Training Derailed

As I think about my journey since restarting cycling in 2008, my attention often turns to points on that timeline where an apparently successful training plan is, for no reason I can remember, abandoned. The problem is that my memory is quite fallible (most peoples' are.) So, when I recently watched a training plan unravel in real time I decided to document it before I forgot how it happened. I started a new training plan last February, my last training plan having derailed the previous November. That new plan was designed to prepare me for metric centuries during the 2020 season. This new plan came apart in March with the arrival of the COVID-19 pandemic, but by April, I was ready to resume it despite the fact that the metric centuries for which I was originally training would not be held this year. It took me three weeks to work back up to that plan, and after three weeks of executing it, my body complained so I cut back a bit for four weeks to recover. I then went back to this plan for another three weeks when life events caused it to derail once again. It is this last derailment that I want to talk about here.

What is this training plan that keeps derailing, and what does "derail" actually mean? That plan is to ride my "maintenance" schedule most weeks, one 34 mile ride with 1800 feet of climbing ridden mostly in Zone 2*, one 23 mile ride with 1300 feet of climbing ridden in a mixture of Zones 2 through 5, and two easy rides ridden in Zone 1. There are two ways I can use this plan to be ready for a metric century. If I do a metric century every month, then two weeks of each month I ride the maintenance plan, one week I ride two to three easy rides to taper (rest) for the metric century which I ride at the end of that week, and then the following week I do five to six easy rides. If I am not currently riding metric centuries, then I ride the maintenance schedule every week. From that base, four weeks before a metric century, I increase the length of the longest ride from 34 to 45 miles. The following week I drop back to the maintenance schedule. Two weeks before the metric century I increase the longest ride from 45 to 55 miles. One week before the metric century I ride the maintenance schedule. The week of and after the metric century is the same as the metric century a month schedule, easy to rest before and recover after the event.

What does it mean to say that the above schedule "derailed"? For more than I year I have consistently completed at least 300 minutes of aerobic exercise every single week, so derail does not mean no riding, it means less riding, but still enough riding to add up to 300 minutes. A common minimal schedule I do to reach that 300 minutes is five one hour, relatively flat rides each week. So, from a health perspective, I'm doing fine. It is only in the context of a regular schedule of metric centuries that my schedule derails. Subsequent to derailment, I may no longer be a month away from being able to complete a metric century (though see below), it might take me two months or more to reach that goal.

What were these "life events" that most recently derailed my training plan? They were a relatively modest pair of events which, upon reflection, were only able to derail the plan because 1) they were on top of other life events which, by themselves, were not enough to derail the plan but brought me close to that eventuality and 2) because my maintenance plan appears to be close to the maximum training load I can sustain. The two things that pushed me over the edge and caused me to drop back to an easier schedule were: 1) Road repair that made it difficult to ride my 23 and 34 mile rides. 2) My grandkids and their family took a vacation. They needed some extra help from me to get ready and that extra effort left me exhausted. They left mid-day on Saturday, and after they left, I had planned to do an easy ride. I was unable to complete (or even start) that ride nor could I do anything else for the rest of the day. I collapsed on the couch for the rest of the day. On Sunday, I completed that short ride but was unable to do anything else thereafter. On Monday, I was scheduled to do my long ride, and had even toyed with the idea of working towards a solo metric century four weeks from now by extending that ride to 45 miles. However, that Monday I was exhausted and opted to spend my limited energy doing some chores and forgoing the ride. On Tuesday, I was still feeling tired but also feeling that this was my last chance to avoid another derailment and even feared slipping below 300 minutes for the week.  If it had not been for the road repairs, I suspect I would have forgone the longer ride but would have completed my normally scheduled 34 mile ride. In retrospect, I don't know if that would have been a wise decision, but faced with finding my way around the road work, it was more than I could manage emotionally so I did a much flatter, easier 38 mile ride instead. Thus was my schedule derailed.

If my maintenance schedule is at the limit of what I can sustain, how is it that I can ever do a metric century? Part of the answer is given by the word "sustain." I have noted multiple times on this blog that it is possible to train to a peak of fitness which is above what I can sustain long term. Thus, I can work my way up to a metric century but then might have to take a break to pay off the resulting fatigue debt resulting from preparing for and riding the metric century. If that were true, then my dream of riding a metric century a month all season long is doomed. Perhaps a more realistic explanation is that what I can sustain on the bike is strongly influenced by what is going on off the bike. When I am unstressed and well rested, I might well be able to maintain a metric century a month schedule, but when life takes its toll, I can barely maintain the easiest version of 300 minutes a week, and in fact I think that is what the story in this post suggests. Key to appreciating this alternative explanation is the dramatic impact of life events on the ability of my body to absorb training, that may be more important than the fine details of exactly how I train. Sadly, this scenario may also doom my metric century a month dream, the odds of going an entire season with no intervening life events are low indeed.

One final point that I don't quite know how to fit in with the rest of this story is that I know from experience the more I do, the more I can do. Right now, I find my 34 mile ride fairly tiring, my legs are inevitably sore by the end. I have found, however, that when I start doing longer rides, that 34 mile ride starts feeling easy. This was particularly evident last Fall, when my preparations for the Golden Hills Metric Century and a subsequent solo metric century worked particularly well, leading to a level of fitness and comfort on the bike I haven't seen for a long time. I sure would like to repeat that! When I look back on that period, they key was not consistency, up through August I had a lot going on in my life and my cycling schedule had been derailed to say the least so that in September, I jumped into the ramp-up to prepare for Golden Hills with very little preparation. Why did that work? When I think about it, the period in question, September through November of 2019, was a period of particularly low stress in my life, and maybe that's what this is all about. I often follow the Tour de France, and have been puzzled by the speculation about how the various favorites are "feeling" and how that bodes for their Tour that year. These guys are professionals! Shouldn't they have a training plan that allows them to tune their fitness and fatigue in a predictable way? To some extent they do, the other discussion about the favorites is which races they have ridden leading up to the Tour to maximize their fitness and minimize their fatigue, but there is an unknown factor as well. Might they be similarly susceptible to what is going on off the bike as I? Maybe this is just the way it is, I have to just keep trying, cutting back on my training when my body tells me I must, being the best that I can be when I am lucky enough to have a stress and illness free period of which I can take advantage.

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* Zones 1 and 2 are a pace I can ride indefinitely, with Zone 2 being harder (faster) than Zone 1. Zone 3 is a pace I can maintain for an hour or so. By the top of Zone 4, I can only manage to ride for 30 minutes. Zone 5 is some degree of sprinting, a pace I can maintain for 15 minutes at the bottom of the zone to 20 seconds at the top (aka Zone 6). 

Intensity and Fatigue

The Road Bike Rider newsletter regularly appears in my inbox, and in it is usually an article by my favorite coach, John Hughes. In a recent article, he answered a question from one of his clients, a 70 year old man (the same age as me.) The question was "Why Do [my] Legs Hurt?" His response was pretty harsh, he pointed out to his client that he had not followed Hughes' directions, but rather had ridden more than he was supposed to, thus the sore legs. I felt rather sorry for the client for being publicly shamed, but at the same time noted that I had been feeling worse than usual and wondered if I deserved a tongue lashing as well. So I looked back at my recent logs to see if I have been faithfully following my own training schedule.

