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Validity and Reliability of A New Low-Cost Linear Position Transducer to Measure Mean Propulsive Velocity: The ADR device
Abstract
Many sports and recreational strength training coaches consider movement velocity essential to improve performance, and velocity-based training has gained attention over the past decade. Furthermore, there is a lack of low-cost, easy to use, and reliable methods to measure movement velocity. Therefore, this current research aims to analyze the validity and reliability of a new linear position transducer device (ADR) for the measurement of barbell mean propulsive velocity. Seventeen trained participants (n = 14 men; n = 3 women; 21.264.0 years) performed an incremental bench press exercise test against five different loads (45%, 55%, 65%, 75%, and 85% 1RM) at maximal concentric velocity. Barbell displacement was derived simultaneously from three devices including: a linear velocity transducer (T-Force, criterion measurement) and two linear position transducers (ADR and Speed4lifts (S4L)). The ADR mean propulsive velocity measurements demonstrated substantial validity compared to both T-Force and S4L at all loads (between the r values and p values r = .86–.99 p \ 0.001). The ADR device was reliable showing very high Intraclass correlation coefficients (ICC (95% CI): 0.95 (0.90–0.98), 0.96 (0.91–0.98), 0.75 (0.55–0.88), 0.91 (0.83–0.93), 0.85 (0.72–0.93) for 45%, 55%, 65%, 75%, and 85% 1RM, respectively); low coefficients of variation (CV (95% CI): 9.93 (7.93–11.93, 11.25 (9.25–13.25), 6.78 (4.78–8.78), 10.95 (8.95–12.95), 14.40 (12.40–16.40) for 45%, 55%, 65%, 75%, and 85% 1RM, respectively), and small standardized typical error values (STE = 0.2–0.6). In conclusion, the ADR device can be considered an affordable, reliable, and valid method to measure movement velocity, thereby making it a practical resource for coaches when assessing velocity-based training at gyms.
... El big data actual de Internet se ha convertido gradualmente en un punto caliente del desarrollo social, y ha sido ampliamente utilizado en varias industrias y ha logrado buenos resultados (Wang & Anhouck, 2022). La validez y confiabilidad de un nuevo transductor de posición lineal de bajo costo para medir la velocidad propulsora media en los deportes (Lopez-Torres et al., 2022), aporta una aplicación a través de la inteligencia artificial que proporciona la predicción de riesgos en los deportes (Qiao, 2022). ...
Resumen. El artículo aborda la tendencia del deporte 4.0 desde una perspectiva bibliométrica. Este tipo de deporte emergente en la cultura depor-tiva contemporánea utiliza tecnologías de la información y la comunicación para mejorar la experiencia deportiva de los atletas, entrenadores, aficionados y otros actores del deporte. El objetivo fue analizar las características, tecnologías y diseños con líneas de innovación en el deporte 4.0 desde las revisiones bibliográficas y su implementación práctica. Los resultados destacan la importancia de Big Data, Internet de las cosas e Inteligen-cia artificial aplicados al deporte 4.0. Se realizó una revisión sistemática de literatura en la base de datos Scopus, examinando 585 artículos publica-dos desde enero de 2012 a junio de 2022. La etapa de implementación de un evento de deporte 4.0 con estudiantes del semillero de investigación Administración y gestión deportiva se basó en la estrategia pedagógica de investigación formativa, iniciativa que permitió aplicar los conocimientos adquiridos en el aula de clases en un entorno real y práctico. Requirió de un proceso cuidadoso y planificado por parte de los investigadores princi-pales, quienes se aseguraron de involucrar a todos los actores relevantes, tanto estudiantes como profesores. La muestra estuvo constituida por 24 estudiantes en el rol de atletas divididos en 12 equipos, 12 estudiantes en el rol de Staff (comisión organizadora) y 100 estudiantes en el rol de espectadores. Se concluye que esta tendencia aborda la complejidad de la cultura deportiva contemporánea con un enfoque holístico e interdiscipli-nario, lo que conlleva a cambios en las buenas prácticas deportivas. Palabras clave: Deporte 4.0, Big data, Internet de las cosas, inteligencia artificial, análisis bibliométrico, cultura deportiva contemporánea. Abstract. The article addresses the sport 4.0 trend from a bibliometric perspective. This type of sport emerging in contemporary sports culture uses information and communication technologies to enhance the sporting experience for athletes, coaches, fans, and other stakeholders in the sport. The objective was to analyze the characteristics, technologies and designs with lines of innovation in sport 4.0 from the bibliographic reviews and their practical implementation. The results highlight the importance of Big Data, the Internet of Things and Artificial Intelligence applied to sport 4.0. A systematic literature review was carried out in the Scopus database, examining 585 articles published from January 2012 to June 2022. The implementation stage of a sport 4.0 event with students from the Sports Administration and Management research hotbed was based on the pedagogical strategy of formative research, an initiative that allowed applying the knowledge acquired in the classroom in a real and practical environment. It required a careful and planned process on the part of the principal investigators, who made sure to involve all relevant stakeholders, both students and teachers. The sample consisted of 24 students in the role of athletes divided into 12 teams, 12 students in the role of Staff (organ-izing committee) and 100 students in the role of spectators. It is concluded that this trend addresses the complexity of contemporary sports culture with a holistic and interdisciplinary approach, which leads to changes in good sports practices.
