Low Carb High Intensity Interval Training Performance

Here’s a new study that looked at the Low Carb diet and High Intensity Interval Training performance (Lukas Cipryan, Daniel J. Plews, Alessandro Ferretti, Phil B. Maffetone, and Paul B. Laursen. Effects of a 4-Week Very Low-Carbohydrate Diet on High-Intensity Interval Training Responses. J Sports Sci Med. 2018 Jun; 17(2): 259–268.).

The purpose of the study was to examine the effects of altering from habitual mixed Western-based (HD) to a very low-carbohydrate high-fat (VLCHF) diet over a 4-week timecourse on performance and physiological responses during high-intensity interval training (HIIT).

Eighteen moderately trained males (age 23.8 ± 2.1 years) consuming their HD (48 ± 13% carbohydrate, 17 ± 3% protein, 35 ± 9% fat) were assigned to 2 groups. One group was asked to remain on their HD, while the other was asked to switch to a non-standardized VLCHF diet (8 ± 3% carbohydrate, 29 ± 15% protein, 63 ± 13% fat) for 4 weeks.

Participants performed graded exercise tests (GXT) before and after the experiment, and an HIIT session (5x3min, work/rest 2:1, passive recovery, total time 34min) before, and after 2 and 4 weeks. Heart rate (HR), oxygen uptake (V̇O2), respiratory exchange ratio (RER), maximal fat oxidation rates (Fatmax) and blood lactate were measured. Total time to exhaustion (TTE) and maximal V̇O2 (V̇O2max) in the GXT increased in both groups, but between-group changes were trivial (ES ± 90% CI: -0.1 ± 0.3) and small (0.57 ± 0.5), respectively.

Between-group difference in Fatmax change (VLCHF: 0.8 ± 0.3 to 1.1 ± 0.2 g/min; HD: 0.7 ± 0.2 to 0.8 ± 0.2 g/min) was large (1.2±0.9), revealing greater increases in the VLCHF versus HD group. Between-group comparisons of mean changes in V̇O2 and HR during the HIIT sessions were trivial to small, whereas mean RER decreased more in the VLCHF group (-1.5 ± 0.1). Lactate changes between groups were unclear.

Adoption of a VLCHF diet over 4 weeks increased Fatmax and did not adversely affect TTE during the GXT or cardiorespiratory responses to HIIT compared with the HD.

I have a lot of respect for Phil Maffetone and Paul Larson. Both are long time advocates of Low Carb Athletics. Phil Maffetone coached Mark Allen to multiple wins at Kona Ironman (Mark Allen Interview: A look back at working with Phil Maffetone and what it means for today’s triathlete).

 

 

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!

 

Ketogenic Ironmen

Nice short study on Keto and Ironman ultra-endurance events (Maunder E, Kilding AE, Plews DJ. Substrate Metabolism During Ironman Triathlon: Different Horses on the Same Courses. Sports Med. 2018 May 18. doi: 10.1007/s40279-018-0938-9.).

Given the finite human capacity for endogenous carbohydrate storage, minimising the endogenous carbohydrate cost associated with performing exercise at competitive intensities should be a goal of Ironman preparation. A range of strategies exist that may help to achieve this goal, including, but not limited to, adoption of a low-carbohydrate diet, exogenous carbohydrate supplementation and periodised training with low carbohydrate availability.

Given the diverse metabolic stimuli evoked by Ironman triathlons at different performance levels, it is proposed that the performance level of the Ironman triathlete is considered when adopting metabolic strategies to minimise the endogenous carbohydrate cost associated with exercise at competitive intensities. Specifically, periodised training with low carbohydrate availability combined with exogenous carbohydrate supplementation during competition might be most appropriate for elite and top-amateur Ironman triathletes who elicit very high rates of energy expenditure.

Conversely, the adoption of a low-carbohydrate or ketogenic diet might be appropriate for some lower performance amateurs (> 12 h), in whom associated high rates of fat oxidation may be almost completely sufficient to match the energy demands required.

