Validity of a Smartphone App Using Artificial Intelligence for the Real-Time Measurement of Barbell Velocity in the Bench Press Exercise.

ABSTRACT: Balsalobre-Fernández, C, Xu, J, Jarvis, P, Thompson, S, Tannion, K, and Bishop, C. Validity of a smartphone app using artificial intelligence for the real-time measurement of barbell velocity in the bench press exercise. J Strength Cond Res 37(12): e640-e645, 2023-The purpose of this study was to explore the validity and within-session reliability of the newly developed My Jump Lab application (app), which uses artificial intelligence techniques to monitor barbell velocity in real time. Twenty-seven sport science students performed 5 repetitions at 50 and 75% of their self-reported bench press 1 repetition maximum (1RM) during a single testing session, whereas barbell velocity was concurrently measured using the app (installed on an iPhone 12 Pro) and the GymAware linear position transducer (LPT). A very high correlation was observed between devices at each loading condition (50% 1RM: r = 0.90 [0.82-0.97]; 75% 1RM: r = 0.92 [0.86-0.98]). Results showed trivial differences between the app and LPT at both 50% 1RM (g = -0.06) and 75% 1RM (g = -0.12). Bland-Altman analysis showed a bias estimate of -0.010 m·s-1 and -0.026 m·s-1 for the 50 and 75% 1RM, respectively. Finally, similar levels of reliability, as revealed by the coefficient of variation, were observed for both devices (50% 1RM: LPT = 6.52%, app = 8.17%; 75% 1RM: LPT = 12.10%, app = 13.55%). Collectively, the findings of this study support the use of My Jump Lab for the measurement of real-time barbell velocity in the bench press exercise.

Calf muscle abilities are related to sprint performance in male Rugby Union players.

OBJECTIVES: To examine the strength of the relationship between plantarflexor power and strength-endurance metrics and 10-m sprint times in male Rugby Union players. A secondary aim was to examine the strength of the relationship within calf muscle metrics. DESIGN: Observational cross-sectional correlational. SETTING: Field-based. PARTICIPANTS: Sixteen male Rugby Union players in the National Provincial Championship. MAIN OUTCOME MEASURES: Participants completed three single-leg calf muscle tests: bodyweight power, weighted power, and strength-endurance. Data were recorded using the Calf Raise application. Three-to-four days later, average and best 10-m sprint performances were collected using timing lights. RESULTS: There were large significant correlations between 10-m sprint performances (average and best times) and calf muscle power (weighted) and strength-endurance (total displacement and work) metrics (r = -0.503 to -0.628). There were large significant correlations between bodyweight and weighted power, weighted power and strength-endurance (total displacement and work), and most strength-endurance metrics (r = 0.520 to 0.943). CONCLUSIONS: Our findings emphasise the importance of triceps surae muscle power and strength-endurance for maximal-effort accelerations and sprint performances in Rugby Union. Our data indicate that weighted power and total work from strength-endurance tests are the most useful metrics for further investigation in the context of short sprints and acceleration.

Effects of Mental Fatigue Induced by Stroop Task and by Social Media Use on Resistance Training Performance, Movement Velocity, Perceived Exertion, and Repetitions in Reserve: A Randomized and Double-Blind Crossover Trial.

This study aimed to explore the effects of mental fatigue (MF) induced by an incongruent Stroop task (ST) and by using social media (SM) compared to watching a documentary (control) on dynamic resistance training. Twenty-one resistance-trained males attended three identical experimental sessions with the only difference of the randomized cognitive task (ST, SM, or control). Each session consisted of (a) baseline MF and motivation visual analogue scale responses, (b) cognitive task, (c) postvisual analogue scale responses, (d) warm-up, and (e) resistance training based on three sets of bench press at 65% of one-repetition maximum till concentric failure. Number of repetitions, ratings of perceived exertion, mean velocity of repetitions, and three repetitions in reserve estimated by subjects were recorded for each set. Both ST (p < .001) and SM (p = .010) effectively induced MF, but only ST impaired the number of repetitions performed in Set 2 (p = .036) and generated higher-than-normal levels of ratings of perceived exertion even reaching significant differences compared to SM in Set 1 (p = .005). However, SM also affected neuromuscular performance by impairing movement velocity in Set 1 (p = .003). The ability of estimating three repetitions in reserve or motivation was not affected by any condition (p range = .362-.979). MF induced by ST impaired the number of repetitions performed, what seems to be mediated by higher-than-normal levels of ratings of perceived exertion. Besides, SM also impaired the ability to apply force against 65% of one-repetition maximum measured by movement velocity.

