared to the highest possible heart rate, maximum heart rate (HRmax). In fact, many coaches and exercise scientists have switched from talking about heart rate (HR) to talking about reserve heart rate (rHR) which is just the measured heart rate minus the resting heart rate. For marking the boundaries of training zones, this makes no difference, but for understanding the relationship between heart rate and power, rHR is the better metric.

Blood Lactate


There are three kinds of muscle fiber, type I, type IIa, and type IIx. Between them, they use three sources of energy to generate ATP and creatine phosphate: anaerobic metabolism of glucose, aerobic metabolism of glucose, and aerobic metabolism of fat. Aerobic means with oxygen. Aerobic metabolism is what I talked about above, the reaction between food and oxygen to generate energy. Anaerobic means without oxygen. Below, I will be describing ways muscles can use glucose to generate energy that don't require oxygen. 

Aerobic metabolism of fat and glucose do not result in lactate production. However they are slow. They are important during low intensity cycling but cannot keep up with the energy demands during high intensity cycling. Anaerobic metabolism of glucose is fast and thus allow for high intensity cycling. One downside of that is that it makes inefficient use of glucose and thus accelerates depletion of glycogen in the muscle fibers, and this depletion is a major source of medium-term fatigue. Another important source of fatigue is that anaerobic metabolism of glucose generates lactate that ultimately builds up in the blood, a major source of short-term fatigue. Between them, glycogen depletion and lactate accumulation limit how long high intensity cycling can continue. 

Generation of lactate by anaerobic glucose metabolism and its release into the blood is a normal part of metabolism and not just by muscles. Many blood cells generate all their energy this way. Even at rest, muscle cells generate and release some lactate. Thus, there is a resting baseline level of lactate in the blood of about 1 millimolar (mM.) During low intensity cycling, most energy comes from the aerobic metabolism of glucose and fat, only a baseline level of lactate is released into the blood, and the blood lactate levels remain at this resting level. As cycling becomes more intense, more and more glucose has to be metabolised anaerobically creating lactate that is released from the muscle into the blood. During this medium intensity exercise a steady state is reached where at any intensity level there is an associated level of lactate in the blood. This phase continues up to a blood concentration of approximately 4 mM. Past that, metabolism of lactate cannot keep up with its production and as a result, there is no steady state, blood lactate continues to go up even at a constant intensity level. However, the rate of increase becomes faster as the intensity of cycling increases. In this third phase of intensity, lactate will relentlessly increase, more or less quickly depending on the intensity of cycling, up to a maximum of approximately 20 mM at which point it becomes impossible to continue. Thus, it is clear there are three fundamentally different intensity zones in terms of lactate. In Zone 1, lactate remains at resting levels and is not an issue in terms of fatigue. In Zone 2, lactate levels increase but the body can manage that lactate. As far as I know, the consequence of these steady state but increased levels of lactate on fatigue is not known. Finally, there is Zone 3 where lactate production exceeds the body's ability to manage it ultimately limiting the ability to continue at that intensity. This is why advocates of Polarized Training like Dr. Stephen Seiler often use a three training zone system based on these lactate levels. To avoid confusion, I will refer to these as Lactate Zones. As a final point I would note that blood lactate levels, like many other things (heart rate for example) vary significantly from person to person. Thus, the absolute values I have given here are typical but by no means accurate for any given cyclist. However, the general patterns are almost universal: no increase, increased but steady, steadily increasing*.

Knowledgeable readers may be raising their eyebrows at this point. Advocates of a blood-lactate-based three training zone system do not talk about lactate levels in the way I have above. Rather, they talk about how specific levels of blood lactate are associated with specific levels of intensity. When they plot a graph of blood lactate concentration as a function of watts generated on a bicycle trainer, they see that it goes up very slowly at first and then at some point starts going up faster. This transition is called the first inflection point and is the boundary between Lactate Zone 1 and Lactate Zone 2. As intensity continues to increase, there is a second increase in the rate at which lactate increases with intensity. This is called the second inflection point and is the boundary between Lactate Zone 2 and Lactate Zone 3. There are two reasons for the difference between how I explain lactate levels and how most coaches and exercise scientists do. The first is that, as I have previously noted, biology is never black and white. I talked about how at low intensity, blood lactate does not increase. That was a simplification. Rather than generating no excess lactate at low intensity, lactate generation starts gradually rather than all at once as I described it. So at these low intensities, in Lactate Zone 1, there is still some increase in blood lactate as intensity increases. The second reason for the difference in how I express things compared to coaches and exercise scientists has to do with the way exercise scientists typically conduct their experiments. Virtually every experiment I have seen has involved an intensity ramp where intensity is increased every few minutes and during each intensity period, a blood sample is taken and lactate measured. In Lactate Zone 3, where I claim lactate is not at equilibrium but rather is relentlessly increasing what is really being measured is how much lactate has accumulated at a specific intensity after the specific number of minutes used in the experimental protocol. Though reported as a lactate concentration, that value is really a rate, how fast lactate is being accumulated at that intensity and thus how much lactate has accumulated during those few minutes. The rate of lactate accumulation times the time the cyclist has been at the intensity being investigated results in the level of lactate that is reported. If the time at each intensity in the experimental protocol were increased, the level of lactate measured would also increase, so the levels reported are dependent on the details of the experimental protocol.

