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28 March 2025

Risk Factors of Ankle Sprain in Soccer Players: A Systematic Review and Meta-Analysis

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1
International Center for Limb Lengthening, Rubin Institute for Advanced Orthopedics, Sinai Hospital of Baltimore, Baltimore, MD 21215, USA
2
School of Medicine, Tehran University of Medical Sciences, Tehran 1416634793, Iran
3
Neuromusculoskeletal Research Center, Department of Physical Medicine and Rehabilitation, School of Medicine, Iran University of Medical Sciences, Tehran 1416634793, Iran
4
School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1983969411, Iran
Sports2025, 13(4), 105;https://doi.org/10.3390/sports13040105
This article belongs to the Special Issue Advances in Sports Injury Prevention and Rehabilitation Strategies
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Abstract

Background: Soccer is associated with substantial injury risk, with reported between 13 to 35 injuries per 1000 player-hours of competitive play. Notably, approximately 77% of soccer-related ankle injuries are attributed to ankle sprain injuries (ASIs). ASI can lead to chronic ankle instability, obesity, and post-traumatic osteoarthritis. This study focuses on identifying factors such as gender, age, body mass index (BMI), and a history of ASIs, which contribute to the development of ASI in soccer players. Methods: A systematic literature search was conducted in October 2023 across databases, including PubMed, Web of Science, Scopus, Cochrane Library, and ProQuest, without applying any filters. Keywords included ankle, ankle joint, sprain, risk factors, etc. Data extraction was performed on the included studies, with findings standardized and analyzed using Stata Statistical Software: Release 17 to determine a weighted treatment effect. Results: Our systematic review included 26 studies. The meta-analysis revealed that a history of ankle sprain is the most significant risk factor for future ASIs. BMI emerged as a risk factor in three out of seven studies, while age and height were significant in one out of six studies each. Gender and weight were not found to significantly affect ASI occurrence. Other factors identified but not subjected to a meta-analysis due to methodological heterogeneity or insufficient studies included playing surface, joint laxity, muscle weakness, match congestion, strength asymmetries, ground reaction forces, balance maintenance, skill level, and playing position. Conclusions: This research contributes valuable insights into the prevention of ASIs in soccer, highlighting the importance of previous ankle sprains and playing surface quality. These findings assist sports professionals in developing optimal conditions and strategies for effective ankle sprain prevention.

1. Introduction

Soccer is widely recognized for its high injury rates, with studies reporting 13 to 35 injuries per 1000 player-hours of competitive play [1]. Among these, ankle sprain injuries (ASIs) stand out as the most prevalent form of injury encountered by youth soccer players [2,3,4]. It is noted that a substantial 77% of ankle injuries within soccer can be attributed to ASI, highlighting the significance of this issue [5]. The investigation into the etiological factors that lead to foot and ankle injuries in soccer is not only crucial for understanding the nature of these injuries but also for developing preventive measures [6].
There is compelling evidence suggesting that individuals with a history of an ASI are at twice the risk of a subsequent ASI within at least one year of the initial occurrence, pointing to a cycle of vulnerability and re-injury [7,8]. Furthermore, chronic ankle instability, obesity, and post-traumatic osteoarthritis represent some of the long-term repercussions of ASI, although these are not exhaustive [7,9,10,11]. Interestingly, neuromuscular training (NMT) warm-up routines have been shown to significantly lower the incidence of acute lower extremity injuries in youth sports by 29 to 60 percent [12,13,14,15,16], indicating the potential for preventive strategies.
Soccer players face unique risk factors for ankle sprains due to the sport’s specific demands; yet, the literature lacks a focused synthesis of these factors. This systematic review and meta-analysis fills this gap, providing novel insights into both intrinsic and extrinsic risk factors to guide targeted prevention strategies and enhance player safety [17,18]. Identifying these factors is essential for formulating targeted ankle sprain prevention methods for young players.
This study aims to explore various potential risk factors for ASI, including gender, age, body mass index (BMI), and previous ASI history, among youth soccer players. By examining these elements in a systematic review, the research seeks to contribute valuable insights into the prevention and management of ASI in soccer.

