Abstract
BACKGROUND
Injuries are common in soccer as it is a contact sport. However, longitudinal studies spanning multiple seasons, focused on injuries in female university soccer players in Japan, are lacking. Additionally, broader comparisons on injury patterns remain unexplored. The purpose of this study was to prospectively investigate injuries among female university soccer players over three seasons.
STUDY DESIGN
Descriptive epidemiological study
METHODS
A total of 108 players from a university women’s soccer team were included. A qualified athletic trainer assessed injury type (injury or disability), site and severity according to injury occurrence (game or practice) over three seasons. An orthopedic surgeon confirmed the resulting injury.
RESULTS
The overall injury rate was 1.62/1000 athlete exposures. The game injury rate (9.86/1000 athlete exposures) was higher than the practice injury rate (1.39/1000 athlete exposures). Sprains were the most common injury in games and practices; concussions and meniscal tears were also common during games. The lower limbs, particularly the ankles and feet, were the most common anatomical injury sites in both games and practices. Severe injuries requiring long-term withdrawal were frequently observed during games and practices. These included anterior cruciate ligament (ACL) tears, meniscal injuries, and severe concussions, all of which necessitated prolonged recovery periods and withdrawal from team activities.
CONCLUSION
Injury prevention measures should be prioritized for female university soccer players, especially to address the high injury rate during games and the frequent occurrence of severe injuries.
LEVEL OF EVIDENCE
3b
Keywords: Epidemiology, Injury surveillance, Injury rate, Women’s soccer
INTRODUCTION
Soccer is an extremely popular sport worldwide. The number of female soccer players in Japan is approximately 27,798, according to the Japan Football Association’s latest report. (JFA, 2023) In recent years, the number of female soccer players has increased. The Japanese victory in the Fédération Internationale de Football Association (FIFA) Women’s World Cup in 2011 served as an opportunity to raise awareness and increase the popularity of women’s soccer. The Japan Women’s Professional Soccer League, commonly known as the WE League, started in 2021. Japan’s female soccer competitions are expected to be further developed and incentivized in the future.
In soccer, injuries occur frequently because of multiple contact plays and sudden physical movements, including sprinting and turning.1,2 Injury surveys of soccer players conducted in other countries have reported that lower-extremity injuries, such as injuries to the thighs, knee joints, and ankle joints, are common. Moreover, soccer players are considered to be at a high risk for injuries, including muscle strains, sprains, and bruises.1,3,4 Previous reports have focused on elite athletes, often male soccer players, with only few injury studies focusing on female soccer players.5 Although several studies have analyzed injuries among female soccer players, most have focused on a single season.6,7 Consequently, longitudinal studies spanning multiple seasons are limited, particularly those examining female university soccer players in Japan.
Additionally, while this study focused on a single women’s university soccer team, broader comparisons, such as those considering injury patterns by position, across different study years, or in relation to other competition levels, remain unexplored.
To date, many soccer-related injury surveys using only one evaluation method have been conducted; nonetheless, the definitions of injury and the calculation methods for its incidence rates differ among reports, making it difficult to compare and study them. Accordingly, FIFA proposed a unified injury investigation method.8 However, few studies in Japan have used this survey method, and surveys that follow up over several seasons are limited. Therefore, the purpose of this study was to prospectively investigate injuries among female university soccer players over three seasons.
METHODS
Participants
This prospective study included a total of 108 players from a university women’s soccer team, all competing at a similar university-level playing standard. Data were collected in real-time over three seasons, with predefined protocols for injury assessment and recording. To analyze the data for each position, the players were divided into four groups according to their position: forward (FW), midfielder (MF), defender (DF), and goalkeeper (GK). The participants practiced two hours a day, six days a week, and had won the ‘All Japan Women’s University Soccer Championship’ and Women’s Soccer League multiple times, for which games were held on weekends. Players were classified based on their school year at the university, which corresponds to their academic level and playing experience. The classifications used were Freshman (1st year), Sophomore (2nd year), Junior (3rd year), and Senior (4th year). These terms were used to standardize the categorization and provide a clear understanding of the participants’ experience level.
The study was approved by the Institutional Human Research Ethics Committee and conducted in compliance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before data collection.
Injury Assessment
All injuries that occurred during the 2018–2020 seasons were investigated. Injuries were recorded separately for each season (2018, 2019, and 2020), and an athlete who sustained injuries in multiple years was counted multiple times. All injuries were recorded by the Japan Sports Association’s qualified trainers, and the injuries were confirmed by an orthopedic surgeon. An “injury” was defined as any of the following: (1) an injury that occurred during a soccer practice or game that prevented the player from participating in a practice or game for longer than 1 day; (2) an injury caused by a single external force during a competition, and (3) an injury caused by gradual worsening of symptoms. The survey items included the type, site, and severity of injury and whether the injury occurred during a game or practice.
