Aerobic Energy Expenditure Comparisons Between One Traditional and CrossFit-Based Exercise Session

This study sought to compare aerobic energy expenditure, recovery VO2, peak heart rate, and peak VO2 achieved across 45 min of exercise and 15 min of recovery performing both traditional and CrossFit®-based exercise. Thirty healthy, physically active participants of both genders (15 men, 15 women) performed a workout following the guidelines of the American College of Sports Medicine (traditional) and a workout following the CrossFit® method. Each workout consisted of a 5 min warm-up (light aerobic exercise and stretching), resistance exercise (both focused on leg exercises), cardiorespiratory exercise (a treadmill run for the traditional exercise and circuit training for the CrossFit®-based exercise) and 5 min cool-down (walking). The cool-down was followed by 10 min of sitting to record recovery values. During each workout the participants wore a K4b2 Cosmed unit to measure energy expenditure and VO2, and a Polar heart rate monitor to measure heart rate. Each measure was compared using a Dependent t-Test. Energy expenditure (468 ± 116 vs. 431 ± 96 kcal, p\u3c0.001), peak heart rate (189 ± 8 vs. 172 ± 8 bpm, p\u3c0.001), peak VO2 (3.22 ± 0.73 vs. 2.81 ± 0.63 L/min, p\u3c0.001) and average 15 min recovery VO2 (0.89 ± 0.24 vs. 0.78 ± 0.18 L/min, p\u3c0.001) were significantly greater in the CrossFit®-based workout. The present study suggests that CrossFit®-based exercise may result in greater aerobic energy expenditure than traditional exercise

The Relationship of Physiological and Fitness Variables to Performance in CrossFit®-based Exercise: Preliminary Findings

CrossFit® is a rapidly expanding exercise program as well as an emerging competitive sport. Little is known regarding the correlation of physical fitness measures and performance in CrossFit®-based events. PURPOSE: The purpose of this study was to determine the relationship between various physiological and fitness variables and performance in a typical CrossFit®-based workout. METHODS: Nine male participants (age = 32.2 ± 3.6 yrs; height = 173.1 ± 9.8 cm; weight = 86.1 ± 11.6 kg; BMI = 28.6 ± 1.2 kg/m2) who had performed CrossFit® as their primary exercise program at least 3 days/week for the past 12 months were recruited. Participants performed a test of maximal aerobic fitness (VO2max), a Wingate test, a DEXA scan, a 1RM Clean and Jerk, and a series of exercises that would typically be included in a CrossFit®-based workout. More specifically, participants performed 15 Wall Ball exercises (20 lbs.), 15 Box Jumps (24 in.), 10 Burpees over a barbell, and 10 Kipping Pullups. If all exercises were completed, the participants repeated the exercises in the same order. The participants were asked to stop after 12 min, and the total number of repetitions completed was recorded. Questions regarding the participants’ exercise history and dietary habits were asked. Spearman’s correlation was used to identify relationships between the variables and performance (number of repetitions completed) during the CrossFit®-based workout. Participants were also grouped into “high” (≥ median) or “low” (\u3c median) groups, and independent samples t-tests were used to compare how each group performed during the CrossFit®-based workout. Statistical significance was set at .05. RESULTS: Performance during the CrossFit®-based workout had strong, positive relationships with strength-to-body weight ratio (r = .686; p = .041), 1RM Clean and Jerk (r = .915; p = .001), and years of experience (r = .869; p = .002). Participants with higher strength-to-body weight ratios (p = .036), lower fatigue index (p = .022), lower body fat percentage (p = .022), higher weight lifted during the 1RM Clean and Jerk (p = .017), and more years of experience (p = .027) completed more repetitions during the CrossFit®-based workout. Significance was not found with any other variable. CONCLUSION: Based on these early findings, anaerobic fatigue resistance, body fat percentage, muscular power, and exercise history appear to be significant predictors of performance in CrossFit®-based workouts

