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ML for Neck Disability Using Muscle and Joint

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 evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Identifier: NCT05291377
Recruitment Status : Not yet recruiting
First Posted : March 22, 2022
Last Update Posted : March 22, 2022
Information provided by (Responsible Party):
Ahmed Ali Mohammed Torad, Kafrelsheikh University

Brief Summary:

conduct machine learning models to identify different aspects that can give us an impression about the disability in patients with neck pain.

By using 17 different classifier and regressor models. to identify disability from emg, pain, ROM and curve measurements

Condition or disease
Neck Pain Disability Physical

Show Show detailed description

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Study Type : Observational [Patient Registry]
Estimated Enrollment : 90 participants
Observational Model: Other
Time Perspective: Cross-Sectional
Target Follow-Up Duration: 1 Week
Official Title: Machine Learning Models for Identifying Disability in Neck Pain Patients Using Muscle and Joint Parameters
Estimated Study Start Date : March 12, 2022
Estimated Primary Completion Date : April 24, 2022
Estimated Study Completion Date : April 24, 2022

Resource links provided by the National Library of Medicine

high disbility group
Group scored more than half of the score of the neck disability index
low disability group
Group scored less than half of the score of the neck disability index

Primary Outcome Measures :
  1. Correlation between neck disability and emg signals [ Time Frame: 2 weeks ]
    using logistic regression, we can get this outcome

  2. Correlation between neck disability and range of motion [ Time Frame: 2 weeks ]
    using logistic regression, we can get this outcome

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   30 Years to 55 Years   (Adult)
Sexes Eligible for Study:   All
Sampling Method:   Probability Sample
Study Population
neck pain patients that scored at least 3 on VAS and experienced the pain for more than 3 months

Inclusion Criteria:

  • Complaining from pain
  • Able to read and write

Exclusion Criteria:

  • no anomalies
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Responsible Party: Ahmed Ali Mohammed Torad, lecturer, Kafrelsheikh University Identifier: NCT05291377    
Other Study ID Numbers: P.T/BAS/1/2022/10
First Posted: March 22, 2022    Key Record Dates
Last Update Posted: March 22, 2022
Last Verified: March 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: the researcher intend to share the code of the analysis with the globe to increase body of knowledge
Supporting Materials: Analytic Code
Time Frame: 2 months after data collection finished
Access Criteria: all authors and related investigators

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Neck Pain
Neurologic Manifestations