Door to Door or Drive to Ride?

My Bianchi Volpe strapped to the back of my car at the start of my exploration of the San Francisco Bay trail in July of 2022. In the background is the San Francisco Bay, and to the left, the Dumbarton Bridge. The ten year old bike rack shown in this picture has since gone to the bike shop in the sky and has been replaced by a more modern rack, one that can accommodate all my bikes, something which the old one could not do.

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There are many good reasons to prioritize riding "door to door", that is, to start and end my rides from home rather than attaching my bike to my car and then driving to the start of a ride ("drive to ride"). One reason is some combination of social responsibility and esthetics; a bike ride is supposed to be an environmentally friendly activity, a feature at least somewhat corrupted by driving to the start of the ride. Another is that a door to door ride is more time efficient. The time to load up the bike, drive to the destination, unload the bike, reload the bike after the ride, and unload it at home, all add significantly to the time it takes for the ride. When time is tight this might be the difference between fitting in a ride or skipping it.

I am far from the most fanatical of door to door cyclists. When I was an active randonneur, I used to read about those randonneurs who would bike significant distances from their home to the start of a brevet and then back to their home when it was over, making an already challenging ride even more difficult. There never has been a time when I didn't drive the start of a group ride. However, for routine training, I usually ride door to door.

Why would I ever want to do a training ride that was not door to door? I would want to in order to have access to rides that are not practical if riddent door to door. The value of such rides became much greater the day I moved from a house in a relatively flat neighborhood in San Carlos to a house in nearby Emerald Hills which was anything but flat. If I wanted a flat ride, and I did, then I would need to drive to it.

Since I restarted cycling back in 2008, the extent to which I have been willing to drive to the start of a ride has varied depending on my circumstances. At first, the only rides I drove to were group rides but in 2010 I discovered an uniquely valuable route that was not practically accessible from home, the Terry Hershey/George Bush park ride. What made this ride worth the drive was that it featured attractive scenery, was long, and was completely car-free. Thus, it lent itself to the riding needed to train for long group rides. This became important in 2012 when I started randonneuring, a version of cycling whose goal was to complete long rides known as brevets. By riding back and forth over the Terry Hershey/George Bush route a few times I was able to accomplish the 90 mile long training ride I needed to prepare for a 200 km (126 mile) brevet. When, in 2014, I decided to stop randonneuring, I decreased my riding on the Terry Hershey/George Bush route and my cycling returned to a more door to door pattern. This door to door pattern continued when I moved to California in 2017. However, I probably should have gone back to a more balanced mix of door to door and drive to ride when I moved into a hilly neighborhood in 2020, but the advantages of door to door riding and inertia delayed that rebalancing. However, by 2022, my pattern had started to shift.

The first thing, post-move, that caused me to ride something other than a door to door ride was my continuing efforts to ride my Hetchins. That Hetchins does not have very low gears so is unrideable door to door. As a way to work around that limitation, in February of 2022, I threw my Hetchins onto my bike rack and drove to the Bay Trail for a reasonably long yet flat test ride. Because that ride was successful, a month later I drove down to another segment of the Bay Trail with the Hetchins on the back of my car to ride with the local Classic and Vintage bike club. As it happened, that ride traversed some parts of the Bay Trail that I had never before ridden which made me want to explore the Bay Trail more on my own. One barrier to exploring the Bay Trail during a door to door ride is the steep climb that would come at the end of the ride to get me back home but another is the issue of time. If I started from home, by the time I got to the part of the Bay Trail I wanted to explore, I would be out of time and would need to turn around and return home leaving no time for exploration. Using the drive to ride strategy was a solution to both these problems. By taking this approach I was able to undertake a series of explorations of the Bay Trail that made my 75th Birthday Ride possible two years later.

What was good for my Hetchins was good for another antique bike I own, a 1963 Bianchi Specialissima. As is the case with my Hetchins, the very narrow gear range on my Specialissima makes it unrideable in my neighborhood, so I threw it on the back of my car, made the very short drive down to my old neighborhood in San Carlos, and the Specialissima rode again. Being willing to drive to ride has made it possible for me to enjoy this somewhat impractical but deeply nostalgic and totally delightful bicycle.

In addition to hills, another change that has impacted my cycling is that I have gotten older. These two changes exacerbate each other and together have caused me to continue to incorporate more drive to ride rides into my schedule, not just to accomodate an antique bike, but to accomodate my aging body. These days, it is more often than not that a week of riding will include at least one ride that is drive to ride, an approach which I very much hope will allow me to continue cycling for years to come.

