ean

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 1

Copyright © February 1st, 2011 by Alan Aragon Home: www.alanaragon.com/researchreview Correspondence: aarrsupport@gmail.com

2 Estimating total caloric needs (macros too).

B y Alan Aragon

5 The impact of protein co-ingestion on muscle

protein synthesis during continuous endurance type exercise. Beelen M, et al. Am J Physiol Endocrinol Metab. 2011 Mar 1. [Epub ahead of print] [Medline]

6 The effects of intermittent or continuous energy restriction on weight loss and metabolic disease risk markers: a randomized trial in young overweight women. Harvie MN, et al. Int J Obes (Lond). 2010 Oct 5. [Epub head of print] [a Medline]

7 Beneficial effect of creatine supplementation in

knee osteoarthritis. Neves M Jr, et al. Med Sci Sports Exerc. 2011 Feb 8. [Epub head of print] [a Medline]

8 Influence of ingesting versus mouth rinsing a carbohydrate solution during a 1-h run. Rollo I, et al. Med Sci Sports Exerc. 2011 Mar;43(3):468-75. [Medline]

9 Anabolic and catabolic hormones and energy

balance of the male bodybuilders during the preparation for the competition. Mäestu J, et al. J Strength Cond Res. 2010 Apr;24(4):1074-81. [Medline]

11 Tripartite Model of the Mind.

By Jamie Hale

12 Why broscience works, part 1. B

y Alan Aragon

13 A correction has been made in the January 2011

issue.

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 2

E

stimating total caloric needs (macros too). By Alan Aragon __________________________________________________ I ntro & background

Estimating total energy needs is important because it’s considered the primary step in constructing a diet – at least on paper. Once total kcals are determined, setting protein, fat, and carbohydrate becomes a rather simple matter of filling in the gaps. I spoke quite a bit about macronutrient manipulation in the Editor’s Cut of the Jan 2009 AARR. In retrospect, it’s missing sufficient discussion of the importance of setting (or at least having a keen awareness of) total kcals. The article was mainly directed towards populations with a relatively narrow range of total energy intake requirements. Furthermore, that article’s focus was to delve into non-linear approaches to macronutrient intake. Thus, its focus was more on manipulating the macronutrients and less on total kcals. My goal for the present article is to provide a more comprehensive means of determining total kcal targets for those who have a preference to use this parameter as a foundation for diet design. T

he main components of total energy expenditure

Most of you who have formally (or informally) studied the fundamentals are already familiar with the 3 main components

f total energy expenditure (TEE): o

1. Resting energy expenditure (REE) is a term used interchangeably with basal metabolic rate (BMR), and resting metabolic rate (RMR). Although each of these are technically not the same thing, it’s impractical to factor in their differences when calculating needs. Those interested in reading about these differences can go to page 112 here.1 REE is about 60-75% of the TEE.2,3 As a matter of trivia, the FAO/WHO/UNU has published a figure as low as 45% of total expenditure.4

2. Thermic effect of activity (TEA) includes voluntary as well as involuntary physical activity. A sub-component of TEA includes non-exercise activity thermogenesis (NEAT). TEA is approximately 15-30% of TEE.2

3. Thermic effect of food (TEF) accounts for roughly 10-15% of the TEE. This range stems from a rough average of the variable thermic effects of each macronutrient. The TEFs of each macronutrient are approximated as follows:5 Protein (25-30%), carbohydrate (6-8%), and fat (2-3%).

T

extbook approaches to predicting resting/basal expenditure

Traditional academic approaches to determining TEE involve calculating the sum of the 3 major components (REE/BMR, TEA, TEF). Perhaps the most commonly used formula for predicting REE/BMR is the Harris-Benedict equation. Keep in mind that there are other ‘texbook’ formulas, and Harris-Benedict is far from the definitive end-all. A systematic review by Frankenfield et al compared four of the most commonly used prediction equations and found the Mifflin-St Jeor equation to be

the most accurate and reliable.6 Another notable method is the Katch-McArdle equation,7 which is perhaps more methodologically sound, since it’s based on lean body mass (LBM).. On a cautionary note, estimating LBM in itself can be problematic. This is in large part because people tend to have inaccurate perceptions of body composition. In my observations, it’s not uncommon for people to assume they have almost half the bodyfat that they actually do. Most recreationally fit guys assume their bodyfat percent is in the high single-digits, when they actually are closer to the low-to-mid double-digits. Women of the same profile often think they’re in the high teens, when in reality they’re closer to the low-to-mid-20’s. A review by Fleck discussing the body composition of elite Olympic athletes puts things into perspective, especially since it’s a safe bet to assume that most recreationally fit individuals as a group are not as lean as world-class competitive athletes. Here’s a summary of Fleck’s findings:8

All groups of athletes were below the average values for % fat of college age men and women of 15% and 25%, espectively. r

Athletes involved in a sport where their body weight is supported have higher % fat values. For example, canoers, kayakers, and swimmers had bodyfat levels at appr ximately 13% (men) and 22% (women). o

Athletes involved in sports where a weight class has to be met to compete (ie, boxing and wrestling) had lower % fat valu s. Men’s bodyfat ranged 6.9-7.9%. e

Sprinters in the 100, 200, and 400 meter events had even lower % fat values, approximately 6.5% (men) and 13.7 (women). Male marathoners averaged 6.4%.

Recent research by Vucetic et al found similar bodyfat levels in national-level male track & field athletes, ranging 5.5-6.3%.9 Although the Mifflin-St Jeor and Katch-McArdle equations are the most worthwhile of the bunch for predicting REE/BMR, I’ll go ahead and outline the Big Four for general reference purposes. I’ll also take the liberty to provide my own formula for estimating REE/BMR. The other formulas have a tendency to shoot too high when it comes to real-world applications. Important note: height is in centimeters, weight is in

ilograms, and age is in years. k H arris-Benedict: Men: 66 + (13.75 x weight) + (5 x height) - (6.76 x age) Women: 655 + (9.56 x weight) + (1.85 x height) - (4.68 x age) O wen: Men: 879 + (10.2 x weight) Women: 795 + (7.2 x weight) M ifflin-St Jeor: Men: (10 x weight) + (6.25 x height) - (5 x age) + 5 Women: (10 x weight) + (6.25 x height) - (5 x age) - 161 K atch-McArdle: Both men & women: 370 + (21.6 x LBM)

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 3

A ragon: Both men & women: 25.3 x LBM For pounds, it’s 11.5 x LBM.

