Background Reference values of body fat for competitive volleyball players are lacking, making it difficult to interpret measurement results. This review systematically summarized published data on the relative body fat of volleyball players and calculated potential differences between sex, measurement method, and competitive level. Methods The protocol followed the Preferred Reported Items for Systematic Reviews and Meta-Analysis guidelines. The literature search was conducted using five electronic databases to retrieve all relevant publications from January 1, 2010, to July 1, 2021. The 63 studies including 2607 players that met the inclusion criteria were analyzed using random-effects models. Data were reported as pooled mean body fat with 95% confidence intervals. Results Body fat for males and females was 12.8% (11.9-13.8%) and 22.8% (21.9-23.7%), respectively. Body fat was 18.3% (16.3-20.4%) measured via skinfolds, 18.4% (15.6-21.2%) via bioelectrical impedance analysis, 24.2% (20.4-28.0%) via dual-energy x-ray absorptiometry and 21.6% (17.4-25.8%) via densitometry. Regional, national, and international-level players had body fat values of 19.5% (17.8-21.2%), 20.3% (18.6-22.0%), and 17.9% (15.7-20.4%), respectively. When the meta-regression was adjusted for the variables sex, measurement method, and competitive level, a significant difference between sex (p < 0.001), dual-energy x-ray absorptiometry and skinfolds (p = 0.02), and national and international-level players (p = 0.02) was found. However, sensitivity analysis revealed that findings regarding measurement method and competitive level were not robust and should, therefore, be interpreted with caution. Conclusions Despite the limitations of published data, this meta-analysis provided pooled values for body fat of male and female volleyball players for different competitive levels and measurement methods.

Abstract Bauer, P, Majisik, A, Mitter, B, Csapo, R, Tschan, H, Hume, P, Martínez-Rodríguez, A, and Makivic, B. Body composition of competitive bodybuilders: a systematic review of published data and recommendations for future work. J Strength Cond Res 37(3): 726–732, 2023—The purpose of this review was to systematically summarize studies measuring the body composition of competitive bodybuilding athletes to provide recommended values for preparation and during competition. The protocol was preregistered with PROSPERO (CRD42020197921) and followed the guidelines of the Preferred Reported Items for Systematic Reviews and Meta-Analysis. A search of 5 electronic databases (PubMed, Web of Science, SportDiscus, CINAHL, and Scopus) was conducted to retrieve all relevant publications from January 1, 2000, up to June 13, 2021. Of 16 studies meeting the inclusion criteria, 6 presented longitudinal data on competition preparation and were discussed in detail. In the general preparation phase, body fat levels of bodybuilding athletes ranged between 15.3 and 25.2% (female) and from 9.6 to 16.3% (male). Close to competition, however, body fat levels were substantially lower, ranging from 8.1 to 18.3% for female and 5.8–10.7% for male athletes. All studies comparing relative body fat values at various time points during competition preparation found significant reductions between 30 and 60% in relative body fat, whereas lean mass was mostly maintained. Findings from the studies included in this review suggest that most bodybuilding competitors keep resistance training volume high while increasing aerobic training volume when preparing for competition. Findings on energy intake and macronutrient distribution were unclear and should be addressed in future studies. Further research, especially on contest preparation, is warranted and should include more details about training programs, nutritional strategies, psychosocial situation, anabolic androgen steroid, and supplement use as well as measurement protocols and preparation.

The aim of this cross-sectional study was to compare dietary intake to published recommendations and to analyze the potential relationship between body composition and dietary intake in collegiate athletes. Eighteen healthy male middle-and long-distance runners (age 20.11 symbolscript 2.72 y; height, 174.7 symbolscript 6.1 cm; body mass, 64.0 symbolscript 7.7 kg), were recruited from a Mexican university track and field team at the beginning of the general preparation phase for national competitions. Participants completed three 24-hour dietary recalls, which were used to estimate dietary intake. Body composition was measured by Dual-energy X-ray absorptiometry (DXA). Athletes displayed high body fat values. Protein intake was significantly higher than published recommendations. Iron, zinc, sodium, and vitamin C intake were significantly higher than recommended values, while potassium and calcium intake were below established recommendations. No significant correlations between body composition variables (i.e body fat, lean body mass, bone mineral content) and dietary intake (i.e energy, macronutrients and selected vitamins and minerals) could be found. These findings suggest that coaches and practitioners should pay close attention to dietary intake and body composition of endurance athletes starting general preparation for competition. Future studies on changes of dietary intake and body composition during off-season and competitive phase, which also track physical activity, are warranted.

