Sprinting, Strength, and Architectural Adaptations Following Hamstring Training in Australian Footballers.

The aim of this study was to determine the sprinting, strength, and architectural adaptations following a hip-dominant flywheel (FLY) or Nordic hamstring exercise (NHE) intervention in Australian footballers. Twenty-seven male athletes were randomized to FLY (n = 13) or NHE (n = 14) training across a 39-week period (inclusive of pre-season and in-season). Biceps femoris long head (BFlh) architecture was assessed throughout. Eccentric hamstring strength and 40 m sprint times (with force-velocity profiling) were assessed at baseline, end of pre-season, and following the intervention. After the intervention, BFlh fascicle length was longer in both groups compared to baseline (FLY: 1.16 cm, 95%CI: 0.66 to 1.66 cm, d = 1.99, p < 0.001; NHE: 1.08 cm, 95%CI: 95%CI 0.54 to 1.61 cm, d = 1.73, p < 0.001). Both groups also increased their eccentric strength (FLY: mean change 82 N, 95%CI 12 to 152 N, d = 1.34, p = 0.026; NHE: mean change 97 N, 95%CI 47 to 146 N, d = 1.77, p = 0.001). After pre-season, the NHE group improved their 5 m sprint time by 3.5% (±1.2%) and were 3.7% (±1.4%) and 2.0% (±0.5%) faster than the FLY group across 5 m and 10 m, respectively. At the end of pre-season, the FLY group improved maximal velocity by 3.4% (±1.4%) and improved horizontal force production by 9.7% in-season (±2.2%). Both a FLY and NHE intervention increase BFlh fascicle length and eccentric strength in Australian Footballers. An NHE intervention led to enhanced acceleration capacity. A FLY intervention was suggested to improve maximal sprint velocity and horizontal force production, without changes in sprint times. These findings have implications for hamstring injury prevention but also programs aimed at improving sprint performance.

Running exposure is associated with the risk of hamstring strain injury in elite Australian footballers.

BACKGROUND: To investigate the association between running exposure and the risk of hamstring strain injury (HSI) in elite Australian footballers. METHODS: Elite Australian footballers (n=220) from 5 different teams participated. Global positioning system (GPS) data were provided for every athlete for each training session and match for the entire 2015 season. The occurrences of HSIs throughout the study period were reported. Receiver operator characteristic curve analyses were performed and the relative risk (RR) of subsequent HSI was calculated for absolute and relative running exposure variables related to distance covered above 10 and 24 km/hour in the preceding week/s. RESULTS: 30 prospective HSIs occurred. For the absolute running exposure variables, weekly distance covered above 24 km/hour (>653 m, RR=3.4, 95% CI 1.6 to 7.2, sensitivity=0.52, specificity=0.76, area under the curve (AUC)=0.63) had the largest influence on the risk of HSI in the following week. For the relative running exposure variables, distance covered above 24 km/hour as a percentage of distance covered above 10 km/hour (>2.5%, RR=6.3, 95% CI 1.5 to 26.7, sensitivity=0.93, specificity=0.34, AUC=0.63) had the largest influence on the risk of HSI in the following week. Despite significant increases in the RR of HSI, the predictive capacity of these variables was limited. CONCLUSIONS: An association exists between absolute and relative running exposure variables and elite Australian footballers' risk of subsequent HSI, with the association strongest when examining data within 7-14 days. Despite this, the use of running exposure variables displayed limited clinical utility to predict HSI at the individual level.

Higher Levels of Morning and Daytime Light Exposure Associated with Positive Sleep Indices in Professional Team Sport Athletes.