F

actoring in physical activity energy expenditure

The next step toward figuring total energy expenditure is multiplying the previously estimated resting expenditure with an activity factor, ranging from sedentary to extremely active. Bear in mind that these activity factors account for lifestyle physical activity in general, not just formal exercise sessions. Note that these activity factors typically shoot high enough to negate the need to add the 10-15% to account for the thermic effect of food. This means that once you’ve multiplied your estimated resting metabolic needs with your active metabolic needs, you’re done

ith the calculation. w

Sedentary (little or no exercise, desk job) TEE = BMR x 1.2

Lightly Active (light exercise/sports 1-3 days/week) TE = BMR x 1.3-1.4 E

Moderately Active (moderate exercise/sports 3-5 days/week) TE = BMR x 1.5-1.6 E

Very Active = (hard exercise/sports 6-7 days/week) TEE = BMR x 1.7-1.8 Extremely Active (very hard daily exercise/sports & physical job or twice-a-day training) TEE = BMR x 1.9-2.0

T

oward building a better mousetrap

I find plenty of gratification in striving to improve current models. Some might consider this an attempt to build a better mousetrap, while others may see this as a more futile attempt to reinvent the wheel. Those of you who have my book Girth Control know that I like to play with formulas and try to strike the best compromise between simplicity and accuracy. I tend to avor simpler models instead of the multi-step textbook models. f

Most existing formulas are designed to estimate current maintenance needs. So, in order to adjust for weight loss or weight gain, an arbitrary surplus or deficit must be assigned, and it’s usually about 500 kcals up or down. I recently developed a formula that accounts for training volume, intensity, and target bodyweight (TBW), which is a surrogate measure for LBM plus a small buffer. This circumvents the problem of having to attempt to measure or estimate your body composition. Rather, you simply have to have an idea of the total bodyweight you realistically are aiming at. Since this formula is geared toward figuring maintenance need of a targeted/goal set of circumstances, it eliminates the need to add or subtract calories arbitrarily for goals other than maintenance. Without further ado, my formula follows (important note: target bodyweight for this formula is in pounds): Target BW x (8‐10 or 9‐11 + avg. total weekly training hours)  Notice that there are 2 separate ranges of multipliers. The lower range (8-10) is more suitable for women since they have a

higher percentage of bodyfat than men, and thus a lower proportion of lean mass. Using the higher range (9-11) on women would have a tendency to overestimate needs. Each range has a certain margin to account for differences in intensity. Low, medium, and high-intensity work can be factored in by using the low, middle, or high end of each range, depending on where your training sessions average during the week. Both formal cardio and weight training sessions must be included when totaling up average weekly training hours. Vigorous recreational physical activity (ie, sports games & practices) hould also be tallied in. s

Note that applying this formula to folks with zero hours of training per week can lead to underestimations if the lower end of each range is used as a multiplier. In the rare case of anyone with zero hours of training or vigorous physical activity per week, I’d suggest simply using the top end of each range (10 for women, 11 for men) as a multiplier. F

illing it in with the macronutrients

Once you’ve figured total calories, then filling them in with the macronutrients is pretty simple. Protein requirements were discussed at length in the opening article of the January 2011 AARR. The research-friendly range of protein intake for the active/athletic population is 1.2-2.2g/kg.10-14 This translates to 0.54-1.0g/lb. However, my personal preference for use in the field is 2-3g/kg (0.9-1.36g/lb) of TBW. Fat is a bit more of a grey area than protein. Currently, there’s no authoritative source to cite when indicating an evidence-based dosing range. The following quote from the Food & Nutrition Board’s most recent

RI report captures the situation nicely:1 D “The amount of total energy as fat in the diet can vary from 10 to 50 percent without differing effects on short-term health (Jéquier, 1999). When men and women were fed isocaloric diets containing 20, 40, or 60 percent fat, there was no difference in total daily energy expenditure (Hill et al., 1991). […] There are insufficient data, however, to identify a defined intake level for fat based on maintaining fat balance or on the prevention of chronic diseases. Therefore, neither an AI (adequate intake) nor

n EAR (estimated average requirement) and RDA are set.” a Traditionally, I’ve recommended a fat intake of 0.4-0.5g per pound of target bodyweight, which metrically equals 0.88-1.1g/kg TBW (which, again, is a proxy for lean mass with a small margin of safety). However, I’ve recently been observing client success with a wider range of intakes, to the order of 0.3-0.6g/lb TBW. This translates to 0.66-1.32g/kg TBW. Once you’ve figured total kcals, protein, and fat, carbohydrates simply comprise the remainder. Let’s run through a sample calculation

n a hypothetical subject with the following characteristics: o ale whose goal bodyweight is 180 lbs. M He must lose weight in order to achieve the 180 lb goal, so this is my cue to error on the high side with protein.

Has an average of 4 weekly training hours, is desk-bound & sede tary otherwise. n

His training is an even mix of high & low intensity, averaging out to moderate (in the middle of the 9-11 range).

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 4

W e’ll start by figuring total kcals using my formula:

Target BW x (8‐10 or 9‐11 + avg. total weekly training hours)  

180 lbs x (10 + 4 avg. total weekly training hours) = 180 x 14 = 2520 kcal

Now we can start filling in total kcals with protein & fat. I’ll choose near the upper-range for fat, and purposely exceed the upper limit of the research-friendly protein dosing, using the middle of my routine range in practice, which is 2-3g/kg (0.9-1.36g/lb). I’m strategically aiming high with protein and fat allotments. This is because his overall activity level is relatively low, and may not be optimally conducive to a proportionally high carbohydrate intake.

Protein = 180 lbs x 1.2 = 216g Fat = 180 lbs x 0.5 = 90g

To calculate carbs, all we need to do is add the sum of the protein & fat kcals, subtract that sum from the total kcals, and

en we’ll arrive at carb kcals. th Protein = 216g x 4 kcal/g = 864 kcal Fat = 90g x 9 kcal/g = 810 kcal

A dd protein & fat kcals: 864 + 810 = 1674 kcal

S ubtract this sum from the total kcals to get carb kcals: 2520 - 1674 = 846 kcal

D ivide by 4 to convert kcals to carb grams: 846 ÷ 4 kcals/g = 212g

In the literature, the recommended carbohydrate intake for strength/power athletes is 5-7 g/kg (2.3-3.2g/lb) and 7-10 g/kg (3.2-4.5g/lb) for endurance athletes.15 Our subject does somewhere between 25-50% of the training volume of competitive athletic populations, and as such, his carbohydrate allotment should reflect that – and it does. G rand totals:

Energy: 2520 kcal Protein: 216g Fat: 90g Carbs: 212g

Formulas must take a backseat to reality As with all formulas, if the number you arrive at is way off from what you know you respond favorably to, scrap it. It can’t be overemphasized that the practicality of formulas is limited to individuals who have a haphazard eating history and are unaware of their maintenance requirements. Those who do have an awareness of their maintenance needs do not have much use for formulas (aside from number-crunching for the sheer joy of it). All formulas are merely ballpark starting points that must be put to trial and adjusted according to individual response. At best, numbers derived from these predictive equations are hypothetical. They must ultimately pass the reality test.

References

1. Institute of Medicine, Food & Nutrition Board. Dietary Reference Intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. The National Academies Press, Washington DC, 2005. [full report - PDF]

2. Genton L, et al. Energy and macronutrient requirements for physical fitness in exercising subjects. Clin Nutr. 2010 Aug;29(4):413-23. [Medline]

3. Shetty P. et al. Energy requirements of adults. Public Health Nutr. 2005 Oct;8(7A):994-1009. [Medline]

4. FAO/WHO/UNU. Human energy requirements. Oct 2001. [full report – PDF]

5. Jequier E. Pathways to obesity. Int J Obes Relat Metab Disord. 2002 Sep;26 Suppl 2:S12-7. [Medline]

6. Frankenfield D, et al. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. 2005 May;105(5):775-89. [Medline]

7. McArdle W, Katch F, KatchmV. Exercise Physiology Energy, Nutrition, and Human Performance, 7th Edition. Lippincott Williams and Wilkins, WoltersKluwer Health, 2010. [LWW]

8. Fleck SJ. Body composition of elite American athletes. Am J Sports Med. 1983 Nov-Dec;11(6):398-403. [Medline]

9. Vucetić V, et al. Morphological differences of elite Croatian track-and-field athletes. Coll Antropol. 2008 Sep;32(3):863-8. [Medline]

