Low Back Pain
Conditions
Brief summary
Low back pain (LBP) is a common musculoskeletal problem that is frequently encountered in the population and can occur at any age. Responsible for the loss of a full healthy year in both the 10-24 and 50-74 age groups, LBP causes significant personal and social losses and increases healthcare costs. In the classification of low back pain, pain that persists for up to 6 weeks is defined as acute, pain that lasts between 6-12 weeks is subacute, and pain that persists for more than 12 weeks is considered chronic low back pain (CLBP). Chronic LBP (CLBP) leads to fear of movement, causing patients to limit their daily activities and social participation to avoid pain. A sedentary lifestyle in LBP patients is a factor that contributes to the chronicity of the disease. While most acute LBP patients recover well within a few weeks or months, the prognosis for patients with chronic low back pain is generally poor. Approximately one-quarter of patients visiting primary care facilities develop chronic LBP. Therefore, identifying the risk factors for chronic LBP, understanding the population at risk of developing chronic LBP, identifying high-risk individuals, and implementing appropriate preventive and therapeutic measures are important. Several musculoskeletal problems have played a role as risk factors in the development of LBP, and identifying and validating these risk factors can provide a potential mechanism through which LBP can be effectively treated. Accurately identifying musculoskeletal problems and risk factors can provide a mechanism to prevent the development of LBP and reduce the socioeconomic burden associated with the condition. Machine learning (ML) is a scientific discipline that uses computer algorithms to identify patterns in large amounts of data and make predictions on new datasets based on these patterns. ML creates models to predict unknown data from historical data and allows us to select the most appropriate algorithm. Additionally, ML algorithms can extract variables that contribute to the prediction of the target variable, and differ from traditional statistical methods in enhancing the accuracy of future data predictions. ML has shown excellent performance in increasing the predictive value of medical imaging and postoperative clinical outcomes. The aim of this study is to compare the joint range of motion in patients with low back pain and healthy individuals, and to detect differences in these ranges using artificial intelligence-supported analysis methods.
Interventions
DIAGNOSTIC_TESTAssessment of Joint Range of Motion
Joint range of motion (ROM) measurements will be conducted to assess the specific ranges of motion of participants' joints.
Sponsors
Pamukkale University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
Inclusion criteria
* Ages 18-65 * Individuals who have experienced lower back pain for at least 3 months * Individuals who have consulted a doctor at least once due to lower back pain * Individuals who agree to participate in the study and have signed the informed consent form
Exclusion criteria
* Individuals who have undergone surgical intervention due to lower back pain * Individuals with acute traumatic injuries * Individuals with neurological or systemic diseases unrelated to the musculoskeletal system * Individuals who have received physiotherapy or surgical treatment for lower back pain in the last three months * Pregnancy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pain Evaluation | 5 minute | This outcome measure assesses changes in pain levels using the Visual Analog Scale (VAS), a validated tool for pain evaluation. Pain intensity will be recorded by having participants rate their pain on a scale from 0 (no pain) to 10 (worst possible pain). Pain assessments will be conducted at baseline and follow-up intervals to track changes in reported pain intensity over time. The VAS provides a quantitative measurement of pain that is sensitive to minor changes, allowing for accurate monitoring of treatment effectiveness or condition progression. |
| Joint Range of Motion (ROM) | 20 minutes | The primary outcome for this clinical study is the evaluation of Joint Range of Motion (ROM) in degrees (°), measured using the Halo digital goniometer. The assessment will include both active and passive ROM for major joints: shoulder, elbow, wrist, neck, lumbar spine, hip, knee, and ankle. Each joint's ROM will be recorded in degrees to provide a quantitative measure of flexibility and mobility. Data will be analyzed against normative values to determine any deviation or restriction in movement. |
Countries
Turkey (Türkiye)
Contacts
Primary ContactSeref Duhan Altug
Outcome results
None listed