Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue?

BACKGROUND: This study investigated the most accurate method for estimating the hip joint center position in clinical 3D gait analysis for young individuals with high amounts of soft tissue. We compared position estimates of five regression-based and two functional methods to the hip joint center position obtained through 3D free-hand ultrasound. METHODS: For this purpose, the data of 14 overweight or obese individuals with a mean age of 13.6 (SD 2.1 yrs) and a BMI of 36.5 (SD 7.1 kg/m2, range 26-52 kg/m2) who underwent standard clinical 3D gait analysis were used. The data of each participant were processed with five regression-based and two functional methods and compared to the hip joint center identified via 3D free-hand ultrasound. FINDINGS: The absolute location errors to 3D free-hand ultrasound for each anatomical plane and the Euclidean distances served as outcomes next to their effects on gait variables. The data suggest that regression-based methods are preferable to functional methods in this population, as the latter demonstrated the highest variability in accuracy with large errors for some individuals. INTERPRETATION: Based on our findings we recommend using the regression method presented by Hara et al. due to its superior overall accuracy of <9 mm on average in all planes and the lowest impact on kinematic and kinetic output variables. We do not recommend using the Harrington equations (single and multiple) in populations with high amounts of soft tissue as they require pelvic depth as input, which can be massively biased when a lot of soft tissue is present around the pelvis.

Current practices in clinical gait analysis in Europe: A comprehensive survey-based study from the European society for movement analysis in adults and children (ESMAC) standard initiative.

BACKGROUND: Clinical gait analysis (CGA) is a systematic approach to comprehensively evaluate gait patterns, quantify impairments, plan targeted interventions, and evaluate the impact of interventions. However, international standards for CGA are currently lacking, resulting in various national initiatives. Standards are important to ensure safe and effective healthcare practices and to enable evidence-based clinical decision-making, facilitating interoperability, and reimbursement under national healthcare policies. Collaborative clinical and research work between European countries would benefit from common standards. RESEARCH OBJECTIVE: This study aimed to review the current laboratory practices for CGA in Europe. METHODS: A comprehensive survey was conducted by the European Society for Movement Analysis in Adults and Children (ESMAC), in close collaboration with the European national societies. The survey involved 97 gait laboratories across 16 countries. The survey assessed several aspects related to CGA, including equipment used, data collection, processing, and reporting methods. RESULTS: There was a consensus between laboratories concerning the data collected during CGA. The Conventional Gait Model (CGM) was the most used biomechanical model for calculating kinematics and kinetics. Respondents also reported the use of video recording, 3D motion capture systems, force plates, and surface electromyography. While there was a consensus on the reporting of CGA data, variations were reported in training, documentation, data preprocessing and equipment maintenance practices. SIGNIFICANCE: The findings of this study will serve as a foundation for the development of standardized guidelines for CGA in Europe.

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis.

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data. Since increased inter-trial variability can have important implications for data post-processing and analysis, this study compared the inter-trial variability of simultaneously recorded markerless and marker-based data. For this purpose, the data of 18 healthy volunteers were used who were instructed to simulate four different gait patterns: physiological, crouch, circumduction, and equinus gait. Gait analysis was performed using the smartphone-based markerless system OpenCap and a marker-based motion capture system. We compared the inter-trial variability of both systems and also evaluated if changes in inter-trial variability may depend on the analyzed gait pattern. Compared to the marker-based data, we observed an increase of inter-trial variability for the markerless system ranging from 6.6% to 22.0% for the different gait patterns. Our findings demonstrate that the markerless pose estimation pipelines can introduce additionally variability in the kinematic data across different gait patterns and levels of natural variability. We recommend using averaged waveforms rather than single ones to mitigate this problem. Further, caution is advised when using variability-based metrics in gait and human movement analysis based on markerless data as increased inter-trial variability can lead to misleading results.

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait.

Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system. 21 healthy volunteers were instructed to walk with four different gait patterns: physiological, crouch, circumduction, and equinus gait. Three-dimensional kinematic data were simultaneously recorded using the markerless and a marker-based motion capture system. The root mean square error (RMSE) and the peak error were calculated between every joint kinematic variable obtained by both systems. We found an overall RMSE of 5.8 (SD: 1.8 degrees) and a peak error of 11.3 degrees (SD: 3.9). A repeated measures ANOVA with post hoc tests indicated significant differences in RMSE and peak errors between the four gait patterns (p ¡ 0.05). Physiological gait presented the lowest, crouch and circumduction gait the highest errors. Our findings indicate a roughly comparable accuracy to IMU-based approaches and commercial markerless multi-camera solutions. However, errors are still above clinically desirable thresholds of two to five degrees. While our findings highlight the potential of markerless systems for assessing gait kinematics, they also underpin the need to further improve the underlying deep learning algorithms to make markerless pose estimation a valuable tool in clinical settings.

Robust deep learning-based gait event detection across various pathologies.

The correct estimation of gait events is essential for the interpretation and calculation of 3D gait analysis (3DGA) data. Depending on the severity of the underlying pathology and the availability of force plates, gait events can be set either manually by trained clinicians or detected by automated event detection algorithms. The downside of manually estimated events is the tedious and time-intensive work which leads to subjective assessments. For automated event detection algorithms, the drawback is, that there is no standardized method available. Algorithms show varying robustness and accuracy on different pathologies and are often dependent on setup or pathology-specific thresholds. In this paper, we aim at closing this gap by introducing a novel deep learning-based gait event detection algorithm called IntellEvent, which shows to be accurate and robust across multiple pathologies. For this study, we utilized a retrospective clinical 3DGA dataset of 1211 patients with four different pathologies (malrotation deformities of the lower limbs, club foot, infantile cerebral palsy (ICP), and ICP with only drop foot characteristics) and 61 healthy controls. We propose a recurrent neural network architecture based on long-short term memory (LSTM) and trained it with 3D position and velocity information to predict initial contact (IC) and foot off (FO) events. We compared IntellEvent to a state-of-the-art heuristic approach and a machine learning method called DeepEvent. IntellEvent outperforms both methods and detects IC events on average within 5.4 ms and FO events within 11.3 ms with a detection rate of ≥ 99% and ≥ 95%, respectively. Our investigation on generalizability across laboratories suggests that models trained on data from a different laboratory need to be applied with care due to setup variations or differences in capturing frequencies.

Modeling biological individuality using machine learning: A study on human gait.

Human gait is a complex and unique biological process that can offer valuable insights into an individual's health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual. We utilized the data from three publicly available datasets comprising 5368 bilateral ground reaction force recordings during level overground walking from 671 distinct healthy individuals. Our results show that individuals can be identified with a prediction accuracy of 99.3% by using the bilateral signals of all three ground reaction force components, with only 10 out of 1342 recordings in our test data being misclassified. This indicates that the combination of bilateral ground reaction force signals with all three components provides a more comprehensive and accurate representation of an individual's gait signature. The highest accuracy was achieved by (linear) Support Vector Machines (99.3%), followed by Random Forests (98.7%), Convolutional Neural Networks (95.8%), and Decision Trees (82.8%). The proposed approach provides a powerful tool to better understand biological individuality and has potential applications in personalized healthcare, clinical diagnosis, and therapeutic interventions.

Overground Walking in a Fully Immersive Virtual Reality: A Comprehensive Study on the Effects on Full-Body Walking Biomechanics.