10. Kreider RB, Campbell B. Protein for exercise and recovery. Phys Sportsmed. 2009 Jun;37(2):13-21. [Medline]

11. Rodriguez NR, et al. 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. [Medline]

12. Campbell B, et al. International Society of Sports Nutrition position stand: protein and exercise. J Int Soc Sports Nutr. 2007 Sep 26;4:8. [Medline]

13. Tipton KD, Wolfe RR. Protein and amino acids for athletes. J Sports Sci. 2004 Jan;22(1):65-79. [Medline]

14. Wilson J, Wilson GJ. Contemporary issues in protein requirements and consumption for resistance trained athletes. J Int Soc Sports Nutr. 2006 Jun 5;3:7-27. [Medline]

15. Burke LM, et al. Guidelines for daily carbohydrate intake: do athletes achieve them? Sports Med. 2001;31(4):267-99. [Medline]

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 5

The impact of protein co-ingestion on muscle protein

ynthesis during continuous endurance type exercise. s Beelen M, et al. Am J Physiol Endocrinol Metab. 2011 Mar 1. [Epub ahead of print] [Medline] PURPOSE: his study investigates the impact of protein co-ingestion with carbohydrate on muscle protein synthesis during endurance type exercise. METHODS: Twelve healthy male cyclists were studied during 2 h of fasted rest, followed by 2 h of continuous cycling at 55% W(max). During exercise, subjects received either 1.0 g•kg(-1)•h(-1) carbohydrate (CHO) or 0.8 g•kg(-1)•h(-1) carbohydrate with 0.2 g•kg(-1)•h(-1) protein hydrolysate (CHO+PRO). Continuous intravenous infusions with L-[ring-(13)C(6)] phenylalanine and L-[ring-(2)H(2)] tyrosine were applied, and blood and muscle biopsies were collected to assess whole-body protein turnover and muscle protein synthesis rates at rest and during exercise conditions. RESULTS: Protein co-ingestion stimulated whole-body protein synthesis and oxidation rates during exercise by 22±3% and 70±17%, respectively (P<0.01). Whole-body protein breakdown rates did not differ between experiments. As a consequence, whole-body net protein balance was slightly negative in CHO and positive in the CHO+PRO treatment (-4.9±0.3 vs 8.0±0.3 μmol phe•kg(-1)•h(-1), respectively; P<0.01). Mixed muscle protein fractional synthetic rates (FSR) were higher during exercise when compared with resting conditions (0.058±0.006 vs 0.035±0.006%•h(-1) in CHO and 0.070±0.011 vs 0.038±0.005%•h(-1) in the CHO+PRO treatment, respectively; P<0.05). FSR during exercise did not differ between experiments (P=0.29).CONCLUSION: We conclude that muscle protein synthesis is stimulated during continuous endurance type exercise activities when ingesting carbohydrate with or without protein. Protein co-ingestion does not further increase muscle protein synthesis rates during continuous endurance type exercise. SPONSORSHIP: None listed. Study strengths This study tackles the timely topic of intra-workout nutrient ingestion. In light of the interest in protein as a part of the mix, this is the first study to examine the effect of protein co-ingested with carbohydrate during endurance-type exercise on both whole-body protein balance as well as skeletal muscle protein synthesis. All subjects were competitive endurance athletes, which minimizes the variability of effects seen in newbies. The treatments compared were isocaloric, and the dosing (1.0g/kg/hr CHO versus 0.8g/kg/hr CHO + 0.2g/kg/hr PRO) has been shown in previous research to be effective in allowing a continuous substrate supply & minimize disturbances in glucose, amino acids, and insulin levels during training. All subjects had the same standardized dinner the night prior to testing in order to control cofounding variations. A crossover was implemented so that each subject underwent both conditions. Study limitations An obvious limitation is the acute (short-term) nature of the study. Although the authors listed the dosing scheme of CHO & CHO + PRO as a strength, it can also be viewed as a limitation. Individuals aiming for a weight or fat loss via maintaining hypocaloric conditions often restrict carbohydrate intake. As a

result, carbohydrate consumption (even during training) may take a hit. The dose range in the present study (0.8-1.0g/kg/hr) reflects an amount closer to the higher-end of carbohydrate consumption. Cases with lesser doses of carbohydrate might unmask the benefits of including protein in the mix. For example, recent research by Ferguson-Stegall,1 a trend toward a greater anti-proteolytic effect (assessed via myoglobin levels) was seen in subjects consuming a low-carb CHO+PRO solution (3% carbohydrate/1.2% protein) compared to a 6% CHO solution consumed during endurance exercise. Further research on lower-carb solutions during training on muscle protein status using the more direct assessment means of the present study are warranted. C omment/application

The main findings of the present study were somewhat dichotomous. Whole-body (in systemic circulation) net protein balance was slightly positive in the CHO+PRO treatment, but slightly negative in the CHO condition during exercise. However, direct analysis of muscle amino acid concentrations did not find any significant difference between conditions. Predictably, mixed muscle fractional synthetic rate (FSR) increased during exercise compared with resting conditions in both treatments, but there was no significant difference in FSR between the CHO+PRO and CHO treatments. The latter finding is important because whole-body protein kinetics don’t necessarily reflect what occurs in skeletal muscle – the bodily

rotein pool that matters most in this context. p Previous research by the present study’s investigators showed different outcomes when resistance training was examined.2 Whole-body protein breakdown was lower in the CHO+PRO treatment compared the CHO, and importantly, FSR was 49% greater in the CHO+PRO treatment as well. However, at 0.15g/kg/hr of protein and/or carbs of the same dose, this was significantly less than the amounts administered in the present study. Interestingly, despite the markedly lower CHO dose, insulin area under the curve in the previous study (28.0 mU/l & 37 mU/l for the CHO & CHO+PRO treatments, respectively) was substantially higher than that of the present study (9.1 mU/l & 18.1 mU/l for the CHO & CHO+PRO treatments, respectively). The authors of the present study attribute these discrepant results from their previous work (especially the differences in FSR) to the “intermittent character of the resistance type exercise in which specific muscle groups of the lower limbs could rest during periods of exercise performed by the upper limbs.” Apparently (but not too surprisingly), when dietary protein is available, muscle protein synthesis can better amplified during resistance training as opposed to endurance training. Still, further non-acute research involving adequate

aily protein is yet to solidify the necessity of this practice. d Aside from muscle protein turnover, another important aspect to consider is exercise performance. Steams et al recently did a meta-analysis/systematic review of the influence of protein & carbohydrate coingestion during exercise on endurance performance.3 They concluded that, “…the ergogenic effect of protein seen in isocarbohydrate studies may be because of a generic effect of adding calories (fuel) as opposed to a unique benefit of protein.”

