Description:
Objectives: To assess the association between workload, subjective wellness, musculoskeletal screening measures and non-contact injury risk in elite Australian footballers. Design: Prospective cohort study. Methods: Across 4 seasons in 70 players from one club, cumulative weekly workloads (acute; 1 week, chronic; 2-, 3-, 4-week) and acute:chronic workload ratio’s (ACWR: 1-week load/average 4-weekly load) for session-Rating of Perceived Exertion (sRPE) and GPS-derived distance and sprint distance were calculated. Wellness, screening and non-contact injury data were also documented. Univariate and multivariate regression models determined injury incidence rate ratios (IRR) while accounting for interaction/moderating effects. Receiver operating characteristics determined model predictive accuracy (area under curve: AUC). Results: Very low cumulative chronic (2-, 3-, 4- week) workloads were associated with the greatest injury risk (univariate IRR = 1.71–2.16, 95% CI = 1.10–4.52) in the subsequent week. In multivariate analysis, the interaction between a low chronic load and a very high distance (adj-IRR = 2.60, 95% CI = 1.07–6.34) or low sRPE ACWR (adj-IRR = 2.52, 95% CI = 1.01–6.29) was associated with increased injury risk. Subjectively reporting “yes” (vs. “no”) for old lower limb pain and heavy non-football activity in the previous 7 days (multivariate adj-IRR = 2.01–2.25, 95% CI = 1.02–4.95) and playing experience (>9 years) (multivariate adj- IRR = 2.05, 95% CI = 1.03–4.06) was also associated with increased injury risk, but screening data were not. Predictive capacity of multivariate models was significantly better than univariate (AUCmultivariate = 0.70, 95% CI 0.64–0.75; AUCunivariate range = 0.51–0.60). Conclusions: Chronic load is an important moderating factor in the workload–injury relationship. Low chronic loads coupled with low or very high ACWR are associated with increased injury risk.
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Objectives: To assess the association between workload, subjective
Description:
Objective: Establish the prevalence of illness symptoms, poor sleep quality, poor mental health symptoms, low energy availability and stress-recovery state in an Olympic cohort late in the 3months prior to the Summer Olympic Games. Methods: Olympic athletes (n=317) from 11 sports were invited to complete questionnaires administered 3months before the Rio 2016 Olympic Games. These questionnaires included the Depression, Anxiety and Stress Questionnaire, Perceived Stress Scale, Dispositional Resilience Scale, Recovery-Stress Questionnaire (REST-Q-52 item), Low Energy Availability in Females Questionnaire (LEAF-Q), Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index and custom-made questionnaires on probiotic usage and travel. Multiple illness (case) definitions were applied. ORs and attributable fractions in the population were used. Factor analyses were used to explore the relationships between variables. Results: The response rate was of 42% (male, n=47, age 25.8±4.1 years; female, n=85, age 24.3±3.9 years). Low energy availability was associated with sustaining an illness in the previous month (upper respiratory, OR=3.8, 95%CI 1.2 to 12). The main factor relating to illness pertained to a combination of anxiety and stress recovery states (as measured by the REST-Q-52 item). All participants reported at least one episode of illness in the last month (100% prevalence). Conclusions: All participants reported at least one illness symptom in the previous month. Low energy availability was a leading variable associated with illness in Olympic-class athletes. The estimates duration of symptoms ranged from 2 to 7 days. Factor analyses show the interdependence of various health domains and support multidisciplinary care.