Objective: Light exposure techniques have been recommended to combat sleep issues caused by disruption to circadian regularity in the athletic population, although studies are lacking. Methods: A total of 17 professional male Australian Football athletes (age ± SD: 22 ± 3 years) wore a wrist actigraph to measure sleep parameters, and a wearable light sensor to measure melanopic equivalent daylight illuminance (mEDI, in lux) for 14 days. Participants completed three sleep questionnaires at the end of the data collection period and completed well-being surveys 6 times. The Sleep Regularity Index (SRI) for each player was also calculated from actigraphy data. Light exposure data were organised into three different timeframes: morning (wake time + 2 hours), daytime (end of morning to 6 pm), and evening (2 hours leading up to bedtime) for analysis. Repeated measures correlation was conducted for objective sleep measures and mEDI values per timeframe. Pearson's correlation was conducted on subjective sleep measures and well-being measures against mEDI values per timeframe. Results: Higher morning light was associated with significantly (p < 0.001) greater total sleep time (r = 0.31). Higher daytime light exposure was associated with higher subjective sleep quality (r = 0.48, p < 0.05). Higher evening light exposure was associated with higher Athlete Sleep Screening Questionnaire (ASSQ) global scores (r = 0.52, p < 0.05). There were no other significant correlations between light exposure and sleep or well-being measures (p > 0.05). Conclusion: Higher morning and daylight exposure levels were associated with various positive objective and subjective sleep measures in professional team sport athletes, supporting the need for education on optimising light exposure to improve circadian function, sleep, and health.

Hamstring Strain Injury Risk Factors in Australian Football Change over the Course of the Season.

BACKGROUND/AIM: This study aimed to determine which factors were most predictive of hamstring strain injury (HSI) during different stages of the competition in professional Australian Football. METHODS: Across two competitive seasons, eccentric knee flexor strength and biceps femoris long head architecture of 311 Australian Football players (455 player seasons) were assessed at the start and end of preseason and in the middle of the competitive season. Details of any prospective HSI were collated by medical staff of participating teams. Multiple logistic regression models were built to identify important risk factors for HSI at the different time points across the season. RESULTS: There were 16, 33, and 21 new HSIs reported in preseason, early in-season, and late in-season, respectively, across two competitive seasons. Multivariate logistic regression and recursive feature selection revealed that risk factors were different for preseason, early in-season, and late in-season HSIs. A combination of previous HSI, age, height, and muscle thickness were most associated with preseason injuries (median area under the curve [AUC], 0.83). Pennation angle and fascicle length had the strongest association with early in-season injuries (median AUC, 0.86). None of the input variables were associated with late in-season injuries (median AUC, 0.46). The identification of early in-season HSI and late in-season HSI was not improved by the magnitude of change of data across preseason (median AUC, 0.67). CONCLUSIONS: Risk factors associated with prospective HSI were different across the season in Australian Rules Football, with nonmodifiable factors (previous HSI, age, and height) mostly associated with preseason injuries. Early in-season HSI were associated with modifiable factors, notably biceps femoris long head architectural measures. The prediction of in-season HSI was not improved by assessing the magnitude of change in data across preseason.

The Fragility Index of Risk Factors for Hamstring Injuries.

OBJECTIVE: To determine the Fragility Index of hamstring injury risk factors, defined as the minimum number of participants who would need to change classification to make a hamstring injury risk factor statistically nonsignificant. DESIGN: Retrospective secondary data analysis. METHODS: Studies that investigated 1 or more risk factors for hamstring injury, and presented sufficient data to develop a 2 × 2 contingency table were included. A systematic literature search and reference screening of a recent hamstring injury systematic review were conducted to identify 78 articles. Relative risk and 95% confidence intervals were determined and then systematically recalculated by removing 1 observation from the high-risk injury count and adding it to the high-risk noninjury count. The Fragility Index for a risk factor was the number of observations required to be moved between groups until the relative risk was no longer significant. RESULTS: The median Fragility Index of all hamstring injury risk factors was 3 (Q1-Q3 = 2-6). The Fragility Index for nonmodifiable risk factors was 3 (Q1-Q3 = 2-6) and 3 (Q1-Q3 = 2-5) for modifiable risk factors. Over 35% of all included hamstring injury risk factors had a Fragility Index of ≤2. CONCLUSION: Most statistically significant hamstring injury risk factors are fragile associations. The interpretation of significant hamstring injury risk factors should consider a range of statistical metrics, and while the Fragility Index should never be considered in isolation, it is an intuitive measure to help assess the robustness of findings. J Orthop Sports Phys Ther 2024;54(10):672-678. Epub 4 September 2024. doi:10.2519/jospt.2024.12300.

The Effect of Eccentric or Isometric Training on Strength, Architecture, and Sprinting across an Australian Football Season.

