Proteinlimit, Muscle Damage & T2DM, Calories, Everyday Biceps T., Hydration, Alcohol, Whey, Caffeine & Your Gainz



Science is a social endeavor. Those who fail to realize that will never reach their full intellectual capacity.


As I pointed out two days ago, there were still a lot of studies to discuss in the May 2016 supplement of Medicine & Science in Sports & Exercise. So, I sat down and went through the rest of them always looking for papers like “Correlations Between Omega-6: Omega-3 Fatty Acid Ratio and Physical and Cognitive Function in Older Adults”, which happens to be the one most people asked for, even though it should be hardly surprising that the scientists’ analysis of data from 28 older adults simply confirmed what you probably know, anyways:

“These preliminary data suggest that dietary omega 6:3 ratio is inversely associated with physical and cognitive function in older adults” (Gurevich. 2016).

Now, this does mean that “consuming a diet with a lower omega-6: omega-3 ratio may promote the maintenance of physical and cognitive function in aging” (ibid. | my emphasis), but this hypothesis is based on correlation and needs to be confirmed in longitudinal RCTs” (ibid.).


No extra-protein necessary after starvation: Data from a recent study suggest that supplementing energy adequate high protein (~2.0 g·kg-1·d-1) diets with additional protein does not enhance LBM recovery from short-term starvation (Sepowitz. 2016).


Body composition (dual energy x-ray absorptiometry) and cross-sectional area (CSA) of the thigh were measured before (BASELINE) and after (POST) a 7-d, near complete starvation caused by Survival, Evasion, Resistance, and Escape School (SERE) in 63 male U.S. Marines (mean ± SD, 25 ± 2 y, 84 ± 9 kg). POST SERE, volunteers were randomized to receive supplements high in carbohydrate (975 kcal, 224 g CHO, 3 g PRO; n=21), moderate in protein (910 kcal, 123 g CHO, 87 g PRO; n=24), or high in protein (1055 kcal, 106 g CHO, 139 g PRO; n=18) during a 27-d recovery period (REC).


Supplements were consumed daily, in addition to their self-selected, ad libitum diet. Dietary intake was calculated using 24-hr recalls and body composition measurements were repeated at the end of REC.


For all participants, total body mass (TBM) (7.2 ± 1.0%; 5.8 ± 1.0 kg; P < 0.05) and CSA (5.9 ± 2.2%; P < 0.05) was lower POST SERE compared to BASELINE. The decline in LBM (4.7 ± 2.5%; 3.1 ± 1.6 kg; P < 0.05) accounted for 53% of the TBM loss. During REC, no differences were observed in total energy intake when self-selected diets and supplement intake were combined (4498 ± 1191 kcal/d; P > 0.05); however, per study design, protein intake was significantly different between groups (high carbohydrate: 1.9 ± 0.6 g/kg/d; moderate protein: 3.1 ± 0.9 g/kg/d; high protein: 3.4 ± 0.9 g/kg/d; P < 0.01). At REC, and independent of group assignment, all participants regained TBM (8.0 ± 2.8%), LBM (5.7 ± 2.9%), and CSA (7.4 ± 3.2%) from POST SERE deficits, resulting in no differences between BASELINE and REC measures (P > 0.05).


New study study rejects the hypothesis that muscle-damaging exercise will decrease glucose tolerance in men: Participants (N=21) were challenged by 5 times of 100-meter downhill sprinting and 20 times of squats training at 30 pounds weight load for 3 days (Ho. 2016). This challenge produced a wide spectrum of increased levels of muscle creatine kinase (CK) in blood, 48 h after the last bout of training. Participants were then divided into two groups according the magnitude of CK increases (low CK: +48% ± 0.3; high CK: +137% ± 0.5, P< 0.05).


Both groups show comparable decreases in blood glucose levels in OGTT, suggesting that muscle-damaging exercise does not appear to decrease but rather improve glycemic control in men.


“Energy Not Protein Or Carbohydrate Intake Attenuates Whole-body Protein Loss During 4-d Arctic Military Training”: The telling title tells it all (Margolis. 2016).