[This corrects the article DOI: 10.1371/journal.pone.0232465.].
This study aimed to evaluate the reliability and validity of a smartphone application (iLOAD) for the monitoring of mean velocity (MV) during resistance training sets. Twenty males completed two identical sessions consisting of one set of 10 repetitions against four loads (25-40-55-70% of the one-repetition maximum [1RM]) during the back squat and bench press exercises. The MV of the five initial repetitions and for the whole set were determined simultaneously with the iLOAD application and a linear velocity transducer (LVT). Two independent researchers operated the iLOAD application during the experimental sessions to evaluate the inter-rater agreement for the assessment of MV. An acceptable but generally lower reliability was observed for iLOAD (CV range: 5.61-9.79%) compared to the LVT (CV range: 4.51-8.18%) at 25-40-55% of 1RM, while the reliability at 75% of 1RM was acceptable for the LVT during the bench press (CV range: 6.37%-8.26%) but it was unacceptable for the iLOAD during both exercises (CV range: 11.3-12.8%) and for the LVT during the back squat (CV range: 11.3-17.4%). Small to moderate differences (ES range: 0.24-1.04) and very high to practically perfect correlations (r range: 0.70-0.90) were observed between the iLOAD and the LVT. A very high agreement was observed between both raters for the recording of MV during the back squat and bench press exercises (r 0.98). Taken together, these results suggest that the iLOAD application can be confidently used to quantify the MV of training sets during the squat and bench press exercises not performed to failure.
This study aimed to analyze the agreement between five bar velocity monitoring devices, currently used in resistance training, to determine the most reliable device based on reproducibility (between-device agreement for a given trial) and repeatability (between-trial variation for each device). Seventeen resistance-trained men performed duplicate trials against seven increasing loads (20-30-40-50-60-70-80 kg) while obtaining mean, mean propulsive and peak velocity outcomes in the bench press, full squat and prone bench pull exercises. Measurements were simultaneously registered by two linear velocity transducers (LVT), two linear position transducers (LPT), two optoelectronic camera-based systems (OEC), two smartphone video-based systems (VBS) and one accelerometer (ACC). A comprehensive set of statistics for assessing reliability was used. Magnitude of errors was reported both in absolute (m s⁻¹) and relative terms (%1RM), and included the smallest detectable change (SDC) and maximum errors (MaxError). LVT was the most reliable and sensitive device (SDC 0.02–0.06 m s⁻¹, MaxError 3.4–7.1% 1RM) and the preferred reference to compare with other technologies. OEC and LPT were the second-best alternatives (SDC 0.06–0.11 m s⁻¹), always considering the particular margins of error for each exercise and velocity outcome. ACC and VBS are not recommended given their substantial errors and uncertainty of the measurements (SDC > 0.13 m s⁻¹).
This aim of this study was to compare the reliability and validity of seven commercially
available devices to measure movement velocity during the bench press exercise.
Fourteen men completed two testing sessions. The bench press one-repetition
maximum (1RM) was determined in the first session. The second testing session
consisted of performing three repetitions against five loads (45-55-65-75-85% of 1RM).
The mean velocity was simultaneously measured using an optical motion sensing
system (Trio-OptiTrack™; “gold-standard”) and seven commercially available devices:
1 linear velocity transducer (T-Force™), 2 linear position transducers (Chronojump™
and Speed4Lift™), 1 camera-based optoelectronic system (Velowin™), 1 smartphone
application (PowerLift™), and 2 inertial measurement units (PUSH™ band and
Beast™ sensor). The devices were ranked from the most to the least reliable as
follows: (I) Speed4Lift™ (coefficient of variation [CV] = 2.61%), (II) Velowin™ (CV =
3.99%), PowerLift™ (3.97%), Trio-OptiTrack™ (CV = 4.04%), T-Force™ (CV = 4.35%),
Chronojump™ (CV = 4.53%), (III) PUSH™ band (CV = 9.34%), and (IV) Beast™
sensor (CV = 35.0%). A practically perfect association between the Trio-OptiTrack™
system and the different devices was observed (Pearson’s product-moment correlation
coefficient (r) range = 0.947-0.995; P < 0.001) with the only exception of the Beast
sensor (r = 0.765; P < 0.001). These results suggest that linear velocity/position
transducers, camera-based optoelectronic systems and the smartphone application
could be used to obtain accurate velocity measurements for restricted linear
movements, while the inertial measurement units used in this study were less reliable
and valid.
