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

NCT07089082 | Active Not Recruiting

• 1 other identifier: B-ER-114-178
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

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

Keywords

mHealth; Precision Medicine; Computer Vision; Skeletal Tracking; Deep Learning

Outcome Measures

Primary Outcomes (1)

• Range of Motion

  Shoulder Range of Motion

  Total 3 times. First trail at day1 (after enrollment) Second trail at 1hour after first trial Third trail is at day2

Study Arms (2)

healthy

healthy subjects with no any shoulder impairment

shoulder impairment

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

Eligibility Criteria

• Age: 20 Years - 65 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Healthy population and Shoulder impaired population.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• National Cheng-Kung University Hospital: lead

Study Sites (1)

National Cheng Kung University

Tainan, TNN, 704, Taiwan

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: July 18, 2025
• First Posted: July 28, 2025
• Study Start: July 1, 2025
• Primary Completion (Estimated): May 28, 2026
• Study Completion (Estimated): July 31, 2026
• Last Updated: July 28, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)