73 Norwegian Soldiers participating in a 4-d arctic military training program (AMT, 51 kM ski march) were randomized to one of three dietary groups; control (CON; n = 18, 3 combat rations per day), protein (PRO; n = 28, 3 rations plus 4, 20 g protein, 250 kcal protein-based snack bars per day), and carbohydrate (CHO; n = 27, 3 rations plus 4, 48 g carbohydrate, 250 kcal carbohydrate-based snack bars per day).


Energy expenditure (D218O) and energy intake were measured daily. Nitrogen balance (NBAL) and whole-body protein turnover were determined at baseline (BL) and on day 3 of AMT using 24 h urine collections and [15N]-glycine.


Protein and carbohydrate intake were highest (P < 0.05) for PRO (mean ± SE, 2.0 ± 0.1 g·kg-1·d-1) and CHO (5.8 ± 0.3 g·kg-1·d-1) but only CHO (3131 ± 122 kcal·d-1) statistically increased (P < 0.05) energy intake above CON (2506 ± 99kcal·d-1). Energy expenditure (6155 ± 60 kcal·d-1) and energy deficit (3313 ± 93 kcal·d-1) were similar across groups. Whole-body net protein balance (-0.24 ± 0.11 g·d-1) and NBAL (-77.1 ± 10.9 mg·kg-1·d-1) were negative at the conclusion of AMT in all groups. In a combined cohort, consuming more energy was associated with higher (P < 0.05) net protein balance (r = 0.57) and NBAL (r = 0.60), independent of macronutrient intake. Soldiers consuming the most energy (3754 ± 94 kcal·d-1) also consumed more (P < 0.05) protein (2.1 ± 0.1 g·kg-1·d-1) and carbohydrate (6.6 ± 0.3 g·kg-1·d-1) than those who consumed the least amount of energy (1783 ± 113 kcal·d-1, 1.2 ± 0.1 g protein·kg-1·d-1 and 3.3 ± 0.3 g carbohydrate·kg-1·d-1), and achieved net protein balance and NBAL during AMT.


Concomittant training doesn’t mess with satellite cells? Not really… While a recent study that combined HIIT session with resistance training (RE) shows that the extra-work does not interfere with the increase in satellite cell density when compared to RE only. However following the concurrent exercise, in this study, there are fewer active satellite cells. “This may attenuate the number of myogenic precursors cells, a key requirement for hypertrophy” (Pugh. 2016) – learn more in this SV article => “Growing Beyond Limits”


Elliptical trainer overestimates energy expenditure: Bad news for CICO over-believers – If you eat “what you burn” (allegedly) you’ll get fat (McLaughlin. 2016)

Let’s do the math. If you work out for 1h, your elliptical will tell you that you have burned 40kcal more than you actually did. That doesn’t sound significant? Well, let’s assume you train thrice a week and eat exactly according to your alleged energy requirements. In that case, you’ll potentially gain … ~600g of body fat 😉


Twenty subjects (10 male, 10 female; 34 ± 12 yr; 175.3 ± 10.7 cm; 77.1 ± 14.1 kg) consented to participate. Each completed three 15-min bouts of elliptical exercise on the same elliptical trainer, with at least 24 hr between exercise bouts. Pedal rates were held constant throughout each bout at 50, 60, or 70 RPM, and resistance was increased incrementally every 5 min from level 5 to 10 to 15. The different cadences were completed in a randomized order between participants. Expired gases were collected continuously throughout the 15 min. Heart rate, distance (mi), and EE from the elliptical readout were recorded every 1 min. RPE was collected twice per resistance level. A two-tailed paired samples t-test was used to compare elliptical EE to measured EE.


A linear regression model was used to evaluate the ability of the elliptical EE to predict measured EE. Significance for all statistical measures was held at an alpha level of 0.05. The difference between EE estimates from the elliptical and measured VO2 was significant (p<0.0001), with the elliptical machine overestimating EE during a 15 minute session by an average of 10.21 kcals. Measured EE in kcals as derived from open circuit spirometry was significantly predicted by elliptical EE according to the equation: Measured EE = 0.95*(Elliptical EE) – 3.161.


Stable (normal) weight = reduced breast cancer risk: Weight gain and cycling are associated with increased risk of postmenopausal breast, colorectal, and endometrial cancer, study shows (Welti. 2016).