The purpose of this study was to analyze the validity, reliability, and accuracy of new wearable and smartphone-based technology for the measurement of barbell velocity in resistance training exercises. To do this, 10 highly trained powerlifters (age = 26.1 ± 3.9 years) performed 11 repetitions with loads ranging 50–100% of the 1-Repetition maximum in the bench-press, full-squat, and hip-thrust exercises while barbell velocity was simultaneously measured using a linear transducer (LT), two Beast wearable devices (one placed on the subjects' wrist –BW–, and the other one directly attached to the barbell –BB–) and the iOS PowerLift app. Results showed a high correlation between the LT and BW (r = 0.94–0.98, SEE = 0.04–0.07 m • s −1), BB (r = 0.97–0.98, SEE = 0.04–0.05 m • s −1), and the PowerLift app (r = 0.97–0.98, SEE = 0.03–0.05 m • s −1) for the measurement of barbell velocity in the three exercises. Paired samples T-test revealed systematic biases between the LT and BW, BB and the app in the hip-thrust, between the LT and BW in the full-squat and between the LT and BB in the bench-press exercise (p < 0.001). Moreover, the analysis of the linear regression on the Bland-Altman plots showed that the differences between the LT and BW (R 2 = 0.004–0.03), BB (R 2 = 0.007–0.01), and the app (R 2 = 0.001–0.03) were similar across the whole range of velocities analyzed. Finally, the reliability of the BW (ICC = 0.910–0.988), BB (ICC = 0.922–0.990), and the app (ICC = 0.928–0.989) for the measurement of the two repetitions performed with each load were almost the same than that observed with the LT (ICC = 0.937–0.990). Both the Beast wearable device and the PowerLift app were highly valid, reliable, and accurate for the measurement of barbell velocity in the bench-press, full-squat, and hip-thrust exercises. These results could have potential practical applications for strength and conditioning coaches who wish to measure barbell velocity during resistance training.
Purpose:
To analyze the load, force and power-velocity relationships, as well as to determine the load that optimizes power output on the pull-up exercise.
Methods:
Eighty-two resistance trained males (Age = 26.8 ± 5.0 yrs.; Pull-up 1RM – normalized per kg of body mass– = 1.5 ± 0.34) performed two repetitions with 4 incremental loads (ranging 70-100%1-RM) in the pull-up exercise while mean propulsive velocity (MPV), force (MPF) and power (MPP) were measured using a linear transducer. Relationships between variables were studied using first and second order least-squares regression, and subjects were divided into three groups depending on their 1-RM for comparison purposes.
Results:
Almost perfect individual load-velocity (R2 = 0.975 ± 0.02), force-velocity (R2 = 0.954 ± 0.04) and power-velocity (R2 = 0.966 ± 0.04) relationships, which allowed to determine the velocity at each %1-RM as well as the maximal theoretical force (F0), velocity (V0) and power (Pmax) for each subject were observed. Statistically significant differences between groups were observed for F0 (p<0.01) but not for MPV at each %1-RM, V0 or Pmax (p>0.05). Also, high correlations between F0 and 1-RM (r = 0.811) and V0 and Pmax (r = 0.865) were observed. Finally, we observed that the load that maximized MPP was 71.0 ± 6.6 %1-RM.
Conclusions:
The very high load-velocity, force-velocity and power-velocity relationships allows to estimate 1-RM by measuring movement velocity, as well as to determine maximal force, velocity and power capabilities. This information could be of great interest for strength and conditioning coaches who wish to monitor pull-up performance.
Purpose:
This investigation examined the validity of two kinematic systems for assessing mean velocity (MV), peak velocity (PV), mean force (MF), peak force (PF), mean power (MP), and peak power (PP) during the full depth free-weight back squat performed with maximal concentric effort.
Methods:
Ten strength-trained men (26.1±3.0 y; 1.81±0.07 m; 82.0±10.6 kg) performed three 1-repetition maximum (1RM) trials on three separate days, encompassing lifts performed at six relative intensities including 20, 40, 60, 80, 90, and 100% of 1RM. Each repetition was simultaneously recorded by a PUSH band, commercial linear position transducer (LPT) (GymAware [GYM]), and compared with measurements collected by a laboratory based testing device consisting of four LPTs and a force plate.