I talk a lot on this blog about "listening to my body." My body "speaks" to me in two way: how I feel (tired, sore, energetic, moody...) and how I perform (speed on a standard ride.) Listening to my body has been the only way I have found to figure out how much and how hard I should be riding, but it is not a panacea; my body cannot design a cycling schedule for me, it can only tell me if the one I am riding is too hard. Furthermore, by the time I get feedback from my body, it may be too late. By the time I feel the effects of overtraining, I may have already built up a fatigue debt that will require me to cut back significantly on my training, perhaps even for months. Thus, it is important that I carefully design and stick to a training schedule to keep that from happening.

How did I come up with my current training schedule? When I moved from Texas to California three years ago, many aspects of my cycling changed. The hills of California made it more difficult for me to do rides at well defined intensities, so I switched to a more "just ride" strategy, relying on those hills to give me the mix of intensities that cycling coaches recommend. So designing a schedule became making a list of local rides that together kept me fit and healthy but which were sustainable so that I did not drift into overtraining. This schedule consisted of a list of the rides I would be doing each week: Alpine on Monday, Cañada on Wednesday, Stafford Park on Thursday, Neighborhood on Saturday. Each ride has a distance and an amount of climbing; 23 miles and 1300 feet, 34 miles and 1800 feet, 7 miles and 200 feet, 12 miles and 500 feet, respectively. From experience, I know about how about how long each ride will take me, 110 minutes, 160 minutes, 40 minutes, 60 minutes. Thus, my schedule provides for 370 minutes of riding containing a reasonable mix of intensities. By listening to my body, I can determine if this schedule is too hard and adjust if necessary. But what if this schedule is not as consistent as it seems? In particular, I don't always ride the same route at the same speed, what is the impact of riding a particular route fast or slow?

It is fairly obvious why my speed might matter. The fatigue generated by a ride is a function of both how long the ride lasts (volume) and how fast I ride it (intensity.) The problem is how to quantitate the intensity of a ride. It is clear to anyone who has done such rides that a 20 mph ride is more than twice as tiring as a 10 mph ride, but how much more? Training intensity is normally described in terms of zones. The zone in which one is riding can be determined by measuring relative heart rate, relative power output, or subjectively, using relative perceived exertion - how hard the ride feels. Many coaches provide tables that list the heart rates and power levels at the boundaries of the different zones or that describe how each zone feels. There are some differences but a lot of commonality between the tables provided by different coaches. Coach Joe Friel, one of the first coaches whose books I read, uses a seven zone system, naming the zones 1, 2, 3, 4, 5a, 5b, and 5c. Historically, this is the zone system I have used on this blog. Coach Hughes, who I now follow, uses a six zone system, naming the zones 1, 2, 3, 4, 5, 6. The Hughes zones are very similar to the Friel zones except that Hughes combines the Friel zones 5a and 5b into zone 5 and names the Friel Zone 5c as Zone 6. As of this post, I am switching from the Friel zone system to the Hughes zone system. Back in Texas, I determined the zone in which I was riding using heart rate (HR) but since moving to California, I determine my zones using relative perceived exertion (RPE). (I have never had the pleasure of using a power meter.) What is missing from all of these zone systems is a quantitative measure of the impact of each zone on fatigue, fitness, or health, the three main consequences of training.

As I have previously blogged, I deeply distrust the metrics used to translate training zones into quantitative intensity levels in the training books I have read. Rather, my estimates of quantitative intensity has been heavily influenced by a paper I reviewed not once but twice on this blog, a paper I refer to as Gillen et al. This paper looked at the impact of intensity on health. The dramatic claim of this paper is that 1 minute of training in Zone 6 (High Intensity Interval Training or HIIT) has the same health benefits as 45 minutes of training in Zone 2. Even if one accepts the claims of Gillen et al., it is not clear that training in Zone 6 would also have 45 times the impact on fatigue and fitness as training in Zone 2, but in the absence of better data on the topic and for the purposes of this post, I am going to both accept the claims of Gillen et al. and assume that they apply to fatigue and fitness as well as to health.

Gillen et al. only compared rides in Zone 2 and Zone 6. However, a long standing claim of the medical community is that vigorous rides (Zones 3 or 4) have twice the benefit of moderate rides (Zone 2), so, relative to a ride in Zone 2, arbitrarily set to an Intensity of 1, I set the Intensity of Zone 6 to 45 as per Gillen et al. and then Zones 3 at 1.5 and Zone 4 at 3 interpolating and extrapolating the "Vigorous" intensity of the Medical community. To estimate values for Zones 1 and 5, I plotted the values for Zones 2, 3, 4, and 6 as determined above, connected them with a smooth curve, and read intensities for Zones 1 and 5 from the graph. In the case of Zone 1, I adjusted the value because didn't match my experience and common sense. The graph gave Zone 1 an intensity of 0.1. The medical community might agree with that value in terms of impact on health, but my experience says that the impact on fatigue is greater than that so I arbitrarily assigned it an intensity of 0.5. These values are listed in the figure at the top of this post.

I feel like I can do a fair job at estimating my RPE at any point in a ride, but what I cannot do with any degree of accuracy is to say, over the course of a variable and hilly ride, what percent of my time I spent in different intensity zones. Four or five months after moving to California, I wore my heart rate monitor on my Alpine ride. By chance, the speed I rode that particular day was my average speed for that ride, 12.3 mph, making this data maximally useful. The software that came with the monitor gave me the percentage of my time in each of the intensity zones as determined by heart rate. This was the data I used to determined that a mix of rides in California would give me approximately the mix of intensities that most coaches would recommend. Shortly after I did this measurement, my heart rate monitor died so I have no way at present to determine how this might change as my ride speed varies. Is there some way I might estimate that? Back in Houston when I was riding around and around the Rice track, always with a heart rate monitor, I did an experiment where I started riding at a heart rate of 120 bpm, recorded my speed, then increased to 130 bpm and 140 and so on until I reached a maximum heart rate of 173 bpm at which point I was riding at 23 mph. From that I concluded that each heart rate zone corresponded to an increase in speed of about 1.5 mph. Applying that to my current Alpine ride is quite a stretch, but in the absence of any better information, I will assume that if I increase my speed on the Alpine ride by 1.5 mph, the intensity zone of the various segments of this ride (uphill, downhill, flat, ...) will increase by about one, e.g. Zone 2 becomes Zone 3 and so on.

So how much does my speed on my Alpine ride vary? As I blogged last time, the last three months have included both the fastest (14.1 mph) and slowest (11.3) speeds on that ride. What determines how fast I ride it? The speed I ride is determined by how I feel. If my legs are feeling strong and I am feeling motivated, I might ride at at an average speed of 14.1 mph. If my legs are sore and I am not feeling motivated, I might ride at at 11.3 mph. I do confess that internal competition has a lot to do with it. If I ride it faster than 13 mph, I feel like that means I am in good shape and it makes me feel good about myself, so when I feel up to it, I am highly motivated to go for a fast time. To be clear, in all cases I am "just riding", even the fastest rides are fun and comfortable. That is not to say that the fast and slow rides are the same as measured by RPE. Even if the faster ride is more comfortable and more fun than the slower one, using RPE, I can definitely tell that the faster ride is harder. And this brings me to the point of this post: what impact does this variable ride speed have on my carefully planned ride schedule? For the sake of simplicity, let's consider two rides, the average 12.3 mph ride for which I have heart rate data and an hypothetical ride at 1.5 mph faster, 13.8 mph. If I take the percent time spent in each zone for the 12.3 mph ride and increase it by 1 as described in the previous paragraph and then translate each of these zones to an intensity as listed in the figure at the top of the post and sum the intensities over the two rides, this indicates that the 13.8 mph ride generates about twice the fatigue of the 12.3 mph ride. This calculation rests on a lot of shaky assumptions stacked on top of one another and therefore is highly suspect, but I have to say, the result feels right to me.