Injuries were classified into two categories: traumatic injuries or overuse injuries. These were further classified into eight category types: sprains, strains, contusions, fractures, concussions, tendinitis, meniscal tears, or others. These categories included various injury types based on the mechanism of injury, severity, and body part involved. Contusions and concussions were classified as traumatic injuries, not overuse injuries, as these types of injuries result from direct impact or sudden movements rather than repetitive stress. The injured sites were classified into four major areas: head (neck, face and scalp), upper limbs (shoulder, upper arm, elbow, forearm, wrist, hand, and finger), trunk (chest, abdomen, upper and lower back), and lower limbs (thighs, gluteus, knee, lower leg, ankle and foot). The severity of injuries were defined according to the number of days from injury to recovery, as recommended by FIFA: 1–3 days as “minimal,” 4–7 days as “mild,” 8–28 days as “moderate,” and 29 days or more as “severe.” The athletic trainer initially classified the injuries based on the mechanism and severity of the injury. The team physician, who was also the surgeon, then confirmed the final diagnosis after reviewing the injury details provided by the athletic trainer. The classification by the athletic trainer was performed prior to the physician’s confirmation.
Athlete exposure (AE) was defined as a competitor’s participation in a game or practice. Game and practice AEs were calculated by multiplying the average number of players participating in each game or practice by the number of games or practices, respectively.
Statistical Analyses
The number of injuries per 1000 AEs was calculated by dividing the number of injuries by the number of AEs and multiplying it by 1000. The practice injury rate (PIR) and game injury rate (GIR) were calculated using 95% confidence intervals (CIs) by game and practice. The injury ratio (IRR) represented the relationship between the GIR and PIR and was calculated by dividing the GIR by the PIR. The IRR CI was used to determine differences in the origin (trauma or overuse), type (sprain, strain, contusion, fracture, concussion, tendinitis, meniscal tear, or other), site, and severity of injury during games and practices. The IRR of the games and practices was compared among the three seasons using the χ2 test. We also calculated the number and percentage of injuries during games and practices according to the player’s position and study year. Statistical analyses were performed using IBM SPSS statistics, ver. 26 (IBM Corp., Armonk, NY, USA).
RESULTS
The number of players included each year was as follows: 2018, 40 participants; 2019, 37 participants; and 2020, 31 participants. The participants had an average age, height, and weight of 19.3±1.2 years, 163.5±5.9 cm, and 58.1±6.9 kg, respectively. Table 1 presents the athletes game and practice hours, by position, used to calculate AE’s for games and practices.
Table 1. Game and practice athlete-hours.
| 2018 | 2019 | 2020 | Total | |
| Number of players | 40 | 37 | 31 | 108 |
| Forward | 11 | 14 | 8 | 33 |
| Midfielder | 13 | 8 | 7 | 28 |
| Defender | 8 | 7 | 8 | 23 |
| Goalkeeper | 8 | 8 | 8 | 24 |
| Game athlete-hours | 1341.4 | 983.7 | 313.0 | 2638.1 |
| Practice athlete-hours | 23877.4 | 41316.0 | 25755.4 | 90948.9 |
| Total athlete-hours | 25218.9 | 42299.7 | 26068.4 | 93587.0 |
NOTE: Game athlete-hours and practice athlete-hours for the year 2020 were lower compared to other study years, primarily due to the restrictions imposed by the COVID-19 pandemic. This may have influenced the results, particularly the injury rate comparisons between study years.
Injury Incidence
Table 2 presents the incidence of injuries. A total of 152 injuries were reported during the 2018–2020 seasons, comprising 110 (72.4%) trauma and 42 (27.6%) overuse injuries. Of these, 126 (82.9%) occurred during practice and 26 (17.1%) during games. The overall injury rate was 1.62/1000 AEs (95% CI 1.37–1.88), trauma injury rate was 1.18/1000 AEs (95% CI 0.96–1.40), and overuse injury rate was 0.45/1000 AEs (95% CI 0.31–0.58). The overall PIR and GIR were 1.39/1000 AEs (95% CI 1.14–1.63) and 9.86/1000 AEs (95% CI 6.07–13.64), respectively. The overall IRR was 7.11/1000 AEs (95% CI 5.98–8.24), indicating that the overall GIR was higher than the overall PIR (p<0.05). A similar trend was observed within each season from 2018 to 2020.
