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ORIGINAL ARTICLE
Injury prediction in aerobic gymnastics based on anthropometric variables Prédiction des blessures en gymnastique aérobic par les variables anthropométriques R. Abalo-Nú˜ nez a,∗, A. Gutiérrez-Sánchez b, M.C. Iglesias Pérez c, M. Vernetta-Santana d a
Department Faculty of Physiotherapy, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO A Xunqueira, University of Vigo, 36005 Pontevedra, Spain b Faculty of Science Education and Sports, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, University of Vigo, Pontevedra, Spain c Statics and Operations Research Department, School of Forestry Engineering, University of Vigo, Pontevedra, Spain d Physical Education and Sports Department, Faculty of Sport Sciences, University of Granada, Pontevedra, Spain Received 14 August 2017; accepted 6 February 2018
KEYWORDS Q angle; Prevention; Sport; Mathematical model
∗
Summary Objective. — Use logistic regression to determine a mathematical model which is able to predict injuries in aerobic gymnastics (AG) athletes, according to certain anthropometric characteristics. Subjects and methods. — In total, 73 athletes were recruited, of whom 51 were gymnasts and 22 were athletes from other sports. The independent variables were anthropometric characteristics of both lower extremities. The dependent variable was injury at the end of the season. Results. — The statistical model indicated that the effect of the Q angle on the likelihood of injury varies depending on the weight of the gymnast. An excessive Q angle is an anthropometric factor that may predispose to injury, especially the left Q angle. Conclusion. — Studies that analyze anthropometric characteristics can contribute to understanding what variables or parameters may cause injuries in athletes. Thus, in the future, intervention strategies could be developed and the onset of certain injuries could be prevented. © 2018 Elsevier Masson SAS. All rights reserved.
Corresponding author. E-mail address: [email protected] (R. Abalo-Nú˜ nez).
https://doi.org/10.1016/j.scispo.2018.02.002 0765-1597/© 2018 Elsevier Masson SAS. All rights reserved.
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MOTS CLÉS Q angle ; Prévention ; Sport ; Modèle mathématique
Résumé Objectifs. — Déterminer un modèle mathématique par régression logistique capable de prédire les blessures chez les athlètes de gymnastique aérobic selon certaines caractéristiques anthropométriques. Méthodes. — Nous avons recruté 73 athlètes, dont 51 étaient des gymnastes et 22 athlètes d’autres sports. Les variables indépendantes étaient les caractéristiques anthropométriques des deux membres inférieurs. La variable dépendante était la blessure à la fin de la saison. Résultats. — Le modèle statistique indique que l’effet de l’angle Q sur la probabilité de blessure varie en fonction du poids de la gymnaste. Un angle Q élevé est un facteur anthropométrique qui peut prédisposer à une blessure, en particulier l’angle Q gauche. Conclusions. — Les études qui analysent les caractéristiques anthropométriques peuvent contribuer à comprendre quelles variables ou paramètres peuvent prédire des blessures chez les athlètes. Ainsi, à l’avenir, des stratégies de prévention pourraient être développées et l’apparition de certaines blessures pourrait être évitée. © 2018 Elsevier Masson SAS. Tous droits r´ eserv´ es.
1. Introduction Sports, training or recreational physical activities are likely to cause injury to those who practice them. However, there are factors that can alter the risk or likelihood that a person who practices sports will be injured. These factors include individual characteristics (such as gender, age) [1], behaviors, skills, use of protective equipment, playing position, characteristics of the sport, level of competition, playing surface, and weather [1—5]. Therefore, the potential risk factors are grouped into characteristics of the athlete, of the sport, and of the environment [6]. From another perspective, several studies have suggested that there are three general factors that play a predominant role in the risk of suffering an injury: improper training techniques, unsuitable or damaged equipment, and biomechanical and anthropometric abnormalities [7]. There are few studies on injuries suffered by elite gymnasts, most of them being of an epidemiological nature [8—11] or conducted with Australian gymnasts [12]. The results of all of them showed injuries in upper and lower limbs, the predominant ones being muscle and tendon injuries. During the exercises in this event category, the joints of the lower extremities are under high-impact loading conditions due to the high number of throws and catches within the jump elements. Gymnasts are subject to a constant repetition of the exercises with the consequent overload, which can lead to acute injuries or, in the worst-case scenario, to chronic injuries to these extremities. Our study is based on this information and on the fact that there are predictive studies in other sports that focused on structural factors through the application of mathematical models in order to predict injuries by means of logistic regression, with encouraging results in the discrimination between the anthropometric parameters related to lower extremity sports injuries [7,13—15]. There are two ways to carry out goniometer measurements: the first one with the subject in a supine position [16], with the knee extended and quadriceps relaxed [17], although some studies perform them with the quadriceps contracted [18]; others perform the measurement in a
standing position [19]. Recently, the study conducted by Freedman et al. [20], which compared different measurements (magnetic resonance imaging, quadriceps extended, isometric contraction of the quadriceps, with 15◦ of knee flexion), has concluded that measurement with a contraction or flexion did not improve the reliability of the angle, and measurement using magnetic resonance imaging or manual measurement varies from 5◦ to 8◦ . Along the same lines, Silva et al. [21] performed static and dynamic Q angle measurements, obtaining better discriminatory results with the latter, suggesting that potentially modifiable variables (dynamic valgus or hip muscle strength) should be considered. Therefore, some authors proposed to directly measure the in vivo patellar kinematics, acquired during tasks that require active quadriceps control, using precise and dynamic measurement techniques [22]. Knowing the risk factors in any type of sports activity is very important because it helps the athlete or coach in predicting the risk of injury to the former [6]. Thus, understanding the injury mechanisms and risk could lead to more effective prevention. Therefore, the objective of this study is to determine a mathematical model by means of a logistic regression to predict injuries in AG athletes based on certain anthropometric characteristics. To achieve this, data from a previous study were replicated [11] and subsequently analyzed with more consistent statistical techniques to ensure relevant results. The hypothesis is that the anthropometric measurements could be an effective indicator for detecting risk factors for lower limb injuries in athletes practicing AG at top national level.
2. Methods 2.1. Participants In total, 73 athletes, analyzed in the study conducted by Abalo et al. [11], formed the cohort. Fifty-one participants made up the experimental group (EG) and were gymnasts (45 women and 6 men). The remaining 22 participants formed
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Table 1 Descriptive statistics (mean and standard deviation) of age, weight and height for athletes and gymnasts, separately.