In retrospect, the conclusion of this analysis seems obvious: if I want to avoid overtraining, I should not ignore how fast I am riding. If I want to get the right balance of ride intensities from the hills of California, I need to have that set of intensities be consistent which means riding at a consistent speed. A week where I ride my two longest and hilliest rides at greater than 13 mph is a very different week than one where I ride them at less than 12 mph. How could I have missed something so obvious? It is, I think, because I viewed ride speed as a "message from my body" telling me, on a day I rode quickly, that all is well. While true, it is also true that how fast I ride is a decision I make which affects my training outcome. Another factor was by my natural competitiveness, I really like arriving home with an average speed over 13 mph. Do I now have to become a mindless drudge, reigning in my enthusiasm and squeezing all spontaneity and joy from my rides? Maybe not, maybe there is a middle ground. Perhaps it is OK to go all out on Monday and see how fast I can do the Alpine ride on a day when I am feeling strong, but then on Wednesday, I should make a point of holding back on the Cañada ride even if I feel like I could be riding it faster. Moderation in all things, as my Grandmother used to say.

So is this the answer to the question that inspired this post, are my annoying periods of feeling tired the result of prior rides that were ridden too fast? My intuition tells me that, at most, this is only part of the answer. It is certainly the case that coaches warn over and over again against exercising more than you think you are, more than you should, and there is the statistic that 65% of cyclists train too much as compared to 25% who train too little. Still, there are other potential reasons I might feel tired: stress from things going on in my life, a sub-symptomatic illness, or trying to ramp up my training too rapidly at the beginning of the season. But at the very least, being aware of the impact of my ride speed on fatigue gives me one more way to respond to the signals my body sends me.

Its Not About the Bike

I currently am riding five bikes:

• A 2016 Public bike, an upright urban bike with an eight speed hub.
• A 2010 Surly Cross Check, a cyclocross/gravel/do everything road bike.
• A 2007 Bianchi Volpe, a cyclocross/gravel/do everything road bike.
• A 1967 Hetchins. Originally a fast touring bike, now a road sport frankenbike.
• A 1960 Bianchi Specialissima. A vintage, top of the line road racing bike.

I have blogged about these bikes over the years so I won't say too much more about them here. Over the last year, I have been rotating them through my LBS, Veloro Bikes, to catch up on maintenance. Each time Veloro finishes with one bike, I ride the next one to be worked on into their shop and pick up the one they have just finished. A few weeks after the Golden Hills Metric Century last October, I took my Hetchins in and picked up my Volpe, and a stunning thing happened: my speed on my standard rides increased dramatically. One of the routes I have ridden the most often, over 130 times, is one which I call the Alpine ride. Until I picked up my Volpe, my speed on that ride varied between a low of 11.3 to a high of 13.2 mph. The first time I rode it on my newly overhauled Volpe, I rode it at 12.5 mph, a pretty average speed. However, the next time I rode it, I rode a new personal best, 13.4 mph. For a variety of reasons, I did not ride another Alpine ride for a while, but my speed on my similar 34 mile ride strongly supported the notion that this was not a fluke. Then in February, I did my Alpine ride at an astonishing (for me) 14.1 mph! I became a believer, Gebhard of Veloro Cycles must have done something magic to my Volpe to make it a full mph faster than any of my other bikes, though for the life of me I could not imagine what that might have been. As it happens, my subsequent training results have debunked that myth, my speed on that Volpe has returned to normal. I have always believed that my Volpe was my fastest bike (with the possible exception of my Specialissima) and Gebhard did put some pretty nice tires on it so it is possible that a small part of that November to February high was due to the bike, but the bulk of it must have been something I accidentally did right in my training. What might I have accidentally done right in my training and what accounts for the fall in my speed thereafter?

I have previously blogged about my training leading up to my last two Metric Centuries of 2019.  I thought I might end the 2019 season on December 8 with a third-in-a-row metric century, another solo ride, but the week before I would have done that ride was rainy and I decided to host a Thanksgiving dinner that week so was unable to prepare and my season ended in November. What happened thereafter was not particularly planned except that I had a determination to maintain the 300 minutes a week of cycling needed for my health and a vague notion that if I could ride a 34 mile ride each week I could get ready for a future metric century relatively quickly. As a result, in December through February I usually did one 34 mile ride each week combined with a variable mixture of shorter rides to add up to something between 300 and 360 minutes. At that point, the COVID-19 pandemic struck. At first, the shelter in place orders used to control the pandemic meant that my rides had to be kept close to home so my 300 minutes of riding consisted of five to six short rides around my neighborhood and the 34 mile ride had to go. After about five weeks, the rules changed so I could do longer rides, including that 34 mile ride, so despite the absence of a season for which to prepare (as a consequence of the pandemic) I decided to ride as if I were preparing for one anyway. In the past, I have found that the 80:20 rule definitely applies to my training, very easy riding keeps me pretty fit. Also, I have recently made my short, neighborhood ride both longer and hillier, and when that was my only ride, I was riding it faster, so I had hoped that when I restarted Alpine rides, I would go back to riding them at 13+ mph. Nonetheless, I took things a bit slow at first and rode nothing longer than 23 miles for two weeks before resuming my 34 mile rides. My plan was to get back to my maintenance schedule of four rides a week of length 12, 12, 23, and 34 miles, maintain that for six weeks, and then up the difficulty. To my disappointment, all of my Alpine rides had speeds of less than 13 mph, and some of them fell below 12 mph. Further, after three weeks of my maintenance schedule, my body was complaining and my speeds were dropping, so I reduced my effort for the next four weeks in response. So, good performance on the last two metric centuries of 2019, outstanding performance on routine rides for the next three to four months of less challenging riding, and then after five weeks of short rides only, my performance fell back to average when I resumed longer rides, and further, I found what had in the past been an easy schedule to be exhausting.

I have a bad habit of over-analyzing my rides, but with that warning, here is how I am currently thinking about what happened. I think the training I did for my 2019 "metric century"* season, especially at the end, left me in very fit. That fitness persisted for a few months at the same time as an easier training schedule allowed me to eliminate virtually all my fatigue. That resulted in the fast rides I initially attributed to a "magic bike." However, by March, the extra fitness from 2019 was gone and then when the pandemic forced me to cut back even more, my fitness fell even further. This manifest itself by slower speeds once I was able to return to my Alpine (and longer) rides but also in a reduced ability of my body to take advantage of training, I need to "get in shape for getting in shape" as the coaches say.

