Recently, interest in time-restricted feeding (TRF) has increased from reports highlighting improvements in body composition and muscular performance measures. Twenty-six recreationally active males were randomly assigned to either TRF (n = 13; ~22.9 years; 82.0 kg; 178.1 cm; 8 h eating window, 25% caloric deficit, 1.8 g/kg/day protein) or normal diet (ND; n = 13; ~22.5 years; 83.3 kg; 177.5 cm; normal meal pattern; 25% caloric deficit, 1.8 g/kg/day protein) groups. Participants underwent 4-weeks of supervised full body resistance training. Changes in body composition (fat mass (FM), fat free mass (FFM), and body fat percentage (BF%)), skeletal muscle cross sectional area (CSA) and muscle thickness (MT) of the vastus lateralis (VL), rectus femoris, (RF), and biceps brachii (BB) muscles, resting energy expenditure (REE), muscular performance, blood biomarkers, and psychometric parameters were assessed. Significant (p < 0.05) decreases were noted in BM, FM, BF%, testosterone, adiponectin, and REE, along with significant increases in BP_1RM, LP_1RM, VJ_HT, VJ_PP, VL_CSA, BB_CSA, and BB_MT in both groups. Plasma cortisol levels were significantly elevated at post (p = 0.018) only in ND. Additionally, FFM was maintained equally between groups. Thus, a TRF style of eating does not enhance reductions in FM over caloric restriction alone during a 4-week hypocaloric diet.

This investigation examined anthropometric, hormonal, and physiological differences between advanced (ADV; n = 8, 27.8 ± 4.2 years, 170 ± 11 cm, 79.8 ± 13.3 kg) and recreational (REC; n = 8, 33.5 ± 8.1 years, 172 ± 14 cm, 76.3 ± 19.5 kg) CrossFit (CF) trained participants in comparison to physically-active controls (CON; n = 7, 27.5 ± 6.7 years, 171 ± 14 cm, 74.5 ± 14.3 kg). ADV and REC were distinguished by their past competitive success. REC and CON were resistance-trained (>2 years) and exercised on 3–5 days·wk^-1 for the past year, but CON utilized traditional resistance and cardiovascular exercise. All participants provided a fasted, resting blood sample and completed assessments of resting metabolic rate, body composition, muscle morphology, isometric mid-thigh pull strength, peak aerobic capacity, and a 3-minute maximal cycle ergometer sprint across two separate occasions (separated by 3–7 days). Blood samples were analyzed for testosterone, cortisol, and insulin-like growth factor-1. Compared to both REC and CON, one-way analysis of variance revealed ADV to possess lower body fat percentage (6.7–8.3%, p = 0.007), greater bone and non-bone lean mass (12.5–26.8%, p ≤ 0.028), muscle morphology characteristics (14.2–59.9%, p < 0.05), isometric strength characteristics (15.4–41.8%, p < 0.05), peak aerobic capacity (18.8–19.1%, p = 0.002), and 3-minute cycling performance (15.4–51.1%, p ≤ 0.023). No differences were seen between REC and CON, or between all groups for resting metabolic rate or hormone concentrations. These data suggest ADV possess several physiological advantages over REC and CON, whereas similar physiological characteristics were present in individuals who have been regularly participating in either CF or resistance and cardiovascular training for the past year.

Resistance training may differentially affect morphological adaptations along the length of uni-articular and bi-articular muscles. The purpose of this study was to compare changes in muscle morphology along the length of the rectus femoris (RF) and vastus lateralis (VL) in response to resistance training. Following a 2-wk preparatory phase, 15 resistance-trained men (24.0 ± 3.0 y, 90.0 ± 13.8 kg, 174.9 ± 20.7 cm) completed pre-training (PRE) assessments of muscle thickness (MT), pennation angle (PA), cross-sectional area (CSA), and echo-intensity in the RF and VL at 30, 50, and 70% of each muscle’s length; fascicle length (FL) was estimated from respective measurements of MT and PA within each muscle and region. Participants then began a high intensity, low volume (4 x 3–5 repetitions, 3min rest) lower-body resistance training program, and repeated all PRE-assessments after 8 weeks (2 d ∙ wk^-1) of training (POST). Although three-way (muscle [RF, VL] x region [30, 50, 70%] x time [PRE, POST]) repeated measures analysis of variance did not reveal significant interactions for any assessment of morphology, significant simple (muscle x time) effects were observed for CSA (p = 0.002) and FL (p = 0.016). Specifically, average CSA changes favored the VL (2.96 ± 0.69 cm², p < 0.001) over the RF (0.59 ± 0.20 cm², p = 0.011), while significant decreases in average FL were noted for the RF (–1.03 ± 0.30 cm, p = 0.004) but not the VL (–0.05 ± 0.36 cm, p = 0.901). No other significant differences were observed. The findings of this study demonstrate the occurrence of non-homogenous adaptations in RF and VL muscle size and architecture following 8 weeks of high-intensity resistance training in resistance-trained men. However, training does not appear to influence region-specific adaptations in either muscle.