Results:
Trials 2 and 3 were used for validity analyses, combining all 120 repetitions indicated the GYM was highly valid for assessing all criterion variables while the PUSH(TM) was only highly valid for estimations of PF (r=0.94; CV=5.4%; ES=0.28; SEE=135.5 N). At each relative intensity, the GYM was highly valid for assessing all criterion variables except for PP at 20% (ES=0.81) and 40% (ES=0.67) of 1RM. Moreover, the PUSH(TM) was only able to accurately estimate PF across all relative intensities (r=0.92-0.98; CV=4.0-8.3%; ES=0.04-0.26; SEE=79.8-213.1 N).
Conclusions:
The PUSH accuracy for determining MV, PV, MF, MP, and PP across all six relative intensities was questionable for the back squat, yet the GYM was highly valid at assessing all criterion variables, with some caution given to estimations of MP and PP performed at lighter loads.
The purpose of this study was to analyse the validity and reliability of a novel iPhone app (named: PowerLift) for the measurement of mean velocity on the bench-press exercise. Additionally, the accuracy of the estimation of the 1-Repetition maximum (1RM) using the load–velocity relationship was tested. To do this, 10 powerlifters (Mean (SD): age = 26.5 ± 6.5 years; bench press 1RM · kg−1 = 1.34 ± 0.25) completed an incremental test on the bench-press exercise with 5 different loads (75–100% 1RM), while the mean velocity of the barbell was registered using a linear transducer (LT) and Powerlift. Results showed a very high correlation between the LT and the app (r = 0.94, SEE = 0.028 m · s−1) for the measurement of mean velocity. Bland–Altman plots (R2 = 0.011) and intraclass correlation coefficient (ICC = 0.965) revealed a very high agreement between both devices. A systematic bias by which the app registered slightly higher values than the LT (P < 0.05; mean difference (SD) between instruments = 0.008 ± 0.03 m · s−1). Finally, actual and estimated 1RM using the app were highly correlated (r = 0.98, mean difference (SD) = 5.5 ± 9.6 kg, P < 0.05). The app was found to be highly valid and reliable in comparison with a LT. These findings could have valuable practical applications for strength and conditioning coaches who wish to measure barbell velocity in the bench-press exercise.
Purpose:
This study aimed to evaluate the accuracy of a novel approach for predicting the one-repetition maximum (1RM). The prediction is based on the force-velocity and load-velocity relationships determined from measured force and velocity data collected during resistance-training exercises with incremental submaximal loads. 1RM was determined as the load corresponding to the intersection of these two curves, where the gravitational force exceeds the force that the subject can exert.
Methods:
The proposed force-velocity-based method (FVM) was tested on 37 participants (23.9 ± 3.1 year; BMI 23.44 ± 2.45) with no specific resistance-training experience, and the predicted 1RM was compared to that achieved using a direct method (DM) in chest-press (CP) and leg-press (LP) exercises.
Results:
The mean 1RM in CP was 99.5 kg (±27.0) for DM and 100.8 kg (±27.2) for FVM (SEE = 1.2 kg), whereas the mean 1RM in LP was 249.3 kg (±60.2) for DM and 251.1 kg (±60.3) for FVM (SEE = 2.1 kg). A high correlation was found between the two methods for both CP and LP exercises (0.999, p < 0.001). Good agreement between the two methods emerged from the Bland and Altman plot analysis.
Conclusion:
These findings suggest the use of the proposed methodology as a valid alternative to other indirect approaches for 1RM prediction. The mathematical construct is simply based on the definition of the 1RM, and it is fed with subject's muscle strength capacities measured during a specific exercise. Its reliability is, thus, expected to be not affected by those factors that typically jeopardize regression-based approaches.
2002;5(3):54-59. The purpose of this investigation was to determine if 1-RM strength could be predicted from a 4-6 RM submaximal strength test with a greater accuracy than the commonly used 7-10 submaximal strength test. Thirty-four healthy males between the ages of 19 and 32 participated in this study. Subjects completed 1-RM, 4-6 RM, and 7-10 RM strength assessments in random order with a minimum of 48 hours between each strength assessment. During each session, subjects performed strength assessments for the bench press, incline press, triceps extension, biceps curl, and leg extension. Multiple regression analysis was used to produce equations for predicting 1-RM strength from 4 to 6 or 7 to 10 repetition maximum tests. The 4-6 RM prediction equations improved the predictive accuracy of 1-RM strength compared to the 7-10 RM prediction equations based on the adjusted R 2 and standard error of estimate. Since no injuries or symptoms of delayed onset of muscle soreness were reported during either the 7-10 RM or the 4-6 RM submaximal strength assessments, the results of this study indicate that when attempting to predict 1-RM strength in healthy, young, males, a 4-6 RM submaximal strength assessment appears to be the more accurate test.
To investigate whether caffeine ingestion counteracts the morning reduction in neuromuscular performance associated with the circadian rhythm pattern.