Table 2. Total injury rates in a university women’s soccer team (2018-20).
| Practice | Game | Total | |||||||||||||||||||||||||||||||||||
| n | (%) | IR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | IRR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | ||||||||||||||||
| Total | All injury | 126 | (100) | 1.39 | ( | 1.14 | - | 1.63 | ) | 26 | (100) | 9.86 | ( | 6.07 | - | 13.64 | ) | 7.11 | ( | 5.98 | - | 8.24 | ) | ＊ | 152 | (100) | 1.62 | ( | 1.37 | - | 1.88 | ) | |||||
| Trauma | 86 | (67.5) | 0.95 | ( | 0.75 | - | 1.15 | ) | 24 | (96.2) | 9.48 | ( | 5.76 | - | 13.19 | ) | 10.02 | ( | 8.15 | - | 11.89 | ) | ＊ | 110 | (72.4) | 1.18 | ( | 0.96 | - | 1.40 | ) | ||||||
| Overuse | 40 | (32.5) | 0.44 | ( | 0.30 | - | 0.58 | ) | 2 | (3.8) | 0.38 | ( | 0.00 | - | 5.55 | ) | 0.86 | ( | 0.60 | - | 1.12 | ) | 42 | (27.6) | 0.45 | ( | 0.31 | - | 0.58 | ) | |||||||
| 2020 | All injury | 36 | (100) | 1.40 | ( | 0.94 | - | 1.85 | ) | 4 | (100) | 12.78 | ( | 0.26 | - | 25.30 | ) | 9.14 | ( | 6.31 | - | 11.98 | ) | ＊ | 40 | (100) | 1.53 | ( | 1.06 | - | 2.01 | ) | |||||
| Trauma | 28 | (75.0) | 1.05 | ( | 0.65 | - | 1.44 | ) | 3 | (75.0) | 9.58 | ( | 0.00 | - | 20.43 | ) | 9.14 | ( | 5.92 | - | 12.36 | ) | ＊ | 31 | (77.5) | 1.19 | ( | 0.77 | - | 1.61 | ) | ||||||
| Overuse | 8 | (25.0) | 0.35 | ( | 0.12 | - | 0.58 | ) | 1 | (25.0) | 3.19 | ( | 2.58 | - | 3.81 | ) | 9.14 | ( | 3.17 | - | 15.12 | ) | ＊ | 9 | (22.5) | 0.35 | ( | 0.12 | - | 0.57 | ) | ||||||
| 2019 | All injury | 52 | (100) | 1.26 | ( | 0.92 | - | 1.60 | ) | 9 | (100) | 9.15 | ( | 3.17 | - | 15.13 | ) | 7.27 | ( | 5.45 | - | 9.09 | ) | ＊ | 61 | (100) | 1.44 | ( | 1.08 | - | 1.80 | ) | |||||
| Trauma | 36 | (69.2) | 0.87 | ( | 0.59 | - | 1.16 | ) | 9 | (100) | 9.15 | ( | 3.17 | - | 15.13 | ) | 10.50 | ( | 7.43 | - | 13.57 | ) | ＊ | 45 | (73.8) | 1.06 | ( | 0.75 | - | 1.37 | ) | ||||||
| Overuse | 16 | (30.8) | 0.39 | ( | 0.20 | - | 0.58 | ) | 0 | 0 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 16 | (26.2) | 0.38 | ( | 0.19 | - | 0.56 | ) | |||||||
| 2018 | All injury | 38 | (100) | 1.59 | ( | 1.09 | - | 2.10 | ) | 13 | (100) | 9.69 | ( | 4.42 | - | 14.96 | ) | 6.09 | ( | 4.42 | - | 7.76 | ) | ＊ | 51 | (100) | 2.02 | ( | 1.47 | - | 2.58 | ) | |||||
| Trauma | 21 | (60.5) | 0.96 | ( | 0.57 | - | 1.36 | ) | 13 | (100) | 9.69 | ( | 4.42 | - | 14.96 | ) | 10.06 | ( | 6.68 | - | 13.44 | ) | ＊ | 34 | (66.7) | 1.35 | ( | 0.90 | - | 1.80 | ) | ||||||
| Overuse | 17 | (39.5) | 0.63 | ( | 0.31 | - | 0.95 | ) | 0 | 0 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 17 | (33.8) | 0.67 | ( | 0.35 | - | 0.99 | ) | |||||||
Abbreviations: CI, confidence interval; IR, injury rate = injuries per 1000 athlete exposures; IRR, injury rate ratio = game/practice injury rate. ＊: p<0.05.
Incidence of Injuries by Position and Year of Study
Table 3 shows the results of injury incidence according to player position and study year. The DF position had the highest frequency of injuries, with 73 (48.0%) cases. There were 50 (32.9%) injuries among MFs, 15 (9.9%) among GKs, and 14 (9.2%) among FWs. DFs had the highest PIR and GIR, 0.67/1000 AEs (95% CI 0.50–0.84) and 4.55/1000 AEs (95% CI 1.98–7.12), respectively. MFs had the highest IRR, 9.72/1000 AEs (95% CI 7.03–12.42). The IRR in GKs was 8.62/1000 AEs (95% CI 4.26–12.98), and in DFs was 6.78/1000 AEs (95% CI 5.23–8.34). The GIRs in DFs, GKs, and MFs were higher than the PIRs (P<0.05). Women in their third year had the highest frequency of injuries, with 46 (30.3%) cases, followed by women in their fourth, second, and first years, with 39 (25.7%), 37 (24.3%), and 30 (19.1%) cases, respectively. The PIR was the highest for players in their third year (0.43/1000 AEs; 95% CI 0.29–0.56). The GIR was the highest for players in their first year (3.03/1000 AEs; 95% CI 0.98–5.13). The IRR was the highest for players in their first year, at 12.54/1000 AEs (95% CI 8.05–17.02), followed by that for those in their second, third, and fourth years (8.04/1000 AEs, 95% CI 5.45–10.64; 6.19/1000 AEs, 95% CI 4.40–7.98; and 3.94/1000 AEs, 95% CI 2.70–5.18, respectively). The GIR was higher than the PIR in all study years (p<0.05).
