MAF at One Month-ish

I did a second MAF baseline yesterday. There was more running than the last MAF baseline. Here’s the first MAF baseline (Heart Rate Training (HRT) – Part 7). I re-crunched my data from the first MAF test. Here’s the heart rate from Strava (I only had the Samsung Watch at the time). I can see I was lower on the heart rate range than now.

Here’s the heart rate data from yesterday – the Polar Strap data.

I only had two points where I went over my MAF rate and that was for a very short time.

Here is the same data from my watch (for apples-apples comparison):

I don’t trust the glitches on both of the watch charts. Not sure what the glitch was, but other than that the data is pretty comparable on both.

Performance Increase?

The idea of MAF is that you will see a performance increase. Here’s the two MAF benchmark split times.

The two mile, three mile, and for mile splits were all about 30 seconds faster so I am making good progress in improving my aerobic fitness.

 

Nerding Out on Data

I like Strava for tracking my MAF runs but it doesn’t work well for me with my Polar Chest Heart Rate Strap (HR-7). So, I’ve switched to Polar Beat/Flow for the HR-7 strap since it’s easier to read the heart rate while running. I still use Strava along with Samsung Health. The watch sends data to Samsung Health and Samsung Health sends data to Strava. I still don’t like the result since the heart rate data gets blocky. Here’s an example:

So how did I get the data?

This is the fore-warned nerdy part. I’ve written a Python script. If you don’t know Python skip the rest of this post since I can’t support the code. If you care, the Python code is here on GitHub. Again, I can’t support the code. It uses libraries that are here.

After running the pyStravaParse code. I then open the CSV file (spreadsheet format) in LibreOffice (a Microsloth EXCEL clone). I can’t support your spreadsheet choice either.

The data looks like:

Time (secs) Lat Lon Elev Heart_Rate (bpm) HRmax (bpm) HRmin (bpm)
0 39.908913 -79.71205 323.7 93 122 112
2 39.908913 -79.71205 323.7 93 122 112

HRmax and HRmin are hard coded as string constants at the start of the code. They are based on your MAF number. They could be replaced by 180-age and 190-age.Data_Time is offset in seconds.

I then select the Time, Heart_Rate, HRmax and HRmin columns like this:

Select Insert, Chart.

Choose Chart Type – XY (Scatter) then Next.

For Data Range you should already be OK if you selected data above. The select Next.

For Data Series you should already be OK. Then select Next.

For Chart Elements enter your title, etc as below. After entering in the titles, select Finish.

You should get a result like this.

To edit the chart double click in the chart. Then right click on one of the numbers on the heart rate axis. You should then see.

Select Format Axis. Then enter your own heart rate range numbers. I selected Minimum of 80 and left the maximum at 140.

You should get something like this.

I also like to move the legend to the bottom and move the graph up a bit.

Not a bad result but it’s easy to see the blockyness of the data. The Polar strap does better. I don’t have data for that same run since I bought the Polar strap later but here’s a recent image.

Not bad!

 

Getting Van Wilder to Boston

The Big Question Remains

How to best fuel Van Wilder’s (Another VO2max Test – Van Wilder) next marathon? It is six weeks away and he’s not far from qualifying for the Boston Marathon. Is it best to shift to a high carb low fat diet for the marathon?

Pace vs Heart Rate Test

We did a test of pace vs heart rate.  The test was done on a track in 800 meter distance increments with speed matched to 10 bpm heart rate steps every 2 laps (of the 400 meter track). We downloaded the average data from the logging application.

We then overlaid the pace vs heart rate data with fat-carb oxidation rates from Van Wilder’s VO2max test. Here’s the resulting curves:

The top curve is running pace. Van Wilder’s fastest pace was almost a 4 minute mile. At that rate, he was have been oxidizing around 22 calories of carbs and 9 calories of fat per minute. The rate is also not sustainable due to the high exertion required.

Van Wilder has to run the marathon in about 3 hours. That’s 26.2 miles/3 hrs = 8.7 mph. That’s 60 min/hr divided by 8.7 miles/hr = 6.9 minutes per mile. That’s a heart rate of 155. That is also around 15 calories per minute from carbs and 12 calories per minute from fat. 15 kCal/min * 60 mins = 900 kCal/hr. If he can feed 360 kCal/hr from carbs that’s a net of 540 lost per hour. In three hours his muscle glycogen will be completely gone (assuming it can all be used).

Probably can’t get there from here – at least at the current performance.

Update 2018-09-14

Van Wilder suffered a hip injury and had to drop out of his qualifying marathon last week. That was the last chance for the season.