Twelve highly resistance-trained men underwent a battery of neuromuscular tests under three different conditions; i) morning (10:00 a.m.) with caffeine ingestion (i.e., 3 mg kg(-1); AM(CAFF) trial); ii) morning (10:00 a.m.) with placebo ingestion (AM(PLAC) trial); and iii) afternoon (18:00 p.m.) with placebo ingestion (PM(PLAC) trial). A randomized, double-blind, crossover, placebo controlled experimental design was used, with all subjects serving as their own controls. The neuromuscular test battery consisted in the measurement of bar displacement velocity during free-weight full-squat (SQ) and bench press (BP) exercises against loads that elicit maximum strength (75% 1RM load) and muscle power adaptations (1 m s(-1) load). Isometric maximum voluntary contraction (MVC(LEG)) and isometric electrically evoked strength of the right knee (EVOK(LEG)) were measured to identify caffeine's action mechanisms. Steroid hormone levels (serum testosterone, cortisol and growth hormone) were evaluated at the beginning of each trial (PRE). In addition, plasma norepinephrine (NE) and epinephrine were measured PRE and at the end of each trial following a standardized intense (85% 1RM) 6 repetitions bout of SQ (POST).
In the PM(PLAC) trial, dynamic muscle strength and power output were significantly enhanced compared with AM(PLAC) treatment (3.0%-7.5%; p≤0.05). During AM(CAFF) trial, muscle strength and power output increased above AM(PLAC) levels (4.6%-5.7%; p≤0.05) except for BP velocity with 1 m s(-1) load (p = 0.06). During AM(CAFF), EVOK(LEG) and NE (a surrogate of maximal muscle sympathetic nerve activation) were increased above AM(PLAC) trial (14.6% and 96.8% respectively; p≤0.05).
These results indicate that caffeine ingestion reverses the morning neuromuscular declines in highly resistance-trained men, raising performance to the levels of the afternoon trial. Our electrical stimulation data, along with the NE values, suggest that caffeine increases neuromuscular performance having a direct effect in the muscle.
When analysing the reliability of ratio-scaled variables, such as walking energy cost, variability of the error term often increases with increasing mean values. This phenomenon is called heteroscedasticity, and it makes the analysis of reliability more complicated. This study presents an examination of heteroscedasticity for walking energy cost before analysing the reliability.
Walking energy cost was collected from 33 children with cerebral palsy (CP), with varying Gross Motor Function Classification System (GMFCS) levels (19 males; 14 females; mean age: 7y 6mo [SD 2y 6mo]; GMFCS levels I [n=16], II [n=7], and III [n=10]). It was assessed by measuring oxygen uptake during 10 minutes of resting and 5 minutes of walking at comfortable speed. Measurements were performed twice, within 4 to 6 weeks. Primary outcomes included gross energy cost, gross non-dimensional energy cost, net energy cost, net non-dimensional energy cost, speed, and non-dimensional speed. Heteroscedasticity was analysed with Bland-Altman plots and Kendall's tau.
Visual inspection of the Bland-Altman plots showed heteroscedasticity for gross energy cost, gross non-energy cost, and net energy cost. This was confirmed by Kendall's tau coefficients. Accordingly, data were logarithmically transformed, and reliability was assessed with ratio statistics. For speed, heteroscedasticity was not observed.
Variability of gross energy cost, gross non-energy cost, and net energy cost, assessed across different GMFCS levels in children with CP, was proportional to the mean, indicating the presence of heteroscedasticity. This finding emphasizes the importance of always performing a heteroscedasticity examination in reliability studies on energy cost and reporting the reliability statistics accordingly.
G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
It is important that sources of variation in gas analysis measurements are identified and described in an accurate and informative manner. In this paper, we discussed the potential sources of error, which should be considered in any measurement study on gas analysis systems. We then covered how errors in various terms associated with gas laws propagate to outcome measurements of gas exchange to help quantify the relative importance of sources of error. Finally, we performed a literature survey to explore the statistical methods researchers have employed to arrive at conclusions on the performance characteristics of gas analysis methods. We found examples of excellent practice in the literature, but there were also gaps in the knowledge of error in gas analysis systems. Consequently, we supplied guidelines for future method comparison studies. These guidelines included (i) a sample size of at least 40 participants and the citation of confidence intervals, (ii) a description of the relationships between systematic and random errors and the size of measured value, (iii) the parallel examination of test-retest error within a method comparison study, and (iv) an a priori-made judgement on how much systematic and random error between methods is acceptable for practical applications. We stressed that this judgement should be based on expert-agreed position statements about acceptable error, which unfortunately have yet to be formulated for gas analysis systems.
