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!


New (Wide?)-Profile Shimano Wheelset for 2014…the RS81

1 03 2013

Here’s the wheelset I’m staring at: 35mm profile, 2:1 lacing pattern, 2-cross straight-pull spokes DS, radial straight-pull NDS. 11-speed compatible. Effective flange diameter and spacing is unknown. Composition of the rim is apparently an aluminum-carbon mix in a tubular clincher cross-section. Also unknown is whether a clincher version will become available. A 24mm and 50mm deep profile is also offered. Weight is unknown. Spoke nipples are external for easy truing. This new road wheel series will be compatible with 8, 9, and 10 speed drive trains too. A lower-level, all-aluminum 30mm rim, the RS31, will be available as well. More info here.

I hope to learn more about this wheelset. It already has most, if not all, the design features I would want in my race-day only wheelset. Maybe now I won’t have to build it, I can just buy it. A LBS didn’t know about it yet nor could recommend a factory contact. Additionally, information is yet to be posted on Shimano’s website. …waiting, waiting, waiting.

Picture of Shimano RS81-24mm profile

2014 Shimano RS81-24mm deep profile

Picture of the new Shimano RS81 Wheel-35mm profile

2014 Shimano RS81-35mm deep profile

Picture of Shimano RS81-50mm profile

2014 Shimano RS81-50mm deep profile

Rim Wear – How to Trash Your Nice Alloy Wheelset

8 02 2013

Like I spoke of in an earlier post, the Seattle winter weather mixed with the down-hill portions of my team’s training routes was making short work of my HED Belgium C2s. When we moved here in July the braking surfaces were flat and smooth. Our mileage really stepped-up in October and the weather, of course, wasn’t to be outdone. If I recollect correctly there have only been three instances where I’ve returned home somewhat dry. Nonetheless, the quantity of water, road grime and other debris created the perfect abrasive for taking-down alloy rim braking surfaces. I was wearing-out Ultegra brake pads at a rate of one set every two weekends. (That’s something over 100 miles every weekend.) I had tried SwissStop GHP2 pads and found that they lasted a little longer and allowed a more tactile modulation for braking in the wet, but the end effect was the same. Eventually, I tried some Kool-Stop Salmon pads, but was fairly into the disc brake upgrade at the time—I didn’t test the pads under the same conditions as the Ultegra and Swisstops. I was fairly disgruntled.  OK… I admit it, I was pissed. I had put much care and effort into the two quality builds. In retrospect, the wheelset had performed as I had intended them to, that is to say, for the time the wheels were in service, I never had to re-true them. Nor did they yield when I  inattentively bashed them into some road-born hazard like a pothole or concrete slab edge misaligned with its neighbor. To date I have not found rim or hub cracking or other signs of fatigue. Have I mentioned the road surface conditions here in the Greater Seattle Area?

I built the rear wheel in September of ’10 and the front wheel in December of ’11. According to my PowerTap database, the rear wheel has 8532 miles of use, and the front has 4334 miles. I had intended this wheelset to last many more miles. However, considering the amount of metal that has worn away, I’m in doubt about the rim’s longevity. In order to find  what usable life remained in the rims I needed to know a couple of things: how much metal in the rim hook wall remained, and the minimum (safe) thickness of that wall. To measure the thickness of the rim hook wall with some degree of accuracy, I needed something like the caliper pictured below. Fortunately I found one online for about $18 excluding shipping costs.

Picture of Outside Caliper Tool

Precision Outside Caliper

I learned this idea from a blog article that I found while searching for information on rim wear and allowable limits. The small offset jaws of this instrument allow for measuring inside the tiny space between the rim hook and the rim bed. This would give me some quantification as to the severity of wear. However, there are two problems that I can expect right-off-the-bat: the first is that measuring at the indicated points in the picture, Fig. 1, will likely give the depth of wear at a point offset from the maximum depth of wear because of the concave wear pattern. This is indicated by the longer blue line in Fig. 2. Granted that I may be splitting-hairs, but I think the measurement should be at the shorter yellow line in Fig. 2. A better method could be to use the depth blade of a typical dial or digital caliper (“3” in Fig. 3) and take measurements at the yellow line in Fig. 2.