Putting Banister To Bed

 

Graph of the Fatigue, Fitness, and Form the Banister model predicted for me based on my training over the the last few years.

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I have blogged a lot about the Banister model. At the bottom of this post you will find a list of my earlier posts on this topic. The input to the Banister Model is daily training Load, where Load is a combination of how long I ride (in minutes) and how hard I ride (which I calculate from my heart rate.) The output of the Banister Model is predicted performance a.k.a. Form. (Think of this as perFORMance.) How well does that prediction match my actual performance? In my most recent post on this topic I said "I have been using the Banister model for more than two years now and it certainly has not been perfect at predicting the impact of my training on my cycling ability, but I have the impression that it does give me hints that are helpful in combination with what my body tells me in optimizing my training." That evaluation is both subjective and qualitative. The management consultant Peter Drucker is famous for his saying "If you cannot measure it, you cannot improve it." In this post I am going to discuss my efforts to quantitatively compare my performance to that predicted by the Banister model.

I have all kinds of subjective impressions about my performance. For example, last May I rode the 60 mile long Art of Survival with my friend Roger and felt that my performance was poor because, despite the fact that I relied heavily on the eAssist of my eBike and despite the fact that he reported that he had not had the opportunity to train enough before the event, I found it very difficult to keep up with him. Obviously that is not a metric I can use to determine if the Banister Model's prediction of my performance is accurate. What could I use? Can I look at the scientific literature concerning the Banister Model for guidance on a metric? I have read a few papers on this topic and the kinds of metrics they use tend to be things like maximum power output during a five minute time trial. (See, for example, this paper.) What I am trying to optimize is average speed during a five hour group ride which is a very different thing. The former is a measure of fast twitch muscle strength whereas the latter relies much more on slow twitch muscle fibers. It would be like trying to determine how strong your legs are by measuring your arms. I am unenthusiastic about using such short term metrics.

I could use a longer time trial to assess my Form. The problem with that is, in my old age, a long time trial is tiring to the extent that the very training I am trying to optimize is disrupted. It is similar in concept to crash testing a car if you only have one car. After the test, you might know the car was safe, but unfortunately it is now destroyed so that knowledge is of no value. So what could I measure that would be relevant to my training goals and would also not disrupt my training? If you look at my recent posts, it's obvious that I have been hoping to use the average speed at which I ride my training rides for that purpose. For many reasons, that is a very imprecise and subjective measure but back in 2021 I did a statistical analysis that my speed on selected subsets of these rides might be useful nonetheless. For this post, I used my speed on a subset of rides all of which were on my Cañada route, where the level of effort I attempted during ride were all the same, and the bicycle I rode was the same. This is how the speed on those rides compared to the performance predicted by the Banister model:

As is shown in this graph, the Banister has essentially no ability to predict my speed on a Cañada ride. R², shown at the top of the graph, is a measure of predictive power that varies between 0 and 1, where a value of 1 means perfect prediction and a value of 0 means no prediction. The value of 0.001 measured here is about as close to 0 as one is likely ever to see.

Why is the Banister model not working for me? There are many plausible explanations, but before I get into them, I think it will be useful to say a little more about how the Banister model works. As noted above, the Banister model takes as its input the Load of each of my bike rides. It then uses that to calculate two hidden variables, Fitness and Fatigue. I call these variables hidden because there is no way to measure Fitness and Fatigue on their own, instead they are used to calculate Form (performance) which can be measured. To calculate Fitness and Fatigue, Banister uses two constants called k1 and k2. It multiplies Load by k1 and adds it to Fitness and multiplies it by k2 and adds it to Fatigue. In modern usage, k1 and k2 are often customized for each athlete but out of the box, the Banister model sets k1 = 1 and k2 = 2 and those are the values I am using. If this was all there was to the model, Fitness and Fatigue would just keep increasing forever. However, because we know that Fitness and Fatigue decrease over time, every day those accumulated values of Fitness and Fatigue are decreased exponentially. Specifically:

FatigueToday = FatigueYesterday x e^-1/Ta

FitnessToday  = FitnessYesterday x e^-1/Tb

Ta and Tb are two more constants whose value can be customized to the athlete, but out of the box, have values of 15 and 45, respectively. I use these out of the box values, and as a result, my calculated Fatigue decreases by half after 10 days and my Fitness decreases by half after 31 days.

Finally Fitness and Fatigue are used to calculate the output of the model which can be measured as follows:

Form = Fitness - Fatigue

...where Form (perFORMance) is related to how fast I can complete a ride. And that is what is not working for me, the Form I calculate has nothing to do with how quickly I complete a ride.

