Enda King

Isokinetic muscle strength and readiness to return to sport following anterior cruciate ligament reconstruction: is there an association? A systematic review and a protocol recommendation

Introduction Following anterior cruciate ligament reconstruction (ACLR), strength is a key variable in regaining full function of the knee. Isokinetic strength is commonly used as part of the return to sport (RTS) criteria. Aim We systematically reviewed the isokinetic strength evaluation protocols that are currently being used following ACLR. A secondary aim was to suggest an isokinetic protocol that could meet RTS criteria. Method Articles were searched using ScienceDirect, PubMed and Sage Journals Online, combined with cross-checked reference lists of the publications. Protocol data and outcome measurements and RTS criteria were extracted from each article included in the review. Results 39 studies met the inclusion criteria and reported their isokinetic strength evaluation protocol following ACLR. The variables that were most commonly used were concentric/concentric mode of contraction (31 studies), angular velocity of 60°/s (29 studies), 3–5 repetitions (24 studies), range of motion of 0–90° (6 studies), and using gravity correction (9 studies). 8 studies reported strength limb symmetry index scores as part of their RTS criteria. Conclusions There was no standardised isokinetic protocol following ACLR; isokinetic strength measures have not been validated as useful predictors of successful RTS. We propose a standard protocol to allow consistency of testing and accurate comparison of future research.

If overuse injury is a ‘training load error’, should undertraining be viewed the same way?

Inappropriately high training loads cause overuse injuries.1 However, it has recently been proposed that overuse injuries should be considered in terms of both ‘overloading’ and ‘underloading’.2 The rationale is that increased injury risk is associated with ‘spikes’ in workload (ie, overloading) and low chronic workloads (ie, underloading), which may leave an athlete predisposed to a ‘spike’ in workload.3 Given that workload is both modifiable and controllable, it has been suggested that ‘overuse injuries’ be considered as ‘training load errors’.2 ,4 Anecdotally, strength and conditioning staff are viewed as the practitioners who ‘break’ the athlete, while medical staff ‘fix’ them. Conversely, conditioning staff may indeed decrease the probability of athletes sustaining an injury by increasing chronic workloads, whereas medical staff may inadvertently increase injury risk by reducing workloads. Given that all coaching staff as well as the performance team (eg, strength and conditioning, sport scientists and physiotherapists) are involved to varying degrees in the training process, an effective solution needs to be multidisciplinary in nature. Periods of underloading and overloading can occur anywhere, from rehabilitation through to game-specific skills and competition, hence communication between athlete, manager …

Athletic groin pain (part 1): a prospective anatomical diagnosis of 382 patients--clinical findings, MRI findings and patient-reported outcome measures at baseline.

Background Athletic groin pain remains a common field-based team sports time-loss injury. There are few reports of non-surgically managed cohorts with athletic groin pain. Aim To describe clinical presentation/examination, MRI findings and patient-reported outcome (PRO) scores for an athletic groin pain cohort. Methods All patients had a history including demographics, injury duration, sport played and standardised clinical examination. All patients underwent MRI and PRO score to assess recovery. A clinical diagnosis of the injured anatomical structure was made based on these findings. Statistical assessment of the reliability of accepted standard investigations undertaken in making an anatomical diagnosis was performed. Result 382 consecutive athletic groin pain patients, all male, enrolled. Median time in pain at presentation was (IQR) 36 (16–75) weeks. Most (91%) played field-based ball-sports. Injury to the pubic aponeurosis (PA) 240 (62.8%) was the most common diagnosis. This was followed by injuries to the hip in 81 (21.2%) and adductors in 56 (14.7%) cases. The adductor squeeze test (90° hip flexion) was sensitive (85.4%) but not specific for the pubic aponeurosis and adductor pathology (negative likelihood ratio 1.95). Analysed in series, positive MRI findings and tenderness of the pubic aponeurosis had a 92.8% post-test probability. Conclusions In this largest cohort of patients with athletic groin pain combining clinical and MRI diagnostics there was a 63% prevalence of PA injury. The adductor squeeze test was sensitive for athletic groin pain, but not specific individual pathologies. MRI improved diagnostic post-test probability. No hernia or incipient hernia was diagnosed. Clinical trial registration number NCT02437942.