2. Materials and Methods

Systematic review protocol
This systematic review was conducted in strict adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocols guideline [19], with its protocol registered under PROSPERO (CRD42022309294).
Eligibility criteria
Our study exclusively incorporated randomized controlled trials and prospective cohort studies that delve into the risk factors for ankle sprains among soccer players. We considered studies spanning various leagues, focusing on athletes who have encountered either a primary or subsequent ankle sprain injury in the context of official matches or training sessions. Eligible injuries included both contact and non-contact incidents leading to ligament stretching or tearing, contributing to ankle instability.
We excluded investigations related to other forms of ankle injuries, such as Achilles tendon ruptures, ankle fractures, peroneal tendonitis, tarsal tunnel syndrome, and osteochondral lesions, to maintain a focused inquiry into ankle sprains. The requirement for study inclusion was publication in English, with a clear disclosure of participant numbers, detailed injury data, follow-up criteria, and the statistical methods employed. Studies targeting non-soccer athletes, those centered on American football or rugby players, and research focusing on traumatic bony injuries, fractures, contusions, and muscle strains were deemed outside the scope of this review.
Search strategy and outcome measures
A comprehensive search through electronic databases, including PubMed, Web of Science, Scopus, Cochrane Library, and ProQuest, was conducted up to October 2023, following a predefined strategy. The search utilized the following keywords: “Ankle [Mesh]”, “Ankle Joint [Mesh]”, “Lateral Ligament, Ankle [Mesh]”, “Sprains and Strains [Mesh]”, “Ankle Injuries [Mesh]”, and “Risk Evaluation and Mitigation [Mesh]” (Appendix A). The search, executed by one author, utilized the Rayyan web tool for managing the identified records [20]. Our metrics for assessing the impact of risk factors on ASI were effect size, mean difference, and odds ratio.
Study selection
Titles and abstracts were initially screened by two authors for relevance, followed by the removal of duplicates and the retrieval of full-text articles for in-depth evaluation against our inclusion criteria. Discrepancies were resolved through discussion or consultation with a third author. Reference lists of selected articles were also reviewed to identify additional relevant studies.
Data extraction and quality assessment
Following study selection, two researchers independently conducted data extraction using a standardized form, capturing details such as study design, participant characteristics, interventions, risk factors, and outcomes. The Joanna Briggs Institute (JBI) critical appraisal tool was employed to assess the risk of bias across four domains: patient selection, index tests, reference standards, and flow and timing [21]. Quality assessment of RCT studies was conducted based on Cochrane tools. Any disagreements were resolved via discussion with a third author.
Evidence synthesis
To synthesize the evidence, we employed textual descriptions, tabulation, and data standardization using Stata software (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX, USA: StataCorp LLC), ensuring a comprehensive analysis. Missing data were addressed using Wan’s statistical method, which considers sample size, median, and variability (range and interquartile range) to estimate missing values accurately [22].
Subgroup analysis was conducted to explore clinical heterogeneity and enhance the understanding of the data. The synthesis of the findings was presented as odds ratios (ORs) and risk ratios (RRs) with 95% confidence intervals (CIs) for dichotomous outcomes and mean difference (MD) for continuous outcomes. To ensure the reliability of our results, we used data from the longest follow-up period for each specific outcome in the included study code.

