HemiparesisNCT04423497
ESTIMATION OF BALANCE STATUS IN HEMIPARETICS
An observational study of Hemiparesis, run by Pamukkale University.
Trial 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. Read more about this study
- Who is it for?
- Ages 35 to 65 years
- Conditions the study is for
- Hemiparesis
- What is being tested?
- Balance Evaluation Systems Test
- How many people
- 66 participants
- Where
- No sites listed yet
- Study dates
- 31 Jul 2016to 31 May 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 35 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Sixty-six volunteers with hemiparesis (23 females, 43 males) participated in the study. The participants informed of the right to withdraw his or her consent at any time. Prior to giving signed consent, all informed thereof.
Who may be able to join
- 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.
Who cannot join
- Having comorbidities affecting their balance,
- Having communication problems.
- Patients who cannot comprehend the directions given to them were excluded from the study.
About this study
Official title: ESTIMATION OF BALANCE STATUS IN PATIENTS WITH HEMIPARESIS: AN ARTIFICIAL NEURAL NETWORK IMPLEMENTATION
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.
Read the 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
- Being tested
- Balance Evaluation Systems Test
- Keywords
- Hemiparesis; Balance; Artificial Neural Networks.
- Medical subject headings
- Paresis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Balance Evaluation Systems TestOther
Balance Evaluation Systems Test application
What is being measured
The primary outcome is the main question the study is designed to answer.
Balance Evaluation Systems Test (BESTest)
Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait
Measured: two years
Artificial Neural Networks Modeling
comparing the manual total BESTest scores with the predicted scores by the ANNs
Measured: two years
Artificial Neural Networks Modeling
determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.
Measured: two years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2020
- Study start31 Jul 2016
- Primary completion31 May 2018
- Study completion31 May 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for HemiparesisStudies of the same condition, open and under way.
- More trials run by Pamukkale UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 66 participants (actual)
Sponsor and investigators
- Lead sponsor
- Pamukkale University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Güzin Kara, Pamukkale University
- Investigators
- Güzin Kara, PhD, PT, Principal investigator, Pamukkale University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04423497
- Sponsor's protocol ID
- 60116787-020/5431
- Status last verified
- Jun 2020
- First submitted
- 2 Jun 2020
- Last updated
- 9 Jun 2020
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- No
Publications
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.
PubMed 21937234Demir 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
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ESTIMATION OF BALANCE STATUS IN PATIENTS WITH HEMIPARESIS: AN ARTIFICIAL NEURAL NETWORK IMPLEMENTATION
- ClinicalTrials.gov ID
- NCT04423497
- Acronym / sponsor ID
- 60116787-020/5431
- Conditions
- Hemiparesis
- Keywords
- Hemiparesis; Balance; Artificial Neural Networks.
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 66 participants (actual)
- Lead sponsor
- Pamukkale UniversityUniversity, hospital or other
- Study start
- 31 Jul 2016 (actual)
- Primary completion
- 31 May 2018 (actual)
- Study completion
- 31 May 2018 (actual)
- First posted
- 9 Jun 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 35 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Sixty-six volunteers with hemiparesis (23 females, 43 males) participated in the study. The participants informed of the right to withdraw his or her consent at any time. Prior to giving signed consent, all informed thereof.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (3 listed)
- Having comorbidities affecting their balance,
- Having communication problems.
- Patients who cannot comprehend the directions given to them were excluded from the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Balance Evaluation Systems TestBalance Evaluation Systems Test application | Other | – |
Primary outcome
Balance Evaluation Systems Test (BESTest)
Time frame: two years
Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait
Artificial Neural Networks Modeling
Time frame: two years
comparing the manual total BESTest scores with the predicted scores by the ANNs
Artificial Neural Networks Modeling
Time frame: two years
determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jun 2020.
- Background: PubMed 21937234Kaczmarczyk 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.
- BackgroundDemir 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
- Individual participant dataSharing planned: No
Related trials for Hemiparesis
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Recruiting
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Kinematic Analysis of Manual Dexterity
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Evaluation of the Safety and Performance of the AWAKE Device in Patients With Hemiplegia or Hemiparesis Secondary to Stroke (ACV).
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Güzin Kara, PhD, PT principal investigator
Pamukkale University
Questions about this trial
What is this clinical trial studying?
An observational study of Hemiparesis, run by Pamukkale University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Jun 2020. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Balance Evaluation Systems Test (BESTest) (time frame: two years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04423497, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jun 2020.