Greetings, newcomers. Here you will find a text I’ve prepared presenting and analyzing a few studies related to fitness and nutrition. Topics discussed include low-carbohydrate diets, alcohol as well as egg’s effects on muscle gains, single-joint exercises and fatigue. If any of this interests you, read on!

In the future, I hope to write more in-depth and higher quality reviews/presentations. I also wish to make videos when time and resources allow.

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The bulk of this article will touch on our first study which was conducted by Ebbeling et al. (2018) investigating whether different macronutrient ratios (carbohydrates and fats specifically) have varying effects on total energy expenditure (TEE, essentially the number of calories you expended within a day) within the context of weight loss maintenance. This study has been making its rounds and has been the topic of discussion and debate amongst scientists recently. Let’s take a look at how this randomized controlled trial was carried out first.

164 participants were all first put through a “run-in phase” in which they lost 12% of their body mass over 9–10 weeks. This was done to properly set up the study as the participants needed to be in a state where they are more likely to gain weight. Furthermore, as mentioned previously, this investigation needed to be done in the scope of weight loss maintenance. Once the first phase was complete, the subjects were randomized into 3 groups with differing macronutrient ratios: 20% carbohydrates (LOW), 40% carbohydrates (MOD) and 60% carbohydrates (HIGH). Protein was controlled at 20% of total dietary intake to prevent confounding by its greater thermic effect (Eisenstein, Roberts, Dallal & Saltzman, 2002). Participants were instructed to maintain this diet for a total of 20 weeks. Insulin responses to oral glucose tests were also recorded at pre-weight loss to see if people with lower or higher insulin responses had more modest/extreme changes in TEE.

Ebbeling et al. (2018)

Results showed that LOW had a TEE 247 kcal/d higher than the TEE in HIGH. It was also discovered that those with higher insulin responses had more extreme changes in TEE with differences between LOW and HIGH being as high as 478 kcal/d with the results favoring LOW.

Ebbeling et al. (2018) — HIGH (pink), MOD (yellow), LOW (purple)

These are fairly substantial results and the differences between groups may be more pronounced than one would expect. Proponents of CICO (calories in, calories out) agree that low-carb diets such as the ketogenic diet have no metabolic advantage over other diets isocalorically so these numbers would definitely cause some heads to turn amongst them. Stephan Guyenet, PhD in neuroscience with specialization in eating behaviors, created an interesting graph presenting all the results of similar studies. On the extreme end of the spectrum, we can find the current study.

Credit: Stephan J. Guyenet

Kevin Hall and Juen Guo wrote a response to the study outlining some potential issues with the methods used (can be read here). Firstly, they pointed out that there was a deviation from the pre-registered plan of statistical analysis. Initially, Ebbeling et al. (2018) intended to use TEE numbers from before the run-in phase but they used TEE results immediately after the run-in phase instead. This raises a problem that involves the use of doubly labeled water (a method of figuring out energy expenditure in humans). As described by Bhutani, Racine, Shriver & Schoeller (2015), there can be some inaccuracy in the use of doubly labeled water when there is a change in diet and a change in diet was occurring during the first TEE measurements (from a caloric deficit to a diet where calories were increased). Furthermore, if we follow the pre-registered plan, we see more reserved and statistically insignificant results.

In defense, the authors of the study at hand replied to the response. They essentially reinforced that the study was rigorously reviewed and they referenced previous replies they had made to other criticisms. Such points included that the reason behind having the first TEE data point after the run-in phase was because it was as close to the randomization as possible which reduces the chances of confounding (having non-measured/controlled variables affect your outcomes). Overall, the study design seems rather questionable and given how outlying the results were (compared to current literature), the generalizability and accuracy of this study is uncertain.

TL;DR: Study showed that low-carb diet has higher metabolism than diets with higher carb ratios in the context of weight loss maintenance. However, the study’s design is contentious with accuracy at risk.

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Our 2nd study by Parr et al. (2014) explored the effects of alcohol intake post-exercise on muscle protein synthesis (an important process for making gains). The researchers took 8 trained men and had them go through three post-exercise trials.

First test = 500ml whey protein (25g) (PRO)

Second test = 1.5g/kg ethanol + 500ml whey protein (25g) (ALC-PRO)

Third test = 1.5g/kg ethanol + enough carbs to match second test’s kcal intake (ALC-CHO).

