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Force, Power, and Morphology Asymmetries as Injury Risk Factors in Physically Active Men and Women

Department of Biostructure, Wroclaw University of Health and Sport Sciences, Paderewskiego 35 Avenue, 51-612 Wrocław, Poland
Department of Biomechanics, Wroclaw University of Health and Sport Sciences, Mickiewicza 58 Street, 51-684 Wrocław, Poland
Author to whom correspondence should be addressed.
Symmetry 2022, 14(4), 787;
Received: 8 March 2022 / Revised: 30 March 2022 / Accepted: 8 April 2022 / Published: 9 April 2022
(This article belongs to the Section Life Sciences)


This study aimed to investigate whether asymmetry of force, power, and tissue morphology are lower limbs (LL) injury risk factors in physically active adults. Fifty-eight men aged 23.8 ± 1.2 years and forty-seven women aged 23.3 ± 1.0 years were examined. Physical activity level was measured by the International Physical Activity Questionnaire, and injury data were collected with the Injury History Questionnaire. The countermovement jump was performed to evaluate force and power. LL tissue composition was evaluated by a bioimpedance analyzer. The symmetry indices were calculated. A comparison between injured and non-injured subjects in both sexes was conducted to determine indices associated with injuries. The symmetry indices cut-off points were calculated to establish values indicating a significant injury risk increase, and logistic regression was performed. The relative peak force asymmetry above 4.049% was associated with increased injury risk in men. The LL skeletal muscle mass asymmetry above 3.584% was associated with a higher injury risk in women. Increased asymmetry in indicated indices by 1% was associated with 19.8% higher injury risk in men and 82.6% in women. Asymmetry proved to be an injury risk factor. However, a more suitable index for men is relative peak force asymmetry, whereas LL skeletal muscle mass asymmetry is more suitable for women.

1. Introduction

Association between physical activity and health is well known both on the physical and mental side. Maintaining a high level of physical activity is associated with appropriate body composition, proper cardiovascular parameters, and improvement of well-being [1,2]. However, on the other hand, there is a risk of musculoskeletal injury [3,4]. Many factors could influence injury occurrence, which is a complex phenomenon [5]. Injury risk factors are classified as extrinsic factors considered, e.g., weather conditions, sports equipment, and intrinsic factors such as body composition, physical performance, or body posture [6,7,8,9]. The identification of injury risk factors is key to prevention [10]. In the general population, physical activity is addressed to improve health status. However, an injury may effectively stop physical activity participation, cause a timed brake, or lead to even absolute abandon [11]. Particularly vulnerable body parts are the lower limbs (LL), which are exploited in many activities [12]. Therefore, it is needed to investigate factors associated with lower limb injury (LLI) risk.
The results presented in past studies suggested functional and morphological asymmetry predispose to injury; however, data are inconsistent [13,14]. The human body is lateralized. One body’s side is dominant in function; therefore, it is more exploited [15,16]. It could cause asymmetrical development of one limb in morphological aspects and functional abilities. As was mentioned, body morphology indices state useful injury risk indicators [7,17,18]. However, there is a need to investigate whether segmental measurements are valuable in injury risk detection. In this case, there are interesting possible differences between lower limbs [14]. Most are considered functional tests screening or anatomical condition assessment [19,20,21]. Few studies consider lower limb tissue composition an injury risk factor, especially asymmetry, which is indicated as an intrinsic risk factor [22,23]. The test that can be used to assess asymmetry is countermovement jump (CMJ), which provides information about muscle force and power [24]. These variables seem to be associated with injury risk detection [25]. However, the past results are not consistent. Some differences in methodology or study samples may influence this [14]. Considering body morphology and physical performance, there is a need to remember the differences between men and women. Generally, men have more muscles tissue, whereas women have gained more fat tissue [26]. Many sex differences were also observed in the CMJ variables [27,28]. So, our approach was to analyze separately between men and women.
The above-mentioned studies suggest lower limbs asymmetry in morphology and physical performance as an injury risk factor. Studies mainly consider groups of athletes. Less is known about the general, physically active adults, who constitute the biggest part of the population. Participation in physical activity is an important issue to maintain good health status, especially after lockdown due to the COVID-19 pandemic [29]. Physical activity is a brilliant solution for physical and mental state improvement. Therefore, it is necessary to identify potential injury risk factors. However, fewer studies consider injury risk factors in physically active adults [30], so this area is worth exploring.
Therefore, this study aimed to investigate whether force, power, and morphology asymmetry are injury risk factors in physically active men and women. Specifically, we aimed to (1) specify which indicators are associated with injuries in men and women; (2) determine the symmetry indices terminal value indicating the point above which the injury risk is growing significantly; and (3) establish the injury risk level regarding analyzed factors asymmetry indices. We hypothesize that asymmetry in analyzed factors will be significantly associated with injury; however, there will be separate injury etiology for men and women. Obtained results have to point out which factors are effective in injury risk detection and which symmetry indices are suitable for physically active men and women. Moreover, we attempt to find a precise value of these indices that may indicate LLI risk. These results can be helpful as a guideline to prevent and reduce injuries’ prevalence associated with physical activity.

