End of Phase II—Two Steps Forward, One Step Back? Using HRV in a Training Plan

31 12 2014

I’ve found curious results when considering heart-rate variability (HRV) readings in the training plan. For the most part, the two analysis domains followed the proper trend, that is to say when the LF/HF ratio was high, the dominance level of HF power was low as was the index for the natural log of rMSSD x 20. That being said, I look back over the respective phases and think about where I am versus where I thought I would be. Maybe there’s more “art” to this than “science.”

Back in September, I wrote about a phased training plan that I would use to organize my off-season training. Soon after, I learned a bit more about HRV and it’s value as a factor for day-to-day training decisions. There are two types of analysis that I pay attention to: time-domain and frequency-domain. Fortunately, the Kubios HRV software puts the results side-by-side for easy comparison.

There were roughly 21 medical and sports journal articles that I studied when I decided to check what I call the “big picture.” That is to say, I would watch the Mean R-R, SDNN, and rMSSD variables from the time-domain analysis, the LF/HF ratio (Fast Fourier Transform,) and LF/HF ratio and HF (AR) variables from the frequency-domain analysis. The reason I did this was because most of the journal articles held conclusions for particular domains (and metrics.) Since I was extracting the report variables all at the same time from the Kubios software, there wasn’t any extra work involved to create the tracking graphs. (I feel that I should at least mention that the “non-linear” group of variables were available, but that I didn’t see enough recommendation or acceptance within the art to call for tracking them.)

Day-to-day decision rationale: I mostly consider the following graph (Figure 1) for training vs. recovery decisions. If the index is low (near or below the lower standard limit,) I opt for a day of rest. If the index is between 47 and 52, I’ll workout, but maybe with a lesser duration. If the index is higher than 52, I’ll definitely put in a hard or extended workout. I do look at the other tracking graphs, and if they point the other way, I’d re-consider…again viewing the big picture. Thus, a higher index indicates increased capacity to engage a tough workout and benefit from it.

Line graph of the natural log of rMSSD times 20

Figure 1. rMSSD variable from the Time-domain analysis

Some remarks about the various phases:

  • Resistance Phase/hypertrophy (P1h)—I had the impression that my legs mass-gain would be more…granted I added 16 pounds of body weight and a 5% increase in body fat compared to this time last year. Then, the BF calipers indicated less than 10% so the current level of 15% objectively puts me at the “fat body” self-ranking. On the other hand, my wife says my thighs definitely got big and my butt got rounder (she says she likes it, whatever.) Graph-wise, I thought I should see larger swings as I lifted the big weights and then into rest/recovery.I still think that I goofed my 1 repetition max test by attempting six lifts when I should have attempted a higher set by using only three lifts (instead of wearing myself out faster with six.) The hypertrophy part was characterized high lifting volume with moderately high resistance.
  • Resistance Phase/strength (P1s)—this portion was characterized as reduced lifting volume and increased resistance. There were some definite sore points within this period, although at the end my system seemed to spring back with some better index numbers at the end. I think the percentage lift points would’ve been higher…noted for next time.
  • Resistance Phase/power (P1p)—characteristics include increased lifting speed, reduced resistance, and sprint interval workouts. This was a volatile combination. Most of the period saw lower index points where near December 10, I forced a couple of recovery days less I continued to dig myself into over-training. The need to lift “faster” eventually found me lifting the squat bar enough to “hop” into the air. Again, these workouts were draining, and it reflected in the index plot.
  • Aerobic Endurance Phase (P2)—the graph shows a higher index trend, which I believe reflects the lower measure of time spent on riding near/above the 76% FTP minimum target. There were sprint and muscle endurance intervals intended to complement the endurance riding, most of the time the difficulty was not in completing the intervals. The problem was in accruing time-in-the-zone (TIZ) at the minimum target for the specified duration. Try as I might and even with best effort, the most effective extraction to stay in zone two (above 76% FTP) was only 44% to 51%. In other words, I could ride for three hours, yet only have half the ride time above my target. If I rode for five hours, again roughly half would be credited towards correct TIZ. So, to accomplish four to six hours at zone, I would have to ride for six to eight hours…not a realistic idea. The cause was this: riding outside, I’m subject to traffic, stop light and signs, pedestrians and speed limits on multi-user trails, etc.—constraints to my effort. There’s nothing I can do to omit those factors. On the other hand, I could decide to do that time requirement on the trainer, and I did for most of it. However, about 3 ½ hours was all I could muster on a trainer, any more than that I just could not get my brain around. Note: the 76% figure comes from Morris’ designation of the lower limit for the endurance zone on his scale. I figure he’s got a good reason for making it that way. Overall, I did not gain the volume target for this period, and that’ll likely hurt me later on. Because the phase intensity/impact was less extensive, the graph shows higher index marks. (My reasoning anyway.) I had played with the idea that this phase indicated that some type of “form” had incurred, but then I hadn’t done much riding comparatively for me to truly accept having any “form.” I had also extended this period by two weeks to try to meet requires. Next year, I’ll try to find a longer, uninterrupted stretch of road like a secondary state highway to meet the TIZ requirements.
  • Aerobic Endurance Phase/rest week (P2r)—this is the built-in rest week for the second phase. It has its own training regimen oriented around rest days and a few (but more intense) short interval workouts. This is also the week that I spent out-sick courtesy of the local flu bug, so not really much training to speak of, nor of much quality rest either. Kind of a waste of a week progress-wise. It’s why the graph’s index numbers are swirling around the bottom of the plot, again, dashed lines indicate non-training days.
  • Phase 3 Supermaximum Sustainable Power Intervals (P3)—the first two days of this period were sick/non-training days. Although I did try part of the first workout during the second day. Not bad results, I made the target wattage, but was not able to complete the full workout. I deemed it “OK” after being sick for a week.