PURPOSE: This study aimed to investigate the effect of an isometric (ISO) or Nordic hamstring exercise (NHE) intervention, alongside a sprint training program on hamstring strength, architecture, and sprinting performance in Australian footballers. METHODS: Twenty-five male athletes undertook NHE ( n = 13) or ISO ( n = 12) training across a 38-wk period (including preseason and in season). Biceps femoris long head (BFlh) architecture, ISO, and eccentric knee flexor strength were assessed at baseline, at the end of preseason (14 wk), and at the conclusion of the intervention. Sprint times and force-velocity profiles were determined at baseline and at the end of preseason. RESULTS: After the intervention, both groups had significant improvements in BFlh fascicle length (NHE: 1.16 cm, 95% CI = 0.68 to 1.63 cm, d = 1.88, P < 0.001; ISO: 0.82 cm, 95% CI = 0.57 to 1.06 cm, d = 1.70, P < 0.001), muscle thickness (NHE: 0.11 cm, 95% CI = 0.01 to 0.21 cm, d = 0.51, P = 0.032; ISO: 0.21 cm, 95% CI = 0.10 to 0.32 cm, d = 0.86, P = 0.002), and eccentric strength (NHE: 83 N, 95% CI = 53 to 114 N, d = 1.79, P < 0.001; ISO: 83 N, 95% CI = 17 to 151 N, d = 1.17, P = 0.018). Both groups also finished the intervention weaker isometrically than they started (NHE: -45 N, 95% CI = -81 to -8 N, d = -1.03, P = 0.022; ISO: -80 N, 95% CI = -104 to -56 N, d = -3.35, P < 0.001). At the end of preseason, the NHE group had improved their 5-m sprint time by 3.3% ± 2.0%), and their maximum horizontal velocity was 3% ± 2.1% greater than the ISO group who saw no changes. CONCLUSIONS: Both ISO and NHE training with a periodized sprinting program can increase BFlh fascicle length, thickness, and eccentric strength in Australian footballers. NHE training also improves 5-m sprint time and maximum velocity. However, both interventions reduced ISO strength. These findings provide unique, contextually relevant insights into the adaptations possible in semiprofessional athletes.

The Dose-Response of the Nordic Hamstring Exercise on Biceps Femoris Architecture and Eccentric Knee Flexor Strength: A Randomized Interventional Trial.

PURPOSE: To examine the dose-response of the Nordic hamstring exercise (NHE) on biceps femoris long head (BFlh) architecture and eccentric knee flexor strength. DESIGN: Randomized interventional trial. METHODS: Forty recreationally active males completed a 6-week NHE training program consisting of either intermittent low volumes (group 1; n = 10), low volumes (group 2; n = 10), initial high volumes followed by low volumes (group 3; n = 10), or progressively increasing volumes (group 4; n = 10). A 4-week detraining period followed each program. Muscle architecture was assessed weekly during training and after 2 and 4 weeks of detraining. Eccentric knee flexor strength was assessed preintervention and postintervention and after 2 and 4 weeks of detraining. RESULTS: Following 6 weeks of training, BFlh fascicle length (FL) increased in group 3 (mean difference = 0.83 cm, d = 0.45, P = .027, +7%) and group 4 (mean difference = 1.48 cm, d = 0.94, P = .004, +14%). FL returned to baseline following detraining in groups 3 and 4. Strength increased in group 2 (mean difference = 53.6 N, d = 0.55, P = .002, +14%), group 3 (mean difference = 63.4 N, d = 0.72, P = .027, +17%), and group 4 (mean difference = 74.7, d = 0.83, P = .006, +19%) following training. Strength returned to baseline following detraining in groups 2 and 3 but not in group 4. CONCLUSIONS: Initial high volumes of the NHE followed by lower volumes, as well as progressively increasing volumes, can elicit increases in BFlh FL and eccentric knee flexor strength. Low volumes of the NHE were insufficient to increase FL, although as few as 48 repetitions in 6 weeks did increase strength.

Screening Hamstring Injury Risk Factors Multiple Times in a Season Does Not Improve the Identification of Future Injury Risk.