3. Results

Based on our search strategy, 1811 unique titles were obtained from four electronic databases. Further application of the inclusion and exclusion criteria narrowed the selection down to 26 studies for detailed data extraction (Figure 1). The details of each of the papers are shown in Table 1.
Figure 1. PRISMA flow diagram of risk factors of ankle sprain in soccer players.
Table 1. Risk factors of ankle sprain extracted from each paper.
Generalized joint hypermobility
Among the risk factors analyzed, generalized joint hypermobility was investigated in two studies [31,41]. One study [41] found no significant difference in ankle sprain incidence related to joint hypermobility, while the other [31] suggested joint laxity could potentially increase the risk of ankle sprain (odds ratio [OR] = 3.38 [0.82–14.00]; p = 0.093), indicating a possible but uncertain risk factor.
Age
Age was evaluated in six articles [25,29,31,34,35,38] as a potential risk factor for ASI. Only one study [25] identified age as a significant risk factor (p < 0.001). Overall, age did not significantly influence ASI risk (mean difference [MD] = 0.36; 95% CI = −0.27, 0.99), as illustrated in Figure 2a (mean difference), Figure 2b (funnel plot), and Figure 2c (Galbraith plot).
Figure 2. (a) Forest plot for age as a risk factor of ankle sprain in soccer players. (b) Funnel plot for age as a risk factor of ankle sprain in soccer players. (c) Galbraith plot for age as a risk factor of ankle sprain in soccer players [25,29,31,34,35,38].
BMI
Seven studies in total [25,29,31,34,35,37,38] examined BMI’s role, detailed in Figure 3a (forest plot), Figure 3b (funnel plot), and Figure 3c (Galbraith plot). Three studies [25,31,38] highlighted BMI as a risk factor for ASI. Specifically, De Ridder et al. [25] reported the most significant difference in BMI between injured and uninjured groups, with injured players having higher BMIs (19.8 ± 1.9 vs. 17.6 ± 1.9; p < 0.001).
Figure 3. (a) Forest plot for BMI as a risk factor for ankle sprain in soccer players. (b) Funnel plot for BMI as a risk factor for ankle sprain in soccer players. (c) Galbraith plot for BMI as a risk factor for ankle sprain in soccer players [25,29,31,34,35,37,38].
Weight
No significant correlation between player weight and ASI was found across studies [25,29,31,34,37,38]. The overall MD in weight between injured and uninjured groups was 1.4 kg [−0.71, 3.51], as shown in Figure 4a–c.
Figure 4. (a) Forest plot for weight as a risk factor of ankle sprain in soccer players. (b) Funnel plot for weight as a risk factor of ankle sprain in soccer players. (c) Galbraith plot for weight as a risk factor of ankle sprain in soccer players [25,29,31,34,37,38].
Height
Height was considered a risk factor in one study by McCann et al. [37], which found taller players to be more prone to ASI (p = 0.01). However, this finding was not corroborated by other studies [29,31,34,35,38], as depicted in Figure 5a–c.
Figure 5. (a) Forest plot for height as a risk factor of ankle sprain in soccer players. (b) Funnel plot for height as a risk factor of ankle sprain in soccer players. (c) Galbraith plot for height as a risk factor of ankle sprain in soccer players [29,31,34,35,38].
Sex
Sex did not emerge as a risk factor in any of the four articles [28,33,35,36] that explored this aspect, with relative risk figures (male to female RR = −0.41 [−0.85, 0.04]) indicating no significant difference, as shown in Figure 6a–c.
Figure 6. (a) Forest plot for gender as a risk factor of ankle sprain in soccer players. (b) Funnel plot for gender as a risk factor of ankle sprain in soccer players. (c) Galbraith plot for gender as a risk factor of ankle sprain in soccer players [28,33,35,36].
Turf
The role of the type of turf as a risk factor for ankle sprain was explored in six studies [23,27,39,40,46,47]. Notably, Ekstrand J. et al. [47] conducted a study between 2003 and 2005 to compare the incidence of ankle sprain on artificial turf to natural grass. In that study, cohort 1, consisting of 10 teams with artificial turf at their home facilities, showed an incidence of 4.83 ankle sprains per 1000 h of match exposure on artificial turf, compared to 2.66 on natural grass (RR = 1.81 [1–3.28]; p < 0.05), indicating a significant increase in risk; however, no significant difference was observed during training sessions. Conversely, in cohort 2, comprising nine teams with natural grass at their home facilities, no significant inter-cohort differences were noted, both for training (RR = 1.26 [0.58–2.73]) and matches (RR = 0.99 [0.49–2.01]). Subsequent analysis by Ekstrand J. et al. [27] extended to 2008 confirmed a significant difference in the incidence of ankle sprain between artificial turf and natural grass in men’s soccer (RR = 1.6 [1.02–2.49]; p < 0.05), but no discernible difference in women’s soccer was found. While Bjørneboe J. et al. [40] observed a trend towards an increased risk of ankle sprain on artificial turf during matches, this was not statistically significant. Kristenson et al., Aoki H. et al., and Soligard T. et al. [23,39,46] found no significant differences in the incidence of ankle sprains between artificial and natural turf.