In closing, I’ll keep watching the “big picture.” Next year this time, I’ll use the lessons learned after this evolution to make the program better.

Thanks for reading. My next post should show how I was able to integrate the BSXinsight Lactate Meter into the training program…and I’m really looking forward to learning that! See ya.


Heart Rate Variability—the Not-So-Hidden Indicator

13 11 2014

I first read about heart rate variability (HRV) in September of 2012. Back then, HRV was background information in the context of a laboratory comparison to examine the relative effectiveness of HRbased training vs. power meter (PM)-based training. The next occurrence was from a team newsletter early this month. Least to say…I took interest.

Basically, HRV is about the variances in duration for your beat-to-beat time period. Here’s what I’m talking about:

Graphic showing the RR or NN endpoints within an ECG graph

The RR (or N-N) periods from the high-point of the QRS complex

Any variance is clear when one RR period differs from another RR period and so on throughout the tracing. So why is this variance important? Well, remember when training stress balance (TSB) used exponential weighted moving average equations to estimate where your training stress level (form and freshness) was? HRV is a direct (although inclusive) indicator of the level of stress that your system is experiencing.

In a fashion, your heartbeat rate is controlled by two systems within your body, the sympathetic nervous system (SN) and the parasympathetic nervous system (PN). The SN system functions to regulate the body’s unconscious actions, like fight-or-flight, and is constantly active at a basic level to maintain system stability. The PN system on the other hand, is responsible for stimulation of “rest and digest” or “feed and breed” activities that occur when the body is at rest. Basically, each system is the opposite and compliment to the other. The SN system increases the uniformity of the RR period, the PN increases dissimilarity. Both these systems “vie for control” and as such, the variability in the length of time between heart beats rises and falls.

Understanding how HRV is affected becomes important to us when we train in our respective sports. The body reacts to various types of stress. In training, we call this stimulation and adaptation. For instance, our bodies compensate for the anticipated future strain by making muscles stronger and/or more efficient. This occurs during the rest cycle, when we sleep. Accordingly, the balance between beat-to-beat uniformity and non-uniformity reflects this state. That is to say, when the body and/or mind is stressed, the variance tends to decrease. When the human body is rested and has capacity to take-on work, the variance increases. However, this “HRV gauge” isn’t specific to a particular stress source—it’s inclusive. This is the part that caught my attention. Why? Because I’m terrible at estimating just how depleted my athletic engine has become.

One particular article of research studied changes in autonomic function related to athletic over-training syndrome. Mourot et al. (2003) discussed how HRV provides information related to the regulation of heart rate in real-life conditions. Their study demonstrated that analysis of HRV using linear and non-linear methods (Poincare Plots specifically,) could be used as a direct indicator of fatigue after prolonged exercise.

Note that you’ll have to make some choices about how you collect and interpret HRV data. You could download an Android application to your smart phone, and have a super simplified green/yellow/red scheme to tell you your stress level, but this version might not give the detail that you want. Or you could spend near $175 USD for a more professional program (maybe with hardware) to give you a bit more detail, but again, you might not learn how that information is derived. I preferred to do the homework, learn the method(s), and gain an understanding at a deeper level. Choose what’s important for you.

I started learning how I could take advantage of this phenomena. Equipment-wise, I have heart rate straps of the ANT+ variety, an ANT+ USB dongle, a Power Tap hub on one of my wheel sets, and I have a computer. Problem was, I didn’t have the Windows-compatible software to analyse an ECG tracing. As it turns out, I didn’t need to mess around with tracings. After four to five days of searching the web, I found the HRV_Tracker application, which records RR intervals. This free software program accepts the data stream that my ANT+ compatible HR strap produces and drops it into a pre-formatted .hrm file. This only produces the data file however, and that’s when I found the Kubios HRV – Heart Rate Variability Analysis Software. This software application is an advanced tool for studying the variability of heart beat intervals.

After you get the software installed and setup, the basic process is to:

  1. Wake-up the next morning and create a daily data file using HRV_Tracker
  2. Open that file using Kubios HRV. Adjust for artifacts and frequency cut-off
  3. Determine which analysis method and metrics to track (time-domain, frequency-domain, or non-linear)
  4. Transfer chosen metrics over to a Minitab 16 statistics file (or Microsoft Excel, or other graphing application)
  5. Decide how the indicated data affects your training decision for the day
Recording Your Data File

I paraphrased the data collection steps from the Medicore SA-3000P clinical manual—simple and straight-forward:

  1. Surrounding environment
    1. Keep the same collection time since HRV is known to have circadian rhythm (morning/evening)
    2. The proper environment
      1. Avoid bright light or noise
      2. Maintain comfortable room temperature
  2. Before the measurement
    1. Avoid caffeine or smoking before recording your data file
    2. Do not eat breakfast
    3. Allow time to adjust to your seated position
  3. During the measurement
    1. Maintain a comfortable sitting position
    2. Don’t move or talk
    3. Don’t close your eyes or fall asleep
    4. Don’t intentionally control your breathing

I wake-up, visit the restroom then return to my desk’s seat, put on my HR strap, then launch the HRV_Tracker software (Figure 1.)

Picture of HRV_Tracker's Software Interface

Figure 1. HRV_Tracker Application Interface

See the Device and Script Recording tool bars? Check the “Help and Support Documentation” section of the index.html file within your installation folder for detail on how to set these up. Something to pay attention to is that if you have the application window at some other size than full-screen…the stage time values will not display. I found this out by happenstance and always maximize the application window now when I’m recording.

