Literature Decoded

Living reprintoriginally published in Sports Medicine 2022CC BY 4.0Read the version of record Opt out

Literature DecodedLiving reprint
Living reprint · journal article Journal version available

Non-contact Anterior Cruciate Ligament Injury Epidemiology in Team-Ball Sports: A Systematic Review with Meta-analysis by Sex, Age, Sport, Participation Level, and Exposure Type

Systematic review and meta-analysis first published in Sports Medicine (2022), reprinted in full under its CC BY 4.0 licence.

Lionel Chia1*ORCID iD 0000-0002-3164-8809, Danilo De Oliveira Silva2, Matthew Whalan3, Marnee J. McKay4, Justin Sullivan4, Colin W. Fuller5, Evangelos Pappas6

1grid.1013.30000 0004 1936 834XFaculty of Medicine and Health, Sydney School of Health Sciences, Discipline of Physiotherapy, The University of Sydney, Sydney, NSW Australia; Cleveland Guardians Baseball Company, Cleveland, OH USA2grid.1018.80000 0001 2342 0938La Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, VIC, Australia3Research and Development Department, Football Australia, Sydney, NSW Australia; grid.1007.60000 0004 0486 528XCentre of Medical and Exercise Physiology, School of Medical, Indigenous & Health Sciences, University of Wollongong, Wollongong, NSW Australia4grid.1013.30000 0004 1936 834XFaculty of Medicine and Health, Sydney School of Health Sciences, Discipline of Physiotherapy, The University of Sydney, Sydney, NSW Australia5Colin Fuller Consultancy Ltd, Sutton Bonington, UK6grid.1013.30000 0004 1936 834XFaculty of Medicine and Health, Sydney School of Health Sciences, Discipline of Physiotherapy, The University of Sydney, Sydney, NSW Australia; grid.1007.60000 0004 0486 528XSchool of Medicine and Illawarra Health and Medical Research Institute, The University of Wollongong, Wollongong, NSW Australia*Corresponding author

Reprinted 2026-10-01 29 min read Living reprint · journal article Version of record: Sports Medicine 2022Licence: CC BY 4.0

Reading mode
In plain languageSystematic review and meta-analysis first published in Sports Medicine (2022), reprinted in full under its CC BY 4.0 licence.

Systematic review and meta-analysis first published in Sports Medicine (2022), reprinted in full under its CC BY 4.0 licence.

No plain-language summary has been written for this reprint yet. The authors' abstract and full text follow, unchanged apart from layout.

Educational summary of research findings; not medical advice. Discuss care decisions with a qualified clinician.

Bottom line

What the evidence supports

How to read it

No plain-language summary has been written for this reprint yet. The authors' abstract and full text follow, unchanged apart from layout.

Limitations

The findings apply to the included study populations and may not generalise to every person or setting.

Disclaimer

Educational summary of research findings; not medical advice. Discuss care decisions with a qualified clinician.

The paper

Full manuscript

Abstract

Background

Not all anterior cruciate ligament (ACL) injuries are preventable. While some ACL injuries are unavoidable such as those resulting from a tackle, others that occur in non-contact situations like twisting and turning in the absence of external contact might be more preventable. Because ACL injuries commonly occur in team ball-sports that involve jumping, landing and cutting manoeuvres, accurate information about the epidemiology of non-contact ACL injuries in these sports is needed to quantify their extent and burden to guide resource allocation for risk-reduction efforts.

Objective

To synthesize the evidence on the incidence and proportion of non-contact to total ACL injuries by sex, age, sport, participation level and exposure type in team ball-sports.

Methods

Six databases (MEDLINE, EMBASE, Web of Science, CINAHL, Scopus and SPORTDiscus) were searched from inception to July 2021. Cohort studies of team ball-sports reporting number of knee injuries as a function of exposure and injury mechanism were included.

Results

Forty-five studies covering 13 team ball-sports were included. The overall proportion of non-contact to total ACL injuries was 55% (95% CI 48–62, I2 = 82%; females: 63%, 95% CI 53–71, I2 = 84%; males: 50%, 95% CI 42–58, I2 = 86%). The overall incidence of non-contact ACL injuries was 0.07 per 1000 player-hours (95% CI 0.05–0.10, I2 = 77%), and 0.05 per 1000 player-exposures (95% CI 0.03–0.07, I2 = 97%). Injury incidence was higher in female athletes (0.14 per 1000 player-hours, 95% CI 0.10–0.19, I2 = 40%) than male athletes (0.05 per 1000 player-hours, 95% CI 0.03–0.07, I2 = 48%), and this difference was significant. Injury incidence during competition was higher (0.48 per 1000 player-hours, 95% CI 0.32–0.72, I2 = 77%; 0.32 per 1000 player-exposures, 95% CI 0.15–0.70, I2 = 96%) than during training (0.04 per 1000 player-hours, 95% CI 0.02–0.07, I2 = 63%; 0.02 per 1000 player-exposures, 95% CI 0.01–0.05, I2 = 86%) and these differences were significant. Heterogeneity across studies was generally high.

Conclusion

This study quantifies several key epidemiological findings for ACL injuries in team ball-sports. Non-contact ACL injuries represented over half of all ACL injuries sustained. The proportion of non-contact to total ACL injuries and injury incidence were higher in female than in male athletes. Injuries mostly occurred in competition settings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40279-022-01697-w.

Key Points

Table 1.
The overall proportion of non-contact to total ACL injuries in team ball-sports was 55%.
Injury incidence of non-contact ACL injuries in team ball-sports was higher in female athletes than in male athletes.
Injury incidence of non-contact ACL injuries in team ball-sports during competition was higher than during training.

Introduction

Anterior cruciate ligament (ACL) injuries commonly occur in team ball-sports1–3 but we do not know how many of these injuries are preventable. ACL injuries that result from contact situations like a tackle are sometimes unavoidable4 compared to those that occur in non-contact situations like twisting and turning in the absence of external contact5. Exercise-based injury risk reduction programs (IRRPs) are a prominent feature in ACL injury risk-reduction efforts6 and these programs seem to have a stronger effect on reducing the risk of non-contact ACL injuries (odds ratio (OR) 0.39) compared with contact ones (OR 0.61)5. Syntheses of information about the epidemiology of non-contact ACL injuries are currently unavailable and this information is important for guiding ACL risk-reduction efforts7, 8. Prior epidemiological reviews on ACL injury incidence did not consider injury mechanism1, 9, combined different exposure types (player-hours converted to player-exposures)10, 11, did not utilize meta-regression analyses to investigate sources of heterogeneity and the association of categorical variables like sex and sport, and did not investigate the proportion of non-contact to total ACL injuries.

We need to better understand the extent of non-contact ACL injuries because they impose a wide-ranging personal, societal and economic burden12–14. ACL injuries are associated with, for example, a sevenfold increase in odds of end-stage osteoarthritis resulting in total knee arthroplasties12, more than US$90,000 per injury to gain a quality-adjusted life-year15, and psychological barriers that may affect recovery, return to sport and an increased risk of sustaining a subsequent injury16. Therefore, we undertook a comprehensive systematic review, meta-analysis and meta-regression to estimate the proportion of non-contact ACL to total ACL injuries, and describe the incidence of non-contact ACL injuries by unit of exposure, sex, age group, sport, participation level, and exposure type in team ball-sports.

Methods

This review is on ACL injuries only and it forms part of a larger systematic review on the epidemiology of non-contact knee injuries sustained in team ball-sports. Future publications will focus on other non-contact knee injuries like gradual-onset knee injuries. The review was prepared and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement17, and was prospectively registered with the PROSPERO International Prospective Register of Systematic Reviews (CRD42020179475). We were informed in an automated PROSPERO message that due to their focus on COVID-19-related systematic review registrations at the time of registration, this submission was automatically published and not checked for eligibility. Patients and public partners were not involved in the design, conduct or interpretation of this systematic review.

Search Strategy and Selection Criteria

Six electronic databases (MEDLINE, EMBASE, Web of Science, CINAHL, Scopus and SPORTDiscus) were systematically searched from inception to July 2021. Search terms consisted of controlled vocabulary and free text, and were mapped to medical subject where possible to capture records of knee injury (e.g., “anterior cruciate ligament rupture”, “patellofemoral pain”, “meniscus tears”) epidemiology (e.g., “prevalence”, “incidence”, “exposure”) in team ball-sports (e.g., “soccer”, “football”, “rugby”, “basketball”). The MEDLINE search strategy is provided in Appendix A1. All records were downloaded to EndNote X8 (Thomson Reuters, USA) where duplicates were removed, then uploaded to Covidence (Covidence systematic review software, Veritas Health Innovation, Melbourne, VIC, Australia; http://www.covidence.org). Bibliographic hand-searches were also performed to supplement the electronic database search.

Studies were included if: (i) the number of ACL injuries as a function of injury mechanism were reported; (ii) they were prospective cohort studies or retrospective cohort studies examining routinely collected data (e.g., league-wide injury surveillance databases and insurance databases); (iii) they featured athletes from field and court-based team ball-sports (Association Football or soccer, futsal, football or American Football, rugby union, rugby league, Gaelic football, Australian football, basketball, netball, handball, volleyball, field hockey, floorball, lacrosse, hurling, baseball, softball, and cricket) because ACL injury mechanisms on these surfaces were more comparable4, 18, 19; (iv) they reported exposure data in terms of athlete-hours, athlete-exposures, or per-event data. Examples of per-event exposure data are the number of tackles in rugby union or number of jumps in volleyball. If a study reported on knee injuries and injury mechanisms separately, but not knee injury as a function of mechanism, study authors were contacted via email to request more information. We excluded studies if more detailed data were not available or authors did not provide the information following two email contact attempts. Studies were also excluded if: (i) the data had been published in earlier papers, such as cases of secondary analysis of routinely collected data; in such situations, the paper that reported the most exposure data was included; (ii) they investigated non-organised or non-competitive sport, such as school-based recreation or physical education classes; (iii) the data recorded were not sport-specific (e.g., hospital emergency department admission records); (iv) they investigated post-surgical populations or re-injury outcomes; (v) they featured athletes competing on ice, sand, in water, or on horseback. Studies were not excluded based on their definition of non-contact injury mechanisms, or lack thereof. Two authors (LC and DS) independently applied selection criteria to screen studies by titles and abstracts, followed by full texts to identify eligible studies. Disagreements were settled through discussion and consensus, and a third author (EP) acted as a tie-breaker if needed.