Participants included 87,882 postmenopausal women (50-79 yrs) from the Women’s Health Initiative Observational Study, categorized by self-reported weight change (weight stable, steady weight gain, lost weight, weight cycled) during adulthood (18-50 yrs). Adjudicated incident breast, colorectal, and endometrial cancer events were collected annually over 20 yrs. Cox models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI).


Relative risk increase for breast, colorectal and endomitrial cancer in post-menopausal women (Welti. 2016)


In this group of women, 31.5% were weight stable, 28.4% weight gainers, 2.9% weight losers, and 37.2 % weight cyclers. During a mean 12.8 years of follow-up, 8,801 (BC=6446, EC=884, CC=1471) incident cancer cases were identified among women who met study criteria. Compared to weight stability, women who identified as weight gainers were at increased risk of BC (HR: 1.19, CI: 1.12-1.26), CC (HR: 1.24, CI 1.08-1.41), and EC (HR: 1.37, CI 1.15-1.63). Weight cycling increased risk of BC (HR 1.08, CI 1.02-1.15) and EC (HR 1.42, CI 1.21-1.68), with a trend toward an increased risk of CC (HR 1.13, CI 1.00-1.28). Weight loss was not associated with cancer risk.


Training your biceps everyday may not be as bad as you’d think: At least in a recent 21-day study, the daily workouts produced both, size and strength gains.


Using a within subject design, five trained individuals were assigned both a control and experimental arm in a randomized fashion. The control arm performed a one repetition maximum (1RM) and maximal voluntary isometric contraction (MVC) every day for 21 straight days, while the experimental arm performed the same 1RM and MVC test in addition to three sets of elbow flexion exercises at 70% 1RM. Pre to post muscle thickness and strength differences within each condition and median differences between conditions were determined using non-parametric Wilcoxin tests. All significance was set at p≤ .05. Data are presented as median (25th, 75th percentile).


Changes in mean muscle size of the biceps after 21 days of training (Dankel. 2016)


1RM strength increased from Pre to Post in both the experimental [Pre: 25.2 (22.6, 33.1), Post: 27.7 (25.1, 34.9) kg] and control [Pre: 22.5 (21.8, 32.6), Post: 25.0 (23.7, 34.2) kg] (p=0.043) arm, with no difference between arms (p=0.345). Individual data plots indicated that muscle growth occurred in the experimental arm for all individuals, however no changes in muscle size were observed at any location in the control arm. Median muscle size differences within the experimental condition were as follows: 50% (Pre: 3.8, Post: 4.2 cm; p=0.066), 60% (Pre: 4.4, Post: 4.7 cm; p=0.034) and 70% (Pre: 4.9, Post: 5.0 cm; p=0.066). Median differences in muscle size between the experimental and control arms found significant differences at the 50% site (p=0.041) with a trend at the 60% (p=0.068) and 70% (p=0.059) sites.


Milk may be a good fluid to hydrate – even in the office: A recent study comparing various beverages people usually consume at the office shows that the ingestion of milk led to a reduced urine output compared with the other drinks, most likely due to its electrolyte content and casein protein content (Galloway. 2016).


The retention of fluid volume following milk ingestion may be important in situations where frequent work breaks need to be avoided,” the authors conclude based on comparison that involved water, coffee, orange juice or semi-skimmed milk.


Hypohydration has your brain shrivel away… temporarily: Scientists from the East Carolina University had nine physically active adults (four male, five female; 23.9 ± 9.3 y | Wittbrodt. 2016) complete three experimental sessions: control (no exercise/heat exposure; CON), HYPO induced by 2.5 h intermittent walking in 45°C, 15% RH, and euhydration (EUH), 2.5 h intermittent walking in the heat with water ingestion to match sweat loss. Brain morphology was assessed using T1- and T2-weighted magnetic resonance images after a 1 h cool down.


Body mass loss was -2.8 ± 0.6% during HYPO with no sex differences (-2.9 ± 0.3, -2.7 ± 0.9%). Men had greater intracranial, cortical white matter, and subcortical grey matter volume (thalamus, basal ganglia, hippocampus, amygdala) compared to women (p < 0.05) cortical grey or white matter volume; but, tended to decrease (p = 0.06) subcortical grey matter by -1.4% (ES: 0.76) and increase (p < 0.05) ventricular volume by 12.5% (ES: 1.6) and cerebrospinal fluid volume by 13.7% (ES: 1.7) compared to EUH. In contrast, EUH differed (p < 0.05) from CON with lower ventricular and cerebrospinal fluid volumes (-5.1%, ES: 0.72; -6.2%, ES: 1.13) but higher intracranial volume (1.4%; ES: 0.84) compared to CON. Intracranial volume was also lower (p < 0.05) by 1.1% (ES: 0.71) during HYPO vs. EUH.