After launching the application, plug-in your USB ANT+ stick. Your HR strap can now talk to your computer. When your ready (and if you’re using a script file) mouse over and click the “Start Scrip” button. Otherwise click “Start” to begin recording. You’ll see something like Figure 2:

Screen picture of the application after start of recording

Figure 2. Record file start screen

Within the “Heart rate status” window you’ll see your HR rate (self-explanatory) and the sequential R-R interval as it’s being recorded in milliseconds. Within the “Script status” window (if you’re using one,) you’ll see the stage message and elapsed time that you’ve implemented in your script file. (Refer to the help file noted before.) I chose three minutes as my recording period mainly because I decided to focus on Low Frequency (LF) and High Frequency (HF) and because these components are distinguished in short-term recordings according to the HRV Guidelines suggested by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Other periods include five minutes, 20 minutes, and 24 hours. Whatever you decide as your recording period, try not to mix different length files.

Once you’ve reached the end of your chosen recording period, the application will automatically save your data file to your folder of choice, Figure 3.

Screen picture of saved .hrv data file

Figure 3. Data file location and file name structure

Note the date and 24-hr time stamp within the file name, this is automatic and it makes it easy to manage your information over time.

Preparing Your Data

There are a couple things I do before copying my metrics over to my tracking file. In sequence, they are: deal with artifacts (if any), and remove the very low-frequency bands (VLF.) An artifact is basically any type of RR interval point that can be considered an outlier. These points are easy to spot…and easier to deal with, consider Figure 4:

Screen picture of an .hrm file opened in Kubios

Figure 4. A raw data .hrm file opened in Kubios

Note the main graphing window, see the four points that lie far outside the rest of the data points? Those are artifacts. You’ll find their causes within the Kubios user manual. All I need to do is get rid of them otherwise they’ll skew the rest of the LF and HF frequency information. In the left-side margin you’ll see a drop-down box labeled “Artifact correction.” Click it and you’ll see a range of options from “none” to “custom.” The idea is to pick the option that will highlight only the outlying points and not the rest of the data stream. Note that your retained data points turn the color green from blue temporarily. I usually pick down the list until this condition is true, thus in Figure 5:

Screen picture of the data file with artifacts selected for exclusion

Figure 5. Artifacts selected with the correction drop box

Here, the “low” correction level has excluded all the artifact points. Click “Apply” to screen the points out. The graph will automatically re-range for you. Click the “Frequency-Domain” button under the horizontal scroll bar for the next step.

Let’s reduce the very low-frequency (VLF) band from our analysis. (It’s the pink area in the FFT and AR spectrum graphs.) Basically this frequency zone (0 Hz to .04 Hz) is background noise for our purposes, so we can get rid of it. Select “Smoothing priors” from the “Method” drop box under “Remove trend components.” You’ll note that the Lambda value field (500) and the estimated frequency cutoff “fc = 0.035 Hz” appear. I debated changing the default value to “390,” to give me “fc = 0.039” but I’m not certain that the effect will be statistically significant. [Edit: I set the default to 0.039 anyway.] To save yourself some time set the detrending method and default Lambda value in your “Analysis options” section of your Preferences settings.  Your screen would look something like Figure 6:

Screen picture of detrended data in the Kubios application

Figure 6. Detrended data file in Kubios

Note that most of the pink VLF band area is gone.

One of the neat things about Kubios is that there are various analysis methods to consider depending on your disposition. Some pros and cons about my particular metrics choices from types of analysis are:

Time-Domain Analysis

  • Pro—the simplest to apply. There are a few available metrics in this category SDNN, pNN50, TINN, and Triangular Index; but the root mean square of successive differences (rMSSD) is considered easiest for convenience by non-expert users. One substantiated argument for rMSSD as the ideal metric comes from this article. Also, another reference for the preference of rMSSD vs. pNN50 because of its mathematical robustness is made by the Task Force for HRV.
  • Con—On the other hand, George Moody, Harvard-MIT Division of Health Sciences and Technology stated in an article published in PhysioNet that, “The commonly quoted scalar measures (…SDNN, pNN50, rMSSD) offer only a limited view of HRV.” He goes on to say that these measures were devised when the standard technology for estimating HRV was a pair of calipers and a hand calculator.

Frequency-Domain Analysis

Available metrics in this category are very-low-frequency (VLF), low-frequency (LF), and high-frequency (HF).

Fast Fourier Transform Approach (FFT)

Autoregressive Modeling Analysis (AR)

  • Pro—this model smooths the frequency components allowing discrimination of the bands, and not depending on pre-selection. In essence, the produced graph is easier to see compared to FFT. According to the Kubios users manual, the AR model, “…yields improved resolution especially for short samples.”
  • Con—A limitation concerns the suitability of the value for the model order. This value will affect the determination of the center frequency and the amplitude of the frequency components, and non-optimal selections may introduce inaccuracies. Previously, Task Force guidelines recommended a range from 8 to 20. More definitively, a study by Boardman et al. (2002) recommended the best model order to be 16, to reduce spikes and smearing, and to produce easily observable peaks. The default model order in the preferences of the software is now 16.

Non-linear Analysis

Poincare plot

  • Pro—using this graphical method, it can be easy to identify artifacts. Refer to Figure 7. Note that in the main graph display, we find a couple of outliers, aka artifacts near the 7:10:35 time mark. Correspondingly, with the view results button “Nonlinear” selected, we see three points within the Poincare plot quite separated from the main cloud; these are the same indicators. I suppose that with practice, deciding on outlying points that are closer to the cloud will become clearer.
Application screen print of Kubios Poincare plot showing artifacts with the data file

Figure 7. Poincare plot and locations of artifact points

  • Con—even though Mourot et al. (2004) demonstrated that Poincare plots reliably delineated shifts in parasympathetic balance, and that “…changes in SD1 and SD1n paralleled changes observed in rMSSD, pNN50, TP, HF, and HF/TP.” it might be difficult to attempt day-to-day decisions about training based on the graph alone. Alternatively, a trend based on SD1 and/or SD2 fluctuation compared to a baseline could be a solution, although I found no study to date has demonstrated this idea.

