Spatiotemporal Patterning and Multivariate Risk of Acute Injuries in Elite Rugby: A Cohort Based on Prospective Surveillance

Spatiotemporal Patterning and Multivariate Risk of Acute Injuries in Elite Rugby: A Cohort Based on Prospective Surveillance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Patterning and Multivariate Risk of Acute Injuries in Elite Rugby: A Cohort Based on Prospective Surveillance Mei Xiaodong, Lu Yingjie, Liao Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7443394/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute injuries are common in rugby and threaten both player health and career longevity. Previous studies often focused on isolated risk factors, while limited research has comprehensively examined the interplay of physiological, environmental, and situational variables. Methods Study conducted a cohort study of 40 elite male players from the Tianjin Rugby Team, monitoring 575 match exposures across 2.5 consecutive seasons (2022–2025). Acute injuries were defined according to international consensus criteria and verified by medical staff. Spatiotemporal distributions (seasonal variation, match stage, playing position, and body site) were analyzed using chi-square and logistic regression. Multivariate models were applied to identify independent risk factors including demographic, training, and environmental variables. Results A total of 143 acute injury events were recorded, with bone and joint injuries most prevalent (48.2%) and the majority classified as moderate-to-severe (78.3%). Injury rate rose significantly to 1.84 per 1,000 player-hours in 2024/25 vs. 1.83 in 2022/23 and advanced competition stages (OR for finals = 7.06 vs. group stage, p < 0.001). Forwards exhibited higher risk than backs (OR = 1.51), and injuries most frequently involved the head/face, upper limbs, and lower limbs. Multivariate regression identified excessive training load (OR = 3.78), extreme temperatures (OR ≈ 6.5–6.8), elite athletic level (OR = 1.84), prior injury history (OR = 5.98), poor sleep quality (OR = 5.64), and mild fatigue (OR = 2.48) as significant predictors. Conclusions Acute injury risk in rugby demonstrates clear spatiotemporal patterns and is strongly influenced by both individual and environmental factors. The model developed provides a practical basis for targeted prevention strategies, including load management, environmental adaptation, and individualized recovery protocols. These findings may assist coaches and medical teams in optimizing training and competition management, while future research should expand cohorts and integrate rugby acute injury spatiotemporal distribution risk factors logistic regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rugby is widely regarded as one of the most physically demanding team sports, involving frequent high-impact collisions, rapid offensive-defensive transitions, and complex technical maneuvers. These characteristics contribute to a substantial burden of acute injuries, which may compromise athletic performance, reduce career longevity, and generate considerable medical and economic costs for both teams and society. For instance, nationwide data from New Zealand Rugby indicate that seasonal injuries can lead to significant wage losses and treatment expenditures, with direct medical costs constituting a greater proportion in high-income nations [1–2] . These realities underscore the urgent need for effective injury prevention strategies.​ Prior research has identified several key contributors to rugby-related injuries, including a history of previous injury, imbalances in muscle strength, such as defective internal/external rotation strength ratios, and accumulated fatigue [3] . Epidemiological studies have further emphasized the predominance of lower limb injuries—accounting for 42.5%-58.4% of cases and including particularly hamstring strains and ankle sprains—while also highlighting a notable incidence of clavicle injuries among male players (13.0 injuries/100 player-games) [4] . Additionally, environmental and situational factors such as playing surface, protective equipment appropriateness, and specific match phases such as ball contests, significantly influence injury odds [5–6] .​ Despite these insights, most studies have examined risk factors in isolation, limiting the broader understanding of how physiological, environmental, and situational determinants interact to elevate injury risk [6] . This gap is particularly evident in the context of Chinese rugby, where Li Yingchao et al. reported a 35.22% injury incidence rate among English rugby players in Chongqing municipality during 2022 (118 cases out of 335 participants) [7] . Compounding this challenge is the significant variability in recovery trajectories—from one week for mild ankle sprains to six to twelve months for anterior cruciate ligament tears, often accompanied by secondary deficits in decision-making speed and performance upon return to play [8] —which underscores the limitations of solitary intervention measures in minimizing cumulative risk. [9–12] ​ The study aims to address these limitations by systematically evaluating the spatiotemporal dynamics of acute injuries in elite rugby players across multiple competitive seasons. Specifically, we investigate how injury risk varies across temporal dimensions (seasonal progression and within-season competition stages) and spatial dimensions (injury locations and player positions). Uniquely integrating these spatiotemporal patterns with individualized variables (such as fatigue and sleep history), our study analyzes a multivariate Chinese rugby cohort to generate the first discriminative equation for acute injury prediction. By establishing this comprehensive risk model, we seek to provide a theoretical foundation for evidence-based risk management strategies, enhanced injury prevention effectiveness, optimized load management, and improved competition planning—ultimately supporting player health and athletic performance in rugby. Methodology Study Design & Data Source This investigation constitutes a retrospective cohort study based on prospective injury monitoring. It involves forty male national professional athletes from the Tianjin rugby team who met the inclusion criteria. Of them, there are 12 members of the national team, representing China at international competitions. Keep on documenting the number of acute injury events occurring during the games in 2.5 continuous seasons (from September 2022 to December 2023 as the first half-season; from March 2024 to June 2025 as the second half-season). There are 28 rounds of league games each season (one game each round for every team. For 15-a-side games, 80 minutes for every game, two 40-minute periods; for 7-a-side games, 14 minutes for every game, two 7-minute periods). The same player may have multiple records of injuries or non-injuries in different games. There are 575 person-times of match exposure data, of which 143 person-times had acute injuries during the exposure time, 432 person-times had no injuries at all. The injury incidence rate reached 1.78 per 1,000 player-hours. Acute injury refers to the injury occurring suddenly during a game or exercise, which makes the athlete unable to continue the current activity or requires treatment, excluding chronic strain or old injury recurrence. Sample Size and Inclusion Criteria Sample size estimation was performed using G*Power 3.1.9.7 for logistic regression (binomial test), with α = 0.05 and power = 0.80. Preliminary data suggested an odds ratio of 1.78, and a minimum of 563 player-match exposures was required. The final dataset included 575 exposures, meeting the statistical requirement. Inclusion criteria were: (1) male athletes aged 18–28 years; (2) registered professional players; (3) complete exposure and injury data; and (4) no history of severe cardiovascular or cerebrovascular disease. Exclusion criteria included chronic musculoskeletal disorders, recurrent injuries prior to the study period, or incomplete questionnaires. Data Collection Procedures Player data were collected before each competition using the online platform “Questionnaire Star,” supplemented with interviews for clarification. Variables included demographic data (age, height, weight, playing experience, competitive level), history of injury, training load, fatigue, and sleep quality. Training load was documented weekly (hours and subjective intensity rating on a 1–10 scale) and validated by cross-checking with coaching staff schedules. Fatigue was assessed using the Fatigue Scale-14 (FS-14), while sleep quality was evaluated with the Pittsburgh Sleep Quality Index (PSQI). Environmental data, including match-day temperature, were also recorded. Injury data were independently verified by team medical staff and categorized according to type (bone/joint, soft tissue, ligament, concussion, or organ), anatomical site (head/face, upper limbs, torso, lower limbs), and severity (trivial to career-ending, based on time lost). Recovery status and return-to-play decisions were confirmed by physicians and physiotherapists. [13] The detailed procedures for data collection and queue construction are provided in