To be clear, this study was done in a crossover design which means all subjects went through all trials with 2 weeks between each trial. For perspective, 1.5g/kg of ethanol is around 5 cans of beer for a 70kg individual. For dietary control, a 24 hour diet diary was logged on the day of each trial and meals were provided pre and post workout. The workout involved some resistance training, cycling as well as some high intensity interval training.

For the main outcome, PRO had the highest rates of muscle protein synthesis outperforming ALC-PRO by 24% and ALC-CARB by 37%. PRO also had the highest MTOR activation (MTOR is an important signalling pathway in the body that has a very well established and studied connection with muscle hypertrophy). PRO even had the highest serum amino acid levels (essentially levels of protein the blood). An irrelevant but interesting finding was that blood alcohol content was higher in ALC-CHO than ALC-PRO at the 6 hour and 8 hour marks post-exercise.

Parr et al. (2014) — Muscle Protein Synthesis Rates Between Groups

Based on these results, we can see that heavy, post-workout alcohol consumption has some detrimental effect on muscle protein synthesis likely due to the oxidative stress and inflammation alcohol can cause. However, the generalizability of the study may be limited by its very small sample size.

TL;DR: Copious alcohol intake after your workout is likely bad for gains.

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The next paper poses the question of why people stop in high intensity aerobic exercise. Staiano et al. (2018) put three theories to the test. The first and most popular theory of why we stop is because our muscles simply fatigue and cannot continue. This theory was challenged by Marcora & Staiano (2010) where the power (how much work done over time) after a time-to-exhaustion (TTE) test was greater than the power needed for the TTE. If muscular fatigue stops exercise, how could one stop exercise while still being able to generate powers greater than required by a TTE? Another theory is based around the pain in our muscles while we exercise. As the aches progressively get worse, it eventually reaches a point beyond toleration and exercise comes to a halt. Finally, the last theory suggests that perception of effort is the main player. If the physical test requires a level of effort beyond the maximum effort you are willing to put in, you consciously stop. It is important to make a distinction between this theory and the pain theory as they both follow different neural processing pathways.

The investigation was split into 2 parts. The first experiment examined effects of a TTE test on rate of perceived exertion (RPE) and maximum voluntary cycling power (MVCP) in 11 male athletes. Baseline VO2 maxes (basically a measurement of endurance) and MVCPs were obtained 48 hours before the actual test. The TTE test was performed at 80% of their MVCP and at a 60rpm cadence. The TTE test would conclude if the cadence dropped below 60rpm for more than 5 seconds. On top of MVCP, electromyography (EMG, measure of muscle activation) and peak torque (measure of force about a point of rotation or joint) were taken through leg extensions before the TTE test. RPE was measured throughout the TTE test. Right after exhaustion, MVCP was again measured and the rest of the variables like EMG and peak torque were also recorded.

This experiment found an expected 35% drop in MVCP in the TTE. However, the MVCP after exhaustion was 200 W greater than the power needed to do the TTE test. Similar patterns were seen in EMG and peak torque. Moreover, a strong negative correlation (r = -0.75, p = 0.009) was seen between RPE and TTE. These findings suggest that the rate of perceived exertion theory is more correlated with stopping aerobic exercise than muscular fatigue.

Staiano et al. (2018) — First bar is variable before TTE and last bar is post-TTE

The second experiment took 12 male/female athletes and put them through similar tests to the first experiment except they looked at pain unpleasantness instead of MVCP. A problem with measuring pain unpleasantness is that people often compare the pain to past occurrences of pain which may lead to an inaccurate description of the pain at hand. To sidestep this issue, the authors had the participants go through a cold pressor test (CPT), a test where you essentially dip your hand in a very cold bucket of water until the cold becomes unbearable. The subjects can then use the pain from that test as a very fresh reference point.

The pain unpleasantness experienced at the limits of the CPT was far greater than the pain unpleasantness at exhaustion (9.7 vs 5.0, out of 10). On top of this, there was no significant correlation between time to CPT limit and TTE but a strong, negative correlation was again found between RPE and TTE (r = -0.83, p = 0.002). Like the first experiment, these results are in support of the perception of effort theory.