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Objectives: The use of microsensor technologies to conduct research and implement interventions in sports and exercise medicine has increased recently. The objective of this paper was to determine the validity and reliability of the ViPerform as a measure of load compared to vertical ground reaction force (GRF) as measured by force plates. Design: Absolute reliability assessment, with concurrent validity. Methods: 10 professional triathletes ran 10 trials over force plates with the ViPerform mounted on the mid portion of the medial tibia. Calculated vertical ground reaction force data from the ViPerform was matched to the same stride on the force plate. Bland–Altman (BA) plot of comparative measure of agreement was used to assess the relationship between the calculated load from the accelerometer and the force plates. Reliability was calculated by intra-class correlation coefficients (ICC) with 95% confidence intervals. Results: BA plot indicates minimal agreement between the measures derived from the force plate and ViPerform, with variation at an individual participant plot level. Reliability was excellent (ICC = 0.877; 95% CI = 0.825–0.917) in calculating the same vertical GRF in a repeated trial. Standard error of measure (SEM) equalled 99.83 units (95% CI = 82.10–119.09), which, in turn, gave a minimum detectable change (MDC) value of 276.72 units (95% CI = 227.32–330.07). Conclusions: The ViPerform does not calculate absolute values of vertical GRF similar to those measured by a force plate. It does provide a valid and reliable calculation of an athlete’s lower limb load at constant velocity.
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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.
Description:
Objectives To examine different timeframes for calculating acute to chronic workload ratio (ACWR) and whether this variable is associated with intrinsic injury risk in elite Australian football players. Design Prospective cohort study. Methods Internal (session rating of perceived exertion: sRPE) and external (GPS distance and sprint distance) workload and injury data were collected from 70 players from one AFL club over 4 seasons. Various acute (1–2 weeks) and chronic (3–8 weeks) timeframes were used to calculate ACWRs: these and chronic load categories were then analysed to determine the injury risk in the subsequent month. Poisson regression with robust errors within a generalised estimating equation were utilised to determine incidence rate ratios (IRR). Results Altering acute and/or chronic timeframes did not improve the ability to detect high injury risk conditions above the commonly used 1:4 week ACWR. Twenty-seven ACWR/chronic load combinations were found to be “high risk conditions” (IRR > 1, p < 0.05) for injury within 7 days. Most (93%) of these conditions occurred when chronic load was low or very low and ACWR was either low (<0.6) or high (>1.5). Once a high injury risk condition was entered, the elevated risk persisted for up to 28 days. Conclusions Injury risk was greatest when chronic load was low and ACWR was either low or high. This heightened risk remained for up to 4 weeks. There was no improvement in the ability to identify high injury risk situations by altering acute or chronic time periods from 1:4 weeks.
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Training loads contribute to sports injury risk but their mitigation has rarely been considered in a sports injury prevention framework. A key concept behind monitoring training loads for injury prevention is to screen for those at increased risk of injury so that workloads can be adjusted to minimise these risks. This review describes how advances in management of workload can be applied as a preventive measure. Primary prevention involves screening for preparticipation load risk factors, such as low training loads, prior to a training period or competition. Secondary prevention involves screening for workloads that are known to precede an injury developing so that modification can be undertaken to mitigate this risk. Tertiary prevention involves rehabilitation practices that include a graded return to training programme to reduce the risk of sustaining a subsequent injury. The association of training loads with injury incidence is now established. Prevention measures such as rule changes that affect the workload of an athlete are universal whereas those that address risk factors of an asymptomatic subgroup are more selective. Prevention measures, when implemented for asymptomatic individuals exhibiting possible injury risk factors, are indicated for an athlete at risk of developing a sports injury. Seven key indicated risks and associated prevention measures are proposed.
