<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04423497</url>
  </required_header>
  <id_info>
    <org_study_id>60116787-020/5431</org_study_id>
    <nct_id>NCT04423497</nct_id>
  </id_info>
  <brief_title>ESTIMATION OF BALANCE STATUS IN HEMIPARETICS</brief_title>
  <official_title>ESTIMATION OF BALANCE STATUS IN PATIENTS WITH HEMIPARESIS: AN ARTIFICIAL NEURAL NETWORK IMPLEMENTATION</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Pamukkale University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Pamukkale University</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      Although Balance Evaluation Systems Test(BESTest) is an important balance assessment tool to&#xD;
      differentiate balance deficits, it is time consuming and tiring for hemiparetic patients.&#xD;
      Using artificial neural networks(ANNs) to estimate balance status can be a practical and&#xD;
      useful tool for clinicians. The aim of this study was to compare manual BESTest results and&#xD;
      ANNs predictive results and to determine the highest contributions of BESTest sections by&#xD;
      using ANNs predictive results of BESTest sections. 66 hemiparetic individuals were included&#xD;
      in the study. Balance status was evaluated using the BESTest. 70%(n=46), of the dataset was&#xD;
      used for learning, 15%(n=10) for evaluation, and 15%(n=10) for testing purposes in order to&#xD;
      model ANNs. Multiple linear regression model(MLR) was used to compare with ANNs.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The demographics and clinical information of the participants' were recorded. Clinical&#xD;
      information consists of some basic medical data for the patients. Hodkinson Mental Test was&#xD;
      used to assess the cognitive status of the participants if they met inclusion criteria.&#xD;
      Balance Evaluation Systems Test was used to assess balance status of the participants.&#xD;
&#xD;
      Feed-forward back-propagation ANNs was used in this study by employing Levenberg-Marquardt&#xD;
      training algorithm. Tangent hyperbolic transfer functions were used in the hidden layer.&#xD;
      Matlab (Version R2017b, Mathworks Inc, USA) was used in ANNs modeling. 70% (n=46), 15% (n=10)&#xD;
      and 15% (n=10) of the data obtained from the participants were used for training, validation&#xD;
      and test in the study, respectively. Multiple linear regression (MLR) models also were used&#xD;
      to compare with ANNs.&#xD;
&#xD;
      Firstly, the ANNs were modeled for the first aim of the study. We used the data of the five&#xD;
      traditional balance tests in the BESTest that did not use the real values (the timing or&#xD;
      distance), but just the classified values (0-3 points in the BESTest) to train ANNs. Five&#xD;
      balance tests were functional reach test (cm), one leg standing test for right and left side&#xD;
      (sec), 6-metre timed walk test (sec) and timed up and go test (sec). Then, we compare the&#xD;
      manual total BESTest scores with the predicted scores by the ANNs.&#xD;
&#xD;
      Secondly, we removed 6 sections of the BESTest one by one and modeled with the remaining 5&#xD;
      sections of the test to estimate the total BESTest score. After this modeling, we removed&#xD;
      each item one by one in the first section and estimated the first section total score. We&#xD;
      repeated the process for all the sections of the BESTest.&#xD;
&#xD;
      Statistical Analysis&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 31, 2016</start_date>
  <completion_date type="Actual">May 31, 2018</completion_date>
  <primary_completion_date type="Actual">May 31, 2018</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Balance Evaluation Systems Test (BESTest)</measure>
    <time_frame>two years</time_frame>
    <description>Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Artificial Neural Networks Modeling</measure>
    <time_frame>two years</time_frame>
    <description>comparing the manual total BESTest scores with the predicted scores by the ANNs</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Artificial Neural Networks Modeling</measure>
    <time_frame>two years</time_frame>
    <description>determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.</description>
  </primary_outcome>
  <enrollment type="Actual">66</enrollment>
  <condition>Hemiparesis</condition>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Balance Evaluation Systems Test</intervention_name>
    <description>Balance Evaluation Systems Test application</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Sixty-six volunteers with hemiparesis (23 females, 43 males) participated in the study. The&#xD;
        participants informed of the right to withdraw his or her consent at any time. Prior to&#xD;
        giving signed consent, all informed thereof.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Being aged between 35-65 years,&#xD;
&#xD;
          -  Able to walk independently or with a walking aid,&#xD;
&#xD;
          -  Able to stand at least 1 minute independently,&#xD;
&#xD;
          -  Having single hemiparesis,&#xD;
&#xD;
          -  Getting at least 8 points from Hodkinson Mental Test.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Having comorbidities affecting their balance,&#xD;
&#xD;
          -  Having communication problems.&#xD;
&#xD;
          -  Patients who cannot comprehend the directions given to them were excluded from the&#xD;
             study.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>35 Years</minimum_age>
    <maximum_age>65 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Güzin Kara, PhD, PT</last_name>
    <role>Principal Investigator</role>
    <affiliation>Pamukkale University</affiliation>
  </overall_official>
  <reference>
    <citation>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.</citation>
    <PMID>21937234</PMID>
  </reference>
  <reference>
    <citation>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</citation>
  </reference>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>June 2, 2020</study_first_submitted>
  <study_first_submitted_qc>June 5, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 9, 2020</study_first_posted>
  <last_update_submitted>June 5, 2020</last_update_submitted>
  <last_update_submitted_qc>June 5, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 9, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Pamukkale University</investigator_affiliation>
    <investigator_full_name>Güzin Kara</investigator_full_name>
    <investigator_title>Physiotherapist, Doctor of Philosophy</investigator_title>
  </responsible_party>
  <keyword>Hemiparesis; Balance; Artificial Neural Networks.</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Paresis</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04423497
org study id: 60116787-020/5431
nct id: NCT04423497
lead sponsor: 
has dmc: No
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.
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
observational model: Other
time perspective: Cross-Sectional
measure: Balance Evaluation Systems Test (BESTest)
time frame: two years
description: Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait
measure: Artificial Neural Networks Modeling
time frame: two years
description: comparing the manual total BESTest scores with the predicted scores by the ANNs
measure: Artificial Neural Networks Modeling
time frame: two years
description: determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.
intervention type: Other
intervention name: Balance Evaluation Systems Test
description: Balance Evaluation Systems Test application
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 35 Years
maximum age: 65 Years
healthy volunteers: No
last name: Güzin Kara, PhD, PT
role: Principal Investigator
affiliation: Pamukkale University
citation: 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.
PMID: 21937234
citation: 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
responsible party type: Principal Investigator
investigator affiliation: Pamukkale University
investigator full name: Güzin Kara
investigator title: Physiotherapist, Doctor of Philosophy
mesh term: Paresis
sharing ipd: No