Table 3. Total injury rates in a university women’s soccer team in 2018–2020 by player position and year of study.
| Practice | Game | Total | |||||||||||||||||||||||||||||||||||
| n | (%) | IR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | IRR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | ||||||||||||||||
| Position | |||||||||||||||||||||||||||||||||||||
| Forward | 14 | (11.1) | 0.15 | ( | 0.07 | - | 0.23 | ) | 0 | 0.0 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 14 | (9.2) | 0.15 | ( | 0.07 | - | 0.23 | ) | |||||||
| Midfielder | 39 | (31.0) | 0.43 | ( | 0.29 | - | 0.56 | ) | 11 | (42.3) | 4.17 | ( | 1.71 | - | 6.63 | ) | 9.72 | ( | 7.03 | - | 12.42 | ) | ＊ | 50 | (32.9) | 0.53 | ( | 0.39 | - | 0.68 | ) | ||||||
| Defender | 61 | (48.4) | 0.67 | ( | 0.50 | - | 0.84 | ) | 12 | (46.2) | 4.55 | ( | 1.98 | - | 7.12 | ) | 6.78 | ( | 5.23 | - | 8.34 | ) | ＊ | 73 | (48.0) | 0.78 | ( | 0.60 | - | 0.96 | ) | ||||||
| Goalkeeper | 12 | (9.5) | 0.13 | ( | 0.06 | - | 0.21 | ) | 3 | (11.5) | 1.14 | ( | 0.00 | - | 2.42 | ) | 8.62 | ( | 4.26 | - | 12.98 | ) | ＊ | 15 | (9.9) | 0.16 | ( | 0.08 | - | 0.24 | ) | ||||||
| School year | |||||||||||||||||||||||||||||||||||||
| 4 | 35 | (27.8) | 0.38 | ( | 0.26 | - | 0.51 | ) | 4 | (15.4) | 1.52 | ( | 0.03 | - | 3.00 | ) | 3.94 | ( | 2.70 | - | 5.18 | ) | ＊ | 39 | (25.7) | 0.42 | ( | 0.29 | - | 0.55 | ) | ||||||
| 3 | 39 | (31.0) | 0.43 | ( | 0.29 | - | 0.56 | ) | 7 | (26.9) | 2.65 | ( | 0.69 | - | 4.62 | ) | 6.19 | ( | 4.40 | - | 7.98 | ) | ＊ | 46 | (30.3) | 0.49 | ( | 0.35 | - | 0.63 | ) | ||||||
| 2 | 30 | (23.8) | 0.33 | ( | 0.21 | - | 0.45 | ) | 7 | (26.9) | 2.65 | ( | 0.69 | - | 4.62 | ) | 8.04 | ( | 5.45 | - | 10.64 | ) | ＊ | 37 | (24.3) | 0.40 | ( | 0.27 | - | 0.52 | ) | ||||||
| 1 | 22 | (17.5) | 0.24 | ( | 0.14 | - | 0.34 | ) | 8 | (30.8) | 3.03 | ( | 0.93 | - | 5.13 | ) | 12.54 | ( | 8.05 | - | 17.02 | ) | ＊ | 30 | (19.7) | 0.32 | ( | 0.21 | - | 0.44 | ) | ||||||
Abbreviations: CI, confidence interval; IR, injury rate = injuries per 1000 athlete exposures; IRR, injury rate ratio = game/practice injury rate. *: p<0.05.
Type of Injury
Table 4 presents the results by injury type. Sprains were the most common injury type (n=54, 35.5%), followed by others (n=32, 21.1%) and strains (n=22, 14.5%). Sprains had the highest PIR and GIR among the injury types, at 0.40/1000 AEs (95% CI 0.27–0.53) and 6.82/1000 AEs (95% CI 3.67–9.98), respectively. Meniscal tears had the highest IRR (22.98/1000 AEs, 95% CI 2.84–43.13), followed by sprains (17.24/1000 AEs, 95% CI 12.64–21.83), concussions (11.49/1000 AEs, 95% CI 4.99–17.99), and contusions (3.45/1000 AEs, 95% CI 1.41–5.48). The GIR was higher than the PIR for meniscal tears, sprains, concussions, and contusions (p<0.05).
