Alan Aragons Research Review January 2014 [Back to Contents] Page 1

Copyright January 1st, 2014 by Alan Aragon

Home: www.alanaragon.com/researchreview

Correspondence: aarrsupport@gmail.com

2 Why nutrition is so confusing to Gary Taubes.

By Alan Aragon

4 Dietary protein distribution positively influences 24-h muscle protein synthesis in healthy adults.

Mamerow MM, Mettler JA, English KL, Casperson SL, Arentson-Lantz E, Sheffield-Moore M, Layman DK,

Paddon-Jones D. J Nutr. 2014 Jan 29. [Epub ahead of print]

[PubMed]

5 Long-term effects of a Palaeolithic-type diet in obese postmenopausal women: a 2-year randomized trial. Mellberg C, Sandberg S, Ryberg M, Eriksson M, Brage S,

Larsson C, Olsson T, and Lindahl B. European Journal of Clinical Nutrition, advance online publication 29 January

2014; doi: 10.1038/ejcn.2013.290 [EJCN]

7 Cardiovascular and ride time-to-exhaustion effects

of an energy drink. Nelson MT, Biltz GR, Dengel DR. J Int Soc Sports Nutr.

2014 Jan 22;11(1):2. [Epub ahead of print] [PubMed]

8 High-Intensity Interval Resistance Training (HIRT)

influences resting energy expenditure and respiratory ratio in non-dieting individuals. Paoli A, Moro T, Marcolin G, Neri M, Bianco A, Palma A,

Grimaldi K. J Transl Med. 2012 Nov 24;10:237. doi:

10.1186/1479-5876-10-237. [PubMed]

10 Statistics arent so bad! By Jamie Hale

12 Will diet beverages make you fat?

By Alan Aragon

13 Interview with Michael Limon, 2014 Gold Coast Classic Champion. By Alan Aragon

Alan Aragons Research Review January 2014 [Back to Contents] Page 2

Why nutrition is so confusing to Gary Taubes.

By Alan Aragon

____________________________________________________

Thanks again, Gary

Gary Taubes shows up in AARR regularly, not only because

hes one of the most provocative and popular journalists in world, but also because he puts out information that I cant sit back and bite my tongue about. Through his best-selling books,

Taubes has played a major role in shaping a large segment of the

diet & health-conscious publics opinion that carbs are the villain in the war against obesity. Taubes has also been

instrumental in prodding much of the publics disdain for the government-issued guidelines which have traditionally been

carbohydrate-friendly and cautionary against fat. This brings us

to Taubes main project called the Nutrition Science Initiative (NuSi), whose goal is to build teams of multidisciplinary researchers from independent universities and institutions, and

we make it possible for them to do targeted, cutting-edge

experiments that will directly address the key questions of

obesity and health.

The rather obvious problem with Taubes at the helm of NuSi is

that he has a built-in conflict of interest. Having a deeply rooted

low-carb diet bias is bound to bleed onto every aspect of the

operation, from the choice of researchers, to the study designs,

and on down to the data interpretation and presentation to the

public. Taubes books are dedicated to promoting the idea that carbohydrate is the inherent crux of the worlds weight and diabetes problem. To orchestrate research that will do anything

but support the premises in his books is highly unlikely.

Its apparently not enough that Taubes navigates the NuSi ship; hes teamed up Peter Attia, a physician who happens to beyou guessed itanother low-carb zealot. Why not balance out the yin with the yang and team up with a fat-phobic carbophile? Im being facetious, of course. An organization whose aim is to

further the march of diet research needs to be led by individuals

with no vested interest in any given fad diet dogma. With NuSi,

we pretty much have the makings of the Atkins Diet Revolution

Reloaded, this time with an extra serving of confirmation bias.

Im not alone in my concerns with NuSi. If you havent already done so, Id encourage you to head back to the August 2013 issue of AARR, where I discuss several letters from researchers

who collectively feel that Taubes aims are misguided, his biases are obvious, and his understanding of the pathogenesis of

obesity is far from sufficient (to me, its actually deranged).

The latest buzz

Taubes is a talented scribe. He may be the very best there is at

making a mundane topic like diet research sound like a boiling

mix of conspiracy, mystery, and intrigue. However, in his latest

New York Times article titled Why Nutrition Is So Confusing hes essentially complaining about the worthlessness of the current body of diet research. This excerpt is worth quoting since

it captures the meat of his speculations about why nutrition is so

confusing, and it also hints towards the NuSi agenda:

Heres another possibility: The 600,000 articles along with several tens of thousands of diet books are the noise generated by a dysfunctional research establishment. Because the nutrition research community has failed to establish reliable, unambiguous knowledge about the environmental triggers of obesity and diabetes, it has opened the door to a diversity of opinions on the subject, of hypotheses about cause, cure and prevention, many of which cannot be refuted by the existing evidence. Everyone has a theory. The evidence doesnt exist to say unequivocally whos wrong.

I flatly disagree with the sentiments Taubes is expressing here.

Theres exactly zero chance that he is going to save the

supposedly sinking ship of obesity and diabetes research by

ushering in his own brand of methodologically perfect

investigations. The current body of nutrition research is

humming along nicely without any further carbophobic bias to

litter the landscape.

As for diabetes, a recent systematic review and meta-analysis by

Ajala et al had three notable findings: 1) The low-carbohydrate,

low-GI, Mediterranean, and high-protein diets all led to a greater

improvement in glycemic control compared with their respective

control diets (which included low-fat, high-GI, American

Diabetes Association, European Association for the Study of

Diabetes, and low-protein diets). 2) The low-carbohydrate diet

was the most effective for raising HDL. 3) The Mediterranean

diet showed the greatest improvements in glycemic control and

weight loss compared to the control diets. To quote their

conclusion:

In conclusion, our review of the existing literature on low-carbohydrate, low-GI, Mediterranean, and high-protein diets suggests that these diets may be effective in improving various markers of cardiovascular risk in people with diabetes and could have a wider role in the management of diabetes. Dietary behaviors and choices are often personal, and it is usually more realistic for a dietary modification to be individualized rather than to use a one-size-fits-all approach for each person.

In other words, Ajala et al found that the government-issued

diets were outperformed by various alternative diets for

managing type 2 diabetes, but they failed to find anything

special about low-carbohydrate diets for this purpose. Wait a

minute, you mean they didnt identify any diet as the singularly superior diabetes solution? Thats correct. If any magic was found for improving glycemic control, it was seen mostly in the

Mediterranean diet, which the authors describe as rich in olive

oil, legumes, unrefined cereals, fruit, and vegetables, low in

meat/meat products, moderate in dairy products (mostly cheese

and yogurt), fish, and wine, with total fat typically at 2535% of calories, and saturated fat at

Alan Aragons Research Review January 2014 [Back to Contents] Page 3

individual to sustain a caloric deficit over time. The composition

of this diet canand should betailored to the individual. Wu

and colleagues recently did an impressively thorough review of

the full range of diet types, from low-carb to low-fat, and

virtually everything in-between.3 The full text in PDF can be

downloaded here, please read it when you get a chance. To quote

their findings:

Moreover, the difference in weight loss among these diets is only 1-2 kg or less, which appears to be of little clinical significance. Thus, overweight and obese people can choose many different weight-loss diets on the basis of their personal preferences.

Imagine that, choosing a weight loss diet based on personal

preference. Apparently, concepts such as flexibility, personal

preference, and individualization fall straight into the ignore

file by Taubes and others that share his magic bullet bias

towards low-carb diets.