As I look back on my 2019 season, I find myself amazed at how foolish some of my decisions seem in retrospect. That said, a recurring factor limiting my performance has been a feeling of being tired much of the time. What is the cause of that tiredness? One obvious candidate is overtraining, but there are others. The last three years of my life have been extremely stressful, beginning with the loss of my wife rapidly followed by a move to California with all the general disruption that caused as well as the impact of that move on the kind of riding I could do. The most recent stress is the COVID-19 pandemic.When I discussed my recent tiredness with my son, his immediate reaction was that my sense of being tired was most likely a response to the stress of the pandemic.  Perhaps I should not think of my cycling career as having started in 1965 or even in 2008 but in 2017 when I moved to California and only then could starting figuring out how to train in the hills of California with a highly stressed 70 year old body. Perhaps I had a lot to learn in 2019, lessons that I had no choice but to learn from experience. 

So what now? One overwhelming factor affecting what I do now is the virtual absence of a 2020 group cycling season. Due to the pandemic, the "metric centuries" I had planned to ride I will not be held this year. I could plan solo metric centuries or arrange rides with my son with whom I am sheltering. The huge difference in our abilities would be a factor, but he is very understanding and we could arrange it so that my challenge rides are his easy rides. That is what I had in mind when I restarted more serious training seven weeks ago, but once again, feeling  tired impacted my plans. One big difference between group challenge rides and solo challenge rides is that the group rides are fixed date so ready or not, I have to ride them when scheduled (or not at all.) Does this force me to be disciplined, to stick to a schedule despite my subjective feelings? Or rather, are the solo rides better because they allow me to listen to my body to optimize my training plans? Only time will tell, stay tuned.

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* That season consisted of five rides, three of which were metric centuries (Art of Survival, Golden Hills, and a solo metric century.) The other two were Eroica California, an easy 35 mile ride, and my one pass version of The Death Ride, shorter than a 62 mile metric century but harder due to the amount of climbing.

I Am Not Average

In case it is not obvious, this is not a real ad. Photo from U.S. News and World Report.

[Part 3 of 3 in a series on whether I should give sprint workouts another try.]

The Problem

Since before my first blog post back in 2012, I have been trying to decide if sprint workouts, AKA brisk or high intensity workouts, would be beneficial for me. On the one hand, when I have introduced sprint workouts into my routine, I have not noticed a lot of benefit. Rather, I find that they frequently drive me into overtraining. On the other hand, everything I have ever read about training highly recommends them.

So what have I been reading? The first thing I started reading were training books written by cycling coaches. I have had a bipartite cycling career, the first part running from 1965 or so and continuing full force through 1970 and then slowly petering out until 1978 when it stopped completely. The second part started in 2008, stopped in 2009, started again in 2010, and has continued with ups and down but no significant breaks into the present. Even back in 1965-1970 when I was racing, I did not have a particularly organized training plan, I just rode from challenge to challenge. When I restarted cycling in 2008, I took the same approach. However, when I tried to train for a 200K (124 mile) brevet (challenge ride), I found it more difficult than expected, and my wife bought me "The Complete Book of Long-Distance Cycling" by Burke and Pavelka, where I first encountered the concept of an organized training plan. Following the plan in that book I successfully prepared for a 200K brevet in the spring of 2012. However, I quickly ran into problems and developed questions which lead me to read a variety of such books. I have found these books extremely helpful as a source of ideas, but my experience has been that some of those ideas seemed to work for me while others didn't. In retrospect, I think this was due to a fundamental limitations of such books. It seems to me that central to the concept of coaching is the interaction between coach and trainee. The coach tries workout ideas, sees how their trainee responds, and adjusts accordingly. This central interplay is, of course, missing from the book experience.

As I continued to explore the world of training advice, I encountered scientific studies that compared the virtues of different training plans. Coaching is based on intuition and experience. These studies are based on science, and as a retired scientist, I found that most enticing. There is a whole ecosystem around such studies: they are reported with varying degrees of inaccuracy by the popular press, they are summarized by scientific, medical, and government entities into guidelines and are one source of ideas that coaches use to develop suggestions for their trainees and to put into their books and articles and blog posts, all of which I read compulsively. In all cases, I try to go back to the original scientific publications and read them critically but with an open mind. To date, I have reviewed about ten different scientific studies on exercise on this blog. In part 1 of this series, I reviewed a scientific study that examined the benefits of sprint workouts for health in the elderly (e.g. me.) In part 2 of this series, I reviewed a scientific study that looked for correlations between training intensity and improvement in cycling performance. In the case of almost all of these studies, I seem to find something in them that makes me question their conclusions. Issues I have commented on to date include:

1. There are too few subjects in a study so the results are not statistically significant.

 

2. There are problems with the study design, things like changing more than one variable at a time, that make the results difficult to interpret.

 

3. The study is observational rather than experimental and as a result, cause and effect cannot be proven.

 

4. The study is a biomarker study and as a result, it is not clear that what I care about (e.g. health) is improved just because the biomarker (e.g. VO2peak) is improved.

 

5. The subjects are very different from me (younger, more athletic) making it unclear if the conclusions of the study apply to me.

I would like to invest a few more words on Issue #5 and focus on one way in which the participants in most studies differ from me: they start with subjects who are not currently exercising. In contrast, I have been cycling more or less continuously for well over a decade. Why do so many studies start with sedentary subjects? I have yet to read an explanation but I have my guesses. One guess is that someone who is not exercising and then starts will exhibit a large increase in fitness. Large effects are easier to study, making this choice attractive to the scientists conducting the study. A second guess is that being sedentary is a fairly uniform state, people who are not exercising at all are relatively similar one to another. If you did a study on people who are already exercising, it is likely that they will have different exercise schedules and thus will be starting from different places relative to their maximum fitness. Thus, the same exercise program would be a step up in difficulty for some, and a step down for others. Is this really a problem? Would we not expect that the exercise program that best helps someone get into shape would be the same that would help someone stay in shape? Maybe, but given the popularity of periodized training, I suspect most coaches would not agree. The training that is best at the beginning of the season when you first restart training is very different than the training that is best at the peak of the season, and I would expect that the training approach which is best when you first start cycling is different than what is best after you have been riding for a few years.

Finally, I would like to introduce one more related issue with almost every scientific study ever done on exercise:

6. Results from such studies are the values averaged across a number of subjects.

 

The problem with that that nobody is average, everyone is unique. I have posted a lot on this blog about individual variation. If everyone is different, does it make sense to do studies at all? If everyone is different, doesn't that mean that everyone just going to have to figure out the best exercise schedule for themselves? I actually don't think so, not from scratch. Although no two people are exactly alike, we do have a lot in common so that, though not perfect, studies comparing exercise protocols which report a average across multiple participants are way better than no study at all and I appreciate having such studies very much. Sure, I have to test their conclusions for myself, but knowing the average response to a particular training routine helps me know where to start. That said, I think it is possible to do such studies in a way that would make them even more useful. 