Mental Fatigue From Smartphone Use or Stroop Task Does Not Affect Bench Press Force-Velocity Profile, One-Repetition Maximum, or Vertical Jump Performance.

The aim of this study was to explore the effects of mental fatigue from smartphone use and Stroop task on bench press force-velocity (F-V) profile, one-repetition maximum (1RM), and countermovement jump (CMJ) performance. Twenty-five trained subjects (age = 25.8 ± 5.7 years) completed three sessions separated by 1 week following a randomized double-blinded crossover design. Each session consisted of F-V relationship, 1RM, and CMJ measurements after performing 30 min of control, social media, or Stroop task. Perceived mental fatigue and motivation were recorded. Mental fatigue, motivation, CMJ height, bench press 1RM, and F-V profile variables (maximal force, maximal velocity, and maximal power) were compared between interventions. Significant differences were found for mental fatigue between interventions (p ≤ .001). Both ST (p ≤ .001) and SM (p = .007) induced higher mental fatigue than control. However, no significant differences between interventions were observed for any other variable (p = .056-.723). The magnitude of the differences between interventions ranged from negligible to small (effect sizes ≤ 0.24). These results suggest that although both ST and SM were effective to induce mental fatigue, neither ST nor SM affected CMJ performance, bench press 1RM, or any variable of the F-V profile compared with the control task.

Mental fatigue impairs physical performance but not the neural drive to the muscle: a preliminary analysis.

Mental fatigue (MF) does not only affect cognitive but also physical performance. This study aimed to explore the effects of MF on muscle endurance, rate of perceived exertion (RPE), and motor units' activity. Ten healthy males participated in a randomised crossover study. The subjects attended two identical experimental sessions separated by 3 days with the only difference of a cognitive task (incongruent Stroop task [ST]) and a control condition (watching a documentary). Perceived MF and motivation were measured for each session at baseline and after each cognitive task. Four contractions at 20% of maximal voluntary contraction (MVIC) were performed at baseline, after each cognitive and after muscle endurance task while measuring motor units by high-density surface electromyography. Muscle endurance until failure at 50% of MVIC was measured after each cognitive task and the RPE was measured right after failure. ST significantly increased MF (p = 0.001) reduced the motivation (p = 0.008) for the subsequent physical task and also impaired physical performance (p = 0.044). However, estimates of common synaptic inputs and motor unit discharge rates as well as RPE were not affected by MF (p > 0.11). In conclusion, MF impairs muscle endurance and motivation for the physical task but not the neural drive to the muscle at any frequency bands. Although it is physiologically possible for mentally fatigued subjects to generate an optimal neuromuscular function, the altered motivation seems to limit physical performance. Preliminarily, our results suggest that the corticospinal pathways are not affected by MF.

Concurrent validity and reliability of a mobile iOS application used to assess calf raise test kinematics.

BACKGROUND: Calf raise test (CRT) is used in rehabilitation and sports medicine to evaluate calf muscle function. The Calf Raise application (CRapp) uses computer-vision algorithms to objectively measure CRT outcomes and replicate laboratory-based metrics that are difficult to measure clinically. OBJECTIVE: To validate the CRapp by examining its concurrent validity and agreement levels against laboratory-based equipment, and its intra- and inter-rater reliability. DESIGN: Observational cross-sectional validation study. METHODS: CRT outcomes (i.e., repetitions, positive work, total height, peak height, fatigue index, and peak power) were assessed in thirteen individuals (6 males, 7 females) on three occasions on both legs using the CRapp, 3D motion capture, and force plate simultaneously. Data were extracted from two markers: below lateral malleolus (n = 77) and on the heel (n = 77). Concurrent validity and agreement were determined from 154 data files using intraclass correlation coefficients (ICC3,k), typical errors expressed as coefficient of variations (CV), and Bland-Altman plots to assess biases and precision. Reliability was assessed using ICC3,1 and CV values. RESULTS: Validity of CRapp outcomes was good to excellent across measures for both markers (mean ICC ≥0.878), precision plots showing good agreement and precision. CV ranged from 0% (repetitions) to 33.3% (fatigue index) and were on average better for the lateral malleolus marker. Inter- and intra-rater reliability were excellent (ICC≥0.949, CV ≤ 5.6%). CONCLUSION: CRapp is valid and reliable within and between users for measuring CRT outcomes in healthy adults. CRapp provides a tool to objectivise CRT outcomes in research and practice, aligning with recent advances in mobile technologies and their increased use in healthcare.