2. Materials and Methods

2.1. Participants

The study sample consisted of 58 men aged 23.8 ± 1.2 (95% CI = 23.5–24.1) years and 47 women aged 23.3 ± 1.0 (95% CI = 23.0–23.6) years, who were students at the University of Physical Education. Details of the subjects were situated in the results section (Table 1). All participants were physically active volunteers with no experience in professional sports and no injuries during the six weeks before starting the study measurements. Before participating in the relevant study, all subjects were required to sign a written consent form. Participants were informed about the study conditions. They could abandon measurements at any time.

2.2. Measurements

All measurements were taken one day in the morning hours in the sequence according to the description of the below-mentioned methods. The measurements were conducted in the Biomechanical Analysis Laboratory at Wroclaw University of Health and Sport Sciences, Poland, with the quality management system certificate (ISO 9001:2009)
The International Physical Activity Questionnaire (IPAQ) established the participants’ physical activity levels. The reliability of this tool regarding the physical activity level of nonprofessional sports adults was confirmed [31]. The IPAQ required self-reported information about the weekly average physical activity time (minutes per week). The received data allow calculation of Metabolic Equivalents of Task (MET). Based on IPAQ physical active criteria, the inclusion MET values were:
3 or more days of vigorous exercise, for a total of at least 1500 MET;
7 or more days of any combination of exercise (walking, moderate, or vigorous exercise) exceeding 3000 MET.
The Injury History Questionnaire (IHQ) was used to collect injury data. The IHQ reliability was determined using Cronbach’s alpha coefficient, which at level 0.836 indicated high reliability of IHQ [30,32]. The IHQ analyzes the number of injuries in the last 12 months concerning the body part. For the relevant study, only data considering the lower limbs injuries were assessed.
A Swiss anthropometer (GPM Anthropological Instruments, DKSH Ltd., Zurich, Switzerland) was used for body height measurements. The InBody230 bioimpedance analyzer assessed body morphology (InBody Co., Ltd., Cerritos, CA, USA). The very high reliability of this device was confirmed [33]. Participants were instructed to avoid alcohol drinking and excessive physical exercise 24 h before the measurements. No food and drink at least 3 h before measurements were allowed. They were also instructed to excrete directly before the measures—the collected data allowed analyzing the body parts. The studied parameters concern lower limbs fat and muscle mass.
CMJ with arm swing was performed to determine the power and force asymmetries. Two synchronized Kistler force plates (9286A; Winterthur, Switzerland) with Kistler MARS Power 2875A software were used to gain separate ground reaction force measurements for both body sides. The sampling frequency of the signal from the force plates was 1000 Hz. The participants were instructed to jump as high as possible. There were no countermovement depth restrictions. The five CMJ attempts were performed with a 60 s break between them, and the further analysis considered the highest CMJ performed by each participant. The following CMJ variables divided into right and left body sides were included in the analysis: relative peak power (peak power during the take-off phase divided by the body mass) and relative peak force (peak ground reaction force during the take-off phase divided by the body mass).
The standardized Symmetry Index (SI) was calculated for parameters gained from CMJ measurements (force and power) and lower limbs morphology (BIA) (fat mass%, fat mass, fat-free mass, muscle mass) from the following equation [34]:
SI = 2 · | Right   side Left   side |   Right   side Left   side   100 %

2.3. Statistics

The Shapiro–Wilk test was used to establish the normality of the distributions of the analyzed variables. The means, standard deviations, and confidence intervals were calculated. The U-Mann–Whitney test was used to compare injured and non-injured subjects concerning sex in case of analyzed factors. The receiver operating characteristic curve (ROC) method was used to establish the cut-off points that indicated critical values of factors associated with increased injury risk. The Youden Index was used for detecting the optimal cut-off point based on the sensitivity (the ratio of true positive cases that were correctly identified by the test) and the specificity (the ratio of true negative cases that were correctly identified by the test) values. The odds ratio of an injury risk was calculated using logistic regression. A level of significance was set at p < 0.05. The Statistica v13.0 by Statsoft Polska was used for statistical analysis.

