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Sports Biomechanics

ISSN: 1476-3141 (Print) 1752-6116 (Online) Journal homepage: https://www.tandfonline.com/loi/rspb20

Letter to editor

Iker J. Bautista & Fernando Martín

To cite this article: Iker J. Bautista & Fernando Martín (2019): Letter to editor, Sports

Biomechanics, DOI: 10.1080/14763141.2019.1640280

To link to this article: https://doi.org/10.1080/14763141.2019.1640280

Published online: 30 Jul 2019.

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Letter to editor

We have read with great interest and care the study carried out by García-Orea,

Belando-Pedreño, Merino-Barrero, and Heredia-Elvar (2019) entitled ‘Validation of

an opto-electronic instrument for the measurement of execution velocity in squat

exercise’DOI: 10.1080/14763141.2019.1597156. We applaud the authors for their

thoughtful approach to the study. The general idea of the study is good and has an

important practical utility, unfortunately there are some aspects, regarding both statis-

tics and results that, in our modest opinion, need to be addressed.

The use of devices to control velocity execution while training with external resis-

tances is an interesting issue (García-Ramos et al., 2017; Spitz, Gonzalez, Ghigiarelli,

Sell, & Mangine, 2019). The cost of these devices ranges from the most economical (i.e.,

video camera and app) to the most expensive (T-Force system) (Courel-Ibañez et al.,

2019). However, regardless of the price, it is crucial that all devices that evaluate

execution velocity do so as accurately as possible. Systematic and/or random errors in

the measurement of the velocities at which the bar moves could generate large errors in

the determination of the intensity of the exercise according to its maximum repetition

(1RM) or when comparing the performance between diﬀerent athletes.

Here is a list of what are, in our opinion, errors that we have detected in the study

mentioned above:

Page 3, ‘experimental design’section. The authors express that they have used

a unifactorial repeated subject design. That is not true. Two independent variables are

available in this study (i.e., load [6 levels] and devices [2 levels]). Therefore, the most

appropriate design for the data collection they have carried out would be a factorial design

(since there is more than 1 independent variable) of repeated measurements. At the

statistical level, this could be solved with an analysis of the variance of repeated measure-

ments (ANOVA RM [2, devices x 6, loads]) where we would ﬁnd the following eﬀects:

(i) Main eﬀect of ‘device’. Regardless of the load factor, the ANOVA compares the

mean of all velocities across both devices (i.e., Velowin vs. T-Force). This is an

interesting eﬀect.

(ii) Main eﬀect of ‘load’. Regardless of the device factor, the ANOVA compares the

mean of all velocities across all loads (i.e., 20 vs. 30 vs. 40 . . .). This is not an

interesting eﬀect.

(iii) Interaction ‘device x load’. The ANOVA compares the mean of all velocities

across load and intensity factors. This is an interesting eﬀect.

It would be interesting for the reader to be able to interpret the results obtained in

terms of main eﬀect of device ‘factor’and interaction eﬀect ‘device x load’of the

aforementioned ANOVA.

SPORTS BIOMECHANICS

https://doi.org/10.1080/14763141.2019.1640280

© 2019 Informa UK Limited, trading as Taylor & Francis Group

Page 6, statistical analysis section. The authors express that they have used the

intraclass correlation coeﬃcient (ICC). However, there are more than 6 diﬀerent

types of ICC depending on the treatment that receives the term error, i.e., whether to

include the systematic and/or random error, if a 1-way or 2-way ANOVA is performed,

if a ﬁxed or random model is used, and if the data come from one single measurement

or from an average of measurements (Weir, 2005). On the other hand, measurement

reliability is deﬁned as stability against multiple repetitions at diﬀerent times. As we

have been able to read in the study, no measurements have been made on diﬀerent days

and only the best repetition of each load has been analysed. Therefore, we do not

understand how the authors can speak about reliability.

Page 6, statistical analysis section. The authors express that only the fastest repetition

at each load is used for the reliability analysis (page 7). They declare to have carried out,

among others, the ICC test, it is therefore impossible to evaluate the reliability of the

device, since to carry this out, at least two measurements should have been taken into

account for each load, and preferably in diﬀerent experimental sessions. We understand

that the ICC has been made to study the concordance between both devices, but the

latter is not a reliability test, only a validation. Moreover, this is not the best statistical

procedure to evaluate that. Bland-Altman plot would be the best procedure to evaluate

the concordance between devices and also assess systematic and random error. Finally,

we understand that the best repetition on device 1 (e.g., T-Force) does not have to

match with the best repetition on device 2 (e.g., Velowin).

Page 7, the authors state that Pearson’s Correlation Coeﬃcient showed values from

r= 0.70 to r= 0.96. This is not consistent with Lin’s CCC results, since Lin’s CCC value

can never be higher than Pearson’s Correlation Coeﬃcient value, a fact that the same

authors take into account on page 6 when they explain the statistical procedures they

have performed.