Overall, while this is correlative data, it does seem that perception of effort is the most important variable to determine when an individual will stop in aerobic exercise. Muscular fatigue and pain likely have relevant roles but their ill effects are controlled by perception of effort. On top of this, perception of effort has more factors than simply just muscle fatigue/pain including external motivational factors or mental status.

TL;DR: Perception of effort is likely the main driver over muscular fatigue / pain for stopping high intensity aerobic exercise. Pain is not very well correlated to stopping and muscular power upon exhaustion from a physical test is still greater than the power required to complete the physical test. These contradict the theories of muscular pain and muscular fatigue respectively. Perception of effort is strongly correlated with time to exhaustion.

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Vliet et al. (2017) examined the metabolic responses in either consuming whole eggs or only egg whites. The consumption of egg yolk is often avoided amongst the public due to the fear of its fat and cholesterol content. Dietary cholesterol has very little effect on serum cholesterol in general and overall mortality which is further explained in a concise Healthline article. The trial was conducted in a crossover design with 10 trained men swapping protocols after 1 week. The subjects either consumed:

3 whole eggs (18g protein, 17g fat, 226 kcal)

Egg whites (18g protein, 0g fat, 73 kcal)

Before consumption of the eggs, an exercise consisting of 4 sets of 10 reps of leg press and leg extensions were completed.

Plasma leucine (an important amino acid for muscle protein synthesis) levels were similar between groups. The same can be said for the insulin responses. Serum triglycerides (what fat gets broken down into) were markedly higher at the 3 hour mark in the whole egg group but returned to baseline after 5 hours like the other group.

Vliet et al. (2017)

While all the signalling pathways were activated to the same extent, muscle protein synthesis was greater in the whole egg group. The authors also explored how quickly leucine was absorbed and how it was metabolized in the body. In the whole eggs group, leucine was more rapidly absorbed but the overall availability of leucine in the blood over time was the same between groups. No differences in leucine metabolism were discovered.

Vliet et al. (2017)

Vliet et al. (2017)

With these results, we must ask why we see higher muscle protein synthesis rates in the whole eggs group despite being equated for protein intake (18g). Some may point to the higher calorie intake in whole eggs but this is likely incorrect. Additional calories on top of a fixed protein intake does not enhance muscle protein synthesis (Staples et al., 2011). Furthermore, the extra fat had no sparing effect on protein metabolism as evidenced by the lack in metabolic differences between groups. The authors proposed that whole eggs have vitamins/minerals, fatty acids and other chemicals which support muscle protein synthesis and that are not found in just the whites of an egg.

TL;DR: Whole eggs are better than egg whites for muscle protein synthesis in young men likely due to unique substances found only in the yolk compared to the whites.

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Finally, we will cover a piece for all the women out there. Barbalho et al. (2018) aimed to see if adding single joint exercises to a multi-joint exercise programme for 17 trained women would have any difference to a programme without them. The inclusion criteria for training history was at least 1 year of resistance training with at least a frequency of 3 times a week and a combination of single-joint and multi-joint exercises.

The women were separated into 2 groups. One went through a multi-joint exercise programme while the other group did the same programme with single-joint exercises added on which means the study was not volume-equated. The periodized programmes lasted 8 weeks with a frequency set at 6 times a week, each muscle group at 2 times a week. Unfortunately, there is no mention of the number of sets completed for each exercise anywhere. All sets were done to failure to match for perceived effort. Outcomes included 10RM tests for a variety of exercises, arm circumference and arm skinfolds.

Barbalho et al. (2018)

After 8 weeks, there were no differences in 10RM tests, arm circumferences or arm skinfold measurements between the two groups. This suggests that adding single-joint exercises may be of no use for improvement of muscle performance or body composition changes. The authors suggest that multi-joint exercises should be done to be time-efficient and they also noted that single-joint exercises may have injury risks (in specific, they used the leg extension as an example).

Firstly, it is worth noting that for legs, the authors added seated knee flexion and calf raises to the programme for all subjects even though it is a single-joint movement. This already shows the clear benefit of single-joint exercises — they can target muscles better than compounds in some cases. In response to injury risk comments, all exercises have some inherent risk especially when done improperly however, a lot of them, when done properly, present value for fitness (leg extensions included).

The use of arm circumference for measurement has its obvious limitations. Ideally the authors would use some ultrasound or MRI scanning to get an accurate image of how much muscle was gained / how much fat was lost. However, the researchers showed good planning in that they measured the circumference 5–7 days after the final training session to account for muscle swelling. The use of 10RM tests was fine to represent changes in muscle performance though 1RM tests, personally, would have been more interesting as we could have seen changes in strength.

