ShoulderNCT07089082
Development and Evaluation of a Smart Technology-Assisted System for Shoulder Joint Physical Therapy Assessment
An observational study of Shoulder, run by National Cheng-Kung University Hospital at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Shoulder
- How many people
- 40 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Jul 2025to 31 Jul 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Healthy population and Shoulder impaired population.
Who may be able to join
- 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.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| healthy | healthy subjects with no any shoulder impairment | |
| shoulder impairment | diagnosed by Doctors or Physical Therapists with any type of non-acute shoulder impairment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Range of Motion
Shoulder Range of Motion
Measured: Total 3 times. First trail at day1 (after enrollment) Second trail at 1hour after first trial Third trail is at day2
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jul 2025
- Study start1 Jul 2025
- Primary completion28 May 2026 (estimated)
- Study completion31 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ShoulderStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Cheng-Kung University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ya-Lan Chiu, National Cheng-Kung University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07089082
- Sponsor's protocol ID
- B-ER-114-178
- Status last verified
- Jul 2025
- First submitted
- 18 Jul 2025
- Last updated
- 28 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Evaluation of a Smart Technology-Assisted System for Shoulder Joint Physical Therapy Assessment
- ClinicalTrials.gov ID
- NCT07089082
- Acronym / sponsor ID
- B-ER-114-178
- Conditions
- Shoulder
- Keywords
- mHealth, Precision Medicine, Computer Vision, Skeletal Tracking, Deep Learning
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- National Cheng-Kung University HospitalUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 28 May 2026 (estimated)
- Study completion
- 31 Jul 2026 (estimated)
- First posted
- 28 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| healthy | – | –healthy subjects with no any shoulder impairment |
| shoulder impairment | – | –diagnosed by Doctors or Physical Therapists with any type of non-acute shoulder impairment |
Who may qualify
- Ages
- 20 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Healthy population and Shoulder impaired population.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Range of Motion
Time frame: Total 3 times. First trail at day1 (after enrollment) Second trail at 1hour after first trial Third trail is at day2
Shoulder Range of Motion
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Jul 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Shoulder, run by National Cheng-Kung University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 28 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Range of Motion (time frame: Total 3 times. First trail at day1 (after enrollment) Second trail at 1hour after first trial Third trail is at day2). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Jul 2025.