Approximate Entropy (ApEn)

  • Pro—this method might work well for an HRV file with less than 50 points, can be applied in real-time, and is also less affected by noise. Additionally, research by Lau et al. (2005) shows that ApEn can be a natural measure of HRV.
  • Con—Based on the first reference in Pro, ApEn could estimate lower than expected for small records. ApEn could also lack relative consistency. Relative difficulties in applying this method in ECG data analysis were examined in this study by Holzinger et al. Furthermore, “ApEn was shown to be a biased statistic.” Pincus S. (1995)

Sample Entropy (SampEn)

An extension of ApEn. Technically explained, “Sample Entropy is the negative natural logarithm of an estimate of the conditional probability that sub-series (epochs) of length m that match point-wise within a tolerance r also match at the next point.”

  • Pro— (still searching for a reason to use this method by itself or in addition to the previous methods.) None found to date.
  • Con—Values for the variables m, and r must be chosen before analysis. As of this writing, various directions have been published. Accordingly, Aktaruzzanman and Sassi (2013) determined the following guidance: “The value m depends on the length of the series and it should be kept small (m = 1) for short series (length ≤ 120 points.)” and “The recommended value of r in the range [0.1 0.2] x STD has been shown to be applicable to a variety of signals.” Additionally, Heffernan et al. in their study of HR and HRV following resistance training, set their embedding value m = 2, and their filtering value (tolerance) r = 0.2 x STD. These latter settings happen to be the default in Kubios HRV.
Understanding Your Data

This is the tough part. Up to now all effort was put towards collecting and processing data. Now for the interpretation, because analysis methods and related metrics all have pros and cons I decided to approach any training decision-making from a more-holistic approach. That is to say, I wouldn’t make decisions based solely on a single method or metric. I would look at a multiple method and metric “picture” of the statistics for that morning and trend. I copy the following prepared metrics from Kubios into my Minitab 16 tracking sheet each morning Date, Mean RR, SDNN, rMSSD, NN50, pNN50, FFT(LF/HF), AR(LF/HF), and HF power (AR results). See Figure 8.

I input these metrics in every morning after creating the .hrm file; easily less than 5 minutes typically. Note that the last column, lnRMSSD, is actually a formula that I inserted into each cell of that column. Basically, it takes the rMSSD figure from column C4 and produces the natural logarithm then multiplies by 20. I then round to an integer for a nicer “real” number. In other words, it’s easier to look at “2” in a graph and not “2.876.”

When I initially started collecting HRV data, one of the first tracking graphs I created was my mean RR over time. This graph doesn’t carry much decision-making weight and I don’t really look at it anymore. It simply confirms that my RR average has decreased along the years, i.e., my average isn’t like it was in my twenties.

Note: all graphs contain a baseline of seven to ten days of non-training HRV measurements. This forms the position of the “mean.” Another way to do this would be to assign the baseline value as a horizontal reference line, and let the mean calculate and display as usual.

Based on my continued reading of research articles, and in some part observation of (then) current sports HRV measuring practice, I created LF/HF ratio tracking graphs from the FFT and AR analysis methods. Those are shown here as Figure 9 and Figure 10. Note that the vertical lines from the “Date” axis are days-off (no training) and that all data points are plotted in the morning before any workout or training:

Graph picture of HRV (LF/HF) over time

Figure 9. Minitab 16 Individuals graph of HRV FFT (LF/HF) over time

For this method, a lower ratio is better, indicating “balance” between the ANS and PNS systems. According to FFT, my system was stressed far above the green average of 4.76. (This coincides with when I started the off-season lifting phase.) Curiously, only the latter date of Oct 29 shows higher stress, as I was lifting weights on the non-rest days. Or does this show adaptation?

Later on, again based on the research articles I was reading, the Autoregressive method seemed the way to go:

Picture of graph of Autoregressive LF/HF ratio over time

Figure 10. Graph of AR LF/HF ratio over time

The AR method reveals more stress days beyond what FFT showed in Figure 9. Again, for recovery, lower numbers are better. Per my understanding, system stress should increment higher during successive training days and reduce after a rest day. That’s basically the pattern here.

I’ve read plenty of studies that suggested that the HF band is indicative of parasympathetic activity. This was another graph that I created to help me understand the big picture. For the HF graph, higher numbers are better (Figure 11.)

Picture of graph of HF values over time

Figure 11. Graph of HF values over time

In the HF graph, higher plots are better, showing an increased level of recovery or capacity for more work. What alarms me here is the downward trend starting Nov 1 (a rest day.) A bit of recovery followed for the morning of Nov 3 (heavy lift day), a continued negative trend for Nov 4 (light lift day), and an expected decrease for the morning of Nov 5 (rest day). A day of rest should have manifested as an increase for the morning reading on Nov 6 (a scheduled heavy lift day), but instead the plot continued its downward trend. At this point I decided not to lift, but to instead take another rest day. If the plot for the morning of Nov 7 shows an increase, then I know I’m recovering. However, if the plot decreases, then I know I’ve dug myself a good hole by training too much and not watching my trend line.

Nov 7: fortunately, the morning reading shows that my system recovery is back up to where I wanted it to be—above the baseline (47.) See Figure 12.