The second problem is that I needed a minimum wall thickness designation. Alternatively, I could use a figure for maximum wear before becoming unsafe. Either way, this whole effort will be for naught without a minimum value. I contacted HED Cycling Products in Minnesota by phone and email around January 15th and followed-up with same, but haven’t been able to receive a usable figure since then. I also asked if a wear indicator (pin-punch) was present, because I couldn’t remember seeing one (I think the answer was “no.”) Other sources on the web have been more forthright with a wall thickness value. A discussion on the Weight Weenies forum suggests 1 mm, and E. C. Zimmermann suggests a minimum of 1 mm or more. I’ve read Jobst Brandt’s posts to find suitable references, but haven’t found a figure as of this writing. (I know I read of one in one of his posts, but haven’t found it yet.)

(Edit 2/13/2013) There are three types of wear indicators that I know about: brake-surface pin punch, brake-surface groove, and an “interior” indicator. The two former indicators allow you to monitor the surface as it wears down. The interior indicators only “appear” after you have worn down to the minimum limit.

Picture of HED Belgium C2 rim

Fig. 1 Cross-section of rim

So what is known? Well, 20 sample depth measurements were taken for each side of the front wheel. Why 20? A sample size of 20 will cover about 78% of the population at a confidence level of 95%. I’m certain that the sample mean represents the true measure of depth around the rim. The mean thickness for the drive side was .5 mm, the non-drive side thickness was .34 mm. I found the difference between sides interesting. Apparently there is  something different going on between the left and right side pads, and rim wear is not symmetrical. I can’t measure the rear wheel because the PowerTap hub is being repaired. I’m going to bet that its wear rate is about the same.

Graphic of rim measurement points

Fig. 2 Measurement points

Graphic of a digital or dial caliper blade probe

Fig. 3 Blade probe for a typical caliper

So there it is. I think I can conclude that the rims are done and it’s time to replace them. The question is, should I use the same C2 rims from HED or search for a suitable replacement? If I use the same rim, there will be no wear indicator, and I’ll have to be especially watchful. If I buy another brand, I could opt for a rim with a pin-punch or groove as an indicator. I really like the ride quality of the wider C2 rims and I’ve grown accustomed to their appearance. I’d prefer to stay with the same hub and spoke so that somewhat limits the ERD of a 700c candidate rim to 592 or thereabouts. What other rims might I choose for the rebuild? Note: the prices are as seen online, without shipping/handling added, weights are approximate.

Brand Joint Wear Indicator Braking Surface Wt (g) Width (mm) Depth (mm) Drilling / ERD/Notes Cost ($) Picture
Pacenti SL23 weld & sleeve Yes CNC 450 24 26 20, 24, 28 / 588 / slightly angled 98 Picture of Pacenti Aero Tubeless
HED Belgium C2 weld none CNC 475 23 24 18, 24, 28, 32 / 592 / angled 119 Picture of HED Belgium C2
A23 sleeve none CNC 426 23 19.5 20, 24, 28, 32, 36 / 601 / angled 82.50 Graphic of Velocity A23
H plus Son Archetype weld none CNC 470 23 25 20, 24, 28, 32 / 594 / slightly angled 69.95 Graphic of H Plus Son Archetype rim
BHS C472w pinned none CNC ~477 23 28 20, 24, 28, 32, 36 / 584 / center drilled 57.95 Grahic of BHS C472w rim

What rim characteristics are important in the final selection? First and foremost is drilling pattern. I’m going to keep my current hubs and spokes so the availability of a 20h and 24h is a must. I understand that the HED Belgium C2 is no longer offered in a 20h. I’m not going to switch from a perfectly usable 20h Chris King R45 front hub. Regrettable for certain. I really liked the C2 rim’s appearance, ride feel, material quality, and durability.

Without belaboring why I’m not going to choose all the other rims. I just discuss why I choose the Pacenti SL23 as the replacement rim. The second factor was rim width. The SL23 has the wide rim, which I’ve grown fond of riding. Since moving to Seattle late in July, I’ve not pinch flatted at all in seven months. (Comparatively, this area has the worst riding surfaces I’ve ridden over.)  I’m not saying that the wide rim has been the direct cause for no pinch-flats, but I’d like to think it was a contributing characteristic.

The tertiary factor was the rim wear indicator. The question for this whole exercise was to decide if the C2s were safe to ride or not. If the Belgiums would have been produced with a wear indicator, this effort would have been far less of an event. (That however, is a moot point as a 20h is apparently not available.)