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 6

The effects of intermittent or continuous energy restriction on weight loss and metabolic disease risk markers: a randomized trial in young overweight women. Harvie MN, et al. Int J Obes (Lond). 2010 Oct 5. [Epub ahead of print] [Medline] BACKGROUND: The problems of adherence to energy restriction in humans are well known. OBJECTIVE: To compare the feasibility and effectiveness of intermittent energy restriction (IER) with continuous energy restriction (CER) for weight loss, insulin sensitivity and other metabolic disease risk markers. DESIGN: Randomized comparison of a 25% energy restriction as IER ( 2710 kJ/day for 2 days/week) or CER ( 6276 kJ/day for 7 days/week) in 107 overweight or obese (mean (±s.d.) body mass index 30.6 (±5.1) kg m(-2)) premenopausal women observed over a period of 6 months. Weight, anthropometry, biomarkers for breast cancer, diabetes, cardiovascular disease and dementia risk; insulin resistance (HOMA), oxidative stress markers, leptin, adiponectin, insulin-like growth factor (IGF)-1 and IGF binding proteins 1 and 2, androgens, prolactin, inflammatory markers (high sensitivity C-reactive protein and sialic acid), lipids, blood pressure and brain-derived neurotrophic factor were assessed at baseline and after 1, 3 and 6 months. RESULTS: Last observation carried forward analysis showed that IER and CER are equally effective for weight loss: mean (95% confidence interval ) weight change for IER was -6.4 (-7.9 to -4.8) kg vs -5.6 (-6.9 to -4.4) kg for CER (P-value for difference between groups=0.4). Both groups experienced comparable reductions in leptin, free androgen index, high-sensitivity C-reactive protein, total and LDL cholesterol, triglycerides, blood pressure and increases in sex hormone binding globulin, IGF binding proteins 1 and 2. Reductions in fasting insulin and insulin resistance were modest in both groups, but greater with IER than with CER; difference between groups for fasting insulin was -1.2 (-1.4 to -1.0) μU ml(-1) and for insulin resistance was -1.2 (-1.5 to -1.0) μU mmol(-1) l(-1) (both P=0.04). CONCLUSIONS: IER is as effective as CER with regard to weight loss, insulin sensitivity and other health biomarkers, and may be offered as an alternative equivalent to CER for weight loss and reducing disease risk. SPONSORSHIP: Breast Cancer Campaign, World Cancer Research Fund, Genesis Appeal Manchester UK, Intramural Research Program of the National Institute on Aging of the NIH, the Danish Research Council for Health and Disease. Study strengths This study is the first to compare the long-term effects of intermittent energy restriction (IER) with continuous energy restriction in humans. The scant amount of previous work looked at periods spanning 12 weeks,4,5 whereas the present trial lasted 6 months. Another strength was the relatively large number of subjects. 18 of the 107 randomized subjects withdrew, which made for a relatively low rate of attrition (86% at 6 months). To bolster compliance, motivational phone calls (every 2 weeks) and monthly in-person meetings (assessing weight & anthropometry) were implemented. Study limitations When listing the study’s strengths, the authors pointed to their chosen protocol as being potentially more realistic than

alternate-day fasting or a linear protocol. Specifically, they felt that a 25% energy reduction via non-proprietary VLCD 2 days per week (75% reduction on VLCD days) without restriction on the other 5 days may be more “achievable” than the aforementioned alternatives. However, when listing the study’s limitations, they reported that 58% of the subjects in the IER group planned on continuing the diet beyond the end of the trial, while 85% of the CER group planned to continue after the ending point of the trial. Neither group received counseling or recommendations about exercise, which can potentially influence the effectiveness (or lack of) when combined with the differing diet schemes. A final limitation worth noting is that body composition was determined via bioelectrical impedance (BIA), which may be acceptable for tracking changes in groups, but lacks reliability when used on individuals.6 C

omment/application

The main finding of the present study was that IER and CER were equally effective for weight loss and improving the clinical endpoints measured. There were only a few of notable differences among broad range of biomarkers of disease risk. A slightly greater improvement in insulin sensitivity in the IER group was seen. Although this wasn’t too small to be detected, it was not large enough to reach statistical significance. The CER group had a greater reductions in dehydroepiandrosterone sulphate (DHEAS) levels, which is associated with lower risk for breast cancer. However, this finding may lack impact, since IER group had a significantly greater menstrual cycle length – which is associated with educed risk for breast cancer. r

The CER protocol’s 27% greater popularity (assessed via subjects’ plans to continue after the end of the trial) runs contrary to the anticipations & hopes of the authors. I find this somewhat surprising, since it’s assumable that 5 “non-dieting” days per week is an attractive proposition. It’s also surprising in the sense that the long (6-month) trial period was insufficient to allow the majority of the subjects to become fully comfortable with the two VLCD days. Overall, the CER treatment was slightly more favorable by the subjects in most parameters. 8% of the IER group reported minor adverse symptoms including lack of energy, headache, and constipation – while none of the participants in the CER reported such symptoms. Unsurprisingly, 15% of the IER subjects complained of hunger, while none of the CER subjects did. 15% of the IER subjects and 7% of the CER subjects reported minor adverse psychological effects. 51% of the IER group reported difficulty adapting the diet into their daily routine, compared to 30% of the CER group. Although the diets were equally effective in most measures, the IER’s 2 energy-restricted days might have been too drastic to circumvent undue diet-related stress. The VLCD was 645 kcals comprised of approximately 1 liter of reduced-fat milk, four 80g portions of vegetables, one portion of fruit, one low-kcal drink, and a vitamin/mineral supplement. Despite its effectiveness, this protocol did not win the hearts of the majority, who preferred the daily 25% reduction. An alternative to the 2-day VLCD/5-day unrestricted would be a variation on the opposite, where 5 days would have an energy restriction of 35%, while 2 days would be

nrestricted. Now that would make an interesting comparison. u

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 7

Beneficial effect of creatine supplementation in knee steoarthritis. o

Neves M Jr, et al. Med Sci Sports Exerc. 2011 Feb 8. [Epub head of print] [a Medline]

INTRODUCTION: The aim of this study was to investigate the efficacy of creatine (CR) supplementation combined with strengthening exercises inknee osteoarthritis (OA). METHODS: A randomized, double-blind,placebo-controlled trial was performed.Postmenopausal women with knee OA were allocated to receive either CR (20 g/dfor one week and 5 g/dthereafter) or placebo (PL) and were enrolled in a lower limb resistance training program. They were assessed at baseline (PRE) and after 12 weeks (POST). The primary outcome was the physical function as measured by the timed-stands test. Secondary outcomes included lean mass, quality of life, pain, stiffness, and muscle strength. RESULTS: Physical function was significantly improved only in the CR group (p=0.006). Additionally, a significant between-group difference was observed (CR-PRE: 15.7 ± 1.4, POST: 18.1 ± 1.8; PL-PRE: 15.0 ± 1.8, POST: 15.2 ± 1.2; p=0.004). The CR group also presented improvements in physical function and stiffness subscales as evaluated by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (p=0.005 and p=0.024, respectively), whereas the PL group did not show any significant changes in these parameters (p>0.05). Additionally, only the CR group presented a significant improvement in lower limb lean mass (p=0.04) as well as in quality of life (p=0.01). Both CR and PL groups demonstrated significant reductions in pain (p<0.05). Similarly, a main effect for time revealed an increase in leg-press 1-RM (p=0.005) with no significant differences between groups (p=0.81). CONCLUSION: CR supplementation improves physical function, lower limb lean mass and quality of life in postmenopausal women with knee OA undergoing strengthening exercises. Clinicaltrials.gov number: NCT00992043. SPONSORSHIP: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) S tudy Strengths

Conceptually, this study is strong since osteoarthritis is such a prevalent disease, and non-sports/fitness applications of creatine supplementation is a sparsely tapped area of study. This is perhaps the first study to specifically examine the effect of creatine supplementation on subjects with osteoarthritis. As an extra measure to increase the bioavailability of the creatine, subjects were instructed to dissolve the supplements in liquid (juice was suggested) before ingestion. Compliance was reinforced by weekly communication with the research staff. The resistance training program was progressive and each session was supervised by a fitness professional. S tudy limitations