the Appendix*. Recruitment and Informed Consent Procedures Recruitment channels: Issuing official invitation letters to registered players by means of the Tianjin Rugby Association, specifying the purpose of research, methods of entry, as well as the right of withdrawal. Content of the informed consent form: It includes the research background, the scope of data collection (health indicators, injury records), privacy protection measures (anonymous processing), the principle of voluntary participation and contact information. Signing process: Members of the research team will explain the content of the informed consent form one-on-one to ensure that participants fully understand it, and then they will sign a paper document, which will be archived for future reference. Exposure and Injury Rate Calculation Match exposure hours were calculated using the formula: Exposure time during matches (hours) = Number of players × Match duration (hours) × Number of matches The incidence of acute injuries was expressed per 1,000 match exposure hours: Incidence rate = (Number of injuries / Total exposure hours) × 1,000 Statistical Analysis All analyses were conducted using SPSS v26.0 (IBM, USA). Categorical variables were summarized as frequencies and percentages, with comparisons performed using chi-square or Fisher’s exact tests. Continuous variables were tested for normality and reported as mean ± SD or median (IQR), with independent t-tests or Mann–Whitney U tests applied as appropriate. Principal component analysis was used to test multicollinearity among independent variables. Logistic regression models (univariate and multivariate, forward stepwise) were applied to estimate odds ratios (ORs) with 95% confidence intervals (CIs). Model fit was evaluated using the Hosmer–Lemeshow test, and statistical significance was set at p < 0.05. Ethical Considerations The study was approved by the Ethics Committee of Tianjin University of Sport (Approval No. 2025-090). Written informed consent was obtained from all participants, with additional guardian consent required for athletes under 18 years old. Data confidentiality and anonymity were maintained throughout the study. Variables and Strategies Demographic Characteristics: Age was grouped into quartiles ( 24 years). Body mass index (BMI) was calculated as weight (kg)/height² (m²) and classified according to Chinese adult reference standards as underweight, normal, overweight, and obese. Athletic Characteristics: Competitive level (elite vs. first-level) and years of rugby participation (< 2 years, 2–5 years, ≥ 5 years) were recorded. Previous injury history (yes/no) was documented through medical verification. Training and Recovery Variables: Training load was assessed weekly using hours of training and subjective intensity ratings (1–10 scale). Fatigue was measured with the Fatigue Scale-14 (FS-14) and categorized as normal (0–9), mild (10–12), or severe (13–14). Sleep quality was evaluated with the Pittsburgh Sleep Quality Index (PSQI), classified as very good (0–5), good (6–10), average (11–15), or poor (16–21). Environmental Variables: Ambient temperature on match days was recorded and classified as low ( 25°C). Spatiotemporal Factors: Season (2022/23, 2023/24, 2024/25), competition stage (group stage, semi-finals, finals), field position (forwards vs. backs), and anatomical site of injury (head/face, upper limbs, torso, lower limbs) were coded as categorical predictors. [12–20] All injury rates expressed per 1,000 player-hours. Results Participant Characteristics A total of 575 player-match exposures were recorded from 40 athletes, with 143 acute injuries over 80,500 exposure hours (1.78 per 1,000 player-hours). The median age was 22 years (IQR: 21–24), and 62.9% of participants competed at the elite level. Previous injury history was common (78.0%) and strongly associated with acute injury occurrence (χ²=16.19, p < 0.001). BMI distribution and years of participation showed no significant relationship with injury risk. As shown in Table 1. The participants' age structure has a bimodal feature: participants under 21 years of age (23.8%) and participants over 24 years of age (15.0%) combine to make up 38.8%. There is no significant relationship between the distribution of grades and injury occurrence (χ² = 4.526, P = 0.033). Table 1. Distribution of BMI classification. BMI classification Non-injured(%) Injured(%) Chi-squaretest (p) 18.5 < x < 24 19.44 26.57 0.162 24 ≤ x < 28 69.44 65.03 ≥ 28 11.11 8.39 Over 90% of the participants had a sports experience of ≥ 5 years. The chi-square test indicated that there was no significant relationship between injury occurrence and duration of sports (χ² = 1.268, P = 0.561). Distribution and Types of Acute Injuries Among the 143 acute injuries, 78.3% were classified as moderate-to-severe. Bone and joint injuries were most frequent (48.2%), followed by soft tissue injuries (34.3%), while concussions and organ injuries were rare (< 5%). Anatomical sites most affected were the lower limbs (42.7%) and upper limbs (41.9%), with fewer injuries to the head/face (7.7%) and torso (7.7%). Spatiotemporal Distribution Seasonal variation: Injury incidence increased across seasons, from 43.3/1,000 exposure hours in 2022/23 to 127.9/1,000 in 2024/25 (χ²=7.64, p = 0.022). The risk in 2024/25 was 1.83-fold higher than in 2022/23.As shown in Fig. 1 . Competition stage: Injury rates escalated from group stage (55.2 per 1,000 player-hours) to semi-finals (82.8 per 1,000 player-hours) and finals (131.7 per 1,000 player-hours), with finals showing a sevenfold higher risk compared with group matches (OR = 7.06, p < 0.001). Field position: Forwards exhibited 17.4 injuries per 1,000 player-hours vs. backs: 7.9 (OR = 1.51, p = 0.046). Injury sites: Logistic regression confirmed elevated risks for injuries to the head/face (OR = 13.55), upper limbs (OR = 13.86), torso (OR = 4.77), and lower limbs (OR = 10.73), compared with other sites (all p < 0.01). As shown in Fig. 2 .and Table 2. Table 2. Impact of spatiotemporal distribution characteristics on the occurrence of acute injuries among rugby players. Variable β value standarderror Wald(2 P-value OR 95%CI Temporal Distribution Characteristics Season 2023/24(ref = 2022/23) 0.107 0.281 0.144 0.704 1.112 0.642–1.928 2024/25(ref = 2022/23) 0.606 0.275 4.857 0.028 1.833 1.069–3.142 Match Phase Semi-finals (ref = group stage) 0.604 0.261 5.346 0.021 1.830 1.096–3.054 Finals (ref = group stage) 1.955 0.262 55.796 <0.001 7.064 4.229–11.798 Spatial Distribution Characteristics Injury Sites Head and face (ref = non-head and face) 2.606 0.392 44.187 <0.001 13.550 6.283–29.219 Upper limbs (ref = non-upper limbs) 2.629 0.336 61.399 <0.001 13.859 7.180–26.750 Torso (ref = non-torso) 1.562 0.536 8.485 0.004 4.769 1.667–13.642 Lower limbs (ref = non-lower limbs) 2.373 0.258 84.352 <0.001 10.733 6.468–17.812 Field Positions Forwards (ref = back players) 0.411 0.207 3.941 0.047 1.509 1.005–2.265 Univariate Analysis Significant associations with injury occurrence were observed for age group, competitive level, previous injury history, training load, ambient temperature, sleep quality, and fatigue (all p < 0.05). No significant effects were found for BMI or years of participation. Shown in Table 3. Table 3. Univariate analysis of acute injuries. Metric Acute Injuries χ² value P-value No. (n = 432) Yes(n = 143) Age ( years ) 8.859 0.031 24 65(15.05) 36(25.17) BMI(kg/m2) 3.641 0.162 ≤ 18.5 0(0) 0(0) 18.5<x<24 84(19.44) 38(26.57) 24 ≤ x<28 300(69.44) 93(65.03) ≥ 28 48(11.11) 12(8.39) Athletic Level 4.526 0.033 Elite 272(62.96) 104(72.73) Level 1 160(37.04) 39(27.27) Years of Participation in Rugby 1.268 0.561 ≤ 2 4(0.93) 2(1.4) 2<x<5 45(10.42) 11(7.69) ≥ 5 383(88.66) 130(90.91) Previous Injury History 16.190 <0.001 No. 95(21.99) 10(6.99) Yes 337(78.01) 133(93.01) Training Load 50.542 <0.001 High 76(17.59) 58(40.56) Medium 238(55.09) 62(43.36) Low 188(43.52) 23(16.08) Temperature 86.869 <0.001 High 24(5.56) 27(18.88) Normal 338(78.24) 52(36.36) Low 70(16.2) 64(44.76) Sleep Quality 34.530 <0.001 Very Good 55(12.73) 23(16.08) Good 356(82.41) 93(65.03) Average 14(3.24) 11(7.69) Poor 7(1.62) 16(11.19) Fatigue Scores 21.206 <0.001 None 338(78.24) 84(58.74) Mild Fatigue 92(21.3) 57(39.86) Severe Fatigue 2(0.46) 2(1.4) *All ORs reference baseline rate of 1.78 per 1,000 player-hours. Multivariate Logistic Regression The independent predictors of acute injury and their corresponding effects on the logarithmic scale are presented in Fig. 3. below. The regression model demonstrated good fit (Hosmer–Lemeshow p > 0.05), indicating adequate discrimination of risk factors. Distribution of Basic Characteristics of Acute Injuries Figure 4 . presents in the 143 acute injuries which rugby players sustained, the greater percentage of them, that is, 78.32% of cases, had been of severe injuries. Minor, as well as mild injuries, had been less common, accounting for 8.39% as well as 13.29% of the total acute injuries, respectively. No instances of trivial injuries or career-ending/non-fatal catastrophic injuries had been reported(shown in Fig. 4 ). Regarding the types of injuries, joint and bone injuries were the most prevalent, representing 