Training Manipulations Based on Acute Heart Rate Variability Measures

Heart rate variability (HRV) is an accurate indicator of sympathetic and parasympathetic nervous system activity. The balance between these systems affects the time between heartbeats. A high variability between heartbeats is equated to a greater influence from the parasympathetic nervous system. In this state, an individual is well rested, and therefore possesses higher readiness to perform physical activity. Through the use of smartphone applications (apps), athletes and coaches can collect accurate short-term HRV readings to assess autonomic nervous system balance. These apps provide a readiness to train score that may prove beneficial in adjusting daily training loads to maximize performance. PURPOSE: The purpose of this study is to characterize the changes in lower-body strength and power before and after a 6-week strength training program while manipulating intensity based on daily HRV readiness measures in female collegiate softball athletes. METHODS: Nine female NCAA Division II Softball athletes completed the 6-week training protocol. Participants were split into an experimental group (E; n = 5; age = 20.5±0.7 yrs, height = 166.9±2.7 cm, weight = 59.9±7.6 kg), who completed the training with the intensity adjusted based off of daily HRV readiness scores, and a control group (C; n = 4; age = 20.6±0.8 yrs, height = 171.7±1.2 cm, weight = 70.7±30.3 kg), who completed the training with no changes in exercise intensity. Measures of HRV were taken prior to each training session and used to calculate readiness scores with the use of a smartphone app. Participants completed 3 strength-training sessions per week throughout the study. Lower-body strength and power measurements were assessed before and after the protocol. One-repetition maximums on the back squat (SQ) and clean (CL) exercises and maximum vertical jump (VJ) height were collected. RESULTS: Lower-body power measurements were increased in the E group (CL: 51.3 vs. 56.9 kg, p = 0.047; VJ: 40.1 vs. 44.7 cm, p = 0.037) and the C group (CL: 56.8 vs. 63.6 kg, p = 0.021; VJ: 41.6 vs. 46.2 cm, p = 0.034), following 6 weeks of strength training. No significant differences were observed in lower body strength measurements in the E group (SQ: 74 vs. 84.1kg, p = 0.21) or the C group (SQ: 75.5 vs. 86.6 kg, p = 0.2). Significant differences were found between the prescribed volume of training and the completed volume of training (25364 vs 21650 kg, p = 0.014) in the E group. No significant differences (p \u3e 0.05) were found with SQ, CL, and VJ measures between the E and C groups following 6 weeks of strength training. No significant differences (p \u3e 0.05) were found in daily HRV measures between the E and C groups. CONCLUSION: Both groups exhibited similar HRV scores throughout the 6-week training protocol. Using daily short-term HRV readings, training intensity can be reduced without leading to any differences in lower-body strength and power improvements in female collegiate softball athletes

The Dose Effect of Whey Protein on Insulin Responses in Pre-Diabetics and Type 2 Diabetics

People with pre-diabetes and type 2 diabetes have shown an increase in insulin secretion after ingesting 55 g of whey protein coupled with a glycemic challenge. However, the effect of lower amounts of whey protein on insulin responses remains unclear. Our hypothesis was that both 20 g and 30 g of whey consumption prior to an oral glucose tolerance test (OGTT) would produce an increase in insulin secretion, with 30 g producing the greatest increase compared to a control. PURPOSE: The purpose of this study was to examine the effect of two different doses of whey protein ingested 30 min prior to a 50 g OGTT on glucose, insulin, C-peptide, and glucagon responses. METHODS: Diabetic or pre-diabetic participants (n=9, mean ± SD; age: 64.3 + 8.1 yrs; BMI: 29.4 + 6.0 kg/m2; body fat percentage: 42.5 + 7.8 %; fasting plasma glucose: 6.9 + 1.2 mmol/l; HbA1c: 6.4 + 0.6 %) completed three trials. The randomly assigned trials consisted of: 250 ml of water (CON), 250 ml of water + 20 g whey (20g), and 250 ml of water + 30 g whey (30g), followed by an OGTT. Blood was collected at -30, 0, 15, 30, 60, 90, 120, and 150 min for the measurement of glucose, insulin, C-peptide, and glucagon. The whey protein mixture was administered immediately following the -30 min blood draw, and the 50 g OGTT began immediately following the 0 min blood draw. Glucose was analyzed using a YSI 2900D glucose analyzer and insulin, C-peptide, and glucagon were measured via multiplex fluorescent detection (MagPix). A one-way repeated measures ANOVA (pRESULTS: Incremental area under the curve (AUC) for glucose presented no difference between the 3 trials. Insulin AUC was significantly increased from CON to 20g (p=0.004, 36.3%), CON to 30g (p=0.002, 61.7%), and 20g to 30g (p=0.030, 18.6%). C-peptide and glucagon AUC significantly increased from CON to 20g (p=0.018, 20.6%; p=0.046, 33.1%) and CON to 30g (p=0.001, 30.1%; p=0.017, 33.7%). CONCLUSION: Whey protein elicited a dose response on plasma insulin, increasing concentrations from CON to 20g, and 20g to 30g, however plasma glucose was unaffected. 20g and 30g displayed similar responses for glucagon. Neither 20 g nor 30 g of whey protein may be adequate to provide glycemic improvement in the disease management of type 2 or pre-diabetes

