Literature DB >> 27912919

The Southampton Hand Assessment Procedure revisited: A transparent linear scoring system, applied to data of experienced prosthetic users.

Johannes G M Burgerhof1, Ecaterina Vasluian2, Pieter U Dijkstra3, Raoul M Bongers4, Corry K van der Sluis2.   

Abstract

STUDY
DESIGN: Cross-sectional.
INTRODUCTION: Southampton Hand Assessment Procedure (SHAP) provides function scores for hand grips (prehensile patterns) and an overall score, the index of function (IOF). The underlying equations of SHAP are not publicly available, which induces opacity. Furthermore, SHAP has been scarcely tested in prosthetic users.
METHODS: Issues with SHAP-IOF are discussed; an alternative scoring system, that is, linear index of function (LIF) and a weighted version (W-LIF) are presented. In LIF, task times are transformed linearly, relative to SHAP norms, and are computed into LIF-prehensile patterns (LIFPP). LIF and IOF were compared using data of 27 experienced prosthetic users.
RESULTS: High correlation and agreement between LIF and IOF was found: LIFPP vs IOFPP ranged from r = 0.880 to r = 0.988, and W-LIF vs IOF had a correlation coefficient of r = 0.984. DISCUSSION: SHAP data of prosthetic users are valuable benchmarks for health care professionals. LIF calculations are a good and cost free alternative for IOF scores. CONCLUSION(S): Measurements with LIF and IOF may be considered similar, but LIF is transparent and easier to use than IOF. LEVEL OF EVIDENCE: Clinical measurement and cross-sectional.
Copyright © 2016 Hanley & Belfus. Published by Elsevier Inc. All rights reserved.

Keywords:  Linear scoring system; New data; Prosthetic users; SHAP

Mesh:

Year:  2016        PMID: 27912919     DOI: 10.1016/j.jht.2016.05.001

Source DB:  PubMed          Journal:  J Hand Ther        ISSN: 0894-1130            Impact factor:   1.950


  5 in total

1.  Upbeat: Augmented Reality-Guided Dancing for Prosthetic Rehabilitation of Upper Limb Amputees.

Authors:  Marina Melero; Annie Hou; Emily Cheng; Amogh Tayade; Sing Chun Lee; Mathias Unberath; Nassir Navab
Journal:  J Healthc Eng       Date:  2019-03-19       Impact factor: 2.682

2.  User training for machine learning controlled upper limb prostheses: a serious game approach.

Authors:  Morten B Kristoffersen; Andreas W Franzke; Raoul M Bongers; Michael Wand; Alessio Murgia; Corry K van der Sluis
Journal:  J Neuroeng Rehabil       Date:  2021-02-12       Impact factor: 4.262

3.  An evaluation of contralateral hand involvement in the operation of the Delft Self-Grasping Hand, an adjustable passive prosthesis.

Authors:  Alix Chadwell; Natalie Chinn; Laurence Kenney; Zoë J Karthaus; Daniek Mos; Gerwin Smit
Journal:  PLoS One       Date:  2021-06-17       Impact factor: 3.240

4.  A novel framework for designing a multi-DoF prosthetic wrist control using machine learning.

Authors:  Chinmay P Swami; Nicholas Lenhard; Jiyeon Kang
Journal:  Sci Rep       Date:  2021-07-22       Impact factor: 4.379

5.  Estimation of Motor Impairment and Usage of Upper Extremities during Daily Living Activities in Poststroke Hemiparesis Patients by Observation of Time Required to Accomplish Hand Dexterity Tasks.

Authors:  Tomoko Tanaka; Toyohiro Hamaguchi; Makoto Suzuki; Daigo Sakamoto; Junpei Shikano; Naoki Nakaya; Masahiro Abo
Journal:  Biomed Res Int       Date:  2019-11-07       Impact factor: 3.411

  5 in total

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