48.25% of the acute injuries. This was followed by soft tissue injuries, which accounted for 34.27%. Other injury types included ligament injuries, concussions, and organ injuries, representing 13.99%, 2.10%, and 1.40% of the acute injuries, respectively (As shown in Fig. 5 ). Results of Multivariate Logistic Regression Analysis This study used binary logistic regression to identify independent risk factors for acute rugby injuries, with principal component analysis confirming no significant multicollinearity (eigenvalues ≥ 0.534). Key findings show that high training loads, extreme temperatures (both high and low), and higher competitive levels (elite athletes facing 1.837 times greater risk than first-level athletes) significantly increase injury risk. Individual factors like prior injury history, poor sleep quality, and mild fatigue are also closely linked to acute injuries, though severe fatigue shows no statistically significant effect. These factors interact synergistically, indicating that injury prevention strategies should comprehensively address seasonal planning, training load management, and recovery processes.As shown in Table 4. Table 4. Multivariate logistic regression analysis of acute injuries Variable β value Standard Error Wald χ2 P-value OR 95%CI Training Load Medium (ref = low) 0.558 0.316 3.118 0.077 1.747 0.940–3.246 High (ref = low) 1.329 0.337 15.515 <0.001 3.779 1.950–7.321 Temperature 62.706 <0.001 High (ref = normal) 1.924 0.265 52.571 <0.001 6.849 4.071–11.521 Low (ref = normal) 1.877 0.363 26.659 <0.001 6.531 3.203–13.315 Athletic Level Elite (ref = level 1) 0.608 0.268 5.155 0.023 1.837 1.087–3.105 Previous Injury History Yes (ref = no) 1.789 0.408 19.191 <0.001 5.982 2.687–13.316 Sleep Quality 21.612 <0.001 Good (ref = very good) -0.521 0.333 2.452 0.117 0.594 0.309–1.140 Average (ref = very good) 0.791 0.607 1.700 0.192 2.207 0.671–7.252 Poor (ref = very good) 1.730 0.618 7.852 0.005 5.643 1.682–18.929 Fatigue Symptoms 13.155 0.001 Mild Fatigue (ref = none) 0.909 0.251 13.142 <0.001 2.481 1.518–4.054 Severe Fatigue (ref = none) 0.102 1.339 0.006 0.939 1.107 0.08-15.271 Constant -4.534 0.617 54.074 <0.001 0.011 Discussion This study systematically examined the spatiotemporal distribution and multivariate risk factors of acute injuries among elite rugby players across multiple competitive seasons. The findings highlight several important contributions to the field of sports medicine and injury prevention. Key Findings and Contributions First, the study confirms that acute injuries in rugby are predominantly moderate-to-severe, with bone and joint injuries most common. Importantly, the risk of injury increases substantially during the later stages of the season and in high-stakes competition phases such as finals. These results underscore the cumulative effect of physical load and competitive stress on player vulnerability. While prior studies have often focused on single factors such as match phase or injury type, our research integrates temporal, spatial, and physiological dimensions, offering a more comprehensive understanding of injury dynamics in rugby. Second, positional differences were evident: forwards experienced significantly higher injury risk than backs, consistent with their greater involvement in high-impact collisions such as scrums and rucks. This positional disparity reinforces the need for tailored prevention strategies, such as enhanced eccentric strength and stability training for forwards. Third, the multivariate regression model identified several independent predictors of acute injury, including high training loads, extreme temperatures, elite competition level, prior injury history, poor sleep quality, and mild fatigue. The combination of physiological, environmental, and behavioral factors highlights the multifactorial nature of injury risk. By quantifying these effects, our model provides an evidence-based framework for individualized injury prevention and early warning systems. Comparison with Previous Literature Our results are broadly consistent with prior research indicating that cumulative training load, fatigue, and previous injury are among the strongest predictors of sports injuries. [21–25] However, this study extends existing knowledge by demonstrating that environmental extremes—both heat and cold—are comparably potent risk factors. The integration of sleep quality and fatigue assessments also advances current models, as these recovery-related variables are often underrepresented in rugby injury surveillance. Practical Implications From an applied perspective, these findings support the implementation of multifactorial monitoring systems in elite rugby. Regular assessment of training load, sleep quality, and fatigue can enable early identification of at-risk players. Coaches and medical teams should adjust workloads, introduce mandatory rotation during congested schedules, and employ targeted interventions such as heat adaptation protocols or recovery-focused training. Moreover, positional demands necessitate role-specific injury prevention programs. Limitations and Future Directions Several limitations should be acknowledged. It is declared as a pilot study based on a single team with a relatively small sample size, which may limit generalizability, and multi-center validation is recommended. Training load was self-reported and not complemented by objective biomarkers such as GPS data, lactate concentration, or heart rate variability. [26–29] Additionally, injury surveillance focused on match play, excluding training-related injuries that may contribute to overall risk. Future research should address these gaps by incorporating larger multicenter cohorts, integrating wearable monitoring technologies, and extending surveillance to both training and competition contexts. Conclusion This study demonstrated that acute injuries among elite rugby players follow distinct spatiotemporal patterns, with risks heightened during later competitive stages, finals, and in players occupying forward positions. Bone and joint injuries were most common, and the majority were of moderate-to-severe grade, underscoring the heavy burden of injury in this sport. Multivariate analysis identified high training load, exposure to extreme temperatures, elite competition level, prior injury history, poor sleep quality, and mild fatigue as independent predictors of acute injury. These findings confirm the multifactorial nature of rugby injury risk and highlight the interplay of physiological, environmental, and situational determinants. From a practical perspective, the results support the development of individualized prevention strategies and monitoring systems. Training loads should be carefully periodized, recovery protocols strengthened, and environmental adaptation strategies adopted. Sleep and fatigue monitoring can provide early warning signals, while position-specific programs may help address the elevated risks faced by forwards. Although limited by sample size and single-team design, this study establishes a quantitative risk model that can serve as a theoretical and practical reference for coaches, medical staff, and policymakers. Future research integrating larger multicenter cohorts and wearable monitoring technologies will further enhance the precision and applicability of such models. In conclusion, the present work provides a robust foundation for evidence-based injury prevention and management in elite rugby, with implications extending to other collision-intensive sports. Declarations Ethic Approval & Consent Participation This study has received ethical approval from the Ethics Committee of the Tianjin University of Sports (approval number: 2025-090). Participants provided consent through an informed consent process reviewed by the Ethics Committee, ensuring compliance with the ethical standards set forth in the 1964 Declaration of Helsinki. Consent for Publication The data, figures, and images presented in this manuscript are original or have been authorized and licensed legally. The study guarantee that ethical standards have been followed in human experiments, and informed consent has been obtained from the subjects. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research did not receive external funding support. Author Contributions Mei Xiaodong (First Author) Research Design and Implementation: Led the design of the study protocol and completed both prospective data collection and retrospective cohort construction. Data Analysis and Model Development: Performed all statistical analyses, including Chi-square tests and logistic regression modeling. Manuscript Writing: Responsible for drafting the abstract, methods, results, discussion, and conclusions. Injury Data Verification: Collaborated with the medical team of the Tianjin British Rugby Team to categorize the severity and follow-up of 143 acute injuries. Lu Yingjie Data Collection and Processing: Established a standardized injury database. Literature Review: Systematically reviewed literature related to risk assessment of sports injuries. Environmental Factor Analysis: Collected and standardized data on covariates such as ambient temperature and training load. Liao Peng (Corresponding Author) Academic Guidance: Proposed a theoretical framework for analyzing the spatiotemporal distribution and multivariate risk coupling. Author Agreement All authors have seen and approved the final version of the submitted manuscript. They guarantee that this article is the author's original work, has not been published in advance, and has not been considered for publication elsewhere. References King DA, Clark TN, Hume PA, Hind KJSm, science h. 