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1.
Med Sci Monit ; 26: e923147, 2020 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-32930152

RESUMO

BACKGROUND For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS). MATERIAL AND METHODS In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied. RESULTS (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach's alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore "gait speed" are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling. CONCLUSIONS The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.


Assuntos
Análise da Marcha/métodos , Transtornos Neurológicos da Marcha/diagnóstico , Acidente Vascular Cerebral/complicações , Adulto , Idoso , Idoso de 80 Anos ou mais , Avaliação da Deficiência , Feminino , Transtornos Neurológicos da Marcha/etiologia , Humanos , Masculino , Pessoa de Meia-Idade , Gravação em Vídeo , Adulto Jovem
2.
Hum Brain Mapp ; 35(4): 1574-86, 2014 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-23568448

RESUMO

During prism adaptation two types of learning processes can be distinguished. First, fast strategic motor control responses are predominant in the early course of prism adaptation to achieve rapid error correction within few trials. Second, slower spatial realignment occurs among the misaligned visual and proprioceptive sensorimotor coordinate system. The aim of the present ultra-highfield (7T) functional magnetic resonance imaging (fMRI) study was to explore cerebellar cortical and dentate nucleus activation during the course of prism adaptation in relation to a similar visuomotor task without prism exposure. Nineteen young healthy participants were included into the study. Recently developed normalization procedures were applied for the cerebellar cortex and the dentate nucleus. By means of subtraction analysis (early prism adaptation > visuomotor, early prism adaptation > late prism adaptation) we identified ipsilateral activation associated with strategic motor control responses within the posterior cerebellar cortex (lobules VIII and IX) and the ventro-caudal dentate nucleus. During the late phase of adaptation we observed pronounced activation of posterior parts of lobule VI, although subtraction analyses (late prism adaptation > visuomotor) remained negative. These results are in good accordance with the concept of a representation of non-motor functions, here strategic control, within the ventro-caudal dentate nucleus.


Assuntos
Adaptação Fisiológica/fisiologia , Adaptação Psicológica/fisiologia , Córtex Cerebelar/fisiologia , Núcleos Cerebelares/fisiologia , Atividade Motora/fisiologia , Percepção Visual/fisiologia , Adulto , Feminino , Dedos/fisiologia , Humanos , Imageamento por Ressonância Magnética , Masculino , Dispositivos Ópticos , Psicofísica , Processamento de Sinais Assistido por Computador , Análise e Desempenho de Tarefas , Fatores de Tempo
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