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Spastic equinus foot is a common deformity in neurologic patients who compromise walking ability. It is related to the imbalance between weak dorsiflexion and overactive plantar flexor muscles. To achieve the best functional results after surgical management, the challenge is to identify the relevant components involved in the deformity using several methods, namely, examination in the supine position, motor nerve blocks allowing transient anesthesia of suspected overactive muscles, and kinematic and electromyographic data collected during an instrumented 3D gait analysis. The procedure is not standardized; its use varies from one team to another. Access to gait analysis laboratories is limited, and some teams do not perform motor nerve blocks. When both examinations are available, instrumental data from the instrumented 3D gait analysis can be used to specify muscle targets for motor blocks, but data collected from both examinations are sometimes considered redundant.This retrospective cohort analysis compared examination in the supine position, temporary motor nerve blocks, and instrumented 3D gait analysis data in 40 adults after brain or spinal cord injuries. Clinical data collected before motor nerve block was not associated with instrumental data to assess calf muscle's overactivity and tibialis anterior function. Improvement of ankle dorsiflexion in the swing phase after tibial motor nerve block was associated with soleus spastic co-contraction during this phase corroborating its involvement in ankle dorsiflexion defects. This study showed the relevance of tibial motor nerve block to remove spastic calf dystonia and facilitate the assessment of calf contracture. It also underlined the need for complementary and specific analyses of the tibialis anterior abnormal activation pattern after motor nerve block to confirm or deny their pathological nature.

Additional file 2. Additional results file. Figure S4. Learning curves of two example patients’ data (#21 and #29) showing the evolution of a specific parameter over the trials (1 to 10) for the 3 sessions. A. Task time (TaskTime, s). B. Movement time (MovementTime, s). C. Peak velocity (PeakVel, cm/s). D. Hand path ratio (HPR, dimensionless). E. Number of peak velocity (nPeak). F. Game score (Score, %). Figure S5. Evolutions of Minimal Detectable Change (MDC, dashed lines) and Intraclass Correlation Coefficient (ICC, solid lines) according to the number of trials (of sessions 2 and 3), taking into account the calculation of reliability (1: only the 10th trial, to 10: the 10th to the 1st trial). Different parameters are represented by different colors and shapes (black (■): peak velocity, gray (♦): score, orange (●): number of peaks, blue (▲): task time, green (⁃): movement time, yellow (▬): HPR). Figure S6. Learning curves of the averaged time of occurrence of the peak velocity during the movement expressed in percentage (PercPeakVel (%) ±SD), showing the evolution of this parameter over the trials (1 to 10) of the 3 sessions. Between-session significance is represented with asterisks (* p

Additional file 1. Additional methodological file. Additional material S1. Custom Matlab code. The Matlab code used for the calculation of kinematic parameters, available on GitHub website. Figure S2. Box plot comparison between kinematics calculation methods. Comparison between the averaged parameter values provided in the summary report of the ArmeoSpring (Armeocontrol Software) and those calculated with the Matlab code (custom code). Statistical results of the paired t-tests for the hand path ratio (A. HPR), Task Time (B. TaskTime in seconds) and the Score (C. Score in percentage) are reported on the figure. Figure S3. Graphical representation of the average number of failed attempts to catch the consecutive balls (targets 1 to 12). Statistical parameters of the ANOVA are shown below the graph. The number of failed attempts to catch target 1 is significantly lower compared to the other targets (*p

International audience; Background: In older people, diabetes is associated with an increased risk of falls and frailty. The value of using posturography for evaluating the risk of falling is unclear. In theory, a time-scale analysis should increase the metrological properties of the posturography assessment. Objectives: This study aimed to determine which posturographic parameters can be used to identify fall-risk patients in a frail diabetic older population and to assess their interest in comparison to usual clinical trials for gait and balance. Design: This is a prospective observational cohort. Settings: frail or pre-frail diabetic patients, in Bordeaux, France. Participants: 84 patients were included in the study (mean age 80.09 years, 64.5% of men).Criteria for inclusion were: age over 70 years, diabetes mellitus for over 2 years, and at least one of the Fried’s frailty criteria. Measurements: Gait and balance assessments were undertaken at baseline: Static posturography, the timed up and go test, short physical performance battery, and (gait) walking speed. Raw data from posturography were used for wavelet analysis. Data on self reported new falls were collected prospectively during 6 months. Results: The posturography parameter most useful was area of 90% confidence ellipse of statokinesigram (COP90area): area under the ROC curve AUC = 0.617 (95% CI, 0.445-0.789) and OR=1.003 (95%CI 1.000-1.005) p =0.05. The optimum clinical test was the time to walk over 4m AUC=0.735 (95%CI, 0.587-0.882) and OR=1.42 (95%CI 1.08-1.87) p= 0.013. Conclusion: Posturography has limited utility for assessment of falls risk in frail older people with diabetes. Gait and balance clinical assessments such as walking speed continue to retain their value.

