Are motor coordination deficits an underlying cardinal feature of Autism Spectrum Disorders (ASD)? Database searches identified 83 ASD studies focused on motor coordination, arm movements, gait, or postural stability deficits. Data extraction involved between-group comparisons for ASD and typically developing controls (N = 51). Rigorous meta-analysis techniques including random effects models, forest and funnel plots, I (2), publication bias, fail-safe analysis, and moderator variable analyses determined a significant standardized mean difference effect equal to 1.20 (SE = 0.144; p <0.0001; Z = 10.49). This large effect indicated substantial motor coordination deficits in the ASD groups across a wide range of behaviors. The current overall findings portray motor coordination deficits as pervasive across diagnoses, thus, a cardinal feature of ASD.

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Quantitative gait assessment is important in diagnosis and management of Parkinson's disease (PD); however, gait characteristics of a cohort are dispersed by patient physical properties including age, height, body mass, and gender, as well as walking speed, which may limit capacity to discern some pathological features. The aim of this study was twofold. First, to use a multiple regression normalization strategy that accounts for subject age, height, body mass, gender, and self-selected walking speed to identify differences in spatial-temporal gait features between PD patients and controls; and second, to evaluate the effectiveness of machine learning strategies in classifying PD gait after gait normalization. Spatial-temporal gait data during self-selected walking were obtained from 23 PD patients and 26 aged-matched controls. Data were normalized using standard dimensionless equations and multiple regression normalization. Machine learning strategies were then employed to classify PD gait using the raw gait data, data normalized using dimensionless equations, and data normalized using the multiple regression approach. After normalizing data using the dimensionless equations, only stride length, step length, and double support time were significantly different between PD patients and controls (p < 0.05); however, normalizing data using the multiple regression method revealed significant differences in stride length, cadence, stance time, and double support time. Random Forest resulted in a PD classification accuracy of 92.6% after normalizing gait data using the multiple regression approach, compared to 80.4% (support vector machine) and 86.2% (kernel Fisher discriminant) using raw data and data normalized using dimensionless equations, respectively. Our multiple regression normalization approach will assist in diagnosis and treatment of PD using spatial-temporal gait data.

Objective Individual muscle activation patterns may be controlled by motor modules constructed by the central nervous system to simplify motor control. This study compared modular control of gait between persons with Parkinson’s disease (PD) and neurologically-healthy older adults (HOA) and investigated relationships between modular organization and gait parameters in persons with PD. Methods Fifteen persons with idiopathic PD and fourteen HOA participated. Electromyographic recordings were made from eight leg muscles bilaterally while participants walked at their preferred walking speed for ten minutes on an instrumented treadmill. Non-negative matrix factorization techniques decomposed the electromyographic signals, identifying the number and nature of modules accounting for 95% of variability in muscle activations during treadmill walking. Results Generally, fewer modules were required to reconstruct muscle activation patterns during treadmill walking in PD compared to HOA (p<.05). Control of knee flexor and ankle plantarflexor musculature was simplified in PD. Activation timing was altered in PD while muscle weightings were unaffected. Simplified neuromuscular control was related to decreased walking speed in PD. Conclusions Neuromuscular control of gait is simplified in PD and may contribute to gait deficits in this population. Significance Future studies of locomotor rehabilitation in PD should consider neuromuscular complexity to maximize intervention effectiveness.

Gait dysfunction and postural instability are two debilitating symptoms in persons with Parkinson’s disease (PD). Tai Chi exercise has recently gained attention as an attractive intervention for persons with PD because of its known potential to reduce falls and improve postural control, walking abilities, and safety at a low cost. The purpose of this report is to investigate the effect of Tai Chi exercise on dynamic postural control during gait initiation and gait performance in persons with idiopathic PD, and to determine whether these benefits could be replicated in two different environments, as complementary projects. In these two separate projects, a total of 45 participants with PD were randomly assigned to either a Tai Chi group or a control group. The Tai Chi groups in both projects completed a 16-week Tai Chi exercise session, while the control groups consisted of either a placebo (i.e., Qi-Gong) or non-exercise group. Tai Chi did not significantly improve Unified Parkinson’s Disease Rating Scale Part III score, selected gait initiation parameters or gait performance in either project. Combined results from both projects suggest that 16 weeks of class-based Tai Chi were ineffective in improving either gait initiation, gait performance, or reducing parkinsonian disability in this subset of persons with PD. Thus the use of short-term Tai Chi exercise should require further study before being considered a valuable therapeutic intervention for these domains in PD.

Both groups significantly improved muscular fitness and body composition as a result of the 13 wk of training. The results show that one-set programs are still effective even after a year of training and that increasing training volume over 13 wk does not lead to significantly greater improvements in fitness for adult recreational weight lifters.