Aren't statistics necessary for helping to determine if the results of a study are real or are due to random chance? Yes they are, but there are different ways of applying statistics and how statistics are used needs to match what is trying to be accomplished. In the context of training for cycling, there are two sources of variability in performance. The first is day to day variability. For a variety of reasons, people have good days and bad days. Usually, that is not very interesting and it gets in the way of comparing different training programs. Imagine I want to determine if a polarized plan or a moderate intensity plan is better to help me prepare for a metric century. I do the polarized plan, ride a metric century, but as luck would have it, I have a bad day that day, so my speed is slow. For the next ride, I prepare using moderate intensity training. That day, I have a really good day, so my speed is fast. I conclude that moderate training is better for me, but this is a flawed conclusion, the random noise introduced by good days and bad days has obscured the true result. I need some way to average out those good days and bad days. The way most studies do this is by averaging the results of several different riders, some of whom are having good days and others having bad days. The problem with that approach is that there is a second source of variability, and that is person to person variability. Let me explain with one example. There is a great deal of anecdotal evidence that as one ages, one needs more recovery between hard rides. Imagine a study to determine the optimal number of rides per week; 3, 4, 5, or 6. Imagine the study groups contained a mixture of riders of different ages. If one looked at the older and the younger riders separately, one might find different optima, maybe 4 days a week for the older riders and 6 days a week for the younger, but if you average everyone together, one might find an average optimum of 5 days a week, optimal for neither group. One obvious way to deal with that particular problem is to subdivide the study groups into groups of similar riders; men vs. women, older vs. younger, serious vs. casual; those who have been riding a long time vs. beginners, and so forth. One problem with that approach is that studies become very large, they require lots of participants to cover all the different subgroups. Another problem which I consider to be even more serious is that I believe there are subgroups of riders who will give very different results in a study who cannot be easily identified, people who look the same but who differ genetically in ways that affect their response to exercise. The solution to that problem is described in the Wikipedia article on N of 1 trials. Rather than average the results of several subjects, one averages the results of several tests on the same subject. In principle, this allows a study to reduce the noise generated by good days and bad days but retain the information on person to person variability. This approach is not perfect either. In the first place, each subject is accumulating training, building up fatigue, and aging as the study progresses. In the second place, it requires very long studies to provide the time needed to test different exercise protocols on each of the subjects in a study. Ideally, a mixture of N of 1 protocols and more conventional protocols on well defined subgroups would complement each other, providing more information than either would alone. However, this only aggravates the problem of needing very large numbers of subjects and long study times.

A Proposed Solution

At long last that brings me to the picture at the top of this post. As a senior, I get a benefit from Medicare and my supplemental insurance plan called "silver sneakers", a free gym membership. Neither Medicare nor my insurance company provide this out of the goodness of their hearts, they provide it because if I exercise, I will be healthier and and as a result they will save money on my medical care. Might that be true for younger people as well? Might it save insurance companies money to encourage exercise by paying for gym membership even for younger customers? Medicare, though it does not cover these younger people, might decide that on top of whatever immediate improvement in health exercise provides to the young, exercise now will make them healthier later when they reach their 60s and begin to be covered by Medicare, and thus save Medicare money in the long run. For the purposes of this post, let's assume that one or both of these is true, and that as a result, a significant number of people become eligible for subsidized gym membership. A requirement of such a subsidy might be that participants agree that in return, they will participate in studies comparing different exercise protocols. This could be a small ask, such studies could be designed to have minimal impact on a participants training plans.

Full disclosure, I have not taken advantage of my Silver Sneakers benefit because my preferred exercise is cycling and the gyms that currently participate in Silver Sneakers don't particularly support cycling. What I have considered is working with Five Rings Cycling Center, an organization that provides coaching for a wide range of cyclists from serious racing cyclists to casual cyclists like me. If my plan to improve the usefulness of scientific studies on cycling were to happen, besides increasing the number of participants, Silver Sneakers would have to increase the range of providers to include groups like Five Rings. What would be the requirements for an organization to participate? First, that they provide a program that the medical community agrees improves health. I would expect that most coaching organizations would easily meet this requirement. Second, that they participate in the scientific study part of the program. A requirement for coaches employed as part of this plan is that they abide by government guidance in designing the plans for their clients. Is any of this at all likely? That is an interesting question, but one well beyond the scope of this post. The thought experiment which is the subject of this post is to imagine, if some of the current constraints were relaxed, how might scientific studies on the benefits of different kinds of exercise be improved. For the purposes of answering that question, assume that the expansion of Silver Sneakers proposed here happened. 

How would that impact scientific research on exercise in general and cycling in particular? Specifically, how would the above plan solve the six common problems with research studies outlined at the top of this post?

1. "There are too few subjects in a study so the results are not statistically significant." The number of potential subjects available to studies would be dramatically increased. My guess is that this problem would become a thing of the past.
2. "There are problems with the study design, things like changing more than one variable at a time, that make the results difficult to interpret." At first glance it might appear that this plan might not help with this problem, but in an organized system like the above there would be more opportunities for investigators to interact with each other which should improve the quality of studies and reduce problems with experimental design.
3. "The study is observational rather than experimental and as a result, cause and effect cannot be proven." Coaches would ask people to do one or another plan. Those that were unwilling would not be included in the study.
4. "The study is a biomarker study and as a result, it is not clear that health is improved just because a biomarker is." Because the studies would go on for a long time, actual health data could be obtained.
5. "The subjects are very different from those wanting to use the results of the study ...  studies start with sedentary subjects." With lots of subjects available, many more subgroups similar to many more users will be available.
6. "The results of almost all studies are reported as an average of a number of subjects." Because subjects are in long term, N of 1 protocols become possible.

Given that the proposed changes in Silver Sneakers has not happened, does this thought experiment have any value? I believe that it does. By clearly imagining what a more ideal study would look like, I feel I am better able to evaluate the studies that actually exist.

Cycling in a Time of COVID-19

A diagram of the SARS-CoV-2 virus, the causative agent of COVID-19 

I am interrupting my series on high intensity workouts for two reasons. 1) Along with virtually everyone else in the world, my life has been disrupted by the COVID-19 pandemic. That disruption has made it harder for me to finish the third and final post. 2) On the other hand, the impact of that disruption on my cycling is something about which I wanted to blog. Thus, I promise to finish that series "real soon now", but for now, I am interrupting it with a post about my experiences cycling during the pandemic.

As I have confessed many times on this blog, I am a card-carrying member of the medical establishment. The COVID-19 pandemic has sparked the same partisan debate as has virtually every other issue in our society and I am not going to be a part of that. I am committed to following the guidelines of the medical establishment, partisan debate or no, but I do confess that is easier said than done. In the first place, there are a multitude of voices speaking for the medical establishment and few of them are immune from partisan influence. Medical professionals have been fired by government officials for expressing their opinion on COVID-19. In the second place, there is a great deal the medical establishment does not yet know about COVID-19. Almost all their advice at this point is their best guess based on fragmentary data and a lot of it is just best practice and common sense behavior for any infectious disease (e.g. washing hands.) Under these circumstances there are going to be legitimate differences of opinion, especially when you get to rather peripheral issues like the wisdom of cycling during a pandemic, even the best of governments are going to disagree. In Spain and Italy, recreational cycling is banned entirely. Fortunately for me, my state (California) and local (San Mateo County) governments have taken a different view, they explicitly allow and even encourage recreational cycling, albeit with some restrictions. The official restrictions are that social distancing should be maintained while riding and that rides should be taken locally, within five miles of home.