Table 4. Injury rates in a university women’s soccer team in 2018–2020 by injury type.
| Practice | Game | Total | ||||||||||||||||||||||||||||||||||
| n | (%) | IR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | IRR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | |||||||||||||||
| Sprain | 36 | (28.6) | 0.40 | ( | 0.27 | - | 0.53 | ) | 18 | (69.2) | 6.82 | ( | 3.67 | - | 9.98 | ) | 17.24 | ( | 12.64 | - | 21.83 | ) | ＊ | 54 | (35.5) | 0.58 | ( | 0.42 | - | 0.73 | ) | |||||
| Strain | 21 | (16.7) | 0.23 | ( | 0.13 | - | 0.33 | ) | 1 | (3.8) | 0.38 | ( | 0.00 | - | 1.12 | ) | 1.64 | ( | 0.96 | - | 2.33 | ) | 22 | (14.5) | 0.24 | ( | 0.14 | - | 0.33 | ) | ||||||
| Contusion | 10 | (7.9) | 0.11 | ( | 0.04 | - | 0.18 | ) | 1 | (3.8) | 0.38 | ( | 0.00 | - | 1.12 | ) | 3.45 | ( | 1.41 | - | 5.48 | ) | ＊ | 11 | (7.2) | 0.12 | ( | 0.05 | - | 0.19 | ) | |||||
| Fracture | 3 | (2.4) | 0.03 | ( | 0.00 | - | 0.07 | ) | 0 | 0.0 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 3 | (2.0) | 0.03 | ( | 0.00 | - | 0.07 | ) | ||||||
| Concussion | 9 | (7.1) | 0.10 | ( | 0.03 | - | 0.16 | ) | 3 | (11.5) | 1.14 | ( | 0.00 | - | 2.42 | ) | 11.49 | ( | 4.99 | - | 17.99 | ) | ＊ | 12 | (7.9) | 0.13 | ( | 0.06 | - | 0.20 | ) | |||||
| Tendinitis | 12 | (9.5) | 0.13 | ( | 0.06 | - | 0.21 | ) | 1 | (3.8) | 0.38 | ( | 0.00 | - | 1.12 | ) | 2.87 | ( | 1.31 | - | 4.43 | ) | 13 | (8.6) | 0.14 | ( | 0.06 | - | 0.21 | ) | ||||||
| Meniscus tear | 3 | (2.4) | 0.03 | ( | 0.00 | - | 0.07 | ) | 2 | (7.7) | 0.76 | ( | 0.00 | - | 1.81 | ) | 22.98 | ( | 2.84 | - | 43.13 | ) | ＊ | 5 | (3.3) | 0.05 | ( | 0.01 | - | 0.10 | ) | |||||
| Other | 32 | (25.4) | 0.35 | ( | 0.23 | - | 0.47 | ) | 0 | 0.0 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 32 | (21.1) | 0.34 | ( | 0.22 | - | 0.46 | ) | ||||||
Abbreviations: CI, confidence interval; IR, injury rate = injuries per 1000 athlete exposures; IRR, injury rate ratio = game/practice injury rate. ＊: p<0.05.
Site of Injury
Table 5 presents the results by injury site. The lower limbs were the most frequently injured site (126 cases, 82.8%). There were 14 (9.21%) head and neck injuries and 11 (7.24%) trunk and back injuries. The PIR of the lower limbs was 1.39/1000 AEs (95% CI 1.25–1.52), and the respective GIR was 8.34/1000 AEs (95% CI 6.56–10.12), which was the highest among those of the different injury sites. The IRR of the upper limbs was the highest, at 34.47/1000 AEs (95% CI 0.00–102.04), followed by that of the head and neck (7.39/1000 AEs, 95% CI, 3.52–11.26) and lower limbs (6.02/1000 AEs, 95% CI 4.97–7.07). The GIR of the upper limbs, head and neck, and lower limbs was higher than the corresponding PIR (P<0.05). Among the lower limbs, the ankles and feet were the most frequently injured sites (47 cases, 30.9%). There were 34 (22.3%) cases of thigh and gluteal injuries and 31 (20.3%) of knee injuries. The PIR of the ankles and feet was 0.52/1000 AEs (95% CI 0.43–0.60), and the GIR was 4.55/1000 AEs (95% CI 3.24–5.86), which were the highest among those of the lower limbs. The elbow and forearm had the highest IRR (34.47, 95% CI 0.00–102.04), followed by the knee (8.90/1000 AEs, 95% CI 5.76–12.03), ankle and foot (8.80/1000 AEs, 95% CI 6.29–11.32), and thigh and gluteus (2.03/1000 AEs, 95% CI 1.35–2.71). The GIR of the elbow and forearm, ankle and foot, knee, and head and neck were higher than the corresponding PIR (p<0.05). Table 6 provides further details of the injuries. In the lower limbs, which were the most frequently injured parts, ankle and foot inversion sprains, knee and anterior cruciate ligament (ACL) injuries, and meniscal injuries were the most common.