Effects of Mental Fatigue on Strength Endurance: A Systematic Review and Meta-Analysis.

The purpose of the present systematic review and meta-analysis was to explore the effects of mental fatigue on upper and lower body strength endurance. Searches for studies were performed in the PubMed/MEDLINE and Web of Science databases. We included studies that compared the effects of a demanding cognitive task (set to induce mental fatigue) with a control condition on strength endurance in dynamic resistance exercise (i.e., expressed as the number of performed repetitions at a given load). The data reported in the included studies were pooled in a random-effects meta-analysis of standardized mean differences. Seven studies were included in the review. We found that mental fatigue significantly reduced the number of performed repetitions for upper body exercises (standardized mean difference: -0.41; 95% confidence interval [-0.70, -0.12]; p = .006; I2 = 0%). Mental fatigue also significantly reduced the number of performed repetitions in the analysis for lower body exercises (standardized mean difference: -0.39; 95% confidence interval [-0.75, -0.04]; p = .03; I2 = 0%). Our results showed that performing a demanding cognitive task-which induces mental fatigue-impairs strength endurance performance. Collectively, our findings suggest that exposure to cognitive tasks that may induce mental fatigue should be minimized before strength endurance-based resistance exercise sessions.

Effects of Concurrent Resistance and Endurance Training Using Continuous or Intermittent Protocols on Muscle Hypertrophy: Systematic Review With Meta-Analysis.

ABSTRACT: Monserdà-Vilaró, A, Balsalobre-Fernández, C, Hoffman, JR, Alix-Fages, C, and Jiménez, SL. Effects of concurrent resistance and endurance training using continuous or intermittent protocols on muscle hypertrophy: Systematic review with meta-analysis. J Strength Cond Res 37(3): 688-709, 2023-The purpose of this systematic review with meta-analysis was to explore the effects of concurrent resistance and endurance training (CT) incorporating continuous or intermittent endurance training (ET) on whole-muscle and type I and II muscle fiber hypertrophy compared with resistance training (RT) alone. Randomized and nonrandomized studies reporting changes in cross-sectional area at muscle fiber and whole-muscle levels after RT compared with CT were included. Searches for such studies were performed in Web of Science, PubMed, Scopus, SPORTDiscus, and CINAHL electronic databases. The data reported in the included studies were pooled in a random-effects meta-analysis of standardized mean differences (SMDs). Twenty-five studies were included. At the whole-muscle level, there were no significant differences for any comparison (SMD < 0.03). By contrast, RT induced greater type I and type II muscle fiber hypertrophy than CT when high-intensity interval training (HIIT) was incorporated alone (SMD > 0.33) or combined with continuous ET (SMD > 0.27), but not compared with CT incorporating only continuous ET (SMD < 0.16). The subgroup analyses of this systematic review and meta-analysis showed that RT induces greater muscle fiber hypertrophy than CT when HIIT is included. However, no CT affected whole-muscle hypertrophy compared with RT.

Validity, reliability, and normative data on calf muscle function in rugby union players from the Calf Raise application.

We examined the validity and reliability of biomechanical outcomes extracted using the Calf Raise application of three calf muscle tests. We then established normative calf muscle function values for male rugby union players accounting for rugby-related factors (position, level) alone and together with clinical factors (age, leg dominance, BMI, previous injury). In total, 120 athletes performed three single-leg calf muscle tests. Twenty athletes participated in application validation; 18 in test-retest reliability; and all in establishing normative equations. Validity of application outcomes against 3D motion and force plate data was good-to-excellent (CV ≤ 6.6%, ICC ≥0.84). Test-retest reliability was good across outcomes following familiarisation (CV < 10%, ICC ≥0.83). Forwards produced superior power than backs during the bodyweight (59 W, p = 0.007) and weighted (73 W, p < 0.001) power tests. Playing level influenced power outcomes (p < 0.009). Super Rugby players were more powerful than Club (both power tests), Provincial (both power tests), and International (bodyweight power). Backs completed more repetitions (3 repetitions, p = 0.001) and positive displacement (30 cm, p = 0.001) than forwards during endurance testing. When accounting for clinical factors; BMI, age, and previous injury explained some of the differences observed between positions and levels. This study provides initial benchmark values of calf muscle function in rugby union.