Quality Assessment

Two authors (LC and MW) independently assessed study quality using a modified six-item Newcastle–Ottawa scale for cohort studies where one star was awarded for each item for a maximum of six stars20. A similar scale was previously used in a systematic review of acute hamstring injuries21. The six items were: (a) population description, (b) population recruitment, (c) surveillance methods, (d) duration of observation, (e) case definition, and (f) others (all other methods) (Appendix A2). Disagreement between assessors was settled through discussion and consensus, with a third author (EP) acting as a tie-breaker if needed. In accordance with previously published systematic reviews and meta-analyses of injury incidence22, 23, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to assess certainty of evidence was not used because this review is not a clinical practice guideline and does not make clinical recommendations24.

Data Extraction and Management

Publication information (authors, year), population characteristics (cohort size, age, participation level, sport, sex), exposure type (training, competition, composite), number of injuries, exposure, unit of exposure, surveillance information (definition of injury, how injuries were recorded, duration of surveillance), and severity of injuries were extracted and recorded on a customised spreadsheet by one author (LC), and double-checked by another author (DS). Disagreements were resolved by a third author (MW). Sample populations were classified into three age groups: children (≤ 12 years), adolescents (13–18 years), and adults (≥ 19 years). Participation level was classified into three categories: amateur (including recreational, high school and intramural athletes), intermediate (including collegiate and semi-professional athletes), and elite (including professional and national-level athletes)9. If not explicitly reported, incidences (per 1000 exposure units) were calculated from the available raw data using the following formula:

Incidence = (Sum of new knee injuries over a specified time)/(Sum of exposure units for all included samples) × 1000.

Statistical Analysis

All meta-analyses were performed in R (V. 3.6.1 and later, the R Foundation for Statistical Computing) using the meta (metarate, metaprop, metareg, and forest.meta functions) and tidyverse packages. Meta-analyses of incidence were carried out using a random effects Poisson regression model (unconditional model – random study effects) to produce forest plots with 95% confidence intervals (CIs)25, 26. The Poisson regression model was selected because of the binary and frequentist nature of the incidence data, corresponding to similar, previously employed methods22, 27. Statistical heterogeneity was assessed using the I2 statistic where I2 < 50% was considered as not important, 50–75% as moderate, and > 75% as high heterogeneity28. Between-study variance was estimated using the maximum-likelihood method. Meta-analyses were only performed when there were three or more included studies. Meta-analyses of proportions were performed using the Freeman-Tukey Double arcsine transformation29, 30. Confidence intervals for individual studies were calculated using the Clopper-Pearson interval, and estimations of between-study variance were performed using the DerSimonian-Laird method. The α level for all meta-analyses was set at 0.05. Funnel plots were used to assess publication bias in studies included in meta-analyses of overall proportion of non-contact to total ACL injuries and overall incidence of non-contact ACL injuries. Like previous systematic reviews31, 32, we conducted subgroup and meta-regression analyses to investigate sources of heterogeneity and the association of the following categorical variables with proportion and incidence of non-contact ACL injuries: exposure type unit, sex, sport, age group, participation level, and exposure type (competition vs. training). For all analyses except meta-analyses of incidence by exposure type, we only synthesised studies when both training and competition data together were available. Additional sub-group analyses were attempted where possible.

Results

The online database and bibliographic hand search yielded 8,015 non-duplicate studies that were screened by title and abstract: 708 potentially eligible studies were identified. Following full-text review of the 708 studies, 45 met the inclusion criteria and were included in this review (Fig. 1)33–77. Two studies shared the same dataset43, 44, and therefore only 44 studies are reflected in Tables 1 and 2.

Fig. 1
Fig. 1. Flow chart of the study selection process
Table 1. Characteristics of the included studies
StudyYearSport(s)LevelStudy durationSexNo. of athletesAgeNo. of non-contact ACL injuriesIncidenceaUnitProportion (%)Non-contact injury definition
Agel et al.472005Basketball, soccerI13M/F-ADSoccer (M): 66
Soccer (F): 161
Basketball (M): 78
Basketball (F): 305		Soccer (M): 0.04
Soccer (F): 0.13
Basketball (M): 0.04
Basketball (F): 0.17		pexSoccer (M): 34
Soccer (F): 41
Basketball (M): 46
Basketball (F): 59		No apparent contact, contact with the ball, or contact with the floor
Agel et al.452007BasketballI16F-AD1690.42pex64No apparent contact, contact with the ball, or contact with the floor
Anderson et al.482019Basketball, lacrosse, soccerI12M/F-ADBasketball (M): 45
Basketball (F): 127
Lacrosse (M): 41
Lacrosse (F): 50
Soccer (M): 12
Soccer (F): 45		Basketball (M): 0.05
Basketball (F): 0.15
Lacrosse (M): 0.09
Lacrosse (F): 0.14
Soccer (M): 0.02
Soccer (F): 0.05		pexBasketball (M): 57
Basketball (F): 69 Lacrosse (M): 69
Lacrosse (F): 71
Soccer (M): 35
Soccer (F): 44		-
Brooks et al.372005UnionE2M63AD10.12phr100Twisting/turning, running, lifting (lineout/kickoff)
Dallalana et al.492007UnionE2M546AD20.01phr22Twisting/turning, running, lifting, lineout, other non-contact (not defined)
Dick et al.462007Am footballI16MAD6940.06pex32No apparent contact
Dönmez et al.502018SoccerA1M1821AD20.35phr13-
Faude et al.512005SoccerE1F165AD70.20phr64Running, change in direction, shooting, jumping, hit by ball, others (not defined)
Fuller et al.43, 442007SoccerI2M/FADM: 11, F: 42M: 0.04, F: 0.13phrM: 48, F: 46No player-to-player or player-surface contact
Fuller et al.392008UnionE1M626AD00.00phr0No player-to-player or player-surface contact
Fuller et al.362010UnionI2M282AD10.42phr17No player-to-player or player-surface contact
Fuller et al.352013UnionE1615AD21.04phr40No player-to-player or player-surface contact
Fuller et al.342017UnionE1M639AD40.21phr80No player-to-player or player-surface contact
Fuller et al.422018UnionI8M3922AD40.30phr24No player-to-player or player-surface contact
Fuller et al.332020UnionE1M646AD00.00phr0No player-to-player or player-surface contact
Fuller and Taylor412020SevensE10M3242AD121.00phr39No player-to-player or player-surface contact
Giza et al.522005SoccerE2F202AD60.07phr75-
Gupta et al.532020SoccerA10M/F-ADOM: 27, F: 106M: 0.02, F: 0.07pexM: 36, F: 53No player-to-player or player-surface contact
Hartmut et al.542010SoccerE1F254AD80.11phr73Twisting, contact with turf, taking a shot, sprinting, or falling
Hollander et al.552018HockeyA1M/FM: 158, F: 74ADM: 0, F: 1M: 0.00, F: 0.09phrM: 0, F: 100No contact with another player, ball or stick
Joseph et al.562013Am football, soccer, volleyball, basketball, baseball, softballA5M/F-ADOAm football (M): 87
Soccer (M): 14
Soccer (F): 44
Volleyball (F): 6
Basketball (M): 13
Basketball (F): 42
Baseball (M): 4
Softball (F): 12		Am football (M): 0.03
Soccer (M): 0.02
Soccer (F): 0.06
Volleyball (F): 0.01
Basketball (M): 0.01
Basketball (F): 0.05
Baseball (M): < 0.01
Softball (F): 0.02		pex-No contact with another player, surface, or apparatus (ball, base, goalpost etc.)
Krutsch et al.572016SoccerI1M408AD60.04phr38-
Leppanen et al.58b2017BasketballI3F-ADOM: 1, F: 3M: 0.04, F: 0.12phr-No direct contact or strike to the involved knee
Leyes et al.592011SoccerI3F55AD, ADOAD: 7, ADO: 4AD: 0.26, ADO: 0.38phrAD: 100, ADO: 100Absence of direct trauma against another player
Loughran et al.602019Am footballI10M-AD1910.06pex37No apparent contact with another player, playing surface, and other
Nilstad et al.612014SoccerE1F173AD50.11phr--
O’Connor et al.772021GaelicA2F132AD00.00phr--
Orchard et al.622001Au footballE8M1643AD630.47pex76Absence of direct contact to the injured knee or leg
Ostenberg and Roos632000SoccerI1F123AD00.00phr0-
Pasanen et al.642008FloorballI1F374AD70.15phr70-
Pasanen et al.652017FloorballE4M/F-ADM: 0, F: 1M: 0.00, F: 0.09phrM: 0, F: 25Absence of body contact, stick contact, ball contact, or unintended collision
Pasanen et al.662018FloorballA3M/F-ADOM: 0, F: 8M: 0.00, F: 0.32phrM: 0, F: 100Absence of direct contact to injured body region or contact with other body parts
Rekik et al.672018SoccerE5M–AD190.04phr51Absence of direct contact (contact with knee) or indirect contact (contact with another body part)
Scranton Jr et al.681997Am footballE5M–AD610.07pex––
Senisik et al.692011SoccerI2.5M64AD110.29pex100–
Taylor et al.402011UnionE1F285AD10.91phr–Injuries not as a result from contact with another player or object
Tondelli et al.762021UnionA1M250AD60.13phr67–
Walden et al.712011SoccerE3M/FM: 2019, F: 310ADM: 39, F: 6M: 0.04, F: 0.06phr62Absence of any physical contact with another player or object at the time of injury
Walden et al.702013SoccerE9M1357AD310.04phr63Injury resulting without player contact
Webb et al.722014LacrosseE1M––00.00phr––
West et al.732020UnionE16M–AD19C: 0.15bphr––
West et al.382020UnionE11M–AD4T: < 0.01bphr––
Whalan et al.742019SoccerI1M–AD50.10phr63An injury that occurred without any contact to the player or the injury site by another player or object (ball, ground or equipment)
Willigenburg et al.752016Am football, unionI3M–ADAm football: 6
Union: 0		Am football: 0.21
Union: 0.00		pex100Injuries that did not follow from a direct hit to the affected area