Picture of a graph of ln rMSSD over time

Figure 12. Graph of ln rMSSD over time

I’d like to think that I avoided a potential over-reaching scenario by understanding the warning signal as shown in Figure 12 for Nov 4 to Nov 6. Good meals and three nights rest allowed the regular workout as scheduled.

Control Limits & Historical Tendency

One of the challenges with this approach is the context of whole system change over time. Throughout the Minitab 16 graphs above you would have noticed the (red) horizontal line(s) above and below the mean value (green) line. These represent warning lines more or less, and they’re temporarily a way for me to easily tell when I’ve went overboard. For now, I’m setting the position of the upper and lower control limits equal to one, and some cases two, standard deviations because I haven’t installed a better mechanism for setting these “gates.”

One solution may be the exponentially weighted moving average (EWMA) equation like that used to calculate training stress balance (TSB). Using initial gain factors of 7 days for short-term and 42 days for long-term, I might be able to forecast the level of recovery. On the other hand, Dr. Larry Creswell, M.D., states in his blog post:

HRV responses to training are not only specific to an individual but also to both the recent and remote training history. The most important observation is that the relationship between HRV and fitness is simply different in well-trained athletes: there can be increases in HRV with no corresponding increase in fitness over a training cycle and there can also be decreases in HRV despite increases in fitness.

Another example of a gate-keeping solution is the BioForce HRV software application. This program color-codes a daily, weekly, and monthly index to give criteria for training decisions. At first glance, the index seems to be an average from the current period as compared to the previous and respective period but I haven’t read anything that explains the calculation method yet; so maybe I’ll have to keep looking.


I know there’s particular applications and authors who maintain that their method or metric is the way to go about tracking of this stuff. I’m not convinced based on what I’ve read thus far which says that a specific method and metric is the true and accurate way. So, what I’ve done is collect methods and metrics to examine as a whole to give me the “big picture” compared to what I expect to see. For example, the following methods and metrics when recorded the next morning after the earlier day’s training should behave thus:


Autoregression (AR LF/HF) graph—plot point should increase from previous; higher ratio indicates additional system stress

Fast Fourier Transform (FFT LF/HF) graph—plot point should increase from previous; higher ratio indicates additional system stress

High Frequency (HF) graph—an indicator of parasympathetic activity or magnitude. The plot point should decrease from the previous.


Mean RR graph—plot point should decrease; a lower value indicates increased sympathetic action (or decreased parasympathetic action.) An extended trend upward could mean general system adaptation. An extended downward trend could mean I’m moving into an over-reaching or over-training state

Natural log of rMSSD index graph—plot point should decrease from the previous as this metric is supposed to be an indicator of parasympathetic system magnitude compared to the sympathetic system.

If the direction of one graph doesn’t agree with the others, or if the plot doesn’t go in the direction I thought it would, the first thing I look for is an input error when I transferred the data values to the tracking spreadsheet (Minitab 16,) or maybe I didn’t do a pre-processing step correctly. If the data checks-out, then I attribute the disagreement between graphs to the “fuzziness” of the deal. After all, I’m not certain this is an exact science (yet?) So far there have been a few contradictions since my start date on October 6, but for the most part, graph behavior has been consistent.

My next issue concerns the size or amount of change from one day’s plot to the next. Does the amount of change make sense? Should the plot point be farther up or down? Well, this condition is why I’m doing it in the first place; to try to predict or understand how much change is involved based on the level of training I did. This condition relates to the “gating” function I spoke of earlier. In other words, when race weekend arrives, where will my system’s recovery level be? Will I be ready for a great effort, or should I wait for the next venue? I think if I can figure-out the pattern, I’ll be able to back plan my training (and recovery) from one of my target races, like an “A” race/stage race/etc.

That was a lot of stuff to put in one blog post. I only have one months-worth of data based on Phase 1‘s training input. The next training level is the Strength Period of Phase 1, which starts this coming Monday. My next post should be (hopefully) much shorter.

Hey, thanks for reading, and let me know any questions you may have. Best of luck for your racing efforts!

Training to Meet the New Season, Part 2

29 01 2014

Link to Part 1

So after discussing how benchmarking helped me compare training goals vs. racing performance, I concluded by saying that I needed to choose a performance target for racing in the new season. I would appreciate having a power file as a Cat 3 on a course such as the Longbranch road race, but I do not have one yet. Dr. Coggan and Andrew Hunter in their book, categorized a table based on the known performance abilities of world champion athletes and untrained cyclists. Their suggested four index durations best describe the different energy systems measured from the vast collection of many cyclists. One division, described as “good” between “untrained” and “world-class,” represents the Cat 3 level of racers. This is the benchmark I’ve chosen. Here are the range of intensities  (w/kg):

5 seconds 1 minute 5 minutes FT
17.24 8.63 5.01 4.18
16.97 8.51 4.91 4.09
16.70 8.40 4.81 4.00
16.43 8.28 4.70 3.91
 16.15 8.17 4.60 3.82
 15.88 8.05 4.50  3.73
15.61  7.94 4.39 3.64
15.34  7.82 4.29 3.55
15.07 7.71 4.19  3.47

Now that I’ve identified my training target, I need a method to get there. I like the concept of targeted systems training as a method of periodization. In this scheme, the idea is to target and overload one intensity area, say the endurance system, before moving to the next. The first area to work on would be the endurance level, followed by sweet spot, threshold, and finally VO2 max. A strength of this method of layers as I call it is that I can maximize the build on a lower layer before using it as the foundation for the next. Other areas of riding may degrade in the meantime, but I can work on those in later efforts.

