Category: Performance Testing

Exercise and Longevity

There’s a couple of recent studies out that look at the effects of exercise using telomere length as a surrogate for longevity. Our telomeres shorten as we age.

The first study is (Beate Ø Osthus, Ida & Sgura, Antonella & Berardinelli, Francesco & Alsnes, Ingvild & Brønstad, Eivind & Rehn, Tommy & Kristian Støbakk, Per & Hatle, Håvard & Wisløff, Ulrik & Nauman, Javaid. (2012). Telomere Length and Long-Term Endurance Exercise: Does Exercise Training Affect Biological Age? A Pilot Study. PloS one. 7. e52769. 10.1371/journal.pone.0052769). The study:

Older endurance trained athletes had longer telomere length compared with older people with medium activity levels (T/S ratio 1.12±0.1 vs. 0.92±0.2, p = 0.04). Telomere length of young endurance trained athletes was not different than young non-athletes (1.47±0.2 vs. 1.33±0.1, p = 0.12).

A second study looked at the effects of the specific mode of exercise (Christian M Werner, Anne Hecksteden, Arne Morsch, Joachim Zundler, Melissa Wegmann, Jürgen Kratzsch, Joachim Thiery, Mathias Hohl, Jörg Thomas Bittenbring, Frank Neumann, Michael Böhm, Tim Meyer, Ulrich Laufs; Differential effects of endurance, interval, and resistance training on telomerase activity and telomere length in a randomized, controlled study , European Heart Journal, ehy585).

The results were interesting.

This randomized, controlled, and prospective training study shows that specific training protocols lead to differential effects on cellular aging. Aerobic endurance and high-intensive interval training, but not resistance training, increases telomerase activity and telomere length in blood mononuclear cells.

This study was fairly impressively powered with 124 subjects.

One hundred and twenty-four healthy previously inactive individuals completed the 6 months study. Participants were randomized to three different interventions or the control condition (no change in lifestyle): aerobic endurance training (AET, continuous running), high-intensive IT (4 × 4 method), or RT (circle training on 8 devices), each intervention consisting of three 45 min training sessions per week.

The specific results were statistically significant.

Telomerase activity in blood mononuclear cells was up-regulated by two- to three-fold in both endurance exercise groups (AET, IT), but not with RT. In parallel, lymphocyte, granulocyte, and leucocyte TL increased in the endurance-trained groups but not in the RT group. Magnet-activated cell sorting with telomerase repeat-ampliflication protocol (MACS-TRAP) assays revealed that a single bout of endurance training—but not RT—acutely increased telomerase activity in CD14+ and in CD34+ leucocytes.

Things to note is that this is an older (~49 years on average), untrained group of people who were at healthy BMI (~24).

Mechanism

The mechanism is interesting.

Exercise Intensity and Blood Sugar

I’ve come to the conclusion that for me as a diabetic intense exercise (at high heart rates) is not good for my blood sugar control. Here’s a study of Type 1 Diabetics which shows the increase in blood sugar from intense exercise (Vinutha S, Paul F, Raymond D, et al. Effect of exercise intensity and blood glucose level on glucose requirements to maintain stable glycaemia during exercise in individuals with type 1 diabetes. Int J Pediatr Endocrinol. 2015;2015(Suppl 1):O39). The study looked at:

Nine young adults with T1D underwent euglycaemic clamps, whereby stable blood glucose levels between 4.5 to 6mmol/L were maintained during the study at basal insulin levels. Participants performed up to 40 minutes of exercise at four different exercise intensities (35%, 50%, 65% and 80% VO2peak) on four separate days following a randomised counterbalanced design. In a subsequent experiment, eight participants underwent either a euglycaemic or hyperglycaemic (9.5 – 10.5mmol/L) clamp at basal insulin levels, during which they performed 40 minutes of exercise at 50% VO2peak, on two separate days. In both studies, glucose infusion rates (GIR) to maintain stable glycaemia were measured during exercise, constant deuterated glucose was infused to determine glucose kinetics and blood samples were collected for the analysis of glucoregulatory hormones.

The result was:

The average GIR to maintain euglycaemia during exercise was 2.0±0.9, 4.0±1.5, and 4.1±1.7g/h (mean±SEM) at 35%, 50% and 65% VO2peak, respectively. These GIRs were all significantly greater than that at 80% VO2peak where no glucose was required (p<0.05). Exercise at 80% VO2peak was associated with a significant rise in catecholamine levels and endogenous glucose production (p<0.05). The average GIR to maintain stable glycaemia during exercise performed during the second experiment at 50% VO2peak was similar at euglycaemia (4.9±2.1g/h) and hyperglycaemia (5.5±2.5g/h; p>0.05).