In Lactate Zone 2, the body is able to clear all the lactate generated by the muscles as fast as it is generated. How does the body do that? There are two general mechanisms. The first is to burn it as fuel. The heart is particularly good at that. The second mechanism is to convert it back into glucose. This happens mostly in the liver and in the kidneys. That glucose is then returned to the blood to be used by muscles and other parts of the body. These same two processes also occur when exercising in Zone 3, but they cannot occur fast enough to keep up with lactate generation by the muscles so lactate levels increase over time.

Oxygen Debt and Afterburn

Anaerobic metabolism of glucose is immensely wasteful. Oxidation of one molecule of glucose yields 38 molecules of ATP. Anaerobic metabolism of glucose to generate lactate yields only 2 molecules of ATP. Worse, conversion of that lactate back into glucose by the kidneys or the liver requires 6 molecules of ATP for a net loss of 4 molecules of ATP for each molecule of glucose used anaerobically by the muscles. From where comes the energy to create that ATP? From the oxidation of fat by the liver and kidneys. When exercise is over, the body has to get rid of all the lactate left in the blood, and that takes oxygen. ATP and creatine phosphate levels in the muscle are likely depleted, and oxidation of fat or glucose must occur after the end of exercise to replenish those. (These are just two examples, there are probably others.) As a result, oxygen use remains elevated after exercise has finished, a process that coaches and athletes refer to as "afterburn." When riding in Lactate Zone 1 or Lactate Zone 2, the oxygen used to burn carbs and fat is delivered more or less as it is being used. When riding in Lactate Zone 3, however, oxygen consumption lags behind energy generation. The athlete's body accumulates an oxygen "debt" that is "paid back" during the afterburn period, a period that can last for hours.

Relative Perceived Exertion


From a theoretical perspective, relative perceived exertion could be related to almost anything. It is a highly subjective, complex metric. All we know about it is that it is produced by subconscious processes in the brain from unknown data and using unknown algorithms. It is interesting, then, that when measured, the relative perceived exertion reported by most athletes is is fairly similar to heart rate and power. Why the subconscious brain of an athlete makes this association between the rate at which calories are being burned and how hard the effort that is burning those calories feels is a mystery of evolution. I won't be discussing it in this post but training effect, short and long term fatigue, and health benefits, things  that our conscious brain think are much more important than calories, are not closely related to calories burned. Thus, it is a good thing that the way training zones are usually used does not require them to be proportional to fitness, health, or fatigue, because (as I have discussed in the past and will be discussing again in the future) they are not.

Putting It All Together




In the above graph, I have replotted the training zone data from  the chart at the top of the post. The value plotted for each zone is the value at the upper boundary of that zone. Thus, Zone "0" is really the lower bound of Zone 1. As noted above, Coach Hughes does not really establish a lower boundary for Zone 1. In theory, Zone 1 extends all the way down to watching TV while sitting on the couch. For purposes of the graph above, what I did was to set a lower boundary for Zone 1 such that it would have about the same width as the other zones. When I did that, I found that I needed two more zones to get to the couch, which are labelled "light" for light exercise and "rest" for resting, or no exercise at all. I adjusted Coach Hughes heart rate data to use relative heart rate (rHR) rather than absolute heart rate. When I did that, I was astonished at how similar the graphs for heart rate and power were.