 

Study of Glycogen and Exercise Studies

Thanks to Luis at Ketogains for pointing to a great study which looks at the studies of Glycogen and Exercise (Pim Knuiman, Maria T. E. Hopman, and Marco Mensink. Glycogen availability and skeletal muscle adaptations with endurance and resistance exercise. Nutr Metab (Lond). 2015; 12: 59.).

…Recent research into the effects of glycogen availability sheds new light on the role of the widely accepted energy source for adenosine triphosphate (ATP) resynthesis during endurance exercise.

Indeed, several studies showed that endurance training with low glycogen availability leads to similar and sometimes even better adaptations and performance compared to performing endurance training sessions with replenished glycogen stores.

The study leads with:

…Glycogen is made and stored in cells of the liver (~100 g) and muscles (~350 – 700 g; depending on training status, diet, muscle fibre type composition, sex and bodyweight) and can be reduced by fasting, low intake of dietary carbohydrates and/or by exercise.

Intermittent Fasting, Low Carbs, exercise. Yep, that’s me.

Glycogen is differently distributed within the muscle fibers (subsarcolemmal ~5-15 %, intermyofibrillar ~75 % and intramyofibrillar ~5-15 %)

And here’s the bit about high intensity workouts:

Glycogen is an essential substrate during high intensity exercise by providing a mechanism by which adenosine tri phosphate (ATP) can be resynthesized from adenosine diphosphate (ADP) and phosphate.

The relative use of energy sources during exercise is mainly determined by the intensity and the duration of the exercise bout, as well as the athlete’s training status.

Fat as source of energy is relatively most dominant during moderate intensity (30-65 % of VO2peak), whereas the relative contribution of carbohydrate oxidation to total energy expenditure becomes greater when exercise intensity increases, with muscle glycogen becoming the most important substrate source

…glycogen availability is essential to power ATP resynthesis during high intensity exercise which relies heavily on glycogenolysis.

Furthermore, it has been well documented that the capability of skeletal muscle to exercise is impaired when the glycogen store is reduced to a certain level, even when there is sufficient amount of other fuels available.

To date, few studies have found an improved training-induced performance effect of conducting the exercise bouts with low glycogen levels compared with replenished glycogen levels

On the subject of resistance training:

… a typical resistance exercise session has been shown to reduce glycogen levels by approximately ~24-40 %. This reduction in glycogen content during exercise is determined by the duration, intensity and volume of the performed exercise bout. The largest reductions in glycogen are seen with high repetitions with moderate load training, an effect that mainly occurs in type II fibers.

Remember glycogen is the storage form of glucose.

Effect of weight loss by ketogenic diet on body composition

An interesting study which is said to show good results for the keto diet and athletic performance (Hyun-seung Rhyu1 and Su-Youn Cho. The effect of weight loss by ketogenic diet on the body composition, performance-related physical fitness factors and cytokines of Taekwondo athletes . J Exerc Rehabil. 2014 Oct; 10(5): 326–331.).

The participants were randomly assigned to 2 groups, 10 participants to each group: the ketogenic diet (KD) group, and the non-ketogenic diet (NKD) group.

The diet/training period was only 3 weeks. The performances were compared:

Aerobic capacity was evaluated by measuring the time taken to finish a 2,000 m sprint. Whereas anaerobic capacity was evaluated by the Wingate test (), by measuring peak power, mean power and fatigue index using a Monark cycle ergometer (Monark 894-E, Sweden). Muscle strength was evaluated based on the measurement of: (1) grip force (TKK 5401, Takei, Japan) and back muscle strength (TKK 5402, Takei, Japan) using a digital measuring instrument, (2) muscle endurance by measuring the number of sit-ups performed in 60 sec, (3) instantaneous reactionary force by measuring time and distance on 100 m sprint and standing broad jump, respectively, and (4) balance by measuring duration on single leg standing with eyes closed.

The diet was only three weeks long. The body composition results were not great for Low Carb.

Changes in body composition

Variables KD (n= 10) NKD (n= 10) F-value


Pre Post Pre Post
Weight (kg) 64.11± 7.19 60.34± 6.59 63.69± 7.64 61.16 ± 7.84 G 0.004
T 89.927*
G×T 3.484
%Body fat (%) 12.59± 3.96 12.21± 3.59 11.31± 2.77 10.23 ± 2.63 G 1.283
T 4.486*
G×T 1.122
Lean body mass (kg) 54.65± 3.93 52.47± 4.67 54.94± 6.50 53.55 ± 8.16 G 0.067
T 10.457*
G×T 0.520
BMI (kg/m2) 21.44± 2.10 20.18± 1.79 21.08± 1.94 20.23 ± 1.97 G 0.032
T 86.936*
G×T 3.282

The KD kids lost twice as much weight which is good. The %Body fat wasn’t changed nearly as much in the KD group as in the NKD group. Worse than that, the KD group lost much more Lean Body Mass. BMI tracked the weight loss again demonstrating the weakness of the BMI measurement for tracking body composition. I think it may be the case that the LBM mass was mostly from lost water weight.

