Replication of Inter-limb asymmetries versus performance in elite team sports athletes

Fort-Vanmeerhaeghe, A., Bishop, C., Buscà, B., Aguilera-Castells, J., Vicens-Bordas, J., & Gonzalo-Skok, O. (2020). Inter-limb asymmetries are associated with decrements in physical performance in youth elite team sports athletes. PloS one, 15(3), e0229440. https://doi.org/10.1371/journal.pone.0229440

Introduction

The paper explored whether there were relationships between inter-limb asymmetries and athletic performance in youth elite team sports. A secondary goal was to establish whether sex influenced inter-limb asymmetries.

The authors performed a cross-sectional study. Eighty-one elite youth team sport athletes were recruited. Athletes were assessed in the anterior star excursion balance test, single-leg countermovement jump, single leg broad jump, 30 m sprint, and a change-of-direction V-cut test.

Original paper can be found here: [Link]

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Data Analysis

Data can be downloaded by following this link: [Link]

Inter-limb asymmetries were calculated as:

\[ asymmetry = \frac{(high – low)}{low} * 100 \]

The authors performed several analyses:

Data was checked for normality using the Kolmogorov-Smirnov test, and within-session reliability was measured using a two-way random ICC with absolute agreement and coefficient of variation.

Student t-tests were run to identify potential differences in asymmetries between the group and each sex. Independent sample t-tests were also run to identify potential differences in asymmetries between sexes.

Cohen’s d was used to assess the magnitude of the difference of effect size for the asymmetries between the sexes. Effect size was interpreted as suggested by Hopkins et al. (2009).

Finally, relationships between asymmetry scores and physical performance were assessed using the Pearson correlation coefficient. Relationships were established for the whole group and for each sex. Strength of the correlation was interpreted as suggested by Hopkins et al. (2009).

The authors did not state whether outliers were removed. Therefore, the analyses performed on the data includes all observations.

Code

Some of the analyses cannot be completed with the data set that was made public.

Set-Up

library(readxl) # to read in data
library(tidyverse, quietly = TRUE) # for tidy code
library(janitor) # clean variable names
library(skimr) # for easy exploratory analyses
library(correlation) # correlation matrices
library(report) # automate reporting of results
library(parameters) # supplemental reporting fns
library(knitr) # clean reporting

Original Data Set

asym <- read_excel("Data asymmetries vs performance.xlsx")

asym <- asym %>% 
  clean_names() %>%  # clean column names
  mutate_if(Negate(is.numeric), as.numeric) %>%  # convert non-numeric to numeric
  mutate(sex = case_when(
    sex == 0 ~ "male",
    sex == 1 ~ "female"
  ))

Code

knitr::kable(head(asym))

codi	age	phv	height	weight	experience	bmi	sex	cmj	x30m_sprint	v_cut_test	olht_skillfull	olhte_non_skillfull	percent_asi_olht	cmj_skillfull	cmj_nonskillfull	percent_asi_cmj	sebt_ant_skillful	sebt_ant_non_skillfull	percent_asi_sebt_ant
1	17.01	NA	1.78	70.5	8	22.25	female	0.25	5.092	7.353	1.73	1.54	10.75	0.18	0.15	16.41	0.0	76.0	2.56
2	17.29	3.49	1.68	59.0	10	20.90	female	0.27	4.507	7.612	1.56	1.41	9.73	0.20	0.17	17.45	62.3	62.3	0.00
3	15.98	3.54	1.78	59.0	9	18.62	female	0.28	4.644	7.523	1.61	1.55	3.85	0.18	0.14	24.61	76.3	76.3	2.62
4	16.56	3.14	1.65	59.0	10	21.67	female	0.27	4.884	7.459	1.50	1.41	6.13	0.16	0.13	15.96	70.7	70.7	3.30
5	16.18	3.07	1.67	60.0	8	21.51	female	0.28	4.747	7.713	1.54	1.50	2.98	0.18	0.14	21.81	73.0	73.0	36.07
6	16.68	3.92	1.75	74.0	7	24.16	female	0.16	NA	NA	1.22	0.67	44.90	0.11	0.09	13.00	57.5	57.5	3.63

