Pamukkale University

Objective: The aim of this study is to investigate the effect of motor imagery on muscle activity, pain, and function in arthroscopic rotator cuff repair.
Methods: As a result of the power analysis (G-Power), 36 participants are planned to be included in this study Block randomization will be used to divide participants into 2 groups, each with at least 18 participants: Group 1 (MI group) and Group 2 (Control group) (Randomizer.org). Both groups will receive a 4-week physical therapy program. MI (Motor Imagination) group will receive a motor imagination program in addition to the physical therapy program. Data will collect using the visual analog scale (VAS), goniometric measurement, circumference measurement, Disabilities of the Arm, Shoulder, and Hand Questionnaire (DASH), Kinesthetic and Visual Imagery Questionnaire- KVIQ-20, Tampa Kinesiophobia Scale, 3-question satisfaction questionnaire, superficial Electromyography (EMG) (BTS Bioengineering Free EMG 100 RT).
Practice Implications: The current study will contribute to understanding how motor imagination affects muscle activity and muscle atrophy.

The aim of this clinical study was to investigate the effects of different electrotherapy interventions on pain and hand function in young individuals with neck pain.
The main question that the study aimed to answer is: are two different electrotherapy interventions superior to each other for pain and hand function? Participants will be assigned to either transcutaneous electrical nerve stimulation, high-voltage pulsed galvanic stimulation, or placebo. Participants will receive a total of 20 sessions of electrotherapy intervention, 20 minutes per day, 3 days a week. Participants will be evaluated before the intervention, after 1 session, and 1 week after the completion of the 20-session intervention.

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.