E elite-level, I intermediate-level, A amateur-level, M male, F female, AD adult, ADO adolescent, pex per 1000 player-exposures, phr per 1000 player-hours, C competition, T training, Au football Australian football, Am football American football, Gaelic Gaelic football, Union Rugby union

aCombined training and competition for all meta-analyses except for meta-analyses by exposure type

bOnly adolescent (under 18 years) data were extracted; data obtained from authors

Table 2. Quality assessment using a modified Newcastle-Ottawa scale
StudyItems
123456
Agel et al.47−+++++
Agel et al.45−+++++
Anderson et al.48−+++−+
Brooks et al.37−+++++
Dallalana et al.49−+++++
Dick et al.46−+++++
Donmez et al.50+++−−−
Faude et al.51++++++
Fuller et al.43, 44++++++
Fuller et al.39++++++
Fuller et al.36++++++
Fuller et al.35++++++
Fuller et al.34++++++
Fuller et al.42++++++
Fuller et al.33++++++
Fuller et al.41++++++
Giza et al.52−+++−+
Gupta et al.53−+++++
Hartmut et al.54+++++++
Hollander et al.55++++++
Joseph et al.56−+++++
Krutsch et al.57++++−+
Leppanen et al.58++++++
Leyes et al.59++++++
Loughran et al.60−+++++
Nilstad et al.61++++−+
O’Connor et al.77−+++−+
Orchard et al.62++++++
Ostenberg et al.63++++−+
Pasanen et al.64++++−+
Pasanen et al.65−++++−
Pasanen et al.66+++−++
Rekik et al.67−+++++
Scranton Jr et al.68−+++−+
Senisik et al.69+−−+−−
Taylor et al.40+++−++
Tondelli et al.76++++−+
Walden et al.71++++++
Walden et al.70−+++++
Webb et al.72++++−+
West et al.73−+++−+
West et al.38−+++−+
Whalan et al.74++++++
Willigenburg et al.75−−++++

+ one star awarded; − no star awarded; Item 1 (population description): 1 star was awarded when the population at risk was fully described in terms of number, competition level, sex, age; Item 2 (Population recruitment): 1 star was awarded when it was described how the population under study was arrived at, and when the entire population participated, or a random sampling (fraction) method was used to follow a sample of the population at risk for non-contact knee injuries; Item 3 (Surveillance methods): 1 star was awarded when it was stated how the incidence of non-contact knee injuries were surveilled; Item 4 (Duration of observation): 1 star was awarded when the duration of observation was stated. If duration of observation was less than 1 season, duration in terms of days/weeks/months should be provided; if not, no star was awarded, Item 5 (Case definition): 1 star was awarded when the study defined both injury and injury mechanisms; Item 6 (Others): 1 star was awarded when all other methods were found appropriate

Description of Included Studies

A total of 2,748 non-contact ACL injuries were recorded across 45 million player-hours and player-exposures combined (5 million player-hours and 40 million player-exposures) from 13 sports (soccer, American Football, rugby union, Australian football, Gaelic football, basketball, netball, volleyball, field hockey, floorball, lacrosse, baseball, softball) (Table 1). Most studies defined injuries based on the “time-loss” definition (89%)11, and injury data were primarily collected and recorded by medical staff (91%). In studies that defined non-contact injury mechanisms (68%), some seemed to consider non-contact and indirect contact mechanisms together58, 62, 75, while the rest defined the non-contact mechanism as no apparent player-player, surface-player, and ball-player contact.

Study Quality Assessment

Initial agreement between reviewers was 80% (212 of 264 items), but all disagreements were subsequently resolved by consensus. Seventeen studies were awarded the maximum six stars and one study scored two stars (Table 2)69. Stars were most often not awarded because the population was not fully described (item (a): 41% awarded no stars), and because non-contact injury mechanisms were not defined (item (e): 32%).

Publication Bias

Visual inspection of the funnel plots indicated that almost all studies in the meta-analyses had low standard errors, possibly due to large cohort sizes (Appendix A3). Studies were missing from the lower right quadrant in the funnel plot to assess publication bias in studies included in meta-analyses of overall proportion of non-contact to total ACL injuries, and this quadrant represents smaller studies with a high proportion of non-contact to total ACL injuries. Studies were evenly distributed in the funnel plot to assess publication bias in studies included in meta-analyses of overall incidence of non-contact ACL injuries.

Proportion of Non-contact Anterior Cruciate Ligament (ACL) Injuries to Total ACL Injuries

The overall proportion of non-contact ACL injuries to total ACL injuries was 55% (95% CI 48–62, I2 = 82%) (Fig. 2).

Fig. 2
Fig. 2. Forest plot of meta-analysis of proportion of ACL injuries sustained by non-contact mechanisms by sex. AD, adults; ADO, adolescents; Am Football, American Football; Au Football, Australian Football; Union, Rugby union; blue square, point estimate; red diamond, combined point estimate and 95% confidence intervals

By Sex

Non-contact ACL injury proportion was higher among female athlete (63%, 95% CI 53–71, I2 = 84%) compared to male athletes (50%, 95% CI 42–58, I2 = 86%) (Fig. 2).

By Sport

The overall proportion of non-contact ACL injuries to total ACL injuries was 66% in floorball (95% CI 15–100, I2 = 73%), 58% in basketball (95% CI 49–67, I2 = 84%), 54% in rugby union (95% CI 18–88, I2 = 42%), 53% in soccer (95% CI 46–61, I2 = 78%), and 38% in American football (95% CI 28–48, I2 = 89%) (Appendix A4-1). We were unable to perform meta-analyses for the other sports because there were less than three included studies (Appendix A4-1). Only the difference between field hockey and American football was significant, as confirmed by meta-regression (β = 0.29, 95% CI 0.03–0.55, p = 0.03) (Appendix B1). There were sufficient studies to sub-group by sport and sex for soccer only. In female soccer athletes, the proportion of non-contact to total ACL injuries was 55% (95% CI 45–65, I2 = 76%) (Appendix A4-2).

By Age Group

The overall proportion of non-contact ACL injuries to total ACL injuries was 55% in adults (95% CI 48–63, I2 = 90%) and 68% in adolescents (95% CI 43–90, I2 = 88%) (Appendix A5-1). After sub-grouping by age group and sex, this proportion was 60% in adult female athletes (95% CI 49–70, I2 = 85%) and 52% in adult male athletes (95% CI 43–60, I2 = 86%) (Appendix A5-2). There were insufficient studies to investigate injury proportions by sex in adolescents (Appendix A5-3)53, 59, 66. None of the included studies investigated children.

By Participation Level

The overall proportion of non-contact to total ACL injuries by participation level was 61% in elite-level (95% CI 50–70, I2 = 16%), 55% in intermediate-level (95% CI 44–65, I2 = 93%), and 56% in amateur-level athletes (95% CI 45–767, I2 = 89%) (Appendix A6-1). After sub-grouping by participation level and sex, this proportion was 65% in elite-level female athletes (95% CI 47–70, I2 = 0%), and 59% in elite-level male athletes (95% CI 45–72, I2 = 31%) (Appendix A6-2). In intermediate-level athletes, the proportion of non-contact to total ACL injuries in females was 58% (95% CI 43–73, I2 = 89%), and in males was 50% (95% CI 36–64, I2 = 88%) (Appendix A6-3). In amateur-level athletes, this proportion was 67% in females (95% CI 52–81, I2 = 85%), and 48% in males (95% CI 35–60, I2 = 82%) (Appendix A6-4).

By Exposure Type

The overall proportion of non-contact to total ACL injuries by exposure type was 42% (95% CI 30–54, I2 = 92%) in competition and 47% in training settings (95% CI 29–64, I2 = 72%) (Appendix A7-1). After sub-grouping by exposure type and sex, the proportion of non-contact to total ACL injuries during competition in female athletes was 58% (95% CI 42–74, I2 = 79%), and 35% in male athletes (95% CI 23–48, I2 = 87%) (Appendix A7-2). This difference between females and male athletes was significant as confirmed by meta-regression (β = − 0.22, 95% CI − 0.42 to − 0.02, p = 0.02) (Appendix B2). In training settings, this proportion was 68% in female athletes (95% CI 0.34–0.95, I2 = 60%) and 36% in male athletes (95% CI 21–53, I2 = 54%) (Appendix A7-3).

Incidence of Non-contact ACL Injuries

The overall incidence of non-contact ACL injuries was 0.07 per 1000 player-hours (95% CI 0.05–0.10, I2 = 77%) (Fig. 3), and 0.05 per 1000 player-exposures (95% CI 0.03–0.07, I2 = 97%) (Fig. 4). Figure 5 displays a summary of injury incidence meta-analyses by player-hours.