Descriptive Statistics

Both Sexes

skim.asym <- skim(asym)
skim.asym <- cbind(skim.asym,
                   tibble(kolmogorov.smirnov = sapply(asym, FUN = function(x) if(is.numeric(x)) round(ks.test(x, "pnorm")$p.value, 2))))
skim.asym <- skim.asym[, c(-1, -4:-9, -12:-16)]
skim.asym <- as.data.frame(sapply(skim.asym, FUN = function(x) if(is.double(x)) round(x, 3) else as.character(x)))

Code

kable(skim.asym)

skim_variable	n_missing	numeric.mean	numeric.sd	numeric.hist	kolmogorov.smirnov
sex	0	NA	NA	NA	0
codi	0	41	23.527	▇▇▇▇▇	0
age	0	15.905	1.127	▆▇▆▇▇	0
phv	2	2.733	1.795	▁▂▇▆▁	0
height	0	1.79	0.2	▁▁▁▁▇	0
weight	0	69.91	11.785	▅▇▃▂▁	0
experience	0	6.089	3.008	▃▃▇▆▂	0
bmi	0	20.228	5.242	▁▁▁▇▂	NULL
cmj	0	0.285	0.073	▆▇▁▁▁	0
x30m_sprint	5	5.146	4.175	▇▁▁▁▁	0
v_cut_test	2	7.181	0.949	▁▁▁▁▇	0
olht_skillfull	1	1.684	0.248	▃▇▆▅▂	0
olhte_non_skillfull	1	1.586	0.258	▁▂▇▆▅	0
percent_asi_olht	1	5.919	6.227	▇▁▁▁▁	0
cmj_skillfull	0	0.161	0.039	▃▇▆▂▁	0
cmj_nonskillfull	0	0.142	0.035	▂▇▆▁▁	0
percent_asi_cmj	0	11.331	8.327	▇▆▂▁▁	0
sebt_ant_skillful	1	68.265	9.771	▁▁▁▅▇	0
sebt_ant_non_skillfull	1	69.172	7.009	▃▆▇▂▁	0
percent_asi_sebt_ant	1	12.848	15.312	▇▁▂▁▁	0

Male

skim.asym.by.sex <- skim(asym %>% 
                    group_by(sex))

skim.asym.by.sex <- skim.asym.by.sex[, c(-1, -8:-12)]
skim.asym.by.sex <- as.data.frame(sapply(skim.asym.by.sex, FUN = function(x) if(is.double(x)) round(x, 3) else as.character(x)))

Code

kable(skim.asym.by.sex %>% 
              filter(sex == "male"))

skim_variable	sex	n_missing	complete_rate	numeric.mean	numeric.sd	numeric.hist
codi	male	0	1	60.724	14.619	▇▅▁▅▇
age	male	0	1	16.125	1.078	▇▅▆▇▇
phv	male	0	1	1.561	2.257	▃▆▇▃▁
height	male	0	1	1.803	0.322	▁▁▁▁▇
weight	male	0	1	75.421	13.827	▆▇▃▅▁
experience	male	0	1	4.731	3.474	▆▇▂▂▅
bmi	male	0	1	21.132	5.036	▁▁▁▇▃
cmj	male	0	1	0.348	0.077	▆▇▁▁▁
x30m_sprint	male	1	0.966	5.64	6.918	▇▁▁▁▁
v_cut_test	male	0	1	6.561	1.306	▁▁▁▁▇
olht_skillfull	male	0	1	1.941	0.155	▆▆▇▇▂
olhte_non_skillfull	male	0	1	1.83	0.175	▂▅▇▇▃
percent_asi_olht	male	0	1	5.719	4.661	▇▇▃▂▁
cmj_skillfull	male	0	1	0.193	0.037	▃▆▇▆▁
cmj_nonskillfull	male	0	1	0.168	0.036	▁▅▇▃▁
percent_asi_cmj	male	0	1	12.647	10.327	▇▆▂▁▁
sebt_ant_skillful	male	1	0.966	69.732	6.07	▂▇▇▅▂
sebt_ant_non_skillfull	male	1	0.966	69.625	8.003	▃▆▇▃▁
percent_asi_sebt_ant	male	1	0.966	15.271	18.869	▇▁▂▁▁

Female

skim.asym.by.sex <- skim(asym %>% 
                    group_by(sex))

skim.asym.by.sex <- skim.asym.by.sex[, c(-1, -8:-12)]
skim.asym.by.sex <- as.data.frame(sapply(skim.asym.by.sex, FUN = function(x) if(is.double(x)) round(x, 3) else as.character(x)))