There are reports that Banister model and the related, widely used Training Peaks software package do work for many athletes so this is a problem with me, not with Banister. Given that, I can now turn to the question of why.

1) Maybe the Banister model is not working for me because my training is too constant.

The idea here is that I try to plan my training so that I ride my Cañada rides on days when I am fairly well rested. Thus, if I never vary my Load and resulting Fatigue much, I cannot expect my ride speed to vary much either; what looks like a lack of correlation (low R²) might be a lack of variation in my level of Form caused by a lack of variation in my Fatigue on the days that I measured my performance. It is not that the Banister model cannot predict my ride speed, rather it is that I never gave the Banister Model anything to work with.

Consistent with this idea, if you look at the graph at the top of this post, it looks as if Form doesn't vary much; it is always uniformly high. This is a bit misleading; Form on that graph uses a different Y axis than does Fitness or Fatigue. (I did that to make it more visible.) In fact, over the set of suitable Cañada rides, Form varies 7-fold from a low of 114 to a high of 765. On the other hand, if I look at all days, not just the days I chose to ride a Cañada ride, the variation is 43-fold, from 21 to 894. Perhaps if I had attempted a Cañada ride on a day where my Form was 21, I might have seen a more dramatic effect on my speed.

2) Maybe the Banister model is not working for me because of confounding factors.

If Point 1 were the whole problem, we would see that ride speed doesn't vary much. In fact, it varies between 9 and 12 miles per hour, a range that feels like a big difference to me. If changes in the Banister-predicted Form, Fitness, and Fatigue don't explain this, what does? As I have noted before, I often feel tired after a stressful day, even if that stress is mental rather than physical, a day when my babysitting responsibilities are particularly heavy, for example. Depending on how large the impact of non-cycling stress is, this could obscure the effects of my training.

3) Maybe the Banister model is not working for me because training speed is a bad metric.

In all my reading, I have never come across anyone who has attempted to use training speed as a metric in the way I have. Maybe there is a reason. Coaches recommend time trials as a metric to measure Form. One aspect of a time trial is that, because the athlete is riding as fast as possible, some confounding factors are eliminated. When I go for a training ride, I might be slow because of the Fatigue from an earlier ride or I might be slow just because I am not in the mood to go fast.

4) Maybe the Banister model is not working for me because I have not customized its constants.

Most current writing about the Banister model recommends not using the out of the out of the box constants but rather fitting these constants to the data from each individual athlete. I am working on software to allow me to do that, but to date, have not completed that work and don't know if or when I will. It is possible I will complete this software, fit the constants, and all of a sudden the Banister model will start working for me. If that happens, I will post about it.

5) Maybe (gasp!) the Banister model is wrong. (Not likely, but hear me out.)

There is a lot about the Banister model that doesn't make intuitive sense to me. It is my understanding that most coaches and exercise scientists believe that it is important that an athlete not do a workout (at least a hard workout) until that athlete has recovered from the previous workout. What "recovered" means varies, but at a minimum, it is when their Form has returned to what it was before the workout. According to the Banister model using the out of the box constants, that takes 16 days. A more stringent version of this rule is that "recovered" means until an athlete's form has peaked. According to the Banister model, this would take 39 days. Both of these numbers seem totally bonkers to me. Similarly, I am puzzled that following any reasonable training program means that, according to the Banister model, Fatigue always remains high. As I was laying awake thinking about this one night before bed, I asked myself what seemed reasonable to me. It seemed to me that a linear model of recovery might better fit my experience than the exponential model of Banister, that my Fatigue would be reduced by a fixed amount each day until it was gone entirely. But by how much each day? My experience has been that it takes me two days to recover from a Cañada ride, so I set up a model where my Fatigue decreased by that much. When I fit the same set of Cañada rides I used above to test the Banister model, I again found no correlation between my level of Fatigue and my speed on a ride. However, I remembered my post from last January where I found that the correlation between heart rate and ride speed improved dramatically when I looked at rides over the course of a few months rather than over the course of a few years, so I tried again, using a shorter time frame. In addition, to improve my chances, I used a time frame during which my predicted Fatigue changed substantially. Here is what I found:

An R² value of 0.229 is very unimpressive and not very useful, but it is a lot higher than 0.001. But that is an unfair comparison, my Linear Fatigue only had an R² value of 0.009 when evaluated over all the Cañada rides. What if I looked at the Banister model over the same reduced time frame? It did improve,  with an R² value of 0.079 for Banister Fatigue and 0.082 for Banister Form but not to a degree to be useful.