Pyramid of Power

Pyramid of Power

Each layer forms a foundation for the layer above. I thought about the “size” of the endurance layer—the wider my triangle, the higher I might peak. Having a wide endurance foundation, or base as some call it, has its roots in many older coaching regimens. Indeed, plenty of cyclists have heard the term “getting in the miles” or “base miles.” So how wide should my base be? Edmund Burke, PhD, author of Serious Cycling, says “4 to 6 hours per day for senior male amateurs.” I don’t have that kind of time each day, but I can manage 10 to 14 hours per week. Another PhD, John A. Hawley,  says that the conditioning or preparatory phase “should last as long as possible for a (state level) rider.” Apparently then, I should keep my time-in-zone continual while integrating work at higher intensities. Here’s a graph for how much time I’ve invested in the first two layers:

Stacked-bar graph depicting accrued hours in training intensity zones

Accrued hours by week in various training intensity zones

The mass of yellow bars represent the endurance-level hours since the off-season began. The wedge of light blue is the amount of time in the sweet spot (or low-tempo area.) The dark blue represents threshold, and pink-VO2 max. As I complete each level, each latter color also forms a “wedge shape” on the graph.

At the start of October I started doing longer, governed rides than my teammates were doing. By governed endurance rides I mean actually watching my HR number on my Joule GPS and ensuring I stayed within my endurance zone.  I learned that on grades such as 4% to 6%, I could pedal my lowest gear (34 x 25) at a pace only a bit faster than walking. It also meant that on the downhills, I’d have to press hard to keep my heart rate from dropping too low. The slower pace meant longer rides, sometimes around five hours. I also tried to pick smoother, flatter routes to maximize the pacing control.

After a couple of months of the routine (now November) my RPE or rated perceived exertion felt lower. It felt like the riding was getting slightly easier. Coincidentally, the general team schedule also called for adding tempo efforts, so what I did was add 6 x 10/5 235 watt minimum average intervals on the tail-end of the indoor workout. Note that I include this “sweet spot” level within the “sub-threshold level.” After all that work, I can show how my average session HR (the red connecting line between groups) has trended lower:

Time-series graph with trend line of six-interval means

Time Record of Six-interval Means after a Three-hour Z2 Session

This graph represents each time that I did an inside trainer session of three hours at endurance level followed by these six intervals in the sweet spot. There are groups that have a lesser number of completions. These were sessions where I was physically worn-out or where I just wasn’t motivated and ended-up bagging the rest of the workout.  

The quadratic method has the best accuracy measures as compared to linear, exponential, or logarithmic. Based on this plot, the best-fit curve between November 26 and Jan 28 has a decreasing slope—this should signal adaptation as I understand it. According to Allen and Cheung (2012), the defining change of improved fitness is signaled by an increase in cardiac output (bpm x stroke volume.) This means that as my system becomes more efficient, I should see a reduction in bpm because stroke volume increases. Of course, once I started tinkering with the target effort  (Jan 7 and 11.), my HR average went back up, which might have meant that my system was reacting to the increased training stress.

I think these two ratings, RPE and decreased average HR/same effort, are the at-hand metrics that will show when I am ready to move to the threshold level of work. Since Dr. Coggan’s sweet spot zone covers 88% to 94% of functional threshold power (FTP), I expect training in this area possibly until March as mid-range power is my weak point. I’ll have reached an initial goal if I can get my 60-minute/FTP greater than 272.

This minor goal relates to the Cat 3 target scheme mentioned before. My best-ever, 60-minute average was 272 watts in August of 2011. That power-to-weight ratio (w/kg) was 3.6, which at my current weight of 162 pounds calculates to about a 266 watt average—fairly reasonable I think, just on the low-end of the target scale. Basically I’m going to try to meet the new target power-to-weight ratios in each category of the target scheme, i.e. 5 second, 1 minute, 5 minute, and 60 minutes as I progress up through the pyramid toward race season.

My definition of success started with learning about training sessions, followed by understanding of when to end a training session. A common factor of my earlier regimens was that I completed a pre-determined workout for a specified time. With a targeted training systems approach however, my advancement to the next level isn’t based simply on completing a number of workouts within a time period. The progression requires adaptation to the training level’s stress. This is my definition of plan success: 

  • To perform within the Cat 3 target range specified in Dr. Coggan’s power profile during training bouts
  • The ability to finish a Cat 3 or Cat 3 Master’s race with or ahead of pack

As always, a good plan haphazardly executed is rather pointless. So to safeguard against straying off-course I’ll use the plan-do-study-act process control and improvement method. I have planned my work as mentioned before and I’ll execute as intended. I’ll track my information like I always do and compare the performance against the training targets. I’ll also make sure that I’m only comparing identical trainer sessions. In the case that a variance occurs I will try to isolate the cause of that departure and learn if I need a plan adjustment. Eventually, I should see a similar graph pattern evolve as noted above for the new training level.

I think it’s time to start working on higher level sweet spot efforts with an intent towards sub-threshold and threshold-level workouts.

Thanks for reading and best wishes on your effort. See you on the course!

Black is the New Black & Training to Meet the New Season, Part 1

31 12 2013

Damn. A lot has happened since I last posted. I re-built the training hoops with Pacenti SL-23s, built-up some tubular race hoops, and mortgaged the farm to pay for next season’s kit. Ha. The last one’s not too far off the mark. I wonder what other team members pay for their kit? Am I correct in thinking our annual kit cost ($400+) is outrageous? At any rate, I suppose the annual “sticker” shock is just part of being a roadie.

Let’s get on with it. The first part of this post will be about how my off-season training has changed a bit since last year at this time and why. I have no doubt that racing as a Cat 3 will put me through my paces, on the other hand, successes should feel really good in that I have accomplished something worth remembering. The last part of this post will discuss a couple of product reviews. Now I usually don’t do reviews outright, but I think the two items are at least worth mentioning.