As noted by the authors, none of the subjects had severe osteoarthritis, so extrapolations to the latter would be speculative. Also, the mechanisms behind the apparent effects were not directly measured (ie, structural changes in cartilage), and thus need further clarification. Diet was assessed on three separate occasions via 24-hour recall, which has limited

reliability and accuracy. A final limitation is that these results in the all-female sample may or may not apply similarly to men. C

omment/application

As shown above, the creatine treatment significantly improved physical function, assessed via the “time-stands” test, which measures the maximum number of stand-ups that can be performed from a standard height (without assistance from the upper body) for 30 seconds. Predictably, creatine significantly increased lower-limb lean mass, but somewhat surprisingly, this did not translate to a significant difference in whole-body lean mass. Subjective reports of improvements in stiffness and

uality of life were also significantly better in the creatine group. q Creatinine clearance was not different between groups, and no adverse effects were reported. The authors note that, “This observation is in agreement with an extensive literature attesting the safety of CR in a broad range of populations, including those suffering from chronic diseases.” The authors supported this by citing research by Bender et al in aged patients with Parkinson’s disease,7 and a review by Persky & Rawson.8 It should be noted that the latter review cautions that although creatine has a good afety profile, there’s still a lack of long-term safety data. s

In related research, a review by Yoshizumi & Tsourounis discussed the apparent safety of creatine taken within reasonable doses.9 However, this paper also cited a case study where acute renal failure occurred in a 20 year-old male taking 20g/day for 4 weeks. This suggests the possibility that a predisposition to renal disease might be exacerbated by excessive creatine dosing. Creatine loading phases typically involve 20g/day for 5-7 days not a month!), followed by a daily maintenance dose of 3-5g. (

Perhaps the most interesting aspect of the present study is the therapeutic potential for creatine as a means to target cartilage repair. Phosphocreatine kinase (CK) is also present in less metabolically active tissues such as bone and cartilage, so it’s not completely implausible that supplementation might either increase the rate of anabolism or reduce the rate of catabolism in these tissues. The problem is, measuring the direct effects of creatine supplementation on cartilage would be prohibitively invasive. A notable point made by the authors was that creatine’s therapeutic effects might not materialize in the absence of exercise. In support of this, Roy et al failed to observe any therapeutic effect of creatine on recovery after total knee arthroplasty.10 It’s also possible that these conditions were too severe for creatine to make a dent. In any case, future studies in this area will interest anyone who isn’t getting any younger.

Influence of ingesting versus mouth rinsing a drate solution during a 1-h run. carbohy

Rollo I, et al. Med Sci Sports Exerc. 2011 Mar;43(3):468-75. Medline[

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 8

] PURPOSE: To investigate the influence of ingesting versus mouth rinsing a carbohydrate-electrolyte solution on 1-h running performance. METHODS: After a 14- to 15-h fast, 10 endurance-trained male runners (mean ± SD: V˙O2peak = 65.0 ± 4.4 mL·kg·min) completed three 1-h performance runs separated by 1 wk. In random order, runners ingested either a 8-mL·kg body mass of either a 6.4% carbohydrate-electrolyte solution (CHO) or a placebo solution (P) 30 min before or a 2-mL·kg body mass at 15-min intervals throughout the 1-h run. On a separate occasion, runners mouth rinsed (R) a 6.4% CHO, i.e., without ingestion, at the same times as in the ingestion trials. RESULTS: Total distances covered in the CHO, P, and R trials were 14,515 ± 756, 14,190 ± 800, and 14,283 ± 758 m, respectively. Runners covered 320 m more (90% confidence interval = 140-510 m, P = 0.01) during the CHO trial compared with the P trial and 230 m more (90% confidence interval = 83-380 m, P = 0.019) in comparison with the R trial. There was no difference in n distance covered between the R and P trials (P = 1.0). CONCLUSIONS: A greater distance was covered after the mouth rinse and ingestion of a 6.4% CHO during a 1-h performance run than when mouth rinsing the same solution or mouth rinsing followed by the ingestion of the same volume of a placebo solution. SPONSORSHIP: Loughborough University. S tudy strengths

The majority of studies in this area have been done on cyclists. This is perhaps the first one to break the mold and examine effects on 1-hour running performance. The subjects were experienced runners, all of whom were accustomed to training sessions (or competitions) lasting at least 1 hour. All participants underwent a “habituation trial” where they completed the hour-long run – but water was ingested instead of the carbohydrate-electrolyte solution. This served to reduce the potential for variability resulting from the ‘shock’ or novelty of the testing protocol. To control intake variables, subjects were instructed to consume a standardized diet 48 hours before the first trial, and to replicate that before each subsequent trial. S tudy limitations

As noted by the authors, the subjects were not “blinded” about the purpose of the study. They were informed about the nature of what was being investigated, and were made aware that ingesting and mouth rinsing carbohydrate–electrolyte drinks have been seen to independently improve 1-hour running performance. With this knowledge in mind, subjects carried a certain degree of expectation bias and confirmation bias going

to the trials. in A notable potential limitation involves the product used for testing. Lucozade is somewhat unique among sports beverages in that it doesn’t contain fructose. Rather, it’s a roughly even mix of dextrose and maltodextrin. It’s possible that a combination of glucose and fructose would have had superior performance effects due to more efficient carbohydrate delivery via separate intestinal transporters of the different carbohydrate subtypes.11 But from a more practical standpoint, Lucozade is

relatively obscure compared to the readily available commercial beverages (ie, Gatorade), the majority of which contain a combination of glucose and fructose. C

omment/application

As shown above, the main finding of the present study was that for 1-hour running performance, consuming the carbohydrate–electrolyte solution significantly outperformed mouth-rinsing the same carbohydrate–electrolyte solution or ingesting the same volume of a placebo solution. These results run contrary to those in the only other study thus far comparing carbohydrate ingestion with mere mouth-rinsing. Oddly, Pottier et al observed the endurance performance superiority of rinsing the mouth with a carbohydrate-electrolyte solution over ingesting it.12 It’s possible that the ingested solution had less oral transit time than the mouth-rinsing treatment. Thus, Pottier et al speculated that the ingested beverage had less contact time with carbohydrate sensors in the mouth that activate reward centers in the central

ervous system (CNS) that drive exercise performance. n Bearing Pottier et al’s results in mind, the authors of the present study took pains to standardize the duration of oral exposure to the carbohydrate beverages. Each solution was held in the mouth for 5 seconds before either being swallowed or spit out. Previous work by the present study’s authors found that mouth-rinsing a carbohydrate–electrolyte solution did not improve 1-hour running performance compared to placebo.13 However, the difference between the present and previous study was that the placebo solution was consumed after the rinse, whereas previously, it was spit out. They therefore speculated that the slight (non-significant) benefit of the mouth-rinsing over placebo was due to a greater ingestion of fluid, rather than any special ffects of the rinse per se. e

The idea that CNS-mediated performance increases via oral carbohydrate sensors is attractive & intriguing. It carries the practical possibility of circumventing energy intake while maintaining the ergogenic effects of carbohydrate ingestion. However, the research thus far showing the performance benefit of mouth-rinsing did so in subjects who exercised in a fasted state. In the only study to-date examining the effect of carbohydrate mouth-rinsing in fed subjects, Beelen et al found that when a high-carb meal was consumed 3 hours pre-trial, no performance differences occurred between the carbohydrate-rinsing treatment and the non-carb placebo.14 It will be nteresting to see how the results play out in future research.. i