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The Influence of in-Season Training Loads on Injury Risk in Professional Rugby Union. International journal of sports physiology and performance (2016) 11(3):350-5. Bjelanovic L, Mijatovic D, Sekulic D, Modric T, Kesic MG, Klasnja A, et al. Injury Occurrence in Amateur Rugby: Prospective Analysis of Specific Predictors over One Half-Season. Medicina (2023) 59(3):579. Murray-Smith S, Williams S, Whalan M, Peoples GE, Sampson JA. The Incidence and Burden of Injury in Male Adolescent Community Rugby Union in Australia. Science and medicine in football (2023) 7(4):315-22. Fuller CW, Molloy MG. Epidemiological Study of Injuries in Men's International under-20 Rugby Union Tournaments. Clinical journal of sport medicine (2011) 21(4):356-8. Brown S, Brughelli M, Cross MR. Profiling Sprint Mechanics by Leg Preference and Position in Rugby Union Athletes. International journal of sports medicine (2016) 37(11):890-7. Tranaeus U, Gledhill A, Johnson U, Podlog L, Wadey R, Wiese Bjornstal D, et al. 50 Years of Research on the Psychology of Sport Injury: A Consensus Statement. Sports medicine (2024) 54(7):1733-48. Li YJ, Wang J, Zhou Y. Research Progress on the Characteristics and Preventive Measures of Contact-Related Injuries in British Rugby Players. Chinese Journal of Sports Medicine (2022) 41(12):966-74. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7443394","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515543429,"identity":"fc8e9766-baf6-4ded-9ba0-33cb8dd01004","order_by":0,"name":"Mei Xiaodong","email":"","orcid":"","institution":"Tianjin University of Sports","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Xiaodong","suffix":""},{"id":515543431,"identity":"51b2508b-2d3c-4413-9edb-3df93c086602","order_by":1,"name":"Lu Yingjie","email":"","orcid":"","institution":"Tianjin University of Sports","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yingjie","suffix":""},{"id":515543433,"identity":"7bce15a9-0b2f-4687-9d71-970bfbd0d50c","order_by":2,"name":"Liao Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3RMQrCMBSA4SeFdIlmbSneoRCog9JepSXQWXBxcAgIHZ3rLSqCcyDQLr2AIKgIPYI4qS3qGjMK5l+SQD4SeAAm0w82sN4bAhB3a49/I+hD3EybfDZ++Vo1iG2dL9P5IdweeePeYDIshNWc1B9DlOZ1w3YlpB6GlBYCjXw1gcDrZ5IFHQGQSSEwctTEvnr9u2Q0g7T92EOH4PYVLkMfQepgEFpkRvNSxk4ZszH2GV1LFCgJIdXmMl3IiCzrZH+bh8NVtWyUpKubZsIBx+103kcdEgHYQuOuyWQy/WNPajNBGb98VJ0AAAAASUVORK5CYII=","orcid":"","institution":"Tianjin University of Sports","correspondingAuthor":true,"prefix":"","firstName":"Liao","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-08-23 22:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7443394/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7443394/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91421062,"identity":"e2aa4ef9-fdaa-43a5-931c-5522d8ae693e","added_by":"auto","created_at":"2025-09-16 10:11:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11696,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of acute injuries among rugby players during seasons. (per 1,000 player-hours) (Note: The last season is not a complete data season.)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/dcab130b0e7ec0ee821387ec.png"},{"id":91419897,"identity":"7b6122e7-7c39-4868-866e-57207abc3cfd","added_by":"auto","created_at":"2025-09-16 10:03:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12760,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of the spatial distribution of acute injuries in rugby players. (Note: The last season is not a complete data season.)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/8ca784f2485d69f5c4437dca.png"},{"id":91419898,"identity":"b8dcd801-9d6e-48a9-96c5-d8ff32cac7d1","added_by":"auto","created_at":"2025-09-16 10:03:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35087,"visible":true,"origin":"","legend":"\u003cp\u003eForecast plot of independent predictors of acute injury.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/2efeb371f34f3996fdde0b03.png"},{"id":91421063,"identity":"7cdea3b8-6fa7-4de1-b48d-048c30ffa6c4","added_by":"auto","created_at":"2025-09-16 10:11:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":190483,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of severity of acute injuries. Distribution based on 143 injuries over 80,500 player-hours.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/f7d70c9372ec54288c4b353d.png"},{"id":91419901,"identity":"c23d635c-bcbe-44c6-8a4d-5732332e9ecc","added_by":"auto","created_at":"2025-09-16 10:03:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96654,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of the injured body. Distribution based on 143 injuries over 80,500 player-hours.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/12b723e93ebd295ed09bd3b3.jpeg"},{"id":97664837,"identity":"2d9bb327-b062-44c2-b7f6-7ca084db7799","added_by":"auto","created_at":"2025-12-08 09:14:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1250202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/13ee10aa-0d74-4f3b-b3b1-dde539641ac7.pdf"},{"id":91419895,"identity":"afee433d-52ae-41b3-a19a-d1310a06ef92","added_by":"auto","created_at":"2025-09-16 10:03:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16068,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7443394/v1/f98310be2a3a7d79f43daaad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Patterning and Multivariate Risk of Acute Injuries in Elite Rugby: A Cohort Based on Prospective Surveillance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRugby is widely regarded as one of the most physically demanding team sports, involving frequent high-impact collisions, rapid offensive-defensive transitions, and complex technical maneuvers. These characteristics contribute to a substantial burden of acute injuries, which may compromise athletic performance, reduce career longevity, and generate considerable medical and economic costs for both teams and society. For instance, nationwide data from New Zealand Rugby indicate that seasonal injuries can lead to significant wage losses and treatment expenditures, with direct medical costs constituting a greater proportion in high-income nations\u003csup\u003e[1\u0026ndash;2]\u003c/sup\u003e. These realities underscore the urgent need for effective injury prevention strategies.​\u003c/p\u003e\u003cp\u003ePrior research has identified several key contributors to rugby-related injuries, including a history of previous injury, imbalances in muscle strength, such as defective internal/external rotation strength ratios, and accumulated fatigue\u003csup\u003e[3]\u003c/sup\u003e. Epidemiological studies have further emphasized the predominance of lower limb injuries\u0026mdash;accounting for 42.5%-58.4% of cases and including particularly hamstring strains and ankle sprains\u0026mdash;while also highlighting a notable incidence of clavicle injuries among male players (13.0 injuries/100 player-games)\u003csup\u003e[4]\u003c/sup\u003e. Additionally, environmental and situational factors such as playing surface, protective equipment appropriateness, and specific match phases such as ball contests, significantly influence injury odds\u003csup\u003e[5\u0026ndash;6]\u003c/sup\u003e.​\u003c/p\u003e\u003cp\u003eDespite these insights, most studies have examined risk factors in isolation, limiting the broader understanding of how physiological, environmental, and situational determinants interact to elevate injury risk\u003csup\u003e[6]\u003c/sup\u003e. This gap is particularly evident in the context of Chinese rugby, where Li Yingchao et al. reported a 35.22% injury incidence rate among English rugby players in Chongqing municipality during 2022 (118 cases out of 335 participants)\u003csup\u003e[7]\u003c/sup\u003e. Compounding this challenge is the significant variability in recovery trajectories\u0026mdash;from one week for mild ankle sprains to six to twelve months for anterior cruciate ligament tears, often accompanied by secondary deficits in decision-making speed and performance upon return to play\u003csup\u003e[8]\u003c/sup\u003e\u0026mdash;which underscores the limitations of solitary intervention measures in minimizing cumulative risk.\u003csup\u003e[9\u0026ndash;12]\u003c/sup\u003e​\u003c/p\u003e\u003cp\u003eThe study aims to address these limitations by systematically evaluating the spatiotemporal dynamics of acute injuries in elite rugby players across multiple competitive seasons. Specifically, we investigate how injury risk varies across temporal dimensions (seasonal progression and within-season competition stages) and spatial dimensions (injury locations and player positions). Uniquely integrating these spatiotemporal patterns with individualized variables (such as fatigue and sleep history), our study analyzes a multivariate Chinese rugby cohort to generate the first discriminative equation for acute injury prediction. By establishing this comprehensive risk model, we seek to provide a theoretical foundation for evidence-based risk management strategies, enhanced injury prevention effectiveness, optimized load management, and improved competition planning\u0026mdash;ultimately supporting player health and athletic performance in rugby.