Athletes Age Height Height Gymnasts Age Weight Height
Mean
Standard deviation
14.59 49.73 1.46
3.93 11.19 0.10
13.61 45.28 1.50
4.59 14.42 0.15
Table 2 RQA LQA DQA BWBRL BWBLL DBWB PRT PLT DPT
the control group (CG) and they were athletes from other sports. They were all women. Table 1 shows the mean age, weight, and height of the EG and CG. As for the level of competition in the EG, 56.86% participated in national competitions, 39.22% in international competitions and 3.92% in regional competitions. However, in the CG, half competed at the regional level, 45.45% at national level, and only 4.55% at international level. As for the number of competitions throughout the season, in the EG 78.43% competed in between 4 and 5 annual competitions, and 11.78% competed up to 6 and 7 times a year. In the CG 63.64% participated in 3 and 4 annual competitions and the maximum number of competitions was 5 (22.73%). There is a direct relationship between the level of competition and the training load in both groups, since the higher level of competition corresponds to more training days. Thus, in the EG, the average number of training days and their standard deviation are as follows: at international level 6.85 + 0.366, national level 4.83 + 1.466, and regional level 3 + 0; the average number of training days and their standard deviation in the CG are: at international level 7 + 0, national level 3.80 + 0.919, and regional level 3.09 + 0.302. The Spanish Federation of Aerobic Gymnastics, coaches, athletes and parents were fully informed about the purpose of this research and gave their consent to the athletes’ participation. All data were processed according to the Organic Law of Protection of Personal Data 15/1999 of December 13, in compliance with the Declaration of Helsinki.
2.2. Design For the development of this research, a quasi-experimental, retrospective and longitudinal study was performed. The independent variables included several anthropometric characteristics of both lower limbs (Table 2). The dependent variable was the injury at the end of the season (injury/no injury). The independent variables were measured by the same person, the first author of this study, on two different days at the same time of the day; hence, there are two records: 1 and 2. In addition, the mean was found by measuring each characteristic three times in both records.
Independent (anthropometric) variables. Record means 1 and 2 of the right Q angle Record means 1 and 2 of the left Q angle Difference of the Q angle Record means 1 and 2 of the bilateral weight-bearing on the right leg Record means 1 and 2 of the bilateral weight-bearing on the left leg Difference of the bilateral weight-bearing Record means 1 and 2 of the perimeter of the right thigh Record means 1 and 2 of the perimeter of the left thigh Difference of the perimeter of the thigh
2.3. Procedures Before discussing the Q angle measurements, it should be noted that it is formed by a line drawn from the anterior superior iliac spine to central patella and a second line drawn from central patella to tibial tubercle. The value of the Q angle is obtained from the intersection between these two lines [23]. The value of this angle is normally in the range 15◦ to 20◦ depending on gender [24,25]. A Q angle larger than this range is often referred to as excessive and is associated with anterior knee pain [26]. The Q angle measurement should be bilateral, since there are asymmetries between the two extremities [14]. The subjects exhibiting a high asymmetry score may be at increased risk of injury. Therefore, for their evaluation, some authors proposed to perform the measurement in the preseason [26]. This angle can be measured using radiographic images [27,28], photography [29,30], or goniometry [16—19]. The most commonly used technique is the latter, due to its simplicity. For this reason, it was carried out in this research according to the method described by Caylor et al. [16]. The perimeter of the thigh and the difference of the bilateral weight-bearing were measured according to the method reported by Fernández Martínez et al. [7]. The material used for the measurements included: a goniometer (Medizintechnik KaWe K02), a pachymeter (TKK), a rule (TKK), a tape measure (Condor) and two Jackson platforms. The first three were used for the Q angle measurement; the tape measure was employed for thigh perimeters and platforms in order to determine the bipedal weight-bearing. A recording sheet was prepared to collect these data. Furthermore, important information related to the injuries that still affected the athletes and gymnasts at the end of the season was collected in the questionnaire validated by Abalo et al. [8].
2.4. Statistical analysis First, the study focused on whether there were significant differences in the distribution of the independent variables between the group of injured and uninjured athletes,
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the group of gymnasts and the control group, respectively. The nonparametric Mann-Whitney U test was used for all the variables and the t-test for independent samples when the normality assumption was accepted. Finally, a multivariate logistic regression model with backward stepwise was obtained. The data were processed with the Statistical Package for the Social Sciences (SPSS) software, v. 18.0 (SPSS Inc., Chicago, IL).
3. Results Out of the 73 athletes, a total of 35 subjects presented previous injuries related to sports practice: 25 (34.25%) in the EG and 10 (13.70%) in the CG. There is a statistically significant relationship between previous injuries and injuries suffered during the season, both in the CG (p-value of Chi-square test = 0.045) and the EG (p-value of Chi-square test = 0.007). Specifically, in the EG, out of the 26 gymnasts who did not have any previous injuries, 2 got injured during the season, whereas out of the 25 gymnasts with previous injuries, 10 got reinjured (Table 3).
In the EG, there is a relationship between the number of training days and injuries. The subjects who suffered injuries at a higher rate were those who trained seven days a week (a total of 9 gymnasts). However, there is no significant relationship between the number of years of practice in this sport and the number of injuries found. In the EG, the subjects who suffered injuries at a higher rate were those who had spent six years practicing this sport, accounting for three injured. All injured in the EG trained more than 2 hours a day. In the group of gymnasts, only the normality by groups (injury/no injury) was accepted for the BWBRL and BWBLL variables (with a significance level of 5%). Therefore, the ttest was applied only to these two variables, which showed the existence of significant differences between the mean values of the right leg weight-bearing of injured and uninjured gymnasts, and similarly for the left leg weight-bearing (Table 4). The Mann-Whitney U test applied to all the anthropometric variables indicated significant differences in the distribution of the injured versus uninjured gymnasts for the following variables: RQA (p = 0.005), LQA (p = 0.003) and
Table 3 Results of Pearson’s Chi-square test and Fisher’s test for the independence contrast between previous injuries and injuries in the season, in the control group, experimental and together.
CG Pearson’s Chi-square Fisher’s test n valid cases EG Pearson’s Chi-square Fisher’s test n valid cases Total Pearson’s Chi-square Fisher’s test n valid cases
Value
gl
Asymptotic significance (bilateral)
4.023c
1
0.45
Exact significance (bilateral)
Exact significance (unilateral)
0.074
0.059
0.009
0.007
0.424
0.229
22 7.393d
1
0.007
51 1.019a
1
0.313
73
The meaning of the letters a and d is the same: 0 boxes (0%) have waited for a count less than 5. The minimum expected count is 5.88 The meaning of the letter c is: 2 boxes (50%) have waited for a count less than 5. The minimum expected count is 3.18.
Table 4 Comparison of means (t-test) and variances (Levene’s test) between injured and uninjured gymnasts (experimental group) for BWBRL and BWBLL variables. Levene’s test
BWBRL Equal variances Different variances BWBLL Equal variances Different variances
T
fd
F
Sig.
4.404
0.041
−2.386 −2.870
49 26.070
5.989
0.018
−1.816 −2.363
49 31.636
Sig.