As far as lessor factors, this rim has a sleeve and weld joint. Although the joint does not necessarily need to be welded, I like the appearance of a smooth, uninterrupted brake surface. The CNC machine work just adds to this effect. The rim’s weight is ball-park with all the others, that being fairly light. This is a training or everyday wheelset. I’ll save the gram-chasing for when I build a set of racing hoops. The fact that the new rim has an ERD of 588 is of some concern. The C2 ERD is 592. Without my spare spoke and Polyax nipple from the original build in front of me I am unable to discern how much take-up is available. I’d like to re-use my CX-Ray spokes if not limited by the ERD. Inspection of the front wheel spoke nipples shows that the tip of the spokes are even with the bottom of the nipple drive slots. How many turns-of-thread that remain is unknown. Plan “B” is to use HM nipple washers to create “artificial” ERD, somewhat like thickening the rim after the fact. The reference suggests adding 1.5 mm to the spoke length calculation so I should be OK here. The addition of 10 grams or so to the wheel build will be insignificant. If that doesn’t work plan “C” is to buy a shorter set of spokes for ~$80. Last, but not least is a (very) subtle aero benefit. I’d like to think that the longer, hub-side of the rim will keep more boundary layer flow than the Belgium, but hey, this is a training wheelset.

So there you have it. The Pacenti SL23 is my replacement choice. Once I get my PowerTap returned from the factory I’ll have to start planning my wheelset rebuild. On that note, many thanks to Brandon from The BikeHubStore for his valuable advice and for discussion on the SL218 rear hub (16:8 drilling). You should check his site out, better yet give him a call. Also, thanks to Andy at the The Bike Repair Shop for his hands-on discussion of rim wear and consequential effects.

Hope your race season starts well!

A Clean Drivetrain is a Quiet Drivetrain

29 01 2013

First use impression and review as used on my commuter bike:
The Zep citrus degreaser works great on breaking-down or dissolving lubricant-laden grime. Plus, it’s biodegradable for easy disposal. I used it straight from the bottle. At $10 to $11 dollars a gallon, fairly inexpensive too. I found it at the local hardware store–highly recommended.

Next is the XLC chain cleaner. The action of the moving chain drives a geared cog, which rotates the brushed cleaning wheel. Fairly effective at removing MOST of the grime from a chain. In comparison with my usual chain-cleaning method, the mechanism will not remove the built-up grime on the outside of chain plates near the rollers that a tooth-brush and a good eye would catch. Since the chain is encased within the housing, the alignment of the chain cleaner is important as you rotate the cranks backward. In other words, the mechanism will bind if the entry angle of the chain exceed the guides. Additionally, the left-side picture of the chain cleaner shows a square piece of black open-cell foam. The purpose of this foam is to act as a squeegee as the chain exits the cleaner. The squeegee is fairly effective, however, please be aware that enough solvent will exit and accumulate on your drivetrain and may drip on your drop cloths on the floor. $25 at your LBS–hesitantly recommended.

Zep biodegradable citrus cleaner

Zep Biodegradable Citrus Cleaner

Picture of the XLC Chain Cleaner

XLC Chain Cleaner


  1. Use a good, biodegradable cleaner that acts fast. The longer you use the chain cleaner, the more solvent will exit the mechanism.
  2. Change your solvent as needed. It doesn’t make sense to wash your chain in solvent that’s saturated.
  3. Lay down drop cloths. The using the chain cleaner can make a mess if the chain is not smoothly and steadily moved through the cleaner.
  4. Dry-off the chain with a pile-type cloth such as an old hand towel. Dry the chain as much as possible before applying new lubricant.
  5. Use a bike stand or something similar to hold the bike still while you rotate the drivetrain backwards.
  6. For the XLC engineers:
    1. The compartment containing the foam squeegee does not drain into the main chamber. Eventually, the accumulating fluid level will prevent effective squeegee action, and draining to the outside of the cleaner body. Allow a drain port at the bottom of the chamber.
    2. Alternate the application angle of the cleaning brushes from normal to the chain plate to a more acute angle. This will improve the sweep area of the brushes leaving less residue on the chain plates.

My race bike receives a higher frequency of cleaning (that is, after every ride) than my commuter bike does. Thus, this chain cleaner would be a good method for cleaning the accumulated chain grime on the commuter. My race bike however, will continue to receive the wash cloth and solvent method for the chain since there is a much lower level of road and oil grunge. Is the $25 worth it? At this point, only if your chain cleaning involves a lot of accumulated grime and crap, and at that point, be prepared to do a lot of wiping down and cleaning-up. On the other hand, if your chain has a master-link, use a dip tank and a tooth-brush. You’ll have faster, cleaner results with less clean-up involved.