Code

kable(skim.asym.by.sex %>% 
              filter(sex == "female"))

skim_variable	sex	n_missing	complete_rate	numeric.mean	numeric.sd	numeric.hist
codi	female	0	1	30	20.157	▇▇▇▁▇
age	female	0	1	15.783	1.145	▇▆▇▇▇
phv	female	2	0.962	3.412	0.967	▁▁▆▇▆
height	female	0	1	1.783	0.077	▅▇▇▇▁
weight	female	0	1	66.837	9.274	▂▇▅▂▂
experience	female	0	1	6.846	2.437	▁▁▇▆▂
bmi	female	0	1	19.724	5.335	▁▁▁▇▇
cmj	female	0	1	0.25	0.04	▃▇▇▆▁
x30m_sprint	female	4	0.923	4.858	0.293	▁▇▆▁▁
v_cut_test	female	2	0.962	7.541	0.315	▃▅▇▅▂
olht_skillfull	female	1	0.981	1.538	0.153	▃▂▇▆▅
olhte_non_skillfull	female	1	0.981	1.447	0.184	▁▁▃▆▇
percent_asi_olht	female	1	0.981	6.033	7.005	▇▁▁▁▁
cmj_skillfull	female	0	1	0.143	0.026	▅▇▇▇▃
cmj_nonskillfull	female	0	1	0.127	0.023	▂▆▇▇▂
percent_asi_cmj	female	0	1	10.597	6.977	▇▅▅▁▁
sebt_ant_skillful	female	0	1	67.475	11.25	▁▁▁▆▇
sebt_ant_non_skillfull	female	0	1	68.929	6.482	▃▆▇▅▁
percent_asi_sebt_ant	female	0	1	11.544	13.028	▇▂▁▁▃

Statistical Analysis

The authors report that they assessed limbs as strong and weak. The following code separates the limb values by strength for further analyses.

asym.stats <- asym %>% 
  mutate(sebt.high = ifelse(sebt_ant_skillful > sebt_ant_non_skillfull, sebt_ant_skillful, sebt_ant_non_skillfull),
         sebt.low = ifelse(sebt_ant_skillful < sebt_ant_non_skillfull, sebt_ant_skillful, sebt_ant_non_skillfull),
         
         olht.high = ifelse(olht_skillfull > olhte_non_skillfull, olht_skillfull, olhte_non_skillfull),
         olht.low = ifelse(olht_skillfull < olhte_non_skillfull, olht_skillfull, olhte_non_skillfull),
         
         cmj.high = ifelse(cmj_skillfull > cmj_nonskillfull, cmj_skillfull, cmj_nonskillfull),
         cmj.low = ifelse(cmj_skillfull < cmj_nonskillfull, cmj_skillfull, cmj_nonskillfull)) 

t-tests

Asymmetries by Group vs by Sex

CMJ

percent.asi.cmj <- asym.stats$percent_asi_cmj
percent.asi.cmj.male <- subset(asym.stats, sex == "male")$percent_asi_cmj
percent.asi.cmj.female <- subset(asym.stats, sex == "female")$percent_asi_cmj

grp.v.male.cmj <- t.test(percent.asi.cmj, percent.asi.cmj.male)
grp.v.female.cmj <- t.test(percent.asi.cmj, percent.asi.cmj.female)

Code

report(grp.v.male.cmj)

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.cmj and
percent.asi.cmj.male (mean of x = 11.33, mean of y = 12.65) suggests that the
effect is negative, statistically not significant, and very small (difference =
-1.32, 95% CI [-5.61, 2.98], t(41.76) = -0.62, p = 0.540; Cohen's d = -0.14,
95% CI [-0.59, 0.31])

Code

report(grp.v.female.cmj)

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.cmj and
percent.asi.cmj.female (mean of x = 11.33, mean of y = 10.60) suggests that the
effect is positive, statistically not significant, and very small (difference =
0.73, 95% CI [-1.92, 3.38], t(121.93) = 0.55, p = 0.585; Cohen's d = 0.10, 95%
CI [-0.25, 0.44])

Star Balance

percent.asi.sebt <- asym.stats$percent_asi_sebt_ant
percent.asi.sebt.male <- subset(asym.stats, sex == "male")$percent_asi_sebt_ant
percent.asi.sebt.female <- subset(asym.stats, sex == "female")$percent_asi_sebt_ant


grp.v.male.sebt <- t.test(percent.asi.sebt, percent.asi.sebt.male)
grp.v.female.sebt <- t.test(percent.asi.sebt, percent.asi.sebt.female)

