BackgroundAmyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease whose primary hallmark is the progressive degeneration of motor neurons in the brainstem, spinal cord, and cerebral cortex that leads to weakness, spasticity, fatigue, skeletal muscle atrophy, paralysis, and even death. Exercise, as a non-pharmacological tool, may generally improve muscle strength, cardiovascular function, and quality of life. However, there are conflicting reports about the effect of exercise training in adults with ALS.AimsThis systematic review and network meta-analysis aim to conduct a mixed comparison of different exercise interventions for function, respiratory, fatigue, and quality of life in adults with ALS.MethodsRandomized controlled trials with ALS participants were screened and included from the databases of PubMed, Medline, and Web of Science. Physical exercise interventions were reclassified into aerobic exercise, resistance training, passive exercise, expiratory muscle exercise, and standard rehabilitation. Patient-reported outcome measures would be reclassified from perspectives of function, respiratory, fatigue, and quality of life. The effect size would be transferred into the percentage change of the total score.ResultThere were 10 studies included, with the agreement between authors reaching a kappa-value of 0.73. The network meta-analysis, which was conducted under the consistency model, identified that a combined program of aerobic exercise, resistance exercise, and standard rehabilitation showed the highest potential to improve quality of life (0.64 to be the best) and reduce the fatigue (0.39 to be the best) for ALS patients, while exercise program of aerobic and resistance training showed the highest potential (0.51 to be the best) to improve ALS patients' physical function. The effect of exercise on the respiratory was still unclear.ConclusionA multi-modal exercise and rehabilitation program would be more beneficial to ALS patients. However, the safety and guide for practice remain unclear, and further high-quality randomized controlled trials (RCTs) with a larger sample are still needed.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021253442, CRD42021253442.

Introduction: The purpose of this study was to evaluate the effect of running-induced fatigue on the characteristic asymmetry of running gait and to identify non-linear differences in bilateral lower limbs and fatigued gait by building a machine learning model.Methods: Data on bilateral lower limb three-dimensional ground reaction forces were collected from 14 male amateur runners before and after a running-induced fatigue experiment. The symmetry function (SF) was used to assess the degree of symmetry of running gait. Statistical parameter mapping (Paired sample T-test) algorithm was used to examine bilateral lower limb differences and asymmetry changes pre- and post-fatigue of time series data. The support vector ma-chine (SVM) algorithm was used to recognize the gait characteristics of both lower limbs before and after fatigue and to build the optimal algorithm model by setting different kernel functions.Results: The results showed that the ground reaction forces were asymmetrical (SF > 0.5) both pre-and post-fatigue and mainly concentrated in the medial-lateral direction. The asymmetry of the medial-lateral direction increased significantly after fatigue (p < 0.05). In addition, we concluded that the polynomial kernel function could make the SVM model the most accurate in classifying left and right gait features (accuracy of 85.3%, 82.4%, and 82.4% in medial-lateral, anterior-posterior and vertical directions, respectively). Gaussian radial basis kernel function was the optimal kernel function of the SVM algorithm model for fatigue gait recognition in the medial-lateral and vertical directions (accuracy of 54.2% and 62.5%, respectively). Moreover, polynomial was the optimal kernel function of the anterior-posterior di-rection (accuracy = 54.2%).Discussion: We proved in this study that the SVM algorithm model depicted good performance in identifying asymmetric and fatigue gaits. These findings can provide implications for running injury prevention, movement monitoring, and gait assessment.