Virtual reality (VR) is an emerging technology offering tremendous opportunities to aid gait rehabilitation. To this date, real walking with users immersed in virtual environments with head-mounted displays (HMDs) is either possible with treadmills or room-scale (overground) VR setups. Especially for the latter, there is a growing interest in applications for interactive gait training as they could allow for more self-paced and natural walking. This study investigated if walking in an overground VR environment has relevant effects on 3D gait biomechanics. A convenience sample of 21 healthy individuals underwent standard 3D gait analysis during four randomly assigned walking conditions: the real laboratory (RLab), a virtual laboratory resembling the real world (VRLab), a small version of the VRlab (VRLab-), and a version which is twice as long as the VRlab (VRLab+). To immerse the participants in the virtual environment we used a VR-HMD, which was operated wireless and calibrated in a way that the virtual labs would match the real-world. Walking speed and a single measure of gait kinematic variability (GaitSD) served as primary outcomes next to standard spatio-temporal parameters, their coefficients of variant (CV%), kinematics, and kinetics. Briefly described, participants demonstrated a slower walking pattern (-0.09 ± 0.06 m/s) and small accompanying kinematic and kinetic changes. Participants also showed a markedly increased gait variability in lower extremity gait kinematics and spatio-temporal parameters. No differences were found between walking in VRLab+ vs. VRLab-. Most of the kinematic and kinetic differences were too small to be regarded as relevant, but increased kinematic variability (+57%) along with increased percent double support time (+4%), and increased step width variability (+38%) indicate gait adaptions toward a more conservative or cautious gait due to instability induced by the VR environment. We suggest considering these effects in the design of VR-based overground training devices. Our study lays the foundation for upcoming developments in the field of VR-assisted gait rehabilitation as it describes how VR in overground walking scenarios impacts our gait pattern. This information is of high relevance when one wants to develop purposeful rehabilitation tools.

3D free-hand ultrasound to register anatomical landmarks at the pelvis and localize the hip joint center in lean and obese individuals.

3D free-hand ultrasound (3DFUS) is becoming increasingly popular to assist clinical gait analysis because it is cost- and time-efficient and does not expose participants to radiation. The aim of this study was to evaluate its reliability in localizing the anterior superior iliac spine (ASIS) at the pelvis and the hip joint centers (HJC). Additionally, we evaluated its accuracy to get a rough estimation of the potential to use of 3DFUS to segment bony surface. This could offer potential to register medical images to motion capture data in future. To evaluate reliability, a test-retest study was conducted in 16 lean and 19 obese individuals. The locations of the ASIS were determined by manual marker placement (MMP), an instrumented pointer technique (IPT), and with 3DFUS. The HJC location was also determined with 3DFUS. To quantify reliability, intraclass correlation coefficients (ICCs), the standard error of measurement (SEm), among other statistical parameters, were calculated for the identified locations between the test and retest. To assess accuracy, the surface of a human plastic pelvic phantom was segmented with 3DFUS in a distilled water bath in 27 trials and compared to a 3D laser scan of the pelvis. Regarding reliability, the MMP, but especially the IPT showed high reliability in lean (SEm: 2-3 mm) and reduced reliability in obese individuals (SEm: 6-15 mm). Compared to MMP and IPT, 3DFUS presented lower reliability in the lean group (SEm: 2-4 mm vs. 2-8 mm, respectively) but slightly better values in the obese group (SEm: 7-11 mm vs. 6-16 mm, respectively). Correlations between test-retest reliability and torso body fat mass (% of body mass) indicated a moderate to strong relationship for MMP and IPT but only a weak correlation for the 3DFUS approach. The water-bath experiments indicated an acceptable level of 3.5 (1.7) mm of accuracy for 3DFUS in segmenting bone surface. Despite some difficulties with single trials, our data give further rise to the idea that 3DFUS could serve as a promising tool in future to inform marker placement and hip joint center location, especially in groups with higher amount of body fat.

Comparison of Bioelectrical Impedance-Based Methods on Body Composition in Young Patients with Obesity.

(1) Background: The determination of body composition is an important method to investigate patients with obesity and to evaluate the efficacy of individualized medical interventions. Bioelectrical impedance-based methods are non-invasive and widely applied but need to be validated for their use in young patients with obesity. (2) Methods: We compiled data from three independent studies on children and adolescents with obesity, measuring body composition with two bioelectrical impedance-based devices (TANITA and BIACORPUS). For a small patient group, additional data were collected with air displacement plethysmography (BOD POD) and dual-energy X-ray absorptiometry (DXA). (3) Results: Our combined data on 123 patients (age: 6-18 years, body mass index (BMI): 21-59 kg/m²) and the individual studies showed that TANITA and BIACORPUS yield significantly different results on body composition, TANITA overestimating body fat percentage and fat mass relative to BIACORPUS and underestimating fat-free mass (p < 0.001 for all three parameters). A Bland-Altman plot indicated little agreement between methods, which produce clinically relevant differences for all three parameters. We detected gender-specific differences with both methods, with body fat percentage being lower (p < 0.01) and fat-free mass higher (p < 0.001) in males than females. (4) Conclusions: Both bioelectrical impedance-based methods provide significantly different results on body composition in young patients with obesity and thus cannot be used interchangeably, requiring adherence to a specific device for repetitive measurements to ascertain comparability of data.