95% CI Lower
Upper
0.021 0.008
−9.89612 −9.21829
−0.84670 −1.52453
0.076 0.024
−9.08559 −8.03161
−0.45995 −0.59404
F: Levene statistic; Sig: p-value; T: T statistic; fd: free degree; CI: confidence interval of mean difference.
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Table 5 Comparison of means (t-test) and variances (Levene’s test) between injured and uninjured athletes (control group) for several independent variables. Levene’s test
RQA Equal variances Different variances BWBRL Equal variances Different variances BWBLL Equal variances Different variances PRT Equal variances Different variances PLT Equal variances Different variances
T
fd
F
Sig.
0.484
0.495
0.226 0.235
20 13.056
0.009
0.925
0.149 0.153
0.798
0.382
0.451
0.674
Sig.
95% CI Lower
Upper
0.824 0.818
−3.26043 −3.23986
4.05186 4.03128
20 12.515
0.883 0.881
−5.56544 −5.65861
6.42258 6.51575
0.599 0.637
20 13.773
0.556 0.535
−3.84069 −3.67295
6.93707 6.76933
0.509
0.322 0.363
20 16.039
0.751 0.721
−4.27112 −3.77215
5.82998 5.33101
0.421
0.159 0.182
20 16.541
0.875 0.858
−4.67461 −4.10718
5.44623 4.87880
F: Levene statistic; Sig: p-value; T: T statistic; fd: free degree; CI: confidence interval of mean difference.
Table 6 Classification table and correct classification percentages of logistic regression to model the probability of injury in gymnasts depending on LQA, BWBRL and PLT. Observed
Predicted Injuries
1 Step injuries No Yes Global percentage
No
Yes
35 5
4 7
Correct percentage
89.7 58.3 82.4
BWBRL (p = 0.025). The p-values for the other variables were: BWBLL (p = 0.056), PRT (p = 0.079) and PLT (p = 0.110). The above analysis was repeated for the control group. Normality test per group (injury/no injury) was accepted for the RQA, BWBRL, BWBLL, PRT and PLT variables. After applying the t-test for independent samples to these variables, no significant differences were detected in their mean values between the groups of injured and uninjured athletes (Table 5). In addition, the Mann-Whitney U test did not detect any significant differences either. The next step consisted of applying a backward stepwise approach with six initial variables: the right and left Q angles, weight-bearing on both legs and the thigh circumference of the right and left leg. LQA, BWBRL and PLT were the selected variables. This model improved the sensitivity of the previous model from 42% to 58% (Table 6) and it also had a better performance in AIC = 47.48. The previous model seemed to suggest that in addition to the angle, the weight and thickness of the leg may affect the occurrence of injury (Table 7).
It should be noted that, regarding the participants’ age in this sample, there was a very strong correlation between age, weight, height, and leg measurements (Table 8). In addition, age, weight and height were significant variables for the injury incidence rate when using univariate logistic regressions. Therefore, it seemed logical to adjust the effect of the Q angle considering the growth of the subjects. Thus, several models were checked maintaining the same LQA and varying age, weight and height, including a term of interaction, with the next model obtaining the best predictive level among those considered and an AIC value of 45.47 (Tables 9 and 10). The presence of interaction indicates that the effect of the Q angle on the probability of injury varies depending on the gymnast’s weight. Specifically, the left Q angle OR when the angle was increased by one degree and the weight was maintained at x is 5.075 × (0.974)x. The prediction equation of this model is given by: P (Y = 1) = 1/(1 + exp(−(−24.95 + 1.624 LQA + 0.392 weight − 0.026 LQA × weight))) Finally, to improve the sensitivity of the previous models, another multivariate logistic model was studied. This model included the age, the three measurements of the left leg and the interactions between age and the mentioned variables (Table 11). Its AIC (40.48), specificity (97%) and sensitivity (83%) were good, but the model had many variables considering the available sample size, thus a larger sample size is required to consider and validate this model.
4. Discussion and conclusions In view of the results, it is observed that the higher the level of the athlete, the training load (days and hours) increases. Consequently, athletes’ exposure time, and the number of opportunities to be injured [8,9,11] also increase. The same happens in the study carried out by Caine et al. [2], who
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R. Abalo-Nú˜ nez et al. Table 7
Results of logistic regression to model the probability of injury in gymnasts depending on LQA, BWBRL and PLT. B
LQA BWBRL PLT Constant
Table 8
Standard error
0.261 0.332 −0.272 0.861
a
0.113 0.176 0.157 4.163
fd
5.314 3.551 3.006 0.043
Sig.
1 1 1 1
Exp (B)
0.021 0.060 0.083 0.836
95% CI
1.298 1.393 0.762 2.366
Lower
Upper
1.040 0.987 0.560
1.621 1.967 1.036
Correlations between variables (Pearson correlation coefficient). Weight
Age Age Weight Height BWBRL BWBLL PRT PLT RQA LQA
Wald
a
1 0.847a 0.828a 0.849a 0.821a 0.715a 0.719a 0.705a 0.694a
0.847 1 0.891a 0.988a 0.988a 0.917a 0.920a 0.640a 0.648a
Height a
0.828 0.891a 1 0.879a 0.875a 0.727a 0.738a 0.642a 0.640a
BWBRL a
0.849 0.988a 0.879a 1 0.956a 0.898a 0.901a 0.628a 0.641a
BWBLL a
0.821 0.988a 0.875a 0.956a 1 0.917a 0.919a 0.659a 0.655a
PRT
PLT a
0.715 0.917a 0.727a 0.898a 0.917a 1 0.955a 0.562a 0.577a
RQA a
0.719 0.920a 0.738a 0.901a 0.918a 0.955a 1 0.558a 0.575a
LQA a
0.705 0.640a 0.642a 0.628a 0.659a 0.562a 0.558a 1 0.952a
0.694a 0.648a 0.640a 0.641a 0.655a 0.577a 0.575a 0.952a 1
0.01 significant correlation.