If you recall the sections above on Blood Lactate and Oxygen Debt and Afterburn it might seem surprising that rHR and power continue to follow each other all the way to the top of Zone 6. I argued that, rHR increases as demand for oxygen increases, and that, in Zones 5 and 6, muscles are using glucose to generate power in the absence of oxygen, so why does rHR continue to rise? I have two guesses, both of which I believe to be true. First, the heart pumps blood for reasons besides just supplying oxygen to an athlete's leg muscles. Some of those reasons might be to move lactate from the muscle to other parts of the body and to provide oxygen to organs like the liver which are generating energy by oxidizing fat to be used to convert lactate back to glucose. The second reason why rHR might parallel power through the top of Zone 6 is that Coach Hughes' Training Zone system has 7 zones, but does not give rHR data above Zone 5 nor power data above Zone 6. (For purposes of the graph, I estimated the rHR data for Zone 6.) It is only in the highest zone, Zone 7 (which Hughes designates not as Zone 7 but as "Sprint") that anaerobic power production really takes off. Hughes defines the top of Zone 6 by a power output of 120% of FTP. Race data shows athletes both professional and amateur reaching peak power outputs of 400% to 500% FTP (an output they can only maintain for 5 seconds or so.) There is no reason for Hughes to delineate the upper boundary of his "Sprint" Zone (Zone 7) because all the athlete needs to know is to go "all out", but that said, the absence of that boundary prevents us from seeing if the large predicted deviation of rHR and power in fact occurs even though common sense tells us that it must. Although there is significant athlete to athlete variation, for virtually all athletes, HRmax, the highest heart rate it is possible to reach, is between 120% and 135% the HR at lactate threshold, far lower than the 400% to 500% power that can be generated compared to the similar FTP.

Of the three commonly used zone metrics plotted, clearly Relative Perceived Exertion (RPE) seems to be the most different. This is despite the fact that, to make it more comparable to the other metrics, I adjusted the values of RPE to be the percentage of the RPE at the top of Zone 4. (It is not at all clear if, given what RPE is, doing so makes any sense.) Given how opaque and subjective RPE is, what is interesting is not that it is somewhat different than power and heart rate, but how similar it is. That said, it is relatively easy to rationalize away even that difference. Because Coach Hughes is a coach, the RPE values he suggests are for a trained athlete. Such an athlete would feel no exertion whatsoever during "light" exercise. An untrained person might have a different experience resulting in different values for RPE at the same heart rate and (relative) power output. I will leave it as an exercise for the reader to imagine how adjusting RPE for such an untrained person would cause them to approach those for rHR and Power.

In Conclusion


The goal of training plans developed by coaches is to increase the various aspects of fitness of their athletes without generating more fatigue than their athletes can handle. To accomplish that goal, the training plans specify different amounts (minutes) of exercise at specific intensities. Calorie consumption is a secondary consideration, if it is a consideration at all. As it happens, the tools available for measuring intensity all measure calorie consumption or the energy output fueled by those calories. This is not a problem as long as it is recognized that what is being measured (heart rate, power output, ...) is not a measure of fitness or fatigue and that the relationship between intensity defined by energy and power will be different than intensity defined by fitness and fatigue.



^ 360 cal/hr: https://www.welovecycling.com/wide/2020/05/14/how-to-convert-watts-into-calories-burned-on-the-bike/
   568 cal/hr: https://captaincalculator.com/health/calorie/calories-burned-cycling-calculator/
   540 cal/hr: https://gearandgrit.com/convert-watts-calories-burned-cycling/
   360 cal/hr: https://mccraw.co.uk/2012/10/14/powertap-meter-convert-watts-calories-burned/

* (This paragraph is based on "Many Factors to Consider When Collecting, Analyzing, and Interpreting Blood Lactate Measurements" in "Physiological Tests for Elite Athletes" Second Edition by Australian Institute of Sport (Author), Rebecca Tanner (Editor), Christopher Gore (Editor) ISBN-13: 978-0736097116, ISBN-10: 0736097112)


Sunday, April 4, 2021

Skeletal Muscle: From Molecules to Movement

 


The relationship between exercise intensity and fatigue has continued to fascinate me and I am constantly prowling the Internet for clues to help me understand what, for me, continues to be a conundrum. Of the many ideas out on the Internet, which ones should I believe? One approach is to purchase a textbook on muscle physiology and exercise to get a solid foundation in the basics, but textbooks are expensive and up until now I have not been willing to do that. Finally I realized that the cost of a textbook could be justified not in terms of any impact it would have on my cycling but rather in satisfying my curiosity, so, after reading reviews on various texts, decided to purchase this one. What have I learned from it?