Fig. 3
Fig. 3. Forest plot of meta-analysis of incidence of non-contact ACL injuries per 1000 player-hours by sex. AD, adults; ADO, adolescents; Am Football, American Football; Au Football, Australian Football; Union, Rugby union; blue square, point estimate; red diamond, combined point estimate and 95% confidence intervals
Fig. 4
Fig. 4. Forest plot of meta-analysis of incidence of non-contact ACL injuries per 1000 player-exposures by sex. AD, adults; ADO, adolescents; Am Football, American Football; Au Football, Australian Football; Union, Rugby union; blue square, point estimate; red diamond, combined point estimate and 95% confidence intervals
Fig. 5
Fig. 5. Summary of selected injury incidence meta-analyses by player-hours. Squares, summary measure; accompanying horizontal line, 95% CI

By Sex

In females, non-contact ACL injury incidence was 0.14 per 1000 player-hours (95% CI 0.10–0.19, I2 = 40%) and 0.06 per 1000 player-exposures (95% CI 0.04–0.11, I2 = 97%) (Figs. 3, 4). In males, injury incidence was 0.05 per 1000 player-hours (95% CI 0.03–0.07, I2 = 48%) and 0.04 per 1000 player-exposures (95% CI 0.03–0.07, I2 = 93%). Only the difference between female and male athletes per 1000 player-hours was significant as confirmed by meta-regression (β = − 1.15, 95% CI − 1.58 to − 0.73, p < 0.01) (Appendix B2).

By Sport

Overall, non-contact injury incidence was 0.06 per 1000 player-hours (95% CI 0.02–0.18, I2 = 68%) in rugby union, 0.08 per 1000 player-hours (95% CI 0.05–0.12, I2 = 84%) and 0.05 per 1000 player-exposures (95% CI 0.03–0.9, I2 = 97%) in soccer, 0.17 per 1000 player-hours in floorball (95% CI 0.09–0.32, I2 = 41%), 0.05 per 1000 player-exposures in basketball (95% CI 0.03–0.11, I2 = 98%), and 0.06 per 1000 player-exposures in American football (95% CI 0.05–0.08, I2 = 87%) (Appendices A8-1 and 8-2). There were insufficient studies for field hockey55, Australian football62, rugby sevens41, lacrosse48, volleyball56, baseball56, softball56 and Gaelic football77 (Table 1).

Sub-grouping by sport and sex was only possible for soccer and basketball. In soccer, injury incidence was higher in female athletes (0.13 per 1000 player-hours, 95% CI 0.09–0.19, I2 = 52%; 0.07 per 1000 player-exposures, 95% CI 0.05–0.11, I2 = 95%) compared to male athletes (0.04 per 1000 player-hours, 95% CI 0.03–0.05, I2 = 0%; 0.03 per 1000 player-exposures, 95% CI 0.01–0.09, I2 = 95%) (Appendices A8-3 and 8-4). Only the difference between female and male soccer athletes per 1000 player-hours was significant as confirmed by meta-regression (β = − 1.09, 95% CI − 1.38 to − 0.81, p < 0.01) (Appendix B2). Injury incidence was higher in female basketball players (0.11 per 1000 player-exposures, 95% CI 0.06–0.20, I2 = 97%) compared to males (0.03 per 1000 player-exposures, 95% CI 0.02–0.05, I2 = 89%) and this difference was significant (β = − 1.34, 95% CI − 2.25 to − 0.43, p < 0.01) (Appendices A8-5 and B2).

By Age Group

The overall incidence of non-contact ACL injuries in adults was 0.07 per 1000 player-hours (95% CI 0.05–0.10, I2 = 75%) and 0.08 per 1000 player-exposures (95% CI 0.06–0.11, I2 = 97%) (Appendices A9-1 and 9-2). In adolescents, the incidence was 0.19 per 1000 player-hours (95% CI 0.09–0.38, I2 = 57%) and 0.02 per 1000 player-exposures (95% CI 0.01–0.04, I2 = 94%) (Appendices 9-1 and 9-2). Only the difference between adults and adolescents per 1000 player-exposures was significant as confirmed by meta-regression (β = − 1.28, 95% CI − 1.87 to − 0.69, p < 0.01) (Appendix B2).

After sub-grouping by age group and sex, adult injury incidence was 0.11 per 1000 player-hours (95% CI 0.04–0.09, I2 = 74%) and 0.08 per 1000 player-exposures in female athletes (95% CI 0.05–0.11, I2 = 93%), and 0.05 per 1000 player-hours (95% CI 0.03–0.07, I2 = 55%) and 0.06 per 1000 player-exposures (95% CI 0.04–0.09, I2 = 89%) in male athletes (Appendices A9-3 and 9–4). The differences between adult male and female athletes per 1000 player-hours and player-exposures were significant as confirmed by meta-regression (β = − 1.08, 95% CI − 1.35 to − 0.81, p < 0.01 and β = − 0.69, 95% CI − 1.29 to − 0.10, p = 0.02, respectively) (Appendix B2). In adolescents, injury incidence was 0.02 per 1000 player-exposures in male athletes (95% CI 0.01–0.02, I2 = 89%), and 0.28 per 1000 player-hours (95% CI 0.17–0.46, I2 = 26%) and 0.03 per 1000 player-exposures for female athletes (95% CI 0.02–0.06, I2 = 91%) (Appendices A9-5 and A9-6). There were insufficient studies of male adolescent athletes to compare incidence per 1000 player-hours between adolescent male and female athletes58, 66.

By Participation Level

Overall, the incidence of non-contact ACL injuries in elite-level athletes was 0.06 per 1000 player-hours (95% CI 0.04–0.09, I2 = 68%), in intermediate-level athletes 0.10 per 1000 player-hours (95% CI 0.06–0.18, I2 = 76%) and 0.10 per 1000 player-exposures (95% CI 0.06–0.17, I2 = 98%), and in amateur-level athletes 0.13 per 1000 player-hours (95% CI 0.07–0.24, I2 = 41%) and 0.03 per 1000 player-exposures (95% CI 0.02–0.05, I2 = 96%) (Appendices A10-1 and 10–2). There were insufficient studies of elite-level cohorts to compare incidence per 1000 player-exposures68. Only the difference between amateur- and intermediate-level athletes per 1000 player-exposures was significant as confirmed by meta-regression (β = 1.03, 95% CI 0.26–1.79, p < 0.01) (Appendix B2).

After sub-grouping by participation level and sex, the incidence of non-contact ACL injuries in elite-level female athletes was 0.10 per 1000 player-hours (95% CI 0.07–0.14, I2 = 19%) and in male athletes was 0.04 per 1000 player-hours (95% CI 0.03–0.05, I2 = 52%) (Appendix A10-3). There were insufficient studies to perform similar analyses per 1000 player-exposures68. The difference between male and female athletes per 1000 player-hours was significant as confirmed by meta-regression (β = − 0.91, 95% CI − 1.31 to − 0.52, p < 0.01) (Appendix B2). At the intermediate-level, injury incidence in females was 0.16 per 1000 player-hours (95% CI 0.12–0.21, I2 = 30%), and in male athletes was 0.04 per 1000 player-hours (95% CI 0.07–0.06, I2 = 0%) and 0.08 per 1000 player-exposures (95% CI 0.04–0.17, I2 = 92%) (Appendices A10-4 and A10-5). There were insufficient studies to meta-analyse data from female intermediate-level athletes47. Only the difference between male and female athletes per 1000 player-hours was significant as confirmed by meta-regression (β = − 1.32, 95% CI − 1.85 to − 0.80, p < 0.01) (Appendix B2). At the amateur-level, injury incidence in females was 0.27 per 1000 player-hours (95% CI 0.14–0.51, I2 = 0%) and 0.05 per 1000 player-exposures (95% CI 0.03–0.09, I2 = 95%), and in males was 0.10 per 1000 player-hours (95% CI 0.06–0.18, I2 = 0%) and 0.02 per 1000 player-exposures (95% CI 0.01–0.04, I2 = 95%) (Appendices A10-6 and A10-7).

By Exposure Type

The overall incidence of injury during competition was 0.48 per 1000 player-hours (95% CI 0.32–0.72, I2 = 77%) and 0.32 per 1000 player-exposures (95% CI 0.15–0.70, I2 = 96%), and during training was 0.04 per 1000 player-hours (95% CI 0.02–0.07, I2 = 63%) and 0.02 per 1000 player-exposures (95% CI 0.01–0.05, I2 = 86%) (Appendices A11-1 and A11-2).

After sub-grouping by exposure types and sex, injury incidence in female athletes during competition was 0.67 per 1000 player-hours (95% CI 0.33–1.35, I2 = 75%) and in training was 0.07 per 1000 player-hours (95% CI 0.05–0.10, I2 = 2%) (Appendix A11-3). There were insufficient studies to perform meta-analysis by player-exposures in females45, 53. For males, injury incidence was 0.37 per 1000 player-hours (95% CI 0.24–0.59, I2 = 72%) and 0.34 per 1000 player-exposures, 95% CI 0.12–0.99, I2 = 96%) during competition while for training was 0.02 per 1000 player-hours (95% CI 0.01–0.05, I2 = 55%) and 0.03 per 1000 player-exposures (95% CI 0.01–0.06, I2 = 83%). All competition to training comparisons reported above were significant as confirmed by meta-regression (Appendix B2).

Additional Sub-group Analyses

There were sufficient studies to perform a meta-analysis by participation level per 1000 player-hours for female soccer athletes. Injury incidence was higher in intermediate- (0.18 per 1000 player-hours, 95% CI 0.11–0.29, I2 = 57%) compared to elite-level athletes (0.10 per 1000 player-hours, 95% CI 0.07–0.15, I2 = 35%) (Appendix A12). This difference was significant as confirmed by meta-regression (β = 0.44, 95% CI < 0.01–0.88, p = 0.05) (Appendix B2).

Discussion

We conducted a systematic review with meta-analysis to estimate the proportion of non-contact to total ACL injuries and describe the incidence of non-contact ACL injuries in team ball-sports. Compared to the two most recent systematic reviews on ACL injury epidemiology, our review captured more ACL injuries, estimated incidence according to player-hours and player-exposures, and performed meta-regression analyses to investigate sources of heterogeneity and to test the influence of sex, age group, sport, participation level and exposure type on effect sizes1, 9. Overall, we found that non-contact ACL injuries represented over half of all ACL injuries sustained in team ball-sports. Non-contact ACL injury proportion was higher in female than male athletes in team ball-sports. Injury incidence was higher in females than males with most injuries occurring during competition team ball-sports. Intermediate-level male and female athletes were more likely to sustain non-contact ACL injuries than amateur-level athletes in team ball-sports. Heterogeneity across studies was generally high.

