Development and Evaluation of a Smart Technology-Assisted System for Shoulder Joint Physical Therapy Assessment

NCT ID: NCT07089082

Last Updated: 2025-07-28

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 40 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-07-01
• Study Completion Date: 2026-07-31

Brief Summary

Shoulder pain is one of the most prevalent musculoskeletal conditions. Evidence-based medicine has identified physical therapy as the most effective intervention for managing shoulder disorders. To ensure accurate diagnosis and effective treatment planning, a comprehensive evaluation that integrates various clinical findings is essential. Without timely and accurate diagnosis and intervention, shoulder pain may recur and fail to improve, limiting therapeutic outcomes.

With technological advancements, the application of mobile devices and artificial intelligence (AI) in clinical settings has become increasingly widespread. Motion capture technologies integrated into mobile platforms offer emerging solutions for clinical challenges. If clinicians are equipped with an intelligent system for shoulder assessment and intervention-one that includes image-based quantitative assessment tools, evidence-based clinical guidelines and data repositories, and home-based rehabilitation support-it may enhance diagnostic precision, increase clinical efficiency, and improve patient adherence to home exercise programs.

The aim of this study is to develop a smart technology-assisted assessment system for orthopedic physical therapy of the shoulder joint and to validate its reliability and validity. This system will provide clinicians with objective, data-driven evaluation results. In future development, it will also offer support in treatment goal setting, intervention planning, and home-based exercise guidance. The proposed intelligent system is expected to serve as an evidence-based clinical aid, enhancing both the precision and efficiency of physical therapy interventions.

Conditions

Shoulder

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

healthy

healthy subjects with no any shoulder impairment

No interventions assigned to this group

shoulder impairment

diagnosed by Doctors or Physical Therapists with any type of non-acute shoulder impairment

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• BMI (body mass index) between 18.5-24.9
• self-perceived good physical condition
• no history of shoulder orthopedic disease or nerve damage

• Non-acute shoulder orthopedic diseases diagnosed by orthopedic physicians or assessed by physical therapists
• may include but are not limited to the following diseases: frozen shoulder, shoulder compression syndrome, shoulder rotator injury, shoulder instability, etc.

Exclusion Criteria

• Any neurological disease that may cause pain
• a history of related surgery in the past six months
• acute inflammation of the shoulder joint
• open wounds in the shoulder joint area
• Principle Investor's teaching students, laboratory assistants

• Minimum Eligible Age: 20 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Cheng-Kung University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Ya-Lan Chiu

Assistant Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

National Cheng Kung University

Tainan City, TNN, Taiwan

Countries

Taiwan

Other Identifiers

B-ER-114-178

Identifier Type: -

Identifier Source: org_study_id