“This study found hypohydration of ~3% body mass loss: 1) decreases intracranial volume and may reduce subcortical grey matter volume; 2) expands ventricle and cerebrospinal fluid volumes; and 3) induces similar changes in the brain structures of men and women. Moreover, after 1 h recovery from exertional heat stress with water replacement, brain structures differ from a control rest condition”, the authors conclude.


If you include EPOC, exercise may burn much more energy than you’d though: The purpose of a recent study from the San Diego State University compared the measured versus predicted caloric cost of prolonged exercise using various work to rest cycles (Pautz. 2016).


Ten subjects performed 2 hours of exercise on a treadmill using six different work to rest cycles. Subjects performed in a random order the following six isocaloric protocols:A: 3.0 mph,1.7% grade; 30 min rest, 30 min workB:3.5 mph, 3.8% grade; 20 min work, 40 min restC: 3.0 mph,1.7% grade; 30 min work, 30 min restD: 2.5 mph, 1% grade; 40 min work, 20 min restE: 2.0 mph, 1% grade; 50 min work, 10 min restF: 1.5 mph, 1.7% grade; 60 min work, 0 min restOxygen consumption was measured every minute using a metabolic cart.

Comparison of measured vs. predicted energy expenditure durin 2h of work and rest (Pautz. 2016).


The measured caloric cost for protocols A and F were not significantly different than predicted (p >.05). However, in protocols B, C, D, and E the measured caloric cost was significantly greater than predicted. Specifically, the measured caloric cost for the 2 hour exercise bouts were 7-15% higher than predicted from ACSM metabolic equations. When the total caloric cost for each two hour period was separated into working and resting components, the measured versus predicted working components were not significantly different. However, the measured resting components were significantly higher than the predicted values by 24-46% for protocols B, C, D, and E (p<.05).


“The increased caloric cost during the resting component is believed to be due to excess post-exercise oxygen consumption. These results suggest that predictive formulas significantly underestimate the total caloric cost during work/rest exercise. Work/rest cycles utilized in an occupational setting may underestimate the total amount of work performed and result in chronic caloric deficits,” Pautz et al. (2016) conclude.


Alcohol doesn’t mess w/ post-workout mTOR expression: At least when the alcohol is ingested after the workout, it did not affect phosphorylation of the mTORC1 signaling pathway following RE in women (Aziz. 2016).


Athletes and alcohol don’t mix, right? If we go by the results of a recent rodent study, this long-standing recommendation appears to be unwarranted for athletes whose main concern are increases in muscle size. Eventually, the jury is however still out there… and chronic alcohol intake certainly ain’t conducive for your strength & size gains | more



A significant main effect (p<0.05) for time was found for mTOR phosphorylation; mTOR phosphorylation was higher at +3h (ALC: 0.102 ± 0.014 AU; PLA: 0.085 ± 0.009 AU) than at PRE (ALC: 0.111 ± 0.021 AU; PLA: 0.112 ± 0.010 AU) and at +5h (ALC: 0.097 ± 0.016; PLA: 0.088 ± 0.007 AU). A trend (p =0.052) for time was found for S6K1 phosphorylation suggesting greater phosphorylation at +3h compared to Pre and +5h. No significant differences between conditions or over time were found for 4E-BP1 phosphorylation.


Whey way more insulinogenic in women: In a recent study 5 health young men and 5 likewise young women consumed 20g/200 mL whey peptide in water after a 12h fast (Kakigi. 2016). One hour after intake of whey peptide, muscle and blood samples were collected. Muscle samples were used to determine the phosphorylation status of mTOR (Ser2448) and S6K1 (Thr389) by using



Interestingly, the whey peptide intake significantly increased the concentration of serum insulin in women (p<0.05), whereas it did not change in men.


That’s news: First study to show that whey peptides spikes insulin in women, but not men (Kagigi. 2016).


