Literature DB >> 9645541

A kinematic and kinetic analysis of the sit-to-stand transfer using an ejector chair: implications for elderly rheumatoid arthritic patients.

B J Munro1, J R Steele, G M Bashford, M Ryan, N Britten.   

Abstract

Twelve elderly female rheumatoid arthritis patients (mean age = 65.5 +/- 8.6 yr) were assessed rising from an instrumented Eser Ejector chair under four conditions: high seat (540 mm), low seat (450 mm), with and without the ejector mechanism operating. Sagittal plane motion, ground reaction forces, and vertical chair arm rest forces were recorded during each trial with the signals synchronised at initial subject head movement. When rising from a high seat, subjects displayed significantly (p < 0.05) greater time to seat off; greater trunk, knee and ankle angles at seat off; increased ankle angular displacement; decreased knee angular displacement; and decreased total net and normalised arm rest forces compared to rising from a low seat. When rising using the ejector mechanism, time to seat off and trunk and knee angle at seat off significantly increased, whereas trunk and knee angular displacement, and total net and normalised arm rest forces significantly decreased compared to rising unassisted. Regardless of seat height or ejector mechanism use, there were no significant differences in the peak, or time to peak horizontal velocity of the subjects' total body centre of mass, or net knee and ankle moments. It was concluded that increased seat height and use of the ejector mechanism facilitated sit-to-stand transfers performed by elderly female rheumatoid arthritic patients. However, using the ejector chair may be preferred by these patients compared to merely raising seat height because it does not necessitate the use of a footstool, a possible obstacle contributing to falls.

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Year:  1998        PMID: 9645541     DOI: 10.1016/s0021-9290(97)00130-9

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  4 in total

1.  Detection of daily physical activities using a triaxial accelerometer.

Authors:  M J Mathie; A C F Coster; N H Lovell; B G Celler
Journal:  Med Biol Eng Comput       Date:  2003-05       Impact factor: 2.602

2.  A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance.

Authors:  Thomas Bennett; Praveen Kumar; Virginia Ruiz Garate
Journal:  Sensors (Basel)       Date:  2022-06-24       Impact factor: 3.847

3.  Effects of pelvic compression belts on the kinematics and kinetics of the lower extremities during sit-to-stand maneuvers.

Authors:  Jong Moon Kim; Hyun Dong Je; Hyeong-Dong Kim
Journal:  J Phys Ther Sci       Date:  2017-08-10

4.  Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning.

Authors:  Javier Andreu-Perez; Luis Garcia-Gancedo; Jonathan McKinnell; Anniek Van der Drift; Adam Powell; Valentin Hamy; Thomas Keller; Guang-Zhong Yang
Journal:  Sensors (Basel)       Date:  2017-09-14       Impact factor: 3.576

  4 in total

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