Clinical Research Directory

Corrective Multisport Training

Background: Postural deviations of the shoulders, pelvis and spine are common in preschool and school-aged children and may contribute to musculoskeletal imbalance and pain. Corrective exercise and organised sport have each been shown to benefit posture, but the effect of combining them in a single programme has not been examined. Aim: To determine the effect of a nine-month corrective multisport (CMS) training programme on upper-body posture in healthy preschool and school-aged children. Methods: In a single-centre, parallel-group randomised controlled trial, 398 healthy children were randomised (1:1, block randomisation stratified by sex) to a corrective multisport (CMS) group or a control (CON) group. The CMS group completed two 60-minute sessions per week for nine months, combining corrective strength and stretching exercises with multisport activities (soccer, basketball, handball, tennis). The CON group continued regular physical education. Upper-body posture was assessed before and after the intervention with the Contemplas three-dimensional (3D) photogrammetric system (Templo software) by assessors blinded to group allocation.

Validity and Reliability of the DeepClinic Posture Assessment Application

Posture assessment is widely used in sports medicine, physiotherapy, and rehabilitation to identify body alignment problems and support clinical decision-making. Mobile applications have become increasingly popular because they are fast, inexpensive, and easy to use. However, these applications must be shown to provide accurate and reliable measurements before they can be recommended for clinical or sports use. The purpose of this observational study is to evaluate the validity and reliability of the DeepClinic mobile application for posture assessment in physically active adults. Approximately 50 participants will undergo standardized posture photography from the front, back, and side views. The images will be analyzed using both the DeepClinic application and the validated APECS posture analysis system. The agreement between the two systems will be examined to determine validity. To evaluate test-retest reliability, posture assessments will be repeated after one week under the same standardized conditions. No treatment or intervention will be provided as part of this study. The findings are expected to provide scientific evidence regarding the accuracy and reliability of the DeepClinic application and its potential use as a practical posture assessment tool in clinical practice, sports science, and rehabilitation.

Artificial Intelligence-Based Postural Feedback for Scoliosis

Due to advancements in technology, Posture-Analyzing and Virtual Reconstructing (PAViR) systems were developed. They used Red-Green-Blue-Depth camera sensors and artificial intelligence to evaluate human posture alignment, muscle balance, and movement without the use of radiation or physical markers. This technology served as a portable, X-ray-like device that was safe and user-friendly, providing detailed views of bone and muscle models alongside easy-to-understand 3D visualizations. However, how to utilize the data obtained from PAViR to support traditional treatment remained unclear. To fill this gap, this study aimed to investigate the effects of AI-based postural feedback on posture correction in patients with idiopathic scoliosis.

Relationship Between Forward Head Posture and Sagittal Lumbopelvic Alignment Among Smartphone Users

Forward head posture is a common postural abnormality characterized by anterior positioning of the head in the sagittal plane. This posture may increase biomechanical stress on the cervical spine and may be associated with compensatory changes in spinal and lumbopelvic alignment. This observational study will investigate the relationship between forward head posture and sagittal lumbopelvic alignment among male smartphone users aged 18 to 35 years. Craniovertebral angle will be measured using the APECS mobile application, and sagittal lumbopelvic alignment parameters will be measured using the Spine3D LiDAR system.