Physiological and Fitness Adaptations after Eight Weeks of High-Intensity Functional Training in Physically Inactive Adults

The purpose of this study was to characterize high-intensity functional training (HIFT) in physically inactive adults. Four men and 10 women who were inexperienced with HIFT and not performing regular physical activity performed HIFT 3 days/week for 8 weeks. Health and fitness measures were assessed before and after the intervention. Resting heart rate (73 ± 12 vs. 68 ± 11 bpm) and resting diastolic blood pressure (71 ± 7 vs. 65 ± 6 mmHg) were reduced, while resting systolic blood pressure remained unchanged. Absolute VO2max (2.53 ± 0.68 vs. 2.69 ± 0.66 L/min) and relative VO2max (32.51 ± 8.84 vs. 34.31 ± 8.63 mL/kg/min) were improved. Lean body mass (48.20 ± 13.37 vs. 49.26 ± 13.81 kg) was increased, but fat mass was unchanged. Performance on the leg press (164.61 ± 54.35 vs. 201.62 ± 67.50 kg), bench press (39.12 ± 20.15 vs. 46.43 ± 21.18 kg), YMCA bench press (26 ± 13 vs. 37 ± 16 reps), one-minute sit-up (25 ± 9 vs. 32 ± 10 reps), and sit-and-reach (30.36 ± 11.36 vs. 32.14 ± 9.66 cm) were all increased. High-intensity functional training may be useful for improving health-related physical fitness parameters in physically inactive adults

Low Energy Availability Prevalence, Sleep Quality, and Dietary Habits in Female ROTC Cadets

BACKGROUND: Energy availability is defined as the difference between energy intake (EI) and exercise energy expenditure (EEE) divided by fat free mass (FFM). Low energy availability (LEA) is a state in which energy intake is insufficient to support all physiological functions and is defined as an energy availability (EA) of \u3c 30 kcal/kg FFM. LEA may be intentional due to body recomposition or social pressure to maintain a certain body image but may also be unintentional due to increased training demands. LEA may lead to hormone dysfunction, sleep disturbance, altered metabolic responses, and other maladaptations on health and performance. Previous studies have reported high prevalence of LEA in both male and female athletes, with a higher prevalence in females. Prevalence of LEA has also been reported in male Reserve Officers’ Training Corps (ROTC) cadets; however, this has not been evaluated in female ROTC cadets in the United States. Therefore, the purpose of the present study is to assess the prevalence of LEA in female ROTC cadets and to assess sleep quality. Additionally, we aim to compare their nutritional practices to the Military Dietary Reference Intakes (MDRIs) and examine resting metabolic rate (RMR) suppression. METHODS: Seventeen female ROTC cadets will be recruited. Following an overnight fast, height, weight, body composition, and RMR will be measured. Participants will then complete the Low Energy Availability in Females Questionnaire (LEAF-Q) to assess LEA symptoms, as well as the Athlete Sleep Behavior Questionnaire (ASBQ) and Pittsburgh Sleep Quality Index (PSQI) to assess sleep. Participants will be fitted for both a hip and wrist worn physical activity monitor to assess energy expenditure and sleep parameters, respectively, for 7 continuous days. During this time, daily EI will be assessed using the Automated Self-Administered 24-hour Dietary Assessment Tool. Following data collection, food and activity records will be reviewed for completion. T-tests will be used to assess sleep, nutrition, and body composition differences between participants with and without LEA. Pearson correlations will be used to compare EA with sleep and body composition. EXPECTED RESULTS: It is hypothesized that LEA, sleep disturbance, and RMR suppression will be highly prevalent in female ROTC cadets. Additionally, it is hypothesized that the MDRIs will not be met by most individuals