I am very reluctant, as a follower of the medical establishment and as someone who tries to be a good citizen, to engage in behavior that is less strict than what is recommended, but I do occasionally use my best judgement to decide to be a bit more conservative than the official recommendations, for example, on social distancing. The official government position is that social distancing while cycling involves keeping a six foot distance from other cyclists. There have been controversial studies and common sense discussions arguing that six feet is too close, that because of the motion of a bicycle and the wind generated thereby, virus might spread much further than six feet behind a cyclist. Because I mostly ride by myself, it is easy for me to sidestep this controversy and simply continue that behavior. Perfection, is, of course, impossible, so when other cyclists pass me or on the very rare occasions when I pass another cyclist, I get closer to them than I would like but I comfort myself that this is all about probabilities, so the fact that such encounters are short lived minimizes their harm.

Because most people cannot go to work, the amount of recreational walking, running, and cycling has increased dramatically. Before the pandemic, I would rarely see a runner or a walker or another cyclist while riding and now I see many more, such that encounters with all three of these groups has become more common. This has impacted my choice of routes. The Bay Trail, a favorite of mine, is simply too crowded for my comfort these days.

Due to an abundance of caution, I initially limited my cycling to my immediate neighborhood. However, this got boring after a while so I undertook the somewhat laborious process of making a snapshot of Google Maps and drawing a circle with a scaled radius of five miles around my home and found that most of my usual rides fit within this constraint. As I was doing this, I was cursing my outdated programming skills, thinking how much easier this task would have been had I been able to automate it, and of course, almost immediately found that someone already had. There is at least one site that allows you to enter your address and the desired circle radius in miles and draws the relevant safe riding circle for you.

"But why risk cycling at all?" many voices on the Internet ask. "Why risk an accident that will increase the burden on an already overwhelmed medical system?" Many of these same voices were questioning cycling long before this pandemic, arguing the same safety issue, and my response also remains the same: the real risks of an accident are outweighed manyfold by the improvement in my health that results from cycling. Sure, this argument is true in the long run, but is it really true in the short run, in the midst of a pandemic? Well, my local governments think so, and so I am going with that. That said, I am trying to adjust the cost-benefit in the face of the short-run conditions. I am always a very cautious cyclist, but I am even more cautious now. And I am also trying to take things just a bit easier. Long term, occasionally pushing myself to exhaustion can be a good thing, but in the short term, such all out efforts temporarily suppress my immune system, so I am trying to cycle enough to stay healthy but putting off any big pushes until COVID-19 is under control.

Stay healthy, wash your hands, and socially distance. See you on the other side of the pandemic.

Polarized Training and Athletic Performance

A Hill Climb

Photo from Tavo Mx on Pinterest

[Part 2 of 3 in a series on whether I should give sprint workouts another try.]

Is Polarized Training Better for Improving Athletic Performance?

In my last post I wrote that I had seen no evidence that I would be either more or less healthy if I substituted sprint workouts for some of my moderate intensity cycling. Thus, from a health perspective, I should do sprint workouts or moderate intensity rides entirely determined by what I prefer. Although health is my most important reason for cycling, having fun is also a very important reason and I think I would have more fun if I could ride a bit faster because that would allow me to do more rides with my friends who, as it happens, are faster than I am. Might sprint workouts make me faster?

Polarized training is training done mostly at very low or very high intensity and minimizes riding at moderate intensity. In the context of this series of posts, a polarized training advocate would argue that I should replace some of my riding at moderate intensity with a mixture of sprint workouts and rides even easier than my moderate rides. Vermeire et al., Journal of Strength and Conditioning Research: September 06, 2019 is a paper about the effectiveness of polarized training at improving athletic performance. I am not an expert on exercise physiology so I don't know the names of the reputable journals in the field, but I do recognize the name of the publisher of this journal, Wolters Kluwer, allowing me to tentatively conclude this is a legitimate scientific publication. The journal's claim to be the Official Journal of the National Strength and Conditioning Association suggests it may be much better than that. On the other hand, a big barrier to my reviewing this paper is that to see the full paper I would have had to purchase the article, all I can see without paying is the abstract. Fortunately, there are things we can confidently learn from the abstract. This study looks as 11 recreational cyclists, not a large number. According to the Bicycling article about this paper, these cyclists "were just beginning spring training. All the riders were preparing for a hill climb in the Alps or Pyrenees." At the beginning of the study, athletes were tested for power output at aerobic threshold (a level of effort than can be maintained for many hours), power output at anaerobic threshold (a level of effort than can be maintained for 30 to 60 minutes) and the maximum power that can be generated going all out, an intensity that can be maintained for less than a minute. These athletes then trained on their own for 12 weeks following whatever training plan they liked, but during that training, the amount of time they spent exercising and their level of effort was recorded. At the end of the 12 weeks, the total training load (a combination of time and intensity) was calculated using several versions of the widely used TRIMP method and, in addition, the percent time they spent:
1) below the aerobic threshold,
2) between the aerobic and anaerobic threshold, and
3) above the aerobic threshold
...were determined. The power tests were repeated, and statistics used to determine if there were any correlations between how the cyclists trained and how much their power measures increased.

The first thing I note about this study is that it is both an observational study and a biomarker study*, and thus, as detailed below, has the limitations of both. The second thing I note is that the questions they chose to ask are not the ones I would choose. The first question they ask is if more training load (as determined by one of the TRIMP calculations) is better. Given the assertion that 65% of competitive cyclists train too hard`, I would assume not and don't consider this a particularly interesting question. The second question would, in principle, be more interesting, and that is does the percent of time spent training at different intensity levels matter? This is the polarized training question: is it better to train mostly at high and low intensity (polarized training), or mostly at moderate intensity? Because this is an observational study, that is, they allowed the athletes to train however they liked, their study contains two barriers to answering this question. First, the range of different training programs chosen by the subjects of the study might not cover the range of exercise plans that ought to be considered. What if none of these 11 particular athletes train in a particularly effective way? The second problem, common to observational studies, is that it is impossible to distinguish between cause and effect. Are the best athletes best because of how they train, or do they chose to train the way they do because of their natural athletic ability? It is very possible that athletes who are more gifted find intense training more enjoyable, but that the reason they perform better is not because of this intense training but due to their natural ability. Finally, there is the issue that this is a biomarker study, and more generally, that an exercise plan can only be judged with reference to its goal. The best training plan to win the Tour de France will be very different than the best training plan to win a gold metal in the Olympic 1000 meter individual sprint.

These reservations aside, what were the results of this study? Taking the results the authors reported in the abstract at face value, none of the TRIMP scores correlated with improvement in any of their three power measurements. However, when they fit an equation containing percent of training done at the three intensity levels to improvement in their power measurements, the power at anaerobic threshold was weakly but positively correlated (more is better) with training below aerobic threshold (easy training), was inversely correlated (less is better) with training between aerobic and anaerobic threshold (moderate intensity training) and strongly correlated (more is better) with training above anaerobic threshold (high intensity training, e.g. sprint workouts.) There were no correlations observed with power at aerobic threshold or maximum power. Thus, overall conclusion of this paper is that polarized training is better than moderate intensity training for increasing power at anaerobic threshold. At best, this is a conclusion limited to one parameter, power at anaerobic threshold, and it is left up to the reader to determine the application of that to their real world goals. Unfortunately, the other explanation for their data is that the best athletes who improve most from training prefer polarized training, that it is their ability, not how they train, that is responsible for their better results. Finally, this paper only includes 11 participants and they were free to choose their own training plans so may not have provided the best set of different amounts of high intensity training. That said, I liked the experimental design of looking at the correlation between amount of time spent at different intensities to various measures of fitness. I would be very interested in seeing a similar study done with many more participants and with a more structured set of training plans.

