No abstract

The present study was designed to evaluate the voluntary post-activation potentiation (PAP) effects of moderate (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 y; body mass, 86.0 ± 13.9 kg; parallel back squat 1-repetition maximum [1-RM], 155.2 ± 30.0 kg) participated in a randomized, cross-over study. After familiarization, the subjects visited the laboratory on three separate occasions. They performed a contrast PAP protocol comprising three sets of either MI (6×60% of 1-RM) or HI back squats (4x90% of 1-RM) or 20 s of recovery (CTRL) alternated with seven CMJs that were performed at 15 s, and 1, 3, 5, 7, 9 and 11 min 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 to those recorded during control condition to calculate the voluntary PAP effect. CMJ performance was decreased immediately after the squats but increased across all three sets of MI and HI between 3 - 7 minutes post-recovery. However, voluntary PAP effects were small or trivial and no difference between the three 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 min of recovery after the squats is needed to benefit from voluntary PAP.

Mitter, B, Hölbling, D, Bauer, P, Stöckl, 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.

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 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.