Nicely put.

 

Measuring Heart Rate

I’ve said it before but it bears repeating.

A man with two clocks never knows what time it is.

Today I went out with my Polar Chest Strap and Samsung Gear Sport watch. I got two very different sets of data. Normally, I would trust the chest strap over the watch since the watch sometimes runs too high. This time I trust my watch over the chest strap. Why?

Two charts

Turns out that Strava can export a GPX file which can be read by EXCEL. (EXCEL complains about the data a few times but opens it OK). Here’s the two charts.

Judging by Past Performance

I’ve walked up the same hills before and have an idea of what happens with my heart rate. By one mile I’ve gone to the post office and started walking back up a hill. The Polar has my heart rate at 70 which seems way too low. The Samsung has my heart rate at 100 or so which makes more sense.

Judging by Rate of Perceived Exertion

The last half of the Samsung data has my heart rate in my MAF range (112-122). That makes sense given where I was in the walk and my rate of perceived exertion. Here’s the MAF range added to the Watch data.

Performance

The splits show I was behind my normal pace (messing with the monitors plus I was on hills) but not that far behind the pace.

Map

Other than going to the Post Office (upper left circle) I did my typical route.

The pace and elevation data are:

Solution?

I think I didn’t wet the Polar strap well enough. It was less than 70 degrees and relatively dry so the strap could have been too dry. I didn’t sweat on the walk either.

Next time I will wet the chest strap better.

 

Dietary Periodization – Strategic Carbs

Do Strategic Carbs work?

This study took a look at the strategic carbs strategy (Louise Burke. Fat adaptation and glycogen restoration for prolonged cycling—recent studies from the Australian Institute of Sport. Australian Journal of Nutrition and Dietetics, vol. 58, no. 2, 2001, p. S23+). The study looked at:

… a period of exposure to high fat, low CHO intake, followed by the restoration of muscle glycogen stores with a high CHO diet.

Such ‘dietary periodisation’ aims to enhance the capacity of both glycolytic and lipolytic systems to oxidative metabolism during prolonged exercise, by increasing the contribution from fat to substrate metabolism while potentially sparing intact muscle glycogen stores

Here are the results:

The fat adaptation diet caused major changes in fuel utilisation during sub-maximal exercise, with at least some of the adaptations persisting on day seven, even in the face of a plentiful CHO supply. As dramatic as these metabolic changes were, they failed to improve the performance of the cyclists’ time trial.

Together with other research, this study fails to find evidence that fat adaptation strategies offer any benefits for the endurance athlete.

The only remaining question is whether there are any advantages for ultra-endurance athletes who compete in events undertaken at a lower intensity and for longer periods (e.g. four hours or more). For these athletes, fat is the predominant fuel source.

 

Protein Before Bed

Here’s an interesting study which indicates that Protein taken before bed stimulates Muscle Protein Synthesis (Tim Snijders, Peter T Res, Joey SJ Smeets, Stephan van Vliet, Janneau van Kranenburg, Kamiel Maase, Arie K Kies, Lex B Verdijk, Luc JC van Loon; Protein Ingestion before Sleep Increases Muscle Mass and Strength Gains during Prolonged Resistance-Type Exercise Training in Healthy Young Men, The Journal of Nutrition, Volume 145, Issue 6, 1 June 2015, Pages 1178–1184).

Methods: Forty-four young men (22 ± 1 y) were randomly assigned to a progressive, 12-wk resistance exercise training program. One group consumed a protein supplement containing 27.5 g of protein, 15 g of carbohydrate, and 0.1 g of fat every night before sleep. The other group received a noncaloric placebo. Muscle hypertrophy was assessed on a whole-body (dual-energy X-ray absorptiometry), limb (computed tomography scan), and muscle fiber (muscle biopsy specimen) level before and after exercise training. Strength was assessed regularly by 1-repetition maximum strength testing.