Athletic groin pain: a systematic review and meta-analysis of surgical versus physical therapy rehabilitation outcomes

Background Athletic groin pain (AGP) is an encompassing term for the multitude of chronic conditions presenting as pain in the inguinal region. The purpose of this review was to compare the return to play rates (RTPrate) and return to play times (RTPtime) between surgical and rehabilitation interventions in the treatment of AGP. Methods A systematic review of English language peer review journals was carried out between 1980 to June 2013 using PubMed, Embase, CINHAL and Google Scholar searching for all papers relating to AGP (and its various pseudonyms) and all surgical and rehabilitative interventions which reported RTPrate and/or RTPtime. AGP literature has been subdivided by many eponymous diagnoses but anatomical diagnostic groupings of (1) abdominal wall, (2) adductor and (3) pubic related pain were used in this review. Meta-analysis was then carried out on the data to compare results between the surgical and rehabilitation groups. Results Fifty-six papers out of the 561 discovered in the initial search were included in the review with 3332 athletes included. Evidence was mostly level IV. Using the Black and Downs checklist we found poor study quality overall with a high risk of bias especially among surgical studies. The results showed comparable RTPrate between surgical and rehabilitative interventions within the three diagnostic groups. Rehabilitation had significantly quicker RTPtime for pubic related groin pain compared to surgery (10.5 weeks and 23.1 weeks respectively). The abdominal group had the fastest return of the three groups for the rehabilitation and surgery. Conclusions The review suggested better outcomes with rehabilitation for pubic-related groin pain with no difference between the adductor and abdominal groups. The review highlighted the poor quality and risk of bias in the literature making accurate comparison difficult.

Athletic groin pain (part 2): a prospective cohort study on the biomechanical evaluation of change of direction identifies three clusters of movement patterns

Background Athletic groin pain (AGP) is prevalent in sports involving repeated accelerations, decelerations, kicking and change-of-direction movements. Clinical and radiological examinations lack the ability to assess pathomechanics of AGP, but three-dimensional biomechanical movement analysis may be an important innovation. Aim The primary aim was to describe and analyse movements used by patients with AGP during a maximum effort change-of-direction task. The secondary aim was to determine if specific anatomical diagnoses were related to a distinct movement strategy. Methods 322 athletes with a current symptom of chronic AGP participated. Structured and standardised clinical assessments and radiological examinations were performed on all participants. Additionally, each participant performed multiple repetitions of a planned maximum effort change-of-direction task during which whole body kinematics were recorded. Kinematic and kinetic data were examined using continuous waveform analysis techniques in combination with a subgroup design that used gap statistic and hierarchical clustering. Results Three subgroups (clusters) were identified. Kinematic and kinetic measures of the clusters differed strongly in patterns observed in thorax, pelvis, hip, knee and ankle. Cluster 1 (40%) was characterised by increased ankle eversion, external rotation and knee internal rotation and greater knee work. Cluster 2 (15%) was characterised by increased hip flexion, pelvis contralateral drop, thorax tilt and increased hip work. Cluster 3 (45%) was characterised by high ankle dorsiflexion, thorax contralateral drop, ankle work and prolonged ground contact time. No correlation was observed between movement clusters and clinically palpated location of the participants pain. Conclusions We identified three distinct movement strategies among athletes with long-standing groin pain during a maximum effort change-of-direction task These movement strategies were not related to clinical assessment findings but highlighted targets for rehabilitation in response to possible propagative mechanisms. Trial registration number NCT02437942, pre results.

Analyzing Human Movements - Introducing A Framework To Extract And Evaluate Biomechanical Data

This study discusses possible sources of discrepancy between findings of previous human motion studies and presents a framework that seeks to address these issues. Motion analysis systems are widely employed to identify movement deficiencies - e.g. patterns that potentially increase the risk of injury or inhibit performance. However, findings across studies are often conflicting in respect to what a movement deficiency is or the magnitude of the relationship to a specific injury. To test the information content of movement data, a framework was build to differentiate between movements performed by a control (NORM) and abnormal (IMP-L and IMP-C) cohort using solely movement data. Movement data was recorded during jumping, hopping and change of direction exercises and was mathematically decomposed into subject scores. Subjects scores were then used to identify the most appropriate machine learning technique, which was subsequently utilized to create a prediction model that classified if a movement was performed by: IMP-L, IMP-C or NORM. The Monte Carlo method was used to obtain a measure of expected accuracy for each step within the analysis. Findings demonstrate that even the worst classification model outperformed the best guess observed and that not all members of the NORM group represent a NORM pattern as they were repeatedly classified as IMP-L or IMP-C. This highlights that some NORM limbs share movement characteristics with the abnormal group and consequently should not be considered when describing NORM.