The P-word

One little wrinkle I want to add here is the P-word, can you

guess what that is? Ill just end the suspense and say it: protein.

Feel free to chuckle at the fact that nowhere in Taubes 1265-

word article is the word protein mentionednot even once.

This clearly indicates a lack of awareness of proteins pivotal

role in optimizing weight loss diets. AARR readers are well

aware of my harping about the common failure to match protein

intake in studies that compare high- versus low-carb diets. With

low-carb diets almost invariably having higher protein (and their

comparators often having inadequate protein), a multitude of

advantages are carried by the low-carb conditions, including

greater satiety, thermic effect, and lean mass preservation.

This brings us to one of the biggest reasons diet research can

appear confusing. Its because researchers have thus far largely neglected to match something as crucial as protein intake. A

recent study Ive referenced repeatedly is by Soenen et al, who systematically demonstrated that its the higher protein content rather than the lower carbohydrate content that imparts the

advantage for weight loss and weight loss maintenance.4 The

differences in protein intake between high- and low-carb

conditions is often substantial, but even small differences in

protein intake can have a significant impact. To illustrate, Ill quote a recent review by Astrup et al,

5 who were discussing the

findings of a recent systematic review and meta-analysis by

Clifton et al:6

A 3 times greater effect on fat mass was found in those studies

where a difference between the diets of 5% energy from

protein was still maintained at the end of the study, which was

nearly 1kg better than the normal protein diets. So, just an

increase in dietary protein content from say 16 to 21% of

energy is enough to produce a reduction in body fat that may

be of relevance for public health.

Conclusions about the confusion

Taubes message is that nutrition is confusing because the current body of diet research consists of the noise generated by a dysfunctional research establishment. The hidden translation Im seeing is that Taubes is craving more scientific validation for his preconception that the foothold of Big Grain and Big

Sugar on the research realm needs to be stopped by NuSi. Of

course, the problem is that this crusade is prone to be tainted by

his own Big Bias.

In addition to variable carbohydrate comparison studies

matching optimized protein intakes, the current body of diet

research is also lacking the inclusion of progressive resistance

training with the diet protocols. If Taubes wants to battle the

diabetes problem in the process of battling the obesity problem,

he needs to get current with the importance of resistance training

for maximizing the effectiveness of exercise programs designed

to improve glucose control.7,8

But of course, this could be tough

since a big part of his gimmick has been railing against the

effectiveness of exercise for weight loss. Conveying the

importance of resistance training could be especially difficult in

Taubes case because the question remains... Does he even lift?

References

1. Taubes G. Why Nutrition Is So Confusing. New York Times. Feb 8, 2013. [NYT]

2. Ajala O, English P, Pinkney J. Systematic review and meta-analysis of different dietary approaches to the management of

type 2 diabetes. Am J Clin Nutr. 2013 Mar;97(3):505-16.

[PubMed]

3. Wu H, Wylie-Rosett J, Qi Q. Dietary Interventions for Weight Loss and Maintenance: Preference or Genetic Personalization?

Curr Nutr Rep. 2013 Dec;2(4):189-98. [Springer Link]

4. Soenen S, Bonomi AG, Lemmens SG, Scholte J, Thijssen MA, van Berkum F, Westerterp-Plantenga MS. Relatively high-

protein or low-carb energy-restricted diets for body weight loss and body weight maintenance? Physiol Behav. 2012 Oct

10;107(3):374-80. [PubMed]

5. Astrup A, Wium Geiker NR, Efficacy of higher protein diets for long-term weight control. How to assess quality of

randomized controlled trials?, Nutrition, Metabolism and

Cardiovascular Diseases (2014), doi:

10.1016/j.numecd.2014.02.003. [Elsevier]

6. Clifton PM, Condo D, Keogh JB. ong term weight maintenance after advice to consume low carbohydrate, higher protein diets

- A systematic review and meta analysis. Nutr Metab

Cardiovasc Dis. 2013 Dec 20. pii: S0939-4753(13)00301-3.

doi: 10.1016/j.numecd.2013.11.006. [Epub ahead of print]

[PubMed]

7. Oliveira C, Simes M, Carvalho J, Ribeiro J. Combined exercise for people with type 2 diabetes mellitus: a systematic

review. Diabetes Res Clin Pract. 2012 Nov;98(2):187-98.

[PubMed]

8. Irvine C, Taylor NF. Progressive resistance exercise improves glycaemic control in people with type 2 diabetes mellitus: a

systematic review. Aust J Physiother. 2009;55(4):237-46.

[PubMed]

Alan Aragons Research Review January 2014 [Back to Contents] Page 4

Dietary protein distribution positively influences 24-h muscle protein synthesis in healthy adults.

Mamerow MM, Mettler JA, English KL, Casperson SL,

Arentson-Lantz E, Sheffield-Moore M, Layman DK, Paddon-

Jones D. J Nutr. 2014 Jan 29. [Epub ahead of print] [PubMed]

BACKGROUND: The RDA for protein describes the quantity that should be consumed daily to meet population needs and to prevent deficiency. Protein consumption in many countries exceeds the RDA; however, intake is often skewed toward the evening meal, whereas breakfast is typically carbohydrate rich and low in protein. DESIGN: We examined the effects of protein distribution on 24-h skeletal muscle protein synthesis in healthy adult men and women (n = 8; age: 36.9 3.1 y; BMI: 25.7 0.8 kg/m

2). By using a 7-d

crossover feeding design with a 30-d washout period, we measured changes in muscle protein synthesis in response to isoenergetic and isonitrogenous diets with protein at breakfast, lunch, and dinner distributed evenly (EVEN; 31.5 1.3, 29.9 1.6, and 32.7 1.6 g protein, respectively) or skewed (SKEW; 10.7 0.8, 16.0 0.5, and 63.4 3.7 g protein, respectively). Over 24-h periods on days 1 and 7, venous blood samples and vastus lateralis muscle biopsy samples were obtained during primed (2.0 mol/kg) constant infusion [0.06 mol/(kgmin)] of l-[ring-13C6]phenylalanine. RESULTS: The 24-h mixed muscle protein fractional synthesis rate was 25% higher in the EVEN (0.075 0.006%/h) vs. the SKEW (0.056 0.006%/h) protein distribution groups (P = 0.003). This pattern was maintained after 7 d of habituation to each diet (EVEN vs. SKEW: 0.077 0.006 vs. 0.056 0.006%/h; P = 0.001). CONCLUSION: The consumption of a moderate amount of protein at each meal stimulated 24-h muscle protein synthesis more effectively than skewing protein intake toward the evening meal. SPONSORSHIP: Supported in part by the Beef Checkoff (D.P.-J.). The study was conducted with the support of the Institute for Translational Sciences at the University of Texas Medical Branch, supported in part by a Clinical and Translational Science Award (UL1TR000071) from the National Center for Research Resources, NIH. Support was also provided by the Claude D. Pepper Older Americans Independence Center NIH/National Institute on Aging grant P30 AG024832.

Study strengths

This study breaks new ground since its the first to assess the 24- hour protein-synthetic effect of protein feeding distribution

within mixed meals (at maintenance caloric targets), as opposed

to the protein-only designs of preceding studies looking at

shorter (12-hour) periods.1,2

A cross-over alleviated the low

statistical power of the small 8-subject sample, allowing each

subject to undergo both conditions, which reduced the

confounding potential of inter-individual variation. Diets were

prepared and provided by the lab. All meals contained a variety

of high-quality proteins of plant and animal origin. Total

macronutrition between the EVEN and SKEW conditions was

matched, and overall control of the dietary variables was tight.

