Machine-learning multi-sensor fall risk screening for Duchenne muscular dystrophy
Official title: Quantitative Analysis of Upper Extremity Functional Movement of the Upper Extremity in Patients With Duchenne Muscular Dystrophy
Run by Seoul National University Hospital · NCT07015632 · Data updated 2026-11-12
This project will try machine-learning and video/sensor methods to track upper limb movement and see if they can help detect fall risk in children and young adults with Duchenne muscular dystrophy.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 10 Years to 30 Years |
| Sex | All |
| Enrollment | 30 (estimated) |
| Sponsor | Seoul National University Hospital Academic / other |
| Locations | 1 site (Seoul, Jongno) |
| Status | Recruiting |
| Started | July 18, 2024 |
| Main results expected | July 30, 2025 |
| Study ends | December 1, 2026 |
| Registry record updated | June 13, 2025 |
| Trial ID | NCT07015632 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal participants are people aged >10 and <30 years with genetically confirmed DMD, Brooke Scale 2–5, and manual muscle test grade <3 for shoulder abduction who can provide informed consent and complete in-person visits and recordings.
✕Probably not a fit
Patients with Brooke Scale of 1 or 6, significant cognitive impairment that prevents participation, or who cannot or will not consent are unlikely to benefit from this protocol.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a prospective observational study of 30 participants with genetically confirmed Duchenne muscular dystrophy who meet specific age and functional criteria. Participants will undergo clinical tests (PUL 2.0, Brooke Scale, and grip strength) and standardized video recordings at baseline, 6 months, and 12 months. Computer-vision analysis and machine-learning algorithms will be applied to the sensor and video data to quantify changes in upper-extremity function over time. The protocol includes safety monitoring, data confidentiality protections, and compensation provisions in the event of adverse effects.
Why it matters
Potential benefit: If successful, the approach could provide more sensitive, objective ways to monitor upper-limb function and fall risk in non-ambulatory DMD patients, aiding clinical decisions and remote follow-up.
How similar studies have gone: Sensor- and machine-learning approaches have shown promising pilot results in neuromuscular and fall-risk research, but ML-based upper-limb evaluation specifically in non-ambulatory DMD remains relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Individuals with a confirmed genetic diagnosis of Duchenne Muscular Dystrophy (DMD) 2. Age over 10 and under 30 years 3. Brooke Scale score between 2 and 5 4. Manual muscle test grade below 3 for shoulder abduction muscles Exclusion Criteria: 1. Individuals who are unable or unwilling to provide informed consent 2. Brooke Scale score of 1 or 6 3. Individuals with cognitive impairments that significantly limit their ability to participate in assessments
Where it's running
-
Seoul National University Hospital
Seoul, Jongno, South Korea Recruiting
Study contacts
- Study coordinator: JungHyun Kim, prof
- Email: kiking0@naver.com
- Phone: 82+1088632341
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.