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Background Clinically it is understood that rapid increases in training loads expose an athlete to an increased risk of injury; however, there are no systematic reviews to qualify this statement. Objective The aim of this systematic review was to determine training and competition loads, and the relationship between injury, illness and soreness. Methods The MEDLINE, SPORTDiscus, CINAHL and EMBASE databases were searched using a predefined search strategy. Studies were included if they analysed the relationship between training or competition loads and injury or illness, and were published prior to October 2015. Participants were athletes of any age or level of competition. The quality of the studies included in the review was evaluated using the Newcastle-Ottawa Scale (NOS). The level of evidence was defined as strong, 'consistent findings among multiple high-quality randomised controlled trials (RCTs)'; moderate, 'consistent findings among multiple low-quality RCTs and/or non-randomised controlled trials (CCTs) and/or one high-quality RCT'; limited, 'one low-quality RCT and/or CCTs, conflicting evidence'; conflicting, 'inconsistent findings among multiple trials (RCTs and/or CCTs)'; or no evidence, 'no RCTs or CCTs'. Results A total of 799 studies were identified; 23 studies met the inclusion criteria, and a further 12 studies that were not identified in the search but met the inclusion criteria were subsequently added to the review. The largest number of studies evaluated the relationship between injuries and training load in rugby league players (n = 9) followed by cricket (n = 5), football (n = 3), Australian Football (n = 3), rugby union (n = 2), volleyball (n = 2), baseball (n = 2), water polo (n = 1), rowing (n = 1), basketball (n = 1), swimming (n = 1), middle-distance runners (n = 1) and various sports combined (n = 1). Moderate evidence for a significant relationship was observed between training loads and injury incidence in the majority of studies (n = 27, 93 %). In addition, moderate evidence exists for a significant relationship between training loads and illness incidence (n = 6, 75 %). Training loads were reported to have a protective effect against injury (n = 9, 31 %) and illness (n = 1, 13 %). The median (range) NOS score for injury and illness was 8 (5-9) and 6 (5-9), respectively. Limitations A limitation of this systematic review was the a priori search strategy. Twelve further studies were included that were not identified in the search strategy, thus potentially introducing bias. The quality assessment was completed by only one author. Conclusions The results of this systematic review highlight that there is emerging moderate evidence for the relationship between the training load applied to an athlete and the occurrence of injury and illness. Implications The training load applied to an athlete appears to be related to their risk of injury and/or illness. Sports science and medicine professionals working with athletes should monitor this load and avoid acute spikes in loads. It is recommended that internal load as the product of the rate of perceived exertion (10-point modified Borg) and duration be used when determining injury risk in team-based sports. External loads measured as throw counts should also be monitored and collected across a season to determine injury risk in throwing populations. Global positioning system-derived distances should be utilised in team sports, and injury monitoring should occur for at least 4 weeks after spikes in loads.
Description:
Background: Electronic methods are increasingly being used to manage health-related data among sporting populations. Collection of such data permits the analysis of injury and illness trends, improves early detection of injuries and illnesses, collectively referred to as health problems, and provides evidence to inform prevention strategies. The Athlete Management System (AMS) has been employed across a range of sports to monitor health. Australian combat athletes train across the country without dedicated national medical or sports science teams to monitor and advocate for their health. Employing a Web-based system, such as the AMS, May provide an avenue to increase the visibility of health problems experienced by combat athletes and deliver key information to stakeholders detailing where prevention programs May be targeted. Objective: The objectives of this paper are to (1) report on the feasibility of utilizing the AMS to collect longitudinal injury and illness data of combat sports athletes and (2) describe the type, location, severity, and recurrence of injuries and illnesses that the cohort of athletes experience across a 12-week period. Methods: We invited 26 elite and developing athletes from 4 Olympic combat sports (boxing, judo, taekwondo, and wrestling) to participate in this study. Engagement with the AMS was measured, and collected health problems (injuries or illnesses) were coded using the Orchard Sports Injury Classification System (version 10.1) and International Classification of Primary Care (version 2). Results: Despite >160 contacts, athlete engagement with online tools was poor, with only 13% compliance across the 12-week period. No taekwondo or wrestling athletes were compliant. Despite low overall engagement, a large number of injuries or illness were recorded across 11 athletes who entered data—22 unique injuries, 8 unique illnesses, 30 recurrent injuries, and 2 recurrent illnesses. The most frequent injuries were to the knee in boxing (n=41) and thigh in judo (n=9). In this cohort, judo players experienced more severe, but less frequent, injuries than boxers, yet judo players sustained more illnesses than boxers. In 97.0% (126/130) of cases, athletes in this cohort continued to train irrespective of their health problems. Conclusions: Among athletes who reported injuries, many reported multiple conditions, indicating a need for health monitoring in Australian combat sports. A number of factors May have influenced engagement with the AMS, including access to the internet, the design of the system, coach views on the system, previous experiences with the system, and the existing culture within Australian combat sports. To increase engagement, there May be a requirement for sports staff to provide relevant feedback on data entered into the system. Until the Barriers are addressed, it is not feasible to implement the system in its current form across a larger cohort of combat athletes.