Study limitations

Acknowledged by the authors was the inability to concurrently

measure muscle protein synthesis (MPS) and breakdown, which

leaves open questions about the other side of protein turnover.

They pointed to the logistical difficulty and invasiveness of a 3-

pool modeling technique in order to assess both synthesis and

breakdown, since measuring the latter is problematic in non-

steady-state conditions such as the pos-exercise or post-prandial

state. For this reason, its common for acute-response studies to only measure MPS. Another limitation they acknowledged

(which is rarely conceded by authors of these types of studies) is

the possibility that a greater total protein intake could have

pushed the anabolic effect further. As things stand, total protein

intake in both conditions was 90 g, which amounted to 1.17

g/kg. While this exceeds the RDA of 0.8 g/kg, it still falls short

of intakes known to maximize muscle anabolism (at least under

maintenance or surplus conditions), which are approximately

1.7-1.8 g/kg.3,4

So, while the protein intake in this study might be relevant for

some populations (such as the elderly and bed-ridden patients), it

lacks relevance to trainees involved in strength and bodybuilding

pursuits, who very commonly consume protein amounts that are

at least double that of the present study. For example, Lowery et

al found that protein-seeking strength trainers reported an intake of 2.5 g/kg.

5 In another example, Kim et al studied the

dietary habits and nutritional status of elite Korean bodybuilders,

who reported an intake of 4.3 g/kg.6 In my observations, those

pursuing muscle mass and strength habitually consume roughly

2.2-3.3 g/kg. This makes the present studys protein intake of roughly 1.2 g/kg pale in comparison. Further limitations were

the short-term nature of the study, as well as the absence of

resistance exercise.

Comment /application

The main finding was that the EVEN condition (roughly 30 g

protein in each of the 3 meals) resulted in 25% greater 24-hour

MPS than in the SKEW condition (roughly 10, 15, & 65 g

protein in the 3 meals, respectively). At this point its important to point out the potential role of hitting the so-called leucine

threshold for influencing the outcomes seen here. A well-

supported hypothesis is that a threshold amount of dietary leucine (approximately 0.05 g/kg, or 2-3 g) is required to

saturate the mTOR pathway and initiate MPS.7 This threshold

dose is attained by roughly 30-40 g high-quality protein. Notice

how only 1 of the 3 meals in SKEW reached the leucine

threshold while all 3 meals in EVEN hit the leucine threshold.

To quote the authors, In conclusion, the consumption of a moderate amount of high-quality protein 3 times a day provides

a more effective means of stimulating 24-h muscle protein

synthesis than the common practice of skewing protein intake

toward the evening meal. I feel that they showed a subtle bias toward the supposed benefit of evenly distributing the protein,

when its possible that the MPS differences would disappear as long as a sufficient dose of protein to elicit a robust anabolic

response was reached in each meal, regardless of skewing. A

consistency of research shows that 20-25 g protein maximizes

the acute anabolic response in younger subjects.4 My hunch is

that if they compared the even (30-30-30 g) distribution with a

skewed one comprised of 20-20-50 g, the differences in 24-hour

MPS would be minimal. However, as stated earlier, the results of

the present study have relevance to older subjects especially those with lower total daily protein intake. A require a higher

protein dose (35-40 g) has been seen to maximize the acute

anabolic response in this population.8,9

Alan Aragons Research Review January 2014 [Back to Contents] Page 5

Long-term effects of a Palaeolithic-type diet in obese postmenopausal women: a 2-year randomized trial.

Mellberg C, Sandberg S, Ryberg M, Eriksson M, Brage S,

Larsson C, Olsson T, and Lindahl B. European Journal of Clinical Nutrition, advance online publication 29 January 2014;

doi: 10.1038/ejcn.2013.290 [EJCN]

BACKGROUND/OBJECTIVES: Short-term studies have

suggested beneficial effects of a Palaeolithic-type diet (PD) on

body weight and metabolic balance. We now report the long-

term effects of a pd on anthropometric measurements and

metabolic balance in obese postmenopausal women, in

comparison with a diet according to the nordic nutrition

recommendations (NNR). SUBJECTS/METHODS: Seventy

obese postmenopausal women (mean age 60 years, body mass

index 33kg/m2) were assigned to an ad libitum PD or NNR diet in a 2-year randomized controlled trial. The primary outcome

was change in fat mass as measured by dual-energy X-ray

absorptiometry. RESULTS: Both groups significantly

decreased total fat mass at 6 months (6.5 and2.6kg) and 24 months (4.6 and2.9kg), with a more pronounced fat loss in the PD group at 6 months (P

Alan Aragons Research Review January 2014 [Back to Contents] Page 6

protein in the PD & NNR respectively were 84.4 g & 85.2 g at

baseline, 93.7 g & 76.5 g at 6 months, and 84.8 g & 73.4 g at 24

months. As you can see, the difference in protein intake

diminished by the final checkpoint from a moderate amount to a

trivial amount. Nevertheless, the difference in protein intake

alone has the potential to account for the significant weight, fat,

and circumference differences seen at the 6-month point, at

which protein content in PD increased from 17.1 to 23.4%. On

the subject of small differences in protein intake having

significant impact, a recent systematic and meta-analysis by

review by Clifton et al noted a threefold greater effect size on fat

mass in studies where the protein difference between diets was

as little as 5%:11

The differences in carbohydrate intake in the present study are

noteworthy as well. Absolute amounts of carbohydrate in the PD

& NNR respectively were 224 g & 222 g at baseline, 120 g &

181.2 g at 6 months, and 136.8 & 189.5 at 24 months. As you

can see, the carbohydrate differences are substantial regardless

of this difference diminishing from the 6-month to the 24-month

point. Reductions in dietary carbohydrate have been consistently

seen to reduce triglyceride levels,12,13

so this outcome is no

surprise.

Another notable outcome was the difference in total energy

intake between groups. The PD group had a 19% and 20% lower

energy intake at 6 and 24 months respectively, while the NNR

group had an 18% and 12% lower energy intake. This raises the

possibility that the PD was more satiating than NNR. The

authors speculate that it may have been the higher

monounsaturated fatty acid (MUFA), polyunsaturated fatty acid

(PUFA), and protein content of the PD that imparted this effect.

While I wouldnt give too much credence to the fatty acid profile of PD playing a meaningful role (no plausible basis for this

immediately comes to mind), I would agree that the protein

difference did play a role. Another thing Id add here is that the food choices in PD could have contributed to the greater

satiating effect, as seen previously in work by Jnsson et al.14

To

quote my commentary in the November 2010 issue of AARR,

where I cite Holt et al:15

Another possible explanation was that the carbohydrate type may have played a role. The Paleo groups fruit intake was the major source of carbohydrate in the diet. It was double that of the Mediterranean group, who consumed a significant proportion of their carbohydrate from cereal grains. Classic work by Holt et al showed that fruits as a group have a high satiating capacity, surpassing other carbohydrate sources, including grain foods by a small margin

Its easy to assume that an increased fruit intake occurred as a result of grain avoidance. However, sucrose intake in the NNR

group showed a greater decrease than that of the PD group,

which makes a higher fruit intake in PD unlikely. Ultimately,

this study is simply not a compelling case for going Paleo (avoiding grains, legumes, and dairy). Once again, this is

primarily due to the failure to match macronutrition. Im wondering how many more Paleo diet comparison studies will

be published before this imbalance is addressed. Notably, this

study does not support the zealously pro-saturated fat/anti-PUFA

position popular among a large segment of the Paleo crowd.