Previous ankle sprain
Previous ASI was a focus of nine studies [4,18,24,26,29,30,33,34,37,38], with five articles [4,24,26,29,37] concluding that a history of ASI significantly increases the risk of future ASI, highlighted by Hagglund M. et al. with a hazard ratio of 2.8 (CI = 0.8–9.6; p = 0.099), as presented in Figure 7a–c.
Figure 7. (a) Forest plot for previous ankle sprain as a risk factor of ankle sprain in soccer players. (b) Funnel plot for previous ankle sprain as a risk factor of ankle sprain in soccer players. (c) Galbraith plot for previous ankle sprain as a risk factor of ankle sprain in soccer players [4,18,24,26,29,30,33,34,37,38].
Other Risk Factors
In addition to the risk factors previously mentioned, our study revealed several other factors being evaluated for their impact on ankle sprains among soccer players (details are discussed in Table 2).
Table 2. Details of the other risk factors of ankle sprain in soccer players.
Match congestion: Carling C. et al. [42] explored match congestion as a potential risk factor, revealing a higher incidence of ankle sprains in the final match of two- and three-match congestion cycles compared to matches outside these congested periods.
Hip strength and muscle force: De Ridder et al. [25] focused on hip strength as an intrinsic risk factor for lateral ankle sprains, finding that stronger posterior chain hip muscles significantly reduced the risk. Conversely, Kawaguchi et al. [34] noted a significant difference in hip abductor muscle forces between injured and uninjured players, indicating the protective role of hip muscle strength against ankle sprains. However, they found no significant differences in knee extension, knee flexion force, muscle flexibility, or the height of the navicular tubercle between groups.
Eccentric isokinetic strength asymmetries and GRF: Fousekis K. et al. [31] identified eccentric isokinetic strength asymmetries in ankle dorsal and plantar flexors as a significant predictor of ankle sprains. Similarly, Fransz D. et al. [43] found that ground reaction force (GRF) in specific directions could significantly predict ankle sprains, highlighting the importance of biomechanical factors in injury risk.
Balance tests: Engebretsen et al. [45] did not find significant differences in balance test scores between injured and uninjured groups. However, Henry T. et al. [18] observed that poorer lower limb relative balance scores increased the risk of non-contact ankle injuries among amateur soccer players. Jupil Ko et al. [35] reported significant differences in Star Excursion Balance Test (SEBT) and Single-Leg Hop Test (SLHT) scores between injured and uninjured groups, suggesting that balance performance could influence ankle sprain risk. Kawaguchi et al. [34] found no difference between the injured and uninjured groups based on their balance measurement method.
Dominant leg and soccer skill level: Faude O. et al. [30] and Kofotolis et al. [4] investigated the role of limb dominance, finding that dominant legs were more prone to ankle sprains. Moreover, skill level was examined as a risk factor, with Ekstrand J. et al. [44] showing that players in higher divisions faced a greater risk of ankle injury. However, Engebretsen et al. [45] and Henry T. et al. [18] found no significant difference in ankle sprain incidence based on soccer skill level or playing experience. Longer soccer experience (years) was also found to be a risk factor for ankle sprain in a study by De Ridder et al. [25].
Intrinsic factors: Engebretsen A. et al. [29] and Kawaguchi et al. [34] looked into various intrinsic factors such as foot type, standing rearfoot alignment, hallux position, anterior drawer, range of motion, and ankle dorsiflexion range of motion. None of these factors showed a significant difference in the incidence of ankle sprains, indicating the complexity of accurately predicting ankle sprain risk based on intrinsic anatomical and physiological characteristics. Henry T. et al. [18] showed that poorer lower limb relative power output on vertical jump (W/Kg) was an independent risk factor for ankle sprain.
Playing Position: The impact of playing position on ankle sprain incidence was examined, revealing mixed results. Engebretsen et al. [29] found no significant difference in sprain rates across positions, suggesting a uniform risk. In contrast, Kofotolis et al. [4] reported that defenders had a higher injury rate than forwards and midfielders, indicating position-specific risks.
Each risk factor’s details and results are outlined in Table 2, while Table A1 assesses the included studies’ quality and Table A2 summarizes the meta-analysis outcomes according to the GRADE criteria.