PURPOSE: To determine if eccentric knee flexor strength and biceps femoris long head (BFlh) fascicle length were associated with prospective hamstring strain injury (HSI) in professional Australian Football players, and if more frequent assessments of these variables altered the association with injury risk. METHODS: Across two competitive seasons, 311 Australian Football players (455 player seasons) had their eccentric knee flexor strength during the Nordic hamstring exercise and BFlh architecture assessed at the start and end of preseason and in the middle of the competitive season. Player age and injury history were also collected in preseason. Prospective HSIs were recorded by team medical staff. RESULTS: Seventy-four player seasons (16%) sustained an index HSI. Shorter BFlh fascicles (<10.42 cm) increased HSI risk when assessed at multiple time points only (relative risk [RR], 1.9; 95% confidence interval [CI], 1.2-3.0). Neither absolute (N) nor relative (N·kg-1) eccentric knee flexor strength was associated with HSI risk, regardless of measurement frequency (RR range, 1.0-1.1); however, between-limb imbalance (>9%), when measured at multiple time points, was (RR, 1.8; 95% CI, 1.1-3.1). Prior HSI had the strongest univariable association with prospective HSI (RR, 2.9; 95% CI, 1.9-4.3). Multivariable logistic regression models identified a combination of prior HSI, BFlh architectural variables and between-limb imbalance in eccentric knee flexor strength as optimal input variables; however, their predictive performance did not improve with increased measurement frequency (area under the curve, 0.681-0.726). CONCLUSIONS: More frequent measures of eccentric knee flexor strength and BFlh architecture across a season did not improve the ability to identify which players would sustain an HSI.

Prediction of Hamstring Injuries in Australian Football Using Biceps Femoris Architectural Risk Factors Derived From Soccer.

BACKGROUND: Hamstring strain injuries are the most common injuries in team sports. Biceps femoris long head architecture is associated with the risk of hamstring injury in soccer. To assess the overall predictive ability of architectural variables, risk factors need to be applied to and validated across different cohorts. PURPOSE: To assess the generalizability of previously established risk factors for a hamstring strain injury (HSI), including demographics, injury history, and biceps femoris long head (BFlh) architecture to predict HSIs in a cohort of elite Australian football players. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Demographic, injury history, and BFlh architectural data were collected from elite soccer (n = 152) and Australian football (n = 169) players at the beginning of the preseason for their respective competitions. Any prospectively occurring HSIs were reported to the research team. Optimal cut points for continuous variables used to determine an association with the HSI risk were established from previously published data in soccer and subsequently applied to the Australian football cohort to derive the relative risk (RR) for these variables. Logistic regression models were built using data from the soccer cohort and utilized to estimate the probability of an injury in the Australian football cohort. The area under the curve (AUC) and Brier score were the primary outcome measures to assess the performance of the logistic regression models. RESULTS: A total of 27 and 30 prospective HSIs occurred in the soccer and Australian football cohorts, respectively. When using cut points derived from the soccer cohort and applying these to the Australian football cohort, only older athletes (aged ≥25.4 years; RR, 2.7 [95% CI, 1.4-5.2]) and those with a prior HSI (RR, 2.5 [95% CI, 1.3-4.8]) were at an increased risk of HSIs. Using the same approach, height, weight, fascicle length, muscle thickness, pennation angle, and relative fascicle length were not significantly associated with an increased risk of HSIs in Australian football players. The logistic regression model constructed using age and prior HSIs performed the best (AUC = 0.67; Brier score = 0.14), with the worst performing model being the one that was constructed using pennation angle (AUC = 0.53; Brier score = 0.18). CONCLUSION: Applying cut points derived from previously published data in soccer to a dataset from Australian football identified older age and prior HSIs, but none of the modifiable HSI risk factors, to be associated with an injury. The transference of HSI risk factor data between soccer and Australian football appears limited and suggests that cohort-specific cut points must be established.

Factors that Impact Self-reported Wellness Scores in Elite Australian Footballers.

INTRODUCTION: This study aimed to 1) identify the impact of external load variables on changes in wellness and 2) identify the impact of age, training/playing history, strength levels, and preseason loads on changes in wellness in elite Australian footballers. METHODS: Data were collected from one team (45 athletes) during the 2017 season. Self-reported wellness was collected daily (4, best score possible; 28, worst score possible). External load/session availability variables were calculated using global positioning systems and session availability data from every training session and match. Additional variables included demographic data, preseason external loads, and strength/power measures. Linear mixed models were built and compared using root mean square error (RMSE) to determine the impact of variables on wellness. RESULTS: The external load variables explained wellness to a large degree (RMSE = 1.55, 95% confidence intervals = 1.52 to 1.57). Modeling athlete ID as a random effect appeared to have the largest impact on wellness, improving the RMSE by 1.06 points. Aside from athlete ID, the variable that had the largest (albeit negligible) impact on wellness was sprint distance covered across preseason. Every additional 2.1 km covered across preseason worsened athletes' in-season wellness scores by 1.2 points (95% confidence intervals = 0.0-2.3). CONCLUSIONS: The isolated impact of the individual variables on wellness was negligible. However, after accounting for the individual athlete variability, the external load variables examined collectively were able to explain wellness to a large extent. These results validate the sensitivity of wellness to monitor individual athletes' responses to the external loads imposed on them.