Validity and Reliability of Strategy Metrics to Assess Countermovement Jump Performance using the Newly Developed My Jump Lab Smartphone Application.

The aim of the present study was to analyse the validity and reliability of the newly developed My Jump Lab smartphone app, which includes the option to calculate time to take-off and the reactive strength index modified (RSI_Mod - calculated as jump height divided by time to take-off), in addition to jump height. Twenty-seven postgraduate sport science students attended a single test session and performed three maximal effort countermovement jumps (CMJ) on twin force plates, whilst concurrently being filmed using the app. Results showed no significant differences in jump height between measurement methods (g = 0.00) or RSI_Mod (g = -0.49), although a significant difference was evident for time to take-off (g = 0.68). When a correction factor was applied to time to take-off data, no meaningful differences were evident (g = 0.00), which also had a knock-on effect for RSI_Mod (g = 0.10). Bland-Altman analysis showed near perfect levels of agreement for jump height with a bias estimate of 0.001 m, whilst time to take-off reported a bias estimate of 0.075 s initially and, 0.000 s once the correction factor was applied. For RSI_Mod, bias estimate was initially -0.048, and 0.006 once calculated with the corrected time to take-off data. Pearson's r correlations were: 0.98 for jump height, 0.81 for time to take-off, and 0.85 for RSI_Mod. Based on the findings from the present study, and with the inclusion of the newly embedded correction factor, My Jump Lab can now be used as both a valid and reliable means of measuring time to take-off and RSI_Mod in the CMJ.

A Systematic Review of The Effects of Different Resistance Training Volumes on Muscle Hypertrophy.

The main goal of this study was to compare responses to moderate and high training volumes aimed at inducing muscle hypertrophy. A literature search on 3 databases (Pubmed, Scopus and Chocrane Library) was conducted in January 2021. After analyzing 2083 resultant articles, studies were included if they met the following inclusion criteria: a) studies were randomized controlled trials (with the number of sets explicitly reported), b) interventions lasted at least six weeks, c) participants had a minimum of one year of resistance training experience, d) participants' age ranged from 18 to 35 years, e) studies reported direct measurements of muscle thickness and/or the cross-sectional area, and f) studies were published in peer-review journals. Seven studies met the inclusion criteria and were included in the qualitative analysis, whereas just six were included in the quantitative analysis. All participants were divided into three groups: "low" (<12 weekly sets), "moderate" (12-20 weekly sets) and "high" volume (>20 weekly sets). According to the results of this meta-analysis, there were no differences between moderate and high training volume responses for the quadriceps (p = 0.19) and the biceps brachii (p = 0.59). However, it appears that a high training volume is better to induce muscle mass gains in the triceps brachii (p = 0.01). According to the results of this review, a range of 12-20 weekly sets per muscle group may be an optimum standard recommendation for increasing muscle hypertrophy in young, trained men.

The role of the neural stimulus in regulating skeletal muscle hypertrophy.

Resistance training is frequently performed with the goal of stimulating muscle hypertrophy. Due to the key roles motor unit recruitment and mechanical tension play to induce muscle growth, when programming, the manipulation of the training variables is oriented to provoke the correct stimulus. Although it is known that the nervous system is responsible for the control of motor units and active muscle force, muscle hypertrophy researchers and trainers tend to only focus on the adaptations of the musculotendinous unit and not in the nervous system behaviour. To better guide resistance exercise prescription for muscle hypertrophy and aiming to delve into the mechanisms that maximize this goal, this review provides evidence-based considerations for possible effects of neural behaviour on muscle growth when programming resistance training, and future neurophysiological measurement that should be tested when training to increase muscle mass. Combined information from the neural and muscular structures will allow to understand the exact adaptations of the muscle in response to a given input (neural drive to the muscle). Changes at different levels of the nervous system will affect the control of motor units and mechanical forces during resistance training, thus impacting the potential hypertrophic adaptations. Additionally, this article addresses how neural adaptations and fatigue accumulation that occur when resistance training may influence the hypertrophic response and propose neurophysiological assessments that may improve our understanding of resistance training variables that impact on muscular adaptations.

Rating of perceived exertion and velocity loss as variables for controlling the level of effort in the bench press exercise.