Alan Aragons Research Review January 2014 [Back to Contents] Page 7

Cardiovascular and ride time-to-exhaustion effects of an energy drink.

Nelson MT, Biltz GR, Dengel DR. J Int Soc Sports Nutr. 2014

Jan 22;11(1):2. [Epub ahead of print] [PubMed]

BACKGROUND: Currently, there are few studies on the

cardiovascular and fatigue effects of commercially available energy

drinks. PURPOSE: This study investigated the effects of Monster

energy drink (Monster Beverage Corporation, Corona, California), on

resting heart rate (HR), heart rate variability (HRV), ride time-to-

exhaustion, peak exercise HR, respiratory exchange ratio (RER), and

peak rating of perceived exertion (RPE). METHODS: The study used a

double-blind, randomized, placebo controlled, crossover design. After

an 8-hr fast, 15 subjects consumed Monster Energy Drink (ED

standardized to 2.0 mg * kg-1 caffeine) or a flavor-matched placebo

preexercise. Resting HR and HRV were determined. After an initial

submaximal workload for 30 minutes, subjects completed 10 min at

80% ventilatory threshold (VT) and rode until volitional fatigue at

100% VT. RESULTS: Resting HR was significantly different (ED:

65+/-10 bpm vs. placebo: 58+/-8 bpm, p = 0.02), but resting HRV was

not different between the energy drink and placebo trials. Ride time-to-

exhaustion was not significantly different between trials (ED: 45.5+/-

9.8 vs. placebo: 43.8+/-9.3 min, p = 0.62). No difference in peak RPE

(ED: 9.1 +/- 0.5 vs. placebo: 9.0 +/- 0.8, p = 1.00) nor peak HR (ED:

177 +/- 11 vs. placebo: 175 +/- 12, p = 0.73) was seen. The RER at 30%

of VT was significantly different (ED: 0.94 +/- 0.06 vs. placebo: 0.91

+/- 0.05, p = 0.046), but no difference between the two conditions were

seen at the other intensities. CONCLUSION: Although preexercise

ingestion of the energy drink does increase resting HR there was no

alteration in HRV parameters. Ride time-to-exhaustion was not

enhanced. SPONSORSHIP: This work was funded in part by the

Intermountain Research and Medical Foundation (Salt Lake City, UT,

USA) (TB).

Study strengths

This is a particularly relevant topic since the consumption of stimulant-based energy drinks are the second-most popular supplements behind multi-vitamins in American adolescent and young adult population,

16 and are also reported to be the

most popular supplement among elite young UK athletes.17

To my knowledge, this is the first study to examine the effect of the popular Monster energy drink on resting heart rate (HR) and HR variability (HRV) as well as endurance capacity. The crossover design enabled each subject to undergo both conditions, thereby reducing the confounding potential of inter-individual variation. Verbal encouragement by training staff ensured maximal performance effort.

Study limitations

There is some debate over the validity of time-to-exhaustion (TTE) as a reliable measure of real-world competitive performance. TTE models have been found to have greater variability and thus poor reproducibility than time trials (which measure the time it takes to do a fixed amount of work, or the amount of work within a fixed amount of time),

18,19 the

authors themselves acknowledged the potential limitation of the caffeine dose used in the experimental treatement, which was 2 g/kg. It can be argued that this does was not high enough to be significantly ergogenic, especially in a population whose habitual caffeine intake was not reported in this manuscript. The bulk of the evidence points to 3-6 mg/kg being effective for

improving performance.20

Another limitation was the lack of standardization of the evening meal prior to performance testing the next day (especially in terms of carbohydrate). Its possible that variations in muscle glycogen content could have confounded the TTE results, but as mentioned earlier, a crossover design served to reduce potential variation across individuals. Comment/application

What follows are the results of selected performance parameters TTE, peak rate of perceived exertion (RPE, 10-point Borg category scale), as well as peak exercise HR:

TTE (min) RPE HR (bpm)

Monster 45.5 9.1 177 Placebo 43.8 9.0 175

The main findings were that Monster failed to increase endurance capacity, nor did it have any significant impact on HRV. These results support previous work by Candow et al, who saw a lack of effect of Red Bull energy drink (sugar-free version) on high-intensity TTE.

21 The caffeine dose in the latter

study within Red Bull was the same as that of the present study (2 mg.kg). In contrast, Forbes et al found the regular version of Red Bull (with sugar, 0.65 g/kg, and the same amount of caffeine as the sugar-free version) to increase muscle endurance measured via number of bench press repetitions, but not power measured via Wingate testing.

22 The latter study is supported

at least partially by a recent systematic review by Conger et al who found that carbohydrate co-ingested with caffeine provides a significant but small effect to improve endurance performance compared with CHO alone.

23

Perhaps the main concern with popular energy drinks such as Monster and Red Bull is safety. Here are the safety-related guidelines on energy drink (ED) and energy shots (ES) consumption, as outlined by the latest position stand of the Journal of the International Society of Sports Nutrition:

24

Many ED and ES contain numerous ingredients; these products in particular merit further study to demonstrate their safety and potential effects on physical and mental performance.

Athletes should consider the impact of ingesting high glycemic load carbohydrates on metabolic health, blood glucose and insulin levels, as well as the effects of caffeine and other stimulants on motor skill performance.

Children and adolescents should only consider use of ED or ES with parental approval after consideration of the amount of carbohydrate, caffeine, and other nutrients contained in the ED or ES and a thorough understanding of the potential side effects.

Indiscriminate use of ED or ES, especially if more than one serving per day is consumed, may lead to adverse events and harmful side effects.

Diabetics and individuals with pre-existing cardiovascular, metabolic, hepatorenal, and neurologic disease who are taking medications that may be affected by high glycemic load foods, caffeine, and/or other stimulants should avoid use of ED and/or ES unless approved by their physician.

Alan Aragons Research Review January 2014 [Back to Contents] Page 8

High-Intensity Interval Resistance Training (HIRT) influences resting energy expenditure and respiratory ratio in non-dieting individuals.

Paoli A, Moro T, Marcolin G, Neri M, Bianco A, Palma A, Grimaldi K. J Transl Med. 2012 Nov 24;10:237. doi: 10.1186/1479-5876-10-237. [PubMed]

BACKGROUND: The benefits of exercise are well established

but one major barrier for many is time. It has been proposed that

short period resistance training (RT) could play a role in weight

control by increasing resting energy expenditure (REE) but the

effects of different kinds of RT has not been widely reported.

METHODS: We tested the acute effects of high-intensity

interval resistance training (HIRT) vs. traditional resistance

training (TT) on REE and respiratory ratio (RR) at 22hours

post-exercise. In two separate sessions, seventeen trained males

carried out HIRT and TT protocols. The HIRT technique

consists of: 6 repetitions, 20seconds rest, 2/3 repetitions, 20 secs

rest, 2/3 repetitions with 2'30 rest between sets, three exercises for a total of 7 sets. TT consisted of eight exercises of 4 sets of

8-12 repetitions with one/two minutes rest with a total amount of

32 sets. We measured basal REE and RR (TT0 and HIRT0) and

22hours after the training session (TT22 and HIRT22).