Modeling the Risk of Team Sport Injuries: A Narrative Review of Different Statistical Approaches.

Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organizations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. There are a number of methods that can be used to identify injury risk factors. However, difficulty in understanding the nuances between different statistical approaches can lead to incorrect inferences and decisions being made from data. Accordingly, this narrative review aims to (1) outline commonly implemented methods for determining injury risk, (2) highlight the differences between association and prediction as it relates to injury and (3) describe advances in statistical modeling and the current evidence relating to predicting injuries in sport. Based on the points that are discussed throughout this narrative review, both researchers and practitioners alike need to carefully consider the different types of variables that are examined in relation to injury risk and how the analyses pertaining to these different variables are interpreted. There are a number of other important considerations when modeling the risk of injury, such as the method of data transformation, model validation and performance assessment. With these technical considerations in mind, researchers and practitioners should consider shifting their perspective of injury etiology from one of reductionism to one of complexity. Concurrently, research implementing reductionist approaches should be used to inform and implement complex approaches to identifying injury risk. However, the ability to capture large injury numbers is a current limitation of sports injury research and there has been a call to make data available to researchers, so that analyses and results can be replicated and verified. Collaborative efforts such as this will help prevent incorrect inferences being made from spurious data and will assist in developing interventions that are underpinned by sound scientific rationale. Such efforts will be a step in the right direction of improving the ability to identify injury risk, which in turn will help improve risk mitigation and ultimately the prevention of injuries.

Session Availability as a Result of Prior Injury Impacts the Risk of Subsequent Non-contact Lower Limb Injury in Elite Male Australian Footballers.

Prior injury is a commonly identified risk factor for subsequent injury. However, a binary approach to classifying prior injury (i.e., yes/no) is commonly implemented and may constrain scientific findings, as it is possible that variations in the amount of time lost due to an injury will impact subsequent injury risk to differing degrees. Accordingly, this study investigated whether session availability, a surrogate marker of prior injury, influenced the risk of subsequent non-contact lower limb injury in Australian footballers. Data were collected from 62 male elite Australian footballers throughout the 2015, 2016, and 2017 Australian Football League seasons. Each athlete's participation status (i.e., full or missed/modified) and any injuries that occurred during training sessions/matches were recorded. As the focus of the current study was prior injury, any training sessions/matches that were missed due to reasons other than an injury (e.g., load management, illness and personal reasons) were removed from the data prior to all analyses. For every Monday during the in-season periods, session availability (%) in the prior 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84 days was determined as the number of training sessions/matches fully completed (injury free) relative to the number of training sessions/matches possible in each window. Each variable was modeled using logistic regression to determine its impact on subsequent injury risk. Throughout the study period, 173 non-contact lower limb injuries that resulted in at least one missed/modified training session or match during the in-season periods occurred. Greater availability in the prior 7 days increased injury probabilities by up to 4.4%. The impact of session availability on subsequent injury risk diminished with expanding windows (i.e., availability in the prior 14 days through to the prior 84 days). Lesser availability in the prior 84 days increased injury probabilities by up to 14.1%, only when coupled with greater availability in the prior 7 days. Session availability may provide an informative marker of the impact of prior injury on subsequent injury risk and can be used by coaches and clinicians to guide the progression of training, particularly for athletes that are returning from long periods of injury.

An Evidence-Based Framework for Strengthening Exercises to Prevent Hamstring Injury.