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003eStudy Design \u0026 Data Source\u003c/h2\u003e\u003cp\u003eThis investigation constitutes a retrospective cohort study based on prospective injury monitoring. It involves forty male national professional athletes from the Tianjin rugby team who met the inclusion criteria. Of them, there are 12 members of the national team, representing China at international competitions. Keep on documenting the number of acute injury events occurring during the games in 2.5 continuous seasons (from September 2022 to December 2023 as the first half-season; from March 2024 to June 2025 as the second half-season). There are 28 rounds of league games each season (one game each round for every team. For 15-a-side games, 80 minutes for every game, two 40-minute periods; for 7-a-side games, 14 minutes for every game, two 7-minute periods). The same player may have multiple records of injuries or non-injuries in different games. There are 575 person-times of match exposure data, of which 143 person-times had acute injuries during the exposure time, 432 person-times had no injuries at all. The injury incidence rate reached 1.78 per 1,000 player-hours. Acute injury refers to the injury occurring suddenly during a game or exercise, which makes the athlete unable to continue the current activity or requires treatment, excluding chronic strain or old injury recurrence.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Size and Inclusion Criteria\u003c/h2\u003e\u003cp\u003eSample size estimation was performed using G*Power 3.1.9.7 for logistic regression (binomial test), with α\u0026thinsp;=\u0026thinsp;0.05 and power\u0026thinsp;=\u0026thinsp;0.80. Preliminary data suggested an odds ratio of 1.78, and a minimum of 563 player-match exposures was required. The final dataset included 575 exposures, meeting the statistical requirement. Inclusion criteria were: (1) male athletes aged 18\u0026ndash;28 years; (2) registered professional players; (3) complete exposure and injury data; and (4) no history of severe cardiovascular or cerebrovascular disease. Exclusion criteria included chronic musculoskeletal disorders, recurrent injuries prior to the study period, or incomplete questionnaires.\u003c/p\u003e\u003cp\u003eData Collection Procedures\u003c/p\u003e\u003cp\u003ePlayer data were collected before each competition using the online platform \u0026ldquo;Questionnaire Star,\u0026rdquo; supplemented with interviews for clarification. Variables included demographic data (age, height, weight, playing experience, competitive level), history of injury, training load, fatigue, and sleep quality. Training load was documented weekly (hours and subjective intensity rating on a 1\u0026ndash;10 scale) and validated by cross-checking with coaching staff schedules. Fatigue was assessed using the Fatigue Scale-14 (FS-14), while sleep quality was evaluated with the Pittsburgh Sleep Quality Index (PSQI). Environmental data, including match-day temperature, were also recorded.\u003c/p\u003e\u003cp\u003eInjury data were independently verified by team medical staff and categorized according to type (bone/joint, soft tissue, ligament, concussion, or organ), anatomical site (head/face, upper limbs, torso, lower limbs), and severity (trivial to career-ending, based on time lost). Recovery status and return-to-play decisions were confirmed by physicians and physiotherapists.\u003csup\u003e[13]\u003c/sup\u003e The detailed procedures for data collection and queue construction are provided in the Appendix*.\u003c/p\u003e\u003cp\u003eRecruitment and Informed Consent Procedures\u003c/p\u003e\u003cp\u003eRecruitment channels: Issuing official invitation letters to registered players by means of the Tianjin Rugby Association, specifying the purpose of research, methods of entry, as well as the right of withdrawal.\u003c/p\u003e\u003cp\u003eContent of the informed consent form: It includes the research background, the scope of data collection (health indicators, injury records), privacy protection measures (anonymous processing), the principle of voluntary participation and contact information.\u003c/p\u003e\u003cp\u003e Signing process: Members of the research team will explain the content of the informed consent form one-on-one to ensure that participants fully understand it, and then they will sign a paper document, which will be archived for future reference.\u003c/p\u003e\u003cp\u003eExposure and Injury Rate Calculation\u003c/p\u003e\u003cp\u003eMatch exposure hours were calculated using the formula:\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure time during matches (hours)\u0026thinsp;=\u0026thinsp;Number of players \u0026times; Match duration (hours) \u0026times; Number of matches\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe incidence of acute injuries was expressed per 1,000 match exposure hours:\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIncidence rate = (Number of injuries / Total exposure hours) × 1,000\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted using SPSS v26.0 (IBM, USA). Categorical variables were summarized as frequencies and percentages, with comparisons performed using chi-square or Fisher\u0026rsquo;s exact tests. Continuous variables were tested for normality and reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR), with independent t-tests or Mann\u0026ndash;Whitney U tests applied as appropriate. Principal component analysis was used to test multicollinearity among independent variables. Logistic regression models (univariate and multivariate, forward stepwise) were applied to estimate odds ratios (ORs) with 95% confidence intervals (CIs). Model fit was evaluated using the Hosmer\u0026ndash;Lemeshow test, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eEthical Considerations\u003c/p\u003e\u003cp\u003e The study was approved by the Ethics Committee of Tianjin University of Sport (Approval No. 2025-090). Written informed consent was obtained from all participants, with additional guardian consent required for athletes under 18 years old. Data confidentiality and anonymity were maintained throughout the study.\u003c/p\u003e\u003cp\u003eVariables and Strategies\u003c/p\u003e\u003cp\u003eDemographic Characteristics: Age was grouped into quartiles (\u0026lt;\u0026thinsp;21, 21\u0026ndash;22, 23\u0026ndash;24, and \u0026gt;\u0026thinsp;24 years). Body mass index (BMI) was calculated as weight (kg)/height\u0026sup2; (m\u0026sup2;) and classified according to Chinese adult reference standards as underweight, normal, overweight, and obese. Athletic Characteristics: Competitive level (elite vs. first-level) and years of rugby participation (\u0026lt;\u0026thinsp;2 years, 2\u0026ndash;5 years, \u0026ge;\u0026thinsp;5 years) were recorded. Previous injury history (yes/no) was documented through medical verification. Training and Recovery Variables: Training load was assessed weekly using hours of training and subjective intensity ratings (1\u0026ndash;10 scale). Fatigue was measured with the Fatigue Scale-14 (FS-14) and categorized as normal (0\u0026ndash;9), mild (10\u0026ndash;12), or severe (13\u0026ndash;14). Sleep quality was evaluated with the Pittsburgh Sleep Quality Index (PSQI), classified as very good (0\u0026ndash;5), good (6\u0026ndash;10), average (11\u0026ndash;15), or poor (16\u0026ndash;21). Environmental Variables: Ambient temperature on match days was recorded and classified as low (\u0026lt;\u0026thinsp;10\u0026deg;C), normal (10\u0026ndash;25\u0026deg;C), or high (\u0026gt;\u0026thinsp;25\u0026deg;C). Spatiotemporal Factors: Season (2022/23, 2023/24, 2024/25), competition stage (group stage, semi-finals, finals), field position (forwards vs. backs), and anatomical site of injury (head/face, upper limbs, torso, lower limbs) were coded as categorical predictors.\u003csup\u003e[12\u0026ndash;20]\u003c/sup\u003e All injury rates expressed per 1,000 player-hours.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant Characteristics\u003c/p\u003e\u003cp\u003eA total of 575 player-match exposures were recorded from 40 athletes, with 143 acute injuries over 80,500 exposure hours (1.78 per 1,000 player-hours). The median age was 22 years (IQR: 21\u0026ndash;24), and 62.9% of participants competed at the elite level. Previous injury history was common (78.0%) and strongly associated with acute injury occurrence (χ\u0026sup2;=16.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). BMI distribution and years of participation showed no significant relationship with injury risk. As shown in Table\u0026nbsp;1. The participants' age structure has a bimodal feature: participants under 21 years of age (23.8%) and participants over 24 years of age (15.0%) combine to make up 38.8%. There is no significant relationship between the distribution of grades and injury occurrence (χ\u0026sup2; = 4.526, P\u0026thinsp;=\u0026thinsp;0.033).