Table 5. Injury rates in a university women’s soccer team in 2018–2020 by injured body part.
| Practice | Game | Total | |||||||||||||||||||||||||||||||||||
| n | (%) | IR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | IRR | ( | 95%CI | ) | n | (%) | IR | ( | 95%CI | ) | ||||||||||||||||
| Head and Neck | Total | 11 | (8.73) | 0.15 | ( | 0.11 | - | 0.20 | ) | 3 | (11.53) | 1.14 | ( | 0.48 | - | 1.79 | ) | 7.39 | ( | 3.52 | - | 11.26 | ) | ＊ | 14 | (9.21) | 0.15 | ( | 0.11 | - | 0.19 | ) | |||||
| Head and Neck | 10 | (7.93) | 0.14 | ( | 0.10 | - | 0.19 | ) | 3 | (11.53) | 1.14 | ( | 0.48 | - | 1.79 | ) | 7.96 | ( | 3.63 | - | 12.28 | ) | ＊ | 13 | (8.55) | 0.14 | ( | 0.10 | - | 0.18 | ) | ||||||
| Face and scalp | 1 | (0.79) | 0.01 | ( | 0.00 | - | 0.02 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 1 | (0.66) | 0.01 | ( | 0.00 | - | 0.02 | ) | |||||||
| Upper limb | Total | 0 | 0.00 | 0.01 | ( | 0.00 | - | 0.00 | ) | 1 | (3.84) | 0.38 | ( | 0.00 | - | 0.76 | ) | 34.47 | ( | 0.00 | - | 102.04 | ) | ＊ | 1 | (0.66) | 0.01 | ( | 0.00 | - | 0.02 | ) | |||||
| Shoulder and upper arm | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | |||||||
| Elbow and forearm | 0 | 0.00 | 0.01 | ( | 0.00 | - | 0.00 | ) | 1 | (3.84) | 0.38 | ( | 0.00 | - | 0.76 | ) | 34.47 | ( | 0.00 | - | 102.04 | ) | ＊ | 1 | (0.66) | 0.01 | ( | 0.00 | - | 0.02 | ) | ||||||
| Wrist, hand, and finger | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | |||||||
| Trunk and back | Total | 11 | (8.73) | 0.12 | ( | 0.08 | - | 0.16 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 11 | (7.24) | 0.12 | ( | 0.08 | - | 0.15 | ) | ||||||
| Chest, abdomen, and upper back | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | |||||||
| Lower back | 11 | (8.73) | 0.12 | ( | 0.08 | - | 0.16 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 11 | (7.23) | 0.12 | ( | 0.08 | - | 0.15 | ) | |||||||
| Lower limb | Total | 104 | (82.53) | 1.39 | ( | 1.25 | - | 1.52 | ) | 22 | (84.60) | 8.34 | ( | 6.56 | - | 10.12 | ) | 6.02 | ( | 4.97 | - | 7.07 | ) | ＊ | 126 | (82.89) | 1.35 | ( | 1.23 | - | 1.47 | ) | |||||
| Thigh and gluteus | 32 | (25.39) | 0.37 | ( | 0.31 | - | 0.44 | ) | 2 | (7.69) | 0.76 | ( | 0.22 | - | 1.29 | ) | 2.03 | ( | 1.35 | - | 2.71 | ) | 34 | (22.37) | 0.36 | ( | 0.30 | - | 0.43 | ) | |||||||
| Knee | 23 | (18.25) | 0.34 | ( | 0.27 | - | 0.41 | ) | 8 | (30.76) | 3.03 | ( | 1.96 | - | 4.10 | ) | 8.90 | ( | 5.76 | - | 12.03 | ) | ＊ | 31 | (20.39) | 0.33 | ( | 0.27 | - | 0.39 | ) | ||||||
| Lower leg | 14 | (11.11) | 0.15 | ( | 0.11 | - | 0.20 | ) | 0 | 0.00 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 14 | (9.21) | 0.15 | ( | 0.11 | - | 0.19 | ) | |||||||
| Ankle and foot | 35 | (27.77) | 0.52 | ( | 0.43 | - | 0.60 | ) | 12 | (46.15) | 4.55 | ( | 3.24 | - | 5.86 | ) | 8.80 | ( | 6.29 | - | 11.32 | ) | ＊ | 47 | (30.92) | 0.50 | ( | 0.43 | - | 0.58 | ) | ||||||
Abbreviations: CI, confidence interval; IR, injury rate = injuries per 1000 athlete exposures; IRR, injury rate ratio = game/practice injury rate. ＊: p<0.05.