There is a growing interest in the analysis of different methods for monitor fatigue during resistance training sessions. This study aimed to (1) analyse the relationships between the percentage of performed repetitions with respect to the maximum possible number (%REP), RPE and magnitude of velocity loss (VL), and (2) examine whether a multiple regression analysis with the RPE and VL as predictor variables could improve the goodness of fit to predict %REP in the bench press exercise performed in a Smith machine. Seven men performed a repetition maximum test, on 3 separate testing sessions, against 3 different absolute loads based on a target mean velocity (MV) according to an individual load-velocity profile (≈1.00, ≈0.70, and ≈0.50 m/s). MV, VL, %REP and RPE were collected and used for analysis. Based upon quadratic polynomial regression analysis strong relationships were reported between the RPE and %REP (r2 = 0.89 and SEE = 9.85%) and between the VL and %REP (r2 = 0.91 and SEE = 9.85%). Multiple regression analysis with the RPE and VL as predictor variables improved the goodness of fit (r2 = 0.94 and SEE = 7.18%) of the model to predict %REP. These results suggest that both RPE and VL are useful variables to accurately estimate %REP in the bench press exercise.

Jump and Change of Direction Speed Asymmetry Using Smartphone Apps: Between-Session Consistency and Associations With Physical Performance.

ABSTRACT: Bishop, C, Perez-Higueras Rubio, M, Gullon, IL, Maloney, S, and Balsalobre-Fernandez, C. Jump and change of direction speed asymmetry using smartphone apps: between-session consistency and associations with physical performance. J Strength Cond Res 36(4): 927-934, 2022-The aims of this study were to (a) quantify the magnitude and direction of asymmetry from jump and change of direction speed (CODS) tests and (b) determine the relationship between these asymmetries and jump and CODS performance, in a test-retest design. Thirty Spanish national-level youth basketball athletes performed single leg countermovement jumps (SLCMJs), single leg drop jumps (SLDJs), and 505 CODS tests, all assessed using the My Jump 2 and CODTimer smartphone applications. All tests showed good to excellent reliability, with no significant differences identified between test sessions in jump, CODS, or asymmetry data. The direction of asymmetry showed substantial levels of agreement between test sessions for jump height during the SLDJ (Kappa = 0.72), but only fair levels of agreement for reactive strength during the SLDJ (Kappa = 0.25), fair levels of agreement for jump height during the SLCMJ (Kappa = 0.29), and slight levels of agreement for total time during the 505 test (Kappa = 0.18). Jump height asymmetry from the SLDJ was significantly associated with reduced jump height (ρ = -0.44), reactive strength (ρ = -0.46) and 505 times (ρ = 0.45-0.48) in test session 1, and reactive strength (ρ = -0.42) and 505 times (ρ = 0.40) in test session 2. These data show that jump height asymmetry from the SLDJ was associated with reduced jump and CODS performance in youth basketball athletes during repeated test sessions. In addition, the same asymmetry metric was the only one to show substantial levels of agreement between test sessions. Owing to the consistency of these data, SLDJ height asymmetry may be a useful metric to measure when monitoring interlimb asymmetries.

Validity of a novel inertial measurement unit to track barbell velocity / Validación de un nuevo sensor inercial para medir la velocidad de ejecución

The objective of this work is to analyze the reliability and validity of the new inertial measurement unit (IMU) PUSHTM Band2.0 to measure barbell velocity. Six healthy males (24.83±3.71years; 69.88±8.36kg; 175.92±4.5cm) participated in this studyand performed several sets on the bench press. Barbell concentric mean (MV) and peak (PV) velocity were recorded witha LT and the IMU. Pearson correlation coefficient shows a very high relationship for MV (r = 0.97; SEE: 0.08 m/s; 95%CI: 0.95-0.98; p< 0.001) and PV (r = 0.97; SEE: 0.13 m/s; 95%CI: 0.96-0.98; p< 0.001). There was a very high agreement for the values ofMV and PV (MV: ICC = 0.945, CI = 0.834–0.974, α = 0.981; PV: ICC = 0.926, CI = 0.708–0.969, α = 0.977). Paired sample t-testrevealed systematic bias for MV (p< 0.001; mean difference between instruments = 0.06 ± 0.09 m/s) and PV (p< 0.001; meandifference between instruments = 0.15 ± 0.18 m/s). Bland-Altman plots showed almost trivial and moderate relationships forMV (r2 = 0.1) and PV (r2 = 0.37). In conclusion, the PUSHTM Band 2.0 was proven to be a valid alternative for measuring barbellvelocity in the bench press.(AU)