Edit 1/29/2013:

The foam squeegee has degraded after the third use somewhat decreasing its effectiveness. I’ll have to locate and adapt an open-cell piece of foam to replace it as a look at XLC’s web site doesn’t show any replacement parts outright.

Edit 2/13/2013:

The foam squeegee has completely fallen apart now on the fourth or fifth use.

A New Team, a New Bike. What’s not to Like?

19 01 2013

Alright, so here we are, smack dab in the middle of the Emerald City puzzle-palace. Actually, it’s not too different from what my expectations foreshadowed. The downtown never stops moving though, and maybe I can use the number of sirens or emergency vehicles that pass our second-story window as a gauge to how nuts the day is going to be…maybe not. I knew moving over here would involve some deviations from the routine I had become accustomed to in Spokane, but these weren’t anything too crazy. Our condo is tiny (500 sf), one of our neighbors has friends come over during the evening hours to beat on drums, pluck strings, and mostly sing on-key (it gets repetitious though). Other neighbors have killer Chihuahuas and bulldogs that sound the daily bark when you walk past their door en route to the elevator. Another neighbor’s bulldog doesn’t make a sound, but its wall-eye provides a comical appearance. For a building this size, I’m surprised that I don’t run-into more people having lived here for five months already. I’m starting to think most of the units here are weekend-use or odd day dwellings as I just don’t hear people moving around—and I’m a light sleeper too.

But enough of that stuff. You didn’t click-in to read about social dwelling commentary. My homeowner’s insurance policy had paid-off only about $1,333 on the theft of my Madone 5.2. So my effort to recreate a decent quality race bike was going to be creative and extensive, and short-term. Every day I was off the bike increased the fitness gap and I wanted to build a new steed as soon as practical. Most of my sources were non-retail, that is to say like Ebay, Craigslist, and local bike shop used-parts and consignment venues. One of my biggest requirements frame-wise was a round seat post. I wanted the extended adjustment granted by flipping the seat post clamp forward, not like my old Madone 5.2 asymmetric post, where I was never able to get my saddle forward enough where I like it. After scrounging around for about two weeks I found a 2012 Scott CR1 Pro frame on Ebay for a cost of $854. The reviews looked good, the company reputation looked good, and the manufacturing process (my master’s in engineering and technology management degree came in handy) looked good. This would be my new frame. Here’s the appearance:

Picture of 2012 Scott CR1 Pro build

2012 Scott CR1 Pro…my race bike for 2013

  • SRAM Force crankset
  • FSA Wing Pro bars and Gossamer brake calipers
  • Shimano ST-6700 shifter and derailleurs
  • Bontrager Race Lite ACC carbon seat post
  • Speedplay X-10 pedals
  • Specialized Toupe saddle
  • ITM Millennium Super Over stem
  • A couple of Bontrager RL bottle cages.
  • My old HED Belgium C2 wheelset (with SL+ Powertap).*

There you have it, a suitable substitute for my old Madone 5.2. In comparison, this frame feels slightly less vertically compliant and has a bit more give in the BB when I put the beans to it while out of the saddle. I’m not going to whine too much though—I like this frame. At any rate, the fit window for this frame is so much better for my body geometry, it just feels better on the road. While I’ve yet to have it in a race, I expect the bike to do fine. Newer models of the Madone 5 series also have a round mast cap that I could flip forwards. I’ll always wonder what this frame would have felt like on the road, but at $2,600 a pop, purchasing one is a pipe-dream at this point.

* A note about the front wheel: before I had completely worn-out the brake tracks on my C2 rims I converted the front-end to a disc compatible brake (just for the foul-weather season). I’ll probably discuss this more in another post. Suffice to say that from October (when the weather here turned wet) to now I’ve likely worn the tracks down to the minimum width. I’m awaiting a response from the engineering folks at HED Wheels on what a safe minimum width would be since these rims were not produced with a track or pin-punch wear indicator. I wasn’t happy at all about this condition— or about going through a set of Ultegra caliper pads every two weekends. That was just nuts. I expect to buy some replacement C2 rims and re-build the wheelset probably sometime next year.

And about those fenders? Yes, well the new team requires us to have fenders on the bike during the wet season. We don’t cancel training because it’s raining. This is more for the teammate behind me than for me. In March we’ll take ’em off when the weather dries up (for the most part). These fenders are from Toba, and I have to say that they work pretty darn well. Installation, even though I did a small bit of customizing, was straightforward.