While female athletes are at a greater risk of ACL injuries compared to male athletes1, 9, 78, this is the first systematic review to confirm that a similar sex disparity also exists for non-contact ACL injury risk. There is no consensus from multi-pronged research investigating the sex disparity in ACL injury rates through anatomical79, physiological80 and biomechanical lenses81, 82, and injury rates in females remain high1–3, 9. A recent review by Parsons et al. called for ACL injury risk-reduction research to consider the influence of societal83 and cultural norms of female athletes84. Parsons and colleagues provided the example that it is not uncommon for girls to be told to ‘get stronger’ to reduce ACL injury risk, but are not provided with equal opportunity and support to do so84. There is a need for a holistic approach to address this injury rate disparity.

Consistent with previous research, athletes were more likely to sustain non-contact ACL injuries in competition than training settings43, 44, 51, 71. Competition settings are often associated with additional internal and external stressors, and failure to manage these stressors may increase injury risk85. Training sessions are usually conducted in a more controlled environment than competition settings; therefore, it should be easier to reduce non-contact ACL injuries in training38. To do so, it seems logical to employ strategies like technique instruction, optimizing workload, and exercise-based IRRPs. However, the purpose of training is to prepare athletes for the physical demands of sport, and a reduction in injury incidence that comes at the expense of team performance may not be acceptable to coaches. While the search continues for the elusive training “sweet-spot” to reduce injury risk while improving performance86, stakeholders should consider cost-effectiveness analyses and systems thinking approaches to assess injury risk reduction opportunities and challenges, as these are usually unique to each sport and setting8, 87.

Our findings were inconclusive regarding the influence of sport, age group and participation level on non-contact ACL injury epidemiology in team ball-sports. In relation to the influence of sport, Montalvo et al. previously reported the highest incidence of ACL injuries in high-impact rotational landing (gymnastics, obstacle course race) and contact sports (soccer, basketball)1. It is not clear, however, if these differences were significant because meta-regression analyses were not performed in that study. One possible explanation for the lack of difference in our findings could be due to the common non-contact ACL injury scenarios and mechanisms across team ball-sports4, 18, 88, 89. In relation to the influence of age group, studies have suggested that children and adolescents are more susceptible to injury compared to adults because of their lower skill levels, physical capacities, and decision-making capabilities90–92. We only found a significant difference in injury incidence when comparing adults to adolescents by player-exposure but not for incidence by player-hours or proportion. None of the included studies investigated children. Sub-grouping by sex did not reveal any significant findings. With respect to participation level, our findings mirror the current state of evidence that it is not clear if amateur- and intermediate-level athletes are more susceptible to injuries, as found in some cohorts1, 9, or if elite-level athletes are more susceptible27, 93. We did find, however, that intermediate-level male and female athletes were more likely to sustain non-contact ACL injuries than amateur-level athletes. We should caution that our findings on the influence of sport, age group, and participation level were from meta-analyses with high heterogeneity, and further sub-group analyses requiring more studies may be needed to determine the influence of these categorical predictors on non-contact ACL injury epidemiology.

Lastly, in order to fully establish the extent of an injury problem to inform the development of injury risk-reduction strategies, injury epidemiology studies must report injury mechanisms7. We had to exclude nearly four times as many studies from our review than those included because they did not report whether the injuries occurred via a direct contact or non-contact mechanism (168 studies excluded vs. 44 included) (Fig. 1). Additionally, authors from 22 out of the 46 included studies had to be contacted because non-contact ACL injury data were not available in the published manuscript33–44, 50, 51, 55, 65, 66, 73–77. To illustrate the importance of reporting injury mechanisms, the Australian Football League introduced rule changes to limit the run-up of ruckmen at the centre bounce that reduced posterior cruciate ligament (PCL) injury risk by half94. They were successful in doing so because they had identified that PCL injuries commonly occurred through knee-to-knee contact mechanisms, and by limiting the run up, ruckmen had lower momentum and were not jumping and lifting their knees up as high during these contests. Without knowledge that most PCL injuries occurred through contact mechanisms, the proposed injury counter-measures would not have been as effective. Therefore, future studies on injury epidemiology should adopt consensus statement guidelines to not just report injury magnitude, but also injury mechanisms and their accompanying definitions11.

Limitations

Firstly, there was substantial heterogeneity among the included studies. This is inevitable in meta-analyses of epidemiological studies and does not invalidate our findings95. We attempted to investigate sources of heterogeneity via random-effects meta-analytical methods and sub-group analyses. Future research should explore potential sources of heterogeneity not investigated in our review. Next, previous knee and ACL injuries increase the risk of subsequent ACL injuries96, but detailed information regarding medical history was not available in most included studies and therefore was not considered in this review. Subgrouping according to index versus re-injuries may improve the generalizability of our findings. Another limitation was that non-contact injury mechanisms were mostly defined in the included studies as the absence of direct player-to-player or player-to-surface contact (Table 1). However, these definitions were unclear on whether indirect contact mechanisms were considered. Indirect contact is defined as physical contact not applied directly to the knee, but contributes to the causal chain of events leading to an ACL injury11. For example, shoulder contact between soccer players jostling in mid-air for a header can result in an external perturbation of the centre of mass that affects knee landing kinematics and eventuates in an ACL rupture. Up to 44% of ACL injuries could be due to indirect contact mechanisms4, and these injuries could arguably be prevented through careful drill design that replicates contact events in sport and instruction of proper technique97. The inclusion of ACL injuries sustained by indirect contact mechanisms would likely provide a more accurate estimate on the incidence and proportion of injuries that are amenable to exercise-based IRRPs. It is probable that ACL injury data might not be reported in studies where no ACL injuries occurred: these studies should report zero cases to prevent effect size overestimation. This review only included studies investigating team ball-sports and our results should not be generalized to all sports. Lastly, including non-published data might affect the validity and reproducibility of this review, so we used systematic and detailed criteria and processes to maintain transparency throughout this process.

Conclusion

Non-contact ACL injuries represented over half of all ACL injuries sustained in team-ball sports. The proportion of non-contact to total ACL injuries and injury incidence was higher in females than males in team ball-sports. Injuries mostly occurred in competition settings in team ball-sports. More research is required to fully understand the influence of sport, age group and participation level on injury proportion and incidence in team ball-sports. Our findings have implications for future ACL epidemiological research in sport, and the development and implementation of effective ACL injury risk reduction efforts in team ball-sports.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material — available with the version of record.

Supplementary material — available with the version of record.

Acknowledgements

Acknowledgement should be made to those authors who responded to our requests for information not included in their published manuscript.

Declarations

Declarations

Open Access funding enabled and organized by CAUL and its Member Institutions. The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Lionel Chia, Danilo De Oliveira Silva, Matthew Whalan, Marnee J. McKay, Justin Sullivan, Colin W. Fuller and Evangelos Pappas declare that they have no conflicts of interest relevant to the content of this review.

Not applicable.

Not applicable.

Not applicable.

All authors contributed to project conception, project planning and interpretation of data. LC conducted the literature search and data synthesis. LC and DS conducted the record screening and data extraction. LC and MW conducted the methodological evaluation. LC drafted the initial manuscript, which was critically revised and approved for submission by all authors. All authors read and approved the final manuscript.

Raw data from data analysis are available upon reasonable request by contacting the corresponding author. The data are not publicly available because a portion of it is obtained directly from authors of the included studies, and subsequent approval from these authors is required.

Not applicable.

Sources

References

97 references, in the article's own order. Citation numbers in the text are this list's numbers (94 records are cited).

  1. Anterior cruciate ligament injury risk in sport: a systematic review and meta-analysis of injury incidence by sex and sport classification

    Montalvo AM, Schneider DK, Webster KE, Yut L, Galloway MT, Heidt RS, et al.

    2019J Athl Train 54(5):472–482 · PMID 31009238doi:10.4085/1062-6050-407-16

  2. Epidemiology of knee injuries among US high school athletes, 2005/06–2010/11

    Swenson DM, Collins CL, Best TM, Flanigan DC, Fields SK, Comstock RD

    2013Med Sci Sports Exerc 45(3):462 · PMID 23059869doi:10.1249/MSS.0b013e318277acca

  3. Epidemiology of 3825 injuries sustained in six seasons of National Collegiate Athletic Association men's and women's soccer (2009/2010–2014/2015)

    Roos KG, Wasserman EB, Dalton SL, Gray A, Djoko A, Dompier TP, et al.

    2017Br J Sports Med 51(13):1029–1034 · PMID 27190140doi:10.1136/bjsports-2015-095718

  4. Systematic video analysis of ACL injuries in professional male football (soccer): injury mechanisms, situational patterns and biomechanics study on 134 consecutive cases

    Della Villa F, Buckthorpe M, Grassi A, Nabiuzzi A, Tosarelli F, Zaffagnini S, et al.

    2020Br J Sports Med 54(23):1423–1432 · PMID 32561515doi:10.1136/bjsports-2019-101247

  5. Evaluation of the effectiveness of anterior cruciate ligament injury prevention programme training components: a systematic review and meta-analysis

    Taylor JB, Waxman JP, Richter SJ, Shultz SJ

    2015Br J Sports Med 49(2):79–87 · PMID 23922282doi:10.1136/bjsports-2013-092358

  6. Intervention strategies used in sport injury prevention studies: a systematic review identifying studies applying the Haddon matrix

    Vriend I, Gouttebarge V, Finch CF, Van Mechelen W, Verhagen EA

    2017Sports Med 47(10):2027–2043 · PMID 28303544doi:10.1007/s40279-017-0718-y

  7. Incidence, severity, aetiology and prevention of sports injuries

    Van Mechelen W, Hlobil H, Kemper HC

    1992Sports Med 14(2):82–99 · PMID 1509229doi:10.2165/00007256-199214020-00002

  8. Assessing the return on investment of injury prevention procedures in professional football

    Fuller CW

    2019Sports Med 49(4):621–629 · PMID 30838519doi:10.1007/s40279-019-01083-z

  9. “What’s my risk of sustaining an ACL injury while playing sports?” A systematic review with meta-analysis

    Montalvo AM, Schneider DK, Yut L, Webster KE, Beynnon B, Kocher MS, et al.