Table 9 Classification table and correct classification percentages of logistic regression to model the probability of injury in gymnasts depending on LQA, weight and their interaction. Observed
Predicted Injuries
1 Step injuries No Yes Global percentage
No
Yes
38 5
1 7
Correct percentage
97.4 58.3 88.2
concluded that the incidence and severity of injuries of advanced level female gymnasts were relatively high. In this sense, dosing the training load may become a crucial factor both for injury prevention in these gymnasts and to optimize their effectiveness. As in previous studies, a statistically significant relationship was found between previous lesions and those suffered during the season. [8]. Regarding the distributions of the anthropometric variables between injured and uninjured athletes in the CG, they were not significantly different. The Q angles, the bilateral weight-bearing and the perimeter were normally distributed. These results are quite different from those obtained in the group of gymnasts, characterized by the lack of normality and the existence of significant differences between the injured and uninjured for RQA, LQA, BWBRL and
BWBLL variables. The first two variables are consistent with the data obtained in the previous study [11]. The Q angle is one of the factors associated with biomechanical defects in knee alignment [11,31]. An increased Q angle can create a lateral valgus force vector that causes a misalignment of force transmission, developing incorrect lateral movements, and thereby a predisposition to injury. The alignment of the knee and hip is associated with the magnitude of the Q angle [31]. The literature mentions that the Q angle may be related to ankle [32] or knee [26,33] injuries, although there are also other factors related to injuries in these joints. Thus, a knee valgus and foot pronation are also often involved in knee injuries [33]. The ankle can be injured due to bone malformations of the surrounding areas, such as the knee and hip [34]. However, certain studies concluded that the Q angle did not seem to be a decisive factor that increases the chances of suffering ankle sprains. The most important factors are those related to athlete’s age, anthropometric characteristics, and prior injuries [32]. An increased femoral anteversion and tibiofemoral angle result in a larger Q angle. Since many knee injuries appear to result from a combination of both movements and forces in the frontal and transverse plane, this may partly explain why the Q angle has been found as an independent predictor of risk of injury to the lower limb [31]. Multiple anatomical factors may influence the magnitude of the Q angle as of pelvic angle, hip rotation, tibial rotation, patella position, and foot position. Consequently, the Q angle may increase with excessive anterior pelvic tilt (by changing the orientation of the acetabulum and femoral internal rotation), femoral anteversion and genu valgum
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Table 10 Results of logistic regression to model the probability of injury in gymnasts depending on LQA, weight and their interaction. B
LQA Weight LQA*weight Constant
1.624 0.392 −0.026 −24.925
Standard error
0.652 0.178 0.012 9.667
Wald
fd
6.1 4.843 4.933 699.647
Sig.
1 1 1 1
0.013 0.028 0.026 0.010
Exp (B)
5.075 1.480 0.974 0.000
95% CI Lower
Upper
1.413 1.044 0.952
18.230 2.099 0.997
*: interaction.
Table 11 Results of logistic regression to model the probability of injury in gymnasts depending on Age, LQA, BWBLL, PLT and interactions between age and previous variables. B
Age LQA BWBLL PLT LQA*age BWBLL*age PLT*age Constant
8.534 2.486 −2.803 2.982 −0.144 0.150 −0.183 −131.039
Standard error
3.407 1.026 1.418 1.313 0.064 0.082 0.081 50.570
Wald
6.276 5.866 3.907 5.160 5.099 3.317 5.104 6.714
fd
1 1 1 1 1 1 1 1
Sig.
0.012 0.015 0.048 0.023 0.024 0.069 0.024 0.010
Exp (B)
5086.156 12.015 0.061 19.730 0.866 1.162 0.833 0.000
95% CI Lower
Upper
6.406 1.607 0.004 1.505 0.765 0.989 0.711
4038082.891 89.837 0.977 258.610 0.981 1.365 0.976
B: model coefficient; Wald: Wald statistic; fd: free degree; Sig: p-value; Exp (B): odds ratio; CI: confidence interval of odds ratio; *: interaction.
(moving the patella medially relative to the anterior superior iliac spine and the tibial tubercle), and external tibial rotation (by moving the tuberosity of the tibia laterally) [31]. Therefore, as noted in a previous study by Abalo et al. with the same sample [11], an excessive Q angle could be an anthropometric factor, which may predispose to injury, especially the left Q angle, thus corroborating the research study conducted by other authors [35,36]. The morphological changes that occur when the body grows are related to the anthropometric variables [37]. Therefore, in this sample there is a strong correlation between age, weight, height, bilateral weightbearing and thigh circumference, but not with Q angles (Table 10). In addition, age, weight and height are significant variables for the injury incidence rate when using univariate logistic regression. Therefore, the effect of the Q angle was adjusted considering the growth of the subjects. Several models were tested maintaining the same LQA varying age, weight and height, including an interaction term. This led to the second best predictive model among the considered levels and an AIC value of 45.47 (Tables 9 and 10). Hence, the last equation for predicting injuries takes into account the Q angle and the athlete’s weight. Therefore, the proposed hypothesis was confirmed. Anthropometric characteristics (weight, height), age [38] and previous injuries are factors that could affect the likelihood of ankle sprains. However, one should take into account other factors too, such as reduced muscle strength, balance
deficits, proprioception, reduced muscle reaction time and misalignment [32]. Regarding the independent variable of weight imbalance, it was included in the studied prediction equations as a discriminating element of injury in athletes [7]. This variable is the result of the bilateral weight-bearing difference. Hence, similarly to De la Cruz Marquez et al. [14] the independent variables of bilateral weight-bearing on the right leg and bilateral weight-bearing on the left leg were included. These variables can be affected by faulty alignments that lead to an unequal distribution of weight, a displacement of the center of gravity and a deviation of the force transmission lines. They may even affect the alignment of the spine, more specifically scoliosis problems and dysmetria of the iliac spine, which can lead to an imbalance of weight-bearing [8]. Identifying features that may cause injuries in athletes [17], in this case weight and the left Q angle, can help in developing intervention strategies and preventing injuries in the lower limbs. According to the obtained data, it could be concluded that the LQA anthropometric variable has the biggest influence on the occurrence of lower limb injuries in competitive AG athletes. The main limitations of the study are determined by the sample and the anthropometric measurement technique. Regarding the sample, a larger number of competitive athletes at international level, from different countries, would be needed to legitimize the external validity of the study. With regard to the anthropometric measurements,
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especially of the Q angle, other protocols could be used, such as an x-ray or a photographic image. With the aim of establishing more accurate diagnoses, an increasing standardization of these techniques [39] would be necessary. It would also be interesting to perform more in-depth research into other variables related to the upper limbs, in an attempt to provide more information about their possible effects on injuries that occur in a variety of technical movements. Finally, based on the data found, one could say that the studies which analyze the anthropometric characteristics can help understanding what variables or parameters can cause injuries in athletes. To this end, future intervention strategies could be developed and the onset of certain injuries could be prevented.
Disclosure of interest The authors declare that they have no competing interest.
Acknowledgements We wish to thank the gymnasts, coaches and the parents clubs who participated in this study.