Code

report(grp.v.male.sebt)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.sebt and
percent.asi.sebt.male (mean of x = 12.85, mean of y = 15.27) suggests that the
effect is negative, statistically not significant, and very small (difference =
-2.42, 95% CI [-10.42, 5.57], t(40.15) = -0.61, p = 0.544; Cohen's d = -0.14,
95% CI [-0.59, 0.31])

Code

report(grp.v.female.sebt)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.sebt and
percent.asi.sebt.female (mean of x = 12.85, mean of y = 11.54) suggests that
the effect is positive, statistically not significant, and very small
(difference = 1.30, 95% CI [-3.62, 6.23], t(120.82) = 0.52, p = 0.601; Cohen's
d = 0.09, 95% CI [-0.25, 0.43])

Broad Jump

percent.asi.olht <- asym.stats$percent_asi_olht
percent.asi.olht.male <- subset(asym.stats, sex == "male")$percent_asi_olht
percent.asi.olht.female <- subset(asym.stats, sex == "female")$percent_asi_olht

grp.v.male.olht <- t.test(percent.asi.olht, percent.asi.olht.male)
grp.v.female.olht <- t.test(percent.asi.olht, percent.asi.olht.female)

Code

report(grp.v.female.olht)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.olht and
percent.asi.olht.female (mean of x = 5.92, mean of y = 6.03) suggests that the
effect is negative, statistically not significant, and very small (difference =
-0.11, 95% CI [-2.50, 2.27], t(97.42) = -0.09, p = 0.925; Cohen's d = -0.02,
95% CI [-0.37, 0.34])

Code

report(grp.v.male.olht)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.olht and
percent.asi.olht.male (mean of x = 5.92, mean of y = 5.72) suggests that the
effect is positive, statistically not significant, and very small (difference =
0.20, 95% CI [-2.02, 2.42], t(66.14) = 0.18, p = 0.858; Cohen's d = 0.04, 95%
CI [-0.36, 0.43])

Asymmetries by Sex

CMJ

male.v.female.cmj <- t.test(percent.asi.cmj.male, percent.asi.cmj.female)

Code

report(male.v.female.cmj)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between percent.asi.cmj.male
and percent.asi.cmj.female (mean of x = 12.65, mean of y = 10.60) suggests that
the effect is positive, statistically not significant, and small (difference =
2.05, 95% CI [-2.28, 6.38], t(42.55) = 0.95, p = 0.345; Cohen's d = 0.23, 95%
CI [-0.25, 0.71])

Star Balance

male.v.female.sebt <- t.test(percent.asi.sebt.male, percent.asi.sebt.female)

Code

report(male.v.female.sebt)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between
percent.asi.sebt.male and percent.asi.sebt.female (mean of x = 15.27, mean of y
= 11.54) suggests that the effect is positive, statistically not significant,
and small (difference = 3.73, 95% CI [-4.34, 11.80], t(41.20) = 0.93, p =
0.357; Cohen's d = 0.23, 95% CI [-0.26, 0.71])

Broad Jump

male.v.female.olht <- t.test(percent.asi.olht.male, percent.asi.olht.female)

Code

report(male.v.female.olht)

Warning: Missing values detected. NAs dropped.

Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference between
percent.asi.olht.male and percent.asi.olht.female (mean of x = 5.72, mean of y
= 6.03) suggests that the effect is negative, statistically not significant,
and very small (difference = -0.31, 95% CI [-2.92, 2.29], t(75.95) = -0.24, p =
0.811; Cohen's d = -0.05, 95% CI [-0.48, 0.38])

Correlations

Both Sexes

cor.group <- correlation(asym.stats,
                            select = c("percent_asi_sebt_ant",
                                       "percent_asi_olht",
                                       "percent_asi_cmj"),
                            select2 = c("x30m_sprint",
                                        "v_cut_test",
                                        "olht.high",
                                        "olht.low",
                                        "cmj.high",
                                        "cmj.low",
                                        "sebt.high",
                                        "sebt.low")) 

Code

print_md(
  summary(cor.group, 
          stars = TRUE,
          include_significance = FALSE)
)

Correlation Matrix (pearson-method)