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 9

Anabolic and catabolic hormones and energy balance of the male bodybuilders during the preparation for t he competition. Mäestu J, et al. J Strength Cond Res. 2010 Apr;24(4):1074-81. [Medline] BACKGROUND/PURPOSE: The purpose of the study was to investigate simultaneous effects of energy balance, caloric intake, and the hormonal anabolic-catabolic balance in bodybuilders prior to competition. METHODS: Fourteen male bodybuilders took part in an 11-week energy-restricted period to reduce body fat. The subjects were divided into the energy-restricted group (ERG) (n = 7), who were preparing for the competition, or the control group (CG) (n = 7) who continued to train regularly and did not change their dietary or training pattern. Participants were tested at 11 weeks (T1), 5 weeks (T2), and 3 days (T3) before competition for diet, body composition, and fasting hormonal assessment. RESULTS: Body mass and body fat percentage of ERG were significantly (p < 0.05) decreased during the study period. In ERG, insulinlike growth factor-1 (IGF-1) and insulin decreased significantly during the 11-week weight-reduction period (p < 0.05). Testosterone was decreased only from week 11 to week 5 (from 20.3 +/- 6.0 to 18.0 +/- 6.8 nmol/L). Changes in IGF-I concentration were significantly related to changes in insulin (r = 0.741), fat mass (r = 0.705), lean body mass (r = 0.696), and body mass (r = 0.652). Changes in insulin concentrations were significantly related to changes in fat mass (r = 0.630) and lean body mass (r = 0.725). CONCLUSIONS: These data indicate that severe energy restriction to extremely low body energy reserves decreases significantly the concentrations of 3 anabolic pathways despite high protein intake. Monitoring of insulin and IGF-1 concentration is suggested to prevent losses in muscle mass in energy-restricted conditions. Other nutritional strategies might be needed to prevent possible catabolic effect during preparation of bodybuilders to competition. SPONSORSHIP: This study was supported by the Estonian Science Foundation, grant number 6671. S tudy strengths

A conceptual strength of this design was its coverage of both the hormonal and body-compositional changes in the competitors. This is also the first study to report the energy expenditure of bodybuilders prepping for a contest. Another design strength was the inclusion of a non-dieting control group (CG - also competitive bodybuilders) for comparison with an energy-restricted group (ERG). Dual X-ray absorptiometry (DXA) was used to assess body composition. S tudy limitations

The outcomes of this study may be limited to the age group (mean age was 28.29), sex (men), and a lack of anabolic/androgenic steroid (AAS) use. The subjects claimed to be free of AAS use during the study, as well as a minimum of 2 years prior to that. All subjects were tested for AAS at the competition they were prepping for, and none of the subjects failed the drug test. While this can also be viewed as a design strength, it also leaves out a large segment of the competitive bodybuilding population that uses AAS.

Comment/application CG kept a steady volume of about 8.3 hours per week, ERG showed a smooth, linear uptrend of training volume, starting at approximately 10 hours per week, graduating up to approximately 16.6 hours per week. While it’s common for dieting bodybuilders to increase training volume as a contest approaches, ERG increased cardio at the expense of strength training. CG kept their resistance training volume at 65% (with cardio taking up the remainder), while ERG began with 53.5% of total training volume as resistance training, which was reduced to 39% (with cardio increased to the majority of the work) during the final stretch of prep. As seen in the chart above, macronutrient composition between the groups was similar, but ERG was higher in protein and carbohydrate (although not to a statistically significant degree). Interestingly, carbohydrate was the dominant macronutrient in both groups (at least 2x greater than protein), even the pre-contest group. In my observations and experience working with drug-free competitors, protein is invariably the dominant macronutrient in the pre-contest phase. Despite the carb-dominance of the diet, ERG’s body fat decreased from 9.6% at the beginning to 6.5% at the end of the trial. The lowest individual value in the group was 4.8%. Protein intake of ERG was high, at 2.3-2.6g/kg. This, in part, explains the maintenance of lean mass. Nevertheless, the authors expressed concern that the decrease in IGF-1 and insulin levels under healthy levels toward the end of the trial could signal muscle catabolism. Another concern for this same reason was ERG’s initial drop in testosterone levels. The solution proposed by the authors for preventing the downregulation of these anabolic indexes would be to increase carbohydrate intake. However, I would counter that doing so might be at the expense of achieving a greater degree of fat loss. The current state of judging bodybuilding contests, at least for the past 20-30 years, has placed an emphasis on leanness. Among competitors in the same weight class, coming in “peeled” is often the stronger deciding factor than having more mass. Calling the pre-contest group the “energy-restricted group” is a bit misleading because this group didn’t actually restrict caloric intake any more than the controls. In fact, CG’s intake was 3051 kcal, while ERG’s intake was higher, averaging 3469 kcal. But as discussed, ERG’s daily energy expenditure (4032.6 kcal) was significantly & progressively higher than CG (3158.1 kcal). Overall, this was an interesting study outlining one of the many possible ways to ‘skin a cat’ for bodybuilding competition.

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 10

1. Ferguson-Stegall L, et al. The effect of a low carbohydrate beverage with added protein on cycling endurance performance in trained athletes. J Strength Cond Res. 2010 Oct;24(10):2577-86. [Medline]

2. Beelen M, et al. Protein coingestion stimulates muscle protein synthesis during resistance-type exercise. Am J Physiol Endocrinol Metab. 2008 Jul;295(1):E70-7. [Medline]

3. Stearns RL, et al. Effects of ingesting protein in combination with carbohydrate during exercise on endurance performance: a systematic review with meta-analysis. J Strength Cond Res. 2010 Aug;24(8):2192-202. [Medline]

4. Ash S, et al. Effect of intensive dietetic interventions on weight and glycaemic control in overweight men with Type II diabetes: a randomised trial. Int J Obes Relat Metab Disord. 2003 Jul;27(7):797-802. [Medline]

5. Hill JO, et al. Evaluation of an alternating-calorie diet with and without exercise in the treatment of obesity. Am J Clin Nutr. 1989 Aug;50(2):248-54. [Medline]

6. Pateyjohns IR, et al. Comparison of three bioelectrical impedance methods with DXA in overweight and obese men. Obesity (Silver Spring). 2006 Nov;14(11):2064-70. [Medline]

7. Bender A, et al. Long-term creatine supplementation is safe in aged patients with Parkinson disease. Nutr Res. 2008 Mar;28(3):172-8. [Medline]

8. Persky AM, Rawson ES. Safety of creatine supplementation. bcell Biochem. 2007;46:275-89. [Medline]

9. Yoshizumi WM, Tsourounis C. Effects of creatine supplementation on renal function. J Herb Pharmacother. 2004;4(1):1-7. [Medline]

10. Roy BD, et al. Creatine monohydrate supplementation does not improve functional recovery after total knee arthroplasty. Arch Phys Med Rehabil. 2005 Jul;86(7):1293-8. [Medline]

11. Jeukendrup AE, Moseley L. Multiple transportable carbohydrates enhance gastric emptying and fluid delivery. Scand J Med Sci Sports. 2010 Feb;20(1):112-21. [Medline]

12. Pottier A, et al. Mouth rinse but not ingestion of a carbohydrate solution improves 1-h cycle time trial performance. Scand J Med Sci Sports. 2010 Feb;20(1):105-11. [Medline]

13. Rollo I, et al. Influence of mouth rinsing a carbohydrate solution on 1-h running performance. Med Sci Sports Exerc. 2010 Apr;42(4):798-804. [Medline]

14. Beelen M, et al. Carbohydrate mouth rinsing in the fed state: lack of enhancement of time-trial performance. Int J Sport Nutr Exerc Metab. 2009 Aug;19(4):400-9. [Medline]

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 11

T ripartite Model of the Mind. By Jamie Hale†

____________________________________________________ Stanovich’s model of the mind builds on the consensus view of cognition termed dual processing model of cognition.1 This view of cognition categorizes cognition into two categories-

ype 1 processing and Type 2 processing.2,3 T K

ey qualities Type 1 (autonomous / heuristic) processing:

Rapid execution. Execution is mandatory when triggering stimuli are encountered.