Conclusion:

For some unknown reason or reasons, the Banister Model is not able to predict my performance. If that changes, I will revisit the subject, but until then, I am through blogging about the Banister model; it's time for me to put the Banister Model to bed.

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Banister Posts:

• "Modeling Fitness, Fatigue, & Form" posted December of 2021
• "My Fitness, Fatigue, and Form" posted May of 2023
• "Cycling Career Saved" posted July of 2023
• "Update on the Banister Model" posted October of 2024

Winter Break

In the blog post I published at the beginning of December, I promised "I will do my best to maintain as much fitness as I can given the weather and will refocus come February 2025." I was probably a bit overconfident in picking February as the precise time when the weather would improve, it is March and the weather is still sketchy, but other than that I feel like I am more or less doing what I had promised, and I will talk more about that below. However, I want to underline what I assure you was a deliberate pessimistic tone to that promise; '... do my best ... given the weather ...'. What's that about?

The Weather

California has a very strong wet season/dry season pattern. During the last seven years, the wet season has run from some time in November or December through some time in February or March. If you look at my cycling log, the impact of this seasonal pattern is unmistakable. My goal for 2024/2025 was to reduce the impact of the rainy season on my fitness level.

Weather impacts my cycling both directly and indirectly. It impacts my cycling directly because, for safety reasons, I don't ride outdoors when the roads are wet. It impacts my cycling indirectly because I know that there will be days that rain prevents me from riding my scheduled ride making it unrealistic to try to work towards any goal that requires a fixed schedule. During the rainy season, my goals have to be both more modest and more flexible, I just try to do the best I can to somehow do enough cycling to maintain my health. This year, the schedule I came up is as follows:

• When the weather cooperates I ride my Cañada route, 17 miles, 90 to 100 minutes, three times a week and recovery rides on my trainer, 30 easy minutes, two times a week, a schedule I referred to hereafter as my Outdoor Schedule.
• If it rains once or twice during the week, sometimes I can move my rides around so I can still stick to the schedule above.
• When I can't just work around the rain, when it rains every day, for example, I ride on my indoor trainer, and therein lies a story.

My Best

I am a huge Clint Eastwood fan, and one of my favorite of his lines is "A man's got to know his limitations." As is the case for many people, a lack of willpower is responsible for much of my suboptimal behavior. I am of the belief, a belief I think most psychologists would support, that simply trying to conjure willpower out of thin air is doomed to failure. Rather, one must treat willpower as a limited resource and deploy it to best effect. I find riding on my trainer boring at best and miserable at worst. The harder I ride on the trainer, the less I can tolerate it. Thus, in the interests of preserving willpower, this year I worked to find routines I could do on my trainer that required a minimum of willpower while generating a maximum of fitness.

I originally set up my trainer for recovery rides, short rides at low intensity (Zone 1*) which have as their purpose facilitating recovery from an earlier, hard ride rather than generating fitness on their own. I was inspired to do this when I moved into the well-named Emerald Hills; those hills made low intensity rides on the road impossible. I find the trainer extremely boring so I originally assumed that recovery rides was all it was good for. This year, I decided to reconsider if I could also use that trainer as a way of dealing with bad weather. The key was not letting Best be the enemy Good. While it is true that given my limited tolerance for boredom meant that my trainer could never be a complete replacement for rides on the road, perhaps there were rides I could do on the trainer that would be better than an uncalled for recovery ride. 

I had previously found one possibility, my Gillen Interval Ride, six sprints in Zone 7. During the last few months, I have found another. While doing the riding that lead to my Counting Talk Test post, I convinced myself that a ride on my trainer in a 71" gear at 70 RPM was a legit Zone 2 ride while at the same time not being that much more unpleasant than a recovery ride at 55 RPM. Thus, on a day where I felt that a recovery ride wouldn't do, I could simply up my RPMs. To be honest, this is still not a great workout but it is much better than nothing and better than a recovery ride. Best of all, this ride is quite sustainable, I can ride it six days a week. If I am feeling especially motivated, I found I could increase the length of that ride from 30 minutes to 60 minutes, doubling the benefit. However, I confess that by the end of those 60 minutes my willpower is exhausted such that I would find it difficult to do more than one of these in a week. So, a sustainable schedule is six Zone 2 trainer rides a week, five of them 30 minutes long, one of them 60 minutes long. Hereafter, I will refer to this as my Indoor Zone 2 Schedule. By way of comparison, the Gillen Intervals take me about 45 minutes to complete and again, I can only manage one of these a week. So my final of the three schedules discussed in this post is five 30 minute trainer rides in Zone 2 and one Gillen Intervals ride, hereafter referred to as my Gillen Schedule.