The first thing I learned is that much of that I thought I knew, I didn't. Coaches proclaim with great certainty facts about how muscles, exercise, training, and fatigue work, facts this book questions. It's not that what these coaches say is necessarily wrong, it is rather that it isn't really known if it is right or wrong, the data just isn't there. In contrast, when this book states a fact, the evidence for that fact along with the reservations with which this evidence should be taken are clearly stated in detail, and thus those facts can be trusted to whatever level of confidence the evidence justifies. This book also tends to be thorough, there are very few of those missing links in the chain of logic that I find so frustrating in other sources. I am not really criticising coaches, they have a job to do and when science fails to provide them with the information they need they have little choice but to make their best guess based on their experience. To the extent I have a criticism at all it is that these guesses are sometimes presented as facts rather than working hypotheses, and this book has helped me tell the difference. On the other hand, though I do not regret for a second purchasing this book, I don't think it is perfect, there are occasional gaps in what it covers that I find disappointing. (I will point these out later in the post.)

So much for the things I unlearned, what did I learn from this book? This book has 200 pages (relatively short) divided into 15 chapters. It is an introductory textbook so covers some very basic, theoretical material that has almost no impact on my exercise and training but provides the foundation for understanding what comes later. My plan was to read this book from front to back, word for word, but so far I have been unable to do that. When I start reading it I cannot stop myself from skipping ahead to the parts that interest me most, using it more as a reference than a textbook. Nonetheless, by this point I have read most of it. The first two chapters cover the basic structure and cell biology of skeletal muscle. I remember learning much of this material during my biochemistry classes oh so many years ago but of course have forgotten much of what I learned, some of what I learned is obsolete, and so reviewing this material was helpful. Chapters 3, 4, and 5 cover how the nervous system controls the muscles. This is the beginning of material that starts overlapping with what coaches talk about. Chapter 6 describes the kinds of "fuel" (energy sources) muscles use, when and why they use these different sources, and the implications of that. This largely confirms what coaches say, though I found it helpful to have a complete discussion together all in one place. Chapters 7 and 8 covers in great detail experimental evidence about the effects of heat on muscle function and other related topics. I confess I have not fully taken advantage of these chapters. Chapters 9 through 12 were the candy that repeatedly distracted me away from whatever chapter I was attempting to read, four chapters on various aspects of fatigue and muscle pain. Those four chapters, along with Chapter 14 on muscle growth and ageing were my favorites in the book. I expected to enjoy Chapter 13 on Energy Efficiency, but it turned out it was too theoretical and removed from real world exercise. The book finishes with a chapter on muscle diseases, far from my primary of interest and a candidate for the chapter I may never read.

So now to the specifics. This book confirms the widespread belief in the training community that human skeletal muscles are made up of three kinds of muscle fibers named, in modern terminology, I, IIa and IIx. (In older terminology, IIx was named IIb.) Type I is low power high endurance, IIx is high power low endurance, and IIa is in between. A concept central to the training literature (confirmed by this book) is that as ride intensity (e.g. speed) increases, use moves from type I to type IIa to type IIx muscle fibers. Another concept not as commonly discussed is that as ride volume (e.g. miles) increases, a low intensity ride where one is using predominantly type I fibers will start using IIa and then IIx fibers as type I fibers start exhausting their glycogen reserves. The following figure from the book confirms both these ideas:


The upper panel varies time at a constant intensity of 31% VO2max, a very low level of effort, a level that a cyclist would typically use for a recovery ride. An interesting point in the graph is that at this intensity, pretty much only type I muscle fibers are used until they have largely exhausted their glycogen reserves which occurs after two to three hours. The book does not suggest any significance to this fact (being very stingy with speculation) but I wonder if recruitment of type II muscle fibers might have something to do with decoupling, the increase in heart rate that occurs at about this point in a long ride in the absence of any increase in effort. The lower panel keeps the time of exercise constant at 120 minutes but varies the intensity. So far, I have found this lower panel less useful.

Imagine I come home from a ride with my glycogen reserves depleted, what happens then? One of the most common pieces of advice from the exercise community is to eat carbs after a ride to replenish glycogen. How long does that take? This next figure from the book addresses that with results that I found striking: 


What about this did I find striking? First, that it takes a full three days to reach maximum glycogen. Second, that the supercompensation is so immediate and so large. After a single bout of exercise, glycogen reserves are increased two and a half fold!