Whole-body biomechanical differences between limbs exist 9 months after ACL reconstruction across jump/landing tasks

Previous studies examining jump tasks after anterior cruciate ligament reconstruction (ACLR) have focused on performance measures without examining joint kinematic and kinetic variables. The aim of this study was to identify differences in biomechanical and performance measures between limbs across tests 9 months after surgery.

Is stiffness related to athletic groin pain

Athletic groin pain (AGP) is a common injury prevalent in field sports. One biomechanical measure that may be of importance for injury risk is stiffness. To date however, stiffness has not been examined in AGP. The primary aim was to determine whether AGP affects vertical and joint stiffness and if so, whether successful rehabilitation is associated with a change in stiffness. Sixty‐five male patients with AGP and fifty male controls were recruited to this study. Assessment included a biomechanical examination of stiffness during a lateral hurdle hop test. Subjects with AGP were tested pre‐ and post‐rehabilitation, while controls were tested once. AGP subjects were cleared for return to play in a median time of 9.14 weeks (5.14‐29.0). Stiffness was significantly different at pre‐rehabilitation in comparison with controls for four of the ten stiffness values examined: ankle plantar flexor, knee extensor, hip abductor, and vertical stiffness (P < .05, D = 0.36‐0.79). Despite clearance for return to play, of these four variables, only hip abductor stiffness changed significantly from pre‐ to post‐rehabilitation (P = .05, D = 0.35) to become non‐significantly different to the uninjured group (P = .18, D = 0.26). These findings suggest that hip abductor stiffness may represent a target for AGP rehabilitation. Conversely, given the clearance for return to play, the lower sagittal plane and vertical stiffness in the AGP group in comparison with the uninjured controls likely represents either a compensatory mechanism to reduce the risk of further injury or a consequence of neuromuscular detraining.

Biomechanical but not timed performance asymmetries persist between limbs 9 months after ACL reconstruction during planned and unplanned change of direction

Whilst anterior cruciate ligament injury commonly occurs during change of direction (CoD) tasks, there is little research on how athletes execute CoD after anterior cruciate ligament reconstruction (ACLR). The aims of this study were to determine between-limb and between-test differences in performance (time) and joint kinematics and kinetics during planned and unplanned CoD. One hundred and fifty-six male subjects carried out 90° maximal effort, planned and unplanned CoD tests in a 3D motion capture laboratory 9 months after ACLR. Statistical parametric mapping (2 × 2 ANOVA; limb × test) was used to identify differences in CoD time and biomechanical measures between limbs and between tests. There was no interaction effect but a main effect for limb and task. There was no between-limb difference in the time to complete both CoD tests. Between-limb differences were found for internal knee valgus moment, knee internal rotation and flexion angle, knee extension and external rotation moment and ankle external rotation moment with lower values on the ACLR side (effect size 0.72-0.5). Between test differences were found with less contralateral pelvis rotation, distance from centre of mass to the ankle in frontal plane, posterior ground reaction force and greater hip abduction during the unplanned CoD (effect size 0.75-0.5). Findings demonstrated that kinematic and kinetic differences between limbs are evident during both CoD tests 9 months after surgery, despite no statistical differences in performance time. Biomechanical differences between tests were found in variables, which have previously been associated with ACL injury mechanism during unplanned CoD.

Supervised learning techniques and their ability to classify a change of direction task strategy using kinematic and kinetic features

This study examines the ability of commonly used supervised learning techniques to classify the execution of a maximum effort change of direction task into predefined movement pattern as well as the influence of fuzzy executions and the impact of selected features (e.g. peak knee flexion) towards classification accuracy. The experiment utilized kinematic and kinetic data from 323 male subjects with chronic athletic groin pain. All subjects undertook a biomechanical assessment and had been divided previously into 3 different movement strategies in an earlier paper. Examined supervised learning techniques were: a decision tree, an ensemble of decision trees, a discriminant analysis model, a naive Bayes classifier, a k-nearest-neighbour model, a multi-class model for support vector machines, a stepwise forward regression model, a neural network and a correlation approach. Performance (measured by comparing the predefined and classified movement pattern) was highest for the correlation approach (82% - CI 81-83%) and support vector machine (80% - CI 79-80%). The percentage of fuzzy observations within the data was between 16 and 25%. The most informative features for classification were: hip flexion and ankle rotation as well as ankle flexion moment, thorax [flexion and frontal sway], abduction angle in [hip and pelvis] and hip rotation. Findings of this study support the assumption that multiple patterns are used to execute a movement task and demonstrate that classification models can predict movement patterns with a high accuracy (~84%).