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9 [35] Livingston LA, Mandigo JL. Bilateral Q angle asymmetry and anterior knee pain syndrome. Clin Biomech 1999;14: 7—13. [36] Byl T, Cole JA, Livingston LA. What determines the magnitude of the Q angle? J Sport Rehabil 2000;9:26—34. [37] Cumming SP, Standage M, Gillison FB, Dompier TP, Malina RM. Biological maturity status, body size, and exercise behaviour in British youth: a pilot study. J Sports Sci 2009;27: 677—86. [38] Purnell M, Shirley D, Nicholson L, Adams R. Acrobatic gymnastics injury: occurrence, site and training risk factors. Phys Ther Sport 2010;11:40—6. [39] Miranda G, Díaz J, Schonstedt. Medidas radiológicas útiles en patología mu´ısculo esquelética cotidiana [Useful radiological measurements in daily ostearticular pathology]. Rev Hosp Cli´ın Univ Chile 2009;20:137—47.
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ORIGINAL ARTICLE
Injury prediction in aerobic gymnastics based on anthropometric variables Prédiction des blessures en gymnastique aérobic par les variables anthropométriques R. Abalo-Nú˜ nez a,∗, A. Gutiérrez-Sánchez b, M.C. Iglesias Pérez c, M. Vernetta-Santana d a
Department Faculty of Physiotherapy, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO A Xunqueira, University of Vigo, 36005 Pontevedra, Spain b Faculty of Science Education and Sports, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, University of Vigo, Pontevedra, Spain c Statics and Operations Research Department, School of Forestry Engineering, University of Vigo, Pontevedra, Spain d Physical Education and Sports Department, Faculty of Sport Sciences, University of Granada, Pontevedra, Spain Received 14 August 2017; accepted 6 February 2018
KEYWORDS Q angle; Prevention; Sport; Mathematical model
∗
Summary Objective. — Use logistic regression to determine a mathematical model which is able to predict injuries in aerobic gymnastics (AG) athletes, according to certain anthropometric characteristics. Subjects and methods. — In total, 73 athletes were recruited, of whom 51 were gymnasts and 22 were athletes from other sports. The independent variables were anthropometric characteristics of both lower extremities. The dependent variable was injury at the end of the season. Results. — The statistical model indicated that the effect of the Q angle on the likelihood of injury varies depending on the weight of the gymnast. An excessive Q angle is an anthropometric factor that may predispose to injury, especially the left Q angle. Conclusion. — Studies that analyze anthropometric characteristics can contribute to understanding what variables or parameters may cause injuries in athletes. Thus, in the future, intervention strategies could be developed and the onset of certain injuries could be prevented. © 2018 Elsevier Masson SAS. All rights reserved.
Corresponding author. E-mail address: [email protected] (R. Abalo-Nú˜ nez).
https://doi.org/10.1016/j.scispo.2018.02.002 0765-1597/© 2018 Elsevier Masson SAS. All rights reserved.
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MOTS CLÉS Q angle ; Prévention ; Sport ; Modèle mathématique
Résumé Objectifs. — Déterminer un modèle mathématique par régression logistique capable de prédire les blessures chez les athlètes de gymnastique aérobic selon certaines caractéristiques anthropométriques. Méthodes. — Nous avons recruté 73 athlètes, dont 51 étaient des gymnastes et 22 athlètes d’autres sports. Les variables indépendantes étaient les caractéristiques anthropométriques des deux membres inférieurs. La variable dépendante était la blessure à la fin de la saison. Résultats. — Le modèle statistique indique que l’effet de l’angle Q sur la probabilité de blessure varie en fonction du poids de la gymnaste. Un angle Q élevé est un facteur anthropométrique qui peut prédisposer à une blessure, en particulier l’angle Q gauche. Conclusions. — Les études qui analysent les caractéristiques anthropométriques peuvent contribuer à comprendre quelles variables ou paramètres peuvent prédire des blessures chez les athlètes. Ainsi, à l’avenir, des stratégies de prévention pourraient être développées et l’apparition de certaines blessures pourrait être évitée. © 2018 Elsevier Masson SAS. Tous droits r´ eserv´ es.
1. Introduction Sports, training or recreational physical activities are likely to cause injury to those who practice them. However, there are factors that can alter the risk or likelihood that a person who practices sports will be injured. These factors include individual characteristics (such as gender, age) [1], behaviors, skills, use of protective equipment, playing position, characteristics of the sport, level of competition, playing surface, and weather [1—5]. Therefore, the potential risk factors are grouped into characteristics of the athlete, of the sport, and of the environment [6]. From another perspective, several studies have suggested that there are three general factors that play a predominant role in the risk of suffering an injury: improper training techniques, unsuitable or damaged equipment, and biomechanical and anthropometric abnormalities [7]. There are few studies on injuries suffered by elite gymnasts, most of them being of an epidemiological nature [8—11] or conducted with Australian gymnasts [12]. The results of all of them showed injuries in upper and lower limbs, the predominant ones being muscle and tendon injuries. During the exercises in this event category, the joints of the lower extremities are under high-impact loading conditions due to the high number of throws and catches within the jump elements. Gymnasts are subject to a constant repetition of the exercises with the consequent overload, which can lead to acute injuries or, in the worst-case scenario, to chronic injuries to these extremities. Our study is based on this information and on the fact that there are predictive studies in other sports that focused on structural factors through the application of mathematical models in order to predict injuries by means of logistic regression, with encouraging results in the discrimination between the anthropometric parameters related to lower extremity sports injuries [7,13—15]. There are two ways to carry out goniometer measurements: the first one with the subject in a supine position [16], with the knee extended and quadriceps relaxed [17], although some studies perform them with the quadriceps contracted [18]; others perform the measurement in a
standing position [19]. Recently, the study conducted by Freedman et al. [20], which compared different measurements (magnetic resonance imaging, quadriceps extended, isometric contraction of the quadriceps, with 15◦ of knee flexion), has concluded that measurement with a contraction or flexion did not improve the reliability of the angle, and measurement using magnetic resonance imaging or manual measurement varies from 5◦ to 8◦ . Along the same lines, Silva et al. [21] performed static and dynamic Q angle measurements, obtaining better discriminatory results with the latter, suggesting that potentially modifiable variables (dynamic valgus or hip muscle strength) should be considered. Therefore, some authors proposed to directly measure the in vivo patellar kinematics, acquired during tasks that require active quadriceps control, using precise and dynamic measurement techniques [22]. Knowing the risk factors in any type of sports activity is very important because it helps the athlete or coach in predicting the risk of injury to the former [6]. Thus, understanding the injury mechanisms and risk could lead to more effective prevention. Therefore, the objective of this study is to determine a mathematical model by means of a logistic regression to predict injuries in AG athletes based on certain anthropometric characteristics. To achieve this, data from a previous study were replicated [11] and subsequently analyzed with more consistent statistical techniques to ensure relevant results. The hypothesis is that the anthropometric measurements could be an effective indicator for detecting risk factors for lower limb injuries in athletes practicing AG at top national level.
2. Methods 2.1. Participants In total, 73 athletes, analyzed in the study conducted by Abalo et al. [11], formed the cohort. Fifty-one participants made up the experimental group (EG) and were gymnasts (45 women and 6 men). The remaining 22 participants formed
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Table 1 Descriptive statistics (mean and standard deviation) of age, weight and height for athletes and gymnasts, separately.
