Purpose: To examine rest redistribution (RR) effects on back squat kinetics and kinematics in resistance-trained women. Methods: Twelve women from strength and college sports (5.0 [2.2] y training history) participated in the randomized crossover design study with 72 hours between sessions (3 total). Participants completed 4 sets of 10 repetitions using traditional sets (120-s interset rest) and RR (30-s intraset rest in the middle of each set; 90-s interset rest) with 70% of their 1-repetition maximum. Kinetics and kinematics were sampled via force plate and 4 linear position transducers. The greatest value of repetitions 1 to 3 (peak repetition) was used to calculate percentage loss, [(repetition 10–peak repetition)/(peak repetition) × 100], and maintenance, {100–[(set mean–peak repetition)/(peak repetition)] × 100}, of velocity and power for each set. Repeated-measures analysis of variance was used for analyses (P < .05). Results: Mean and peak force did not differ between conditions. A condition × repetition interaction existed for peak power (P = .049) but not for peak velocity (P = .110). Peak power was greater in repetitions 7 to 9 (P < .05; d = 1.12–1.27) during RR. The percentage loss of velocity (95% confidence interval, –0.22% to –7.22%; P = .039) and power (95% confidence interval, –1.53% to –7.87%; P = .008) were reduced in RR. Mean velocity maintenance of sets 3 (P = .036; d = 1.90) and 4 (P = .015; d = 2.30) and mean power maintenance of set 4 (P = .006; d = 2.65) were greater in RR. Conclusion: By redistributing a portion of long interset rest into the middle of a set, velocity and power were better maintained. Therefore, redistributing rest may be beneficial for reducing fatigue in resistance-trained women.

Fields, JB, Merrigan, JJ, White, JB, and Jones, MT. Body composition variables by sport and sport-position in elite collegiate athletes. J Strength Cond Res 32(11): 3153–3159, 2018—To assess body composition measures by sport and sport-position. Elite collegiate athletes participated (n = 475): men's and women's soccer (MSOC, n = 67; WSOC, n = 110); men's and women's swimming (MSWIM, n = 26; WSWIM, n = 22); men's and women's track and field (MTF, n = 29; WTF, n = 24); women's lacrosse and volleyball (WLAX, n = 84; WVB, n = 73); and baseball (BASE, n = 40). One-way analysis of variances assessed differences across sport and sport-position. Post hoc analysis was Tukey honestly significant difference (p ≤ 0.05). For men, BASE and MSWIM had the highest body fat percentage (BF%) (BASE: 16.3 ± 5.2%; MSWIM: 14.2 ± 3.5%). MSOC (11.5 ± 5.3%, 0.13 ± 0.72 kg) and MTF (9.8 ± 5.1%, 0.11 ± 0.08 kg) had the lowest BF% and fat mass (FM)-to-fat-free mass (FFM) ratio (FM:FFM). Fat mass did not differ between MSOC (9.1 ± 4.9 kg), MTF (7.7 ± 5.9 kg), and MSWIM (11.1 ± 3.1 kg). Fat mass for MSOC and MTF was lower than BASE (14.1 ± 5.2). For women, WVB displayed the highest BF% (25.4 ± 5.1%), FM (18.5 ± 5.2 kg), FFM (53.3 ± 5.1 kg), and body mass (BM) (71.8 ± 8.4 kg), but did not differ from WSWIM in BF%, FM, FFM, and BM. WTF had the lowest BF% (12.9 ± 4.0%), FM (7.5 ± 2.5 kg), BM (58.2 ± 4.4 kg), and FM:FFM (0.15 ± 0.05 kg). VB had the highest FFM (53.3 ± 5.1 kg). Body composition differences were observed between sport-positions (p < 0.01). Body composition differed across sport and sport-position, which may be attributed to sport-specific physiological demands.

