ESTIMATION OF BALANCE STATUS IN HEMIPARETICS

NCT ID: NCT04423497

Last Updated: 2020-06-09

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 66 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2016-07-31
• Study Completion Date: 2018-05-31

Brief Summary

Although Balance Evaluation Systems Test(BESTest) is an important balance assessment tool to differentiate balance deficits, it is time consuming and tiring for hemiparetic patients. Using artificial neural networks(ANNs) to estimate balance status can be a practical and useful tool for clinicians. The aim of this study was to compare manual BESTest results and ANNs predictive results and to determine the highest contributions of BESTest sections by using ANNs predictive results of BESTest sections. 66 hemiparetic individuals were included in the study. Balance status was evaluated using the BESTest. 70%(n=46), of the dataset was used for learning, 15%(n=10) for evaluation, and 15%(n=10) for testing purposes in order to model ANNs. Multiple linear regression model(MLR) was used to compare with ANNs.

Detailed Description

The demographics and clinical information of the participants' were recorded. Clinical information consists of some basic medical data for the patients. Hodkinson Mental Test was used to assess the cognitive status of the participants if they met inclusion criteria. Balance Evaluation Systems Test was used to assess balance status of the participants.

Feed-forward back-propagation ANNs was used in this study by employing Levenberg-Marquardt training algorithm. Tangent hyperbolic transfer functions were used in the hidden layer. Matlab (Version R2017b, Mathworks Inc, USA) was used in ANNs modeling. 70% (n=46), 15% (n=10) and 15% (n=10) of the data obtained from the participants were used for training, validation and test in the study, respectively. Multiple linear regression (MLR) models also were used to compare with ANNs.

Firstly, the ANNs were modeled for the first aim of the study. We used the data of the five traditional balance tests in the BESTest that did not use the real values (the timing or distance), but just the classified values (0-3 points in the BESTest) to train ANNs. Five balance tests were functional reach test (cm), one leg standing test for right and left side (sec), 6-metre timed walk test (sec) and timed up and go test (sec). Then, we compare the manual total BESTest scores with the predicted scores by the ANNs.

Secondly, we removed 6 sections of the BESTest one by one and modeled with the remaining 5 sections of the test to estimate the total BESTest score. After this modeling, we removed each item one by one in the first section and estimated the first section total score. We repeated the process for all the sections of the BESTest.

Statistical Analysis

Conditions

Hemiparesis

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: CROSS_SECTIONAL

Interventions

Balance Evaluation Systems Test

Balance Evaluation Systems Test application

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Being aged between 35-65 years,
• Able to walk independently or with a walking aid,
• Able to stand at least 1 minute independently,
• Having single hemiparesis,
• Getting at least 8 points from Hodkinson Mental Test.

Exclusion Criteria

• Having comorbidities affecting their balance,
• Having communication problems.
• Patients who cannot comprehend the directions given to them were excluded from the study.

• Minimum Eligible Age: 35 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Pamukkale University

OTHER

Sponsor Role: lead

Responsible Party

Güzin Kara

Physiotherapist, Doctor of Philosophy

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Güzin Kara, PhD, PT

Role: PRINCIPAL_INVESTIGATOR

Pamukkale University

References

Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J, Gajewski J. Associations between gait patterns, brain lesion factors and functional recovery in stroke patients. Gait Posture. 2012 Feb;35(2):214-7. doi: 10.1016/j.gaitpost.2011.09.009. Epub 2011 Sep 19.

Reference Type: BACKGROUND

PMID: 21937234 (View on PubMed)

Demir U, Kocaoğlu S, Akdoğan E. Human impedance parameter estimation using artificial neural network for modelling physiotherapist motion. Biocybernetics and Biomedical Engineering. 2016; 36(2): 318-326

Reference Type: BACKGROUND

Other Identifiers

60116787-020/5431

Identifier Type: -

Identifier Source: org_study_id