Athletes Age Height Height Gymnasts Age Weight Height
Mean
Standard deviation
14.59 49.73 1.46
3.93 11.19 0.10
13.61 45.28 1.50
4.59 14.42 0.15
Table 2 RQA LQA DQA BWBRL BWBLL DBWB PRT PLT DPT
the control group (CG) and they were athletes from other sports. They were all women. Table 1 shows the mean age, weight, and height of the EG and CG. As for the level of competition in the EG, 56.86% participated in national competitions, 39.22% in international competitions and 3.92% in regional competitions. However, in the CG, half competed at the regional level, 45.45% at national level, and only 4.55% at international level. As for the number of competitions throughout the season, in the EG 78.43% competed in between 4 and 5 annual competitions, and 11.78% competed up to 6 and 7 times a year. In the CG 63.64% participated in 3 and 4 annual competitions and the maximum number of competitions was 5 (22.73%). There is a direct relationship between the level of competition and the training load in both groups, since the higher level of competition corresponds to more training days. Thus, in the EG, the average number of training days and their standard deviation are as follows: at international level 6.85 + 0.366, national level 4.83 + 1.466, and regional level 3 + 0; the average number of training days and their standard deviation in the CG are: at international level 7 + 0, national level 3.80 + 0.919, and regional level 3.09 + 0.302. The Spanish Federation of Aerobic Gymnastics, coaches, athletes and parents were fully informed about the purpose of this research and gave their consent to the athletes’ participation. All data were processed according to the Organic Law of Protection of Personal Data 15/1999 of December 13, in compliance with the Declaration of Helsinki.
2.2. Design For the development of this research, a quasi-experimental, retrospective and longitudinal study was performed. The independent variables included several anthropometric characteristics of both lower limbs (Table 2). The dependent variable was the injury at the end of the season (injury/no injury). The independent variables were measured by the same person, the first author of this study, on two different days at the same time of the day; hence, there are two records: 1 and 2. In addition, the mean was found by measuring each characteristic three times in both records.
Independent (anthropometric) variables. Record means 1 and 2 of the right Q angle Record means 1 and 2 of the left Q angle Difference of the Q angle Record means 1 and 2 of the bilateral weight-bearing on the right leg Record means 1 and 2 of the bilateral weight-bearing on the left leg Difference of the bilateral weight-bearing Record means 1 and 2 of the perimeter of the right thigh Record means 1 and 2 of the perimeter of the left thigh Difference of the perimeter of the thigh
2.3. Procedures Before discussing the Q angle measurements, it should be noted that it is formed by a line drawn from the anterior superior iliac spine to central patella and a second line drawn from central patella to tibial tubercle. The value of the Q angle is obtained from the intersection between these two lines [23]. The value of this angle is normally in the range 15◦ to 20◦ depending on gender [24,25]. A Q angle larger than this range is often referred to as excessive and is associated with anterior knee pain [26]. The Q angle measurement should be bilateral, since there are asymmetries between the two extremities [14]. The subjects exhibiting a high asymmetry score may be at increased risk of injury. Therefore, for their evaluation, some authors proposed to perform the measurement in the preseason [26]. This angle can be measured using radiographic images [27,28], photography [29,30], or goniometry [16—19]. The most commonly used technique is the latter, due to its simplicity. For this reason, it was carried out in this research according to the method described by Caylor et al. [16]. The perimeter of the thigh and the difference of the bilateral weight-bearing were measured according to the method reported by Fernández Martínez et al. [7]. The material used for the measurements included: a goniometer (Medizintechnik KaWe K02), a pachymeter (TKK), a rule (TKK), a tape measure (Condor) and two Jackson platforms. The first three were used for the Q angle measurement; the tape measure was employed for thigh perimeters and platforms in order to determine the bipedal weight-bearing. A recording sheet was prepared to collect these data. Furthermore, important information related to the injuries that still affected the athletes and gymnasts at the end of the season was collected in the questionnaire validated by Abalo et al. [8].
2.4. Statistical analysis First, the study focused on whether there were significant differences in the distribution of the independent variables between the group of injured and uninjured athletes,
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the group of gymnasts and the control group, respectively. The nonparametric Mann-Whitney U test was used for all the variables and the t-test for independent samples when the normality assumption was accepted. Finally, a multivariate logistic regression model with backward stepwise was obtained. The data were processed with the Statistical Package for the Social Sciences (SPSS) software, v. 18.0 (SPSS Inc., Chicago, IL).
3. Results Out of the 73 athletes, a total of 35 subjects presented previous injuries related to sports practice: 25 (34.25%) in the EG and 10 (13.70%) in the CG. There is a statistically significant relationship between previous injuries and injuries suffered during the season, both in the CG (p-value of Chi-square test = 0.045) and the EG (p-value of Chi-square test = 0.007). Specifically, in the EG, out of the 26 gymnasts who did not have any previous injuries, 2 got injured during the season, whereas out of the 25 gymnasts with previous injuries, 10 got reinjured (Table 3).
In the EG, there is a relationship between the number of training days and injuries. The subjects who suffered injuries at a higher rate were those who trained seven days a week (a total of 9 gymnasts). However, there is no significant relationship between the number of years of practice in this sport and the number of injuries found. In the EG, the subjects who suffered injuries at a higher rate were those who had spent six years practicing this sport, accounting for three injured. All injured in the EG trained more than 2 hours a day. In the group of gymnasts, only the normality by groups (injury/no injury) was accepted for the BWBRL and BWBLL variables (with a significance level of 5%). Therefore, the ttest was applied only to these two variables, which showed the existence of significant differences between the mean values of the right leg weight-bearing of injured and uninjured gymnasts, and similarly for the left leg weight-bearing (Table 4). The Mann-Whitney U test applied to all the anthropometric variables indicated significant differences in the distribution of the injured versus uninjured gymnasts for the following variables: RQA (p = 0.005), LQA (p = 0.003) and
Table 3 Results of Pearson’s Chi-square test and Fisher’s test for the independence contrast between previous injuries and injuries in the season, in the control group, experimental and together.
CG Pearson’s Chi-square Fisher’s test n valid cases EG Pearson’s Chi-square Fisher’s test n valid cases Total Pearson’s Chi-square Fisher’s test n valid cases
Value
gl
Asymptotic significance (bilateral)
4.023c
1
0.45
Exact significance (bilateral)
Exact significance (unilateral)
0.074
0.059
0.009
0.007
0.424
0.229
22 7.393d
1
0.007
51 1.019a
1
0.313
73
The meaning of the letters a and d is the same: 0 boxes (0%) have waited for a count less than 5. The minimum expected count is 5.88 The meaning of the letter c is: 2 boxes (50%) have waited for a count less than 5. The minimum expected count is 3.18.