They are computationally inexpensive. Not dependent on input form higher level control systems. Many type 1 processes can run in parallel without interfering with each other and they can run with type 2 processing.

The defining feature of Type 1 processing is its autonomy. Type 1 processes are involved in a variety of useful things- face recognition, depth perception, proprioception, and language ambiguity regulation, etc, which are all beyond our awareness. Type 2 processing contrasts with Type 1 processing on each of ts critical qualities. i

K

ey qualities Type 2 (nonautonomous / analytical) processing:

Slow execution. Computationally expensive. Focus of awareness. Only one or very few Type 2 processes can run in parallel.

Often language-based and rule-based. Type 2 processing is what is often called controlled processing, and it’s the type of processing occurring when we discuss things ike conscious problem-solving. l

Stanovich’s Tripartite Model of the Mind bifurcates Type 2 processing into the categories- algorithmic mind, and reflective mind. So, the Tripartite Model of Mind suggests that the mind be divided into: autonomous mind, algorithmic mind, and reflective mind. This model has been devised to expand on what the previous dual process view ignored- individual differences that contribute to differences in Type 2 processing. Individual differences in Tripartite Framework:1

Reflective Mind Individual differences in rational thinking dispositions

Algorithmic Mind Individual differences in fluid intelligence

Autonomous Mind Few continuous individual differences

Algorithmic and reflective minds are characterized by continuous individual differences. Intelligence tests assess various aspects of algorithmic efficiency, but that is all they directly assess. Rationality encompasses two things: thinking

dispositions of the reflective mind and algorithmic efficiency. Individual differences in rational thought can occur due to differences in the algorithmic mind or because of individual differences of thinking dispositions (reflective mind). “[T]he reflective mind is concerned with goals of the system, beliefs relevant to those goals, and the choice of action that is optimal given the system’s goals and beliefs. It is only at the level of the reflective mind that issues of rationality come into play. Importantly, the algorithmic mind can be evaluated in terms of efficiency but not rationality”, says Stanovich.1 It is important to distinguish typical performance situations from optimal performance situations. Under conditions of typical performance explicit instructions are not given to maximize performance. These conditions are similar to what occurs in everyday situations when given few constraints. Typical performance assessments assess the reflective mind- they assess in part epistemic regulation and goal prioritization. In contrast, optimal performance situations are those where explicit instructions are given to maximize performance. Tests under these conditions assess the processing efficiency of the algorithmic mind. Tests of intelligence or cognitive aptitude are optimal performance assessments, whereas tests that assess rational thinking skills are assessed under typical performance onditions. c

The key difference between the reflective mind and the algorithmic mind is reflected in another distinction in the measurement of individual differences- the distinction between thinking dispositions and cognitive abilities. Cognitive abilities are measures of the efficiency of the algorithmic mind. Thinking positions or cognitive styles reflect belief formation, and belief perseverance. Other thinking dispositions that have been investigated are: goal hierarchy, actively open minded thinking, need for cognition, dogmatism, and need for closure. Thinking dispositions are important psychological characteristics that underpin rational thinking and rational behavior. Cognitive abilities assessed on intelligence tests are not about:

Personal goals and their regulation. Tendency to change beliefs when faced with contrary evidence.

Argument & evidence evaluation. Cognitive abilities assessed on intelligence tests are not measurements of rationality, but measurements of algorithmic-level cognitive capacity. In conclusion, intelligence tests assess important cognitive abilities; however, they are severely incomplete measurements of good thinking. It is time to put intelligence in its place and abort the deification of this often misunderstood area of mental life.

R

eferences 1. Stanovich KE. What intelligence tests miss: The psychology of

rational thought. New Haven, CT: Yale University Press, 2009. [YYP] 2. Kahneman D. A psychological point of view: Violations of rational

rules as a diagnostic of mental processes. Behavioral and Brain Sciences. 2001;23: 681-3. [BBS]

3. Evans JS. The heuristic-analytic theory of reasoning: extension and evaluation. Psychon Bull Rev. 2006 Jun;13(3):378-95. [Medline]

________________________________________________________________ †Visit Jamie Hale's Psych Central page, and his personal websites, maxcondition.com & knowledgesummit.net.

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 12

W

hy broscience works, part 1. By Alan Aragon ____________________________________________________ I ntro & background

I don’t know who actually invented the term, but in August of 2008, I decided to immortalize its definition. If you run a Google search on broscience, the top link will lead you to urbandictionary.com, and the highest-rated definition is written

y me: b “Broscience is the predominant brand of reasoning in bodybuilding circles where the anecdotal reports of jacked dudes are considered more credible than scientific research.”

A more succinct but broader definition of broscience is simply, fitness mythology. Broscience is the tenacious set of rules and concepts that govern the bizarre behavior of health and fitness enthusiasts. Something that’s tough for folks like myself to admit is that broscience doesn’t die because a lot of it actually works. The reason it bugs me to admit that broscience is often effective is because the purported mechanisms and explanations behind these concepts are false or imagined. Another reason I hate to admit that a lot of it works is because of the inherent implication that it’s the best & only way. As we’ll see, some examples of broscience are closer to real science than others, while some stuff is just way out there. There are plenty of examples, so I’ll play it safe and figure that this article might not be the only installment. After each bolded BS ‘rule’ I’ll offer my personal take on why it might be effective. “High reps will get you toned & lean, low reps will get you

ulky & strong.” b Okay, so I had to throw the nonsensical application of the word “toned” in there, because everyone still hears it mentioned in actual commercials for fitness products (and in real life). The thing about this bit of BS is that it’s not entirely false. But let’s first start with the obvious problems of viewing this concept in isolation. It ignores energy balance & diet composition, which can be limiting factors in the effects of repetition range and

tensity of load on changes in body composition and strength. in Now. let’s ignore the important foundational stuff for a moment. Let’s also ignore the fact that high & low are subjective terms and must first be given specific definitions. Given the same intensity of effort (ie, all sets in question taken to fatigue), it’s possible that doing 3 sets of 15-20 reps could burn more calories than 3 sets of 5-8 reps. This would especially be true for trainees who are less experienced with heavier loads, and would be more comfortable pushing themselves through more reps using lighter weights. Not holding back as much through a higher volume of work indeed can burn more calories, and in turn, better contribute to an energy deficit, thereby helping the trainee become leaner. And what about low reps for size and strength? Again, if we ignore energy balance and diet composition as limiting factors,

this aspect of the broscience is not entirely false either. Although it’s given that progressive overload at any rep range can cause gains, The “sweet spot” for hypertrophy appears to be roughly 70-85% of your single-repetition maximum (1RM).1 In most cases, this falls somewhere in the range of 6-12 reps per set (the literature also lists 8-10), depending on how close to failure each set is taken. Regarding goals oriented toward strength, the literature again falls toward the traditional low-rep fare. Not that position stands are unquestionable gospel, but the ACSM’s latest recommendation for strength programs in advanced trainees suggests doing “a wider loading range from 1 to 12 RM in a periodized fashion with eventual emphasis on heavy loading (1-6 RM)”2 “

Stick to clean foods in order to get lean and healthy.”

