Effect of body composition on Functional Movement Screen scores in elite youth male soccer players

Abstract

BACKGROUND:

The Functional Movement Screen (FMS) is a clinical assessment tool used to determine musculoskeletal dysfunctions and asymmetries in athletes.

OBJECTIVE:

The aim of this study was to investigate whether FMS scores differed between elite youth male soccer players with low body fat percentage and those with normal body fat percentage and between those with and without a history of soccer injury.

METHODS:

Fifty-three elite youth male soccer players were included in the study. The participants’ injury histories were recorded, followed by body composition assessment and FMS tests. The participants were grouped according to body fat percentage and injury history for data analysis.

RESULTS:

The mean age, weight and height of the participants were 17.11 $\pm$ 0.91 years, 68.78 $\pm$ 7.41 kg and 1.77 $\pm$ 0.57 meters, respectively. Comparisons of the groups according to body fat percentage and injury history revealed no differences in FMS scores ( $p>$ 0.05).

CONCLUSION:

Lower body fat percentage did not confer an advantage or disadvantage to elite youth male soccer players in terms of FMS scores FMS scores provide limited information to predict injuries in elite youth male players.

Keywords

Football body fat injuries sports body composition athletes

1. Introduction

Body composition is one of the main components of sports-related physical fitness [1]. It has been reported that body composition is associated with aerobic performance and anaerobic fitness in athletes. Achieving optimum body composition contributes to maximizing aerobic and anaerobic performance [2, 3]. In addition, it has been suggested that both insufficient body fat and excessive body fat may be a contributing factor in musculoskeletal injuries [4]. The optimum body fat percentage for athletes is between 6% and 18% [5].

The Functional Movement Screen (FMS) was developed to determine decreased joint mobility, decreased core stabilization, and muscle strength imbalances that can be observed during dynamic and functional movements [6]. The cut-off score for FMS composite score is 14; those with scores lower than this value are considered at risk for musculoskeletal injuries [7]. The FMS has high intrarater and interrater test reliability. The intersession intraclass correlation coefficient and standard error of measurement values for FMS composite score were reported as 0.92 and 0.51, respectively, while the interrater intraclass correlation coefficient and standard error of measurement values were reported as 0.98 and 0.25, respectively. Furthermore, with the exception of the hurdle step test, the intersession Cohen’s kappa value of each test was reported between 0.69 and 0.84, while the interrater Cohen’s kappa values were between 0.75 and 1.00 [8].

A relationship between FMS and sports injuries has been demonstrated in the literature. In team sports, FMS composite score has moderate power in predicting injuries [9]. In addition, asymmetry in tests performed bilaterally and individual test scores provide more information on injury prediction [10]. It was reported that athletes with asymmetry in tests performed bilaterally or an individual test score of 1 were 2.73 times more likely to sustain an injury (relative risk $=$ 2.73, 95% confidence interval $=$ 1.36, 5.4; $p=$ 0.001) [11]. Although it was reported that the deep squat and hurdle step tests are more closely associated with injuries, the relationship between each individual test and specific injuries is unclear [10]. In addition, the relationship between FMS scores and injuries in elite youth soccer players requires further research [12].

Body composition affects locomotor skills [13]. Both excessive and insufficient body fat negatively affect muscle strength and power [14]. Excessive body fat also adversely affects postural control, range of motion, and flexibility [15, 16]. Therefore, it is expected that FMS scores will be affected by the body composition. Indeed, although limited, there is some evidence regarding the effect of body composition on FMS scores. A negative correlation between FMS score and body mass index (BMI) was reported in children and adults with no history of musculoskeletal injury [17, 18, 19]. There are also studies investigating the relationship between FMS and BMI and body fat percentage in athletes. In a study of American football players, FMS composite score and individual test scores decreased as BMI and body fat percentage increased [20]. In another study evaluating a mixed population of soccer players, volleyball players, and rugby players, it was reported that high fat percentage negatively affected FMS composite score [21]. In brief, there have been few studies investigating the relationship between body composition and FMS scores in athletes, and these studies focused on the effect of excessive body fat. However, the impact of insufficient body fat on FMS scores has not been examined in athletes.

The objectives of this study were: (a) to investigate whether the FMS composite, subcategory, and individual test scores of elite youth male soccer players with low body fat percentage differ significantly from those of players with normal body fat percentage, and (b) to determine whether FMS scores are related to reported injury history.

2. Methods

2.1 Study design

This was a cross-sectional, prospective study. Body fat percentage and injury history were determined as independent variables; FMS composite score, subcategory scores, and individual test scores were the dependent variables.