International audience; Electromyographic (EMG) raw signals are sensitive to intrinsic and extrinsic factors. Consequently, EMG normalization is required to draw proper interpretations of standardized data. Specific recommendations are needed regarding a relevant EMG normalization method for participants who show atypical EMG patterns, such as poststroke subjects. This study compared three EMG normalization methods ("isometric MVC", "isokinetic MVC", "isokinetic MVC kinematic-related") on muscle activations and the antagonist-agonist co-contraction index. Fifteen post-stroke subjects and fifteen healthy controls performed active elbow extensions, followed by isometric and isokinetic maximum voluntary contractions (MVC). Muscle activations were obtained by normalizing EMG envelopes during active movement using a reference value determined for each EMG normalization method. The results showed no significant difference between the three EMG normalization methods in poststroke subjects on muscle activation and the antagonist-agonist co-contraction index. We highlighted that the antagonist-agonist co-contraction index could underestimate the antagonist co-contraction in the presence of atypical EMG patterns. Based on its practicality and feasibility, we recommend the use of isometric MVC as a relevant procedure for EMG normalization in post-stroke subjects. We suggest combined analysis of the antagonist-agonist co-contraction index and agonist and antagonist activations to properly investigate antagonist co-contraction in the presence of atypical EMG patterns during movement.

BACKGROUND: Kinematic analysis and clinical outcome measures with established responsiveness contribute to the quantified assessment of post-stroke upper-limb function, the selection of interventions and the differentiation of motor recovery patterns.OBJECTIVE: This systematic review and meta-analysis aimed to report trends in use and compare the responsiveness of kinematic and clinical measures in studies measuring the effectiveness of constraint-induced movement, trunk restraint and bilateral arm therapies for upper-limb function after stroke.METHODS: In this systematic review, randomised controlled trials implementing kinematic analysis and clinical outcome measures to evaluate the effects of therapies in post-stroke adults were eligible. We searched 8 electronic databases (MEDLINE, EMBASE, Web of Science, Scopus, CINAHL, CENTRAL, OTseeker and Pedro). Risk of bias was assessed according to the Cochrane Risk of Bias domains. A meta-analysis was conducted for repeated design measures of pre- and post-test data providing estimated standardised mean differences (SMDs).RESULTS: We included reports of 12 studies (191 participants) reporting kinematic smoothness, movement duration and efficiency, trunk and shoulder range of motion, control strategy and velocity variables in conjunction with assessment by Motor Activity Log, Fugl-Meyer Assessment and Wolf Motor Function Test. Responsiveness was higher (i.e., non-overlap of 95% confidence intervals [CIs]) for Motor Activity Log score (SMD for amount of use 1.0, 95% CI 0.75-1.25, pCONCLUSION: These results are consistent with current literature supporting the use of combined kinematic and clinical measures for comprehensive and accurate evaluation of post-stroke upper-limb function. Future research should include other design trials and rehabilitation types to confirm these findings, focusing on subgroup analysis of type of rehabilitation intervention and functional levels.

International audience; Spasticity and spastic co-contraction are expressions of muscle overactivity that occur in spastic paresis syndrome after a brain injury. The objective of the present pilot study was to improve our understanding of the respective adverse consequences of spasticity and spastic co-contraction on motor disability. In contrary to spasticity, spastic co-contraction is strongly associated with motor impairment in subjects with brain injury. Therapies should be directed toward reducing spastic co-contraction in order to improve motor function. Objective: To elucidate the adverse consequences of spasticity and spastic co-contraction of elbow flexors on motor impairment and upper limb functional limitation. Design: A pilot case-controlled prospective observational study. Subjects: Ten brain-injured adults, and 10 healthy controls. Methods: The co-contraction index was computed from electromyographic recordings of elbow flexors during sub-maximal (25% Maximal Voluntary Contraction) isometric elbow extension. Spasticity was assessed with the Tardieu scale, upper limb limitation using a goniometer during active elbow extension, motor selectivity with the Fugl-Meyer Assessment for the upper limb, and motor function with the Action Research Arm Test. Results: Greater co-contraction occurred in patients with brain injury compared with controls. In contrast to spasticity, strong associations were found between the co-contraction index, the limitation of active elbow extension, the Fugl-Meyer Assessment, and the Action Research Arm Test. Conclusion: This pilot study suggests that spastic co-contraction rather than spasticity is an important factor in altered upper limb motricity in subjects with brain injury, leading to abnormal restricting arm movement patterns in subjects with more severe motor impairment. Practical applications directly concern the pre-and post-therapeutic evaluation of treatments aimed at improving motor skills in subjects with brain injury.