In the past on this blog I have cautioned against overinterpreting results from a single study, and yet the last two figures are just that. Why am I taking them so seriously? It is a matter of trust. This book comes highly recommended from a variety of credible sources and so I assume that, although the figures are drawn from a single experiment, the results they show are representative; that the authors of the book made sure there is enough other evidence in the scientific literature in support of these particular experiments so that they can be trusted.

Examples of other things I learned from this book are:
  • Lactate transport out of the muscle and into the blood where it can be carried away is what limits  the rate of anaerobic glycolysis in muscle, not the rate at which glycolysis occurs. In evidence of that, blood lactate remains elevated for 10 minutes after the end of exercise.
  • The muscle pain that occurs during high intensity exercise correlates with potassium levels in the fluids surrounding the muscles, not those of lactate or pH.

Limitations of This Book


I complained at the top of the post that there were limitations in what this book covers. I'd like to moderate that complaint a bit, it is possible that what I am seeing as a limitation is a strength, that the information that I see as missing is not missing because the authors failed to put it in the book but because it doesn't really exist. Similarly, there may be "facts" that seem plausible, seem like reasonable extensions of what is known, which most experts would bet on, but which have not been experimentally verified with a sufficient degree of rigor to satisfy the authors of this book. When I would hit one of these gaps, I would run off to Wikipedia or search the Internet trying to fill it, and I often could. But could I trust these sources? As I rule, I tend to trust Wikipedia, but I'm sure it has its own strengths and weaknesses. Although I am sure that in some cases my alternative sources are wrong, I am pretty sure that there are other cases where the information I find elsewhere could and should have been in the book. So, with all those reservations, here are some examples of gaps:
  • The two kinds of fuel used by muscle to generate ATP and thus muscle power are fat (lipids) and carbohydrate (glucose/glycogen.) The book discusses the importance of the kinetics of anaerobic glycolysis, oxidation of glucose, and oxidation of lipids to the endurance athlete and thus the importance of utilizing lipids to spare glycogen but does not discuss how long the lipid stored in muscle lasts, the kinetics of utilization of muscle lipids vs lipids from other parts of the body, nor the impact of glucose from foods eaten while exercise, the information needed to build a complete picture out of their kinetic argument.
  • I was delighted to read about Satellite Cells in Chapter 10 of this book. These seemed like they might be the muscle stem cells in which I am so interested. It turns out they are, but I had to go to Wikipedia to find that out.
I think I am working myself up to a series of posts on exercise and fatigue, and if I am, this is the first of those posts. The way this topic is organizing itself is that I am presenting it in multiple posts and getting the foundational material out of the way first so that the climactic post will be shorter and more readable. If I pull this off, this post will be the first of those foundational posts. In any case, I expect to refer back to this book often.



Tuesday, March 2, 2021

My Training Log


This is a very complex and dense spreadsheet format I came up with to compactly represent a short form of my ride training log in a form that works for me. It probably is not that clear to anyone else, sorry! That said:

Everything to the left of the darker vertical line is ride data per day. Everything to the right of that line is summary data. For the rides before the break (2008) the number in the cell is miles ridden. For the rides after the break (2020) the number in the cell is minutes ridden. Thus, these two numbers are completely non-comparable. For those daily cells, the color code indicates the ride type: Yellow is my weekly long ride. Blue is a "Pace" ride. Green is an easy ride. Red is an intense ride, e.g. intervals. In the summary column "Total Min." (the total of the minutes ridden that week) a Yellow color indicates I have met or exceeded the Medical Community's ideal recommendation of 300 minutes of aerobic exercise per week, a Green color indicates I have reached their minimal recommendation of 150 to 300 minutes per week. The summary column "ave min/wk" is the same except that it is a running average over the previous year.



As I have previously blogged, I stopped cycling some time around 1979 not to resume it until August 1, 2008 when I rode my newly renovated Bianchi Specialissima 5 miles home from the bike shop. Being the person I am, the first thing I did after catching my breath was to create a spreadsheet to capture the data on that and all future rides for me and my wife. That first spreadsheet looked like this:


(DLS Miles and DLS MPH are for me, David Lloyd Steffen, AS Miles and AS MPH are the same for my wife, Agi Schönbrunn).

