ML for Neck Disability Using Muscle and Joint

Machine Learning Models for Identifying Disability in Neck Pain Patients Using Muscle and Joint Parameters

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

Study Overview

• Status: Completed
• Conditions: Neck Pain; Disability Physical
• Intervention / Treatment: Other

Detailed Description

ninety patients from both sexes suffering from mechanical neck dysfunction were participated in this study.

Patients were participated in the study if they fulfilled the following criteria

• Ninety patients from both sexes with their ages ranged from 20-35 years old.
• Subjects Referred from orthopedic consultant with chronic mechanical neck pain (<3months duration)
• The neck disability index is above 15, a minimal score to reflect the presence of at least a mild neck pain disorder
• cervical lordotic curve less than 34°
• Subjects whose dull aching pain increased by sustained posture, neck movement, palpation of cervical musculatur
• Agreements of patients.

Exclusion criteria:

• Cervical disc problems or cervical spondylosis.
• History of previous neck trauma or head injuries.
• Ankylosing spondylitis
• Acute inflammation, contracture or surgery affecting cervical spine.
• Current participation in supervised physical therapy for neck pain.
• Any skin disease or injury that may affect technique.
• Osteoporotic and rheumatic arthritic patients.
• Positive skin sensitivity test to kinesiotape.
• Unhealed wounds or scars at the treated area
• Visual or auditory problems Pain intensity was measured by the VAS. The patients were asked to mark on the line of VAS to the point that they felt the pain. Then the score was determined by measuring from the left end of the line to the point that the patient marked It was measured by The NDI which is a 10-item questionnaire consist of pain intensity, personal care, lifting, reading, headache, concentration, work, driving, sleeping and recreation. The subject was instructed to circle one of the six options which describes the severity of each item (0-5)55.Then the marks were counted and divided by 50 or 45 if one section was missing with total score ranging from 0 (no pain or disability) to 50 (severe pain and disability) 57. Then was multiplied by 100 for the percentage (score/ 50) x 100=---% points88. A score of 10-28% is considered mild disability, 30-48% is moderate, 50-68% is sever and 72% or more is complete

Activation pattern of the examined ms was recorded and analyzed using electromyography as follow:

Skin preparation67 After history taking and physical examination, subjects were allowed to rest for 10 minutes for acclimatization. During this period each subject was prepared for the experimental set as follow;

• The site of the electrode placement had been shaved when needed
• The skin was cleaned with alcohol with a piece of cotton to reduce skin impedance at the site of recorded muscle and at the site of the reference electrode

Electrodes positions10:

The electrodes sites were located on each subject's dominant side as follows:

levator scapulae:was centered lateral to the C3-4 spinous process between the posterior margin of the sternocleidomastoid and the anterior margins of the upper trapezius Upper trapezius: 2 cm lateral to the midpoint of a line drawn between C7 spinous process and the posterolateral acromion Reference electrode: was situated over the C7 spinous process. The sites of electrodes placement were determined using a marker and a tape measurement and confirmed through palpation and manual resistance.

• Study Type: Observational
• Enrollment (Actual): 90

Contacts and Locations

Study Locations

• Egypt
  • Cairo, Egypt
    • Faculty of physical therapy, Kafrelsheik university

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 30 years to 55 years (Adult)
• Accepts Healthy Volunteers: N/A

• 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

Description

Inclusion Criteria:

• Complaining from pain
• Able to read and write

Exclusion Criteria:

• no anomalies

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation between neck disability and emg signals	using logistic regression, we can get this outcome	2 weeks
Correlation between neck disability and range of motion	using logistic regression, we can get this outcome	2 weeks

Collaborators and Investigators

• Sponsor: Kafrelsheikh University

Study record dates

Study Major Dates

• Study Start (Actual): August 12, 2022
• Primary Completion (Actual): March 24, 2023
• Study Completion (Actual): March 24, 2023

Study Registration Dates

• First Submitted: March 10, 2022
• First Submitted That Met QC Criteria: March 20, 2022
• First Posted (Actual): March 22, 2022

Study Record Updates

• Last Update Posted (Actual): April 24, 2023
• Last Update Submitted That Met QC Criteria: April 20, 2023
• Last Verified: April 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Neck Pain
• Other Study ID Numbers: P.T/BAS/1/2022/10

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: the researcher intend to share the code of the analysis with the globe to increase body of knowledge
• IPD Sharing Time Frame: 2 months after data collection finished
• IPD Sharing Access Criteria: all authors and related investigators
• IPD Sharing Supporting Information Type: ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No