Strength training is a valuable component of hamstring strain injury prevention programmes; however, in recent years a significant body of work has emerged to suggest that the acute responses and chronic adaptations to training with different exercises are heterogeneous. Unfortunately, these research findings do not appear to have uniformly influenced clinical guidelines for exercise selection in hamstring injury prevention or rehabilitation programmes. The purpose of this review was to provide the practitioner with an evidence-base from which to prescribe strengthening exercises to mitigate the risk of hamstring injury. Several studies have established that eccentric knee flexor conditioning reduces the risk of hamstring strain injury when compliance is adequate. The benefits of this type of training are likely to be at least partly mediated by increases in biceps femoris long head fascicle length and improvements in eccentric knee flexor strength. Therefore, selecting exercises with a proven benefit on these variables should form the basis of effective injury prevention protocols. In addition, a growing body of work suggests that the patterns of hamstring muscle activation diverge significantly between different exercises. Typically, relatively higher levels of biceps femoris long head and semimembranosus activity have been observed during hip extension-oriented movements, whereas preferential semitendinosus and biceps femoris short head activation have been reported during knee flexion-oriented movements. These findings may have implications for targeting specific muscles in injury prevention programmes. An evidence-based approach to strength training for the prevention of hamstring strain injury should consider the impact of exercise selection on muscle activation, and the effect of training interventions on hamstring muscle architecture, morphology and function. Most importantly, practitioners should consider the effect of a strength training programme on known or proposed risk factors for hamstring injury.

Predictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers.

PURPOSE: Three of the most commonly identified hamstring strain injury (HSI) risk factors are age, previous HSI, and low levels of eccentric hamstring strength. However, no study has investigated the ability of these risk factors to predict the incidence of HSI in elite Australian footballers. Accordingly, the purpose of this prospective cohort study was to investigate the predictive ability of HSI risk factors using machine learning techniques. METHODS: Eccentric hamstring strength, demographic and injury history data were collected at the start of preseason for 186 and 176 elite Australian footballers in 2013 and 2015, respectively. Any prospectively occurring HSI were reported to the research team. Using various machine learning techniques, predictive models were built for 2013 and 2015 within-year HSI prediction and between-year HSI prediction (2013 to 2015). The calculated probabilities of HSI were compared with the injury outcomes and area under the curve (AUC) was determined and used to assess the predictive performance of each model. RESULTS: The minimum, maximum, and median AUC values for the 2013 models were 0.26, 0.91, and 0.58, respectively. For the 2015 models, the minimum, maximum and median AUC values were, correspondingly, 0.24, 0.92, and 0.57. For the between-year predictive models the minimum, maximum, and median AUC values were 0.37, 0.73, and 0.52, respectively. CONCLUSIONS: Although some iterations of the models achieved near perfect prediction, the large ranges in AUC highlight the fragility of the data. The 2013 models performed slightly better than the 2015 models. The predictive performance of between-year HSI models was poor however. In conclusion, risk factor data cannot be used to identify athletes at an increased risk of HSI with any consistency.

Architectural Changes of the Biceps Femoris Long Head after Concentric or Eccentric Training.

PURPOSE: To determine the architectural adaptations of the biceps femoris long head (BFlh) after concentric or eccentric strength training interventions and the time course of adaptation during training and detraining. METHODS: Participants in this intervention (concentric training group [n = 14], eccentric training group [n = 14], male subjects) completed a 4-wk control period, followed by 6 wk of either concentric- or eccentric-only knee flexor training on an isokinetic dynamometer and finished with 28 d of detraining. Architectural characteristics of BFlh were assessed at rest and during graded isometric contractions using two-dimensional ultrasonography at 28 d prebaseline; baseline; and days 14, 21, and 42 of the intervention and then again after 28 d of detraining. RESULTS: BFlh fascicle length was significantly longer in the eccentric training group (P < 0.05; d range, 2.65-2.98) and shorter in the concentric training group (P < 0.05; d range, -1.62 to -0.96) after 42 d of training compared with baseline at all isometric contraction intensities. After the 28-d detraining period, BFlh fascicle length was significantly reduced in the eccentric training group at all contraction intensities compared with the end of the intervention (P < 0.05; d range, -1.73 to -1.55). There was no significant change in fascicle length of the concentric training group after the detraining period. CONCLUSIONS: These results provide evidence that short-term resistance training can lead to architectural alterations in the BFlh. In addition, the eccentric training-induced lengthening of BFlh fascicle length was reversed and returned to baseline values after 28 d of detraining. The contraction mode specific adaptations in this study may have implications for injury prevention and rehabilitation.