Table 6. Injury details of a university women’s soccer team in 2018–2020.
| Practice | Game | |||||||||||||||||||
| Head and Neck | ||||||||||||||||||||
| Head and Neck | Concussion | Concussion | ||||||||||||||||||
| Face and scalp | Nasal bone fracture | NA | ||||||||||||||||||
| Upper limb | ||||||||||||||||||||
| Shoulder and upper arm | NA | NA | ||||||||||||||||||
| Elbow and forearm | NA | Medial collateral ligament injury | ||||||||||||||||||
| Wrist, hand, and finger | NA | NA | ||||||||||||||||||
| Trunk and back | ||||||||||||||||||||
| Chest, abdomen, and upper back | NA | NA | ||||||||||||||||||
| Lower back | Lumbar spondylosis, sacral stress fracture, myofascial low back pain | NA | ||||||||||||||||||
| Lower limb | ||||||||||||||||||||
| Thigh and gluteus | Quadriceps fasciitis, rectus femoris muscle tear, adductor muscle tear, hamstring muscle strain, iliopsoas muscle strain, femoral lateral epicondyle strain, thigh muscle strain, external obturator muscle strain, groin pain | Thigh muscle contusion | ||||||||||||||||||
| Knee | Anterior cruciate ligament injury/rupture, meniscus injury, patellar tendonitis, Infrapatellar Fat Pad Syndrome, patella bruise | Anterior cruciate ligament injury/rupture, meniscus injury | ||||||||||||||||||
| Lower leg | Peroneal tendonitis, Achilles tendinopathy, medial tibial stress syndrome, triceps surae contusion, peroneal muscle injury, tibia stress fracture | NA | ||||||||||||||||||
| Ankle and foot | Ankle inversion sprain (anterior talofibular ligament injury, calcaneofibular ligament injury), talus bone contusion, Lisfranc joint injury, ankle valgus sprain (triangular ligament injury), Plantar fasciopathy | Ankle inversion sprain (anterior talofibular ligament injury, calcaneofibular ligament injury), heel fat pad inflammation | ||||||||||||||||||
Injury Severity
Table 7 presents the injury severity results. Most cases were severe (n=63, 41.4%), followed by moderate (n=49, 32.2%), mild (n=18, 11.8%) and minimal (n=22, 14.5%) cases. Severe injuries had a PIR of 0.52/1000 AEs (95% CI 0.37–0.66) and a GIR of 6.06/1000 AEs (95% CI 3.09–9.04), the highest among the different injury severities. Severe injuries also had the highest IRR (11.74/1000 AEs, 95% CI 0.00–14.63), followed by mild (9.85/1000 AEs, 95% CI 0.00–14.40) and moderate injuries (4.81/1000 AEs, 95% CI 0.00–6.16). The GIR was higher than the PIR in severe, moderate, and mild injuries (P<0.05).
Table 7. Injury rates according to severity of injury in a university women’s soccer team in 2018–2020.
| Practice | Game | Total | ||||||||||||||||||||||||||||||||||
| n | (%) | IR | ( | 95%CI) | n | (%) | IR | ( | 95%CI) | IRR | ( | 95%CI) | n | (%) | IR | ( | 95%CI) | |||||||||||||||||||
| Minimal | 22 | (17.5) | 0.24 | ( | 0.14 | - | 0.34 | ) | 0 | 0.0 | 0.00 | ( | 0.00 | - | 0.00 | ) | 0.00 | ( | 0.00 | - | 0.00 | ) | 22 | (14.5) | 0.24 | ( | 0.14 | - | 0.33 | ) | ||||||
| Mild | 14 | (11.1) | 0.15 | ( | 0.07 | - | 0.23 | ) | 4 | (15.4) | 1.52 | ( | 0.03 | - | 3.00 | ) | 9.85 | ( | 0.00 | - | 14.40 | ) | ＊ | 18 | (11.8) | 0.19 | ( | 0.10 | - | 0.28 | ) | |||||
| Moderate | 43 | (34.1) | 0.47 | ( | 0.33 | - | 0.61 | ) | 6 | (23.1) | 2.27 | ( | 0.45 | - | 4.09 | ) | 4.81 | ( | 0.00 | - | 6.16 | ) | ＊ | 49 | (32.2) | 0.52 | ( | 0.38 | - | 0.67 | ) | |||||
| Severe | 47 | (37.3) | 0.52 | ( | 0.37 | - | 0.66 | ) | 16 | (61.5) | 6.06 | ( | 3.09 | - | 9.04 | ) | 11.74 | ( | 0.00 | - | 14.63 | ) | ＊ | 63 | (41.4) | 0.67 | ( | 0.51 | - | 0.84 | ) | |||||
Abbreviations: CI, confidence interval; IR, injury rate = injuries per 1000 athlete exposures; IRR, injury rate ratio = game/practice injury rate. ＊: p<0.05.