4. Discussion

Our meta-analysis identified a history of ankle sprain as the most significant risk factor for future ankle sprain injuries (ASIs) and also has the most significant clinician implication, with body mass index (BMI) also emerging as an important contributing factor. These findings highlight the need for clinicians to closely evaluate soccer players with a history of ankle sprains or elevated BMI, as they may be at increased risk of future ASIs. Furthermore, given the prominent role of prior ankle sprains as a risk factor, further research is warranted to optimize the timing, methods, and effectiveness of rehabilitation strategies.
Previous ankle sprain
The recurrence of ankle sprains was identified as a substantial risk factor, with studies [24,29,37] underscoring its impact. However, Hägglund et al. [32] and Henry et al. [18] did not observe previous ankle injuries as significant, suggesting that factors such as youthfulness of the sample, competitive level, or playing conditions might play a role. Contrarily, Brinkman et al. systematic review [48] highlighted a prior ankle sprain’s role in increasing injury risk due to scar tissue formation. Moreover, it can also result in a decreased range of motion or weakened strength, indirectly impacting the likelihood of future injuries [49]. Given these findings, implementing evidence-based rehabilitation approaches is crucial to prevent recurrence and mitigate the long-term effects of prior injuries. This represents a key clinical implication of our systematic review and meta-analysis. Recent studies [50] emphasized the importance of phased rehabilitation strategies, including balance training, neuromuscular exercises, and sport-specific drills tailored to the recovery timeline. Implementing these approaches in clinical practice could significantly reduce the risk of recurrent ankle sprains and improve patient outcomes.
Turf
Turf quality was considered a risk factor in two studies [27,47], but Kristenson et al. [46] and Bjørneboe et al. [40] did not find a significant difference; they claimed that a lack of increase in the rate of ankle sprain could be interpreted as a continuous improvement in the quality of artificial turf playing surfaces used in football. On the other hand, Williams et al.’s [51] analysis revealed that there was evidence suggesting an elevated risk of ankle injuries when playing on artificial turf in 8 out of 14 cohorts, with incidence rate ratios ranging from 0.71 to 5.20. However, it is important to note that none of the likelihood categories reached values exceeding 95% (indicating very likely harm). Notably, they found evidence of a harmful effect associated with ankle injuries incurred during soccer matches and training on artificial turf, specifically among elite male players [27,40,47], young female players, [52], and collegiate male players during matches [53]. Conversely, a beneficial effect was inferred for soccer matches involving youths [39] and collegiate females (unlike the trivial effect during training) [53]. Artificial turf, from a biomechanical standpoint, has higher frictional coefficients compared to natural grass, leading to increased rates of foot and ankle injuries [54].
External ankle support
The utilization of external ankle supports plays a crucial role in preventing ankle sprains. It reduces ankle mobility, thereby potentially decreasing injury risk, but it does not impair performance in sprint speed, agility run tasks, or kicking accuracy [55,56,57,58,59,60,61,62,63]. Its efficacy is evident across different athletic groups, including male and female soccer players [64,65,66,67], professional female basketball players [68], and ballet dancers [69], especially among those with a history of ankle sprains. However, the findings from Bailey et al. [70] and Briem et al. [71] highlight that kinesiology tape may not provide the same protective benefits to healthy soccer players, pointing to the importance of selecting the appropriate type of ankle support based on the athlete’s specific needs and injury history. However, methodological differences and the utilization of different devices make direct comparisons difficult. Our interpretation is that external ankle devices can avoid the occurrence and reoccurrence of ankle sprain by providing mechanical support and increasing proprioception at the ankle and can enhance muscle response of the fibularis longus by maintaining greater levels of muscle activation, leading to a decrease in the risk of ankle sprains [62,63,67,72,73,74]. Recent reviews emphasize that the effectiveness of external ankle supports is linked to mechanical stabilization, proprioceptive enhancement, and improved activation of the fibularis longus muscle, which provides lateral stability to the ankle. Activating this muscle helps counteract inversion forces and prevent sprains, especially in individuals with chronic ankle instability [75,76,77].