3. Results

Table 1 includes descriptive statistics of lower limbs morphology, force, and power, and SI for measured factors for men and women. Men’s physical activity level was established at 4543 ± 2079 MET (95% CI = 3996–5090), and LLI prevalence was 25.86%. Women’s physical level was 3773.37 ± 2054.01 (95% CI = 3170–4376), and LLI prevalence was 29.79%.
Table 2 shows a t-test comparison between injured and non-injured subjects. The difference in relative peak force–SI was statistically significant in men. Injured men had a higher SI. On the other hand, statistically significant higher LL skeletal muscle mass–SI was noted among injured subjects who were women. No other statistically significant differences were observed. Therefore, only the relative peak force SI for men and LL muscle mass SI for women were included in the next part of the statistical analysis.
The next analysis step included the cut-off point for significantly different factors for both sexes. For men, the relative peak force–SI cut-off point was established at 4.049 (Figure 1), whereas for women, in terms of LL, the skeletal muscle mass–SI cut-off point was 3.584 (Figure 2). Subjects with higher results of the appropriate index are more likely to be injured.
In Table 3, logistic regression models for men and women according to analyzed factors statistically significant differentiated between injured and non-injured subjects. Men with a relative peak force–SI increased by 1% are 19.8% more likely to be injured. Women with LL skeletal muscle mass–SI scores that are higher by 1% have increased injury risk by 82.5%.

4. Discussion

Our results confirm asymmetry as an intrinsic injury risk factor. However, there were revealed significant differences between men and women. Relative peak force asymmetry in males and LL muscle mass asymmetry in females were effective in injury risk detection, which indicates possible sex differences in injury risk etiology.
CMJ is a widely used test that provides valuable data about generating force and power. Using two force plates allows for analyzing side differences, which allows for a deeper evaluation of measurements. Sannicandro et al. [35] suggest using lower limb strength asymmetry measurements as a valuable tool to screen physical performance in soccer training. The same authors [35] indicated a positive relationship between functional movement quality and CMJ height (indirectly describing power capacity). Due to the association of functional movement quality [30] with injury, there is a possible share of force and power obtained during CMJ. It is suggested that injury risk detection is more effective when analyzing data from both separately measured body sides [36]. In our study, injured men had a higher relative peak force SI than those without injury. A similar observation was seen in the research of Jordan et al. [37]. In the group of male and female athletes after ACL reconstruction, higher asymmetry in CMJ peak power during the take-off phase and lower limbs muscle mass (compared to non-injured groups) were observed. Asymmetry in muscle function seems to be a common issue in LLI subjects [38]. In serious injury such as an ACL reconstruction, one limb’s observed impaired function can persist even for two years [39]. It also indicates that reach-limb symmetry measures below >90% in strength and one-legged CMJ height are associated with increased ACL injury [40]. Our results showed that a relative peak force asymmetry of 4% is associated with higher LLI risk among men. Increasing this SI by 1% is associated with a 19.8% higher injury risk. Without a doubt, lower limbs asymmetry is associated with elevated injury risk. The results provided by Hart et al. [25] agree with our observation, where the previous injury was also associated with CMJ peak force asymmetry in countermovement and take-off phases. Additionally, it is binding with a greater risk of future injury. From another perspective, muscle function provides sufficient data useful in LLI risk detection. There is an association between skeletal muscle mass with CMJ force and power [41]. Markovic et al. [42] and Crosier et al. [43] indicated muscle strength asymmetry as an injury risk factor in the prospective term. It is known that some physical performance factors are injury risk factors [8,9]. However, many coexisting factors such as sex, physical activity, and sport influence injury risk [44].
Our study indicated that in women, LL muscle mass asymmetry is more suitable for injury risk detecting. The literature provides many observations concerning injury risk detecting concerning body composition. However, some results did not confirm this state [45]. Fewer studies investigate the inter-limbs differences in terms of injury risk. It was shown that the most common indices, BMI (body mass index), FMI (fat mass index), or MFR (muscle–fat ratio), are helpful in injury risk detection. Inter-sex differences were noted that implicated the need to respect sex differences [9,17,18]. Fewer studies concern body parts that cause some hardship. Studies mainly focus on whole-body indices [7,17,18]. However, in the study mentioned above by Jordan et al. [37], differences in tissue composition between limbs are associated with injury risk elevation. In our research, the LLI risk is higher than at approximately 3.5% of asymmetry LL muscle mass in women, with an 82% higher injury risk with increased SI by 1%. It was observed that a decrease in strength and muscle mass LL is associated with a higher injury risk [46]. Generally, LL tissue composition influences its function in terms of postural stability [47], which is related to ankle injuries [48] and external forces absorption in various activities. The lower extremity with less muscle mass could absorb fewer external forces than the second extremity, leading to injury [49]. The proportion of muscle–fat tissue seems to influence injury occurrence [50]. In women, increased BMI is associated with injury risk [51]. Skeletal muscle mass is considered an injury risk factor. There was an observed increased injury risk with muscle mass loss [52].
Our study adds confirmation that asymmetry is an injury risk factor [13,14]. There is a need to emphasize that previous injury could deepen the earlier asymmetry and increase the LLI risk [53]. Therefore, there is a need to appropriately rehabilitate after injury and decrease asymmetry in prevention [54]. Our results revealed separate injury etiology between men and women. It suggests using different prevention methods due to sex. The solution could be implementing unilateral exercises that should lead to the equal development of body sides in terms of morphology and physical performance. However, there is a need to confirm this in further studies. Moreover, in future studies, there is a need to verify if indicated indices and cut-off point values for men and women effectively predict injury in prospective terms (long-term observations). We are aware of some limitations of our study. A bigger study sample could provide stronger evidence.