Plasma essential and branched-amino acids concentrations significantly increased after whey peptides intake in both men and women (p<0.05). At resting, there were no differences in the phosphorylation of mTOR and S6K1 between men and women. After intake of whey peptide, the phosphorylation of mTOR and S6K1 significantly increased in both muscles of men and women compared with those of resting levels (p<0.05), and the increase in S6K1 phosphorylation in women was significantly higher than that in men (p<0.05). The phosphorylation of S6K1 after whey peptide intake was significantly correlated with doses of whey peptide per body weight (g/kg) (r2=0.7128, p<0.05).


Dehydration worsens your glucose response by 9%: In a randomized-crossover design, five healthy individuals (80 % male) aged 28 ± 4 y, were dehydrated in a sauna (55–85°C) for 45 minutes between 1700–1900 hours, before either remaining dehydrated (consuming maximum 200 mL) or rehydrating with 150 % of individual weight losses throughout the evening. Participants then arrived at the laboratory the next morning in a fasted state at 0800 hours and provided a urine sample to verify hydration status based on urine osmolality, before commencing an oral glucose tolerance test (75 g glucose solution in 89 mL water). Venous blood samples were drawn at baseline and every 15 minutes for 120 minutes. Trials were separated by seven days, with diet and physical activities replicated for 24 h prior to each. Data were analysed via visual checking of trends and calculating the incremental area under the curve (iAUC).


Ok, the effect is not earth-shattering, but should be kept in mind when you monitor your glucose levels (Carrol. 2016).


Body mass was reduced by 1.2 ± 0.8 kg. The following week, participants matched this weight loss or remained in the sauna for 45 minutes (whichever came first). Urine osmolality was significantly higher when dehydrated than rehydrated (1069 ± 67 and 606 ± 292 mOsm). The iAUC for blood glucose was higher in the dehydrated trial than the rehydrated trial (72.9 ± 45.5 vs. 66.6 ± 49.1 mmol*120min/L. This was reflective of a similar time-course of initial response but then an attenuated concentration in the hydrated trial from 45 minutes onwards. Blood lactate concentrations were also lower in the rehydrated group, although the differences in the time-course of the initial response were only apparent from 75 minutes.


Powerlifting – too little volume to benefit from caffeine? 7 mg/kg BW of CAF does not improve measures of force, power, or fatigue during single-set to failure resistance training exercises in habitual caffeine users, recent study in eighteen young, healthy college aged males shows (Sanders. 2016). These results indicate that CAF supplementation may be affected by training bout volume (sets x reps), due to the lack of improvement in F.I., i.e. local muscular endurance.


If volume is the focus of training (i.e., hypertrophy phases), the rest-pause resistance training method should be utilized when bench pressing, study shows: A recent study compared one repetition maximum (1RM), muscle activity (EMG), and volume differences between rest-pause or traditional resistance training (Korak. 2016). Trained males (N = 20) were randomly assigned to either a rest-pause or a traditional training group. Training sessions were completed twice a week for 4 weeks and consisted of four sets of bench press to volitional fatigue at 80% of pretest 1RM with 2-minutes rests between sets. Each participant completed a bench press 1RM before and after the training intervention. Total volume completed was recorded on each training day. Muscle activity of the pectoralis major was measured on the first and last training days. The RMS signals of the last repetition in the last set were normalized to the RMS peak values of the first repetition in the first set for each participant during the 1st and 8th training sessions.


A 2-way repeated measures ANOVA indicated both groups significantly increased their 1RMs following the four week training protocol (p < .05). However, no significant differences were found in 1RM and muscle activity between the two groups (p > .05).


The rest-pause technique allowed the subjects to lift sign. more weight (Korak. 2016).


Lastly, an independent samples t-test indicated total volume lifted was significantly higher for the rest-pause group (M = 56,778lbs, SD = 23,522lbs, N = 10) in comparison to the traditional training group (M = 38,315lbs, SD = 7,870lbs, N = 10). T (18) = 2.354, p <.05.


Reason enough for the scientists to conclude: “While strength and muscle activity changes did not differ between groups, the rest-pause group achieved greater increases in volume than the traditional group. If volume is the focus of training (i.e., hypertrophy phases), the rest-pause resistance training method should be utilized. Future studies should assess changes in muscle size between these bench press methods” (Korak. 2016).