Page 7, normal test results section. The authors state that the variable Mean Velocity

for Velowin did not have a normal distribution, but they express a p= 0.172, a value

that evidences the normality of the variable. This is probably a writing error.

Page 7, Table 2. The authors state ‘Table 2. Coeﬃcient of Variation (CV), Intra-class

Correlation Coeﬃcient (ICC) and Lin’s Concordance Coeﬃcient (CCC) for each variable

with both devices in Squat exercise’. However, the variable ‘device’is not observed

anywhere.

Page 7, Table 2. The authors present the SEM values for each of the loads and as

a function of each of the variables analysed (i.e., mean velocity, mean propulsive

velocity and peak velocity). It is striking that the SEM average of all loads is equal to

0.296 m/s; that the ICC is, on average, 0.94; and that Pearson’s correlation coeﬃcient

has ﬂuctuated between 0.70 and 0.96 (the authors have not expressed in which loads

they have obtained such coeﬃcients). The ICC, depending on which formula it is used,

may or may not include systematic error. However, the SEM is aﬀected by the

systematic and random error. In this case, Pearson’s correlation coeﬃcient cannot

detect systematic errors, but it can detect random errors (Weir, 2005). For this reason,

a type of statistical analysis such as the Bland-Altman graph is missing. Firstly, the error

could have been expressed in terms of systematic and random components. Secondly,

the presence of heterocedasticity could have been analysed.

2LETTER

Page 8, ‘ANOVA’section. As I have previously argued, the experimental design of

the cited study was an ANOVA RM (2 x 6). The authors should have expressed, for

each dependent variable (i.e., VM, VMP and VP), the statistics of Snedecor’s F, the

degrees of freedom and the eﬀect size.

We ﬁrmly believe that the main problem lies in the experimental design of the manuscript

itself. On one hand, one thing is to evaluate the reliability of a device and/or evaluation

procedure and, on the other hand, to evaluate the concordance between diﬀerent devices

(T-Force vs. Velowin). Due to all the arguments we have presented above, we consider that

the assertion made by the authors in the conclusions of the study ‘The main ﬁnding of this

study was the high reliability and concurrent validation of the Velowin opto-electronic system

for measuring the execution velocity’is not supported by the results presented in the manu-

script. We are not saying that such a device (i.e., Velowin) is not valid or reliable, however,

with the results obtained in the study, making such a claim is risky. In fact, a paper recently

published by Courel-Ibañez et al. (2019) reported a correlation coeﬃcient between T-Force

and Velowin of r= 0.991 in full squat exercise and the limits of agreement’svalueswere

−0.08 m/s and 0.05 m/s for systematic and random error, respectively.

Disclosure statement

No potential conﬂict of interest was reported by the authors.

References

Courel-Ibañez, J., Martínez-Cava, A., Morán-Navarro, R., Escribano-Peñas, P., Chavarren-

Cabrero, J., González-Badillo, J. J., & Pallarés, J. G. (2019). Reproducibility and repeatability

of ﬁve diﬀerent technologies for bar velocity measurement in resistance training. Annals of

Biomedical Engineering,47, 1523–1538. doi:10.1007/s10439-019-02265-6

García-Orea, G. P., Belando-Pedreño, N., Merino-Barrero, J. A., & Heredia-Elvar, J. R. (2019)

Validation of an opto-electronic instrument for the measurement of execution velocity in

squat exercise. Sports Biomechanics. doi:10.1080/14763141.2019.1597156

García-Ramos, A., Torrejón, A., Feriche., B., Morales-Artacho, A. J., Pérez-Castilla, A., Padial, P.,

&Haﬀ,G.G.(2017). Prediction of the máximum number of repetitions and repetitions in

reserve from barbell velocity. International Journal of Sports Physiology and Performance,13,

353–359. doi:10.1123/ijspp.2017-0302

Spitz, E. W., Gonzalez, A. M., Ghigiarelli, J. J., Sell, K. M., & Mangine, G. T. (2019). Load-velocity

relationships of the back vs. Journal of Strength and Conditioning Research,32, 301–306.

doi:10.1519/JSC.0000000000002962

Weir, J. P. (2005). Quantifying tet-retest reliability using intraclass correlation coeﬃcient and the

SEM. Journal of Strength and Conditioning Research,19, 231–240. doi:10.1519/15184.1

Iker J. Bautista

Physical Education and Sport Science, University of Granada, Granada, Spain

ikerugr@gmail.com http://orcid.org/0000-0002-7409-6290

Fernando Martín

Physical Education and Sports, University of Valencia, Valencia, Spain

http://orcid.org/0000-0003-1996-8276

Received 13 June 2019; Accepted 1 July 2019

SPORTS BIOMECHANICS 3