The purpose of this study was to assess the body composition of male and female basketball athletes (n = 323) across season, year, and sport-position using air displacement plethysmography. An independent sample t-test assessed sport-position differences. An analysis of variance was used to assess within-subjects across season (pre-season, in-season, and off-season), and academic year (freshman, sophomore, and junior). For both men and women basketball (MBB, WBB) athletes, guards had the lowest body fat, fat mass, fat free mass, and body mass. No seasonal differences were observed in MBB, but following in-season play for WBB, a reduction of (p = 0.03) in fat free mass (FFM) was observed. Across years, MBB showed an increase in FFM from freshman to sophomore year, yet remained unchanged through junior year. For WBB across years, no differences occurred for body mass (BM), body fat (BF%), and fat mass (FM), yet FFM increased from sophomore to junior year (p = 0.009). Sport-position differences exist in MBB and WBB: Guards were found to be smaller and leaner than forwards. Due to the importance of body composition (BC) on athletic performance, along with seasonal and longitudinal shifts in BC, strength and conditioning practitioners should periodically assess athletes BC to ensure preservation of FFM. Training and nutrition programming can then be adjusted in response to changes in BC.

Sex differences in isometric elbow extensor strength are eliminated when expressed relative to muscle volume. Relationships of echo intensity and body fat were different between men and women and may be indicative of greater adipose infiltration in women.

Ishida, A, Travis, SK, Draper, G, White, JB, and Stone, MH. Player position affects relationship between internal and external training loads during Division I collegiate female soccer season. J Strength Cond Res 36(2): 513-517, 2022-The purpose of this study was to investigate how competition phase and player position affect the relationship between internal and external training loads (ITL and ETL, respectively) in collegiate female soccer. Seventeen players participated (21.8 6 1.7 years; 165.1 6 6.2 cm; and 63.7 6 7.9 kg). Nineteen match-plays (10 nonconference and 9 conference) were completed during the 2019 competitive season, including 270 observations of 17 players (defenders 5 5, midfielders 5 9, and forwards 5 3). Internal training load was assessed using session rating of perceived exertion (sRPE). External training load included total distance and high-speed running (HSR) distance. A linear mixed model was compiled with fixed effects of total distance, HSR, competition phase, and player position (defenders, midfielders, and forwards) and random effects of player. There were statistically significant main effects for total distance (p , 0.001), HSR (p 5 0.047) and player position (p 5 0.045) on the prediction model of sRPE. However, the main effect of competition phase did not statistically contribute to the prediction model of sRPE (p 5 0.38). In the final model, total distance (p , 0.001) and player position for forwards (p 5 0.008) were significant predictors of sRPE. However, there was no statistically significant fixed effect of HSR on sRPE (p 5 0.15). The final model explained 60.6% of the variance in sRPE (R 2 5 0.60), whereas the random effect also explained 6.1% of the variance (R 2 5 0.06). Our findings indicated that total distance and player position were strong predictors of sRPE. The relationship between ITL and ETL should be monitored by player position in female soccer players.

Laboratory assessments of maximal oxygen uptake (VO2max) are considered the “gold standard” for ascertaining cardiovascular fitness, but they are not always practical for use in team sport settings. Therefore, the purpose of the current study was to compare the criterion assessment of VO2max on a treadmill to the progressive, multistage 20-m shuttle run test (i.e., Beep test), and to determine the predictability of 6 previously established Beep test predictive equations (i.e., Chatterjee, Flouris, Leger, Leger and Gadoury, Ramsbottom, St. Clair-Gibson). Collegiate women field hockey athletes (n = 65, mean±SD: age 19.6 ± 1.2 years; weight 64.7 ± 6.1 kg) completed criterion VO2max (mean ± SD: 46.4 ± 4.6 mL·kg−1·min−1) and Beep tests to volitional fatigue. According to Bland–Altman and Ordinary Least Products Regressions, the Ramsbottom (46.5 ± 4.2 mL·kg−1·min−1) and Flouris (46.3 ± 3.8 mL·kg−1·min−1) equations were considered valid predictions of criterion measured VO2max (46.4 ± 4.6). The Chatterjee, Leger, Leger and Gadoury, and St. Clair-Gibson equations overestimated VO2max, and are not recommended for use with women collegiate field hockey athletes. The Ramsbottom and Flouris estimates of VO2max from 20-m shuttle performances may be used in this population. For accurate estimates of VO2max, the clientele’s age, fitness level, and training history should be considered when selecting equations.