But how about performance?

  • KD did better on the 2,000 meter sprint after training than before. The NKD didn’t show much of an improvement. However, the timeframe listed in the results was 500 minutes. I don’t know how to make sense of that timeframe. It took the participants over 8 hours to sprint for a mile and a quarter?  Could the units be in seconds? If it was seconds then the time would be 8 minutes to run a little over a mile. That is possible in high school athletes.
  • What is striking is how much worse both of the groups did after training. Both groups lost peak power and mean power and the KD group lost significantly more power than the NKD group.
  • Another striking parameter was how much worse the NKD group got in anaerobic fatigue after the training. That is surprising.
  • The KD group did worse on grip strength gains.
  • The KD group did not improve as much on back muscle strength gains.
  • Both groups took longer on the 100m sprint.
  • Both groups could not jump as far in the broadjumps after training.
  • Equally disturbing was the lack of improvement in the sit-ups on the NKD group compared with the KD group.

All in all this points to a fatigued group for the final tests. It is possible to speculate that the KD group was less fatigued because the performed at a lower rate during the week or two of keto adaptation and stored up strength during that period. They show less signs of fatigue.

Issues with the study

  • One of the issues is that the performance tests were performed after fasting for 12-hours. That seems like a unlikely scenario which greatly benefits the Lower Carb cohort. In a more likely scenario both groups would have followed their diet.
  • There seems to have been no test such as RER or even measurements of urine ketones to verify ketosis in the Low Carb cohort.
  • The High School students were given lists of foods to eat. There were no food logs and no verification of compliance. There was no dietary analysis at all.
  • Muscle endurance was done by seeing how many situps could be done in 60 seconds.
  • There was no control group in diet.
  • The standard deviation bars on the data were much bigger than the change effects.

Taekwondo is characterized as:

a comprehensive physical exercise involving high intensity movement of the muscles and joints of the whole body at the mean 85–95% HR-max

Yet, I can’t find an explanation of the time duration of Taekwondo.

 

Volek Talks about the FASTER Study

A video from 2015 where Dr. Volek talks about the FASTER Study (Jeff S. Volek, Daniel J. Freidenreich, Catherine Saenz, Laura J. Kunces, Brent C. Creighton, Jenna M. Bartley, Patrick M. Davitt, Colleen X. Munoz, Jeffrey M. Anderson, Carl M. Maresh, Elaine C. Lee, Mark D. Schuenke, Giselle Aerni, William J. Kraemer, Stephen D. Phinney. Metabolic characteristics of keto-adapted ultra-endurance runners. Metabolism, Volume 65, Issue 3, March 2016, Pages 100-110.).

  1. Fat adapted athletes become “bonk proof” (see my post about that).
  2. Group of ultra-runners.
  3. More athletes volunteered than could be tested.
  4. Matched groups.
  5. LCD group was 70-20-10 F-P-C.
  6. HCD group was 25-15-60 P-F-C.
  7. Day 2 was three hours at 65% of VO2max (see my post about that). He later stated it ended up being at 64% of VO2max.
  8. They thought peak fat oxidation would be lower due to other studies documenting lower rates. They could have told from any low carb VO2max test that the peak rates were higher in Low Carb dieters.
  9. It looks as if they picked the 65% number based on this study (Achten J1, Gleeson M, Jeukendrup AE. Determination of the exercise intensity that elicits maximal fat oxidation. Med Sci Sports Exerc. 2002 Jan;34(1):92-7.).
  10. Volek showed the same graph from the VESPA article with the shift up and to the right of the fat oxidation curve (see my post about that).
  11. The statistically identical glycogen levels before, after and at the end of recovery were a surprise to Volek (as they are to me). Does fat allow the glycogen stores to refill? He thinks there is a chronic adaptation in LC athletes. It isn’t likely to be peripheral insulin resistance since the athlete’s muscles were biopsied to measure the glycogen levels, right? Alaskan sled dogs may provide a clue?
  12. Athletes were on LC for an average of 19 months.
  13. Gene expression differences between the two groups still being analyzed. Glycogen metabolism gene differences.
  14. LDL Cholesterol levels were much higher in LC athletes. HDL was also much higher in LC athletes.
  15. LDL Particle distributions were better in LC athletes (fewer smaller and more large LDL).
  16. Insulin Resistance scores were much better in LC athletes (top 1% of population).
  17. Half the high carb athletes have switched to low carb diet after the study.