\u003c/p\u003e\u003cp\u003eTable 1. Distribution of BMI classification.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI classification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-injured(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInjured(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChi-squaretest (p)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026thinsp;\u0026lt;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOver 90% of the participants had a sports experience of \u0026ge;\u0026thinsp;5 years. The chi-square test indicated that there was no significant relationship between injury occurrence and duration of sports (χ\u0026sup2; = 1.268, P\u0026thinsp;=\u0026thinsp;0.561).\u003c/p\u003e\u003cp\u003eDistribution and Types of Acute Injuries\u003c/p\u003e\u003cp\u003eAmong the 143 acute injuries, 78.3% were classified as moderate-to-severe. Bone and joint injuries were most frequent (48.2%), followed by soft tissue injuries (34.3%), while concussions and organ injuries were rare (\u0026lt;\u0026thinsp;5%). Anatomical sites most affected were the lower limbs (42.7%) and upper limbs (41.9%), with fewer injuries to the head/face (7.7%) and torso (7.7%).\u003c/p\u003e\u003cp\u003eSpatiotemporal Distribution\u003c/p\u003e\u003cp\u003eSeasonal variation: Injury incidence increased across seasons, from 43.3/1,000 exposure hours in 2022/23 to 127.9/1,000 in 2024/25 (χ\u0026sup2;=7.64, p\u0026thinsp;=\u0026thinsp;0.022). The risk in 2024/25 was 1.83-fold higher than in 2022/23.As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCompetition stage: Injury rates escalated from group stage (55.2 per 1,000 player-hours) to semi-finals (82.8 per 1,000 player-hours) and finals (131.7 per 1,000 player-hours), with finals showing a sevenfold higher risk compared with group matches (OR\u0026thinsp;=\u0026thinsp;7.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eField position: Forwards exhibited 17.4 injuries per 1,000 player-hours vs. backs: 7.9 (OR\u0026thinsp;=\u0026thinsp;1.51, p\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\u003cp\u003eInjury sites: Logistic regression confirmed elevated risks for injuries to the head/face (OR\u0026thinsp;=\u0026thinsp;13.55), upper limbs (OR\u0026thinsp;=\u0026thinsp;13.86), torso (OR\u0026thinsp;=\u0026thinsp;4.77), and lower limbs (OR\u0026thinsp;=\u0026thinsp;10.73), compared with other sites (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.and Table\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eTable 2. Impact of spatiotemporal distribution characteristics on the occurrence of acute injuries among rugby players.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandarderror\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald(2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal Distribution Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023/24(ref\u0026thinsp;=\u0026thinsp;2022/23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.642\u0026ndash;1.928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024/25(ref\u0026thinsp;=\u0026thinsp;2022/23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.069\u0026ndash;3.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMatch Phase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemi-finals (ref\u0026thinsp;=\u0026thinsp;group stage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.096\u0026ndash;3.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinals (ref\u0026thinsp;=\u0026thinsp;group stage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.229\u0026ndash;11.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial Distribution Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInjury Sites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead and face (ref\u0026thinsp;=\u0026thinsp;non-head and face)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.283\u0026ndash;29.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper limbs (ref\u0026thinsp;=\u0026thinsp;non-upper limbs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.180\u0026ndash;26.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTorso (ref\u0026thinsp;=\u0026thinsp;non-torso)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.667\u0026ndash;13.642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower limbs (ref\u0026thinsp;=\u0026thinsp;non-lower limbs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.468\u0026ndash;17.812\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eField Positions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForwards (ref\u0026thinsp;=\u0026thinsp;back players)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.005\u0026ndash;2.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnivariate Analysis\u003c/p\u003e\u003cp\u003eSignificant associations with injury occurrence were observed for age group, competitive level, previous injury history, training load, ambient temperature, sleep quality, and fatigue (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant effects were found for BMI or years of participation. Shown in Table\u0026nbsp;3.\u003c/p\u003e\u003cp\u003eTable 3. Univariate analysis of acute injuries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAcute Injuries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eχ\u0026sup2; value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. (n\u0026thinsp;=\u0026thinsp;432)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes(n\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge ( years )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103(23.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25(17.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u0026ndash;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128(29.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43(30.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136(31.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(27.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65(15.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36(25.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026lt;x\u0026lt;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84(19.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38(26.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;x\u0026lt;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300(69.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93(65.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48(11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(8.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAthletic Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e272(62.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104(72.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160(37.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(27.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of Participation in Rugby\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026lt;x\u0026lt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45(10.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(7.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e383(88.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130(90.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Injury History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95(21.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(6.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e337(78.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133(93.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining Load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76(17.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(40.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238(55.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62(43.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188(43.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(16.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(5.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27(18.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e338(78.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52(36.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70(16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64(44.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery Good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55(12.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(16.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e356(82.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93(65.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(7.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(11.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue Scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e338(78.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84(58.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92(21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57(39.