Fields, JB, Lameira, DM, Short, JL, Merrigan, JM, Gallo, S, White, JB, and Jones, MT. Relationship between external load and self-reported wellness measures across a collegiate men's soccer preseason. J Strength Cond Res 35(5): 1182–1186, 2021—Monitoring athlete training load is important to training programming and can help balance training and recovery periods. Furthermore, psychological factors can affect athlete's performance. Therefore, the purpose was to examine the relationship between external load and self-reported wellness measures during soccer preseason. Collegiate men soccer athletes (n = 20; mean ± SD age: 20.3 ± 0.9 years; body mass: 77.9 ± 6.8 kg; body height: 178.87 ± 7.18cm; body fat: 10.0 ± 5.0%; V̇o 2max: 65.39 ± 7.61ml·kg−1·min−1) participated. Likert scale self-assessments of fatigue, soreness, sleep, stress, and energy were collected daily in conjunction with the Brief Assessment of Mood (vigor, depression, anger, fatigue, and confusion). Total distance (TD), player load (PL), high-speed distance (HSD, >13 mph [5.8 m·s−1]), high inertial movement analysis (IMA, >3.5 m·s−2), and repeated high-intensity efforts (RHIEs) were collected in each training session using positional monitoring (global positioning system/global navigation satellite system [GPS/GNSS]) technology. Session rate of perceived exertion (sRPE) was determined from athlete's post-training rating (Borg CR-10 Scale) and time of training session. Multilevel models revealed the bidirectional prediction of load markers on fatigue, soreness, sleep, energy, and sRPE (p < 0.05). Morning ratings of soreness and fatigue were predicted by previous afternoon's practice measures of TD, PL, HSD, IMA, RHIE, and sRPE. Morning soreness and fatigue negatively predicted that day's afternoon practice TD, PL, HSD, IMA, RHIE, and sRPE. Morning ratings of negative mood were positively predicted by previous day's afternoon practice HSD. In addition, negative morning mood states inversely predicted HSD (p = 0.011), TD (p = 0.002), and PL (p < 0.001) for that day's afternoon practice. Using self-reported wellness measures with GPS/GNSS technology may enhance the understanding of training responses and inform program development.

Collegiate athletes are exposed to high volume loads during preseason training. Monitoring training load can inform training and recovery periods. Therefore, the purpose was to examine changes in and bidirectional relationship between external and internal load metrics in men collegiate soccer athletes (n 5 20; age, 20 6 1 year). Internal load measures of heart rate variability (HRV), salivary testosterone (T) and cortisol (C), and self-assessment wellness and ratings of perceived exertion scales were collected daily. External load measures of total distance, player load, highspeed distance, high inertial movement analysis, and repeated high-intensity efforts were collected in each training session using global positioning system/global navigation satellite system technology. A 1-way analysis of variance determined weekly changes in external load, physiological, hormonal, and subjective self-assessment measures of internal load. Bidirectional prediction of external load markers and self-assessment measures on physiological and hormonal markers of internal load were assessed by hierarchical linear regression models (p , 0.05). External load measures, C, energy, sleep, and rate of perceived exertion (RPE) decreased (p , 0.01), whereas T, T:C ratio, anger, depression, and vigor increased (p , 0.01) from week 1 to week 2. Morning C positively predicted afternoon external load and post-training RPE (p , 0.05); T:C ratio negatively predicted afternoon external load and post-training RPE (p , 0.05); and morning HRV negatively predicted post-training RPE (p 5 0.031). Despite reduced hormonal stress and external load across weeks, negative perceptions of fatigue increased, suggesting fatigue patterns may have a delayed response. Load may have a more belated, chronic effect on perceptions of fatigue, whereas hormonal changes may be more immediate and sensitive to change. Practitioners may wish to use a variety of external and internal load measures to understand athletes' stress responses to training.