 

MAF Plus 20

Peter Defty (of OFM fame) suggests that fat adapted athletes can increase their MAF number from 180 – age (with correction factors added/subtracted) to 200 – age (same correction factors) (Primal Endurance Podcast – #90: Peter Defty Talks Optimized Fat Metabolism).

His reasoning is that the heart rate is 10-15 beats per minute faster in fat adapted athletes (from the FASTER data). He reasoned that Maffetone came up with the number based on non-fat adapted athletes and that once fat adapted the number can be shifted up.

Tempting Idea, but…

I’ve had the same thoughts before and I’d really like to accept Defty’s ideas since I’m getting tired of mostly walking. I’d like to run more. But I’ve also had no injuries in the past few months. Recovery has been so easy that I’m finding myself doing two MAF efforts a day. I’d hate to jeopardize that.

I don’t think I’m getting much faster doing MAF, but I wonder if sticking with MAF and doing intervals would improve my speed. I do feel like I am improving my leg strength at MAF and they are not a limiting factor when I’m out for more than an hour.

The limiting part of MAF is that after 4 or 5 miles I can only run a few steps until I have to start walking again.

MAF is MAF

Of course, Maffetone’s approach is that MAF is MAF. And it’s 180 – age (with correction factors).  The program is fixed and doesn’t need to be changed. The athlete who is not yet fat adapted will burn more carbohydrates at MAF and the athlete who is fat adapted will burn more fat at MAF. This shift away from carbohydrate reliance to fat adaptation is the goal of MAF when done with the recommended lower carbohydrate diet.

20 Beat Shift – VO2 Data

To see what a 20 beat increase would do, take a look at my VO2max fat/carbohydrate oxidation curve. At my MAF (122 bpm) I am currently burning nearly all fat and very little carbohydrates.

Shifting up by 20 bpm from 122 to 142 just happens to be the 50-50 crossover point of calories from fat and carbohydrates. This will cause glycogen depletion which has good and bad aspects. My current view is that staying out of that range is the smartest idea since cycling glycogen doesn’t promote lower glycogen stores since the body responds by over saturating glycogen stores.

Shifting right by 20 bpm could have the advantage of causing a further shift of the curve to the right and increasing my fat oxidation at that same heart rate. If that is the effect then it would be positive since in the end I could have a higher VO2max and improved fitness.

Critique of MAF number

One difficultly of the Maffetone MAF number is that there’s no real explanation of the basis for the number. Maffetone himself says that the number can be adjusted based on actual metabolic tests but he never exactly explains how to adjust the number nor exactly what he based the number on other than observation of a lot of his clients/patients. The number fit the tests within a few beats but Maffetone never explains the derivation of the number in enough details to explain what lab test he used and what the correlation to the tests is. Maffetone has spent a lot of energy explaining what it isn’t (lactic threshold, VO2max, percent of max heart rate, etc) but not a lot explaining what it is. Without tying it to some external metric it’s hard to judge the value of the metric.

Is MAF at the cross-over point for a non-fat adapted athlete but the point of maximum fat burning in a fat adapted athlete? It is true from my data that 122 is the sweet spot. It is literally the peak of fat oxidation (the black theoretical curve fitted line) where no carbohydrates are being burned. Ten beats lower is still in the prime fat burning zone. For me, lower numbers are even ketone burning (evidenced by the RER of less than 0.7).

Rate of Perceived Exertion (RPE)

Mowing my lawn raises my heart rate beyond the MAF range and makes me sweat. MAF makes me sweat when it’s warm outside but it’s a pretty gentle pace. I could do exercises at 142 max and it would be fine. I know because I’ve mowed the lawn (and done CrossFit) at higher rates.

I don’t think I am going to change what I am doing at the moment but I will bear it in mind for the future. I did 5 sessions last week of 5Km or longer and I’d like to keep up the volume.

 

MAF Training And Metabolic Syndrome

There’s an interesting study which looked at two months of training at FATmax to see what the effects on Metabolic Syndrome (Dumortier M, Brandou F, Perez-Martin A, Fedou C, Mercier J, Brun JF. Low intensity endurance exercise targeted for lipid oxidation improves body composition and insulin sensitivity in patients with the metabolic syndrome. Diabetes Metab. 2003 Nov;29(5):509-18). The study showed good improvements from MAF level of training intensity.