Acute Effects of Concurrent Exercise on Biomarkers of Angiogenesis and Cardioprotection in Sedentary Adults: Preliminary Findings

There is evidence that performing brief bouts of aerobic-type exercise before each set of resistance exercise (i.e., integrated concurrent exercise) leads to superior health and fitness outcomes than when the modalities are performed independently (i.e., serial concurrent exercise). This advantage may be due in part to an exaggeration of functional hyperemia leading to an augmented cardiovascular adaptive response. PURPOSE: To analyze circulating levels of an endothelial shear stress-induced biomarker (microRNA-126) and a biomarker of cardiovascular development (microRNA-222) before and after volume- and time-matched serial and integrated concurrent exercise sessions in young, healthy, sedentary adults. METHODS: One female and three male participants (age = 27.8 ± 6 yrs; height = 172.8 ± 4.6 cm; weight = 71.8 ± 17.6 kg; BMI = 23.9 ± 4.9 kg/m2; VO2max = 30.13 ± 4.92 ml/kg/min) who were healthy and had performed no more than 1 hr of structured physical activity per week over the previous year completed all procedures. Participants performed one-repetition maximum tests on the Leg Press, Leg Curl, and Leg Extension exercises, and also completed a maximal cycling test. At least one week after testing, participants performed one of two exercise patterns: 3 sets of 10 repetitions of each resistance exercise followed by 20 min of cycling (serial), or 2 min of cycling performed before each set of resistance exercise (integrated). At least three weeks after the first exercise session, the participants performed the other session. Blood was collected before each exercise session, immediately after each exercise session, and 1 and 3 hours after each exercise session. RNA was extracted from the frozen plasma samples and microRNAs were quantified using PCR analysis. Values were normalized to a spike-in control and adjusted for plasma volume shifts. Fold-change of target microRNAs from baseline were calculated. Data were analyzed using a two-way ANOVA with repeated measures. Significance was set at 0.05. RESULTS: MicroRNA-126 changed 0.24 vs. 1.70-fold immediately post-exercise, 0.23 vs 0.75-fold 1 hr post-exercise, and 0.26 vs. 1.29-fold 3 hr post-exercise following serial and integrated concurrent exercise, respectively. There was no time effect (p = 0.34), no exercise effect (p = 0.85), and no interaction effect (p = 0.58). MicroRNA-222 changed 0.20 vs. 3.07-fold immediately post-exercise, 0.21 vs 1.21-fold 1 hr post-exercise, and 0.22 vs. 2.09-fold 3 hr post-exercise following serial and integrated concurrent exercise, respectively. There was no time effect (p = 0.26), no exercise effect (p = 0.73), and no interaction effect (p = 0.41). CONCLUSION: Although not statistically significant, a more robust response from integrated concurrent exercise compared to serial concurrent exercise was observed. At this early stage, it is unclear if these results will persist with the addition of more participants

Physiological and Fitness Adaptations Following Eight Weeks of CrossFit® Exercise