Reliability of walking and stair climbing kinematics in a young obese population using a standard kinematic and the CGM2 model.

BACKGROUND: Recently, the successor of the Conventional Gait Model, the CGM2 was introduced. Even though achievable reliability of gait kinematics is a well-assessed topic in gait analysis for several models, information about reliability in difficult study samples with high amount of subcutaneous fat is scarce and to date, not available for the CGM2. Therefore, this study evaluated the test-retest reliability of the CGM2 model for difficult data with high amount of soft tissue artifacts. RESEARCH QUESTION: What is the test-retest reliability of the CGM2 during level walking and stair climbing in a young obese population? Is there a clinically relevant difference in reliability between a standard direct kinematic model and the CGM2? METHODS: A retrospective test-retest dataset from eight male and two female volunteers was used. It comprised standard 3D gait analysis data of three walking conditions: level walking, stair ascent and descent. To quantify test-retest reliability the Standard Error of Measurement (SEM) was calculated for each kinematic waveform for a direct kinematic model (Cleveland clinic marker set) and the CGM2. RESULTS: Both models showed an acceptable level of test-retest reliability in all three walking conditions. However, SEM ranged between two and five degrees (∘) for both models and, thus, needs consideration during interpretation. The choice of model did not affect reliability considerably. Differences in SEM between stair climbing and level walking were small and not clinically relevant (<1°). SIGNIFICANCE: Results showed an acceptable level of reliability and only small differences between the models. It is noteworthy, that the SEM was increased during the first half of swing in all walking conditions. This might be attributed to increased variability resulting for example from inaccurate knee and ankle axis definitions or increased variability in the gait pattern and needs to be considered during data interpretation.

GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait.

The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. Therefore, we present GAITREC, a comprehensive and completely annotated large-scale dataset containing bi-lateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bi-lateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.

Input representations and classification strategies for automated human gait analysis.

BACKGROUND: Quantitative gait analysis produces a vast amount of data, which can be difficult to analyze. Automated gait classification based on machine learning techniques bear the potential to support clinicians in comprehending these complex data. Even though these techniques are already frequently used in the scientific community, there is no clear consensus on how the data need to be preprocessed and arranged to assure optimal classification accuracy outcomes. RESEARCH QUESTION: Is there an optimal data aggregation and preprocessing workflow to optimize classification accuracy outcomes? METHODS: Based on our previous work on automated classification of ground reaction force (GRF) data, a sequential setup was followed: firstly, several aggregation methods - early fusion and late fusion - were compared, and secondly, based on the best aggregation method identified, the expressiveness of different combinations of signal representations was investigated. The employed dataset included data from 910 subjects, with four gait disorder classes and one healthy control group. The machine learning pipeline comprised principle component analysis (PCA), z-standardization and a support vector machine (SVM). RESULTS: The late fusion aggregation, i.e., utilizing majority voting on the classifier's predictions, performed best. In addition, the use of derived signal representations (relative changes and signal differences) seems to be advantageous as well. SIGNIFICANCE: Our results indicate that great caution is needed when data preprocessing and aggregation methods are selected, as these can have an impact on classification accuracies. These results shall serve future studies as a guideline for the choice of data aggregation and preprocessing techniques to be employed.

KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis.

In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.

Automatic Classification of Functional Gait Disorders.

This paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with GDs and data from 161 healthy controls (N). Patients were manually classified into four classes with different functional impairments associated with the "hip", "knee", "ankle", and "calcaneus". Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA) based representations, and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis. Based on this analysis, two classification experiments are pursued: distinction between healthy and impaired gait (N versus GD) and multiclass classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient, have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step toward the automated classification of GDs and a first performance baseline for future developments in this direction.

