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The Low Energy Availability in Females Questionnaire (LEAF-Q) was validated to identify risk of the female athlete triad (triad) in female endurance athletes. This study explored the ability of the LEAF-Q to detect conditions related to low energy availability (LEA) in a mixed sport cohort of female athletes. Data included the LEAF-Q, SCOFF Questionnaire for disordered eating, dual-energy X-ray absorptiometry-derived body composition and bone mineral density, Mini International Neuropsychiatric Interview, blood pressure, and blood metabolic and reproductive hormones. Participants were grouped according to LEAF-Q score (≥8 or <8), and a comparison of means was undertaken. Sensitivity, specificity, and predictive values of the overall score and subscale scores were calculated in relation to the triad and biomarkers relevant to LEA. Fisher's exact test explored differences in prevalence of these conditions between groups. Seventy-five athletes (18–32 years) participated. Mean LEAF-Q score was 8.0 ± 4.2 (55% scored ≥8). Injury and menstrual function subscale scores identified low bone mineral density (100% sensitivity, 95% confidence interval [15.8%, 100%]) and menstrual dysfunction (80.0% sensitivity, 95% confidence interval [28.4%, 99.5%]), respectively. The gastrointestinal subscale did not detect surrogate markers of LEA. LEAF-Q score cannot be used to classify athletes as "high risk" of conditions related to LEA, nor can it be used as a surrogate diagnostic tool for LEA given the low specificity identified. Our study supports its use as a screening tool to rule out risk of LEA-related conditions or to create selective low-risk groups that do not need management as there were generally high negative predictive values (range 76.5–100%) for conditions related to LEA. [ABSTRACT FROM AUTHOR]

Objectives: To assess the effect of multiple high-risk-scenario (HRS) exposures on noncontact injury prediction in elite Australian footballers. Design: Retrospective cohort study. Methods: Sessional workload data (session rating of perceived exertion, global positioning system–derived distance, sprint distance, and maximum velocity) from 1 club (N = 60 players) over 3 seasons were collated; several established HRSs were also defined. Accumulated HRS sessional exposures were calculated retrospectively (previous 1–8 wk). Noncontact injury data were documented. Univariate and multivariate Poisson regression models determined injury incidence rate ratios (IRRs) while accounting for moderating effects (preseason workload volume and playing experience). Model performance was evaluated using receiver operating characteristics (area under curve). Results: Very low (0–8 sessions: IRR = 5.76; 95% confidence interval [CI], 1.69–19.66) and very high (>15 sessions: IRR = 4.70; 95% CI, 1.49–14.87) exposures to >85% of an individual's maximal velocity over the previous 8 wk were associated with greater injury risk compared with moderate exposures (11–12 sessions) and displayed the best model performance (area under curve = 0.64). A single session corresponding to a very low chronic load condition over the previous week for all workload variables was associated with increased injury risk, with sprint distance (IRR = 3.25; 95% CI, 1.95–5.40) providing the most accurate prediction model (area under curve = 0.63). Conclusions: Minimal exposure to high-velocity efforts (maximum speed exposure and sprint volume) was associated with the greatest injury risk. Being underloaded may be a mediator for noncontact injury in elite Australian football. Preseason workload and playing experience were not moderators of this effect. [ABSTRACT FROM AUTHOR]

Background: The aims of this retrospective study were to (i) provide a description of sleep quality in elite athletes as measured by the Pittsburgh Sleep Quality Index (PSQI), (ii) provide normative PSQI data, (iii) identify differences across sex and sport, (iv) identify components that contribute to high PSQI scores and (v) assess PSQI test–retest reliability. Methods: The PSQI was completed by 479 athletes (371 female and 108 male) across 20 Olympic team and individual sports. For ordinal and categorical variables, the Wilcoxon rank sum test and Chi Squared tests were used, respectively. A random forest regression was built to determine the importance of each PSQI component. Test–retest reliability was assessed using two-way mixed effects intraclass correlation coefficients. Results: Fifty-two percent of athletes had a global PSQI score ≥ 5. Team sport athletes reported significantly longer sleep onset latency times but longer sleep durations compared with individual sport athletes. Sleep onset latency and sleep quality made the greatest contribution to the global PSQI scores. The PSQI demonstrated variability over periods of 2 months or more, with a minimal detectable change of 3 arbitrary units (AU). Conclusion: Long sleep onset latency and poor perceived sleep quality made the greatest contribution to the high PSQI scores observed in approximately half of elite athletes investigated. The PSQI should be administered at regular intervals due to variability within individuals over periods of 2 months or more. Individual questionnaire items or component scores of the PSQI may be useful for practitioners in guiding decision-making regarding sleep interventions in athletes. [ABSTRACT FROM AUTHOR]