Anthropometric measurements
Even though anthropometric characteristics did not show conclusive results, some studies performed on athletes found a relationship between BMI and the history of ankle sprain. Research conducted on football players investigating the risk factors associated with ankle sprains revealed that a high BMI and a history of previous ankle sprains, when occurring together, can significantly increase the likelihood of experiencing an ankle sprain. McHugh et al. [36] identified a history of a previous ankle sprain and a high BMI in male athletes as the only risk factors. In support of the current findings, a study conducted by Tyler et al. [78], which also investigated the correlation between past injuries and high BMI as potential risk factors among football players, indicated that players with a history of ankle sprains who were also overweight had a significantly higher injury rate, specifically 19 times greater compared to players with no prior ankle sprain and those with normal weight. Importantly, the impact of a previous ankle sprain on injury occurrence was more substantial than the effect of high BMI alone. Specifically, the injury incidence was 6.6 times higher in players with a history of ankle sprains and 3.9 times higher in those classified as overweight individuals. In a meta-analysis performed by Mason et al. [79], previous ankle sprain injury (odds ratio = 2.74, p < 0.001), higher body mass index (SMD = 0.50, p < 0.001), and higher weight (SMD = 0.24, p = 0.02) were identified as risk factors in male athletes. However, body characteristics did not show a significant effect on female athlete injuries. The reason that anthropometric measurements were not recognized as a risk factor was the small difference between the body mass index of the players studied in the included articles.
Age
We did not find age to be a strong risk factor for ankle sprain. In contrast to our findings, Faude et al. [17], discovered that younger athletes suffered from more fractures, fewer strains and sprains [80,81]. However, similar to our results, Willems et al. [82] and Powers et al. [38] did not find a significant difference. The lack of significance in our findings could be due to the sample size and the particular group that was studied.
Limitations
Our study has several limitations, but the main limitation is that the data reporting varied between the articles that were included. For example, for some studies, injuries were reported per hour of playing time and some injuries were reported from the number of the matches that were played. Also, in the previous ankle sprain category, some studies reported the number of feet that were injured and other studies reported the injuries by the number of players. Another limitation that can be noted is the high heterogeneity of some of the studies.

5. Conclusions

This study enhances our knowledge of preventing ankle sprains in soccer players. The research indicates that factors like turf quality and a history of ankle sprains are key considerations for experiencing ankle sprains. However, due to significant variations in study methodologies, additional research is necessary to identify the most effective strategies for reducing ankle sprain occurrences and relative risks in this demographic of soccer players. This study serves as a valuable resource for physicians and sports experts, aiding them in decision-making concerning warm-up protocols, preventive measures, and the assessment of playing surface quality for effective ankle injury prevention.

Author Contributions

A.H.H., A.N.-A., B.F. and C.J.M.: Study Design and Critical Revision. A.R.M., M.M.M.N., S.P.T., S.E. and M.B.C.: Primary Drafting and Analysis. All authors have read and agreed to the published version of the manuscript.