    2019Br J Sports Med 53(16):1003–1012 · PMID 29514822doi:10.1136/bjsports-2016-096274

  10. The influence of methodological issues on the results and conclusions from epidemiological studies of sports injuries

    Brooks JH, Fuller CW

    2006Sports Med 36(6):459–472 · PMID 16737340doi:10.2165/00007256-200636060-00001

  11. Group IOCIIEC, Bahr R, Clarsen B, Derman W, Dvorak J, Emery CA, et al. International olympic committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sports 2020 (including the STROBE extension for sports injury and illness surveillance (STROBE-SIIS)). Orthop J Sports Med. 2020;8(2):2325967120902908.10.1177/2325967120902908PMC702954932118084

    Authors not recorded

    · PMID 32118084doi:10.1177/2325967120902908

  12. ACL and meniscal injuries increase the risk of primary total knee replacement for osteoarthritis: a matched case–control study using the Clinical Practice Research Datalink (CPRD)

    Khan T, Alvand A, Prieto-Alhambra D, Culliford DJ, Judge A, Jackson WF, et al.

    2019Br J Sports Med 53(15):965–968 · PMID 29331994doi:10.1136/bjsports-2017-097762

  13. Risk of knee osteoarthritis after different types of knee injuries in young adults: a population-based cohort study

    Snoeker B, Turkiewicz A, Magnusson K, Frobell R, Yu D, Peat G, et al.

    2020Br J Sports Med 54(12):725–730 · PMID 31826861doi:10.1136/bjsports-2019-100959

  14. Injuries affect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study

    Hägglund M, Waldén M, Magnusson H, Kristenson K, Bengtsson H, Ekstrand J

    2013Br J Sports Med 47(12):738–742 · PMID 23645832doi:10.1136/bjsports-2013-092215

  15. Eggerding V, Reijman M, Meuffels DE, van Es E, van Arkel E, van den Brand I, et al. ACL reconstruction for all is not cost-effective after acute ACL rupture. Br J Sports Med. 2021 (published online first: 18 March 2021).10.1136/bjsports-2020-102564PMC868565633737313

    Authors not recorded

    · PMID 33737313doi:10.1136/bjsports-2020-102564

  16. Psychological aspects of anterior cruciate ligament injuries

    Ardern CL, Kvist J, Webster KE

    2016Oper Tech Sports Med 24(1):77–83doi:10.1053/j.otsm.2015.09.006

  17. explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

    Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, PRISMA, et al.

    2020BMJ 2021:372 · PMID 33781993doi:10.1136/bmj.n160

  18. Mechanisms of ACL injury in professional rugby union: a systematic video analysis of 36 cases

    Montgomery C, Blackburn J, Withers D, Tierney G, Moran C, Simms C

    2018Br J Sports Med 52(15):994–1001 · PMID 28039125doi:10.1136/bjsports-2016-096425

  19. Magnetic resonance imaging and intra-articular findings after anterior cruciate ligament injuries in ice hockey versus other sports

    Kluczynski MA, Kang JV, Marzo JM, Bisson LJ

    2016Orthop J Sports Med 4(5):2325967116646534 · PMID 27294167doi:10.1177/2325967116646534

  20. Wells GA, Shea B, O’Connell Da, Peterson J, Welch V, Losos M, et al. The Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Oxford; 2000

    Authors not recorded

  21. Incidence of acute hamstring injuries in soccer: a systematic review of 13 studies involving more than 3800 athletes with 2 million sport exposure hours

    Diemer WM, Winters M, Tol JL, Pas HI, Moen MH

    2021J Orthop Sports Phys Ther 51(1):27–36 · PMID 33306929doi:10.2519/jospt.2021.9305

  22. Injury profile in women’s football: A systematic review and meta-analysis

    López-Valenciano A, Raya-González J, Garcia-Gómez JA, Aparicio-Sarmiento A, Sainz de Baranda P

    2021Sports Med 51:423–442 · PMID 33433863doi:10.1007/s40279-020-01401-w

  23. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

    Vos T, Barber RM, Bell B, Bertozzi-Villa A, Biryukov S, Bolliger I, et al.

    2015Lancet 386(9995):743–800 · PMID 26063472doi:10.1016/S0140-6736(15)60692-4

  24. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations

    Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al.

    2008BMJ 336(7650):924–926 · PMID 18436948doi:10.1136/bmj.39489.470347.AD

  25. Bagos PG, Nikolopoulos GK. Mixed-effects Poisson regression models for meta-analysis of follow-up studies with constant or varying durations. Int J Biostat. 2009;5(1):Article 21

    Authors not recorded

  26. Meta-analysis of incidence rate data in the presence of zero events

    Spittal MJ, Pirkis J, Gurrin LC

    2015BMC Med Res Methodol 15(1):1–16 · PMID 25925169doi:10.1186/s12874-015-0031-0

  27. A meta-analysis of injuries in senior men’s professional rugby union

    Williams S, Trewartha G, Kemp S, Stokes K

    2013Sports Med 43(10):1043–1055 · PMID 23839770doi:10.1007/s40279-013-0078-1

  28. Measuring inconsistency in meta-analyses

    Higgins JP, Thompson SG, Deeks JJ, Altman DG

    2003BMJ 327(7414):557–560 · PMID 12958120doi:10.1136/bmj.327.7414.557

  29. Meta-analysis of prevalence

    Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T

    2013J Epidemiol Community Health 67(11):974–978 · PMID 23963506doi:10.1136/jech-2013-203104

  30. Prevalence and risk factors for back pain in sports: a systematic review with meta-analysis

    Wilson F, Ardern CL, Hartvigsen J, Dane K, Trompeter K, Trease L, et al.

    2021Br J Sports Med 55:601 · PMID 33077481doi:10.1136/bjsports-2020-102537

  31. International incidence of psychotic disorders, 2002–17: a systematic review and meta-analysis

    Jongsma HE, Turner C, Kirkbride JB, Jones PB

    2019Lancet Public Health 4(5):e229–e244 · PMID 31054641doi:10.1016/S2468-2667(19)30056-8

  32. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis

    Panagioti M, Khan K, Keers RN, Abuzour A, Phipps D, Kontopantelis E, et al.

    2019BMJ 366:1–11 · PMID 31315828doi:10.1136/bmj.l4185

  33. Rugby World Cup 2019 injury surveillance study

    Fuller C, Taylor A, Douglas M, Raftery M

    2020S Afr J Sports Med 32:1–6 · PMID 36818969doi:10.17159/2078-516X/2020/v32i1a8062

  34. Rugby world cup 2015: world rugby injury surveillance study

    Fuller CW, Taylor A, Kemp SP, Raftery M

    2017Br J Sports Med 51(1):51–57 · PMID 27461882doi:10.1136/bjsports-2016-096275

  35. Rugby world cup 2011: international rugby board injury surveillance study

    Fuller CW, Sheerin K, Targett S

    2013Br J Sports Med 47(18):1184–1191 · PMID 22685124doi:10.1136/bjsports-2012-091155

  36. Risk of injury associated with rugby union played on artificial turf

    Fuller CW, Clarke L, Molloy MG

    2010J Sports Sci 28(5):563–570 · PMID 20391085doi:10.1080/02640411003629681

  37. A prospective study of injuries and training amongst the England 2003 Rugby World Cup squad

    Brooks JH, Fuller C, Kemp S, Reddin DB

    2005Br J Sports Med 39(5):288–293 · PMID 15849293doi:10.1136/bjsm.2004.013391

  38. Patterns of training volume and injury risk in elite rugby union: an analysis of 1.5 million hours of training exposure over eleven seasons

    West SW, Williams S, Kemp SP, Cross MJ, McKay C, Fuller CW, et al.

    2020J Sports Sci 38(3):238–247 · PMID 31755824doi:10.1080/02640414.2019.1692415

  39. International rugby board rugby world cup 2007 injury surveillance study

    Fuller C, Laborde F, Leather R, Molloy MG

    2008Br J Sports Med 42(6):452–459 · PMID 18539659doi:10.1136/bjsm.2008.047035

  40. Injury surveillance during the 2010 IRB women's rugby world cup

    Taylor AE, Fuller CW, Molloy MG

    2011Br J Sports Med 45(15):1243–1245 · PMID 21947815doi:10.1136/bjsports-2011-090024

  41. Ten-season epidemiological study of match injuries in men’s international rugby sevens

    Fuller CW, Taylor A

    2020J Sports Sci 38(14):1595–1604 · PMID 32286146doi:10.1080/02640414.2020.1752059

  42. Eight-season epidemiological study of injuries in men’s international Under-20 rugby tournaments

    Fuller CW, Taylor A, Raftery M

    2018J Sports Sci 36(15):1776–1783 · PMID 29252097doi:10.1080/02640414.2017.1418193

  43. Fuller CW, Dick RW, Corlette J, Schmalz R. Comparison of the incidence, nature and cause of injuries sustained on grass and new generation artificial turf by male and female football players. Part 2: training injuries. Br J Sports Med. 2007;41(suppl 1):i27–32.10.1136/bjsm.2007.037275PMC246525217646247

    Authors not recorded

    · PMID 17646247doi:10.1136/bjsm.2007.037275

  44. Fuller CW, Dick RW, Corlette J, Schmalz R. Comparison of the incidence, nature and cause of injuries sustained on grass and new generation artificial turf by male and female football players. Part 1: match injuries. Br J Sports Med. 2007;41(suppl 1):i20–6.10.1136/bjsm.2007.037267PMC246525417646246

    Authors not recorded

    · PMID 17646246doi:10.1136/bjsm.2007.037267

  45. Descriptive epidemiology of collegiate women's basketball injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2003–2004

    Agel J, Olson DE, Dick R, Arendt EA, Marshall SW, Sikka RS

    2007J Athl Train 42(2):202 · PMID 17710168

  46. Descriptive epidemiology of collegiate men's football injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2003–2004

    Dick R, Ferrara MS, Agel J, Courson R, Marshall SW, Hanley MJ, et al.