5. Conclusions

Higher asymmetry is associated with increased injury risk. However, there are some sex differences. Men with relative peak force asymmetry above 4% are more likely to be injured. Moreover, increasing the relative peak force asymmetry by 1% is associated with a 19.8% higher injury risk. Women with a cut-off point above 3.5% for lower limb skeletal muscle mass asymmetry are more likely to be injured. The injury risk for females is growing by 82.6%, with 1% higher asymmetry in lower limbs muscle mass. It means separate injury etiology between sexes.

Author Contributions

Conceptualization, D.K.; methodology, D.K., A.S. and J.D.; software, A.S. and J.D.; validation, D.K., A.S. and J.D.; formal analysis, A.S. and J.D.; investigation, D.K.; resources, D.K. and J.D.; data curation, A.S. and J.D.; writing—original draft preparation, D.K. and A.S.; writing—review and editing, D.K. and J.D.; visualization, D.K.; supervision, A.S. and J.D.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

The Senate Research Ethics Committee approved the research at the Wroclaw University of Health and Sport Sciences, following the ethical requirements for human experiments under the Helsinki Declaration (consent number 16/2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Penedo, F.J.; Dahn, J.R. Exercise and well-being: A review of mental and physical Health benefits associated with physical activity. Curr. Opin. Psychiatry 2005, 18, 189–193. [Google Scholar] [CrossRef] [PubMed]
  2. Warburton, D.E.R.; Bredin, S.S.D. Health benefits of physical activity: A systematic review of current systematic reviews. Curr. Opin. Cardiol. 2017, 32, 541–556. [Google Scholar] [CrossRef] [PubMed]
  3. Meeuwisse, W.H.; Tyreman, H.; Hagel, B.; Emery, C. A dynamic model of etiology in sport injury: The recursive nature of risk and causation. Clin. J. Sport Med. 2007, 17, 215–219. [Google Scholar] [CrossRef] [PubMed]
  4. Złotkowska, R.; Skiba, M.; Mroczek, A.; Bilewicz-Wyrozumska, T.; Król, K.; Lar, K.; Zbrojkiewicz, E. Negatywne skutki aktywności fizycznej oraz uprawiania sportu. Hygeia Public Health 2015, 50, 41–46. [Google Scholar]
  5. Bittencourt, N.F.; Meeuwisse, W.H.; Mendonça, L.D.; Nettel-Aguirre, A.; Ocarino, J.M.; Fonseca, S.T. Complex systems approach for sports injuries: Moving from risk factor identification to injury pattern recognition—Narrative review and new concept. Br. J. Sports Med. 2016, 50, 1309–1314. [Google Scholar] [CrossRef][Green Version]
  6. Saragiotto, B.T.; Di Pierro, C.; Lopes, A.D. Risk factors and injury prevention in elite athletes: A descriptive study of the opinions of physical therapists, doctors and trainers. Braz. J. Phys. Ther. 2014, 18, 137–143. [Google Scholar] [CrossRef][Green Version]
  7. Domaradzki, J.; Koźlenia, D. The performance of body mass component indices in detecting risk of musculoskeletal injuries in physically active young men and women. PeerJ 2022, 10, e12745. [Google Scholar] [CrossRef]
  8. de la Motte, S.J.; Gribbin, T.C.; Lisman, P.; Murphy, K.; Deuster, P.A. Systematic Review of the Association Between Physical Fitness and Musculoskeletal Injury Risk: Part 2-Muscular Endurance and Muscular Strength. J. Strength Cond. Res. 2017, 31, 3218–3234. [Google Scholar] [CrossRef]
  9. de la Motte, S.J.; Lisman, P.; Gribbin, T.C.; Murphy, K.; Deuster, P.A. Systematic Review of the Association Between Physical Fitness and Musculoskeletal Injury Risk: Part 3-Flexibility, Power, Speed, Balance, and Agility. J. Strength Cond. Res. 2019, 33, 1723–1735. [Google Scholar] [CrossRef]
  10. Emery, C.A.; Pasanen, B. Current trends in sport injury prevention. Best Pract. Res. Clin. Rheumatol. 2019, 33, 3–15. [Google Scholar] [CrossRef]
