MedTrace Logo

ESTIMATION OF BALANCE STATUS IN HEMIPARETICS

NCT04423497 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 66

Last updated 2020-06-09

No results posted yet for this study

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.

Conditions

  • Hemiparesis

Interventions

OTHER

Balance Evaluation Systems Test

Balance Evaluation Systems Test application

Sponsors & Collaborators

  • Pamukkale University

    lead OTHER

Principal Investigators

  • Güzin Kara, PhD, PT · Pamukkale University

Eligibility

Min Age
35 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-07-31
Primary Completion
2018-05-31
Completion
2018-05-31

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04423497 on ClinicalTrials.gov