Is the reliability of 3D kinematics of young obese participants dependent on the hip joint center localization method used?

The aim of this study was to investigate if the test-retest reliability for three-dimensional (3D) gait kinematics in a young obese population is affected by using either a predictive (Davis) or a functional (SCoRE) hip joint center (HJC) localization approach. A secondary goal was to analyze how consistent both methods perform in estimating the HJC position. A convenience sample of ten participants, two females and eight males with an age-based body mass index (BMI) above the 97th percentile (mean±SD: 34.2±3.9kg/m2) was recruited. Participants underwent two 3D gait analysis sessions separated by a minimum of one day and a maximum of seven days. The standard error of measurement (SEM) and the root mean square error (RMSE) of key kinematic parameters along with the root mean square deviation (RMSD) of the entire waveforms were used to analyze the test-retest reliability. To get an estimate of the consistency of both HJC localization methods, the HJC positions determined by both methods were compared to each other. SEM, RMSE, and RMSD results indicate that the HJC position estimations between both methods are not different and demonstrate moderate to good reliability to estimate joint kinematics. With respect to the localization of the HJC, notable inconsistencies ranging from 0 to 5.4cm were observed. In conclusion, both approaches appear equally reliable. However, the inconsistent HJC estimation points out, that accuracy seems to be a big issue in these methods. Future research should attend to this matter.

Trunk muscle activation levels during eight stabilization exercises used in the functional kinetics concept: A controlled laboratory study.

BACKGROUND: To ensure accurate implementation of stabilization exercises in rehabilitation, physical therapists need to understand the muscle activation patterns of prescribed exercise. OBJECTIVE: Compare muscle activity during eight trunk and lumbar spine stabilization exercises of the Functional Kinetics concept by Klein-Vogelbach. METHODS: A controlled laboratory study with a single-group repeated-measures design was utilized to analyze surface electromyographic intensities of 14 female and 6 male young healthy participants performing eight exercises. Data were captured from the rectus abdominis, external/internal oblique and lumbar paraspinalis. The normalized muscle activation levels (maximum voluntary isometric contraction, MVIC) for three repetitions during each exercise and muscle were analyzed. RESULTS: Side bridging (28 ± 20%MVIC) and advanced planking (29 ± 20%MVIC) reached the highest activity in the rectus abdominis. For external and internal oblique muscles, side bridging also showed the greatest activity of 99 ± 36%MVIC and 52 ± 25%MVIC, respectively. Apart from side bridging (52 ± 14%MVIC), the supine roll-out (31 ± 12%MVIC) and prone roll-out (31 ± 9%MVIC) showed the greatest activity for the paraspinalis. The advanced quadruped, seated back extension and flexion on chair/Swiss Ball, prone roll-out and advanced one-leg back bridging only yielded negligible muscle activities for the rectus abdominis (< 5%MVIC). CONCLUSION: Based on the data obtained, recommendations for selective trunk muscle activation during eight stabilization exercises were established, which will guide physical therapists in the development of exercises tailored to the needs of their patients.

Within-assessor reliability and minimal detectable change of gait kinematics in a young obese demographic.

INTRODUCTION: Three-dimensional gait analysis (3DGA) in obese populations is a difficult task due to a great amount of subcutaneous fat. This makes it more challenging to identify anatomical landmarks, thus leading to inconsistent marker placement. Therefore, the purpose of this study was to investigate the test-retest reliability for kinematic measurements of obese children and adolescents. METHODS: Nine males and two females with an age-based BMI above the 97th percentile (age: 14.6±2.6years, BMI: 33.4±4.4kg/m2) were administered to two 3DGA sessions. To quantify reliability of discrete parameters the intraclass correlation coefficient (ICC2,k), standard error of measurement (SEM) and minimal detectable change (MDC) were calculated. To quantify waveform similarity, the coefficient of multiple correlation (CMC) and the linear fit method (LFM) were used. RESULTS: From 28 kinematic parameters, 23 showed acceptable ICCs (≥0.70) and the remaining parameters demonstrated moderate values. These were peak hip extension during stance (0.58), mean pelvis rotation (0.60), mean anterior pelvic tilt (0.64), peak knee flexion during swing (0.67) and peak hip abduction during swing (0.69). The SEM was below 5° for all parameters. The MDC for the sagittal, frontal, and transversal plane were on average 7.5°±2.2, 4.6°±1.3 and 6.0°±0.9 respectively. Both the LFM and CMC showed, in general, moderate to good reliability except for pelvis tilt and hip rotation. CONCLUSION: Data demonstrated acceptable error margins especially for the sagittal and frontal plane. Low reliability for the pelvis tilt indicates that great effort is necessary to position the pelvic markers consistently during repeated sessions.