RESULTS: HIRT showed a greater significant increase

(p

Alan Aragons Research Review January 2014 [Back to Contents] Page 9

1. Areta JL, Burke LM, Ross ML, Camera DM, West DW,

Broad EM, Jeacocke NA, Moore DR, Stellingwerff T, Phillips SM, Hawley JA, Coffey VG. Timing and distribution of protein ingestion during prolonged recovery from resistance exercise alters myofibrillar protein synthesis. J Physiol. 2013 May 1;591(Pt 9):2319-31. [PubMed]

2. Moore DR, Areta J, Coffey VG, Stellingwerff T, Phillips SM, Burke LM, Clroux M, Godin JP, Hawley JA. Daytime pattern of post-exercise protein intake affects whole-body protein turnover in resistance-trained males. Nutr Metab (Lond). 2012 Oct 16;9(1):91. [PubMed]

3. 4. Rodriguez NR, DiMarco NM, Langley S; American Dietetic Association; Dietitians of Canada; American College of Sports Medicine: Nutrition and Athletic Performance. Position of the American Dietetic Association, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and athletic performance. J Am Diet Assoc. 2009 Mar;109(3):509-27. [PubMed]

4. Phillips SM, Van Loon LJ. Dietary protein for athletes: from requirements to optimum adaptation. J Sports Sci. 2011;29 Suppl 1:S29-38. [PubMed]

5. Lowery LM, Daugherty A, Miller B, Dye S, Liming L. The effect of habitually large protein intake on renal function of strength athletes: an update. J In Soc Sports Nutr. 2011 Nov, 8(Suppl 1):P33 [JISSN]

6. Kim H, Lee S, Choue R. Metabolic responses to high protein diet in Korean elite bodybuilders with high-intensity resistance exercise. J Int Soc Sports Nutr. 2011 Jul 4;8(1):10. [Epub ahead of print] [Pubmed]

7. Norton LE, Wilson GJ. Optimal protein intake to maximize muscle protein synthesis: examinations of optimal meal protein intake. Agro Food Industry Hi-Tech. 2009;20(2). [AFIHT] [full-text PDF]

8. Pennings B1, Groen B, de Lange A, Gijsen AP, Zorenc AH, Senden JM, van Loon LJ. Amino acid absorption and subsequent muscle protein accretion following graded intakes of whey protein in elderly men. Am J Physiol Endocrinol Metab. 2012 Apr 15;302(8):E992-9. [Pubmed]

9. Yang Y1, Breen L, Burd NA, Hector AJ, Churchward-Venne TA, Josse AR, Tarnopolsky MA, Phillips SM. Resistance exercise enhances myofibrillar protein synthesis with graded intakes of whey protein in older men. Br J Nutr. 2012 Nov 28;108(10):1780-8. [Pubmed]

10. Gaesser GA1, Angadi SS, Sawyer BJ. Exercise and diet, independent of weight loss, improve cardiometabolic risk profile in overweight and obese individuals. Phys Sportsmed. 2011 May;39(2):87-97. [Pubmed]

11. Clifton PM, Condo D, Keogh JB. ong term weight maintenance after advice to consume low carbohydrate, higher protein diets - A systematic review and meta analysis. Nutr Metab Cardiovasc Dis. 2013 Dec 20. pii: S0939-4753(13)00301-3. doi: 10.1016/j.numecd.2013.11.006. [Epub ahead of print] [PubMed]

12. Schwingshackl L, Hoffmann G. Comparison of effects of long-term low-fat vs high-fat diets on blood lipid levels in overweight or obese patients: a systematic review and meta-analysis. J Acad Nutr Diet. 2013 Dec;113(12):1640-61. [Pubmed]

13. Santos FL, Esteves SS, da Costa Pereira A, Yancy WS Jr, Nunes JP. Systematic review and meta-analysis of clinical trials of the effects of low carbohydrate diets on cardiovascular risk factors. Obes Rev. 2012 Nov;13(11):1048-66. [Pubmed]

14. Jnsson T, Granfeldt Y, Erlanson-Albertsson C, Ahrn B, Lindeberg S. A paleolithic diet is more satiating per calorie than a mediterranean-like diet in individuals with ischemic heart disease. Nutr Metab (Lond). 2010 Nov 30;7:85. [Pubmed]

15. Holt SH, Miller JC, Petocz P, Farmakalidis E. A satiety index of common foods. Eur J Clin Nutr. 1995 Sep;49(9):675-90. [Pubmed]

16. Campbell B1, Wilborn C, La Bounty P, Taylor L, Nelson MT, Greenwood M, Ziegenfuss TN, Lopez HL, Hoffman JR, Stout JR, Schmitz S, Collins R, Kalman DS, Antonio J, Kreider RB. International Society of Sports Nutrition position stand: energy drinks. J Int Soc Sports Nutr. 2013 Jan 3;10(1):1. [Pubmed]

17. Petrczi A, Naughton DP, Pearce G, Bailey R, Bloodworth A, McNamee M. Nutritional supplement use by elite young UK athletes: fallacies of advice regarding efficacy. J Int Soc Sports Nutr. 2008 Dec 15;5:22. [Pubmed]

18. Jeukendrup A, Saris WH, Brouns F, Kester AD. A new validated endurance performance test. Med Sci Sports Exerc. 1996 Feb;28(2):266-70. [PubMed]

19. Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sports Med. 2001;31(3):211-34. [PubMed]

20. Goldstein ER, Ziegenfuss T, Kalman D, Kreider R, Campbell B, Wilborn C, Taylor L, Willoughby D, Stout J, Graves BS, Wildman R, Ivy JL, Spano M, Smith AE, Antonio J. International society of sports nutrition position stand: caffeine and performance. J Int Soc Sports Nutr. 2010 Jan 27;7(1):5. [PubMed]

21. Candow DG, Kleisinger AK, Grenier S, Dorsch KD. Effect of sugar-free Red Bull energy drink on high-intensity run time-to-exhaustion in young adults. J Strength Cond Res. 2009 Jul;23(4):1271-5. [PubMed]

22. Forbes SC, Candow DG, Little JP, Magnus C, Chilibeck PD. Effect of Red Bull energy drink on repeated Wingate cycle performance and bench-press muscle endurance. Int J Sport Nutr Exerc Metab. 2007 Oct;17(5):433-44. [PubMed]

23. Conger SA, Warren GL, Hardy MA, Millard-Stafford ML. Does caffeine added to carbohydrate provide additional ergogenic benefit for endurance? Int J Sport Nutr Exerc Metab. 2011 Feb;21(1):71-84. [PubMed]

24. Campbell B, Wilborn C, La Bounty P, Taylor L, Nelson MT, Greenwood M, Ziegenfuss TN, Lopez HL, Hoffman JR, Stout JR, Schmitz S, Collins R, Kalman DS, Antonio J, Kreider RB. International Society of Sports Nutrition position stand: energy drinks. J Int Soc Sports Nutr. 2013 Jan 3;10(1):1. [PubMed]

25. Schuenke MD, Mikat RP, McBride JM. Effect of an acute period of resistance exercise on excess post-exercise oxygen consumption: implications for body mass management. Eur J Appl Physiol. 2002 Mar;86(5):411-7. [PubMed]

26. Heden T, Lox C, Rose P, Reid S, Kirk EP. One-set resistance training elevates energy expenditure for 72 h similar to three sets. Eur J Appl Physiol. 2011 Mar;111(3):477-84. [PubMed]

27. Fagerli B. Myo-reps a time-efficient method for maximum muscle growth. May 1, 2012. [Borgefagerli.com]

Alan Aragons Research Review January 2014 [Back to Contents] Page 10

Statistics arent so bad!

By Jamie Hale

____________________________________________________

Learning about stats will help you think in terms of probabilities,

and allow you to gain a better understanding of research data.

