Trial Review

Technical difficulties have been reported by some users of the search function and is being investigated by technical staff. Thank you for your patience and apologies for any inconvenience caused.

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been endorsed by the ANZCTR. Before participating in a study, talk to your health care provider and refer to this information for consumers
Trial registered on ANZCTR


Registration number
ACTRN12622001540707
Ethics application status
Approved
Date submitted
4/12/2022
Date registered
13/12/2022
Date last updated
13/12/2022
Date data sharing statement initially provided
13/12/2022
Type of registration
Prospectively registered

Titles & IDs
Public title
Posture and musculoskeletal system disorders in post-COVID-19 individuals
Scientific title
Posture and musculoskeletal system disorders in individuals who have had COVID-19
Secondary ID [1] 308545 0
Nil known
Universal Trial Number (UTN)
U1111-1285-7304
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
postural disorder 328392 0
musculoskeletal system disorders 328393 0
pain 328395 0
COVID-19 328396 0
Condition category
Condition code
Infection 325419 325419 0 0
Other infectious diseases
Respiratory 325420 325420 0 0
Other respiratory disorders / diseases
Musculoskeletal 325516 325516 0 0
Other muscular and skeletal disorders

Intervention/exposure
Study type
Observational
Patient registry
False
Target follow-up duration
Target follow-up type
Description of intervention(s) / exposure
In this study, it was planned to reveal the results of posture measurement evaluated by artificial intelligence method, pain status and musculoskeletal disorders in individuals who have had COVID-19. All evaluations of posture and musculoskeletal system disorders will be completed within a maximum of one hour in the field of university practice.
Intervention code [1] 324990 0
Not applicable
Comparator / control treatment
No control group.
Control group
Uncontrolled

Outcomes
Primary outcome [1] 333282 0
The pain severity will be evaluated using Numerical Rating Scale.
Timepoint [1] 333282 0
at survey time
Secondary outcome [1] 416463 0
The presence of postural disorders will be evaluated with the posture analysis package based on the artificial intelligence concept created by Physiosoft and Becure.
Timepoint [1] 416463 0
at survey time
Secondary outcome [2] 416464 0
The score of musculoskeletal disorders will be evaluated using Cornell Musculoskeletal Disorders Questionnaire.
Timepoint [2] 416464 0
at survey time

Eligibility
Key inclusion criteria
Inclusion criteria for the individuals who have had COVID-19 were:
*adult individuals aged 18 and over
*volunteering to participate in the study,
*Individuals who can understand and answer the questionnaires
*Individuals who were diagnosed with COVID-19 (individuals with a positive Polymerase Chain Reaction (PCR) test result, compatible with COVID-19 infection as a result of lung X-ray or lung tomography despite negative PCR test results) and discharged after recovery/home quarantine completed
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
Exclusion criteria for the individuals who have had COVID-19 were:
* Individuals with any physical or mental disability/disease and/or cognitive impairment
• Individuals newly diagnosed with COVID-19, therefore in quarantine at home or receiving treatment in hospital
• Individuals with suspected COVID-19
• Pregnant women

Study design
Purpose
Natural history
Duration
Cross-sectional
Selection
Defined population
Timing
Prospective
Statistical methods / analysis
The sample size required for the study was calculated using the Raosoft sample size calculator program. It was determined that at least 37 individuals should be included in the group of those with COVID-19 in order for this study to reach an a value of 0.05, and a power of 95%, to determine the response of pain rate (13.3%) in the research group.
At the end of the study, statistical analyzes will be made using the SPSS 15.0 program. By using visual (histogram and probability graphs) and analytical methods (Kolmogorov-Smirnov/Shapiro-Wilk tests), the conformity of all variables to normal distribution will be investigated. Descriptive analyzes will be given using frequency (n) and percentage (%) values for categorical variables, median, minimum and maximum values for non-normally distributed variables, mean and standard deviation (×±ss) for normally distributed variables.
The Independent Sample t test (Student t test) will be used to compare the variables that fit the normal distribution, the Mann-Whitney U test will be used to compare the data that do not fit, and the Chi-square test will be used to compare the uncountable data. The relationships between the non-normally distributed variables will be determined by Spearman and the relationships between the normally distributed variables will be determined by the Pearson correlation analysis method. The probability of error in statistical analysis will be determined as p<0.05.

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
31/01/2023
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
37
Accrual to date
Final
Recruitment outside Australia
Country [1] 25165 0
Turkey
State/province [1] 25165 0
Izmir

Funding & Sponsors
Funding source category [1] 312793 0
Government body
Name [1] 312793 0
Scientific and Technological Research Council of Turkey (TUBITAK)
Country [1] 312793 0
Turkey
Primary sponsor type
Individual
Name
GÜLSAH BARGI
Address
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country
Turkey
Secondary sponsor category [1] 314427 0
Individual
Name [1] 314427 0
HELIN ÖNCEL
Address [1] 314427 0
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country [1] 314427 0
Turkey
Secondary sponsor category [2] 314429 0
Individual
Name [2] 314429 0
SIBEL DENIZ
Address [2] 314429 0
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country [2] 314429 0
Turkey
Secondary sponsor category [3] 314430 0
Individual
Name [3] 314430 0
SARA MOHAMMADNEJADIAN
Address [3] 314430 0
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country [3] 314430 0
Turkey

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 312080 0
Izmir Democracy University Non-Interventional Clinical Research of the Ethics Committee
Ethics committee address [1] 312080 0
Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir
Ethics committee country [1] 312080 0
Turkey
Date submitted for ethics approval [1] 312080 0
01/06/2022
Approval date [1] 312080 0
24/06/2022
Ethics approval number [1] 312080 0
2022/07-04

Summary
Brief summary
Thanks to artificial intelligence and machine learning technology, evaluations such as balance and foot pressure measurement can be made in patients today, as well as applications such as exercise and patient follow-up in rehabilitation, solutions to biomedical problems can be offered. Artificial intelligence and machine learning will continue to play an important role in education, training, patient care and research in the future. On the other hand, although various psychological and physical problems have been shown in individuals who have had COVID-19 during the prolonged COVID-19 pandemic process, musculoskeletal disorders and posture problems in these individuals remain unclear. For this reason, in this study, it was aimed to reveal the results of posture measurement evaluated by artificial intelligence method, pain status and musculoskeletal disorders in individuals who have had COVID-19.
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 123398 0
Dr GÜLSAH BARGI
Address 123398 0
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country 123398 0
Turkey
Phone 123398 0
+905317938766
Fax 123398 0
+90 232 260 1004
Email 123398 0
gulsah.bargi@idu.edu.tr
Contact person for public queries
Name 123399 0
Dr GÜLSAH BARGI
Address 123399 0
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country 123399 0
Turkey
Phone 123399 0
+902322601001
Fax 123399 0
+90 232 260 1004
Email 123399 0
gulsah.bargi@idu.edu.tr
Contact person for scientific queries
Name 123400 0
Dr GÜLSAH BARGI
Address 123400 0
Izmir Democracy University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Mehmet Ali Akman Quarter, 13th street, No. 2, 35140 Güzelyali, Konak, Izmir, Turkey.
Country 123400 0
Turkey
Phone 123400 0
+902322601001
Fax 123400 0
+90 232 260 1004
Email 123400 0
gulsah.bargi@idu.edu.tr

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
No/undecided IPD sharing reason/comment
I can not share the data of individuals included in the study in our country within the scope of the personal data protection law.


What supporting documents are/will be available?

No Supporting Document Provided



Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
No additional documents have been identified.