Two Crazy, but Scientifically Proven Workouts for You to Try if You Want to Gain Muscle While Losing Fat or Cut Your Body Fat in Half to Finally Reach a Sub 10% Body Fat Level | more


Study says risk of rhabdo is low for crossfitters: The overall risk of exertional rhabdomyolysis (ER) may be minimal, especially if a participant understands their body’s limitations in regard to the intensity of CrossFit®, the authors of a recent study conclude based on data from 101 CrossFit® participants and 56 ACSM guided participants (Bellovary. 2016).


In those people, “[t]he top five perceived hardest workouts based on frequency were Fran (47), Murph (27), Fight Gone Bad (10), Helen (9) and Filthy 50 (9)” and only one subject of the 101 CrossFit® participants reported ER. Working out augments and prolongs protein synthesis in response to protein ingestion: Cleverly done study confirms what you probably already expected.


Retrospective signalling analysis was carried out on muscle tissue from a previously published study. Twenty-three resistance trained males consumed a high protein breakfast before resting for 3 h. Following a bout of unilateral resistance exercise (8 × 10 leg press and leg extension exercises; 80% 1 RM) participants consumed a whey protein isolate drink (containing 10, 20 or 40 g protein). The activity of p70S6K1 was measured at 0 and 4 h in rested and exercised legs using a [γ-32P] ATP kinase assay. Statistical analysis of the fold change (0-4 h) in p70S6K1 activity between rested and exercised legs was conducted using a paired samples t-test (protein doses were pooled). Linear regression analysis was performed on the difference in fold change of p70S6K1 between rested and exercised (x-axis) against the previously published myofibrillar muscle protein synthesis data (y-axis). Effect sizes were calculated and reported as Cohen’s d with confidence intervals (CI).


Fold-change in muscle protein synthesis in trained and untrained arm after the ingestion of 40g (Manaughton. 2016).


The fold change in p70S6K1 activity in response to protein ingestion from 0-4 h was 62% higher (p=0.004; d=0.61; CI=0.14 to 1.08) in the exercised leg (1.8 ± 1.3 fold; mean ± SD) compared with the rested leg (1.1 ± 0.8 fold). Regression analysis revealed a significant linear relationship between the difference in p70S6K1 activity fold change between rested and exercised legs and previously published myofibrillar muscle protein synthesis data (p=0.031; r2=0.204; y=1.28+0.11*x).


HIIT vs. Steady-State for Fat Loss: Can EPOC Explain the Benefits of Intense Interval Training (HIIT, SIE, HIE)? | more.


HIIT increases EPOC only briefly: High intensity aerobic intervals resulted in greater EPOC than moderate aerobic exercise of the same energy expenditure, but only for the first 10 minutes post exercise (Simmons. 2016). The data comes from nine subjects (5 male, 4 female; age 26 ± 2 yr; height 170 ± 3 cm; mass 68.5 ± 3.3 kg; VO2max 41.9 ± 2.1 who participated in moderate (MOD) and high intensity aerobic interval (HIAI) trials in a counterbalanced order separated by at least 48 hours. HIAI was 10×1-min intervals at 90% Pmax, w/ 1-min active rest (60% Pmax). MOD was 30 min at 50% Pmax.


Traditional, Contrast, And Pre-exhaustive Training: A recent study investigated the extent of recovery from the acute effect of three resistance-training modalities, Traditional Training (TT), Contrast Training (CT) and Pre-Exhaustive Training (PE), using three performance measures critical to power performance sports. Ten (7 male and 3 female) participants age 22.2 +/- 1.62 yrs, height 180.66 +/- 9.08 cm, weight 75.85 +/- 8.2 kg, percent body fat 16.5 +/- 7.22 % participated in the study. The average 1-RM squat was 105.23 +/- 37.76 kg. Training modalities were randomized prior to testing. One hour following training sessions, participants were tested on three performance measures in the following order: vertical jumping (VJ; height, cm), T-test for agility (T-test; time, sec), and repeated sprint (RS; time, sec).