Funding

No external funding was received for this paper.

Data Availability Statement

Available based upon reasonable request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Search Protocol
Databases
  • MEDLINE/PubMed
  • Scopus
  • CENTRAL
  • Web of Science
  • ProQuest
Restrictions
No language, filter, or date restriction
Search strategy
#1 Ankle [Mesh]
#2 Ankle Joint [Mesh]
#3 Lateral Ligament, Ankle [Mesh]
#4 Syndesmos*
#5 External Lateral Ligament
#6 #1 OR #2 OR #3 OR #4 OR #5
#7 Sprains and Strains [Mesh]
#8 sprain*
#9 strain*
#10 rupture*
#11 Instabilit*
#12 unstable
#13 function*
#14 #7 OR #8 OR #9 OR #10 OR #11 OR #12 OR #13
#15 #6 AND #14
#16 Ankle Injuries [Mesh]
#17 #15 OR #16
#18 Risk Factors [Mesh]
#19 Risk Evaluation and Mitigation [Mesh]
#20 Risk*
#21 Relative risk
#22 Incidence*
#23 Epidemiolog*
#24 Survey
#25 Patterns
#26 Prevalence*
#27 #18 OR #19 OR #20 OR #21 OR #22 OR #23 OR #24 OR #25 OR #26
#28 Soccer [Mesh]
#29 Football [Mesh]
#30 Athlet*
#31 Sport*
#32 #28 OR #29 OR #30 OR #31
#33 #17 AND #27 AND #32
Specific issues:
[Mesh] only can be used in PubMed, Central.
Central
[((([mh Ankle] OR [mh “Ankle Joint”] OR [mh “Lateral Ligament, Ankle”] OR “Ankle Syndesmos*” OR “External Lateral Ligament”) AND ([mh Sprains and Strains] OR sprain* OR strain* OR rupture OR Instabilit* OR unstable OR function*)) OR ([mh“Ankle Injuries”])) AND ([mh“Risk Factors”] OR [mh Risk Evaluation and Mitigation] OR Risk* OR Relative risk OR Incidence* OR Epidemiolog* OR Survey OR Pattern* OR Prevalence*) AND ([mh Football] OR [mh soccer] OR Sport* OR Athlet*)]:ti,ab,kw
MEDLINE (through PUBMED)
(((Ankle [mh] OR “Ankle Joint” [mh] OR “Lateral Ligament, Ankle” [mh] OR “Ankle Syndesmos*” OR “External Lateral Ligament”) AND (Sprains and Strains [Mesh] OR sprain* OR strain* OR rupture OR Instabilit* OR unstable OR function*)) OR (“Ankle Injuries” [mh])) AND (“Risk Factors” [mh] OR Risk Evaluation and Mitigation [Mesh] OR Risk* OR Relative risk OR Incidence* OR Epidemiolog* OR Survey OR Pattern* OR Prevalence*) AND (Football [Mesh] OR Soccer [Mesh] OR Sport* OR Athlet*)
Web of Science
(((TS=(((Ankle OR “Ankle Joint” OR “Lateral Ligament, Ankle” OR “Ankle Syndesmos*” OR “External Lateral Ligament”) AND (Sprains and Strains OR sprain* OR strain* OR rupture OR Instabilit* OR unstable OR function*)) OR (“Ankle Injuries”)) AND (“Risk Factors” OR Risk Evaluation and Mitigation OR Risk* OR Relative risk OR Incidence* OR Epidemiolog* OR Survey OR Pattern* OR Prevalence*) AND (Football OR Soccer OR Sport* OR Athlet*)))
Scopus
(((Ankle OR “Ankle Joint” OR “Lateral Ligament, Ankle” OR “Ankle Syndesmos”* OR “External Lateral Ligament”) AND (Sprains and Strains [Mesh] OR sprain* OR strain* OR rupture OR Instabilit* OR unstable OR function*)) OR (“Ankle Injuries”)) AND (“Risk Factors” OR Risk Evaluation and Mitigation OR Risk* OR Relative risk OR Incidence* OR Epidemiolog* OR Survey OR Pattern* OR Prevalence*) AND (Football OR Soccer OR Sport* OR Athlet*)