Over time, two things happened:
  1. I wanted a denser format, one to make it easier to see trends over longer time. I came up with the format shown at the top of the post. Currently, I have select data from every ride I have ever taken in that spreadsheet, from August 1, 2008 through today.
  2. I wanted to capture more information and comments than could be kept in a spreadsheet so came up with the following long, text format:



Monday, 2020-10-19
BP, HR, Weight: 136/92; 67 bpm; 172.8 lbs.
Ride time: 10:00am-1:00pm
Temperature/Humidity/Wind: Before, 64/66/2. After, 76/50/8
Subjective Weather: Clear, Pleasant
Dress: Helmet & Liner, Short Sleeve Jersey, Adventure Cycling Shorts & Briefs,
Sunglasses, Cycling Gloves, Wool Socks & Cycling Shoes.
Clothing Appropriateness: Good.
Ride location: New Alpine-Cañada
Plan: Long Ride
Bike: BV
Ave Speed: 12.8
Distance: 33 mi
Climbing: 1700 ft
Duration: 155 min
Subjective Fatigue: Felt strong. Enthusiasm low before start, moderate during.
Mood: :-|
Comments: Complicated week so I get in rides where I can. Scouting ride. Can I make
this easy ride easier by taking the direct path to the lake and then Jefferson
home?
Current Plan: Discover my new rhythm.




Yes, than really means I was entering data three times. I continued to do that until my wife died in 2017, at which point I dropped the middle format, the first spreadsheet I had developed, and went down from triple entry to double.

Am I insane? I don't think so, or at least I think I am managing this particular insanity effectively. I usually enjoy cycling very much, but I always enjoy recording data about cycling. When I would finish triple entering my data, I would feel a sense of loss that the fun of recording my ride was over. Other than that, is there any value to this (multi-part) log? Many coaches advise keeping a training log to help monitor progress and to head off overtraining, and I have found my log to satisfy that function quite nicely. The weather and clothing fields have also been useful to help me decide what to wear on a ride based on Google's weather prediction for the day. When I have wanted to summarize my riding for this blog, my Training Log(s) have been invaluable.

Is that all there is? I'm afraid not. Before I retired, I was Director of Biomedical Informatics for the Dan L. Duncan Cancer Center, which means I used computers and wrote programs to help the Center do its cancer research. I truly love coding, and missed it when I retired. As I developed the long text form of my training log, I had in mind that this should all be in a computer database, so developed and used that form in a way to facilitate uploading it into such a database. An early version of this database now exists containing every bit of data for every ride I have ever done. Does this mean I am doing triple data entry again? It does not. Because of how I use the long text database, I can upload that into the database and avoid reentry. Will the computer version of the database replace the text version? In theory in could, but in practice not a chance, it is just a hobby to keep an old retiree amused. It is possible when I am creating summaries of my rides, for a blog post for example, the database might come in handy, but developing a data entry front end to replace my text file will probably only happen in The World To Come.

How does this all work? The way I log my rides has stabilized since my move to California, and this is how it goes: When I get up the morning before a ride, I weigh myself and measure my blood pressure and (resting) heart rate. Just before I leave, I record the weather. When I start my ride, I turn on Strava on my cell phone. When I get home, I again record the weather and also the data from Strava. To minimize boring data entry, I copy a previous entry from the text log and change only those parts that changed for the current ride. I then (double) enter a few select data elements into the summary spreadsheet. Every so often, I update the database. The way the database works is that any missing "fields" (things like Distance: or Climbing:) are entered as blanks in the database, I only need to record fields relevant to the specific ride. That changes, especially over time. Back in Houston, my routine use of a heart rate monitor and availability of the dead-flat, zero traffic Rice Track allowed me to capture data that I can no longer capture here in California, so that fields for those data are not present in my current text file but because that data was recorded in those Rice Track rides, those fields are in the database. I think as good a way as any to end this obsessive-compulsive post is to list those fields:



General Text Comments:
  Bike Ridden
  Ride Type (Long Ride, Fast ride, Easy Ride, ...)
  Plan for this Training Period
  Ride Location
  Other Comments

Health and Wellbeing:
  Blood Pressure
  Resting Heart Rate
  Weight
  General Health
  Mood
  How Tired Did I Feel?
  