Velocity-based training is a method used to monitor resistance-training programs based on repetition velocities measured with tracking devices. Since velocity measuring devices can be expensive and impractical, trainee's perception of changes in velocity (PCV) may be used as a possible substitute. Here, 20 resistance-trained males first completed 1 Repetition Maximum (RM) tests in the bench-press and squat. Then, in three counterbalanced sessions, participants completed four sets of eight repetitions in both exercises using 60%1RM (two-sessions) or 70%1RM. Starting from the second repetition, participants reported their PCV of each repetition as a percentage of the first repetition. Accuracy of PCV was calculated as the difference between PCV and actual changes in velocity measured with a linear-encoder. Three key findings emerged. First, the absolute error in the bench-press and squat was ≈5.8 percentage-points in the second repetition, and increased to 13.2 and 16.7 percentage-points, respectively, by the eighth repetition. Second, participants reduced the absolute error in the second 60%1RM session compared to the first by ≈1.7 in both exercises (p≤ 0.007). Third, participants were 4.2 times more likely to underestimate changes velocity in the squat compared to the bench-press. The gradual increments in the absolute error suggest that PCV may be better suited for sets of fewer repetitions (e.g., 4-5) and wider velocity-loss threshold ranges (e.g., 5-10%). The reduced absolute error in the second 60%1RM session suggests that PCV accuracy can be improved with practice. The systematic underestimation error in the squat suggests that a correction factor may increase PCV accuracy in this exercise.
Velocity-based training (VBT) requires the monitoring of lift velocity plus the prescribed resistance weight. A validated and reliable device is needed to capture the velocity and power of several exercises.
Objectives:
The study objectives were to examine the validity and reliability of the Elite Form Training System® (EFTS) for measures of peak velocity (PV), average velocity (AV), peak power (PP), and average power (AP).
Design:
Validity of the EFTS was assessed by comparing measurements simultaneously obtained via the Qualisys Track Manager software (C-motion, version 3.90.21, Gothenburg, Sweden) utilizing 6 motion capture cameras (Oqus 400, 240 Hz, Gothenburg, Sweden).
Methods:
Six participants performed 6 resistance exercises in 2 sessions: power clean, dead lift, bench press, back squat, front squat, and jump squat.
Results:
Simple Pearson correlations indicated the validity of the device (0.982, 0.971, 0.973, and 0.982 for PV, AV, PP, and AP respectively) and ranged from 0.868 to 0.998 for the 6 exercises. The test-retest reliability of the EFTS was shown by lack of significant change in the Pearson correlation (<0.3% for each variable) between the 2 sessions. The multiple count error rate was 2.0% and the missed count error rate was 2.1%.
Conclusions:
The validity and reliability of the EFTS were classified as excellent across all variables and exercises with only one exercise showing a slight influence by the velocity of the movement.
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.
We compared the effects of two resistance training (RT) programs only differing in the repetition velocity loss allowed in each set: 20% (VL20) vs 40% (VL40) on muscle structural and functional adaptations. Twenty-two young males were randomly assigned to a VL20 (n = 12) or VL40 (n = 10) group. Subjects followed an 8-week velocity-based RT program using the squat exercise while monitoring repetition velocity. Pre- and post-training assessments included: magnetic resonance imaging, vastus lateralis biopsies for muscle cross-sectional area (CSA) and fiber type analyses, one-repetition maximum strength and full load-velocity squat profile, countermovement jump (CMJ), and 20-m sprint running. VL20 resulted in similar squat strength gains than VL40 and greater improvements in CMJ (9.5% vs 3.5%, P < 0.05), despite VL20 performing 40% fewer repetitions. Although both groups increased mean fiber CSA and whole quadriceps muscle volume, VL40 training elicited a greater hypertrophy of vastus lateralis and intermedius than VL20. Training resulted in a reduction of myosin heavy chain IIX percentage in VL40, whereas it was preserved in VL20. In conclusion, the progressive accumulation of muscle fatigue as indicated by a more pronounced repetition velocity loss appears as an important variable in the configuration of the resistance exercise stimulus as it influences functional and structural neuromuscular adaptations.
When conducting a double-blind clinical trial to evaluate a new treatment, the investigator is faced with the problem of what to give the control group. If there is no existing acceptable and beneficial treatment against which the new treatment can be compared, the reasonable approach would be to give the control group no treatment at all, but this raises concerns that subjects may have hidden expectations or preferences that could influence the outcome. One solution is the use of a harmless “sham” treatment that is similar in all aspects such that the subjects and those administering the treatments cannot determine whether a subject is in the study group or the control group. This sham treatment is called the placebo. This entry discusses historical usage, difficulties with the placebo research design, and the placebo effect. The term Placebo Domino … (“I shall please the Lord …”) appears in a 5th-century placebo ...