As far as the new team? I knew from the WSBA rankings there were at least three teams in the area. One of them I could not find any contact information for…strange as it sounds. The second one wasn’t too forthcoming about their meet-the-team ride or any organized rides for that matter (just weird). I found this web page on the last team I wanted to learn about.  That’s more organization than any team I’ve seen to date. This team’s big. On the weekend training rides, as many as, or more than as many people show up as some race categories that I’ve competed in. The training’s proceeding well and I’ll start the season with the largest base foundation that I’ve ever had. Thus far the team seems to be a good fit, and I have some good expectations for the coming season.

The team’s shop host is fantastic, and the mechs are top-notch. (Surprise! their benches are clean and organized. I haven’t seen that very often.) If an item that I wanted wasn’t available on the shelf, it was there in two to three days. I wouldn’t hesitate referring my friends to them as I know they would be treated right.

By the way, the west side (as I call this side of the state) has more metal in the road surface than I ever imagined. That is, man-hole covers, pipe access covers, grates, and caps. Wet metal and road tires do not mix too well. And if that wasn’t enough, the concrete slabs that comprise some of the roads have uneven edges, as if frost heave has lifted them enough to make you think a pinch-flat is imminent. The roads here are a challenge in them selves. I miss the rural roads of Spokane. Yeah, their roads may be chip-sealed, but you could roll scores of miles and not see one man-hole cover.

What’s not to like huh? See you on the road.

Change…is Inevitable

9 01 2013

I suppose that racing and training is just the same as everything else…just the same as life is, in other words, full of variability, hidden meanings, and hidden assumptions. 2012’s end rolled through a summer’s conclusion full of change and into a winter beset with hints of future promise.

Late in June my better half happened upon a career move apparently timed just for her. With this new profession, she would take avocation to an occupation. Since I was completing my master’s program with WSU, which was not geographically constrained particularly, I suggested we pull anchor and move. Economically, the greater Seattle area holds much more opportunity than Spokane, there are aviation-related companies and manufacturers here (a plus), and there’s the sea’s proximity. This latter point means much to me. As a child, my father was stationed at submarine bases, and as such, the sea or at least large bodies of water, became a part of me. Returning to the sea seemed like returning home in a way. Additionally, the timing of graduation in the spring and the outlook of a refreshing and challenging second career was most welcome.

Reality wasn’t going to be completely encouraging though. In the last week of July, while expediently packing-up our household, I allowed a slip in home security, which permitted thieves to enter my garage and waltz away with my race bike and wheels. A gut-punch, $3,280 loss, and away with it rolled any chance of attaining my goal for a category three upgrade that season. I was grounded in the whole sense of the word.  My fitness and whatever form remaining quickly faded to obscurity. I would have to start all-over. Not three weeks later, a friend’s road bike was stolen from his garage too. Apparently there’s a racket for high-end road bikes.

The Madone 5.2. Gone to wherever thieves have taken it. Serial # WTC227T074C

As chance would have it, we have temporarily settled into a small condo in downtown Seattle. Quite small, 500 square feet I think. But, I can study on this small coffee table and stool, and I can have my commuter bike and Barb’s road bike stuffed into the living room so we don’t have to use the elevator to get them from storage in the basement. Her commuter bike will have to go in the bedroom for now. I can shop for food and other essentials at Pike’s Market and adjacent stores within walking distance…so far so good.

Next post,  a new team, a new bike.  Thanks for reading.

PowerTap Wheel Build Cost

31 12 2011

I noticed that some folks were wondering how much it costs to build a PowerTap wheel so I dug up my receipts. So, without belaboring the point, here they are:

Component Cost Source Notes
PowerTap Hub $977.07 LBS included the Cervo head unit Picture of the PowerTap SL+
Sapim CX-Ray spokes $89.52 Wheelbuilder.com  includes shipping cost of $9.44/UPS Picture of Sapim CX-Ray spoke
HED Belgium C2 rim $112.50 Two Wheel Transit  includes shipping cost of $10.80 Pic of HED Belgium C2

You might have noticed that Wheelbuilder offers their own comparable PowerTap builds at $1,090.27 (includes shipping cost with no electronics). This is a pretty good deal on a custom build for those of you who would rather not fuss with the assembly process or the time. When I built my version over a year ago, I had very specific requirements—and I also like to do my own projects too so the extra cost was worth it to me. And finally, many months later I built the matching front wheel.

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