2.2 Participants

A total of 53 athletes between 17 and 19 years of age who trained in the soccer academy of a sports club were included in the study.

Inclusion criteria were: playing soccer at the same soccer academy for at least 6 months and having no musculoskeletal injury that prevented participation in training and matches at the time of the study. Exclusion criteria were: consuming stimulants such as caffeine in the 12 hours before the study procedure, performing high-intensity training in the 24 hours before the study procedure, and consuming alcohol or taking anti-inflammatory, muscle relaxant, or analgesic drugs in the 48 hours before the study procedure [12].

2.3 Study procedure

The participants were interviewed to collect demographic information and evaluate their eligibility based on the exclusion and inclusion criteria. Those who met the study criteria were further questioned about their injury history and underwent body composition measurements and FMS testing.

Only time-loss soccer injuries, defined as those incurred during soccer training and matches after the players were licensed by the football federation and resulting in the player being unable to fully participate in future training sessions or matches, were included in their injury history. For all time-loss injuries, the players were questioned about the location and type of injury, time lost from participation in training and matches due to the injury, and whether the injuries recurred. Injuries were recorded as lower limb, upper limb, spine, or head injuries according to the location reported by the players. Lower extremity injuries were classified as bone, muscle, and joint injuries. The injuries were also divided into 4 severity groups based on the time lost from participation in training and matches: mild (1–3 days), minor (4–7 days), moderate (8–28 days), and major ( $>$ 28 days) [22].

The participants’ height and weight were measured and BMI was calculated as body weight divided by height squared (kg/m ${}^{2}$ ) [23]. Skinfold thickness measurements were used to estimate body fat percentage. Skin thickness in the chest, axilla, triceps, subscapular, abdominal, suprailiac, and thigh regions was measured using a caliper (Lange Skinfold Caliper, Beta Technology, Santa Cruz, CA, USA). Three measurements were taken from each region and the mean value was used for analysis. The equation developed by Jackson and Pollock was used to estimate body density and fat percentage [24]. Participants were divided into 2 groups according to their body fat percentage: low ( ${<}$ 6%) and normal (6–18%) [5].

The FMS involves 7 tests: the deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability. The tester explains each test and asks the participant to perform the desired movement 3 times and the best score is recorded as the test score [6]. Tests are scored between 0 and 3. A score of 0 is given if the test cannot be completed due to pain, 1 if the test cannot be performed, 2 if the test can be done with compensation, and 3 if the test can be done without compensation. For tests performed bilaterally (hurdle step, in-line lunge, shoulder mobility, active straight leg raise, rotary stability), the lowest of the scores recorded for the right and left sides was accepted as the test score. The maximum composite score that can be obtained from all 7 tests is 21 [25, 26]. In addition, the test battery can be divided into 3 subcategories. Movement score is calculated by summing the deep squat, hurdle step, and in-line lunge tests scores; mobility score is the sum of shoulder mobility and active straight leg raise tests scores; and reflex stabilization score is the sum of trunk stability push-up and rotary stability tests scores [27]. All participants were evaluated according to the FMS test protocol by the same tester, who was a sports physician and a certified FMS expert. The test protocol was performed using the official FMS kit (Functional Movement Systems, Lynchburg, VA, USA).

2.4 Ethics

The study was approved by the Non-Drug and Medical Device Research Ethics Committee of Necmettin Erbakan University Faculty of Medicine (meeting number 96 held on 18.10.2019, decision number 2019/2116). The study was conducted according to the principles of the Declaration of Helsinki.

2.5 Data analyses

FMS scores and injury history were compared between participants in the low body fat percentage group (LBFPG, ${<}$ 6%) and normal body fat percentage group (NBFPG, 6–18%). In addition, FMS scores and body composition parameters were compared between participants with no history of injury (non-injury group; NIG) and those who reported at least 1 time-loss soccer injury since being licensed by the football federation (injury group; IG).

All data were analyzed using SPSS statistical software (version 21.0, IBM Corp., Armonk, NY, USA). Statistics were calculated for the FMS composite, subcategory, and individual test scores of the groups. Mean and standard deviation were calculated for numerical data. Number and percentage distributions were determined for nominal and ordinal data. Mann-Whitney U test was used to compare FMS scores between groups. Receiver operating characteristic (ROC) curve analysis was performed to determine cut-off values for FMS composite score. Chi-square test for four-fold contingency table was used to analyze the reported injury rate and features. Logistic regression models were used to calculate the probability of FMS composite scores predicting reported injuries.