Weather:
  Temperature at the Start of the Ride
  Humidity at the Start of the Ride
  Wind Speed at the Start of the Ride
  Temperature at the End of the Ride
  Humidity at the End of the Ride
  Wind Speed at the End of the Ride
  How did the Weather Feel, Subjectively?
  Time of Day when Ride Started
  Time of Day when Ride Ended
  Clothing Worn
  Clothing Appropriateness
  
Data Returned by Strava:
  Average Speed
  Total Feet of Climbing
  Total Miles
  Total Time of Ride

Data Only Returned by Bike Computer:
  Average Cadence
  Average Heart Rate

Rice Track/MAF Test Specific:
  Gears
  Decoupling
  Ability to Control Heart Rate
  Maximum Heart Rate
  Minutes in Zone 1
  Minutes in Zone 2
  Minutes in Zone 3
  Minutes in Zone 4
  Minutes in Zone 5

Monday, February 1, 2021

Dr. Stephen Seiler and Social Media


Dr. Stephen Seiler, research physiologist and world expert on polarized training, explaining the distribution of intensities at which the best endurance athletes train in his TEDx talk.



Training has been an important topic on this blog from it's beginning. At first I blogged about the books I had read about training. Later, I switched to scientific papers which, in most cases, I found from links on social media which lead to print media which lead to a scientific publication. Recently, one of these social media links led me somewhere else: to a podcast. This podcast featured an interview of one of the foremost academic experts on polarized training, Dr. Stephen Seiler. I have long enjoyed political podcasts, but this was the first podcast I had listened to on the topic of exercise science and I found it extremely enlightening. I was very impressed with Dr. Seiler and, because I am a fellow scientist, I recognized in him a kindred spirit.  I may, at some point, review his work critically in but in this post I am going to describe the way getting training ideas from Twitter, podcasts, scientific lectures, and TED talks differs from both training books on the one hand and scientific papers on the other.

On the web page containing the podcast interview with Dr. Seiler were links to Dr. Seiler's Twitter and ResearchGate* accounts which I followed. I downloaded some of his papers from ResearchGate and started following him on Twitter. His tweets are useful in and of themselves, but also useful are the links to other videos contained therein. To date, I have listened to some more podcasts, a TEDx talk (shown in the picture at the top of this post) and a professional lecture. Despite being very different platforms, these videos^ were similar in terms of the kind of information I got from them and I will talk about them as a group, lumping them all together rather than attributing ideas to individual ones.

How does getting training ideas from videos compare to getting them from training books by (non-scientist) coaches or from scientific publications? The biggest issue with getting ideas from books is one that Dr. Seiler touched on in one of his podcast interviews, the issue of trust. What reason do I have to I think the ideas in any particular book are correct? Seiler acknowledges that trust between an athlete and coach is a delicate thing that has to be earned. This reflects not only that athletes need to learn to trust their coach, but also that some coaches should not be trusted, so the athlete is right to be cautious. On the other hand, books usually contain comprehensive discussions of an overall training strategies targeted at specific, well-defined goals, for example, training for your first 100 mile long ride. If you trust a training book, you are set, you know exactly how to train. Scientific papers, at their best, require substantially less trust because the entire purpose of the scientific method is to allow proof to replace trust, not by providing absolute certainty, science does not do that, but providing the data needed to evaluate the strength of evidence supporting its conclusions. Unfortunately, to provide such transparency usually requires investigating very narrowly defined questions which lack the comprehensiveness and context which are the strong suit of training books. You may be convinced by the conclusions of a scientific study but have no idea what it suggests for your training plans. I am finding Dr. Seiler's talks so useful because they occupy an in-between place in the dichotomy between coaching books and scientific papers, providing more context than the papers and more transparency of evidence than the books.