El objetivo de este trabajo es analizar la fiabilidad y validez de la nueva unidad de medición inercial (IMU) PUSHTM Band 2.0para medir la velocidad de la barra. Seis hombres sanos (24.83 ± 3.71 años; 69.88 ± 8.36 kg; 175.92 ± 4.5 cm) participaron eneste estudio y realizaron varias series en el press de banca. La velocidad concéntrica de barra (MV) y la velocidad pico (PV)se registraron con un LT y la IMU. El coeficiente de correlación de Pearson muestra una relación muy alta para MV (r = 0.97;SEE: 0.08 m/s; IC 95%: 0.95-0.98; p <0.001) y PV (r = 0.97; SEE: 0.13 m/s; 95% IC: 0,96-0,98; p <0,001). Hubo un acuerdo muyalto para los valores de MV y PV (MV: ICC = 0.945, CI = 0.834–0.974, α = 0.981; PV: ICC = 0.926, CI = 0.708–0.969, α = 0.977). Laprueba t de muestras relacionadas reveló un sesgo sistemático para MV (p <0.001; diferencia media entre instrumentos = 0.06± 0.09 m/s) y PV (p <0.001; diferencia media entre instrumentos = 0.15 ± 0.18 m/s). Las gráficas de Bland-Altman mostraronrelaciones casi triviales y moderadas para VM (r2 = 0.1) y VP (r2 = 0.37). En conclusión, se demostró que PUSHTM Band 2.0 esuna alternativa válida para medir la velocidad de la barra en el press de banca.(AU)

Smartphone and Tablet Software Apps to Collect Data in Sport and Exercise Settings: Cross-sectional International Survey.

BACKGROUND: Advances in smartphone technology have facilitated an increase in the number of commercially available smartphone and tablet apps that enable the collection of physiological and biomechanical variables typically monitored in sport and exercise settings. Currently, it is not fully understood whether individuals collect data using mobile devices and tablets, independent of additional hardware, in their practice. OBJECTIVE: This study aims to explore the use of smartphone and tablet software apps to collect data by individuals working in various sport and exercise settings, such as sports coaching, strength and conditioning, and personal training. METHODS: A total of 335 practitioners completed an electronic questionnaire that surveyed their current training practices, with a focus on 2 areas: type of data collection and perceptions of reliability and validity regarding app use. An 18-item questionnaire, using a 5-point Likert scale, evaluated the perception of app use. RESULTS: A total of 204 respondents reported using apps to directly collect data, with most of them (196/335, 58.5%) collecting biomechanical data, and 41.2% (138/335) respondents reported using at least one evidence-based app. A binomial general linear model determined that evidence accessibility (ß=.35, 95% CI 0.04-0.67; P=.03) was significantly related to evidence-based app use. Age (ß=-.03, 95% CI -0.06 to 0.00; P=.03) had a significant negative effect on evidence-based app use. CONCLUSIONS: This study demonstrates that practitioners show a greater preference for using smartphones and tablet devices to collect biomechanical data such as sprint velocity and jump performance variables. When it is easier to access information on the quality of apps, practitioners are more likely to use evidence-based apps. App developers should seek independent research to validate their apps. In addition, app developers should seek to provide clear signposting to the scientific support of their software in alternative ways.

The Implementation of Velocity-Based Training Paradigm for Team Sports: Framework, Technologies, Practical Recommendations and Challenges.

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load-velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as "muscle power" are commented upon.

Use of Machine-Learning and Load-Velocity Profiling to Estimate 1-Repetition Maximums for Two Variations of the Bench-Press Exercise.

