Literature DB >> 22094178

The "peg test": a novel technique for dexterity evaluation in hand immobilized with a splint.

L Noël1, M-C Blancher, J-P Kempf, A Bodin, S Facca, H Khalifa-Dubert, P Liverneaux.   

Abstract

Manual dexterity tests are difficult to apply in the clinical practice. They are either destined for specific lesions, or are rather complicated and time-consuming. The aim of this study was to evaluate a new manual dexterity test for hand immobilized with a splint: the "peg test". Our series included 86 persons (without any hand trauma or disease) of mean age 42 years of which 77 were right-handed. Materials included five pegs, splints immobilizing the DIP joint, a desk and a stopwatch. Each subject moved the pegs from a horizontal to a vertical position using the thumb-ring pinch grip. This was repeated three times without a splint, with a palmar splint, with a simple dorsal splint and with a dorsal splint glued to the nail, then once more without a splint. A time score was recorded for each trial. A point was deduced at each fall of a peg for result analysis. Results showed that a learning curve affected performance, but that dominance and age before learning had no effect. After learning performance diminished with these variables in the following order: without splint, then more so with a glued splint, even more with a simple dorsal splint and most with a palmar one. The peg test fulfills metrological requirements of dexterity tests: test-retest (intra-observer) reliability (reproducibility), inter-observer reliability (both measured by correlation coefficient), and validity of instrument of measurement. Simple, rapid and reliable, it is perfectly adapted to manual dexterity evaluation in hand immobilized with a splint.
Copyright © 2011 Elsevier Masson SAS. All rights reserved.

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Year:  2011        PMID: 22094178     DOI: 10.1016/j.main.2011.09.007

Source DB:  PubMed          Journal:  Chir Main        ISSN: 1297-3203


  1 in total

1.  An Accessible, Open-Source Dexterity Test: Evaluating the Grasping and Dexterous Manipulation Capabilities of Humans and Robots.

Authors:  Nathan Elangovan; Che-Ming Chang; Geng Gao; Minas Liarokapis
Journal:  Front Robot AI       Date:  2022-04-25
  1 in total

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