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BACKGROUND: Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics. METHODS: This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ). RESULTS: At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (rSpearman=0.64) and to a lesser extent to changes in movement duration (rSpearman=0.51) while TDSM were very responsive to changes in movement duration (rSpearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (rSpearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations). CONCLUSIONS: Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke. TRIAL REGISTRATION: NCT01383512, https://clinicaltrials.gov/ , June 27, 2011.
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Movimiento , Rehabilitación de Accidente Cerebrovascular , Accidente Cerebrovascular , Extremidad Superior , Humanos , Masculino , Femenino , Extremidad Superior/fisiopatología , Persona de Mediana Edad , Movimiento/fisiología , Anciano , Fenómenos Biomecánicos , Accidente Cerebrovascular/fisiopatología , Accidente Cerebrovascular/complicaciones , Rehabilitación de Accidente Cerebrovascular/métodos , Reproducibilidad de los Resultados , Paresia/etiología , Paresia/rehabilitación , Paresia/fisiopatología , Adulto , Recuperación de la Función/fisiologíaRESUMEN
Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provides highly relevant clinical information. This information is used to guide therapeutic choices; however, it is underused in diagnostic processes, probably because of the lack of standardization of data collection methods. Therefore, 3D gait analysis is currently used as an assessment rather than a diagnostic tool. In this work, we aimed to determine if deep learning could be combined with 3D gait analysis data to diagnose gait disorders in children. We tested the diagnostic accuracy of deep learning methods combined with 3D gait analysis data from 371 children (148 with unilateral cerebral palsy, 60 with neuromuscular disease, 19 toe walkers, 60 with bilateral cerebral palsy, 25 stroke, and 59 typically developing children), with a total of 6400 gait cycles. We evaluated the accuracy, sensitivity, specificity, F1 score, Area Under the Curve (AUC) score, and confusion matrix of the predictions by ResNet, LSTM, and InceptionTime deep learning architectures for time series data. The deep learning-based models had good to excellent diagnostic accuracy (ranging from 0.77 to 0.99) for discrimination between healthy and pathological gait, discrimination between different etiologies of pathological gait (binary and multi-classification); and determining stroke onset time. LSTM performed best overall. This study revealed that the gait pattern contains specific, pathology-related information. These results open the way for an extension of 3D gait analysis from evaluation to diagnosis. Furthermore, the method we propose is a data-driven diagnostic model that can be trained and used without human intervention or expert knowledge. Furthermore, the method could be used to distinguish gait-related pathologies and their onset times beyond those studied in this research.
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Parálisis Cerebral , Aprendizaje Profundo , Enfermedades Neuromusculares , Accidente Cerebrovascular , Niño , Humanos , Parálisis Cerebral/diagnóstico , Fenómenos Biomecánicos , Marcha , Enfermedades Neuromusculares/diagnósticoRESUMEN
(1) Aim: The aim of this study was to determine the relationship between lower limb bone deformities and body functions, activity, and participation in ambulant children with CP and whether changing bone morphology affects outcomes in these domains. (2) Methods: A systematic literature search (PROSPERO CRD42020208416) of studies reporting correlations between measures of lower limb bone deformities and measures of body function, activity or participation, or post-surgical outcomes in these domains was conducted from 1990 to 2023 in Medline, Scopus, and Cochrane Library. We assessed study quality with the Checklist for Case Series (CCS) and a quality assessment developed by Quebec University Hospital. Meta-analysis was not possible; therefore, descriptive synthesis was performed. (3) Results: A total of 12 of 3373 screened articles were included. No studies evaluated the relationships between bone deformities and activity or participation, or the effect of isolated bone surgery on these domains. Correlations between bone deformities and body functions were poor-to-moderate. Internal hip rotation during gait improved after femoral derotation osteotomy. (4) Conclusions: A shift in paradigm is urgently required for the research and management of bone deformities in children with CP to include the activity and participation domains of the ICF, as well as consider more psychological aspects such as self-image.
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INTRODUCTION: Anterior cruciate ligament (ACL) injuries are frequent in handball, and altered sensory integration may contribute to increased injury risk. Recent evidence showed that proprioceptive postural control strategies differ among athletes. The aim of this study was to evaluate the relationship between proprioceptive strategy and biomechanics during side-cutting maneuvers. METHODS: A total of 47 handball players performed anticipated and unanticipated cutting tasks. Their postural proprioceptive strategy was then characterized according to the perturbation of the center of pressure displacement generated by the muscle vibration on a firm and foam surface. Individuals able to reweight proprioception from ankle to lumbar signals according to the stability of the support were defined as flexible. Conversely, athletes maintaining an ankle-steered strategy on foam surface were characterized as rigid. Statistical parametric mapping analysis was used to compare pelvic and lower limb side-cutting kinematics, kinetics, and EMG activity from seven muscles 200 ms before and after initial contact (IC) using a two-way ANOVA (group-condition). RESULTS: Twenty athletes (11 females and 9 males, 18.5 yr) were characterized as flexible and 20 athletes (12 females and 8 males, 18.9 yr) as rigid. No interaction between condition and proprioceptive profile was observed. More ipsilateral pelvic tilt before IC and lower vastus lateralis (VL) activity immediately after IC was observed during CUT ant . When comparing proprioceptive strategy, rigid individuals exhibited less preactivity of the semitendinosus ( P < 0.001) and higher VL activity ( P = 0.032). Conversely, rigid showed higher gluteus medius preactivity ( P < 0.05) and higher VL activity 100 ms after IC ( P < 0.001). Ankle was also more internally rotated before and during the stance phase ( P < 0.05) among rigid athletes. CONCLUSIONS: Rigid handball players exhibited at-risk determinants for anterior cruciate ligament injuries during side-cutting maneuvers.
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Lesiones del Ligamento Cruzado Anterior , Ligamento Cruzado Anterior , Masculino , Femenino , Humanos , Fenómenos Biomecánicos , Electromiografía , Atletas , Equilibrio Postural , Articulación de la Rodilla/fisiologíaRESUMEN
Recently, a staging system using 4 grades has been proposed to quantify the extent of cardiac damage associated with aortic stenosis (AS), namely AS-related cardiac damage staging (ASCDS). ASCDS is independently associated with all-cause mortality and important clinical outcomes. To evaluate whether it might be associated with the occurrence of conduction system disorders after TAVI, a total of 119 symptomatic patients with severe AS who underwent a TAVI were categorized according to ASCDS: group 1 (13.5%): no or LV damage; group 2 (58.8%): left atrial/mitral valve damage, atrial fibrillation (AF); group 3 (27.7%): low-flow state, pulmonary vasculature/tricuspid valve/RV damage. After TAVI, 34% of patients exhibited LBBB and 10% high-degree atrioventricular block (HD-AVB). No patient in group 1 developed HD-AVB whereas new LBBB was frequent in groups 2 and 3. Twenty-one patients presented with paroxysmal AF with a higher rate for each group increment (group 1: n = 0, 0%; group 2: n = 11, 15.7%; group 3: n = 10, 30.3%) (p = 0.012). Patients in group 3 had the higher rate of permanent pacemaker implantation (PPMI) (group 1: n = 1, 6.3%; group 2: n = 7, 10%; group 3: n = 9, 27.3%) (p = 0.012). In conclusion, ASCDS might help identify patients at higher risk of conduction disorders and PPMI requirement after TAVI.
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Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.