Sometimes, depending on weather, I do a mix of my outdoor schedule and one one of these trainer schedules. Finally, I occasionally break out of my rut and do something altogether different, but for the remainder of this post I will focus on the three basic schedules described above, my Outdoor Schedule, my Indoor Zone 2 Schedule, and my Gillen Schedule.

Before moving on, I would like to share a thought: I am very lucky to have found outdoor cycling, which I can do most of the year, as a form of exercise I enjoy. Even on a day that I am not in the mood for a ride, a 90 minute outdoor ride is more pleasant and easier to complete than an easier (lower intensity) 30 minute ride on my trainer, and when the weather is nice and I am in a mood to ride, a 400 minute ride can be quite fun.

How Did I Do?

My goal was to do the best I could, given the weather, to cycle enough to maintain my health. Back in 2022, I blogged about cycling for health, describing routines recommended by Coach Hughes, the coach I follow, and by the Medical Community. I am not going to repeat that post, so if you are interested in the details, look there, but briefly, both recommend a Minimal Schedule and an Optimal Schedule. (Coach Hughes also recommends a Super-Optimal schedule which is well beyond my reach so I will speak of it no further.) 

My Outdoor Schedule exceeds the requirements of the Medical Community's Optimum Schedule and Coach Hughes Minimal Schedule. It does not meet Coach Hughes Optimal Schedule but it comes close, it checks all the boxes and has about 80% of the recommended minutes.

My Indoor Zone 2 Schedule exceeds the Medical Community's Minimal Schedule and has about 70% of the minutes of the Medical Community's Optimal Schedule. Coach Hughes is very prescriptive in terms of recommending a specific number of minutes at specific Intensities, but if I can be allowed to count minutes in Zone 2 as minutes in Zone 1, then this schedule meets Coach Hughes minimal schedule except for the minutes in Zone 3. In my opinion, this counts as quite close. However, it comes nowhere near Coach Hughes Optimal Schedule. Clearly, this schedule is way better than nothing and is way better than what I have done in prior years but is clearly less desirable than my Outdoor Schedule.

My Gillen Schedule is the trickiest to compare in that neither the Medical nor Coach Hughes recommendations anticipate the High Intensity Interval Training (HIIT) that is central to this schedule and so I have to estimate some equivalences to make that comparison. For comparison to the Medical Schedule, I am going to use the equivalence claimed in the original Gillen et al. paper that one minute of HIIT is equivalent to 45 minutes of Moderate Intensity exercise. Using that equivalence, my Gillen Schedule easily exceeds the Medical Community's Minimal Schedule and has about 80% of the minutes of the Medical Community's Optimal Schedule making it a bit better than my Indoor Zone 2 schedule. My Gillen Schedule comes nowhere near Coach Hughes Optimal Schedule. Compared to his Minimal Schedule, the total minutes, given reasonable equivalences, is pretty close. I would say it comes about as close as my Indoor Zone 2 schedule but interestingly, deviating in the opposite direction. My Indoor Zone 2 Schedule is missing High Intensity and my Gillen Schedule is missing Low Intensity. This might suggest some mix of these two schedules could be my best compromise for maintaining my health.

In summary, doing the above analysis has left me pretty satisfied that I did a reasonable job this winter of working around both the weather and my distaste for riding on my trainer with schedules that maintain my health. How does this year compare to last year?

Compared to this same time last year, the amount of riding I have been doing has gone up but my speed has gone down. I compared December 2023/January 2024 to December 2024/January 2025. Last year I averaged 208 minutes per week of riding as compared to 291 minutes a week this year. To compare speed, I considered rides on the Cañada route where my intention was a Pace ride (Zones 2 and 3) and where I completed the ride on my Bianchi Volpe (no eAssist.) Last year, I rode 11 rides that qualified and completed them at an average speed of 10.7 miles per hour. This year, I rode 10 rides that qualified and completed them at an average speed of 10.1 miles per hour. There are a lot of ways to look at this data to try to explain away this decrease, and to be honest, I don't think my speed has really decreased by 0.6 miles per hour in just one year, but I think there is no doubt my speed has been decreasing over the last several years, and that decrease does not come from a reduction in training.  This is illustrated in the next two graphs.