    2007J Athl Train 42(2):221 · PMID 17710170

  47. Anterior cruciate ligament injury in national collegiate athletic association basketball and soccer: a 13-year review

    Agel J, Arendt EA, Bershadsky B

    2005Am J Sports Med 33(4):524–531 · PMID 15722283doi:10.1177/0363546504269937

  48. Anterior cruciate ligament injury risk by season period and competition segment: an analysis of National Collegiate Athletic Association injury surveillance data

    Anderson T, Wasserman EB, Shultz SJ

    2019J Athl Train 54(7):787–795 · PMID 31322904doi:10.4085/1062-6050-501-17

  49. The epidemiology of knee injuries in English professional rugby union

    Dallalana RJ, Brooks JH, Kemp SP, Williams AM

    2007Am J Sports Med 35(5):818–830 · PMID 17293461doi:10.1177/0363546506296738

  50. Injuries among recreational football players: results of a prospective cohort study

    Dönmez G, Korkusuz F, Özçakar L, Karanfil Y, Dursun E, Kudas S, et al.

    2018Clin J Sport Med 28(3):249–254 · PMID 28727642doi:10.1097/JSM.0000000000000425

  51. Injuries in female soccer players: a prospective study in the German national league

    Faude O, Junge A, Kindermann W, Dvorak J

    2005Am J Sports Med 33(11):1694–1700 · PMID 16093546doi:10.1177/0363546505275011

  52. Injuries in women’s professional soccer

    Giza E, Mithöfer K, Farrell L, Zarins B, Gill T

    2005Br J Sports Med 39(4):212–216 · PMID 15793089doi:10.1136/bjsm.2004.011973

  53. Sex-based differences in anterior cruciate ligament injuries among United States High School Soccer Players: an epidemiological study

    Gupta AS, Pierpoint LA, Comstock RD, Saper MG

    2020Orthop J Sports Med 8(5):2325967120919178 · PMID 32528989doi:10.1177/2325967120919178

  54. Injuries in women's soccer: a 1-year all players prospective field study of the women's Bundesliga (German premier league)

    Hartmut G, Becker A, Walther M, Hess H

    2010Clin J Sport Med 20(4):264–271 · PMID 20606511doi:10.1097/JSM.0b013e3181e78e33

  55. Epidemiology of injuries in outdoor and indoor hockey players over one season: a prospective cohort study

    Hollander K, Wellmann K, Zu Eulenburg C, Braumann K-M, Junge A, Zech A

    2018Br J Sports Med 52(17):1091–1096 · PMID 29936428doi:10.1136/bjsports-2017-098948

  56. A multisport epidemiologic comparison of anterior cruciate ligament injuries in high school athletics

    Joseph AM, Collins CL, Henke NM, Yard EE, Fields SK, Comstock RD

    2013J Athl Train 48(6):810–817 · PMID 24143905doi:10.4085/1062-6050-48.6.03

  57. Increase in ACL and PCL injuries after implementation of a new professional football league

    Krutsch W, Zeman F, Zellner J, Pfeifer C, Nerlich M, Angele P

    2016Knee Surg Sports Traumatol Arthrosc 24(7):2271–2279 · PMID 25293676doi:10.1007/s00167-014-3357-y

  58. Stiff landings are associated with increased ACL injury risk in young female basketball and floorball players

    Leppänen M, Pasanen K, Kujala UM, Vasankari T, Kannus P, Äyrämö S, et al.

    2017Am J Sports Med 45(2):386–393 · PMID 27637264doi:10.1177/0363546516665810

  59. Leyes JY, Pérez LT, de Olano CC. Lesión del ligamento cruzado anterior en fútbol femenino. Estudio epidemiológico de tres temporadas. Apunts Medicina de l’Esport. 2011;46(171):137–43

    Authors not recorded

  60. Incidence of knee injuries on artificial turf versus natural grass in National Collegiate Athletic Association American football: 2004–2005 through 2013–2014 seasons

    Loughran GJ, Vulpis CT, Murphy JP, Weiner DA, Svoboda SJ, Hinton RY, et al.

    2019Am J Sports Med 47(6):1294–1301 · PMID 30995074doi:10.1177/0363546519833925

  61. Risk factors for lower extremity injuries in elite female soccer players

    Nilstad A, Andersen TE, Bahr R, Holme I, Steffen K

    2014Am J Sports Med 42(4):940–948 · PMID 24500914doi:10.1177/0363546513518741

  62. Intrinsic and extrinsic risk factors for anterior cruciate ligament injury in Australian footballers

    Orchard J, Seward H, McGivern J, Hood S

    2001Am J Sports Med 29(2):196–200 · PMID 11292045doi:10.1177/03635465010290021301

  63. Injury risk factors in female European football. A prospective study of 123 players during one season

    Östenberg A, Roos H

    2000Scand J Med Sci Sports. 10(5):279–285 · PMID 11001395doi:10.1034/j.1600-0838.2000.010005279.x

  64. Injury risk in female floorball: a prospective one-season follow-up

    Pasanen K, Parkkari J, Kannus P, Rossi L, Palvanen M, Natri A, et al.

    2008Scand J Med Sci Sports 18(1):49–54 · PMID 17490461doi:10.1111/j.1600-0838.2007.00640.x

  65. Injuries during the international floorball tournaments from 2012 to 2015

    Pasanen K, Bruun M, Vasankari T, Nurminen M, Frey WO

    2017BMJ Open Sport Exerc Med. 2:e000217 · PMID 28890804doi:10.1136/bmjsem-2016-000217

  66. Acute injuries in Finnish junior floorball league players

    Pasanen K, Hietamo J, Vasankari T, Kannus P, Heinonen A, Kujala UM, et al.

    2018J Sci Med Sport 21(3):268–273 · PMID 28716691doi:10.1016/j.jsams.2017.06.021

  67. ACL injury incidence, severity and patterns in professional male soccer players in a Middle Eastern league

    Rekik RN, Tabben M, Eirale C, Landreau P, Bouras R, Wilson MG, et al.

    2018BMJ Open Sport Exerc Med. 4:e000461 · PMID 30498577doi:10.1136/bmjsem-2018-000461

  68. A review of selected noncontact anterior cruciate ligament injuries in the National Football League

    Scranton PE, Whitesel JP, Powell JW, Dormer SG, Heidt RS, Losse G, et al.

    1997Foot Ankle Int 18(12):772–776 · PMID 9429878doi:10.1177/107110079701801204

  69. Posterior tibial slope as a risk factor for anterior cruciate ligament rupture in soccer players

    Şenişik S, Özgürbüz C, Ergün M, Yüksel O, Taskiran E, İşlegen Ç, et al.

    2011J Sports Sci Med 10(4):763 · PMID 24149571

  70. Regional differences in injury incidence in European professional football

    Waldén M, Hägglund M, Orchard J, Kristenson K, Ekstrand J

    2013Scand J Med Sci Sports 23(4):424–430 · PMID 22092416doi:10.1111/j.1600-0838.2011.01409.x

  71. Anterior cruciate ligament injury in elite football: a prospective three-cohort study

    Waldén M, Hägglund M, Magnusson H, Ekstrand J

    2011Knee Surg Sports Traumatol Arthrosc 19(1):11–19 · PMID 20532869doi:10.1007/s00167-010-1170-9

  72. Injuries in elite men’s lacrosse: an observational study during the 2010 world championships

    Webb M, Davis C, Westacott D, Webb R, Price J

    2014Orthop J Sports Med 2(7):2325967114543444 · PMID 26535349doi:10.1177/2325967114543444

  73. Trends in match injury risk in professional male rugby union: a 16-season review of 10 851 match injuries in the English Premiership (2002–2019): the professional rugby injury surveillance project

    West SW, Starling L, Kemp S, Williams S, Cross M, Taylor A, et al.

    2020Br J Sports Med 55(12):676 · PMID 33046453doi:10.1136/bjsports-2020-102529

  74. The incidence and burden of time loss injury in Australian men’s sub-elite football (soccer): a single season prospective cohort study

    Whalan M, Lovell R, McCunn R, Sampson JA

    2019J Sci Med Sport 22(1):42–47 · PMID 29884595doi:10.1016/j.jsams.2018.05.024

  75. Comparison of injuries in American collegiate football and club rugby: a prospective cohort study

    Willigenburg NW, Borchers JR, Quincy R, Kaeding CC, Hewett TE

    2016Am J Sports Med 44(3):753–760 · PMID 26786902doi:10.1177/0363546515622389

  76. Tondelli E, Boerio C, Andreu M, Antinori S. Impact, incidence and prevalence of musculoskeletal injuries in senior amateur male rugby: epidemiological study. Phys Sportsmed. 2021:1–7.10.1080/00913847.2021.192404533906560

    Authors not recorded

    · PMID 33906560doi:10.1080/00913847.2021.1924045

  77. Injuries in collegiate ladies Gaelic footballers: a 2-season prospective cohort study

    O’Connor S, Bruce C, Teahan C, McDermott E, Whyte E

    2020J Sport Rehabil 30(2):261–266 · PMID 32473586doi:10.1123/jsr.2019-0468

  78. Bram JT, Magee LC, Mehta NN, Patel NM, Ganley TJ. Anterior cruciate ligament injury incidence in adolescent athletes: a systematic review and meta-analysis. Am J Sports Med. 2020:0363546520959619.10.1177/036354652095961933090889

    Authors not recorded

    · PMID 33090889doi:10.1177/0363546520959619

  79. Sex-based differences in the anthropometric characteristics of the anterior cruciate ligament and its relation to intercondylar notch geometry: a cadaveric study

    Chandrashekar N, Slauterbeck J, Hashemi J

    2005Am J Sports Med 33(10):1492–1498 · PMID 16009992doi:10.1177/0363546504274149

  80. Anterior cruciate ligament research retreat VIII summary statement: an update on injury risk identification and prevention across the anterior cruciate ligament injury continuum, March 14–16, 2019, Greensboro, NC

    Shultz SJ, Schmitz RJ, Cameron KL, Ford KR, Grooms DR, Lepley LK, et al.