What have I learned from Dr. Seiler's talks?
  1. Dr. Seiler uses two kinds of data to study which training plans work "best." (The definition of "best" will vary, depending on the individual athlete's goals, of course.) First, he does experiments in the lab where he measures the effect of different training plans (e.g. 4 minute vs. 8 minute vs. 16 minute intervals) on different parameters associated with athletic performance (e.g. functional threshold power.) Second, he studies the training plans used by successful coaches and athletes. The argument here is that this is an evolutionary approach to determining the best training plans in the real world. Those coaches and athletes who use less good training plans have less good results and are removed from his study group by natural selection. It is this second kind of data that was the basis for the graph shown in the photo at the top of this post.
  2. For a lot of his work, he uses a three zone intensity scale rather than the more common five to seven zone systems`. He defines his three zones based on the rate at which blood lactate increases with increased exercise intensity. There are two inflection points where the rate of increase goes up, and that defines the Zone 1 to Zone 2 boundary and the Zone 2 to Zone 3 boundary. In more than one interview he was asked to translate those boundaries into metrics more commonly available to average cyclists, heart rate for example. Personally, I found those translations problematic, different translations he gives seem to me to give different boundaries. He is also asked to translate his three zones into the more typical five zone, heart rate-based system. He responds that Zone 1 in his three zone system corresponds to Zones 1 and 2 in a five zone system, that Zone 2 corresponds to Zone 3, and that his Zone 3 corresponds to Zones 4 and 5 in the five zone system. However, when he describes the boundaries specifically, it seems to me that his Zone 3 actually corresponds to Zone 5 rather than 4 and 5. The boundary between his Zone 1 and Zone 2 seems less problematic, though it might be in the middle of Zone 2 in a five zone system rather than at the top.
  3. A naive framing of Polarized Training could be made that all training should be in Zones 1 and 3 (in the three zone system) and little or none in Zone 2. As I listened to Dr. Seiler talk, it is clear that he is much more concerned about not substituting Zone 2 for Zone 1 and keeping Zone 1 training at 80% to 90% of total training than he is about the distribution of training between Zones 2 and 3. This is based on his studies of real world coaching. He does see examples of successful athletes who train in Zone 2, but not of those to substitute higher intensities for Zone 1; all successful athletes that he has studied spend 80% to 90% of their time exercising in Zone 1.
  4. As a retired academic, I can attest to the fact that paradigm definition is often more important for academic success than for scientific progress. Thus, the cynic might argue that Dr. Seiler's coining of the term "Polarized Training" really comes down to repackaging and renaming things that coaches have been saying for decades. Again, this post is not a critical examination of Dr. Seiler's published works. Nonetheless, I will say is that in his podcasts, he is suitably modest and balanced in presenting his ideas so in this context I think that criticism would carry less weight.
  5. A relief to me is that Dr. Seiler is much more relaxed about how important the differences between training plans are than some of the more rigid writing on the topic suggest. For an older, casual athlete like me, he might almost suggest doing whatever I want, only making sure to exercise consistently and not overdoing it. His research is focused on getting world class athletes to their very peak of performance where small differences become critical, so his focus on these small differences makes sense in the context in which he works. As one example of this, he noted that doing only long, slow rides will increase VO2max, normally considered one of the main targets of high intensity training.
  6. A bike ride is characterized by volume (how many minutes you ride) and intensity (how "fast" or hard you ride.) A simplification that most people make, me included, is that doubling the volume (minutes) doubles the fatigue. In one video, Seiler was discussing the well known phenomenon of decoupling, that on a long ride at a fixed intensity, heart rate will stay constant for a while and then start to increase even though speed, power, etc. remains constant. Imagine a three hour ride where heart rate starts to rise at the end of hour 2. Seiler speculated that might mean that the fatigue generated during hour 3 might be greater than during hours 1 and 2.
As mentioned above, Dr. Stephen Seiler is associated with one style of training, Polarized Training. If one were to name the opposing school of training, it might be Sweet Spot Training, associated with Dr. Andrew Coggan. I hope to discuss Sweet Spot Training in a future post so with that in mind I have been trying to learn about it. Because of my experiences described in this post, I have been listening to podcasts about Sweet Spot Training and have found them to be as useful as Dr. Seiler's podcasts described here. Stay tuned to read about what I have learned.



* ResearchGate is a social media platform for scientists. It is a bit like LinkedIn except it is much more focused on user-provided data. For example, it is a place where a scientist can put their research publications for others to read.

^ Some Videos featuring Dr. Stephen Seiler:
  1. Flo Cycling Podcast (Sept. 30 2018. Scroll down to episode 13. Flo cycling sells bicycle wheels for racing.)
  2. Scientific Triathlon: Polarised training with Stephen Seiler, PhD | EP#177, April 15, 2019 by Mikael Eriksson
  3. Stephen Seiler·TEDx Arendal: How "normal people" can train like the world's best endurance athletes
  4. Fast Talk podcast, ep. 75: High intensity training with Dr. Stephen Seiler
  5. How to Create Multi-Year Development Plans, with Dr. Stephen Seiler and Sondre Skarli
  6. CoachCast by TrainingPeaks: Episode 9: Stephen Seiler
  7. Load, Stress, Strain: Understanding the difference can make you fitter and faster!
` On this blog I originally used a seven zone system (1, 2, 3, 4, 5a, 5b, 5c) but have recently switched to a six zone system (1, 2, 3, 4, 5, 6). In both cases, Zones 1-4 correspond to those in the standard five zone system, and the remaining zones are all subdivisions of Zone 5.