The first graph illustrates the drop in my ride speed since my move to California in 2017. This is one of several analyses I have done which show my speed falling over this time period. I have posted about this before. In this analysis, I collected the highest speed on the Alpine route for each month since my move and plotted that as a function of time:

The blue points connected by the jagged line are the data. The red line is the best fit to that data. The R² value of 0.34 suggests that about 34% of the variation is my monthly maximum speed can be explained by its decrease over time and that about 66% is something else. The probability that there has been no decrease in my maximum speed over time is very low. Having said that, what has caused that correlation is open to many explanations. One obvious explanation is that for whatever reason (e.g. increasing boredom) how much I have trained has decreased over time. The next graph addresses that possibility:

Here I have plotted the total number minutes I have cycled each week since my move to California. Again, the data is in blue, the best fit line is in red. Although my training has varied a lot week to week, on average, there is no correlation of that with time. Of course, there are many ways my training could have and has changed that would not be reflected by time on the bike and I have looked for and will continue to look for changes that might be relevant, but this does suggest that my decrease in speed is not due to a change to my training. The next most obvious explanation is that my decrease in speed results from aging. Eight years is a significant length of time at my age so that certainly is a reasonable suggestion.

Whatever the cause, this decrease in my speed has been very impactful. I feel like I can no longer keep up with my friends Roger and Dave even with the help of my Orbea Gain eBike. Not being able to ride with them eliminates what had been a great source of inspiration.  In the past, the need to get ready for rides with Roger and Dave gave me the inspiration to ramp back up come spring, but absent that, what do I have to look forward to this year? Last year, I compensated for that a bit with my Birthday Ride. However, as I noted at the time, that seems like that was a one time thing rather than an ongoing source of inspiration. Coming up with a new source of inspiration, with new ways to have fun on my bicycle, is perhaps my most important cycling-related goal right now.

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* How hard I ride, e.g. how fast, can be expressed in terms of Training Zones, Zone 1 (very easy) through Zone 7 (very hard.)

The Counting Talk Test

Back in November, I reviewed a series of videos from the online cycling magazine GCN. In that review I mentioned that the authors felt that heart rate was not a good way to identify one's Zone 2, but that breathing rate was. The criterion they suggested for the top of Zone 2 was the point where conversation is forced but possible. This metric is used, in various forms, by many coaches and is called the Talk Test. I do not find the criterion used by GCN easy to use because it is vague; what exactly does it mean for conversation to be forced but possible? Recently, I encountered an alternative version of the Talk Test which I found much easier to use, a version called the Counting Talk Test.

The Counting Talk Test (CTT) is a way of quantitating the Talk Test. In the Counting Talk Test, you exhale, take a deep breath, then count "One one thousand, two one thousand, three one thousand..." until you cannot count one number higher without taking a breath. This measurement is made while resting and then during exercise. The results of the Counting Talk Test is expressed as the percentage of number a patient can reach during exercise relative to what they can reach while resting. The table at the top of the post shows the results of my first attempt to use that test. What I did was get on my trainer, set the gears at 61", ride at 60 rpm for five minutes and then executed the CTT. I then increased the gear to 71" (maintaining my cadence at 60 RPM), rode for five more minutes and then repeated the CTT. I repeated this for  80". 91", and 106". At 106" I was only able to continue for 2 minutes. I then reduced the gear to 71" and measured CTT after 5 then 3 minutes. As I acquired more experience with the CTT, I have gotten better executing it. One consequence of this is that I have decided that my resting CTT is closer to 23 counts rather than the 16 counts measured in that first test, so I now calculate my percentages relative to that constant value of 23.

I encountered the Counting Talk Test in the Journal of Exercise Physiologyonline (JEPonline) Volume 5 Number 1 February 2002. This paper, and in fact every paper I have encountered that investigates the Counting Talk Test, considers it in a medical context rather than a sports context (which is why I used the word patient rather than athlete in the previous paragraph.) The medical community mostly uses a three zone Intensity system, Easy, Moderate and Vigorous, as opposed to the seven Intensity zone (Zones 1 through Zone 7) that the coach I follow, Coach John Hughes, uses. However, over the years I have come to the conclusion that the top of most coaches' Zone 2 and the top of the medical communities' Moderate exercise zone are both equal to each other and equal to the aerobic threshold. Since locating my aerobic threshold is probably the most important use I have for the Talk Test, the difference in training zone systems was not a big limitation. What was a big limitation is that the authors of this paper chose not to distinguish between Moderate and Vigorous exercise but considered the two together (they reported a value for the Counting Talk Test of 55% at the bottom of Moderate Exercise and 30% at the top of Vigorous Exercise.) The value I needed, the boundary between Moderate and Vigorous exercise, was not reported. A search for a scientific study that did report that value lead to Frontiers in Physiology Volume 13 article 832647 2022

The Frontiers in Physiology paper was, in fact, a comparison between the Counting Talk Test and an alternative they developed, the Regulated Monosyllabic Talk Test. I was not persuaded to switch to their Regulated Monosyllabic Talk Test but did find this paper useful because it reported the values for the Counting Talk Test at lower and upper boundaries of the Moderate (63% to 48%) and Vigorous (48% to 32%) exercise zones.