This study aimed to provide reference values for body fat (BF) of basketball players considering sex, measurement method, and competitive level. A systematic literature research was conducted using five electronic databases (PubMed, Web of Science, SPORTDiscus, CINAHL, Scopus). BF values were extracted, with analyses conducted using random-effects models and data reported as percentages with 95% confidence intervals (CI). After screening, 80 articles representing 4335 basketball players were selected. Pooled mean BF was 13.1% (95% CI 12.4–13.8%) for male players and 20.7% (95% CI 19.9–21.5%) for female players. Pooled mean BF was 21.4% (95% CI 18.4–24.3%) measured by dual-energy X-ray absorptiometry (DXA), 15.2% (95% CI 12.8–17.6%) via bioelectrical impedance analysis (BIA), 12.4% (95% CI 10.6–14.2%) via skinfolds and 20.0% (95% CI 13.4–26.6%) via air displacement plethysmography. Pooled mean BF across competitive levels were 13.5% (95% CI 11.6–15.3%) for international, 15.7% (95% CI 14.2–17.2%) for national and 15.1% (95% CI 13.5–16.7%) for regional-level players. As the meta-regression revealed significant effects of sex, measurement method and competitive level on BF, the meta-analysis was adjusted for these moderators. The final model revealed significant differences in BF between male and female players (p < 0.001). BF measured by DXA was significantly higher than that measured by BIA or skinfolds (p < 0.001). International-level players had significantly lower BF than national and regional-level players (p < 0.05). Despite the limitations of published data, this meta-analysis provides reference values for BF of basketball players. Sex, measurement method and competitive level influence BF values, and therefore must be taken into account when interpreting results.

To identify the relationship between load and the number of repetitions performed to momentary failure in the pin press exercise, the present study compared different statistical model types and structures using a Bayesian approach. Thirty resistance-trained men and women were tested on two separate occasions. During the first visit, participants underwent assessment of their one-repetition maximum (1-RM) in the pin press exercise. On the second visit, they performed sets to momentary failure at 90%, 80% and 70% of their 1-RM in a fixed order during a single session. The relationship between relative load and repetitions performed to failure was fitted using linear regression, exponential regression and the critical load model. Each model was fitted according to the Bayesian framework in two ways: using an across-subjects pooled data structure and using a multilevel structure. Models were compared based on the variance explained (R2) and leave-one-out cross-validation information criterion (LOOIC). Multilevel models, which incorporate higher-level commonalities into individual relationships, demonstrated a substantially better fit (R2: 0.97-0.98) and better predictive accuracy compared to generalised pooled-data models (R2: 0.89-0.93). The multilevel 2-parameter exponential regression emerged as the best representation of data in terms of model fit, predictive accuracy and model simplicity. The relationship between load and repetitions performed to failure follows an individually expressed exponential trend in the pin press exercise. To accurately predict the load that is associated with a certain repetition maximum, the relationship should therefore be modelled on a subject-specific level.

The present study was designed to evaluate the test-retest consistency of repetition maximum tests at standardized relative loads and determine the robustness of strength-endurance profiles across test-retest trials. Twenty-four resistance-trained males and females (age, 27.4 ± 4.0 y; body mass, 77.2 ± 12.6 kg; relative bench press one-repetition maximum [1-RM], 1.19 ± 0.23 kg•kg-1) were assessed for their 1-RM in the free-weight bench press. After 48 to 72 hours, they were tested for the maximum number of achievable repetitions at 90%, 80% and 70% of their 1-RM. A retest was completed for all assessments one week later. Gathered data were used to model the relationship between relative load and repetitions to failure with respect to individual trends using Bayesian multilevel modeling and applying four recently proposed model types. The maximum number of repetitions showed slightly better reliability at lower relative loads (ICC at 70% 1-RM = 0.86, 90% highest density interval: [0.71, 0.93]) compared to higher relative loads (ICC at 90% 1-RM = 0.65 [0.39, 0.83]), whereas the absolute agreement was slightly better at higher loads (SEM at 90% 1-RM = 0.7 repetitions [0.5, 0.9]; SEM at 70% 1-RM = 1.1 repetitions [0.8, 1.4]). The linear regression model and the 2-parameters exponential regression model revealed the most robust parameter estimates across test-retest trials. Results testify to good reproducibility of repetition maximum tests at standardized relative loads obtained over short periods of time. A complementary free-to-use web application was developed to help practitioners calculate strength-endurance profiles and build individual repetition maximum tables based on robust statistical models.