Description:
Background: Electronic methods are increasingly being used to manage health-related data among sporting populations. Collection of such data permits the analysis of injury and illness trends, improves early detection of injuries and illnesses, collectively referred to as health problems, and provides evidence to inform prevention strategies. The Athlete Management System (AMS) has been employed across a range of sports to monitor health. Australian combat athletes train across the country without dedicated national medical or sports science teams to monitor and advocate for their health. Employing a Web-based system, such as the AMS, May provide an avenue to increase the visibility of health problems experienced by combat athletes and deliver key information to stakeholders detailing where prevention programs May be targeted. Objective: The objectives of this paper are to (1) report on the feasibility of utilizing the AMS to collect longitudinal injury and illness data of combat sports athletes and (2) describe the type, location, severity, and recurrence of injuries and illnesses that the cohort of athletes experience across a 12-week period. Methods: We invited 26 elite and developing athletes from 4 Olympic combat sports (boxing, judo, taekwondo, and wrestling) to participate in this study. Engagement with the AMS was measured, and collected health problems (injuries or illnesses) were coded using the Orchard Sports Injury Classification System (version 10.1) and International Classification of Primary Care (version 2). Results: Despite >160 contacts, athlete engagement with online tools was poor, with only 13% compliance across the 12-week period. No taekwondo or wrestling athletes were compliant. Despite low overall engagement, a large number of injuries or illness were recorded across 11 athletes who entered data—22 unique injuries, 8 unique illnesses, 30 recurrent injuries, and 2 recurrent illnesses. The most frequent injuries were to the knee in boxing (n=41) and thigh in judo (n=9). In this cohort, judo players experienced more severe, but less frequent, injuries than boxers, yet judo players sustained more illnesses than boxers. In 97.0% (126/130) of cases, athletes in this cohort continued to train irrespective of their health problems. Conclusions: Among athletes who reported injuries, many reported multiple conditions, indicating a need for health monitoring in Australian combat sports. A number of factors May have influenced engagement with the AMS, including access to the internet, the design of the system, coach views on the system, previous experiences with the system, and the existing culture within Australian combat sports. To increase engagement, there May be a requirement for sports staff to provide relevant feedback on data entered into the system. Until the Barriers are addressed, it is not feasible to implement the system in its current form across a larger cohort of combat athletes.
Description:
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.
How much is enough in rehabilitation? High running workloads following lower limb muscle injury delay return to play but protect against subsequent injury
Description:
Objectives: Examine the influence of rehabilitation training loads on return to play (RTP) time and subsequent injury in elite Australian footballers. Design: Prospective cohort study. Methods: Internal (sessional rating of perceived exertion: sRPE) and external (distance, sprint distance) workload and lower limb non-contact muscle injury data was collected from 58 players over 5 seasons. Rehabilitation periods were analysed for running workloads and time spent in 3 rehabilitation stages (1: off-legs training, 2: non-football running, 3: group football training) was calculated. Multi-level survival analyses with random effects accounting for player and season were performed. Hazard ratios (HR) and 95% confidence intervals (CI) for each variable were produced for RTP time and time to subsequent injury. Results: Of 85 lower limb muscle injuries, 70 were rehabilitated to RTP, with 30 cases of subsequent injury recorded (recurrence rate = 11.8%, new site injury rate = 31.4%). Completion of high rehabilitation workloads delayed RTP (distance: >49,775 m [reference: 34,613–49,775 m]: HR 0.12, 95%CI 0.04–0.36, sRPE: >1266 AU [reference: 852–1266 AU]: HR 0.09, 95%CI 0.03–0.32). Return to running within 4 days increased subsequent injury risk (3–4 days [reference: 5–6 days]: HR 25.88, 95%CI 2.06–324.4). Attaining moderate-high sprint distance (427–710 m) was protective against subsequent injury (154–426 m: [reference: 427–710 m]: HR 37.41, 95%CI 2.70–518.64). Conclusions: Training load monitoring can inform player rehabilitation programs. Higher rehabilitation training loads delayed RTP; however, moderate-high sprint running loads can protect against subsequent injury. Shared-decision making regarding RTP should include accumulated training loads and consider the trade-off between expedited RTP and lower subsequent injury risk.