A perpetually debated topic is the importance of clean foods for health and/or optimal body composition. The problem with this debate is that it’s crippled from the get-go. Without any unifying definition of “clean”, it’s literally impossible to make assertions or claims based on this common descriptor. Since there’s rarely (if ever) any agreement over what “clean” means, then cannot be any productive discussions or debates over it. But beyond this, a judgment call of clean or dirty involves looking at foods in a vacuum; in isolation from the rest of the diet. It’s useless to look at individual foods without considering dose & context, since those exact properties determine the nature of the food’s contribution to the diet as a whole. For example, red wine can contribute to a relatively healthy or relatively unhealthy diet depending on whether 2 glasses per day or 2 bottles per day are consumed. So, is red wine a clean food? It

epends on dose & context. d Still, this doesn’t stop people from arguing over it clean versus dirty foods; no way. Right now, someone is wrong on the internet, and this dire issue must be rectified. So, I thought it might be fun to discuss the definitions of “clean” across the denominations and historical eras in nutrition & fitness culture. I’ll begin with the 1980’s, which is when the ‘fitness revolution’ eally started picking up steam. r

1980’s Clean: Fat is evil stuff, fat-free anything is best, so go ahead and splurge on fat-free cookies, fat-free cinnamon rolls, and fat-free dressings. Carbohydrates are king, whole grain foods are the best thing you can eat. Wheaties in the morning means you’re serious about your

ealth. Fruits and vegetables are good for you. h

1990’s Clean: Low-fat is okay, since now there are neutral fats (unsaturated) and deadly fats (saturated). Avoid cholesterol-containing foods, and remember that yolks are only good for making tempera paint. Avoid red meat, that stuff will surely stop your clock from ticking. Skinless chicken breast and water-packed tuna or bust. Upping your protein might help if you’re an athlete, but regular folks on high-protein diets might get osteoporosis and malfunctioning kidneys. Grain-based foods are still okay, so are other starchy foods. However, some of them have a high glycemic index (GI), which can spike insulin and wreak havoc on health and body composition. High-GI food must be vigilantly avoided – except postworkout,

where they must be immediately consumed in large amounts. Postworkout dextrose should be a staple for all in pursuit of athletic goals. Beans and legumes are superfoods because of their low GI. Multiple small meals around the clock is a sure way to stoke the metabolic fire while controlling appetite. Fruits and vegetables are still good for you, but the ones with a high-GI should be avoided (except postworkout).

2000’s Clean: Saturated fat and cholesterol are no longer the bad guys, trans fats are the killers. People are violating Paleolithic ancestral dietary patterns by consuming a disproportionately high amount of omega-6 fatty acids, and not enough omega-3 fatty acids. Fish oil is apparently the cure for all of the world’s diseases, granted no one wants to just die from those fishy burps all day long. It turns out carbohydrate in general is bad for your health, whole grain goodness is one big government conspiracy, and simple sugar can kill you. Fructose in particular is poison to the body, and is more worthy of lacing the tips of arrows than being consumed. Protein is king, it’s infallible. The more meat the better – as long as it’s organic, free-range, and grass-fed. Grains, dairy, legumes, added salt, added sugar, and alcoholic beverages are the downfall of the human species because they are Neolithic foods that violate our evolutionary biology. Soy is a particularly evil legume, despite its staple consumption in Eastern countries with excellent health profiles. Supplemental BCAA gets a pass for being non-Paleolithic; that stuff will make or break your physique goals, so the rules of evolutionary correctness don’t apply to it. Don’t think you’re doing your health a favor by forsaking sugar, then replacing it with aspartame or sucralose, you have to go with stevia or erythritol. Vegetables are still good for you, except for nightshades such as white potato, which contain inflammatory alkaloids (sweet potatoes are fine because they’re not from the nightshade family). Fruits are still good for you, except for ones that contain a lot of fructose, so stick to berries just to be safe.

Alan Aragon’s Research Review – February 2011 [Back to Contents] Page 13

As you can see, not only is "clean" a meaningless abstraction, but it’s something that seems to progressively evolve towards complex states of stupidity as the decades wear on. So, does clean eating work? Yes it does, but not for the myriad ill-supported reasons. Clean eating works because when people emotionally latch on to a newly-discovered dietary “truth”, they take ownership of their diets, and adhere to it more consistently. After all, they’ve been betrayed and misled by the mainstream, and why contribute to lies & conspiracy. Attempts to eat clean also motivate folks to avoid certain foods, and oftentimes, entire food groups. This, in turn, limits their caloric intake. Another benefit is that most whole/unprocessed foods are considered clean, and these foods happen to be less nergy-dense – thus they can better contribute to weight/fat loss. e

And what about unnecessary complexity? Supplementing the diet with isolated food constituents that are already abundant in the diet (ie, amino acids) imparts the perception of being high-tech or cutting-edge. It keeps the person thinking that the extra

step is being taken to reach or exceed the goal faster, even though the process is more laborious. Many folks equate suffering & complexity with success, and pleasure & simplicity with failure. Can people succeed on pleasure and simplicity? Yes, but this is often too foreign a concept for many to grasp; it’s almost as if most people are too deeply indoctrinated into the no pain, no gain mentality. But this doesn’t mean that this approach does not breed success. A certain degree of suffering and complexity can be used as a means to maintain day-long dietary awareness, control, and adherence. There also seems to be a sense of pride and accomplishment in people whose diets force them to endure suffering, complexity, and unnecessary expense. However, the dark side of any obsessive or inflexible approach to dieting is an increased risk of developing or exacerbating an eating disorder.3-5 R

eferences 1. Wernbom M, et al. The influence of frequency, intensity,

volume and mode of strength training on whole muscle cross-sectional area in humans. Sports Med. 2007;37(3):225-64. [Medline]

2. ACSM. American College of Sports Medicine position stand. Progression models in resistance training for healthy adults. Med Sci Sports Exerc. 2009 Mar;41(3):687-708. [Medline]

3. Stewart TM, et al. Rigid vs. flexible dieting: association with eating disorder symptoms in nonobese women. Appetite. 2002 Feb;38(1):39-44. [Medline]

4. Smith CF, et al. Flexible vs. Rigid dieting strategies: relationship with adverse behavioral outcomes. Appetite. 1999 Jun;32(3):295-305. [Medline]

5. Gonzalez VM, Vitousek KM. Feared food in dieting and non-dieting young women: a preliminary validation of the Food Phobia Survey. Appetite. 2004 Oct;43(2):155-73. [Medline]

A correction has been made in the January 2011 issue. Two of the studies I reviewed had the same citation info, and this has been fixed. Additionally, I fixed a couple of typos, thanks to all who spotted them & alerted me. Please re-download he issue, here’s the t log-in page. This video clip shows a uniquely endurance-based hunting technique by Koisan tribesmen in Southern Africa. The amount of skill and respect for nature these hunters have is humbling & utterly fascinating. WARNING to sensitive viewers -- the nimal dies at the end of the clip, and the footage is explicit. a

If you have any questions, comments, suggestions, bones of contention, cheers, jeers, guest articles you’d like to submit, or any feedback at all, send it over to aarrsupport@gmail.com.