The purpose of the current study was to compare the ability of five different methods to estimate eccentric-concentric and concentric-only bench-press 1RM from load-velocity profile data. Smith machine bench-press tests were performed in an eccentric-concentric (n = 192) and a concentric-only manner (n = 176) while mean concentric velocity was registered using a linear position transducer. Load-velocity profiles were derived from incremental submaximal load (40-80% 1RM) tests. Five different methods were used to calculate 1RM using the slope, intercept, and velocity at 1RM (minimum velocity threshold-MVT) from the load-velocity profiles: calculation with individual MVT, calculation with group average MVT, multilinear regression without MVT, regularized regression without MVT, and an artificial neural network without MVT. Mean average errors for all methods ranged from 2.7 to 3.3 kg. Calculations with individual or group MVT resulted in significant overprediction of eccentric-concentric 1RM (individual MVT: difference = 0.76 kg, p = 0.020, d = 0.17; group MVT: difference = 0.72 kg, p = 0.023, d = 0.17). The multilinear and regularized regression both resulted in the lowest errors and highest correlations. The results demonstrated that bench-press 1RM can be accurately estimated from load-velocity data derived from submaximal loads and without MVT. In addition, results showed that multilinear regression can be used to estimate bench-press 1RM. Collectively, the findings and resulting equations should be helpful for strength and conditioning coaches as they would help estimating 1RM without MVT data.

Effects of an eccentric overload and small-side games training in match accelerations and decelerations performance in female under-23 soccer players.

BACKGROUND: The aims of this study were: 1) to investigate the impacts that an eccentric overload training (EOT) and a small-side game training (SSGT) have on the characteristics of the accelerations (ACC) and decelerations (DCC) of the players in a soccer match; and 2) to determine if EOT and SSGT could affect the ACC and DCC reduction over time in a soccer match. METHODS: Twenty-three female soccer players from a Spanish professional club were split into three groups: a small-sided game training group (SGG), an eccentric overload training group (EOG) and a control group (CG). RESULTS: The SSG improved the high intensity distance performed (ES [CI]=0.72 [0.22; 1.22]), the number of high intensity actions (ES [CI]=0.65 [0.01; 1.29]), the percentage of repeated high intensity actions (ES [CI]=0.54 [-0.17; 1.25]), the initial velocity of the ACC (ES [CI]=0.55 [-0.08; 1.17]) and the percentage of repeated accelerations (ES [CI]=0.87 [-0.18; 1.91]) with respect to the control group. The EOG obtained better results in distance travelling accelerating (ES [CI]=0.84 [0.09; 1.60]) and decelerating (ES [CI]=0.87 [0.23; 1.51]) above 3 m/s2, maximum ACC (ES [CI]=1.92 [0.90; 2.94]) and DCC (ES [CI]=1.29 [0.44; 2.14]) and the average of maximum ACC (ES [CI]=0.89 [0.23; 1.54]) and DCC (ES [CI]=1.08 [0.62; 1.55]) with respect to the CG. A decrement in the ACC and DCC performance was observed between the first and last 15 minutes of the competition, except for the EOG. CONCLUSIONS: The SSG obtained mainly improvements in variables related with efforts repetitions and the capacity of maintaining the ACC and the DCC over time, while improvements in the EOG were related to intensity in the ACC and DCC.

Repetitions in Reserve and Rate of Perceived Exertion Increase the Prediction Capabilities of the Load-Velocity Relationship.

ABSTRACT: Balsalobre-Fernández, C, Muñoz-López, M, Marchante, D, and García-Ramos, A. Repetitions in reserve and rate of perceived exertion increase the prediction capabilities of the load-velocity relationship. J Strength Cond Res 35(3): 724-730, 2021-This study aimed to (a) analyze the relationships between relative load (i.e., %1 repetition maximum; 1RM) and movement velocity, repetitions in reserve (RIR) and rate of perceived exertion (RPE) in competitive powerlifters and (b) examine whether a multiple linear regression model with the movement velocity, RIR, and RPE as predictor variables could improve the goodness of fit of the load-velocity relationship. Ten competitive powerlifters performed an incremental loading test (from 50 to 100% 1RM) on the full-squat, hip-thrust, and bench press exercises. Barbell velocity was measured using a linear position transducer, while RIR and RPE were registered immediately after each set. Velocity (r2: 0.747-0.887), RIR (r2: 0.857-0.928), and RPE (r2: 0.908-0.933) were moderately to highly related to relative load. A higher amount of variance of the relative load was explained when the RIR and RPE were added to velocity in a multiple regression model in comparison with the load-velocity relationship (r2: 0.924-0.947). Moreover, it was observed that, in all cases, individual load-velocity, load-RIR, and load-RPE relationships had higher r2 scores than the generalized load-velocity relationship. Incorporating the RIR and RPE as predictors of the relative load along with movement velocity into a multiple linear regression was shown to provide better estimations of the %1RM than using a linear load-velocity relationship.