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*All ORs reference baseline rate of 1.78 per 1,000 player-hours.\u003c/p\u003e\u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\u003cp\u003eThe independent predictors of acute injury and their corresponding effects on the logarithmic scale are presented in Fig.\u0026nbsp;3. below.\u003c/p\u003e\u003cp\u003eThe regression model demonstrated good fit (Hosmer\u0026ndash;Lemeshow p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating adequate discrimination of risk factors.\u003c/p\u003e\u003cp\u003eDistribution of Basic Characteristics of Acute Injuries\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. presents in the 143 acute injuries which rugby players sustained, the greater percentage of them, that is, 78.32% of cases, had been of severe injuries. Minor, as well as mild injuries, had been less common, accounting for 8.39% as well as 13.29% of the total acute injuries, respectively. No instances of trivial injuries or career-ending/non-fatal catastrophic injuries had been reported(shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding the types of injuries, joint and bone injuries were the most prevalent, representing 48.25% of the acute injuries. This was followed by soft tissue injuries, which accounted for 34.27%. Other injury types included ligament injuries, concussions, and organ injuries, representing 13.99%, 2.10%, and 1.40% of the acute injuries, respectively (As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eResults of Multivariate Logistic Regression Analysis\u003c/p\u003e\u003cp\u003eThis study used binary logistic regression to identify independent risk factors for acute rugby injuries, with principal component analysis confirming no significant multicollinearity (eigenvalues\u0026thinsp;\u0026ge;\u0026thinsp;0.534). Key findings show that high training loads, extreme temperatures (both high and low), and higher competitive levels (elite athletes facing 1.837 times greater risk than first-level athletes) significantly increase injury risk. Individual factors like prior injury history, poor sleep quality, and mild fatigue are also closely linked to acute injuries, though severe fatigue shows no statistically significant effect. These factors interact synergistically, indicating that injury prevention strategies should comprehensively address seasonal planning, training load management, and recovery processes.As shown in Table\u0026nbsp;4.\u003c/p\u003e\u003cp\u003eTable 4. Multivariate logistic regression analysis of acute injuries\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald χ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining Load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium (ref\u0026thinsp;=\u0026thinsp;low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.940\u0026ndash;3.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh (ref\u0026thinsp;=\u0026thinsp;low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.950\u0026ndash;7.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh (ref\u0026thinsp;=\u0026thinsp;normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.071\u0026ndash;11.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (ref\u0026thinsp;=\u0026thinsp;normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.203\u0026ndash;13.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAthletic Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElite (ref\u0026thinsp;=\u0026thinsp;level 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.087\u0026ndash;3.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Injury History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes (ref\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.687\u0026ndash;13.316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood (ref\u0026thinsp;=\u0026thinsp;very good)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.309\u0026ndash;1.140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage (ref\u0026thinsp;=\u0026thinsp;very good)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.671\u0026ndash;7.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor (ref\u0026thinsp;=\u0026thinsp;very good)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.682\u0026ndash;18.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue Symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Fatigue (ref\u0026thinsp;=\u0026thinsp;none)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.518\u0026ndash;4.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Fatigue (ref\u0026thinsp;=\u0026thinsp;none)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08-15.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically examined the spatiotemporal distribution and multivariate risk factors of acute injuries among elite rugby players across multiple competitive seasons. The findings highlight several important contributions to the field of sports medicine and injury prevention.\u003c/p\u003e\u003cp\u003eKey Findings and Contributions\u003c/p\u003e\u003cp\u003eFirst, the study confirms that acute injuries in rugby are predominantly moderate-to-severe, with bone and joint injuries most common. Importantly, the risk of injury increases substantially during the later stages of the season and in high-stakes competition phases such as finals. These results underscore the cumulative effect of physical load and competitive stress on player vulnerability. While prior studies have often focused on single factors such as match phase or injury type, our research integrates temporal, spatial, and physiological dimensions, offering a more comprehensive understanding of injury dynamics in rugby.\u003c/p\u003e\u003cp\u003eSecond, positional differences were evident: forwards experienced significantly higher injury risk than backs, consistent with their greater involvement in high-impact collisions such as scrums and rucks. This positional disparity reinforces the need for tailored prevention strategies, such as enhanced eccentric strength and stability training for forwards.\u003c/p\u003e\u003cp\u003eThird, the multivariate regression model identified several independent predictors of acute injury, including high training loads, extreme temperatures, elite competition level, prior injury history, poor sleep quality, and mild fatigue. The combination of physiological, environmental, and behavioral factors highlights the multifactorial nature of injury risk. By quantifying these effects, our model provides an evidence-based framework for individualized injury prevention and early warning systems.\u003c/p\u003e\u003cp\u003eComparison with Previous Literature\u003c/p\u003e\u003cp\u003eOur results are broadly consistent with prior research indicating that cumulative training load, fatigue, and previous injury are among the strongest predictors of sports injuries.\u003csup\u003e[21\u0026ndash;25]\u003c/sup\u003e However, this study extends existing knowledge by demonstrating that environmental extremes\u0026mdash;both heat and cold\u0026mdash;are comparably potent risk factors. The integration of sleep quality and fatigue assessments also advances current models, as these recovery-related variables are often underrepresented in rugby injury surveillance.\u003c/p\u003e\u003cp\u003ePractical Implications\u003c/p\u003e\u003cp\u003eFrom an applied perspective, these findings support the implementation of multifactorial monitoring systems in elite rugby. Regular assessment of training load, sleep quality, and fatigue can enable early identification of at-risk players. Coaches and medical teams should adjust workloads, introduce mandatory rotation during congested schedules, and employ targeted interventions such as heat adaptation protocols or recovery-focused training. Moreover, positional demands necessitate role-specific injury prevention programs.\u003c/p\u003e\u003cp\u003eLimitations and Future Directions\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. It is declared as a pilot study based on a single team with a relatively small sample size, which may limit generalizability, and multi-center validation is recommended. Training load was self-reported and not complemented by objective biomarkers such as GPS data, lactate concentration, or heart rate variability.\u003csup\u003e[26\u0026ndash;29]\u003c/sup\u003e Additionally, injury surveillance focused on match play, excluding training-related injuries that may contribute to overall risk. Future research should address these gaps by incorporating larger multicenter cohorts, integrating wearable monitoring technologies, and extending surveillance to both training and competition contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that acute injuries among elite rugby players follow distinct spatiotemporal patterns, with risks heightened during later competitive stages, finals, and in players occupying forward positions. Bone and joint injuries were most common, and the majority were of moderate-to-severe grade, underscoring the heavy burden of injury in this sport.\u003c/p\u003e\u003cp\u003eMultivariate analysis identified high training load, exposure to extreme temperatures, elite competition level, prior injury history, poor sleep quality, and mild fatigue as independent predictors of acute injury. These findings confirm the multifactorial nature of rugby injury risk and highlight the interplay of physiological, environmental, and situational determinants.