3. Results

The data of 53 players who met the inclusion criteria were analyzed. The mean age, weight, and height of the participants were 17.11 $\pm$ 0.91 years, 68.78 $\pm$ 7.41 kg, and 1.77 $\pm$ 0.57 m, respectively. Four players (7.5%) were goalkeepers, 16 (30.2%) were defenders, 28 (52.8%) were midfielders, and 5 (9.4%) were strikers. The mean body fat percentage was 7.221 $\pm$ 2.195 and the mean FMS composite score was 14.736 $\pm$ 2.754.

Table 1
FMS scores according to body fat percentage

	LBFPG ( $n=$ 17)		NBFPG ( $n=$ 36)
	Median	Min-Max	Median	Min-Max	$p$
Deep squat	2	1-3	2	1-3	0.190
In-line lunge	2	2-3	3	1-3	0.722
Hurdle step	2	1-3	2	1-3	0.077
FMS ${}_{\text{Movement}}$	7	5-9	6	3-9	0.184
Shoulder mobility	2	1-3	2	1-3	0.502
Active straight leg raise	2	1-3	2	1-3	0.303
FMS ${}_{\text{Mobility}}$	4	2-6	4.50	3-6	0.313
Trunk stability push-up	3	1-3	3	1-3	0.130
Rotary stability	2	1-2	2	1-3	0.758
FMS ${}_{\text{Stability}}$	5	3-5	5	3-6	0.136
FMS ${}_{\text{Composite}}$	16	11-20	16	7-20	0.665

FMS: Functional Movement Screen, LBFPG: Low body fat percentage group, NBFPG: Normal body fat percentage group.

Table 2

Characteristics of injuries reported in injury history

Characteristics of injuries	$n$	%
Injury history
None	16	30.19
At least 1	37	69.81
Injury severity
Mild (1–3 days)	13	12.50
Minor (4–7 days)	26	24.04
Moderate (8–28 days)	40	38.46
Major ( $>$ 28 days)	26	25.00
Type of injury
Lower extremity, joint	23	22.12
Lower extremity, muscle	56	53.85
Lower extremity, bone	8	7.69
Spine	5	4.81
Upper extremity	8	7.69
Head	4	3.85
Recurrence
Yes	18	17.31
No	86	82.69

Table 3

FMS scores by injury history

	NIG ( $n=$ 16)		IG ( $n=$ 37)
	Median	Min-Max	Median	Min-Max	$p$
Deep squat	2	1-3	2	1-3	0.891
In-line lunge	2	1-3	2	1-3	0.916
Hurdle step	3	1-3	3	1-3	0.939
FMS ${}_{\text{Movement}}$	7	3-9	6	4-9	0.706
Shoulder mobility	2	1-3	3	1-3	0.046
Active straight leg raise	2	1-3	2	1-3	0.773
FMS ${}_{\text{Mobility}}$	4	3-6	5	2-6	0.291
Trunk stability push-up	3	1-3	3	1-3	0.356
Rotary stability	2	1-2	2	1-3	0.692
FMS ${}_{\text{Stability}}$	5	3-5	5	3-6	0.509
FMS ${}_{\text{Composite}}$	15	7-18	15	1-20	0.324

FMS: Functional Movement Screen, NIG: Non-injury group, IG: Injury group.

3.1 FMS scores according to body composition

Mean body fat percentage in the LBFPG and NBFPG was 5.15 $\pm$ 0.50% and 8.20 $\pm$ 1.99% and the mean BMI values were 21.34 $\pm$ 1.61 and 22.04 $\pm$ 1.51, respectively. The difference between the groups’ FMS scores was not statistically significant ( $p>$ 0.05) (Table 1). The groups also had similar injury history ( $p>$ 0.05). No significant relationship was detected between FMS composite score and BMI or body fat percentage.

3.2 FMS scores according to self-reported injury history

Thirty-seven (69.81%) of the participants reported at least one time-loss injury since being licensed by the football federation. The participants’ injury characteristics are presented in Table 2. Mean body fat percentage and BMI were 7.73 $\pm$ 2.41% and 21.943 $\pm$ 1.612 in the NIG and 7.00 $\pm$ 2.09% and 21.76 $\pm$ 1.56 in the IG, respectively. Body composition parameters were similar in both groups ( $p>$ 0.05). Of the FMS scores, only shoulder mobility scores differed significantly between the groups ( $p{<}$ 0.05); all other scores were similar ( $p>$ 0.05) (Table 3).

In ROC curve analysis, a cut-off point of 14.50 was determined for FMS composite score. Comparison of participants with scores above and below this cut-off point revealed no significant differences in injury history or body composition ( $p>$ 0.05).