Background: Injuries are common in rugby sevens, but studies to date have been limited to short, noncontinuous periods and reporting of match injuries only. Purpose: To report the injury incidence rate (IIR), severity, and burden of injuries sustained by men and women in the Australian rugby sevens program and to provide the first longitudinal investigation of subsequent injury occurrence in rugby sevens looking beyond tournament injuries only. Study Design: Descriptive epidemiology study. Methods: Ninety international rugby sevens players (55 men and 35 women) were prospectively followed over 2 consecutive seasons (2015-2016 and 2016-2017). All medical attention injuries were reported irrespective of time loss. Individual exposure in terms of minutes, distance, and high-speed distance was captured for each player for matches and on-field training, with the use of global positioning system devices. The IIR and injury burden (IIR × days lost to injury) were calculated per 1000 player-hours, and descriptive analyses were performed. Results: Seventy-three players (81.1%) sustained 365 injuries at an IIR of 43.2 per 1000 player-hours (95% CI, 43.0-43.3). As compared with male players, female players experienced a lower IIR (incidence rate ratio, 0.91; 95% CI, 0.90-0.91). Female players also sustained a higher proportion of injuries to the trunk region (relative risk, 1.75; 95% CI, 1.28-2.40) but a lower number to the head/neck region (relative risk, 0.58; 95% CI, 0.37-0.93; P =.011). The majority (80.7%) of subsequent injuries were of a different site and nature than previous injuries. A trend toward a reduced number of days, participation time, distance, and high-speed distance completed before the next injury was observed after successive injury occurrence. Conclusion: Female players have a lower IIR than male players, with variation of injury profiles observed between sexes. With a surveillance period of 2 years, subsequent injuries account for the majority of injuries sustained in rugby sevens, and they are typically different from previous types of sustained injuries. After each successive injury, the risk profile for future injury occurrence appears to be altered, which warrants further investigation to inform injury prevention strategies in rugby sevens. [ABSTRACT FROM AUTHOR]

Background: Accounting for subsequent injuries is critical for sports injury epidemiology. The subsequent injury categorisation (SIC-1.0) model was developed to create a framework for accurate categorisation of subsequent injuries but its operationalisation has been challenging.Objectives: The objective of this study was to update the subsequent injury categorisation (SIC-1.0 to SIC-2.0) model to improve its utility and application to sports injury datasets, and to test its applicability to a sports injury dataset.Methods: The SIC-1.0 model was expanded to include two levels of categorisation describing how previous injuries relate to subsequent events. A data-driven classification level was established containing eight discrete injury categories identifiable without clinical input. A sequential classification level that sub-categorised the data-driven categories according to their level of clinical relatedness has 16 distinct subsequent injury types. Manual and automated SIC-2.0 model categorisation were applied to a prospective injury dataset collected for elite rugby sevens players over a 2-year period. Absolute agreement between the two coding methods was assessed.Results: An automated script for automatic data-driven categorisation and a flowchart for manual coding were developed for the SIC-2.0 model. The SIC-2.0 model was applied to 246 injuries sustained by 55 players (median four injuries, range 1-12), 46 (83.6%) of whom experienced more than one injury. The majority of subsequent injuries (78.7%) were sustained to a different site and were of a different nature. Absolute agreement between the manual coding and automated statistical script category allocation was 100%.Conclusions: The updated SIC-2.0 model provides a simple flowchart and automated electronic script to allow both an accurate and efficient method of categorising subsequent injury data in sport. [ABSTRACT FROM AUTHOR]