The patients exhibited a significant reduction in body weight (- 2.6 +/- 0.7 kg; P=0.002), fat mass (- 1.55 +/- 0.5 kg; P=0.009), waist (- 3.53 +/- 1.3 cm; P<0.05) and hip (- 2.21 +/- 0.9 cm; P<0.05) circumferences, and improved the ability to oxidize lipids at exercise (crossover point: + 31.7 +/- 5.8 W; P<0.0001; LIPOX(max): + 23.5 +/- 5.6 W; P<0.0001; lipid oxidation: + 68.5 +/- 15.4 mg.min(-1); P=0.0001). No clear improvement in either lipid parameters or fibrinogen were observed.

There were significant improvements in the markers of Metabolic Syndrome.

The surrogates of insulin sensitivity evidenced a decrease in insulin resistance: HOMA%S (software): + 72.93 +/- 32.64; p<0.05; HOMA-IR (simplified formula): – 2.42 +/- 1.07; P<0.05; QUICKI: + 0.02 +/- 0.004; P<0.01; SI=40/I: + 3.28 +/- 1.5; P<0.05. Significant correlations were found between changes in body weight and HOMA-IR and between changes in LIPOX(max) and QUICKI.

Here’s a longer term study which shows positive results over a longer time period (Drapier E (2018) Long term (3 years) weight loss after low intensity endurance training targeted at the level of maximal muscular lipid oxidation. Integr Obesity Diabetes 4).

Average weight loss was -2.95 ± 0.37 kg after 3 months, -4.56 ± 0.68 kg after 1 year, -5.31 ± 1.26 kg at 2 years and -8.49 ± 2.39 kg at 3 years.

The beauty of this study was that it compared low intensity exercise to a low fat diet.

This study shows that this low intensity exercise training maintains its weight-reducing effect 3 years while diet is no longer efficient, and that this effect is initially related to muscular ability to oxidize lipids but that metabolic and behavioral adaptations have been further developed and contribute to a long lasting effect.

The results are powerful.

Here’s a third related study (J. O. Holloszy and E. F. Coyle. Adaptations of skeletal muscle to endurance exercise and their metabolic consequences. Journal of Applied Physiology 1984 56:4, 831-838).

The major metabolic consequences of the adaptations of muscle to endurance exercise are a slower utilization of muscle glycogen and blood glucose, a greater reliance on fat oxidation, and less lactate production during exercise of a given intensity.

These adaptations play an important role in the large increase in the ability to perform prolonged strenuous exercise that occurs in response to endurance exercise training.

From the results:

…Probably the most important of these is an increase in mitochondria with an increase in respiratory capacity. One consequence of the adaptations induced in muscle by endurance exercise is that the same work rate requires a smaller percentage of the muscles’ maximum respiratory capacity and therefore results in less disturbance in homeostasis.

A second consequence is increased utilization of fat, with a proportional decrease in carbohydrate utilization, during submaximal exercise.

 

Polar App Zones

The heart rate zones in the Polar Flow app are misleading. Here’s a recent run.

The problem with the Polar app is that the zones are based on assumptions which may not apply for you. More specifically your fuel is a mixture between fat and carbohydrates and the hard edges these applications show don’t reflect a mixture.

I had my own VO2max tested in a lab. I know what my fuel mixture is at a particular heart rate. My Polar app shows me in fat burning below 111 bpm and “fit” above that point. My MAF range is 112-122. My VO2Max test showed my fat/carb burning at 90% / 10% at a heart rate of 124.

But I am fat adapted keto for two years so I am primarily a fat burner. This is why MAF works well for me. My 100% fat burning heart rate is 117 which center of MAF. As long as I am in the MAF zone I’m burning nearly all fat – even though the Polar Flow program says otherwise.

FASTER Study – Three Hour Magic Number

In previous posts I’ve taken a critical look at the FASTER study (FASTER Again – Checking a number on Ben Greenfield’s data). In particular, I took at look at Ben Greenfield’s three hour data (FASTER10 – Ben Greenfield – Three Hour VO2 testing). Ben looked like he still had gas left in his tank after three hours of sub-maximal running.

The Vegan

But what about Damian Stoy (FASTER Subject 43)? He’s a vegan who is not at all fat fueled. He never got more fuel from fat than 50% and that was at 45% of his VO2max.

At 64% of VO2max, Damian was getting nearly zero of his energy from fat. His carbohydrate oxidation rate was ~10 kcal/min. So, in three hours of running, Damian burned ~1800 kcals which has to be close to his entire glycogen stores.

My conclusion? Beyond this time and intensity being carb fueled isn’t a great choice. The reason that marathons are 26 miles is historical and practical. People just can’t run hard for longer times.

 

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!