In logistic regression analysis, the participants’ FMS scores were found to be insufficient to predict injuries ( $p>$ 0.05). The results of logistic regression models between reported injuries and FMS scores are shown in Table 4.

Table 4
Logistic regression models results between reported injuries and FMS scores

Variable	Odds ratio	95% CI	$p$
Reported injuries
Deep squat	1.14	0.39–3.30	0.812
In-line lunge	0.72	0.19–2.76	0.634
Hurdle step	0.65	0.22–1.93	0.437
FMS ${}_{\text{Movement}}$	0.95	0.62–1.46	0.811
Shoulder mobility	2.99	0.94–9.52	0.063
Active straight leg raise	0.56	0.18–1.78	0.324
FMS ${}_{\text{Mobility}}$	1.41	0.80–2.49	0.231
Trunk stability push-up	1.21	0.43–3.41	0.725
Rotary stability	0.79	0.09–6.63	0.827
FMS ${}_{\text{Stability}}$	1.49	0.71–3.17	0.295
FMS ${}_{\text{Composite}}$	1.13	0.75–1.70	0.570

FMS: Functional Movement Screen, CI: Confidence interval.

4. Discussion

This study was conducted to investigate the relationship between BMI and body fat percentage and FMS composite, subcategory, and individual test scores. Our results showed that there was no difference in any of the FMS scores between soccer players with low body fat and those with normal body fat percentages, and therefore, body fat did not constitute an advantage or disadvantage in terms of FMS scores in elite youth soccer players. In addition, approximately 70% of the participants reported a history of at least 1 time-loss injury, but we detected no difference in FMS scores between players with and without injuries. Also based on injury history; the FMS test is not effective in predicting injuries and injury history did not affect FMS scores in the elite youth soccer players in our study.

In elite professional soccer players, body fat percentage limits are determined such that optimum performance can be achieved without injury [28]. However, there is uncertainty about the ideal body fat percentage for youth soccer players to prevent injuries and achieve maximum physical performance [29]. Santos et al. cited 5 ${}^{\text{th}}$ and 95 ${}^{\text{th}}$ percentile values of 7.51% and 17.11% [5]. Fields et al. reported a mean body fat percentage of 11.50 $\pm$ 5.30% among male soccer players in their study of the relationship between elite collegiate athletes’ sports branch and playing position and their body composition [30]. Cárdenas-Fernández et al. reported the mean body fat percentage as 13.29 $\pm$ 2.57% in their study investigating differences in body composition and somatotype of youth soccer players according to playing position and performance [31]. In the present study, the mean body fat percentage of elite youth male soccer players was found to be 7.32. The differences in results between these studies may be related to differences in factors such as training methods, ethnicity, and level of soccer played.

Specific reference values for FMS scores in elite youth male soccer players have not yet been established. Newton et al. reported that the FMS composite scores of England Premier league youth academy soccer players was 15.55 $\pm$ 2.25 [32]. Marques et al. reported the FMS composite score of elite youth soccer players between the ages of 14 and 20 was 13.00 $\pm$ 1.61 [12]. This score was 14.74 $\pm$ 2.75 in the present study. Again, differences in ethnicity, training, and evaluators may explain the variation in study results.

Two previous studies have investigated the relationship between FMS score and body composition in athletes [20, 21]. Nicolozakes et al. examined the relationship between FMS scores and body fat percentage and BMI in American football players and observed a negative correlation between body fat percentage and FMS composite score. In addition, the authors reported that a greater proportion of obese athletes scored below the cut-off value of 14 compared to players with normal weight [20]. In the present study, it was observed that body fat percentage and BMI did not affect FMS scores. This discrepancy in results may be a result of the studies being conducted in different sports branches. Another study by Campa et al. examined the FMS scores and body composition parameters of a group of athletes including soccer, volleyball, and rugby players. The authors reported that FMS composite score was negatively correlated with body fat percentage and BMI. Examining the details of the study, it is seen that rugby athletes had lower FMS composite score and higher body fat percentage, which influenced the results of the study [21]. However, because the FMS scores and body composition of the soccer players included in that study were not analyzed separately and no statistics were provided, it was not possible to compare them with the results of this study.

In soccer, a relationship between body fat percentage and injuries is known to exist, although it has not been fully clarified [1]. Excessive body fat reduces the efficiency of movements such as jumping, long-distance running, and sprinting and increases the risk of injury by changing the force and deformation moments acting on the tissues during these movements [34, 35, 14]. Arnason et al. reported that high fat percentage in soccer players is a risk factor for soccer injuries, especially strains [35]. On the other hand, insufficient body fat can contribute to injuries due to the external forces that the athlete is exposed to through intervention from an opponent, and it causes overloading of other tissues in the absorption of repetitive forces the player is exposed to during activities such as running and shooting. Kemper et al. reported that having a low body fat percentage increases the risk of injury in youth soccer players, and there is no relation between high body fat percentage and soccer injuries [4]. In the present study, it was determined that having low body fat percentage in elite youth male soccer players did not make any difference regarding the reported injury rate and injury characteristics. The use of different body fat percentage measurement methods and classification systems and different characteristics of the selected samples may explain the differences in the results of the studies.