Mitter, B, Holbling, D, Bauer, P, Stockl, M, Baca, A, and Tschan, H. Concurrent validity of field-based diagnostic technology monitoring movement velocity in powerlifting exercises. J Strength Cond Res 35(8): 2170-2178, 2021-The study was designed to investigate the validity of different technologies used to determine movement velocity in resistance training. Twenty-four experienced powerlifters (18 male and 6 female; age, 25.1 +/- 5.1 years) completed a progressive loading test in the squat, bench press, and conventional deadlift until reaching their 1 repetition maximum. Peak and mean velocity were simultaneously recorded with 4 field-based systems: GymAware (GA), FitroDyne (FD), PUSH (PU), and Beast Sensor (BS). 3D motion capturing was used to calculate specific gold standard trajectory references for each device. GA provided the most accurate output across exercises (r = 0.99-1, ES = -0.05 to 0.1). FD showed similar results for peak velocity (r = 1, standardized mean bias [ES] = -0.1 to -0.02) but considerably less validity for mean velocity (r = 0.92-0.95, ES = -0.57 to -0.29). Reasonably valid to highly valid output was provided by PU in all exercises (r = 0.91-0.97, ES = -0.5 to 0.28) and by BS in the bench press and for mean velocity in the squat (r = 0.87-0.96, ES = -0.5 to -0.06). However, BS did not reach the thresholds for reasonable validity in the deadlift and for peak velocity in the squat, mostly due to high standardized mean bias (ES = -0.78 to -0.63). In conclusion, different technologies should not be used interchangeably. Practitioners who require negligible measurement error in their assessment of movement velocity are advised to use linear position transducers over inertial sensors.

Previous research has suggested that viewing the color red immediately prior and during motor performance facilitates motor action. However, viewing red at a delay prior to performance should cause a decline in motor action. In the current study we tested these assumptions. Participants performed maximal voluntary isometric contractions (MVIC) of the right knee extensors and rated the quality of each attempt. In the first experiment (n = 58) instructions in red, blue or gray (matched on lightness/saturation) were shown in a randomized order on a computer screen from five seconds prior to and during MVICs. Results revealed a significantly higher rate of torque development and better ratings when viewing red. In the second experiment (n = 32) color manipulated instructions (red, blue, gray) were shown from 50 to 40 seconds prior to MVICs. The results did not show the assumed decline when viewing red at a delay prior to motor action.

Objectives: The aim of the present meta-analytical review was to determine the effectiveness of training programmes combining higher-load and lower-load exercises in one workout (i.e. complex training [CT]) on lower-body performance. Design: Systematic review and meta-analysis. Methods: A search of five electronic databases (PubMed, Web of Science, SportDiscus, CINAHL and Scopus) was conducted to identify all publications up to 7 March 2018. Meta-analyses were performed using a random-effects model with the dependent variables countermovement jump (CMJ) height, squat jump (SJ) height, one-repetition maximum (1-RM) squat performance and sprint time for 5 m, 10 m, 20 m, 30 m and 40 m, respectively. Results: The analysis comprised 33 studies and a total of 1064 healthy participants. The meta-analysis revealed that CT is effective in improving CMJ (95% confidence interval (CI] 5.6%-12.3%), SJ (95% CI 8.0%-17.4%), 1-RM squat (95% CI 16.4%-30.7%) and sprint performance (5 m = 95% CI -14.8% to -0.9%, 10 m = 95% CI -6.0% to -2.1%, 20 m = 95% CI -7.4% to -1.4%, 30 m = 95% CI -8.0% to -0.6%). However, when directly compared to traditional training methods, only 1-RM squat strength performance and 20 m sprint time were superior following CT interventions (95% CI 0.2%-13.7% and 95% CI -1.6% to -0.1%, respectively) Conclusions: a is an acceptable method for improving jump, strength and sprint performance in athletes. Compared to traditional training methods, CT seems to produce superior training effects only for 1-RM squat and 20 m sprint performance; however, these findings were influenced by single studies and should be therefore interpreted with circumspection. (C) 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Bauer, P, Sansone, P, Mitter, B, Makivic, B, Seitz, LB, and Tschan, H. Acute effects of back squats on countermovement jump performance across multiple sets of a contrast training protocol in resistance-trained men. J Strength Cond Res 33(4): 995-1000, 2019-This study was designed to evaluate the voluntary postactivation potentiation (PAP) effects of moderate-intensity (MI) or high-intensity (HI) back squat exercises on countermovement jump (CMJ) performance across multiple sets of a contrast training protocol. Sixty resistance-trained male subjects (age, 23.3 ± 3.3 years; body mass, 86.0 ± 13.9 kg; and parallel back squat 1-repetition maximum [1-RM], 155.2 ± 30.0 kg) participated in a randomized, crossover study. After familiarization, the subjects visited the laboratory on 3 separate occasions. They performed a contrast PAP protocol comprising 3 sets of either MI (6 × 60% of 1-RM) or HI back squats (4 × 90% of 1-RM) or 20 seconds of recovery (CTRL) alternated with 7 CMJs that were performed at 15 seconds, and 1, 3, 5, 7, 9 and 11 minutes after the back squats or recovery. Jump height and relative peak power output recorded with a force platform during MI and HI conditions were compared with those recorded during control condition to calculate the voluntary PAP effect. Countermovement jump performance was decreased immediately after the squats but increased across all 3 sets of MI and HI between 3 and 7 minutes after recovery. However, voluntary PAP effects were small or trivial, and no difference between the 3 sets could be found. These findings demonstrate that practitioners can use MI and HI back squats to potentiate CMJs across a contrast training protocol, but a minimum of 3 minutes of recovery after the squats is needed to benefit from voluntary PAP.