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Abstract This study analysed the effect of imposing a pause between the eccentric and concentric phases on the biological within-subject variation of velocity- and power-load isoinertial assessments. Seventeen resistance-trained athletes undertook a progressive loading test in the bench press (BP) and squat (SQ) exercises. Two trials at each load up to the one-repetition maximum (1RM) were performed using 2 techniques executed in random order: with (stop) and without (standard) a 2-s pause between the eccentric and concentric phases of each repetition. The stop technique resulted in a significantly lower coefficient of variation for the whole load-velocity relationship compared to the standard one, in both BP (2.9% vs. 4.1%; P = 0.02) and SQ (2.9% vs. 3.9%; P = 0.01). Test-retest intraclass correlation coefficients (ICCs) were r = 0.61-0.98 for the standard and r = 0.76-0.98 for the stop technique. Bland-Altman analysis showed that the error associated with the standard technique was 37.9% (BP) and 57.5% higher (SQ) than that associated with the stop technique. The biological within-subject variation is significantly reduced when a pause is imposed between the eccentric and concentric phases. Other relevant variables associated to the load-velocity and load-power relationships such as the contribution of the propulsive phase and the load that maximises power output remained basically unchanged.
Little is known about the competitive performance characteristics of elite rowers. We report here analyses of performance times for finalists in world-class regattas from 1999 to 2009.
The data were official race times for the 10 men's and 7 women's single and crewed boat classes, each with ∼ 200-300 different boats competing in 1-33 of the 46 regattas at 18 venues. A linear mixed model of race times for each boat class provided estimates of variability as coefficients of variation after adjustment for means of calendar year, level of competition (Olympics, world championship, World Cup), venue, and level of final (A, B, C, …).
Mean performance was substantially slower between consecutive levels of competition (1.5%, 2.7%) and consecutive levels of finals (∼ 1%-2%). Differences in the effects of venue and of environmental conditions, estimated as variability in mean race time between venues and finals, were extremely large (∼ 3.0%). Within-boat race-to-race variability for A finalists was 1.1% for single sculls and 0.9% for crewed boats, with little difference between men and women and only a small increase in lower-level finalists. Predictability of performance, expressed as intraclass correlation coefficients, showed considerable differences between boat classes, but the mean was high (∼ 0.63), with little difference between crewed and single boats, between men and women, and between within and between years.
The race-to-race variability of boat times of ∼ 1.0% is similar to that in comparable endurance sports performed against water or air resistance. Estimates of the smallest important performance enhancement (∼ 0.3%) and the effects of level of competition, level of final, venue, environment, and boat class will help inform investigations of factors affecting elite competitive rowing performance.
In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
This study analyzed the contribution of the propulsive and braking phases among different percentages of the one-repetition maximum (1RM) in the concentric bench press exercise. One hundred strength-trained men performed a test with increasing loads up to the 1RM for the individual determination of the load-power relationship. The relative load that maximized the mechanical power output (P(max)) was determined using three different parameters: mean concentric power (MP), mean power of the propulsive phase (MPP) and peak power (PP). The load at which the braking phase no longer existed was 76.1+/-7.4% 1RM. P(max) was dependent on the parameter used: MP (54.2%), MPP (36.5%) or PP (37.4%). No significant differences were found for loads between 40-65% 1RM (MP) or 20-55% 1RM (MPP and PP), nor between P(max) (% 1RM) when using MPP or PP. P(max) was independent of relative strength, although certain tendency towards slightly lower loads was detected for the strongest subjects. These results highlight the importance of considering the contribution of the propulsive and braking phases in isoinertial strength and power assessments. Referring the mean mechanical values to the propulsive phase avoids underestimating an individual's true neuromuscular potential when lifting light and medium loads.
This study examined the possibility of using movement velocity as an indicator of relative load in the bench press (BP) exercise. One hundred and twenty strength-trained males performed a test (T1) with increasing loads for the individual determination of the one-repetition maximum (1RM) and full load-velocity profile. Fifty-six subjects performed the test on a second occasion (T2) following 6 weeks of training. A very close relationship between mean propulsive velocity (MPV) and load (%1RM) was observed (R (2)=0.98). Mean velocity attained with 1RM was 0.16+/-0.04 m x s(-1) and was found to influence the MPV attained with each %1RM. Despite a mean increase of 9.3% in 1RM from T1 to T2, MPV for each %1RM remained stable. Stability in the load-velocity relationship was also confirmed regardless of individual relative strength. These results confirm an inextricable relationship between relative load and MPV in the BP that makes it possible to: 1) evaluate maximal strength without the need to perform a 1RM test, or test of maximum number of repetitions to failure (XRM); 2) determine the %1RM that is being used as soon as the first repetition with any given load is performed; 3) prescribe and monitor training load according to velocity, instead of percentages of 1RM or XRM.