Both of these papers had as their goal the use of some version of the Talk Test to find the boundaries of the Moderate and/or Vigorous Exercise zones. To do that, they had to know where these boundaries are and then measure the value of their Talk Tests at these boundaries. How did they define these boundaries? Unfortunately, they did so using Heart Rate, exactly what GCN (and others) have argued should be avoided. In response to that concern, I compared the CTT to the version of the Talk Test used by GCN and determined that the percentages given in the Frontiers in Physiology appear to be about right, at least for me.

How does the CTT compare to heart rate as a way to measure Intensity? The main thing I use heart rate for is to measure average Intensity of a ride and thus cumulative Load over time and the CTT is impractical for that purpose. The reason I initially pursued it was that it was one more test that I could use to determine if I had correctly located my aerobic threshold. Having done that I now continue to use now and again it because it is free of some of the complexities of heart rate. Heart rate can be higher or lower based on fatigue, stress, caffeine, environmental temperature, etc. Based on my experience so far, CTT seems to be much less sensitive to such considerations. Also, heart rate is a lagging indicator, it takes time for it to increase after Intensity increases. As best I can tell, CTT responds to an increase in Intensity with little or no delay.

Does the aerobic threshold I measure using CTT agree with what I had previously assumed it was? The answer to that question is not so simple, as is illustrated by the following output from the software used by my heart rate monitor:

This ride was on my Trainer and done at a constant effort, 70 RPM in a 71 inch gear. The Counting Talk Test yielded a value of 11 counts which corresponds to 48%, the top of Zone 2. However, my average heart rate over the course of that ride is 123 beats per minute which corresponds to the middle of Zone 2. Is this a disagreement? Perhaps, but note that, although I held my Intensity constant over the 30 minute of the ride, my heart rate was not constant but increased during at least the first half of the ride. If I look at only the second half of the ride, my heart rate, estimated by the white line, is at 132 beats per minute, much closer to the top of Zone 2. I suspect that my heart rate during the first half of the ride is not a good indicator of Intensity because heart rate lags behind Intensity. If this explanation is correct, then the Counting Talk Test agrees with the other approaches I have used to locate my lactate threshold.

In summary, I think the Counting Talk Test is a better way to estimate breathing rate than the approaches suggested by the exercise community to date. To be most useful, the exercise community would have to adopt the CTT (or something similar) and calibrate it against reliable laboratory measures of Intensity like blood glucose or O2/CO2 levels in the breath. This would provide a clear, accurate way for the average athlete to locate their aerobic threshold quickly, easily, and inexpensively. 

Heart Rate and Intensity

If I ride a given route quickly, it will leave me more tired than if I ride it slowly despite the fact that I spend more time riding at the slow speed. That assertion  is widely accepted by the vast majority of exercise scientists, coaches, and athletes. How to quantitate that is more controversial. For some purposes, speed (e.g. miles per hour) is used for such a quantitation. For example, many coaches recommend periodic time trials on a fixed course where the speed at which one can ride that time trial is a measure of Fitness. But because simple speed is affected by so many factors (e.g. hills) it is not usually used as a routine measure of Intensity, the generalized term used to quantitate the impact of different riding speeds. The most common measures used to routinely calculate Intensity are heart rate, power, and relative perceived exertion (RPE.) RPE is the simplest, it is just how hard a ride feels. That is the metric I used in last month's post to determine if I had been riding too much or too hard.

Power is something like a normalized version of speed in that it corrects for factors like hills that affect speed. It is measured using a power meter which is incorporated into the drivetrain of the bicycle. These are widely available if somewhat pricey. Power (like speed) needs to be calibrated for differences in the fitness and ability of different athletes. 150 watts of power might be at the upper end of the amount of Intensity I can manage while being an easy ride for a better cyclist, and the meaning of 150 watts would be different for me at the beginning of the season when I am not very fit versus at my peak fitness after I have trained for an important event. I have never owned a power meter and so I use speed on standard routes as something of a proxy for power.