    2019J Athl Train 54(9):970–984 · PMID 31461312doi:10.4085/1062-6050-54.084

  81. Sex differences in landing biomechanics and postural stability during adolescence: a systematic review with meta-analyses

    Holden S, Boreham C, Delahunt E

    2016Sports Med 46(2):241–253 · PMID 26542164doi:10.1007/s40279-015-0416-6

  82. Decrease in neuromuscular control about the knee with maturation in female athletes

    Hewett TE, Myer GD, Ford KR

    2004J Bone Jt Surg 86(8):1601–1608 · PMID 15292405doi:10.2106/00004623-200408000-00001

  83. Anterior cruciate ligament injury mechanisms through a neurocognition lens: implications for injury screening

    Gokeler A, Benjaminse A, Della Villa F, Tosarelli F, Verhagen E, Baumeister J

    2021BMJ Open Sport Exerc Med 7(2):e001091 · PMID 34055386doi:10.1136/bmjsem-2021-001091

  84. Parsons JL, Coen SE, Bekker S. Anterior cruciate ligament injury: towards a gendered environmental approach. Br J Sports Med. 2021 (published online first: 10 March 2021).10.1136/bjsports-2020-10317333692033

    Authors not recorded

    · PMID 33692033doi:10.1136/bjsports-2020-103173

  85. Competition stress in sport performers: stressors experienced in the competition environment

    Mellalieu SD, Neil R, Hanton S, Fletcher D

    2009J Sports Sci 27(7):729–744 · PMID 19424897doi:10.1080/02640410902889834

  86. The training—injury prevention paradox: should athletes be training smarter and harder?

    Gabbett TJ

    2016Br J Sports Med 50(5):273–280 · PMID 26758673doi:10.1136/bjsports-2015-095788

  87. Bolling C, Mellette J, Pasman HR, Van Mechelen W, Verhagen E. From the safety net to the injury prevention web: applying systems thinking to unravel injury prevention challenges and opportunities in Cirque du Soleil. BMJ Open Sport Exerc Med. 2019;5:e000492.10.1136/bmjsem-2018-000492PMC640754130899551

    Authors not recorded

    · PMID 30899551doi:10.1136/bmjsem-2018-000492

  88. Three distinct mechanisms predominate in non-contact anterior cruciate ligament injuries in male professional football players: a systematic video analysis of 39 cases

    Waldén M, Krosshaug T, Bjørneboe J, Andersen TE, Faul O, Hägglund M

    2015Br J Sports Med 49(22):1452–1460 · PMID 25907183doi:10.1136/bjsports-2014-094573

  89. Mechanisms for noncontact anterior cruciate ligament injuries: knee joint kinematics in 10 injury situations from female team handball and basketball

    Koga H, Nakamae A, Shima Y, Iwasa J, Myklebust G, Engebretsen L, et al.

    2010Am J Sports Med 38(11):2218–2225 · PMID 20595545doi:10.1177/0363546510373570

  90. A systematic review of sensorimotor function during adolescence: a developmental stage of increased motor awkwardness?

    Quatman-Yates CC, Quatman CE, Meszaros AJ, Paterno MV, Hewett TE

    2012Br J Sports Med 46(9):649–655 · PMID 21459874doi:10.1136/bjsm.2010.079616

  91. Current trends in sport injury prevention

    Emery CA, Pasanen K

    2019Best Pract Res Clin Rheumatol 33(1):3–15 · PMID 31431273doi:10.1016/j.berh.2019.02.009

  92. The incidence and prevalence of ankle sprain injury: a systematic review and meta-analysis of prospective epidemiological studies

    Doherty C, Delahunt E, Caulfield B, Hertel J, Ryan J, Bleakley C

    2014Sports Med 44(1):123–140 · PMID 24105612doi:10.1007/s40279-013-0102-5

  93. Injury incidence and injury patterns in professional football: the UEFA injury study

    Ekstrand J, Hägglund M, Waldén M

    2011Br J Sports Med 45(7):553–558 · PMID 19553225doi:10.1136/bjsm.2009.060582

  94. Decreased incidence of knee posterior cruciate ligament injury in Australian Football League after ruck rule change

    Orchard JW, Seward H

    2009Br J Sports Med 43(13):1026–1030 · PMID 19850572doi:10.1136/bjsm.2009.063123

  95. Dyslipidaemia in Africa—comment on a recent systematic review–Authors' reply

    Noubiap JJ, Balti EV, Bigna JJ, Echouffo-Tcheugui JB, Kengne AP

    2019Lancet Glob Health 7(3):e308–e309 · PMID 30553650doi:10.1016/S2214-109X(18)30517-5

  96. Kaeding CC, Pedroza AD, Reinke EK, Huston LJ, Consortium M, Spindler KP. Risk factors and predictors of subsequent ACL injury in either knee after ACL reconstruction: prospective analysis of 2488 primary ACL reconstructions from the MOON cohort. Am J Sports Med. 2015;43(7):1583–90.10.1177/0363546515578836PMC460155725899429

    Authors not recorded

    · PMID 25899429doi:10.1177/0363546515578836

  97. Buckthorpe M. Recommendations for movement re-training after ACL reconstruction. Sports Med. 2021:1–18.10.1007/s40279-021-01454-533840081

    Authors not recorded

    · PMID 33840081doi:10.1007/s40279-021-01454-5

Article record

The record

Status

Living reprint · journal article Journal version available

Status
Version of record: Sports Medicine 2022
Journal version
Sports Medicine (2022) · doi:10.1007/s40279-022-01697-w
Confirmed
2022-05-27
Licence
CC BY 4.0

This reprint has no DOI of its own: cite the journal article.

The journal's version is the version of record. This page re-typesets it under the article's licence; where they differ, the journal's version is authoritative.

Version history

  1. Published in Sports Medicine2022-05-27
  2. Living reprint on Literature Decoded2026-10-01
  3. Reprinted here2026-10-01
  4. This HTML2026-10-01

Cite this article

Citation
Chia L, Silva DDO, Whalan M, McKay MJ, Sullivan J, Fuller CW, et al. Non-contact Anterior Cruciate Ligament Injury Epidemiology in Team-Ball Sports: A Systematic Review with Meta-analysis by Sex, Age, Sport, Participation Level, and Exposure Type. Sports Med. 2022;52(10):2447–2467. doi:10.1007/s40279-022-01697-w
BibTeX
@article{Chia2022Noncontact,
  title   = {Non-contact Anterior Cruciate Ligament Injury Epidemiology in Team-Ball Sports: A Systematic Review with Meta-analysis by Sex, Age, Sport, Participation Level, and Exposure Type},
  author  = {Lionel Chia and Danilo De Oliveira Silva and Matthew Whalan and Marnee J. McKay and Justin Sullivan and Colin W. Fuller and Evangelos Pappas},
  journal = {Sports Medicine},
  year    = {2022},
  volume  = {52},
  number  = {10},
  pages   = {2447–2467},
  doi     = {10.1007/s40279-022-01697-w},
  pmid    = {35622227}
}
RIS
TY  - JOUR
TI  - Non-contact Anterior Cruciate Ligament Injury Epidemiology in Team-Ball Sports: A Systematic Review with Meta-analysis by Sex, Age, Sport, Participation Level, and Exposure Type
AU  - Lionel Chia
AU  - Danilo De Oliveira Silva
AU  - Matthew Whalan
AU  - Marnee J. McKay
AU  - Justin Sullivan
AU  - Colin W. Fuller
AU  - Evangelos Pappas
JO  - Sports Medicine
PY  - 2022
VL  - 52
IS  - 10
SP  - 2447
EP  - 2467
DO  - 10.1007/s40279-022-01697-w
SN  - 0112-1642
UR  - https://doi.org/10.1007/s40279-022-01697-w
ER  -

Cite the original article (the version of record), not this reprint.

Take it with you

One self-contained HTML file: the text, data and figures, readable offline.

Disclaimer and limitations

Educational summary of research findings; not medical advice. Discuss care decisions with a qualified clinician.

The findings apply to the included study populations and may not generalise to every person or setting.

Licence and attribution

This page reproduces Non-contact Anterior Cruciate Ligament Injury Epidemiology in Team-Ball Sports: A Systematic Review with Meta-analysis by Sex, Age, Sport, Participation Level, and Exposure Type by Lionel Chia, Danilo De Oliveira Silva, Matthew Whalan, Marnee J. McKay, Justin Sullivan, Colin W. Fuller, Evangelos Pappas, first published in Sports Medicine 2022;52(10):2447–2467, doi:10.1007/s40279-022-01697-w, PMID 35622227, PMC9136558. © The Author(s) 2022. It is used under the CC BY 4.0 licence.

Changes made:

The words, data, figures and conclusions are the authors’. Literature Decoded is not the publisher of record and does not claim endorsement by the authors or the journal. The version of record is the journal’s; cite it.

Licence statement in the article: “Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.”

Are you an author of this article? Claim it or ask us to take it down.

Cite this article

Living reprint · journal article