Appendix B

Table A1. Quality assessment of cohort studies based on the JBI tool.
Table A1. Quality assessment of cohort studies based on the JBI tool.
AuthorYearStudy DesignQ1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11
Aoki, H [23]2010prospective cohortyesyesyesunclearyesyesyesyes (1 year)unclearunclearyes
Arni Arnason [24]2004prospective cohortyesyesyesyesyesyesyesyes (4 months)yesyesyes
Carling, C [42]2015prospective cohortyesyesyesnonoyesyesyes (6 seasons)unclearnoyes
De Ridder, R [25]2016prospective cohortyesyesyesyesyesyesyesyes (3 seasons)yesyesyes
Soligard T [39]2012prospective cohortyesyesunclearunclearnot applicableyesunclearyes (3 seasons)yesunclearyes
Ekstr, J [26]1983prospective cohortunclearyesyesnonoyesyesyes (12 months)unclearnoyes
Ekstr, J [27]2011prospective cohortyesyesyesyesyesyesyesyes (February 2003 to October 2008)noyesyes
Ekstr, J [47]2006prospective cohortyesyesyesyesyesyesyesyes (2003–2004 season)noyesyes
Ekstr, J [47]1990prospective cohortyesyesyesnonoyesyesyes (12 months)unclearnoyes
Emery, C [28]2005prospective cohortyesyesyesunclearnoyesyesyes (13 weeks)yesunclearyes
Engebretsen, A. H [29]2010prospective cohortyesyesyesyesyesyesyesyesyesyesyes
Faude, O [30]2006prospective cohortyesyesyesyesunclearyesyesyes (10 months)yesyesyes
Fousekis, K [31]2012prospective cohortyesyesyesyesyesyesyesyes (10 months)yesyesyes
Fransz, D [43]2018prospective cohortyesyesyesyesyesyesyesyes yesyesyes
Hägglund, M [32]2006prospective cohortyesyesyesyesyesyesyesyes (2 seasons: 2001–2002)yesyesyes
Henry, T [18]2016prospective cohortyesyesyesyesyesyesyesyes (2 seasons: 2008–2009)unclearnoyes
Kristenson [46] 2013prospective cohortnoyesunclearunclearnot applicableyesyes2010–2011yesnoyes
Bjørneboe J [40]2010prospective cohortnoyesyesunclearnot applicableyesyesyes 2004–2007yesunclearyes
McHugh [36]2006prospective cohortyesyesyesyesyesyesyes2 yearsyesnot applicableyes
McCann [37]2018prospective cohortyesyesyesyesunclearyesyesunclearyesnot applicableyes
Christopher M [38]2017prospective cohortyesyesyesyesunclearyesyes2 yearsyesnot applicableyes
Kawaguchi [34]2021prospective cohortyesyesyesyesunclearyesyes2019 seasonyesnot applicableyes
Kofotolis [4]2006prospective cohortyesyesyesyesunclearyesyes2 yearsyesnot applicableyes
Vieira [41]2012prospective cohortyesyesunclearyesunclearyesunclear2009 seasonyesnot applicableunclear
Jupil ko [35]2018prospective pilot studyyesyesyesyesunclearyesyes2014/2015yesnot applicableyes
Table A2. Quality assessment of RCT studies based on Cochrane tools.
Table A2. Quality assessment of RCT studies based on Cochrane tools.
AuthorRandom Sequence GenerationAllocation ConcealmentBlinding of Participants and PersonnelBlinding of Outcome AssessmentIncomplete Outcome DataSelective ReportingOther Bias
Emery C 2010 [33]yesyesyesyesNoprobably not detected

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