  11. Ristolainen, L.; Kettunen, J.A.; Kujala, U.M.; Heinonen, A. Sport injuries as the main cause of sport career termination among Finnish top-level athletes. Eur. J. Sport Sci. 2012, 12, 274–282. [Google Scholar] [CrossRef]
  12. Krutsch, W.; Krutsch, V.; Hilber, F.; Pfeifer, C.; Baumann, F.; Weber, J.; Schmitz, P.; Kerschbaum, M.; Nerlich, M.; Angele, P. 11.361 sports injuries in a 15-year survey of a Level I emergency trauma department reveal different severe injury types in the 6 most common team sports. Sportverletz. Sportschaden 2018, 32, 111–119. [Google Scholar] [CrossRef] [PubMed]
  13. Zuzgina, O.; Wdowski, M. Asymmetry of dominant and non-dominant shoulders in university level men and women volleyball players. Hum. Mov. 2019, 20, 19–27. [Google Scholar] [CrossRef]
  14. Helme, M.; Tee, J.; Emmonds, S.; Low, C. Does lower-limb asymmetry increase injury risk in sport? A systematic review. Phys. Ther. Sport Off. J. Assoc. Chart. Physiother. Sports Med. 2021, 49, 204–213. [Google Scholar] [CrossRef] [PubMed]
  15. Promsri, A.; Longo, A.; Haid, T.; Doix, A.M.; Federolf, P. Leg Dominance as a Risk Factor for Lower-Limb Injuries in Downhill Skiers-A Pilot Study into Possible Mechanisms. Int. J. Environ. Res. Public Health 2019, 16, 3399. [Google Scholar] [CrossRef][Green Version]
  16. Guan, Y.; Bredin, S.; Taunton, J.; Jiang, Q.; Wu, N.; Li, Y.; Warburton, D. Risk Factors for Non-Contact Lower-Limb Injury: A Retrospective Survey in Pediatric-Age Athletes. J. Clin. Med. 2021, 10, 3171. [Google Scholar] [CrossRef]
  17. Hollander, N.A.; Finestone, A.S.; Yofe, V.; Bader, T.; Magnezi, R. The association between increased body mass index and overuse injuries in Israel defense forces conscripts. Obes. Facts 2020, 13, 152–165. [Google Scholar] [CrossRef]
  18. Havenetidis, K.; Paxinos, T.; Kardaris, D.; Bissas, A. Prognostic potential of body composition indices in detecting risk of musculoskeletal injury in army officer cadet profiles. Physician Sportsmed. 2017, 45, 114–119. [Google Scholar] [CrossRef]
  19. Gonell, A.C.; Romero, J.A.; Soler, L.M. Relationship Between The Y Balance Test Scores And Soft Tissue Injury Incidence In A Soccer Team. Int. J. Sports Phys. Ther. 2015, 10, 955–966. [Google Scholar]
  20. Isern-Kebschull, J.; Mechó, S.; Pruna, R.; Kassarjian, A.; Valle, X.; Yanguas, X.; Alomar, X.; Martinez, J.; Pomés, J.; Rodas, G. Sports-related lower limb muscle injuries: Pattern recognition approach and MRI review. Insights Into Imaging 2020, 11, 108. [Google Scholar] [CrossRef]
  21. Cowan, D.N.; Jones, B.H.; Frykman, P.N.; Polly, D.W.; Harman, E.A., Jr.; Rosenstein, R.M.; Rosenstein, M.T. Lower limb morphology and risk of overuse injury among male infantry trainees. Med. Sci. Sports Exerc. 1996, 28, 945–952. [Google Scholar] [CrossRef] [PubMed]
  22. Toomey, C.M.; Whittaker, J.L.; Nettel-Aguirre, A.; Reimer, R.A.; Woodhouse, L.J.; Ghali, B.; Doyle-Baker, P.K.; Emery, C.A. Higher Fat Mass Is Associated With a History of Knee Injury in Youth Sport. J. Orthop. Sports Phys. Ther. 2017, 47, 80–87. [Google Scholar] [CrossRef] [PubMed][Green Version]
  23. Mala, L.; Maly, T.; Cabell, L.; Hank, M.; Bujnovsky, D.; Zahalka, F. Anthropometric, Body Composition, and Morphological Lower Limb Asymmetries in Elite Soccer Players: A Prospective Cohort Study. Int. J. Environ. Res. Public Health 2020, 17, 1140. [Google Scholar] [CrossRef] [PubMed][Green Version]
  24. Struzik, A.; Winiarski, S.; Zawadzki, J. Inter-limb asymmetry of leg stiffness in national second-league basketball players during countermovement jumps. Symmetry 2022, 14, 440. [Google Scholar] [CrossRef]