Over the past decade, CrossFit® has been rapidly growing in popularity. There are currently over 5,000 CrossFit® gyms around the world and the number of gyms is increasing each year. Health professionals are unable to provide clear recommendations for CrossFit® due to the limited amount of existing research that includes the program as an exercise intervention. PURPOSE: To characterize the cardiorespiratory responses, body composition, and other health-related physical fitness parameters before and after 8 weeks of CrossFit® exercise in sedentary adults. METHODS: Fourteen participants, including men (n = 4; age = 30 ± 8 yrs; height = 176.03 ± 7.57 cm; weight = 114.6 ± 43.56 kg) and women (n = 10; age = 26 ± 6 yrs; height = 163.21 ± 7.53 cm; weight = 70.71 ± 18.28 kg) who were inexperienced with CrossFit® and had not performed regular structured physical activity for the past 12 months, completed all procedures. All participants completed 8 weeks of CrossFit®, exercising 3 days per week at a CrossFit® gym in Lewisville, TX. Each workout lasted about 1 hour and consisted of a warm-up, resistance exercise, circuit training that included a combination of aerobic and resistance exercise, and flexibility training. Resting heart rate, resting blood pressure, cardiorespiratory fitness, body composition, muscular strength, muscular endurance, and muscular flexibility were assessed before and after the 8-week protocol. Dependent t-Tests were performed to determine any differences between the two time points. A significance level of 0.05 was used. RESULTS: Following the 8-week CrossFit® program, resting heart rate (73 ± 12 vs. 68 ± 11 bpm; p = 0.006) and resting diastolic blood pressure (71 ± 7 vs. 65 ± 6 mmHg; p = 0.01) were reduced, while resting systolic blood pressure (112 ± 13 vs. 108 ± 12 mmHg; p = 0.13) remained unchanged. Absolute VO2peak (2.53 ± 0.68 vs. 2.69 ± 0.66 L/min; p = 0.003) and relative VO2peak (32.51 ± 8.84 vs. 34.31 ± 8.63 ml/kg/min; p = 0.003) were improved. Lean body mass (106.03 ± 29.41 vs. 108.38 ± 30.38 lbs; p = 0.006) was increased, but fat mass (71.57 ± 44.98 vs. 70.22 ± 44.52 lbs; p = 0.23) did not change. Performance on the leg press (5RM; 362 ± 120 vs. 444 ± 149 lbs; p \u3c 0.001), bench press (5RM; 86 ± 44 vs. 102 ± 47 lbs; p \u3c 0.001), YMCA bench press (26 ± 13 vs. 37 ± 16 reps; p \u3c 0.001), one-minute sit-up (25 ± 9 vs. 32 ± 10 reps; p \u3c 0.001), and sit-and-reach (30.36 ± 11.36 vs. 32.14 ± 9.66 cm; p = 0.01) were all increased. No significant or chronic injuries were reported. CONCLUSION: CrossFit® may be a safe and effective exercise program for improving health-related physical fitness parameters in sedentary adults

In vitro mitochondrial and myogenic gene expression is influenced by formoterol in human myotubes

Abstract Background Exercise is an effective treatment for establishing and maintaining skeletal muscle health. The interconnected cascade of gene expression pathways related to myogenesis, mitochondrial homeostasis, and thyroid hormone metabolism are critical to skeletal muscle health. This in vitro study was conducted to investigate the effects of exercise mimetic (formoterol) stimulation on human skeletal muscle cell signaling during myogenesis, and to provide insight on potential targets for future studies exploring therapies for skeletal muscle atrophy. Human myoblasts were cultured and differentiated to evaluate the effects of exercise mimetic stimulation on gene expression during mid and late myogenesis. Results We characterized the expression of 24 genes related to myogenesis, mitochondrial biogenesis, thyroid hormone metabolism, and cellular homeostasis and found that 21 genes were altered in response to formoterol, thus affecting related skeletal muscle pathways. Additionally, formoterol stimulation resulted in a myogenic program that appears to favor prolonged myoblast proliferation and delayed myotube maturation. Robust, yet distinctive effects of exercise mimetic stimulation on gene expression during mid-myogenesis and at terminal differentiation occurred. For instance, MYF5 increased in D6 FORM compared to other groups (p < 0.001) while MYOD and MYOG both decreased expression in the FORM groups compared to CON (p < 0.01). Secondly, mitochondrial biogenesis genes were stimulated following formoterol administration, namely PGC-1α, PGC-1β, and TFAM (p < 0.05). Uniquely in our study, thyroid hormone metabolism related genes were differentially expressed. For instance, DIO2 and DIO3 were both stimulated following formoterol administration (p < 0.05). Conclusions The results of our study support the groundwork for establishing further experiments utilizing exercise signaling as a clinical treatment in models targeting dysfunctional skeletal muscle cell growth