The relationship between FMS scores and the effectiveness of the test scores in predicting possible injuries have been examined in previous studies. Some researchers claimed that both the FMS composite score and individual test scores are effective in predicting injuries [36]. In contrast, other studies have indicated that FMS composite score is not effective in predicting musculoskeletal injuries, while individual FMS tests are successful in predicting injuries [11]. Also some authors reported that both FMS composite and individual test scores are insufficient to predict injuries [37]. These conflicting results cause serious confusion about the effectiveness of FMS scores in predicting injuries [38]. Although FMS is frequently used for injury prediction in soccer, the prevailing evidence suggests that the FMS does not provide sufficient information to predict injuries in soccer. Rusling et al. reported that of the 7 FMS tests, only the deep squat and trunk stability push-up tests were associated with noncontact injuries in their study including players in a professional football club academy [39]. In another study, Newton et al. examined the relationship between FMS scores and noncontact injuries in England Premier League soccer academy players and concluded that FMS test scores obtained at the beginning of the season could not predict noncontact injuries during the season [32]. The results of the present study also showed that the FMS scores alone did not provide sufficient information to predict injuries based on injury history. Results of the studies conducted to date indicate that in soccer, the FMS scores provide useful information about functional deficits and asymmetries, but FMS scores alone are not sufficient to predict injuries [37]. This may be due to the multifactorial nature of most injuries [40].

To the best of our knowledge, only one previous study investigated the effect of injury history on FMS scores in athletes. Chimera et al. reported that FMS composite scores were lower in injured athletes than non-injured athletes in their study conducted in athletes from different sports branches [41]. However, no study in the literature examined the effect of injury history on FMS scores in an elite soccer player population. Therefore, our findings are important in terms of filling this gap in the literature. According to the results of this study, the injury history of elite youth male soccer players did not affect their FMS composite scores. Previous injuries can cause some motor deficits such as strength imbalance and range of motion limitations [42]. These motor deficits are expected to affect the FMS scores [41]. However, the occurrence of motor deficits after injury depends on many factors such as the severity of the injury, whether the injury requires surgery, and the rehabilitation received after the injury [43]. Therefore, new studies that take these factors into account when evaluating the effect of injury history on FMS scores are needed.

The present study has some limitations. In general, athletes have the perception that past injuries may create an obstacle for their current career. Therefore, the participants may have been reluctant to report some past injuries due to this concern. In addition, the participants may not have mentioned some injuries that they considered insignificant, resulting in these being excluded from the study. Another limitation of the study is that the mean body fat percentage of the players in the normal group was fairly close to the lower limit. It is not known whether there will be a difference in FMS scores between players with low body fat percentage and those with normal body fat percentage whose mean value is closer to the upper limit of the reference range. This point may be taken into account in future research. Finally, as there are no body fat percentage reference values for the ethnicity represented by the study sample, reference values of a different ethnic group were used, which represents another limitation of the study. Future studies can investigate the relationship between FMS and other anthropometric properties such as extremity circumference and length measurements, which will further clarify the relationship between anthropometry and FMS scores.

5. Conclusion

In this study, there was no relationship between body composition and FMS composite, subcategory, or individual test scores in elite youth male soccer players. As a result, having low body fat did not create an advantage or disadvantage in terms of FMS scores for elite youth male soccer players. Injury history was also not associated with FMS composite, subcategory, or individual test scores in elite youth male soccer players. FMS scores provide important information about functional deficits and asymmetries in elite youth male soccer players; however, FMS scores alone are not sufficient to predict injuries in this group.

Footnotes

Conflict of interest

None to report.

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Effect of body composition on Functional Movement Screen scores in elite youth male soccer players

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Study design

2.2 Participants

2.3 Study procedure

2.4 Ethics

2.5 Data analyses

3. Results

Table 1 FMS scores according to body fat percentage

3.2 FMS scores according to self-reported injury history

Table 4 Logistic regression models results between reported injuries and FMS scores

5. Conclusion

Footnotes

Conflict of interest

References

Table 1
FMS scores according to body fat percentage

Table 4
Logistic regression models results between reported injuries and FMS scores