Table 4 Comparison of means (t-test) and variances (Levene’s test) between injured and uninjured gymnasts (experimental group) for BWBRL and BWBLL variables. Levene’s test
BWBRL Equal variances Different variances BWBLL Equal variances Different variances
T
fd
F
Sig.
4.404
0.041
−2.386 −2.870
49 26.070
5.989
0.018
−1.816 −2.363
49 31.636
Sig.
95% CI Lower
Upper
0.021 0.008
−9.89612 −9.21829
−0.84670 −1.52453
0.076 0.024
−9.08559 −8.03161
−0.45995 −0.59404
F: Levene statistic; Sig: p-value; T: T statistic; fd: free degree; CI: confidence interval of mean difference.
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Table 5 Comparison of means (t-test) and variances (Levene’s test) between injured and uninjured athletes (control group) for several independent variables. Levene’s test
RQA Equal variances Different variances BWBRL Equal variances Different variances BWBLL Equal variances Different variances PRT Equal variances Different variances PLT Equal variances Different variances
T
fd
F
Sig.
0.484
0.495
0.226 0.235
20 13.056
0.009
0.925
0.149 0.153
0.798
0.382
0.451
0.674
Sig.
95% CI Lower
Upper
0.824 0.818
−3.26043 −3.23986
4.05186 4.03128
20 12.515
0.883 0.881
−5.56544 −5.65861
6.42258 6.51575
0.599 0.637
20 13.773
0.556 0.535
−3.84069 −3.67295
6.93707 6.76933
0.509
0.322 0.363
20 16.039
0.751 0.721
−4.27112 −3.77215
5.82998 5.33101
0.421
0.159 0.182
20 16.541
0.875 0.858
−4.67461 −4.10718
5.44623 4.87880
F: Levene statistic; Sig: p-value; T: T statistic; fd: free degree; CI: confidence interval of mean difference.
Table 6 Classification table and correct classification percentages of logistic regression to model the probability of injury in gymnasts depending on LQA, BWBRL and PLT. Observed
Predicted Injuries
1 Step injuries No Yes Global percentage
No
Yes
35 5
4 7
Correct percentage
89.7 58.3 82.4
BWBRL (p = 0.025). The p-values for the other variables were: BWBLL (p = 0.056), PRT (p = 0.079) and PLT (p = 0.110). The above analysis was repeated for the control group. Normality test per group (injury/no injury) was accepted for the RQA, BWBRL, BWBLL, PRT and PLT variables. After applying the t-test for independent samples to these variables, no significant differences were detected in their mean values between the groups of injured and uninjured athletes (Table 5). In addition, the Mann-Whitney U test did not detect any significant differences either. The next step consisted of applying a backward stepwise approach with six initial variables: the right and left Q angles, weight-bearing on both legs and the thigh circumference of the right and left leg. LQA, BWBRL and PLT were the selected variables. This model improved the sensitivity of the previous model from 42% to 58% (Table 6) and it also had a better performance in AIC = 47.48. The previous model seemed to suggest that in addition to the angle, the weight and thickness of the leg may affect the occurrence of injury (Table 7).
It should be noted that, regarding the participants’ age in this sample, there was a very strong correlation between age, weight, height, and leg measurements (Table 8). In addition, age, weight and height were significant variables for the injury incidence rate when using univariate logistic regressions. Therefore, it seemed logical to adjust the effect of the Q angle considering the growth of the subjects. Thus, several models were checked maintaining the same LQA and varying age, weight and height, including a term of interaction, with the next model obtaining the best predictive level among those considered and an AIC value of 45.47 (Tables 9 and 10). The presence of interaction indicates that the effect of the Q angle on the probability of injury varies depending on the gymnast’s weight. Specifically, the left Q angle OR when the angle was increased by one degree and the weight was maintained at x is 5.075 × (0.974)x. The prediction equation of this model is given by: P (Y = 1) = 1/(1 + exp(−(−24.95 + 1.624 LQA + 0.392 weight − 0.026 LQA × weight))) Finally, to improve the sensitivity of the previous models, another multivariate logistic model was studied. This model included the age, the three measurements of the left leg and the interactions between age and the mentioned variables (Table 11). Its AIC (40.48), specificity (97%) and sensitivity (83%) were good, but the model had many variables considering the available sample size, thus a larger sample size is required to consider and validate this model.
4. Discussion and conclusions In view of the results, it is observed that the higher the level of the athlete, the training load (days and hours) increases. Consequently, athletes’ exposure time, and the number of opportunities to be injured [8,9,11] also increase. The same happens in the study carried out by Caine et al. [2], who
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R. Abalo-Nú˜ nez et al. Table 7
Results of logistic regression to model the probability of injury in gymnasts depending on LQA, BWBRL and PLT. B
LQA BWBRL PLT Constant
Table 8
Standard error
0.261 0.332 −0.272 0.861
a
0.113 0.176 0.157 4.163
fd
5.314 3.551 3.006 0.043
Sig.
1 1 1 1
Exp (B)
0.021 0.060 0.083 0.836
95% CI
1.298 1.393 0.762 2.366
Lower
Upper
1.040 0.987 0.560
1.621 1.967 1.036
Correlations between variables (Pearson correlation coefficient). Weight
Age Age Weight Height BWBRL BWBLL PRT PLT RQA LQA
Wald
a
1 0.847a 0.828a 0.849a 0.821a 0.715a 0.719a 0.705a 0.694a
0.847 1 0.891a 0.988a 0.988a 0.917a 0.920a 0.640a 0.648a
Height a
0.828 0.891a 1 0.879a 0.875a 0.727a 0.738a 0.642a 0.640a
BWBRL a
0.849 0.988a 0.879a 1 0.956a 0.898a 0.901a 0.628a 0.641a
BWBLL a
0.821 0.988a 0.875a 0.956a 1 0.917a 0.919a 0.659a 0.655a
PRT
PLT a
0.715 0.917a 0.727a 0.898a 0.917a 1 0.955a 0.562a 0.577a
RQA a
0.719 0.920a 0.738a 0.901a 0.918a 0.955a 1 0.558a 0.575a
LQA a
0.705 0.640a 0.642a 0.628a 0.659a 0.562a 0.558a 1 0.952a
0.694a 0.648a 0.640a 0.641a 0.655a 0.577a 0.575a 0.952a 1
0.01 significant correlation.