Repetitions to fatigue (RTF) using less than a 1 repetition maximum (1RM) load (RepWt) have been shown to be a good predictor of 1RM strength in men, but such information is scarce in women. The purpose of this study was to evaluate the accuracy of current prediction equations to estimate 1RM bench press performance and to determine whether resistance training changes the capability to predict 1RM from muscular endurance repetitions in young women. Members (n = 103) of a required wellness course were measured for 1RM bench press and RTF using randomly assigned percentages between 60% and 90% of the 1RM (RepWt) before and after 12 weeks of progressive resistance training. The %1RM used to perform RTF remained the same for each individual after training (75.6% +/- 10.3%) as before. One repetition maximum bench press increased significantly after training (28% +/- 21%). Although the change in the group average for RTF (0.6 +/- 6.1) was not significant, the correlation between pretraining and posttraining RTF was moderate (r = 0.66; p < 0.01), and individual differences in percentage change in RTF were substantial (27% +/- 99%). The percentage change in 1RM was not significantly related to initial 1RM (r = -0.05), but it was negatively related to the change in RTF (r = -0.40; p < 0.01). Prediction equations were more accurate in the pretraining and posttraining conditions, in which fewer than 10 RTF were used. Resistance training may alter the relationship between strength and muscle endurance across a wide range of RTF in young women without compromising the accuracy of predicting maximal strength.
Reliability refers to the reproducibility of values of a test, assay or other measurement in repeated trials on the same individuals. Better reliability implies better precision of single measurements and better tracking of changes in measurements in research or practical settings. The main measures of reliability are within-subject random variation, systematic change in the mean, and retest correlation. A simple, adaptable form of within-subject variation is the typical (standard) error of measurement: the standard deviation of an individual's repeated measurements. For many measurements in sports medicine and science, the typical error is best expressed as a coefficient of variation (percentage of the mean). A biased, more limited form of within-subject variation is the limits of agreement: the 95% likely range of change of an individual's measurements between 2 trials. Systematic changes in the mean of a measure between consecutive trials represent such effects as learning, motivation or fatigue; these changes need to be eliminated from estimates of within-subject variation. Retest correlation is difficult to interpret, mainly because its value is sensitive to the heterogeneity of the sample of participants. Uses of reliability include decision-making when monitoring individuals, comparison of tests or equipment, estimation of sample size in experiments and estimation of the magnitude of individual differences in the response to a treatment. Reasonable precision for estimates of reliability requires approximately 50 study participants and at least 3 trials. Studies aimed at assessing variation in reliability between tests or equipment require complex designs and analyses that researchers seldom perform correctly. A wider understanding of reliability and adoption of the typical error as the standard measure of reliability would improve the assessment of tests and equipment in our disciplines.
This study evaluated the influence of cadence on the Young Men's Christian Association (YMCA) bench press test for predicting 1 repetition maximum (1RM) bench press test performance. Fifty-eight medical students (37 men, 21 women) were evaluated for anthropometric variables (age, height, weight, fat-free mass, and percent fat), 1RM bench press, and 2 cadence tests of muscular endurance performed at cadences of 30 and 60 repetitions per minute (reps.min(-1)). Each test was performed on a separate day, with 5 days rest in between. There was no significant difference among the number of repetitions performed at each cadence by men, whereas women performed significantly more repetitions at the slower cadence. Repetitions at either cadence were good predictors of 1RM bench press in both genders (men: 30 reps.min(-1), r(2) = 0.757, standard error of the estimate [SEE] = 8.0 kg; 60 reps.min(-1), r(2) = 0.884, SEE = 8.2 kg; women: 30 reps.min(-1), r(2) = 0.754, SEE = 3.1 kg; 60 reps.min(-1), r(2) = 0.816, SEE = 2.7 kg). The addition of anthropometric dimensions to the regression equations did not improve predictive accuracy. Using both fast and slow cadences, the YMCA bench press test can provide a valid estimation of 1RM performance in untrained young men and women.
The study of measurement error, observer variation and agreement between different methods of measurement are frequent topics in the imaging literature. We describe the problems of some applications of correlation and regression methods to these studies, using recent examples from this literature. We use a simulated example to show how these problems and misinterpretations arise. We describe the 95% limits of agreement approach and a similar, appropriate, regression technique. We discuss the difference vs. mean plot, and the pitfalls of plotting difference against one variable only. We stress that these are questions of estimation, not significance tests, and show how confidence intervals can be found for these estimates.
Encyclopedia of Research Design
- N J Salkind
Salkind NJ. Encyclopedia of Research Design. SAGE
Publications, Inc, 2010.[AQ: 3]