 

 

VESPA and FASTER

Vespa has a graph on their site that shows %VO2max vs Fat oxidation in Low Carb and High Carb athletes from the FASTER study (Fat Adaptation: The Emerging Science from FASTER). Here’s the chart as it appears on the Vespa site:

I can’t find this graph in the FASTER Study paper  (Jeff S. Volek, Daniel J. Freidenreich, Catherine Saenz, Laura J. Kunces, Brent C. Creighton, Jenna M. Bartley, Patrick M. Davitt, Colleen X. Munoz, Jeffrey M. Anderson, Carl M. Maresh, Elaine C. Lee, Mark D. Schuenke, Giselle Aerni, William J. Kraemer, Stephen D. Phinney. Metabolic characteristics of keto-adapted ultra-endurance runners. Metabolism, Volume 65, Issue 3, March 2016, Pages 100-110.).

But I do have some of the VO2max data tests from two of the athletes; Ben Greenfield and Damian Stoy. And Ben was LCD and Damian was HCD. So we should be able to check the graph using their data.

Here is Ben’s curve:

Here is Damian’s curve:

Peak Values

Damian’s peak rate of fat oxidation at around 0.35 g/min was about one-third of Ben’s top rate of around 1.1 g/min. So in this regard the curves do match the relative magnitudes in the Vespa graph.

Shifted Values?

The VESPA graph for the LCD vs the HCD shows a shift to the right for the peak fat oxidation for LCD as compared with HCD. In fact, the VESPA graph shows the peak of the LCD at 70% of VO2max and shows the peak of the HCD at 50%.

This doesn’t match Ben’s data at all. Ben’s fat oxidation peak is clearly around 55% of VO2max.

There is a small shift to the left for vegan Damian Stoy. His peak is somewhere around 45%.

I want to see the other data to see if Ben is at one end of the LC data but he pretty clearly doesn’t match the %VO2max vs maximum fat oxidation rate that the VESPA graph implies.

Why Should I Care?

I care because my own data matches Ben Greenfield’s data.

NIKO NIKO Pace

There is another slow steady heart rate training called Niko Niko. Here’s a good page describing it (NIKO NIKO PACE – THE GENTLE PATH TO SUCCESS).

The formula for heart rate is different than the Maffetone MAF heart rate. Niki Niko uses:

138 minus half your age.

So for me at 58 that would be 138 – (58/2) = 109 bpm. That’s not too far below my MAF heart rate of 112-122. It it at an interesting point on myVO2max test results as well. A heart rate of 109 actually has a higher fat oxidation rate than the extrapolated curve (where the real world data veers from the idealized curve). It is certainly a good point for fat burning.

The MAF number has shifted my performance upwards in just the past three weeks. Here’s my splits from my MAF test three weeks ago compared with my MAF 5 mile this AM. I’ve had to go from walking to a little jogging and walking mixed together.

That’s a pretty good improvement – around a minute and a half.

Slower But Fitter?

An interesting study put a group of endurance athletes on a Ketogenic diet and measured their performance as well as body composition changes (Zinn C, Wood M, Williden M, Chatterton S, Maunder E. Ketogenic diet benefits body composition and well-being but not performance in a pilot case study of New Zealand endurance athletes. J Int Soc Sports Nutr. 2017 Jul 12;14:22.). The study concluded:

All athletes increased their ability to utilise fat as a fuel source, including at higher exercise intensities.

Mean body weight was reduced by 4 kg ± SD 3.1 (p = 0.046; effect size (ES):0.62), and sum of 8 skinfolds by 25.9 mm ± SD 6.9; ES: 1.27; p = 0.001).

But how was their performance?

Mean time to exhaustion dropped by ~2 min (±SD 0.7; p = 0.004; ES: 0.53). Other performance outcomes showed mean reductions, with some increases or unchanged results in two individuals (VO2 Max: -1.69 ml.kg.min ± SD 3.4 (p = 0.63); peak power: -18 W ± SD 16.4 (p = 0.07), and VT2: -6 W ± SD 44.5 (p = 0.77).

Was this an adaptation problem?

Athletes reported experiencing reduced energy levels initially, followed by a return of high levels thereafter, especially during exercise, but an inability to easily undertake high intense bouts. Each athlete reported experiencing enhanced well-being, included improved recovery, improvements in skin conditions and reduced inflammation.

In the end the athletes likes the health benefits even with the performance losses.