\u003c/p\u003e\u003cp\u003eFrom a practical perspective, the results support the development of individualized prevention strategies and monitoring systems. Training loads should be carefully periodized, recovery protocols strengthened, and environmental adaptation strategies adopted. Sleep and fatigue monitoring can provide early warning signals, while position-specific programs may help address the elevated risks faced by forwards.\u003c/p\u003e\u003cp\u003eAlthough limited by sample size and single-team design, this study establishes a quantitative risk model that can serve as a theoretical and practical reference for coaches, medical staff, and policymakers. Future research integrating larger multicenter cohorts and wearable monitoring technologies will further enhance the precision and applicability of such models.\u003c/p\u003e\u003cp\u003eIn conclusion, the present work provides a robust foundation for evidence-based injury prevention and management in elite rugby, with implications extending to other collision-intensive sports.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch4\u003eEthic Approval \u0026amp; Consent Participation\u003c/h4\u003e\n\u003cp\u003eThis study has received ethical approval from the Ethics Committee of the Tianjin University of Sports (approval number: 2025-090). Participants provided consent through an informed consent process reviewed by the Ethics Committee, ensuring compliance with the ethical standards set forth in the 1964 Declaration of Helsinki.\u003c/p\u003e\n\u003ch4\u003eConsent for Publication\u003c/h4\u003e\n\u003cp\u003eThe data, figures, and images presented in this manuscript are original or have been authorized and licensed legally. \u0026nbsp;The study guarantee that ethical standards have been followed in human experiments, and informed consent has been obtained from the subjects.\u003c/p\u003e\n\u003ch4\u003eConflict of Interest\u003c/h4\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eFunding\u003c/h4\u003e\n\u003cp\u003eThis research did not receive external funding support.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eAuthor Contributions\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eMei Xiaodong\u0026nbsp;\u003c/strong\u003e(First Author)\u003c/p\u003e\n\u003cp\u003eResearch Design and Implementation: Led the design of the study protocol and completed both prospective data collection and retrospective cohort construction.\u003c/p\u003e\n\u003cp\u003eData Analysis and Model Development: Performed all statistical analyses, including Chi-square tests and logistic regression modeling.\u003c/p\u003e\n\u003cp\u003eManuscript Writing: Responsible for drafting the abstract, methods, results, discussion, and conclusions.\u003c/p\u003e\n\u003cp\u003eInjury Data Verification: Collaborated with the medical team of the Tianjin British Rugby Team to categorize the severity and follow-up of 143 acute injuries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLu Yingjie\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData Collection and Processing: Established a standardized injury database.\u003c/p\u003e\n\u003cp\u003eLiterature Review: Systematically reviewed literature related to risk assessment of sports injuries.\u003c/p\u003e\n\u003cp\u003eEnvironmental Factor Analysis: Collected and standardized data on covariates such as ambient temperature and training load.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiao Peng\u003c/strong\u003e (Corresponding Author)\u003c/p\u003e\n\u003cp\u003eAcademic Guidance: Proposed a theoretical framework for analyzing the spatiotemporal distribution and multivariate risk coupling.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eAuthor Agreement\u003c/h4\u003e\n\u003cp\u003eAll authors have seen and approved the final version of the submitted manuscript. They guarantee that this article is the author\u0026apos;s original work, has not been published in advance, and has not been considered for publication elsewhere.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKing DA, Clark TN, Hume PA, Hind KJSm, science h. Match and Training Injury Incidence in Rugby League: A Systematic Review, Pooled Analysis, and Update on Published Studies. Sports medicine and health science (2022) 4(2):75-84.\u003c/li\u003e\n\u003cli\u003eQuarrie K, Gianotti S, Murphy I. Injury Risk in New Zealand Rugby Union: A Nationwide Study of Injury Insurance Claims from 2005 to 2017. Sports medicine (Auckland, NZ) (2020) 50(2):415-28. Epub 2019/09/12. \u003c/li\u003e\n\u003cli\u003eLiu H, Garrett WE, Moorman CT, Yu BJJos, science h. Injury Rate, Mechanism, and Risk Factors of Hamstring Strain Injuries in Sports: A Review of the Literature. 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Science and medicine in football (2023) 7(4):315-22.\u003c/li\u003e\n\u003cli\u003eFuller CW, Molloy MG. Epidemiological Study of Injuries in Men\u0026apos;s International under-20 Rugby Union Tournaments. Clinical journal of sport medicine (2011) 21(4):356-8.\u003c/li\u003e\n\u003cli\u003eBrown S, Brughelli M, Cross MR. Profiling Sprint Mechanics by Leg Preference and Position in Rugby Union Athletes. International journal of sports medicine (2016) 37(11):890-7.\u003c/li\u003e\n\u003cli\u003eTranaeus U, Gledhill A, Johnson U, Podlog L, Wadey R, Wiese Bjornstal D, et al. 50 Years of Research on the Psychology of Sport Injury: A Consensus Statement. Sports medicine (2024) 54(7):1733-48.\u003c/li\u003e\n\u003cli\u003eLi YJ, Wang J, Zhou Y. Research Progress on the Characteristics and Preventive Measures of Contact-Related Injuries in British Rugby Players. Chinese Journal of Sports Medicine (2022) 41(12):966-74.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rugby, acute injury, spatiotemporal distribution, risk factors, logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-7443394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7443394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAcute injuries are common in rugby and threaten both player health and career longevity. Previous studies often focused on isolated risk factors, while limited research has comprehensively examined the interplay of physiological, environmental, and situational variables.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eStudy conducted a cohort study of 40 elite male players from the Tianjin Rugby Team, monitoring 575 match exposures across 2.5 consecutive seasons (2022\u0026ndash;2025). Acute injuries were defined according to international consensus criteria and verified by medical staff. Spatiotemporal distributions (seasonal variation, match stage, playing position, and body site) were analyzed using chi-square and logistic regression. Multivariate models were applied to identify independent risk factors including demographic, training, and environmental variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 143 acute injury events were recorded, with bone and joint injuries most prevalent (48.2%) and the majority classified as moderate-to-severe (78.3%). Injury rate rose significantly to 1.84 per 1,000 player-hours in 2024/25 vs. 1.83 in 2022/23 and advanced competition stages (OR for finals\u0026thinsp;=\u0026thinsp;7.06 vs. group stage, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Forwards exhibited higher risk than backs (OR\u0026thinsp;=\u0026thinsp;1.51), and injuries most frequently involved the head/face, upper limbs, and lower limbs. Multivariate regression identified excessive training load (OR\u0026thinsp;=\u0026thinsp;3.78), extreme temperatures (OR\u0026thinsp;\u0026asymp;\u0026thinsp;6.5\u0026ndash;6.8), elite athletic level (OR\u0026thinsp;=\u0026thinsp;1.84), prior injury history (OR\u0026thinsp;=\u0026thinsp;5.98), poor sleep quality (OR\u0026thinsp;=\u0026thinsp;5.64), and mild fatigue (OR\u0026thinsp;=\u0026thinsp;2.48) as significant predictors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAcute injury risk in rugby demonstrates clear spatiotemporal patterns and is strongly influenced by both individual and environmental factors. The model developed provides a practical basis for targeted prevention strategies, including load management, environmental adaptation, and individualized recovery protocols. These findings may assist coaches and medical teams in optimizing training and competition management, while future research should expand cohorts and integrate\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Patterning and Multivariate Risk of Acute Injuries in Elite Rugby: A Cohort Based on Prospective Surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 10:03:41","doi":"10.21203/rs.3.rs-7443394/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e3fa3d8-d560-4cd0-a13e-f252ec48d211","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T00:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 10:03:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7443394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7443394","identity":"rs-7443394","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}