Statistics are not easy, but with some effort the basics can be

learned by most people. We can begin the demystifying by

providing a simple definition:

Statistic: One number that summarizes a property or

characteristic of a set of numbers

Descriptive and inferential statistics

Descriptive statistics are numerical measures that describe a

population by providing information on the central tendency of

the distribution, the width of distribution (dispersion, or

variability), the shape of distribution (Jackson, 2009). Inferential

statistics are procedures that allow us to make an inference from

a sample to the population. That is, we are able to make

generalizations about a population based on the information

derived from the sample.

The importance of statistics

A key reason we need statistics is to be able to effectively

interpret research. Without statistics it would be very difficult to

analyze the collected data and make decisions based on the data.

Statistics give us an overview of the data and allow us to make

sense of what is going on. Without statistics, in many cases, it

would be extremely difficult to find meaning in the data.

Statistics provides us with a tool to make an educated inference.

Most scientific and technical journals contain some form of

statistics. Without an understanding of statistics, the statistical

information contained in the journal will be meaningless. An

understanding of basic statistics will provide you with the

fundamental skills necessary to read and evaluate most results

sections. The ability to extract meaning from journal articles,

and the ability to evaluate research from a statistical perspective

are basic skills that will increase your knowledge and

understanding of the article of interest.

Gaining knowledge in the area of statistics will help you become

a better-informed consumer. If you understand basic statistical

concepts, you will be in a better position to evaluate the

information you have been given. Recently I asked Dr. Jonathan

Gore (Hale, 2012) the following question: Why is a basic

understanding of stats important for the public?

My answer to why stats is important is that pretty much everything operates based on probability. Even some of the

"hard" sciences are starting to realize that phenomena that used

to only require a basic equation are now having to factor in

probability to account for all that they observe. To understand

events that occur in our daily lives, including understanding

other peoples behaviors, the economy, and health, we have to

address probabilities rather than basic equations. When I talk

with religious people about the importance of statistics, and they

question its relevance, I say, Statistics is the best tool for humans to understand how Gods creation works. We may never know the complete picture, but statistics give us the best

possible estimate.

Beware of person-who statistics!

Results of scientific studies are stated in probabilistic terms.

Science is not in the business of making claims of absolute

certainty (refer to bead model of truth). When science describes,

predicts or explains something, it is understood that the

conclusion is tentative. This willingness to admit fallibility is

one of sciences biggest strengths. In virtually every other area of knowledge acquisition, admitting fallibility is not a virtue, but

a weakness. Person-who statistics: situations in which well-

established statistical trends are questioned because someone

knows a person who went against the trend (Stanovich, 2007). For example, Look at my grandpa, he is ninety years old, has been smoking since he was in thirteen, and is still healthy, this statement is implying smoking is not bad for health.

Learning to think probabilistically is an important trait, and can

lead to more accurate thinking. Person-who statistics is an

ubiquitous phenomenon. People like assertions that reflect

certainty. Statistical, scientific thinking is not about absolute

certainty. The conclusions drawn from scientific research are

probabilistic- generalizations that are correct most of the time,

but not every time. People often weight anecdotal evidence more

heavily than probabilistic information. This is an error in

thinking, leads to bad decisions, and often, irrational thinking.

Recently, I was asked, what is the minimal amount someone

needs to know about statistics in order to read the Results of a

study? It is hard to provide an answer to this question. The

statistics reported in the Results section varies. Although it is

hard to provide an answer regarding the previously mentioned

question, it will be beneficial in regards to a basic understanding

of stats to review and understand the questions and answers

provided below.

Research methods & statistics: FAQ

What is a frequency distribution table?

A frequency distribution table presents all of the individual

scores in the distribution. Disorganized scores are placed in

order from lowest to highest, grouping together individuals who

have the same score. The frequency distribution table allows a

quick look at the entire range of scores. The frequency

distribution also allows you to see the location of a single score

relative to the other scores. When there is a large range of scores

it is recommended that a grouped frequency table be used. Keep

in mind; a key purpose for constructing a frequency table is to

reflect a relatively simple, organized picture of the entire range

of scores. However, when the number of scores is large using a

frequency table is not practical, is time consuming and not

simple to read. Presenting the scores in a relatively simple,

organized manner requires a group frequency distribution table.

Alan Aragons Research Review January 2014 [Back to Contents] Page 11

When using the group frequency distribution table groups of

scores are presented rather than individual scores. The groups

are called class intervals. Intervals are often presented when

individual scores arent as important as the range of scores, such as when teachers check to see how many students received As,

Bs, Cs, etc. on an exam.

What is the distinction between a parameter and a statistic?

Parameter- is a value, usually numerical, that describes a

characteristic of the population. Populations yield different

parameters, depending on the characteristic of interest.

Populations yield parameters, and samples yield statistics.

Parameters and statistics are numerical measures that represent

characteristics of populations and samples. A parameter is

generally derived from measurements of individuals in a

population (entire group- people, nonhuman animals or objects-

a researcher is interested in). A statistic is generally derived

from measurements of individuals in a sample (participants/

subjects in a study used to represent population of interest).

How do descriptive statistics describe a distribution?

Descriptive statistics are numerical measures that describe a

distribution by providing information on the central tendency of

the distribution, the width of distribution (variability or

dispersion), and the shape of the distribution. Descriptive

statistics describe, organize, and summarize information.

Understanding descriptive statistics will make learning how to

use inferential statistics much easier. Inferential statistics often

use descriptive statistics when making calculations.

What is alpha level?

Alpha level refers to the level at which we find statistical

significance (difference is large enough that it probably did not

occur due to chance, there is a real difference). For example, if

we say a finding is statistically significant at .05 alpha level, we

mean that our finding could have occurred by chance only 5% of

the time. If we use an alpha level of .01 we mean that our

finding could have occurred by 140 chance only 1% of the time.

The alpha level (level of significance) is a probability value, in a

hypothesis test, that is used to define the concept of very unlikely. Very unlikely means the result is very unlikely due to chance.

Statistics are difficult to learn for many people. Are there any

suggestions that can be used to enhance learning?

Focused attention: Minimize distractions (be attentive to desired sensory inputs while ignoring distractors-

unwanted sensory inputs).

Deep processing: Think deeply about the meaning of the material you are studying.

Memory connections: Try to connect the material you are attempting to learn to other items you already have in

memory.

Spaced Study effects: Multiple, short study sessions promote learning better than long marathon like sessions.

As an example, three 1-hour sessions will be more

beneficial than one 3-hour session.

Testing: Test yourself on a regular basis.

Minimize stress: High stress levels are detrimental to working memory and the formation of explicit long term

memory.

In the words of Dr. Osbaldiston (Research Methods and

Statistics Teacher, Eastern Kentucky University) Repetition is the mother of all skills

If you are interested in learning more about Research Methods

and Stats refer to the sources below:

Recommended sources

Gravetter, F.J., Wallnau, L.B. (2013). Statistics for the Behavioral

Sciences (9th edition). Australia: Wadsworth Cengage Learning.

Jackson, S.L. (2009). Research Methods and Statistics: A critical

thinking approach (3rd edition). Australia: Wadsworth Cengage

Learning.

Keshav, S. (2007). How to read a paper. ACM Sigcomm Computer

Communication Review, 37(3), 83-84.

Little, J.W., & Parker, R. (2010). How to read a scientific paper.

Online http://www.biochem.arizona.edu/classes/bioc568/papers.htm

Mitchell, M.L., & Jolley, J.M. (2010). Research Design Explained

(7th edition). Belmont, CA: Wadsworth Cengage Learning.