Supersetting is fun, time-efficient, exhausting and based on the reasonable assumption that you can benefit from training agonist + antagonist together, but does it build size & strength? more


Time to complete training protocols was significantly different between all 3 protocols (P<0.001); importantly though, there was no difference in volume of total weight lifted during each protocol. Following the protocols, performance tests revealed significant differences between Control VJ performance (59.56 ± 11.71cm) and TT (56.52cm ± 11.90cm) (p≤ 0.05), CT (55.38cm ± 12.88cm) (p≤ 0.01), and PE (57.02cm ± 11.95cm) (p≤ 0.05), respectively. Our results also demonstrated significantly slower times to complete T-test following TT (11.49s ± 1.13s) and CT (11.41s ± 0.95s) protocols, compared to Control (11.27s ± 0.77s) (P0.05).


“Significant differences in performance were found between the TT, CT, and PE treatments and Control protocols in maximal vertical jump height, agility capabilities, and repeated sprint ability. These data suggest that modifying exercise order beyond traditional resistance-training protocols might improve performance variables due to improved recovery and improved efficiency during subsequent same-day training,” the authors conclude.


Exercise order matters for fat burning: The order of exercise between aerobic and resistance exercises does not affect substrate oxidation during aerobic exercise, but aerobic exercise before resistance exercise enhanced fat oxidation during post-exercise period more so than when resistance exercise precedes aerobic exercise (Morishima. 2016).


That’s it for this special: No, there’s not going to be another compilation of study results in two days. I hope I have addressed anything that’s potentially interesting and if I overlooked your favorite, you can still read them yourself and then post and complain on Facebook!



  1. Aziz, et al. “Effect Of Acute Alcohol Ingestion On Resistance Exercise-Induced Muscle MTORC1 Signaling In Women.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 581.
  2. Bellovari, et al. “The Perceived Demands of CrossFit®” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 860.
  3. Carrol, et al. “The Effect of Hydration Status on Glycemic Control: A Pilot Study.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 745.
  4. Dankel, et al. “Muscle adaptation to 21 Straight Days of Elbow Flexor Exercise in Trained Individuals.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 452.
  5. Galloway, et al. “Hydration Potential of Commonly Consumed Drinks in an Office-Working Environment.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 567.
  6. Gurevich, et al. “Correlations Between Omega-6: Omega-3 Fatty Acid Ratio and Physical and Cognitive Function in Older Adults.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 422.
  7. Ho, et al. “Improved Glucose Tolerance By An Acute Bout Of Muscle Damaging Exercise.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 899.
  8. Kakigi, et al. “Dose-Dependent Effects of Whey Peptide Intake on mTOR Signaling in Human Skeletal Muscle.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 584.
  9. Korak, et al. “Effects Of Rest-pause Vs Traditional Bench Press Training On Muscle Strength, Electromyography, And Lifting Volume.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 807.
  10. Macnaughton, et al. “Resistance Exercise Sustains p70S6K1 Activity in Response to Protein Ingestion at 4 hours.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 819.
  11. Margolis, et al. “Energy Not Protein Or Carbohydrate Intake Attenuates Whole-body Protein Loss During 4-d Arctic Military Training.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 444.
  12. Morishima, et al. “Impact Of Order Of Exercises On Substrate Oxidation In Healthy Men.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 1026.
  13. Pugh, et al. “Satellite Cell Reponse to Concurrent Resistance Exercise and High Intensity Interval Training in Overweight/Obese Individuals.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 453–454.
  14. Sanders, et al. “The Effect of Caffeine Supplementation on Power and Fatigue in Recreationally Trained College Aged Male.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 857.
  15. Sepowitz, et al. “Supplementing An Energy Adequate High Protein Diet With Additional Protein Is Not Necessary For Recovery Of Lean Body Mass After Short-term Starvation.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 443.
  16. Simmons, et al. “EPOC Following High Intensity Aerobic Intervals and Moderate Intensity Aerobic Exercise.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 863.
  17. Sokmen, et al. “The Effects Of Traditional, Contrast, And Pre-exhaustive Training Methods On Performance Variables.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 942.
  18. Welti, et al. “Weight Fluctuation And Cancer Risk In Post-Menopausal Women: The Women’s Health Initiative.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 514.
  19. Wittbroth, et al. “Impact Of Hypohydration And Exercise-heat Stress On Brain Structure In Men And Women.” Medicine & Science in Sports & Exercise: May 2016 – Volume 48 – Issue 5S – p 566–



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