DISCUSSION
A prospective injury study was conducted in female collegiate soccer players over three seasons. Overall, injuries tended to occur more frequently in games than in practices, as reported in previous studies.9,10 Furthermore, the current results revealed that the incidence of soccer-related injuries was higher during games than during practices regardless of the playing level, position, or study year. The injury rate was the highest among MFs and DFs during games and practices. Previous reports have shown that GKs have a lower injury rate than players in other positions and MFs experience the highest number of injuries among field players.11,12 Although a similar trend was observed here, where the number of injuries was the highest for DFs and the lowest for FWs, it is difficult to clarify whether these numbers were influenced by player’s sex (only females were studied) or competition level. In contrast, a previous report showed no difference in the frequency of injuries between positions.13
There was no significant difference in the injury rate sustained during practices between study years. The reasons for the high injury rate among first-year university players may include changes in practice environment, extended practice periods, improving quality, differing levels of competition and mental stress, and decreasing physical strength due to examinations. However, the authors did not record the medical history of players before entering the university. After entering university and before starting full-scale competitive activities each player’s physical condition was assessed including medical history, muscle strength, and flexibility, and athletes were screened for injuries. The authors speculate that prior influences at the site of trauma before entering university may also have influenced the high injury rate among first-year players. However, within their first year at university, the injury rate among first-year students decreased over time, potentially due to improvements in their physical conditioning or their increased experience. Regarding injury site, similar to previous reports,6,8,14,15 the ankle and knee joints were the most common injury sites, with ligament injuries the most common. The ankle joint is the most frequently injured site during sports activities, accounting for approximately 30% of all sports injuries, of which ankle joint sprains account for approximately 70%. In addition, the re-injury rate is very high and has a significant impact on performance; therefore, the implementation of a preventive program has been described as essential.16 The risk of sustaining ACL injuries in female soccer players is 2.67 times higher than that in male soccer players.17 This difference has been attributed to the lower activity of the hamstrings and gluteus maximus in women than in men when changing direction and landing, which are frequent movements in soccer.18,19 In addition, direction-changing motions unintentionally increase knee valgus angle and valgus moment.20
The FIFA 11+ is a preventive program that was created by the FIFA Medical Assessment and Research Centre.21,22 Numerous interventions have verified the usefulness of this preventive program, reporting a significant decrease in the total injury incidence rate and incidence of overuse injuries and severe injuries, confirming its preventive effect.23,24 In addition, FIFA 11+ reduces the valgus moment of the knee joint, which is considered a risk factor for ACL injuries that frequently occur in female soccer players, and has been recently reported to improve sprinting and jumping abilities.25–28 As such, FIFA 11+ is a useful program, not only for injury prevention but also for performance improvement and may be useful for these female soccer players.
Concussions have also been previously reported to be a frequent and significant injury in female collegiate soccer players,29 with an estimated incidence approximately 2.4 times higher than that in their male counterparts.29 In this study, concussions were one of the most common injuries, highlighting the importance of identifying concussion in female soccer players. Most concussions in soccer are caused by contact with the ball rather than interpersonal contact.29 Heading is a typical movement unique to soccer required for passing and shooting, however the head suffers a direct physical impact. In the past, based on this factor, changes in rules and concussion-preventive measures were adopted.30,31 Wearing headgear is a typical example of this. However, previous reports have provided both positive and negative outcomes regarding headgear wearing; thus, its effectiveness is currently unclear.30–32 In addition, although the importance of concussion prevention is clear, few workplaces are implementing preventive measures.33 Education regarding sex-related differences is urgent, and further research is required.
In the current results, it was clear that injury severity was higher in games than in practices. Severe injuries were the most common, as were injuries requiring long recovery times. Compared with practice, the intensity of contact is higher in games, and there are many unpredictable factors including variability in opponents. Therefore, it is possible that more severe injuries occur in games, due to higher intensity and unpredictable factors such as opponent variability, while practice may have more potential for injury prevention. In addition to lower-extremity injuries, such as ankle sprains, the participants in this study had several severe injuries that required time to recover including ACL injuries, knee meniscus injuries, and concussions. The severity of these injuries has been previously reported, but the findings have been inconsistent.14,34 Since this study used the FIFA severity classification, it is difficult to compare the current results with those of previous reports, as only a few studies have used the same classification system. In the future, the authors plan to conduct further investigations based on a unified definition of injury and classification of severity.
Longitudinal epidemiological data collection is the first step toward injury prevention. Although many studies on soccer-related injuries have focused on male individuals and elite athletes, this is one of the first long-term injury studies on female university soccer players. Based on this research, concussion and knee injury preventive measures should be further considered for female soccer players, in addition to preventative measures for other lower-extremity injuries.
This study has some limitations. First, only a single soccer team was included; injury type and frequency may vary depending on the competition level, practice environment, and regional characteristics. Second, a detailed analysis of the injury mechanism was not possible because the authors could not investigate the three relevant categories (contact, non-contact, and other). Finally, a detailed classification of the training content was not performed, prohibiting assessment of training related variables with injury.
CONCLUSIONS
This study is one of the first long-term injury surveys of female university soccer players. The results indicate that soccer-related injuries were found to occur more frequently in games than in practice regardless of the playing level, position, or study year. In addition to injuries to the lower limbs, serious injuries requiring long recovery times were also observed, including concussions.
Conflicts of interest
The authors report no conflicts of interest.
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