  25. Hart, L.M.; Cohen, D.D.; Patterson, S.D.; Springham, M.; Reynolds, J.; Read, P. Previous injury is associated with heightened countermovement jump force-time asymmetries in professional soccer players. Transl. Sports Med. 2019, 2, 256–262. [Google Scholar] [CrossRef]
  26. Bredella, M.A. Sex Differences in Body Composition. Adv. Exp. Med. Biol. 2017, 1043, 9–27. [Google Scholar] [CrossRef]
  27. Struzik, A. Measuring Leg Stiffness during Vertical Jumps: Theory and Methods, 1st ed.; Springer: Cham, Switzerland, 2019. [Google Scholar] [CrossRef]
  28. Kibler, W.B.; Chandler, T.J.; Uhl, T.; Maddux, R.E. A musculoskeletal approach to the preparticipation physical examination: Preventing injury and improving performance. Am. J. Sports Med. 1989, 17, 525–531. [Google Scholar] [CrossRef]
  29. Dor-Haim, H.; Katzburg, S.; Revach, P.; Levine, H.; Barak, S. The impact of COVID-19 lockdown on physical activity and weight gain among active adult population in Israel: A cross-sectional study. BMC Public Health 2021, 21, 1521. [Google Scholar] [CrossRef]
  30. Koźlenia, D.; Domaradzki, J. Prediction and injury risk based on movement patterns and flexibility in a 6-month prospective study among physically active adults. PeerJ 2021, 9, e11399. [Google Scholar] [CrossRef]
  31. Biernat, E.; Stupnicki, R.; Gajewski, A. International physical activity questionnaire (IPAQ) _Polish version. Pol. J. Sports Med. 2013, 29, 1–15. [Google Scholar]
  32. Aron, A.; Aron, E.N.; Coups, E.J. Statistics for Psychology, 6th ed.; Pearson International Edition: Upper Saddle River, NJ, USA, 2012. [Google Scholar]
  33. McLester, C.N.; Nickerson, B.S.; Kliszczewicz, B.M.; McLester, J.R. Reliability and Agreement of Various InBody Body Composition Analyzers as Compared to Dual-Energy X-Ray Absorptiometry in Healthy Men and Women. J. Clin. Densitom. Off. J. Int. Soc. Clin. Densitom. 2020, 23, 443–450. [Google Scholar] [CrossRef] [PubMed]
  34. Viteckova, S.; Kutilek, P.; Svoboda, Z.; Krupicka, R.; Kauler, J.; Szabo, Z. Gait symmetry measures: A review of current and prospective methods. Biomed. Signal Process. Control 2018, 42, 89–100. [Google Scholar] [CrossRef]
  35. Sannicandro, I.; Cofano, G.; Rosa, A.R.; Traficante, P.; Piccinno, A. Functional movement screen and lower limb strength asymmetry in professional soccer players. Br. J. Sports Med. 2017, 51, 381–382. [Google Scholar] [CrossRef]
  36. Mitchell, A.; Holding, C.; Greig, M. The Influence of Injury History on Countermovement Jump Performance and Movement Strategy in Professional Soccer Players: Implications for Profiling and Rehabilitation Foci. J. Sport Rehabil. 2021, 30, 768–773. [Google Scholar] [CrossRef]
  37. Jordan, M.J.; Aagaard, P.; Herzog, W. Lower limb asymmetry in mechanical muscle function: A comparison between ski racers with and without ACL reconstruction. Scand. J. Med. Sci. Sports 2015, 25, e301–e309. [Google Scholar] [CrossRef] [PubMed]
  38. Holsgaard-Larsen, A.; Jensen, C.; Mortensen, N.H.; Aagaard, P. Concurrent assessments of lower limb loading patterns, mechanical muscle strength and functional performance in ACL-patients—A cross-sectional study. Knee 2014, 21, 66–73. [Google Scholar] [CrossRef] [PubMed]
  39. Castanharo, R.; da Luz, B.S.; Bitar, A.C.; D’Elia, C.O.; Castropil, W.; Duarte, M. Males still have limb asymmetries in multijoint movement tasks more than 2 years following anterior cruciate ligament reconstruction. J. Orthop. Sci. Off. J. Jpn. Orthop. Assoc. 2011, 16, 531–535. [Google Scholar] [CrossRef]
  40. O’Malley, E.; Richter, C.; King, E.; Strike, S.; Moran, K.; Franklyn-Miller, A.; Moran, R. Countermovement Jump and Isokinetic Dynamometry as Measures of Rehabilitation Status After Anterior Cruciate Ligament Reconstruction. J. Athl. Train. 2018, 53, 687–695. [Google Scholar] [CrossRef][Green Version]