Morling, B. (2012). Research Methods in Psychology: Evaluating a

World of Information. New York, NY: W.W. Norton & Company,

Inc.

Myers, A., & Hansen, C. (2002). Experimental Psychology (5th

edition). Australia: Wadsworth Thomson Learning.

Patten, M.L. (2004). Understanding Research Methods: An

overview of the essentials (4th edition). Glendale, CA: Pyrczak

Publishing.

Pyrczak, F., & Bruce, R.R. (2003). Writing Empirical Research

Reports: A basic guide for students of the behavioral sciences. Los

Angeles, CA: Pyrczak Publishing.

Shaughnessy, J.J., & Zechmeister, E.B. (1990). Research Methods

in Psychology (2nd edition). New York, NY: McGraw-Hill

Publishing Company.

____________________________________________________

Jamie Hale, M.S. (Experimental Psychology), is a university instructor, author, primary researcher, science writer, and fitness & nutrition consultant. He is an experimental researcher specializing in behavioral nutrition and cognitive science. He has conducted primary research in the areas of attention, memory, and behavioral nutrition. He is available for lectures and seminars. Jamie can be contacted at Jamie.hale1@gmail.com. Visit Knowledge

Summit (www.knowledgesummit.net) to learn more about Jamie.

Alan Aragons Research Review January 2014 [Back to Contents] Page 12

Will diet beverages make you fat? By Alan Aragon

____________________________________________________

Introduction

Consider the following headlines, all popping up within the past

year in high-profile online news media:

Diet soda could cause weight gain, not loss MSN News Study: Diet soda doesn't help you lose weight USA Today 10 reasons to give up diet soda Fox News

The list goes on and on. Its a safe assumption that the publics perceptions of various health claims are strongly influenced by

news stories, especially those with plenty of exposure and

repetition. The claim in question is that artificially sweetened

(diet) beverages are just as fattening, or even more so, than

sugar-sweetened beverages. Well now, isnt that a bummer! But of course, as science-minded folks, our duty is to consider the

weight of the research evidence. But before we do that, lets begin with a look at how the concern over diet beverages might

have originated.

More obesity, more suspects

Trends of overweight and obesity prevalence show the sharpest

rise occurring in the 1980s through the late 1990s, after which

point a much flatter but statistically significant increase has

occurred until the present time.1 More than two-thirds of the

adult population qualifies as overweight, and more than a third

qualifies as obese. Since the early 1960s, the prevalence of adult

obesity has more than doubled in the present day (13.4% versus

35.7% now). If you reach enough, diet beverage consumption

trends can be interpreted to have loosely followed the obesity

trend, since non-nutritive sweetener intake increased markedly

in the United States and globally over the past 3 decades.2

Cephalic phase stimulation & insulin response

It has been hypothesized that diet beverages can cause cephalic

phase (before food enters the stomach) stimulation of neurogenic

and hormonal factors that can increase appetite. The converse

has been hypothesized as well; that a lack of cephalic phase

stimulation leads to increased subsequent energy intake.

However, both of these proposed mechanisms involving the

cephalic phase lack a consistent or compelling evidence basis.2

Concerns over insulin release via non-nutritive sweeteners have

also been raised. Diet sweeteners vary in their ability to raise

insulin in the cephalic phase. For example, aspartame has failed

to show an insulinogenic effect, while the less commonly used

saccharin has shown an insulinogenic effect. However, this

concern with insulinogenesis is somewhat moot, since acute

insulin elevation is actually appetite-suppressive.3

Moving past the short-term: observational data

Short-term data provides valuable food for thought, but

outcomes over the longer-term are what hold more relevance.

Trials lasting a period of weeks or months can reveal body

composition and/or bodyweight changes, whereas acute response

studies leave us with large question marks. Along similar lines

when discussing relevance, observational research often gets

shafted for being unable to demonstrate causation, but its part of the evidence puzzle nonetheless, and thus should not be ignored.

Perhaps not too surprisingly, observational data examining the

relationship of diet beverages and bodyweight is mixed, running

the gamut outcomes from a positive association, no association,

to an inverse association.4-6

An important concept to keep in

mind is the potential for reverse causality. In other words, obese

individuals may have a tendency to consume diet beverages in

order to try to lose weight (or mitigate weight gain), as opposed

to the diet beverages themselves causing the state of obesity.

This substantial body of equivocal results begs for the rigor of

controlled experimental research, so well look at that next.

Interventional data

Interventional research carried out past acute periods is where

we find the strongest data for or against the use of diet beverages

since they have the potential to establish causation. The body of

controlled data is not vast, but its telling, nevertheless. Trials involving normal-weight and obese subjects lasting 3-10 weeks

comparing sugar-sweetened beverages with diet beverages have

shown the latter to result in weight loss as opposed to weight

gain in the non-diet beverage intake.8,9

Longer trials lasting 6

months have shown either modest weight loss,10

a lack of

diffecence,11

or the prevention of weight gain when sugar-

sweetened beverages were replaced with diet beverages.12

So,

based on the evidence as a whole, the claim that diet beverages

will make you fat is a load of you-know-what.

References

1. Weight Control information Network, US Department of Health and Human Services/National Institute of Diabetes and Digestive and Kidney Diseases. Overweight and Obesity Statistics. Last Modified: March 12, 2013. [WIN]

2. Mattes RD, Popkin BM. Nonnutritive sweetener consumption in humans: effects on appetite and food intake and their putative mechanisms. Am J Clin Nutr. 2009 Jan;89(1):1-14. [PubMed]

3. Pliquett RU, Fhrer D, Falk S, Zysset S, von Cramon DY, Stumvoll M. The effects of insulin on the central nervous system--focus on appetite regulation. Horm Metab Res. 2006 Jul;38(7):442-6. [PubMed]

4. Fowler SP, Williams K, Resendez RG, Hunt KJ, Hazuda HP, Stern MP. Fueling the obesity epidemic? Artificially sweetened beverage use and long-term weight gain. Obesity (Silver Spring). 2008 Aug;16(8):1894-900. [PubMed]

5. Vanselow MS, Pereira MA, Neumark-Sztainer D, Raatz SK. Adolescent beverage habits and changes in weight over time: findings from Project EAT. Am J Clin Nutr. 2009 Dec;90(6):1489-95. [PubMed]

6. Stellman SD, Garfinkel L. Artificial sweetener use and one-year weight change among women. Prev Med. 1986 Mar;15(2):195-202. [PubMed]

7. Ludwig DS, Peterson KE, Gortmaker SL. Relation between consumption of sugar-sweetened drinks and childhood obesity: a prospective, observational analysis. Lancet. 2001 Feb 17;357(9255):505-8. [PubMed]

8. Tordoff MG, Alleva AM. Effect of drinking soda sweetened with aspartame or high-fructose corn syrup on food intake and body weight. Am J Clin Nutr. 1990 Jun;51(6):963-9. [PubMed]

9. Raben A, Vasilaras TH, Mller AC, Astrup A. Sucrose compared with artificial sweeteners: different effects on ad libitum food intake and body weight after 10 wk of supplementation in overweight subjects. Am J Clin Nutr. 2002 Oct;76(4):721-9. [PubMed]

10. Ebbeling CB, Feldman HA, Osganian SK, Chomitz VR, Ellenbogen SJ, Ludwig DS. Effects of decreasing sugar-sweetened beverage consumption on body weight in adolescents: a randomized, controlled pilot study. Pediatrics. 2006 Mar;117(3):673-80. [PubMed]

11. Ebbeling CB1, Feldman HA, Chomitz VR, Antonelli TA, Gortmaker SL, Osganian SK, Ludwig DS. A randomized trial of sugar-sweetened beverages and adolescent body weight. N Engl J Med. 2012 Oct 11;367(15):1407-16. [PubMed]

12. de Ruyter JC1, Olthof MR, Seidell JC, Katan MB. A trial of sugar-free or sugar-sweetened beverages and body weight in children. N Engl J Med. 2012 Oct 11;367(15):1397-406. [PubMed]

Alan Aragons Research Review January 2014 [Back to Contents] Page 13

Interview with Michael Limon, 2014 Gold Coast Classic Champion.

