Search

Purpose: Transverse myelitis (TM)-associated dysesthesia is diverse and frequently resistant to treatment. This study explored the comprehensive effects of a novel transcutaneous electrical nerve stimulation (TENS) approach tailored to an individual's specific dysesthesia profile in a patient with TM. Patient and method: A 52-year-old woman with severe dysesthesias in the left C8 sensory area caused by TM underwent an A-B-A-B-A design intervention. The baseline (phase A) and intervention (phase B) phases were both 7 days. Tingling and allodynia were scored on an 11-point numerical rating scale pre-, post-, and 1 h post-treatment. Upper limb activities during daily living were assessed using a wrist-worn accelerometer. The intervention phase consisted of 60-min sessions of TENS two times daily. Furthermore, the intervention and carry-over effects of TENS were evaluated using Tau-U and Bayesian unknown change point models. Results: The effects of TENS resulted in the immediate improvement in tingling, allodynia, and upper limb activity. Long-term effects of TENS affected tingling and upper limb activity; however, no impacts on allodynia were observed. Conclusion: This novel TENS approach shows promise as an effective treatment, even in rare and treatment-resistant dysesthesia associated with TM. [ABSTRACT FROM AUTHOR]

Post-stroke gait control is a complex, often fail to account for the heterogeneity and continuity of gait in existing gait models. Precisely evaluating gait speed adjustability and gait instability in free-living environments is important to understand how individuals with post-stroke gait dysfunction approach diverse environments and contexts. This study aimed to explore individual causal interactions in the free-living gait control of persons with stroke. To this end, fifty persons with stroke wore an accelerometer on the fifth lumbar vertebra (L5) for 24 h in a free-living environment. Individually directed acyclic graphs (DAGs) were generated based on the spatiotemporal gait parameters at contemporaneous and temporal points calculated from the acceleration data. Spectral clustering and Bayesian model comparison were used to characterize the DAGs. Finally, the DAG patterns were interpreted via Bayesian logistic analysis. Spectral clustering identified three optimal clusters from the DAGs. Cluster 1 included persons with moderate stroke who showed high gait asymmetry and gait instability and primarily adjusted gait speed based on cadence. Cluster 2 included individuals with mild stroke who primarily adjusted their gait speed based on step length. Cluster 3 comprised individuals with mild stroke who primarily adjusted their gait speed based on both step length and cadence. These three clusters could be accurately classified based on four variables: Ashman’s D for step velocity, Fugl-Meyer Assessment, step time asymmetry, and step length. The diverse DAG patterns of gait control identified suggest the heterogeneity of gait patterns and the functional diversity of persons with stroke. Understanding the theoretical interactions between gait functions will provide a foundation for highly tailored rehabilitation. [ABSTRACT FROM AUTHOR]