Does power at anaerobic threshold matter to me? Interestingly, after having given this considerable thought, I honestly don't know. I can't ride at anaerobic threshold for much more than 30 minutes and the rides I want to be faster on are metric centuries (62 miles long) and take me more like 300 minutes to complete so it might seem that power at anaerobic threshold would not be all that important. However, these centuries are not a single uniform effort. Rather, their effort varies over the course of the ride. My goal is to improve my ability to stay with my faster friends and maybe if I had more power for short bursts at anaerobic threshold I could stay with them under situations where I am now getting dropped, situations like heading up a hill or when the pace temporarily picks up, but who knows, and that's the problem with biomarker studies. Even if I were persuaded that polarized training would increase my power at anaerobic threshold (the biomarker) there is no way to know for sure if my ability to stay with my friends on a metric century ride will be improved.

I don't want to be negative about this paper, its limitations are due to the difficulties of doing exercise research, difficulties about which I have previously blogged. Sure, its results are far from definitive, but its experimental design is a clever response to the difficulties of these kinds of studies and I think that fairness demands recognizing that it is one more brick in the wall supporting polarized training, albeit a small one. Rather than criticizing this paper I would rather think about what it would take to get past the difficulties that plague this and other studies of exercise, and that is what I will do in the third and final installment in this series. Stay tuned.

---

* An observational study is one in which participants are not randomly assigned to different protocols (e.g. training plans) but rather chose their own training plan. They are observed, and those who chose one kind of plan are compared to those who chose a different kind of plan. The problem with observational studies is that the two groups are not "matched", those who chose one plan might be different on average than those to chose the other, affecting the results. At first glance this might seem like nit picking, but it is not, especially in exercise studies. People who are naturally more athletic will find it easier and more comfortable to exercise harder and will be overrepresented in the hard exercise groups. How much of their better performance is due to their harder training, and how much their athletic ability? On the other hand, in many cases an observational study is sometimes the only study one can do, an interventional study, where participants are randomly assigned to different protocols is just not possible for any of a variety of reasons. Observational studies ought not be dismissed, but they should be treated with more caution than an interventional study. 

   A biomarker study is one where what is measured is not the thing in which one is interested (e.g. the ability to race up a hill) but something easier to measure that is believed to be related, (e.g. power at anaerobic threshold.) The problem with biomarker studies is that the biomarker might not, in fact, be related to the thing in which one is interested.

` From an article by Coach John Hughes: "My friend and fellow cycling coach Neal Henderson says that 65% of the athletes he sees train too much, 25% train too little and 10% get it right – the pros who are paid to perform."

^ Quote by Thumper, in the Disney film "Bambi".

Reconsidering Sprints

[Part 1 of 3 in a series on whether I should give sprint workouts another try.]

I started looking at published training plans in about 2012, and from the beginning, I have been skeptical about the value of  sprint workouts (aka brisk training rides aka interval training aka high intensity interval training aka HIIT.) To understand why, consider one example from my recent past. At the end of last season (November 2019) my cycling ability was as good as it has been since I moved to California two and a half years ago. During the 8 to 10 months prior to that I did no sprint workouts. Just before that, about 9 to 10 months ago, my ability was as low as it had been since the move, and it was my impression that my low ability was the result of cumulative fatigue caused by the sprint workouts I had been doing back then. Why, given my many similar experiences, do I still think about giving sprint workouts one more try? Because the exercise community (e.g. the coach-authors*, including the one coach-author I still follow, Coach John Hughes) keep pushing them. But should I be ignoring this advice, as some of my readers have suggest, because it is meant for young racers, not for old fitness and recreation riders like me? Hughes is my favorite coach-author because he focuses on a wider range of cyclists than most, including older and less competitive cyclists like me. Recently, he published two articles (this one and that one) on roadbikerider about the value of interval training for two of his clients, 76 and 62 years old. To be fair, these are stronger. more competitive cyclists than I, but in his book "Anti-Aging: 12 Ways You Can Slow the Aging Process" Hughes also recommends high intensity training for older riders exercising for health. In that book, he claims that a recent scientific paper demonstrated that "intensity training increased the number of mitochondria where energy is produced but neither moderate cardio or strength training increased the number of mitochondria." I have previously noted that I was eager to review the paper from whence Hughes got this assertion and this post is that review. In summary, and with apologies to Coach Hughes, his statement is not supported by the paper he cites and explaining why gives me an opportunity to discuss some important principles useful for understanding a study like this.

Is High Intensity Interval Training Healthier than Moderate Intensity Continuous Training?

The paper I am discussing in this post is Robinson et al., 2017, Cell Metabolism 25, 581–592. The publisher of this paper is Cell Press, one I recognize from my years as a scientist as a highly prestigious publisher, and the study was done at the similarly prestigious Mayo Clinic, so I consider it likely to be a high quality paper. After reviewing it, I have no significant quarrels with the paper itself, but only with how Hughes interprets it. This paper does not attempt to compare the benefits of one exercise program to another, that is, it does not try to answer the question posed in the heading for this section. Rather, this paper is primarily about the changes in gene and protein expression underlying training effects. Because the primary goal of the paper was not to compare the benefits different exercise protocols, the experiments in the paper were not designed to that end. However, this paper does contain data that might speak to the issue, so we can look at it with that in mind, but need to do so with caution.

Two broad categories I use to classify scientific studies are:

1. Observational versus Interventional studies. Interventional are better, but not always possible.
2. Biomarker versus Actual Endpoint studies. Actual Endpoint are better, but not always possible.

This paper is interventional rather than observational (that is, it randomly assigns participants to different exercise protocols), so avoids the problems of an observational study, but it is a biomarker study and has the problems associated with that. Specifically, in this case, what we care about (the actual endpoint) is health, but it is almost impossible to measure that directly in an interventional study, so we study something we can measure that we believe is linked to health. The biomarkers used in this study are:

• VO2peak (a measure very similar to VO2max)
• Insulin Responsiveness (relevant to adult onset diabetes, for example)
• Muscle Mitochondrial Activity
• Fat Free Mass (Muscle Mass)
• Leg Strength

All five of these biomarkers are generally believed to be good indicators of health so in the interests of brevity I am going to assume that the results of this study are, in fact, relevant to health.

The overall design of this study was to compare four exercise protocols across two groups of participants, a young group (18-30 years old) and an older group (65-80 years old). None of the participants were exercising regularly before the study. This is a common but unfortunate precondition of many exercise studies. I will talk both about why this is common and why it is unfortunate in a later part of this series but will ignore it for this post. This is a fairly small study, each of the eight groups contains only 7 to 11 participants making the statistics of comparison between these groups pretty weak. As a result, only the largest effects can be seen and there is lots of possibility for both false positive and false negative results.

