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.

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. 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. 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. 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.

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.

The aim of this study was to determine the validity of three smartphone applications measuring barbell movement velocity in resistance training and comparing them to a commercially available linear transducer. Twenty competitive powerlifters (14 male and 6 female) completed a progressive loading protocol in the squat, bench press and deadlift (sumo or conventional) until reaching 90% of the highest load they had achieved in a recent competition. Mean velocity was concurrently recorded with three smartphone applications: Qwik VBT (QW), Metric VBT (MT), MyLift (ML), and one linear transducer: RepOne (RO). 3D motion capturing (Vicon) was used to calculate specific gold standard trajectory references for the different systems. A total of 589 repetitions were recorded with a mean velocity of (mean ± standard deviation [min-max]) 0.44 ± 0.17 [0.11–1.04] m·s^-1, of which MT and ML failed to identify 52 and 175 repetitions, respectively. When compared to Vicon, RO and QW consistently delivered valid measurements (standardized mean bias [SMB] = 0 to 0.21, root mean squared error [RMSE] = 0.01 to 0.04m·s^-1). MT and ML failed to deliver a level of validity comparable to RO (SMB = -0.28 to 0.14, RMSE = 0.04–0.14m·s^-1), except for MT in the bench press (SMB = 0.07, RMSE = 0.04m·s^-1). In conclusion, smartphone applications can be as valid as a linear transducer when assessing mean concentric barbell velocity. Out of the smartphone applications included in this investigation, QW delivered the best results.