Intervention study of finger-movement exercises and finger weight-lift training for improvement of handgrip strength among the very elderly

Objectives:To examine the effects of finger-movement exercises and finger weight-lift training on handgrip strength and Activities of Daily Living Scale(ADLS)values.Methods:A total of 80 very elderly adults(aged
80 years)were assigned to either an intervention group(n?40)or a control group(n?40).Subjects in the intervention group performed finger-movement exercises and weight-lift training for a period of 3 months,while subjects in the control group received no intervention,and were unaware of the interventions received in the other group.Results:After completing 3 months of finger-movement exercises and weight-lift training,the average handgrip strength of the 40 participants in the intervention group had increased by 2.1 kg,whereas that in the control group decreased by 0.27 kg(P<0.05).After receiving intervention,the number of subjects in the intervention group with an ADLS score>22 points decreased by 7.5%(P<0.05,vs.pre-intervention).Conclusions:The combined use interventionwith finger-movement exercises and proper finger weight-lift training improved the handgrip strength andADLS values of very elderly individuals.These rehabilitation exercisesmay be used to help the elderlymaintain their self-care abilities.

Investigating Neural Representation of Finger-Movement Directions Using Electroencephalography Independent Components

There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution of EEG from that of the ocular artifacts. To overcome this issue, we designed an experiment in which EEG’s temporal dynamics and spatial information are evaluated together to improve the performance of brain-computer interface (BCI) for classifying finger movement directions. Six volunteers participated in the study. We examined their EEG using decoding analyses. Independent components (ICs) that represented brain-source signals successfully classified the directions of the finger movements with higher rates than chance level. The weight analyses of the classifiers revealed that maximal performance of the classification was recorded at the latencies prior to the onset of finger movements. The weight analyses also revealed the relevant cortical areas including the right lingual, left posterior cingulate, left inferior temporal gyrus, and right precuneus, which indicated the involvement of the visuospatial processing. We concluded that combining spatial distribution and temporal dynamics of the scalp EEG may improve BCI performance.