Parameter	x30m_sprint	v_cut_test	olht.high	olht.low	cmj.high	cmj.low	sebt.high	sebt.low
percent_asi_sebt_ant	-0.08	0.01	0.16	0.20	0.03	-0.01	0.05	0.06
percent_asi_olht	-0.08	0.06	-0.10	-0.44**	-0.09	-0.12	-0.16	-0.16
percent_asi_cmj	0.26	0.01	0.16	0.12	0.11	-0.25	0.15	0.09

p-value adjustment method: Holm (1979)

Male

cor.male <- correlation(asym.stats %>% 
                              filter(sex == "male"),
                            select = c("percent_asi_sebt_ant",
                                       "percent_asi_olht",
                                       "percent_asi_cmj"),
                            select2 = c("x30m_sprint",
                                        "v_cut_test",
                                        "olht.high",
                                        "olht.low",
                                        "cmj.high",
                                        "cmj.low",
                                        "sebt.high",
                                        "sebt.low")) 

Code

print_md(
  summary(cor.male, 
          stars = TRUE,
          include_significance = FALSE)
)

Correlation Matrix (pearson-method)

Parameter	x30m_sprint	v_cut_test	olht.high	olht.low	cmj.high	cmj.low	sebt.high	sebt.low
percent_asi_sebt_ant	-0.13	0.05	0.14	0.27	-0.17	-0.27	0.07	0.10
percent_asi_olht	-0.13	0.15	-0.04	-0.56*	0.12	0.08	-5.24e-03	-0.05
percent_asi_cmj	0.36	0.10	0.16	0.17	0.03	-0.47	0.27	0.29

p-value adjustment method: Holm (1979)

Female

cor.female <- correlation(asym.stats %>% 
                              filter(sex == "female"),
                            select = c("percent_asi_sebt_ant",
                                       "percent_asi_olht",
                                       "percent_asi_cmj"),
                            select2 = c("x30m_sprint",
                                        "v_cut_test",
                                        "olht.high",
                                        "olht.low",
                                        "cmj.high",
                                        "cmj.low",
                                        "sebt.high",
                                        "sebt.low")) 

Code

print_md(
  summary(cor.female, 
          stars = TRUE,
          include_significance = FALSE)
)

Correlation Matrix (pearson-method)

Parameter	x30m_sprint	v_cut_test	olht.high	olht.low	cmj.high	cmj.low	sebt.high	sebt.low
percent_asi_sebt_ant	0.11	0.19	0.08	0.08	0.06	0.11	-1.43e-03	0.05
percent_asi_olht	-5.85e-03	0.02	-0.17	-0.64***	-0.22	-0.28	-0.23	-0.18
percent_asi_cmj	0.14	0.10	0.08	-0.02	0.06	-0.30	0.02	8.83e-03

p-value adjustment method: Holm (1979)

Discussion

The results from the paper resemble those that were assessed in this post. Although, there are some key differences that should be addressed.

Primarily, the number of male and female participants in the provided data set do not match the reported number of males and females. This could be a typo in either the publication or the data set during anonymization. Moreover, there are missing observations in the data set that were not accounted for. This can be seen in the Descriptive Statistics section.

There are marked differences in mean and SD values. Although a significance test was not performed between the two, the results in this post should not differ from the publication. If issues arose from the misclassification of a participant, there shouldn’t be stark contrasts between the results here and the original publication. Rather, the individual’s results should have a minimal impact on the outcomes here.

Although the data was tested for normality, the histograms reveal that much of the data was not actually normally distributed. This is due to various issues like outliers remaining in the data set. Subsequent analyses showed that some parameters included values that exceeded five SDs. Since these values exceed three SDs, rationale dictates that the values are either improperly recorded or are not representative of the sample size, and should be removed.

Finally, the majority of the significance values (p-values) for the t-tests and correlations do not coincide between these results and the publication.

It is unclear why the results in this post do not match those in the original publication. It is prudent to say that the analyses in this post were completed incorrectly. Unfortunately, there is enough evidence suggesting that many of the analyses were completed correctly and that the results in the publication are subject to various typos or that statistical analyses were misinterpreted.

Further analyses should be completed that remove outliers and transform data to ensure that the data is normally distributed prior to completing statistical analyses. Many of the distributions returned to the user (see above) can be either log-transformed or a Box-Cox transformation, both of which can be easily back-transformed.

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