Table 9 Classification table and correct classification percentages of logistic regression to model the probability of injury in gymnasts depending on LQA, weight and their interaction. Observed
Predicted Injuries
1 Step injuries No Yes Global percentage
No
Yes
38 5
1 7
Correct percentage
97.4 58.3 88.2
concluded that the incidence and severity of injuries of advanced level female gymnasts were relatively high. In this sense, dosing the training load may become a crucial factor both for injury prevention in these gymnasts and to optimize their effectiveness. As in previous studies, a statistically significant relationship was found between previous lesions and those suffered during the season. [8]. Regarding the distributions of the anthropometric variables between injured and uninjured athletes in the CG, they were not significantly different. The Q angles, the bilateral weight-bearing and the perimeter were normally distributed. These results are quite different from those obtained in the group of gymnasts, characterized by the lack of normality and the existence of significant differences between the injured and uninjured for RQA, LQA, BWBRL and
BWBLL variables. The first two variables are consistent with the data obtained in the previous study [11]. The Q angle is one of the factors associated with biomechanical defects in knee alignment [11,31]. An increased Q angle can create a lateral valgus force vector that causes a misalignment of force transmission, developing incorrect lateral movements, and thereby a predisposition to injury. The alignment of the knee and hip is associated with the magnitude of the Q angle [31]. The literature mentions that the Q angle may be related to ankle [32] or knee [26,33] injuries, although there are also other factors related to injuries in these joints. Thus, a knee valgus and foot pronation are also often involved in knee injuries [33]. The ankle can be injured due to bone malformations of the surrounding areas, such as the knee and hip [34]. However, certain studies concluded that the Q angle did not seem to be a decisive factor that increases the chances of suffering ankle sprains. The most important factors are those related to athlete’s age, anthropometric characteristics, and prior injuries [32]. An increased femoral anteversion and tibiofemoral angle result in a larger Q angle. Since many knee injuries appear to result from a combination of both movements and forces in the frontal and transverse plane, this may partly explain why the Q angle has been found as an independent predictor of risk of injury to the lower limb [31]. Multiple anatomical factors may influence the magnitude of the Q angle as of pelvic angle, hip rotation, tibial rotation, patella position, and foot position. Consequently, the Q angle may increase with excessive anterior pelvic tilt (by changing the orientation of the acetabulum and femoral internal rotation), femoral anteversion and genu valgum
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Table 10 Results of logistic regression to model the probability of injury in gymnasts depending on LQA, weight and their interaction. B
LQA Weight LQA*weight Constant
1.624 0.392 −0.026 −24.925
Standard error
0.652 0.178 0.012 9.667
Wald
fd
6.1 4.843 4.933 699.647
Sig.
1 1 1 1
0.013 0.028 0.026 0.010
Exp (B)
5.075 1.480 0.974 0.000
95% CI Lower
Upper
1.413 1.044 0.952
18.230 2.099 0.997
*: interaction.
Table 11 Results of logistic regression to model the probability of injury in gymnasts depending on Age, LQA, BWBLL, PLT and interactions between age and previous variables. B
Age LQA BWBLL PLT LQA*age BWBLL*age PLT*age Constant
8.534 2.486 −2.803 2.982 −0.144 0.150 −0.183 −131.039
Standard error
3.407 1.026 1.418 1.313 0.064 0.082 0.081 50.570
Wald
6.276 5.866 3.907 5.160 5.099 3.317 5.104 6.714
fd
1 1 1 1 1 1 1 1
Sig.
0.012 0.015 0.048 0.023 0.024 0.069 0.024 0.010
Exp (B)
5086.156 12.015 0.061 19.730 0.866 1.162 0.833 0.000
95% CI Lower
Upper
6.406 1.607 0.004 1.505 0.765 0.989 0.711
4038082.891 89.837 0.977 258.610 0.981 1.365 0.976
B: model coefficient; Wald: Wald statistic; fd: free degree; Sig: p-value; Exp (B): odds ratio; CI: confidence interval of odds ratio; *: interaction.
(moving the patella medially relative to the anterior superior iliac spine and the tibial tubercle), and external tibial rotation (by moving the tuberosity of the tibia laterally) [31]. Therefore, as noted in a previous study by Abalo et al. with the same sample [11], an excessive Q angle could be an anthropometric factor, which may predispose to injury, especially the left Q angle, thus corroborating the research study conducted by other authors [35,36]. The morphological changes that occur when the body grows are related to the anthropometric variables [37]. Therefore, in this sample there is a strong correlation between age, weight, height, bilateral weightbearing and thigh circumference, but not with Q angles (Table 10). In addition, age, weight and height are significant variables for the injury incidence rate when using univariate logistic regression. Therefore, the effect of the Q angle was adjusted considering the growth of the subjects. Several models were tested maintaining the same LQA varying age, weight and height, including an interaction term. This led to the second best predictive model among the considered levels and an AIC value of 45.47 (Tables 9 and 10). Hence, the last equation for predicting injuries takes into account the Q angle and the athlete’s weight. Therefore, the proposed hypothesis was confirmed. Anthropometric characteristics (weight, height), age [38] and previous injuries are factors that could affect the likelihood of ankle sprains. However, one should take into account other factors too, such as reduced muscle strength, balance
deficits, proprioception, reduced muscle reaction time and misalignment [32]. Regarding the independent variable of weight imbalance, it was included in the studied prediction equations as a discriminating element of injury in athletes [7]. This variable is the result of the bilateral weight-bearing difference. Hence, similarly to De la Cruz Marquez et al. [14] the independent variables of bilateral weight-bearing on the right leg and bilateral weight-bearing on the left leg were included. These variables can be affected by faulty alignments that lead to an unequal distribution of weight, a displacement of the center of gravity and a deviation of the force transmission lines. They may even affect the alignment of the spine, more specifically scoliosis problems and dysmetria of the iliac spine, which can lead to an imbalance of weight-bearing [8]. Identifying features that may cause injuries in athletes [17], in this case weight and the left Q angle, can help in developing intervention strategies and preventing injuries in the lower limbs. According to the obtained data, it could be concluded that the LQA anthropometric variable has the biggest influence on the occurrence of lower limb injuries in competitive AG athletes. The main limitations of the study are determined by the sample and the anthropometric measurement technique. Regarding the sample, a larger number of competitive athletes at international level, from different countries, would be needed to legitimize the external validity of the study. With regard to the anthropometric measurements,
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especially of the Q angle, other protocols could be used, such as an x-ray or a photographic image. With the aim of establishing more accurate diagnoses, an increasing standardization of these techniques [39] would be necessary. It would also be interesting to perform more in-depth research into other variables related to the upper limbs, in an attempt to provide more information about their possible effects on injuries that occur in a variety of technical movements. Finally, based on the data found, one could say that the studies which analyze the anthropometric characteristics can help understanding what variables or parameters can cause injuries in athletes. To this end, future intervention strategies could be developed and the onset of certain injuries could be prevented.
Disclosure of interest The authors declare that they have no competing interest.
Acknowledgements We wish to thank the gymnasts, coaches and the parents clubs who participated in this study.
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