Description:
Objectives: Examine the influence of rehabilitation training loads on return to play (RTP) time and subsequent injury in elite Australian footballers. Design: Prospective cohort study. Methods: Internal (sessional rating of perceived exertion: sRPE) and external (distance, sprint distance) workload and lower limb non-contact muscle injury data was collected from 58 players over 5 seasons. Rehabilitation periods were analysed for running workloads and time spent in 3 rehabilitation stages (1: off-legs training, 2: non-football running, 3: group football training) was calculated. Multi-level survival analyses with random effects accounting for player and season were performed. Hazard ratios (HR) and 95% confidence intervals (CI) for each variable were produced for RTP time and time to subsequent injury. Results: Of 85 lower limb muscle injuries, 70 were rehabilitated to RTP, with 30 cases of subsequent injury recorded (recurrence rate = 11.8%, new site injury rate = 31.4%). Completion of high rehabilitation workloads delayed RTP (distance: >49,775 m [reference: 34,613–49,775 m]: HR 0.12, 95%CI 0.04–0.36, sRPE: >1266 AU [reference: 852–1266 AU]: HR 0.09, 95%CI 0.03–0.32). Return to running within 4 days increased subsequent injury risk (3–4 days [reference: 5–6 days]: HR 25.88, 95%CI 2.06–324.4). Attaining moderate-high sprint distance (427–710 m) was protective against subsequent injury (154–426 m: [reference: 427–710 m]: HR 37.41, 95%CI 2.70–518.64). Conclusions: Training load monitoring can inform player rehabilitation programs. Higher rehabilitation training loads delayed RTP; however, moderate-high sprint running loads can protect against subsequent injury. Shared-decision making regarding RTP should include accumulated training loads and consider the trade-off between expedited RTP and lower subsequent injury risk.
Knee flexion strength is significantly reduced following competition in semi-professional Australian Rules football athletes : Implications for injury prevention programs
Description:
Objectives Forearm injuries are common and debilitating to elite rowers. Chronic exertional compartment syndrome, intersection syndrome and proximal radial bone stress injuries have been documented in this population. This paper explores the imaging findings related to these conditions in asymptomatic elite rowers. Design Observational study. Methods 19 asymptomatic senior elite and under-23 rowers currently competing at National level or above underwent ultrasound (US), Magnetic Resonance Imaging (MRI) and muscle functional MRI evaluation of their forearms. A comprehensive evaluation sheet identifying characteristics of bone stress, intersection syndrome and chronic exertional compartment syndrome was utilised based on a literature search and review by senior clinicians working with this population. Results Peritendinous fluid of Extensor Carpi Radialis Longus (n=10, 53%) or Extensor Carpi Radialis Brevis (n=6, 32%) was a common finding on US. MRI had a higher rate of identification than US. Extensor Digitorum (Coeff=−1.76, 95%CI −3.04 to −0.49), Flexor Carpi Radialis (Coeff=−2.86, 95%CI −5.35 to −0.38) and Flexor Carpi Ulnaris (Coeff=−3.31, 95%CI −5.30 to −1.32), Pronator Teres (Coeff=−3.94, 95%CI −6.89 to −0.99), and Supinator (Coeff=−168, 95%CI −3.28 to −0.02) showed statistically significant changes immediately post-exercise. Mild proximal radial marrow hyperintensity was present (n=15, 78.9%) with three participants (15.8%) also having mild periosteal oedema of the radius. Conclusions Imaging findings commonly seen in symptomatic populations are observed in elite, asymptomatic rowers. Care should be taken when diagnosing bone stress injuries, intersection syndrome and compartment syndrome on imaging findings alone. Data presented can be utilised as a normative dataset for future case studies.