Results: Muscle strength increased after resistance exercise training to a significantly greater extent in the protein-supplemented (PRO) group than in the placebo-supplemented (PLA) group (+164 ± 11 kg and +130 ± 9 kg, respectively; P < 0.001). In addition, quadriceps muscle cross-sectional area increased in both groups over time (P < 0.001), with a greater increase in the PRO group than in the PLA group (+8.4 ± 1.1 cm2 vs. +4.8 ± 0.8 cm2, respectively; P < 0.05).

Both type I and type II muscle fiber size increased after exercise training (P < 0.001), with a greater increase in type II muscle fiber size in the PRO group (+2319 ± 368 μm2) than in the PLA group (+1017 ± 353 μm2P < 0.05).

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.

 

Athlete Van Wilder – Part 2

In an earlier post, I took a look at Van Wilder (Another VO2max Test – Van Wilder). He was a low carb guy but has bumped up the carbs. quite a bit. The day before the VO2max he ate quite a few carbs.

Let’s look close to see if our VO2max curves are comparable to the Volek FASTER chart. Here’s the FASTER chart:

The HCD (High Carbohydrate Diet) looks like an inverted parabola and can be modeled as a 2^X function. Due to the distorted hump, the LCD (Low Carbohydrate Diet) looks like a higher order polynomial (more than a square = 2nd order). Assuming a 3rd order polynomial would model the curve better. Revisiting the VO2max data for Van Wilder (as a third order polynomial):

Here is my VO2max data (also as a third order polynomial):

My curve looks more shifted to the right similar to the FASTER graph. Both of the graphs have the same 50%-50% calories point at about 80% of VO2max.

Van Wilder’s MFBGS (Maximum Fat Burning Glucose Sparing) point is at around 43% of his VO2max. My own MFBGS is quite a bit higher at around 61% of my VO2max. Does that provide any real advantage? Certainly my 20+ years older doesn’t help me out.

Van Wilder’s MAF Number

Van Wilder is a well trained athlete but he’s constantly injured. He should have a reduced MAF number but let’s assume he’s at the 180 – 35 = 145 heart rate.

Van Wilder’s MFBGS point is at around 118 bpm. At his MAF number he’s well into carbohydrate burning (around 65% fat and 35% carbs).

 

Train Low, Compete High

One popular Low Carb strategy is to train low and compete high. The basic strategy is to do all training in a fat adapted state and then switch to a higher carb state a day or two before competition.  A study took a look at this methodology (Havemann L, West SJ, Goedecke JH, Macdonald IA, St Clair Gibson A, Noakes TD, Lambert EV. Fat adaptation followed by carbohydrate loading compromises high-intensity sprint performance. J Appl Physiol 2006 Jan;100(1):194-202.). The study consisted of six days of High Fat diet to a High Carb diet on the 7th day. The study looked at the performance on the 8th day. The purpose of the carb fueling was to fill glycogen stores before the final tests.

The ingestion of a HFD for 6 days resulted in a shift in substrate metabolism toward a greater reliance on fat and a reduction in CHO oxidation. The increase in fat oxidation in the present study persisted despite 1 day of CHO loading on day 7 as demonstrated by the lower resting RER (0.77  0.02 vs. 0.88  0.05, Fig. 2) and higher circulating FFA (Table 7) during exercise after HFD-CHO compared with HCD-CHO on day 8.

Here’s what was valuable about this 2006 study.

The study is unique in that it is the first study to investigate the effect of high-fat feeding, followed by CHO loading, on endurance exercise, including high-intensity sprints that simulate actual race situations.

In spite of being on a High Carb diet the effects of the High Fat diet persisted. This could be seen in a lower RER value indicating increased fat oxidation. However, the sprint performance was not as good. From the discussion:

It was hypothesized that the potential glycogen-sparing effect of this dietary strategy (3) would be most beneficial for exercise that included high-intensity sprint bouts, where muscle glycogen is the predominant fuel. However, in contrast to our hypothesis, the HFD-CHO strategy actually compromised high-intensity 1-km sprint performance.