Objective: Treadmill interventions have been shown to promote ‘normal’ walking patterns, as they facilitate the proper movement and timing of the lower limbs. However, prior reviews have not examined which intervention provides the most effective treatment of specific gait impairments in neurological populations. The objective of this systematic review was to review and quantify the changes in gait after treadmill interventions in adults with neurological disorders. Data Sources: A keyword search was performed in four databases: PubMed, CINAHL, Scopus, and Web of Science (January 2000–December 2021). We performed the search algorithm including all possible combinations of keywords. Full-text articles were examined further using forward/backward search methods. Study Selection: Studies were thoroughly screened using the following inclusion criteria: study design: Randomized Controlled Trial (RCT); adults ≥55 years old with a neurological disorder; treadmill intervention; spatiotemporal gait characteristics; and language: English. Data Extraction: A standardized data extraction form was used to collect the following methodological outcome variables from each of the included studies: author, year, population, age, sample size, and spatiotemporal gait parameters including stride length, stride time, step length, step width, step time, stance time, swing time, single support time, double support time, or cadence. Data Synthesis: We found a total of 32 studies to be included in our systematic review through keyword search, out of which 19 studies included adults with stroke and 13 studies included adults with PD. We included 22 out of 32 studies in our meta-analysis that examined gait in adults with neurological disorders, which only yielded studies including Parkinson’s disease (PD) and stroke patients. A meta-analysis was performed among trials presenting with similar characteristics, including study population and outcome measure. If heterogeneity was >50% (denoted by I²), random plot analysis was used, otherwise, a fixed plot analysis was performed. All analyses used effect sizes and standard errors and a p < 0.05 threshold was considered statistically significant (denoted by *). Overall, the effect of treadmill intervention on cadence (z = 6.24 *, I² = 11.5%) and step length (z = 2.25 *, I² = 74.3%) in adults with stroke was significant. We also found a significant effect of treadmill intervention on paretic step length (z = 2.34 *, I² = 0%) and stride length (z = 6.09 *, I² = 45.5%). For the active control group, including adults with PD, we found that overground physical therapy training had the largest effect on step width (z = −3.75 *, I² = 0%). Additionally, for PD adults in treadmill intervention studies, we found the largest significant effect was on step length (z = 2.73 *, I² = 74.2%) and stride length (z = −2.54 *, I² = 96.8%). Conclusion: Treadmill intervention with sensory stimulation and body weight support treadmill training were shown to have the largest effect on step length in adults with PD and stroke.

Background: Age-related changes in the cortical control of standing balance may provide a modifiable mechanism underlying falls in older adults. Thus, this study examined the cortical response to sensory and mechanical perturbations in older adults while standing and examined the relationship between cortical activation and postural control.Methods: A cohort of community dwelling young (18–30 years, N = 10) and older adults (65–85 years, N = 11) performed the sensory organization test (SOT), motor control test (MCT), and adaptation test (ADT) while high-density electroencephalography (EEG) and center of pressure (COP) data were recorded in this cross-sectional study. Linear mixed models examined cohort differences for cortical activities, using relative beta power, and postural control performance, while Spearman correlations were used to investigate the relationship between relative beta power and COP indices in each test.Results: Under sensory manipulation, older adults demonstrated significantly higher relative beta power at all postural control-related cortical areas (p < 0.01), while under rapid mechanical perturbations, older adults demonstrated significantly higher relative beta power at central areas (p < 0.05). As task difficulty increased, young adults had increased relative beta band power while older adults demonstrated decreased relative beta power (p < 0.01). During sensory manipulation with mild mechanical perturbations, specifically in eyes open conditions, higher relative beta power at the parietal area in young adults was associated with worse postural control performance (p < 0.001). Under rapid mechanical perturbations, specifically in novel conditions, higher relative beta power at the central area in older adults was associated with longer movement latency (p < 0.05). However, poor reliability measures of cortical activity assessments were found during MCT and ADT, which limits the ability to interpret the reported results.Discussion: Cortical areas are increasingly recruited to maintain upright postural control, even though cortical resources may be limited, in older adults. Considering the limitation regarding mechanical perturbation reliability, future studies should include a larger number of repeated mechanical perturbation trials.

Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann–Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory–motor (i.e., SMN/VIN) networks.

Tai Chi is a Chinese martial art that provides an adaptive and accessible exercise for older adults with varying functional capacity. While Tai Chi is widely recommended for its physical benefits, wider adoption in at-home practice presents challenges for practitioners, as limited feedback may hamper learning. This study examined the feasibility of using a wearable sensor, combined with machine learning (ML) approaches, to automatically and objectively classify Tai Chi expertise. We hypothesized that the combination of wrist acceleration profiles with ML approaches would be able to accurately classify practitioners’ Tai Chi expertise levels. Twelve older active Tai Chi practitioners were recruited for this study. The self-reported lifetime practice hours were used to identify subjects in low, medium, or highly experienced groups. Using 15 acceleration-derived features from a wearable sensor during a self-guided Tai Chi movement and 8 ML architectures, we found multiclass classification performance to range from 0.73 to 0.97 in accuracy and F1-score. Based on feature importance analysis, the top three features were found to each result in a 16–19% performance drop in accuracy. These findings suggest that wrist-wearable-based ML models may accurately classify practice-related changes in movement patterns, which may be helpful in quantifying progress in at-home exercises.

Tai Chi provides an age-appropriate exercise to decrease fall risks in older adults. However, the exact mechanism underlying the benefits of Tai Chi practice remains an open question. Thus, this study examined how aging and Tai Chi practice impact adaptation to sensory and motor perturbations while standing. We hypothesized that older Tai Chi practitioners would exhibit a decreased reliance on visual processes as sensory and motor perturbations increased, relative to naive healthy older adults. Using rambling and trembling decompositions of the center of pressure (COP) and frequency-domain features, we examined changes in low (0–0.3 Hz), medium (0.3–1 Hz), and high (1–3 Hz) frequency components, reflecting contributions from the visual, vestibular/somatosensory, and proprioceptive systems, respectively, in healthy young adults (HYA), healthy older adults (HOA), and Tai Chi practicing older adults (TCOA). Our results revealed statistically significant condition-by-group interactions in high-frequency COP-x and rambling-x and COP-y components, medium-frequency COP-y components, and all low-frequency components in COP and trembling (p < 0.05). Further, a significant trial-by-group interaction in high-frequency rambling-y was observed (p < 0.05). These results indicate age and Tai-chi-related differences in modulation of sensory contributions to balance as perturbations increase, and with repeated practice, which merit further investigation.