Serratus anterior and trapezius muscle activity during knee push-up plus and knee-plus exercises performed on a stable, an unstable surface and during sling-suspension.

OBJECTIVES: Push-up plus variations are commonly prescribed to clients during shoulder rehabilitation. The purpose of this study was to compare electromyographic (EMG) activities of the serratus anterior (SA), upper (UT), and lower trapezius (LT) during a knee push-up plus and knee-plus exercise performed on various surfaces. STUDY DESIGN: Within-subjects Repeated-Measure Design. PARTICIPANTS: 19 healthy, young female participants performed both exercises on a stable and unstable surface and during sling-suspension. OUTCOME MEASURES: Surface EMG activities were recorded and average amplitudes were presented as a percentage of the maximal voluntary contraction. A two-way repeated-measures ANOVA was performed to determine differences in activity for each muscle. RESULTS: SA showed no significant differences between exercises and was independent of the base of support (p > 0.05). Muscle activity of UT (95% CI [1.2, 1.4]) and LT (95% CI [2.4, 3.5]) showed slightly greater values when performing the knee push-up plus compared to the knee-plus exercise. CONCLUSIONS: The isolated protraction of the shoulder girdle in a kneeling position is as sufficient as the push-up plus in activating the SA selectively. Therefore, we recommended this exercise for clients who are unable to perform an entire push-up or should avoid detrimental stress on the shoulder joint.

The effects of a strength and neuromuscular exercise programme for the lower extremity on knee load, pain and function in obese children and adolescents: study protocol for a randomised controlled trial.

BACKGROUND: Childhood obesity is one of the most critical and accelerating health challenges throughout the world. It is a major risk factor for developing varus/valgus misalignments of the knee joint. The combination of misalignment at the knee and excess body mass may result in increased joint stresses and damage to articular cartilage. A training programme, which aims at developing a more neutral alignment of the trunk and lower limbs during movement tasks may be able to reduce knee loading during locomotion. Despite the large number of guidelines for muscle strength training and neuromuscular exercises that exist, most are not specifically designed to target the obese children and adolescent demographic. Therefore, the aim of this study is to evaluate a training programme which combines strength and neuromuscular exercises specifically designed to the needs and limitations of obese children and adolescents and analyse the effects of the training programme from a biomechanical and clinical point of view. METHODS/DESIGN: A single assessor-blinded, pre-test and post-test randomised controlled trial, with one control and one intervention group will be conducted with 48 boys and girls aged between 10 and 18 years. Intervention group participants will receive a 12-week neuromuscular and quadriceps/hip strength training programme. Three-dimensional (3D) gait analyses during level walking and stair climbing will be performed at baseline and follow-up sessions. The primary outcome parameters for this study will be the overall peak external frontal knee moment and impulse during walking. Secondary outcomes include the subscales of the Knee injury and Osteoarthritis Outcome Score (KOOS), frontal and sagittal kinematics and kinetics for the lower extremities during walking and stair climbing, ratings of change in knee-related well-being, pain and function and adherence to the training programme. In addition, the training programme will be evaulated from a clinical and health status perspective by including the following analyses: cardiopulmonary testing to quantify aerobic fitness effects, anthropometric measures, nutritional status and psychological status to characterise the study sample. DISCUSSION: The findings will help to determine whether a neuromuscular and strength training exercise programme for the obese children population can reduce joint loading during locomotion, and thereby decrease the possible risk of developing degenerative joint diseases later in adulthood. TRIAL REGISTRATION: ClinicalTrials NCT02545764 , Date of registration: 24 September 2015.