  41. Bell, D.R.; Sanfilippo, J.L.; Binkley, N.; Heiderscheit, B.C. Lean mass asymmetry influences force and power asymmetry during jumping in collegiate athletes. J. Strength Cond. Res. 2014, 28, 884–891. [Google Scholar] [CrossRef][Green Version]
  42. Markovic, G.; Šarabon, N.; Pausic, J.; Hadžić, V. Adductor Muscles Strength and Strength Asymmetry as Risk Factors for Groin Injuries among Professional Soccer Players: A Prospective Study. Int. J. Environ. Res. Public Health 2020, 17, 4946. [Google Scholar] [CrossRef]
  43. Croisier, J.L.; Ganteaume, S.; Binet, J.; Genty, M.; Ferret, J.M. Strength imbalances and prevention of hamstring injury in professional soccer players: A prospective study. Am. J. Sports Med. 2008, 36, 1469–1475. [Google Scholar] [CrossRef] [PubMed]
  44. Bahr, R.; Holme, I. Risk factors for sports injuries—A methodological approach. Br. J. Sports Med. 2003, 37, 384–392. [Google Scholar] [CrossRef] [PubMed]
  45. Melloni, M.; De Almeida Ávila, J.; Páscoa, M.A.; De Oliveira Barbeta, C.J.; Cirolini, V.X.; Gonçalves, E.M.; Guerra-Júnior, G. Can anthropometric, body composition, and bone variables be considered risk factors for musculoskeletal injuries in Brazilian military students? BMC Musculoskelet. Disord. 2018, 19, 377. [Google Scholar] [CrossRef] [PubMed]
  46. Konishi, Y.; Oda, T.; Tsukazaki, S.; Kinugasa, R.; Hirose, N.; Fukubayashi, T. Relationship between quadriceps femoris muscle volume and muscle torque after anterior cruciate ligament rupture. Knee Surg. Sports Traumatol. Arthrosc. Off. J. ESSKA 2011, 19, 641–645. [Google Scholar] [CrossRef]
  47. Ryew, C.C.; Lee, A.R.; Hyun, S.H. Effect of muscle mass asymmetric between upper and lower limbs on the postural stability and shock attenuation during landing. J. Exerc. Rehabil. 2019, 15, 488–492. [Google Scholar] [CrossRef]
  48. McGuine, T.A.; Keene, J.S. The effect of a balance training program on the risk of ankle sprains in high school athletes. Am. J. Sports Med. 2006, 34, 1103–1111. [Google Scholar] [CrossRef]
  49. Yeow, C.H.; Lee, P.V.; Goh, J.C. Effect of landing height on frontal plane kinematics, kinetics and energy dissipation at lower extremity joints. J. Biomech. 2009, 42, 1967–1973. [Google Scholar] [CrossRef]
  50. McCaskie, C.J.; Sim, M.; Newton, R.U.; Hart, N.H. Lower-limb injury in elite Australian football: A narrative review of kinanthropometric and physical risk factors. Phys. Ther. Sport Off. J. Assoc. Chart. Physiother. Sports Med. 2021, 52, 69–80. [Google Scholar] [CrossRef]
  51. Freckleton, G.; Cook, J.; Pizzari, T. The predictive validity of a single leg bridge test for hamstring injuries in Australian Rules Football Players. Br. J. Sports Med. 2014, 48, 713–717. [Google Scholar] [CrossRef]
  52. Georgeson, E.C.; Weeks, B.K.; McLellan, C.; Beck, B.R. Seasonal change in bone, muscle and fat in professional rugby league players and its relationship to injury: A cohort study. BMJ Open 2012, 2, e001400. [Google Scholar] [CrossRef][Green Version]
  53. Murphy, D.F.; Connolly, D.A.; Beynnon, B.D. Risk factors for lower extremity injury: A review of the literature. Br. J. Sports Med. 2003, 37, 13–29. [Google Scholar] [CrossRef] [PubMed][Green Version]
  54. Tajdini, H.; Letafatkar, A.; Brewer, B.W.; Hosseinzadeh, M. Association between kinesiophobia and gait asymmetry after ACL reconstruction: Implications for prevention of reinjury. Int. J. Environ. Res. Public Health 2021, 18, 3264. [Google Scholar] [CrossRef] [PubMed]
Figure 1. The cut-off point for relative peak force—SI for increased injury risk. AUC = 0.704 (95% CI = 0.551–0.857); p = 0.0089; Youden index = 0.43.