Description:
Objectives: To investigate seasonal injury incidence and musculoskeletal screening as a predictor of injury in elite Olympic class sailors. Methods: A 12-month analysis of injury surveillance data was performed for elite Australian sailors (age = 16-30 years, N= 22). Pre-season musculoskeletal screening (incorporating mobility, stability and neural tests) and seasonal injury data were analysed for predictive relationships, and associations between potential predictor variables and injury status. Results: Injuries requiring medical attention occurred at a rate of 3.6 injuries/athlete, while injuries resulting in disability occurred at a rate of 0.6 injuries/athlete, with the lumbar spine the main site of injury (23% and 33%, respectively). Wrist and hand injury resulted in the highest number of days of disability (110 days), followed by injury to the lumbar spine (87 days). Across the season 75% of injuries to the lumbar spine occurred in the latter half of the season. The only screening measure predictive of injured/uninjured status was better left-sided single-leg decline-squat performance (OR= 0.29; 95% CI = 0.09-0.88; p = 0.03), while increasing age was significantly (p = 0.03) associated with thoracic (OR = 1.48; 95% CI = 1.03-2.12) and lumbar spine (OR= 1.46; 95% CI = 1.04-2.04) injury. Conclusions: Though clinically useful, current screening protocols do not adequately assess the risk of seasonal injury in elite Olympic class sailors, and should be revised. Due to the increased risk of spinal injury and potential lost/modified participation in older Olympic class sailors, injury prevention activities should be individualised and age appropriate. (C) 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
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Objectives: To investigate the impact of training modification on achieving performance goals. Previous research demonstrates an inverse relationship between injury burden and success in team sports. It is unknown whether this relationship exists within individual sport such as athletics. Design: A prospective, cohort study (n = 33 International Track and Field Athletes; 76 athlete seasons) across five international competition seasons. Methods: Athlete training status was recorded weekly over a 5-year period. Over the 6-month preparation season, relationships between training weeks completed, the number of injury/illness events and the success or failure of a performance goal at major championships was investigated. Two-by-two table were constructed and attributable risks in the exposed (AFE) calculated. A mixed-model, logistic regression was used to determine the relationship between failure and burden per injury/illness. Receiver Operator Curve (ROC) analysis was performed to ascertain the optimal threshold of training week completion to maximise the chance of success. Results: Likelihood of achieving a performance goal increased by 7-times in those that completed >80% of planned training weeks (AUC, 0.72; 95%CI 0.64-0.81). Training availability accounted for 86% of successful seasons (AFE=0.86, 95%CI, 0.46 to 0.96). The majority of new injuries occurred within the first month of the preparation season (30%) and most illnesses occurred within 2-months of the event (50%). For every modified training week the chance of success significantly reduced (OR=0.74, 95%CI 0.58 to 0.94). Conclusions: Injuries and illnesses, and their influence on training availability, during preparation are major determinants of an athlete's chance of performance goal success or failure at the international level. (C) 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Description:
Objectives: To investigate inter and intra-rater reliability of hand held (HHD) and externally fixed (EFD) dynamometry for shoulder internal (IR) and external rotation (ER) strength and their correlation to isokinetic testing. Design: Within participant, inter and intra-rater reliability study. Participants: Twenty active, healthy male and female participants underwent testing by two examiners. Outcome measures: Intra-class coefficients (ICC), percentage standard error of measurement (%SEM), and percentage minimal detectable change (%MDC) were calculated for inter-rater, intra-day and intra-rater, inter-week reliability. Maximum and average of three repetitions were compared to the isokinetic results at three speeds (60 degrees/sec, 180 degrees/sec, 240 degrees/sec) for both concentric and eccentric contractions. Results: Inter and intra-tester values demonstrated good to high agreement (HHD, ICC range = 0.89 -0.97, %SEM = 4.80-8.60%, %MDC = 13.29-23.70%; EFD, ICC = 0.88-0.96, %SEM = 6.60-11.00%, % MDC = 18.40-30.04%). HHD and EFD showed moderate to very strong correlations to the isokinetic testing (HHD, r = 0.45-0.86; EFD, r = 0.49-0.83). Conclusions: The results of this study indicate that both EFD and HI-ID are suitable for clinical practice and research. Hand-held dynamometry is preferred due to its higher intra- and inter-rater reliability and smaller MDC and lower SEM. (C) 2016 Elsevier Ltd. All rights reserved.