Two months ago, when I posted about Zone 2 training and how the reporter Manon Lloyd used herself as a guinea pig to investigate Zone 2 training, something I did not mention is that when Lloyd developed a training plan with her coach, he specifically advised her to use both a power meter and a heart rate monitor. Why both? In many ways, power is considered to be the superior metric. It responds instantly to changes in Intensity and is independent of many factors that affect heart rate, things such as stress, caffeine, etc. However, power is rapidly affected by training and that changes are significant. At the beginning of her six weeks of training, if Lloyd rode at the top of Intensity Zone 2, she could generate 140 watts of power. At the end of her six weeks of training, she could generate 160 watts of power at the same level of Intensity. Thus, had she used only power to guide her training, she would have been training at too low an Intensity at the end of her training. Heart rate is also affected by training, but on a much longer time scale. Although not given in the videos, it is likely that Lloyd's Zone 2 heart rate at the beginning and end of her six weeks of training would have been similar or identical. In short, for the purpose of keeping her effort within Intensity Zone 2, power was the better metric over the course of one ride but heart rate was the better metric over the course of six weeks of training.

Although I use RPE as my primary measure of average ride Intensity, I use average heart rate as an important secondary measure. The goal of this post was to determine how reliable average heart rate is and specifically, to compare average heart rate of a ride to the speed of that ride on a standard route. I used two standard routes for that comparison, my Cañada route and my Alpine route. As noted above, the impact of training on ride speed would be expected to confound that comparison. As I train and become more fit, I would expect my speed a given heart rate to increase. To remove that confounding factor, I did my comparison over a period of time during which I felt that my fitness stayed relatively constant. The results for the Cañada route are shown at the top of this post. The results for the Alpine route are shown here:

Encouragingly, the results for the two routes were similar. I will focus here on the results for the Alpine route. This graph shows the results for 19 rides on the Alpine route from June 30, 2024 and October 24, 2024. It begins after I had gotten in shape for metric centuries, a period during which I was attempting to maintain my fitness but not trying to increase it. For each ride, I plotted the average speed of that ride on the vertical axis as a function of the average heart rate for that ride on the horizontal axis. The line is the best fit line through those points. The equation for that line (which can be ignored) is shown at the top of the graph as well as the value for R-squared (R².) R-squared is a measure of how effective heart rate is at predicting ride speed. R-squared varies between 0 and 1, and if both heart rate and ride speed were perfect measures of ride Intensity, R-squared would be 1. The value measured for the Alpine route is approximately 0.7, very similar to that measured on the Cañada route. This is a pretty good correlation between speed and heart rate which means that both of them are pretty good measures of Intensity, but that one or both of them are imperfect measures. Besides Intensity, what might affect ride speed? The two things I can think of are fitness and wind. As noted above, I have done my best to control for fitness and my intuition suggests that wind is not a major factor. Besides Intensity, what might affect heart rate? Unfortunately, many things: caffeine, emotional state, and fatigue* to name but a few. This would suggest that speed is a better indicator of Intensity than is heart rate, so why not just use speed? 

There are two reasons not to use speed instead of heart rate as a measure of Intensity:

1. Speed is only a good indicator for one specific route. It is possible that I might be able to map some kind of equivalence for two commonly ridden routes like the Alpine and Cañada routes, though I worry that even this level of data processing could lead to false conclusions. Perhaps more importantly, many of my rides are ridden over a wide variety of routes, none of which I ride commonly enough to provide the data needed to map speed onto Intensity. That is why many cyclists use power meters.
2. The speed corresponding to a given level of Intensity changes with training. The graph at the top of this post shows data for rides on the Cañada route between June 17 and October 26 of 2024. If I do a similar analysis for rides on the Cañada route between July of 2022 and October of 2024, the R-squared value drops from a respectable 0.70 to a useless 0.36. This is because variations in how much I was training and my resulting fitness varied substantially over that longer time period.

Where does this all leave me? In a pretty good place, I think. It is definitely true that heart rate is an imperfect measure of Intensity for any single ride. During the June through October 2024 time period, four of my rides on the Alpine route resulted in an average heart rate of 126 beats per minute. The speeds of those four rides varied between 10.8 and 11.5 miles per hour. However, the most important use I make of average heart rate applies not to single rides but rather to accumulation of fatigue over time, and for that purpose, these variations tend to cancel each other out so that average heart rate is a perfectly adequate metric. In that context, the R-squared value of 0.7 I determined from the comparison of speed and heart rate gives me confidence that recording my average heart rate is helpful to my training. But how do I use that to estimate accumulated fatigue? I plan to discuss that in a future post.

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* Many coaches advise that when an athlete is fatigued, their heart rate will be lower than usual at a given level of effort. A rationalization sometimes given for that result is that the athlete's heart is "too tired" to beat any faster.