It is possible that the single-joint exercises were not allocated enough volume to show actual effects. This is difficult to unravel as the number of sets or any measurement of volume was not presented in the text. It is unknown as to why the researchers did not measure changes in leg size. Since the upper arm was the only body part measured, it may be imprecise to say that single-joint exercises are not valuable for the body as a whole.

Finally, there was close to no dietary control in this study. It is possible that the one group consumed more calories or protein than another group which would skew results otherwise known as confounding.

TL;DR: Study found no differences between absence and addition of single-joint exercises to a multi-joint exercise programme for muscle performance or body composition measures in trained women. However, the study is riddled with doubts in design.

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That just about does it for this week’s studies. I hoped you enjoyed the read. If you have any questions or ideas you’d like to discuss, leave a comment. My ability to read and evaluate studies is something I am still working on so any criticism is appreciated. Lastly, share this to anyone who you’d think would enjoy reading this as well!

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Barbalho, M., Coswig, V. S., Raiol, R., Steele, J., Fisher, J., Paoli, A., & Gentil, P. (2018). Effects of Adding Single Joint Exercises to a Resistance Training Programme in Trained Women. Sports, 6(4), 160.

Bhutani, S., Racine, N., Shriver, T., & Schoeller, D. A. (2015). Special Considerations for Measuring Energy Expenditure with Doubly Labeled Water under Atypical Conditions. Journal of Obesity and Weight Loss Therapy, 5(2), .

Ebbeling, B. .C, Feldmen, H. .A & Klein, G. .L. (2018). Effects of a low carbohydrate diet on energy expenditure during weight loss maintenance: randomized trial. British Medical Journal, 363(8177), .

Eisenstien, J., Roberts S. B., Dallal, G., & Saltzman, E. (2002). High-protein weight-loss diets: are they safe and do they work? A review of the experimental and epidemiologic data. Nutrition Reviews, 60(7), 189–200.

Hall, K., & Guo, S.(2018). No Significant Effect of Dietary Carbohydrate versus Fat on the Reduction in Total Energy Expenditure During Maintenance of Lost Weight. Retrieved 30 November, 2018, from https://www.bmj.com/content/363/bmj.k4583/rr-16

Ludwig , D. S. (2018). Author Response to Hall and Guo Regarding Data Reanalysis and Other Criticisms. Retrieved 30 November, 2018, from https://www.bmj.com/content/363/bmj.k4583/rr-17

Marcora, S. M., & Staiano, W. (2010). The limit to exercise tolerance in humans: mind over muscle?. European Journal of Applied Physiology, 109(4), 763–770.

McDonell, K. (2016). Why Dietary Cholesterol Does Not Matter (for most people). Retrieved 30 November, 2018, from https://www.healthline.com/nutrition/dietary-cholesterol-does-not-matter

Parr, E. B., Camera, D. M., Areta, J. L., Burke, L. M., Phillips, S. M., Hawley, J. A., & Coffey, V. G. (2014). Alcohol Ingestion Impairs Maximal Post-Exercise Rates of Myofibrillar Protein Synthesis following a Single Bout of Concurrent Training. Public Library of Science One, 9(2), e88384.

Staiano, W., Bosio, A., de Morree H. M., Rampinini, E., & Marcora, S. (2018). The cardinal exercise stopper: Muscle fatigue, muscle pain or perception of effort?. Progress in Brain Research, 240(11), 175–200.

Staples, A. W., Burd, N. A., West, D. W., Currie, K. D., Atherton, P. J., Moore, D. R., Rennie, M. J.,… Phillips, S. M. (2011). Carbohydrate does not augment exercise-induced protein accretion versus protein alone. Medicine & Science in Sports & Exercise, 43(7), 1154–1161.

Vliet, S., Shy, E. L., Sawan, S. A., Beals, J. W., West, D. WD., Skinner, S. K., Ulanov, A. V.,… Burd, N. A. (2017). Consumption of whole eggs promotes greater stimulation of postexercise muscle protein synthesis than consumption of isonitrogenous amounts of egg whites in young men. The American Journal of Clinical Nutrition, 106(6), 1401–1412.