First off, thanks for agreeing to do the interview. Please tell

the readers some background how you got into bodybuilding

(age you started, what sparked the interest, & what factors

contributed to your decision to compete). Also, please outline

your competitive history.

I was given my first full-body workout machine in junior high. It

was a bench press, which had a few attachments to allow full

body exercise to be executed. After high school football was

over I still enjoyed spending hours in the gym and constantly

improving my strength and size. It was at this point I decided to

become a personal trainer and help others reach their goals as

well. While working for Golds Gym I was constantly surrounded by pictures of great bodybuilders from the past on

the walls. It was then I realized I wanted to compete. I was about

26 at the time. At 27 years old I did my first competition at

Muscle Beach on the 4th

of July, 2010. It was a great learning

experience. I took a couple years off to complete a radiology

program and after graduating in the summer of 2013, I started

hitting the weights with the purpose of competing again. On

February 8th

[of 2014] I competed in the NPC Gold Coast

Classic. I worked very hard and it paid off. I took 1st place in

lightweight Novice, 1st place in lightweight unlimited, and

Overall Novice. It was a great night.

Congratulations on winning both the novice & open in your

weight class - all on your second contest ever, and against

some tough competition. Is there any advice you can give

about fitting in the rigors of training and dieting for a

contest with having a life outside of that - or did you

basically put everything in your life (job, friends, family) on

temporary hold while prepping?

Competing in a contest is very time-consuming and requires

100% dedication if you want to do well. Once you figure out a

good routine and how to cook in bulk so that your food is

prepared ahead of time for a few days it makes it much easier to

spend the extra hours of the day having fun. I strongly believe

that family always comes first so its important to keep your personal priorities in place.

What was your starting/off-season BF% & total bodyweight,

as well as your contest BF% & bodyweight? I realize these

questions might be moot if you did not take quantitative

measures of these parameters. How many weeks did you

allot to get dialed into contest shape?

My starting-off weight was about 185lbs and around 13-15%

body fat. As I began to prep I took that down to 161.5lbs and 4-

5% body fat on the day of the competition. I started getting

dialed in about 3 months out from the contest as the months

before were used to put on as much strength and muscle as

possible.

What was the most difficult part of contest prep for you?

Some have cited hunger & mood as major issues, others cite

the tediousness of preparing the diet a particular way. What

were your biggest challenges?

My biggest challenges would have to be controlling my hunger

and sticking to my meal plan and not snacking in between. The

Alan Aragons Research Review January 2014 [Back to Contents] Page 14

closer I got to the competition the harder it was. Some days were

harder than others. Being moody comes along with the territory,

but if you have self control it shouldnt be a problem.

What was your weight training split each week? Please

include sets, & reps. About how many average weekly hours

of weight training did you do?

During the bulking months my training split was 5 days a week,

hitting every body part hard once a week. Most workouts I used

a 10-12 rep range with 3 sets. I usually tried to hit a total of 15-

20 sets per workout. As I got closer to competition, I increased

the rep range to 12-15 reps and 3-4 sets. I also started hitting

every body part twice a week with one heavy day and one light

day.

What was your weekly cardio regimen?

As for cardio, I started doing 3-4 days a week of about 30-45

minutes fasted cardio in the early AM. I then increased that to 5-

6 days a week. As I hit two months out I added in 30 minutes of

cardio after my PM weight training.

Did you track calorie and macronutrient intake during

prep?

I started out around 3200 calories. 3200 calories was what I

needed to maintain my weight, in order to lose a pound of body

fat a week I simply deducted 500 calories a day. I tracked my

macro nutrients as often as possible. I started with a 40-40-20

(P-C-F) split, and as I got closer to competition I increased my

protein and fats and lowered my carbs. I was somewhere around

a 60-10-30 by the time I was ready for the show.

So, just to get it straight, did that 500 kcal drop from 3200 to

2700 take you all the way to stage weight, or did you have to

push down towards the 2000's toward the end of prep?

I only dropped the calories once to 2700. As my cardio and

workout intensity increased, dropping any lower wouldve most likely had me burning away muscle and too much weight.

Got it. What was your supplement regimen? Did it differ in

the off-season compared to prep?

As for supplements, they definitely helped and are necessary to

aid you training. I took a multivitamin, vitamin C, an Omega 3-

6-9 complex and Vitamin D daily. I took 2-3 protein shakes a

day. One shake during the day, one post work out, and another at

night which was casein protein with some extra glutamine. I

always took a pre-workout to fuel my weight training and

BCAA during the workout to aid in recovery.

What was your showtime peaking strategy?

Showtime peaking is essential as it can make or break your final

look. Sodium intake was lowered as much as possible by 2-3

weeks out. The last week some good advice I received was to

carb deplete and then start carb loading by mid week. At this

point you would begin to lower your water intake as well. As a

dry hard look it was judges look for.

Do you have a particular post-contest eating strategy in

terms of transitioning into maintenance intake and perhaps

moving into a surplus for bulking - or are you going to hold

your condition for shoots, movie roles, etc :)

Post-contest I think its important to increase your caloric intake and definitely treat yourself unless you plan on doing photo

shoots and that. If so, youve got to stick to your diet and maintain until youre ready to start bulking.

What did you do better this time around compared to your

first contest? Is there anything you plan on doing differently

for your next contest?

My nutrition played the biggest role in improving from my first

competition. I learned a lot and plan and using every bit of it to

bring a better package to the stage next time. Its always better to be ahead of schedule then trying to cut corners if youre behind on your training.

Thanks Michael, all the best to you on your next contest.

____________________________________________________

Michael graciously gave me permission to provide his email (michael.anthony.limon@gmail.com) if anyone wants to contact him directly with questions or comments about his most recent contest prep & victory.

Dan Green is a multi-world record-holding powerlifter who

recently lifted a total of 2083 lbs at a bodyweight of 242 lbs in

raw competition. Here is some live seminar footage (length:

21:19). Notice the hilarious section from 11:12-12:50 where he

conveys a general unawareness & disregard for nutrition details

when someone asks him what about his diet keeps him

bodybuilder-lean.

If you have any questions, comments, suggestions, bones of

contention, cheers, jeers, guest articles youd like to submit, or any feedback at all, send it over to aarrsupport@gmail.com.

Table of ContentsEditor's Cut: Why nutrition is so confusing to Gary Taubes.Nutrition & ExerciseDietary protein distribution positively influences 24-h muscle protein synthesis in healthy adults. Long-term effects of a Palaeolithic-type diet in obese postmenopausal women: a 2-year randomized trial.

Supplementation: Cardiovascular and ride time-to-exhaustion effects of an energy drink.Less Recent Gem: High-Intensity Interval Resistance Training (HIRT) influences resting energy expenditure and respiratory ratio in non-dieting individuals.Study Comment ReferencesIn The Lay Press: Statistics arent so bad!Will diet beverages make you fat?Interrogating the Winner: Interview with Gold Coast Classic Champ Michael Limon