Figure 1. The cut-off point for relative peak force—SI for increased injury risk. AUC = 0.704 (95% CI = 0.551–0.857); p = 0.0089; Youden index = 0.43.
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Figure 2. The cut-off point for LL skeletal muscle mass–SI for increased injury risk. AUC = 0.705 (95% CI = 0.532–0.0877); p = 0.0200; Youden index = 0.37.
Figure 2. The cut-off point for LL skeletal muscle mass–SI for increased injury risk. AUC = 0.705 (95% CI = 0.532–0.0877); p = 0.0200; Youden index = 0.37.
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Table 1. Descriptive statistics. Mean, standard deviations, and confidence intervals for measured variables.
Table 1. Descriptive statistics. Mean, standard deviations, and confidence intervals for measured variables.
No Injury (n = 40)Injury (n = 18)No Injury (n = 33)Injury (n = 14)
Mean ± SD 95% CIMean ± SD 95% CIMean ± SD 95% CIMean ± SD 95% CI
Body mass (kg)80.5 ± 11.184.3 ± 8.561.2 ± 9.063.6 ± 12.7
Body height (kg)1.80 ± 0.21.81 ± 0.11.7 ± 0.11.67 ± 0.1
Body mass index—BMI (kg/m2)24.8 ± 2.725.8 ± 2.521.7 ± 2.622.6 ± 2.8
Relative peak force—SI (%)4.0 ± 3.18.5 ± 7.87.3 ± 5.97.8 ± 6.4
3.0 – 5.04.6–12.45.8–9.44.3–11.7
Relative peak power—SI (%)4.4 ± 3.44.0 ± 3.74.9 ± 4.45.7 ± 4.3
LL fat mass%—SI (%)4.8 ± 4.13.9 ± 3.72.3 ± 2.02.8 ± 3.0
LL fat mass kg—SI (%)4.1 ± 3.93.9 ± 3.72.9 ± 2.13.9 ± 6.1
2.9- 5.41.5–5.22.8–3.70.3–7.9
LL free fat mass—SI (%)2.8 ± 1.32.5 ± 1.32.2 ± 1.44.2 ± 3.5
2.5–3.41.8–3.21.7–2.72.1- 6.7
LL muscle mass—SI (%)2.9 ± 1.32.5 ± 1.31.9 ± 1.23.3 ± 2.2
Abbreviations: LL—lower limbs; SI—symmetry index.
Table 2. Descriptive statistics of symmetry indices for subjects according to injury state. U-Mann–Whitney test comparison between injured and non-injured men and women.
Table 2. Descriptive statistics of symmetry indices for subjects according to injury state. U-Mann–Whitney test comparison between injured and non-injured men and women.
SexSymmetry Index (%)RanksZp
No InjuryInjury
MenRelative peak force 1033.00678.00−2.46230.0138 *
Relative peak power 1235.00476.000.91600.3597
LL fat mass % 1229.00482.000.81520.4150
LL fat mass kg 1224.00487.000.73110.4647
LL free-fat mass1253.00458.001.21850.2230
LL skeletal muscle mass 1245.50465.501.09250.2746
WomenRelative peak force 776.50351.500.34890.7271
Relative peak power 758.00370.000.77930.4358
LL fat mass % 777.50350.500.32570.7447
LL fat mass kg 817.50310.50−0.58160.5609
LL free-fat mass 714.00414.001.80280.0714
LL skeletal muscle mass 697.50430.502.18660.0288 *
Abbreviations: LL—lower limbs; * Statistically significant p < 0.05.
Table 3. Injury risk prediction models for relative peak force–SI in men and LL skeletal muscle mass–SI in women.
Table 3. Injury risk prediction models for relative peak force–SI in men and LL skeletal muscle mass–SI in women.
FactorRateSEWaldGU 95% CIOROR 95% CIp
Men—Relative peak force–SI 0.1810.0736.1180.038–0.3241.1981.038–1.3830.013 *
Women- LL skeletal muscle mass–SI0.6020.2605.3690.009–1.1111.8261.097–3.0380.021 *
* Statistically significant p < 0.05.
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Koźlenia, D.; Struzik, A.; Domaradzki, J. Force, Power, and Morphology Asymmetries as Injury Risk Factors in Physically Active Men and Women. Symmetry 2022, 14, 787.

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Koźlenia D, Struzik A, Domaradzki J. Force, Power, and Morphology Asymmetries as Injury Risk Factors in Physically Active Men and Women. Symmetry. 2022; 14(4):787.

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Koźlenia, Dawid, Artur Struzik, and Jarosław Domaradzki. 2022. "Force, Power, and Morphology Asymmetries as Injury Risk Factors in Physically Active Men and Women" Symmetry 14, no. 4: 787.

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