The four exercise protocols were a no exercise control group (SED for sedentary), a High Intensity Interval Training group (HIIT), a Resistance Training group (RT, e.g. weight training), and something they called combined training (CT) which was a mix of resistance and aerobic training. I am going to ignore the details of RT and the resistance part of CT. HIIT involved 5 sessions a week of aerobic exercise. The high intensity part was done 3 times a week and consisted of 4 intervals, each done for 4 minutes at an intensity of > 90% of VO2peak, with 3 minutes recovery between intervals. Converting 90% VO2peak to my training intensity levels, this would correspond to the border between Zone 5b and 5c. For me, this would be a very hard workout. The remaining 2 days a week were 45 minutes at a VO2peak of 70%, roughly equivalent to my MAF tests, an easy workout. The aerobic part of the CT protocol consisted of five days a week of 30 minutes at a VO2peak of 70%, like my MAF test only shorter. Because these protocols involve different intensities, to compare them would require a way to quantitate intensity, and there is no consensus in either the exercise or medical communities as to how to do that. (I have previously blogged about this issue.) That said, based on my intuition and experience I strongly believe that the HIIT protocol involves significantly more aerobic exercise than the CT protocol. Thus, there are three differences between HIIT and CT: the total amount of aerobic exercise, the distribution of that exercise across intensity levels, and the fact that CT includes resistance in addition to aerobic training. If the HIIT protocol were found to be better than the CT and RT protocols, it would not be possible to know if that was due to the greater amount of aerobic exercise in the HIIT protocol, the the higher intensity in the HIIT protocol, or the absence of resistance training in the HIIT protocol. These are very serious and realistic concerns, but having noted them, I will move on.

A lesson offered to virtually all scientists-in-training is "absence of proof is not proof of absence." This is particularly true in a statistical context. Traditionally, the scientific community has used a P value of 0.05 as the cutoff for statistical significance. This corresponds to a 1 in 20 chance that the difference observed is due to chance, or that you are 95% sure the difference is real. If you compare two things and the P value is less than 0.05, then you say the difference is significant. If the P value is greater than 0.05, then you say, not that there is no difference, but that the difference is not significant; that you have not demonstrated a difference. A difference may or may not exist, you simply cannot tell from the experiment you did. Because of the small number of subjects, this is a big issue for this study. Consider the assertion made by Hughes, that HIIT improved mitochondrial activity while CT did not. Here is the data from the paper that speaks to that assertion:

Increase in Muscle Mitochondrial Activity Resulting from Exercise

The dotted line at 0 is placed at the mitochondrial activity at the beginning of the study. If the value is the same as that after 12 weeks of study, then no improvement was observed. Each of the filled in squares or open circles represents the average improvement for the 7-11 participants after the 12 weeks of the protocol shown at the bottom of the graph. The result for each of the participants will be different, and that is how we estimate the variability in the results. Because of that variability, we are not certain that the average of these few participants gives us the exact value of how much each protocol improves mitochondrial activity, and this is shown by the lines above and below the average which give us the range in which we think the answer probably lies (with 95% certainty.) Those groups where the authors are at least 95% sure that mitochondrial activity improved are marked with asterisks. (More asterisks means less chance that the difference is due to chance, that we are more than 95% sure.) First note the SED group, the control group that didn't exercise at all. For both young and older participants, their average mitochondrial activity is lower than before the start of the 12 weeks, but that the difference is not statistically significant, we are not 95% sure that difference is real. In fact, we expect that it is not real, there is no reason for it to change in the absence of exercise. If we only look at the averages, then all three exercise protocols increased mitochondrial activity for both younger and older participants. However, in only three cases can we be 95% sure that is not just due to chance. Maybe RT helps, maybe not. Maybe older participants benefit from CT, maybe not. But now, compare each group not to where they started (the dotted line) but to each other. For example, compare the older HIIT and older CT groups. Although the authors of the study do not compare these two groups, we can estimate the difference by eye, and to my eye, the difference between them is not statistically significant. The difference that is observed is that the comparison between the mitochondrial activity in the HIIT group before and after they participated in the exercise protocol is statistically different, but that the difference in the CT group is not statistically different. I suspect that this is the basis for Hughes' assertion that HIIT improves mitochondrial activity and CT does not. If so, that is an incorrect conclusion from the data. The correct conclusion is that we have significance evidence that the HIIT protocol improves mitochondrial activity, but in the case of the CT group, it might improve mitochondrial activity or it might not, this experiment was not able to answer the question. In short, we cannot say that the HIIT protocol is better at improving mitochondrial activity in older participants than the CT protocol. Based on that, there is no justification for adding a sprint workout to my training.

Let's consider a second example:

Increase in VO2peak (maximum oxygen uptake) Resulting from Exercise

This graph looks at increases in VO2peak with exercise. VO2peak and the very similar VO2max are perhaps the most common biomarker used as a stand-in for the health benefits of exercise. These measure the maximum amount of oxygen a subject can use when exercising as hard as possible. Starting again with SED, the control group, we see the surprising yet statistically significant result that younger participants had their VO2peak decrease over 12 weeks. Despite the fact that this is statistically significant, absent some explanation for this result, I would be inclined to believe that is due to chance. After all, 95% sure is not 100% sure. When we look at the HIIT protocol, we see that younger participants seem to benefit more than older. To me, that says that sprint workouts might have been fine when I was young, but now that I am old, they might not be so helpful. In this case, the difference was so striking the authors calculated the probability that this difference was due to chance, and got a P value of 0.0037, meaning that they were more than 99% sure this didn't happen by chance. For young participants, the difference between HIIT and RT is even more striking; interval training (HIIT) is, with high probability, better than weight training (RT) for improving cardiovascular fitness. In fact, there is no evidence that RT helps cardiovascular fitness at all. (This is the result that would be predicted by both the scientific and exercise communities.) When we compare the benefits of HIIT and CT for younger participants, it looks to my eye to be on the very edge of significance, that we might be 95% sure that HIIT is better at improving CT for young participants. However, when we compare older participants, the average improvement for CT is greater than for HIIT, but but to my eye this difference is not statistically significant. That is, CT and HIIT are, as best we can tell, equally good at improving the cardiovascular fitness of older participants.

Conclusions

My conclusion after reading this paper is that it provides no evidence that sprint workouts improve the health of an older rider like me. Once again, absence of proof is not proof of absence, but if anything, the evidence in this paper suggests that sprint workouts provide no more or less benefit beyond what moderate intensity workouts provide, I should do sprint or moderate intensity workouts as I prefer. Why might I choose to do sprint workouts? First, because they take less time. I hate riding on a trainer, and when doing so, might prefer sprint workouts to get off the trainer as quickly as possible. Second, just because sprint workouts appear to offer me no health benefits doesn't mean that they might not help me become a better recreational cyclists. In my next post, I will ask if sprint workouts might be better than moderate intensity riding at helping me to increase my average speed on a ride, to help me keep up with my faster friends.

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* I coined the term coach-author to help highlight the difference between working one on one with a coach and from taking advice from a book or article written by a coach. In the first case, I would refer to the coach as a coach. In the second case, I refer to the coach who is the author of the training book as a "coach-author". The reason this is important is if I were working with a good coach one on one, they would see how I responded to different training plans and would adjust their recommendations accordingly. In contrast, once a coach puts a training plan into a book or article, it becomes one size fits all, it might work for me or it might not.