So after the pummeling I received at last season’s Longbranch race, I needed to take a closer look at what training factors helped me race better.  Having added the results of my various races to the time spent training and racing in the various zones gave me an idea of what input can produce a particular output. At this point this idea is simply anecdotal because one of the biggest questions I have about racing and training is to quantify exactly what input will produce exactly what output. Here is what the data looks like:

Stacked bar graph of accrued HR endurance time and mid-level power zone time

Time in Zone (all data)

The main yellow color represents the amount of time I spent in the heart rate (HR) zone two.  I use HR Z2 to quantify how much training I have completed at the endurance intensity level. Next, you might notice the dashed vertical lines. These lines represent events on the timeline such as races or changes in the annual plan such as “off-season.” Last, each dashed-line number represents my finish placing in a particular race. (Yeah, I did more racing in 2012 which is why the event labeling is crammed together.) Just as the graph key denotes, shades of blue denote the accumulated time in each power zone. Note that the power zones 1 or 2 are not included as I use my HR metric (yellow bar) to represent that. In the years previous I accrued training time in the following distribution:

Year HR Z2 Pwr Z3 Pwr Z4 Pwr Z5 Pwr Z6 Podium1-3 Finish4-10 Finish11-20
2011 50% 1% 8% 1% 1% 1 2 1
2012 45% 1% 12% 1% 1% 2 3 5
2013 18% 1% 11% 2% 1% 1 1 1
2014 YTD 85% 4% 2% 1% 1%

Naturally, one should refrain from concluding causation from perceived correlation. Here, it would be premature to say that the extra time during the 2012 race season spent in power zone four caused the increase in placings. I know there seems more to the story than just that one factor. Specifically, what factor(s) of my training benefit me the most? Is the answer simply that the successful performance criteria experienced during the above races matched or were exceeded by the same metrics undertaken during training? If this is true then the process of backplanning or benchmarking a particular races winning performance should produce a training regimen, which when completed properly, would produce the needed adaptations to closely mimic those performance requirements. Or in other words―if you can do it in training, you can do in a race.

What might those benchmarks look like? If I use the above structure the Ravensdale road race intensities distribution would look like this:

Race Pwr Z3 Pwr Z4 Pwr Z5 Pwr Z6 Pwr Z7 Podium 1-3
Ravensdale RR 0% 6% 2% 2% 14% 1

But these numbers really don’t break-down into the essential parts needed to form a training session, namely, duration and intensity. What if we use Dr. Coggan’s power-profile structure to describe the same data?

Duration Watts W/kg
5 seconds 983 13.05
1 minute 520 6.91
5 minutes 333 4.42
60 minutes 196 2.60

A stepped, progressive training program could produce the  durations and intensities identified in the above structure. What might this program look like? This is the data record from the weeks before the Ravensdale road race:

Stacked bar graph for intensities, 2013 off-season to Ravensdale road race

Data Record for Intensities in Zone, Off-season to Ravensdale Road Race

The Ravensdale RR is indicated by the vertical, dashed-gray line with the “1,” and the off-season start is denoted as “rs13.” At the time, I used Carmichael’s “energy string theory.” thinking that the adaptation to stresses from the higher intensities would include those from the lower intensities, albeit with the decreased benefit timeline also mentioned by Carmichael.

The bench-marked race has the following intensity distribution compared to training beforehand:

Data Period  HR2 Pwr Z3 Pwr Z4 Pwr Z5 Pwr Z6 Pwr Z7
Off-season/training prior to race  15% 1% 11% 2% 1% 4%
Ravensdale RR  – 0% 6% 2% 2% 14%

At this point I regret not wearing my heart strap so I would know how much time I spent aerobically. Power-wise, I spent 76% of race time in zones one and two, so that helps a little. To characterize the course, my PowerTap’s PowerAgent file shows that most significant efforts 5 minutes and less (Pwr Z7) at wattages greater than 333 occurred in the last 5 minutes of the race. The remaining efforts of 10 minutes and more at wattages less than 257 occurred earlier in the race for a total of about 2 hours and 6 minutes. This suggests to me that the Pwr Z7 intensities were used to win the race with a sprint to the finish line, and that lower intensity efforts were used to sustain, maneuver, and finish the race with the pack.

How did the training effort support this win? Examining the data periods shows two key areas in my opinion. These areas are power zones 4 and 7. I think training in these areas, along with the supporting base layers of endurance and tempo training, enabled me to perform in these two areas during the race. I also think this microcycle is a good example of how specific and quantified training can lead to adaptations enabling equal or higher performance during racing.

That will do for Part 1. I will have to decide to benchmark a Cat 3 race (if data is available) or use Coggan’s Power Profile Output (w/kg) to derive the training schedule. Part 2 will deal with my decision and how I planned my training for the up-coming season, stay tuned.


So here’s the quick gouge on two pieces of gear that I really am pleased with buying. The first is the Gore Countdown Glove.

Picture of the Gore Countdown Glove

Gore Countdown Glove

Thus far this glove has prevented my fingers from transforming into painful ice picks while out riding and training in Seattle’s wonderful fall climate. They are warm, comfortable, waterproof, and windproof. As in many examples of product design there are trade offs. For me these trade offs are breathability and tactile feel. I use Shimano’s STI Ultegra shifters and I had to get used to the slight extra length of finger-space off the tips of my fingers when pushing the down-shift levers. (I use USA size XXXL.) The second quality- breathability is not as good. Whenever the temperature is greater than 40ºF and my effort level stays in the endurance zone, my hands stay dry and warm. However, when pushing into tempo effort levels and higher, my hands will get damp with a bit of chill.Temperatures below 40ºF will have my finger tips feeling a bit cold but generally they are fairly comfortable. Last, a bit more on the tactile feel. The median and ulnar nerve bundles traverse the base of the palm near the wrist. I’d like a bit thicker/denser padding on either side of these nerve paths to isolate the bundles from compression and vibration for increased comfort during rides longer than two hours and up to five hours long. Overall, these gloves were well-worth the ~$80 retail price from the shop that supports my team.