The 2018 CrossFit Open (CFO) was the initial stage of an annual competition that consisted of five weekly workouts. Current evidence suggests that a variety of fitness parameters are important for progressing beyond this stage, but little is known about which are the most important. To examine relationships between CFO performance, experience, and physiological fitness, sixteen experienced (>2 years) athletes (30.7 ± 6.9 years, 171 ± 12 cm, 78.0 ± 16.2 kg) volunteered to provide information about their training and competitive history, and then complete a battery of physiological assessments prior to competing in the 2018 CFO. Athletes’ resting energy expenditure, hormone concentrations, body composition, muscle morphology, cardiorespiratory fitness, and isometric strength were assessed on two separate occasions. Spearman correlations demonstrated significant (p < 0.05) relationships between most variables and performance on each workout. Stepwise regression revealed competition experience (R² = 0.31–0.63), body composition (R² = 0.55–0.80), vastus lateralis cross-sectional area (R² = 0.29–0.89), respiratory compensation threshold (R² = 0.54–0.75), and rate of force development (R² = 0.30–0.76) to be the most common predictors. Of these, body composition was the most important. These fitness parameters are known targets with established training recommendations. Though preliminary, athletes may use these data to effectively train for CFO competition.

Serafini, PR, Feito, Y, and Mangine, GT. Self-reported measures of strength and sport-specific skills distinguish ranking in an international online fitness competition. J Strength Cond Res 32(12): 3483–3493, 2018—To determine if self-reported performance measures could distinguish ranking during the 2016 CrossFit Open, data from 3,000 male (n = 1,500; 27.2 ± 8.4 years; 85.2 ± 7.9 kg; 177.0 ± 6.5 cm) and women (n = 1500, 28.7 ± 4.9 years; 63.7 ± 5.8 kg; 163.7 ± 6.6 cm) competitors was used for this study. Competitors were split by gender and grouped into quintiles (Q1–Q5) based upon their final ranking. Quintiles were compared for one-repetition maximum (1RM) squat, deadlift, clean and jerk (CJ), snatch, 400 m sprint, 5,000 m run, and benchmark workouts (Fran, Helen, Grace, Filthy-50, and Fight-Gone-Bad). Separate one-way analyses of variance revealed that all competitors in Q1 reported greater (p ≤ 0.05) 1RM loads for squat (men: 201.6 ± 19.1 kg; women: 126.1 ± 13.0 kg), deadlift (men: 232.4 ± 20.5 kg; women: 148.3 ± 14.5 kg), CJ (men: 148.9 ± 12.1 kg; women: 95.7 ± 8.4 kg), and snatch (men: 119.4 ± 10.9 kg; women 76.5 ± 7.6 kg) compared with other quintiles. In addition, men in Q1 (59.3 ± 5.9 seconds) reported faster (p ≤ 0.05) 400 m times than Q3 only (62.6 ± 7.3 seconds), but were not different from any group in the 5,000 m run. Women in Q2 (67.5 ± 8.8 seconds) reported faster (p ≤ 0.05) 400 m times than Q3–Q5 (73.5–74.8 seconds), and faster (21.3 ± 1.8 minutes, p < 0.02) 5,000 m times than Q4 (22.6 ± 2.2 minutes) and Q5 (22.6 ± 1.9 minutes). Faster (p ≤ 0.05) Fran times were reported by Q1 (men: 138.2 ± 13.3 seconds; women: 159.4 ± 28.3 seconds) compared with other groups, while the results of other workouts were variable. These data indicate that the most successful athletes excel in all areas of fitness/skill, while lower-ranking athletes should focus on developing strength and power after achieving sufficient proficiency in sport-specific skills.