Last, I would be remiss if I did not mention the fine cycling jacket issued by Showers Pass.

Picture of the Shower's Pass Elite Pro Cycling Jacket

Shower’s Pass Elite Pro Cycling Jacket

This Elite Pro jacket is likely the finest piece of outer wear I have ever purchased. I wear a size medium. This torso-hugging jacket breathes really well and is equipped with side vents via water-resistant zippers. Additionally, a rear zipper just below your neck allows any hot moisture to vent away (in my experience.) Furthermore, the extended tail of the jacket is long enough to cover you while tucked into your drops.

Two minor features that I also like are the off-set main zipper near your neck. I have many center-sewn zippers on base layers, jerseys and the like that stack-up on the front of the neck where the zipper pulls can foul each other. Oh, I almost forgot to mention that the product designers had the foresight to specify the sleeve length under bent-elbow conditions…like when you are tucked-in. This way your sleeve cuff does not retract and open a wind gap when you bend your elbow. That is some intelligent product design huh? Windproof, waterproof, small-packing, with seamed tape panels. This jacket is a GOOD piece of gear.

As an aside, our team had designated the jacket’s color black. I rather like it as it doesn’t matter what design the new annual kit will be, black goes well with anything.

The Winter Doldrums: In the Clutches of the Off-Season

16 12 2010

Well here it is–winter, cold, boring, endless skies of drab and gray. Classic symptoms of cabin fever mark my weeks. It seems it won’t ever be warm again. I forgot what it feels like to ride with just a jersey and bibs.

I’ve been on my trainer since September. (Yeah, I hear you periodization populists remarking about the appropriate phases.) Last years volume-based plan crashed and burned as life fired salvo after salvo of its vocational guns. I don’t mind the trainer, not at all. I like being able to focus on specific energy systems; it’s very quantifiable. It’s not riding with the guys in my squad that bugs the heck out of me during this time.

This off-season plan is based on intensity vs. duration, goal-based training sessions that has plenty of recovery time built-in to avoid over-training. In this regard the program has proceeded quite well. I’m experimenting with using Dr. Coggan’s Training Stress Balance (TSB) methodology too.

All of my workouts target the FTP and a bit of the glycolytic energy systems. In November, I tested for my lactate threshold points and have been doing targeted training sessions ever since. There’s no doubt that the greatest (initial?) gains in increasing FTP are made when training is done just under, at, and just above threshold. So by just completing the sessions I was able to increase my respective wattage categories (horizontal axis) by an average of 12.3% from late September to early in December. This increase is represented by the difference in area between the graph lines:

Graph of off-season training results

Seeing visual results like this helps motivate me during this part of the season. At the end of December I should see an increase in my overall FTP, and that will be motivating too. Part of my success has been the inclusion of a power meter during training…couldn’t measure this without it.

So I’ll continue with the plan, complete the workouts, ensure that I recover, measure and record the data, and keep putting in the DVDs. (Note that during a lot of these workouts I can’t focus on the movie. But that’s just fine.)

I hope your winter training goes well. See you in the spring.

Training Update…Adaptation Revealed

9 02 2010

It’s been just over three weeks since my last post. So I feel myself delinquent in sending this out. Nonetheless, some key events have occurred since then, namely, starting a new job. Not a problem though. During a recurring road trip last week, I thought of my new employment from a fresh perspective–as a somewhat fresh service retiree, I’m now enjoying my “fun job”.

I think about bikes (road bikes and road racing that is) all day. Now I’m surrounded by bikes inside my areas premier bike shop–North Division Bike Shop. I awake in the morning thinking, “Right on!” I’m very pleased.

Back to task. In my earlier post, I discussed how I would execute my plan to arrive at my training goals. In this case, increasing my functional threshold power (FTP). Since February began, I’ve been conducting interval training and measuring my output and results in wattage via the meter I use. I’ve also ensured that I rest sufficiently between the increased intensity sessions. Eating per the information in a previous post, made certain that I had the fuel I needed to do the work and adapt from the training stimulus.

I can tell that all those base miles and endurance training sessions has had a positive effect–I have a lot more air to breath (perceptively) at the 87%-94% range of my heart rate. I’m moving more lactate out of my muscle groups with the added aerobic capacity. The result is that I can turn a bigger chain length (50/15) to get more watts at the same training stress I felt before. Here’s the results graph:

FTP Plot over Time

The blue line represents data, the red line is a trend line projected to the end of March. If I maintain the same progress, I should break the 300 watt level around March 15th. “Adjusted FTP” means I decreased 30-minute test results by 2.5% and 20-minute test results by 5%. Coggan and Hunter explain the 5% adjustment in their text. I threw-in the arbitrary/proportional 2.5% just to be conservative. That’s a 9.76% increase since November 30th, not bad I think.

Nonetheless, I’d like to expect that boosting my training stimulus with VO2 max. interval sessions near the end of February will act in accordance with Carmichael’s “endurance string theory.”

I’ll test again at the end of February. At that time, I’ll need to make any adjustments to keep me on-track for the season’s opening races.

Race hard…train harder. See you when it’s all over.

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