Degenerative Lumbar Spine DiseasesNCT07133724
Digital Health for Lumbar Degeneration
A study testing AI-Based Smart Assessment and Rehabilitation Training in people with Degenerative Lumbar Spine Diseases, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
This study will integrate wireless wearable sensors, smartphone imaging, and multimodal artificial intelligence (AI) to address the rehabilitation needs of patients with lumbar degeneration. Read more about this study
- Who is it for?
- Ages 50 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Degenerative Lumbar Spine Diseases
- What is being tested?
- AI-Based Smart Assessment and Rehabilitation Training
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Aug 2025to 31 Jul 2028
- Phase
- Interventional
- 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 50 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age between 50-80 years to capture the typical characteristics of lumbar degeneration in this age group.
- No history of low back pain lasting more than one week or severe enough to interrupt work within the past year.
- Normal lumbar functional mobility.
- Ability to walk independently for more than 10 meters.
Who cannot join
- Presence of systemic joint diseases such as ankylosing spondylitis, rheumatoid arthritis, or multiple sclerosis, which may significantly affect lumbar mobility and gait patterns.
- Central nervous system disorders (e.g., spinal cord injury, stroke, or Parkinson's disease) that may influence gait and motor control.
- Vestibular system disorders, to avoid balance abnormalities interfering with gait testing.
- History of spinal or lower limb surgery, as postoperative changes may affect the accuracy of gait data.
- Inability to communicate or follow instructions.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Hospital
Taipei, 100
Wei-Li Hsu, Ph.D., 886-2-3366-8127, wlhsu@ntu.edu.tw
About this study
Official title: Digital Health for Aging: A Multimodal AI-Based Smart Assessment and Rehabilitation Training System for Lumbar Degeneration
This study will integrate wireless wearable sensors, smartphone imaging, and multimodal artificial intelligence (AI) to address the rehabilitation needs of patients with lumbar degeneration. Patients will undergo comprehensive functional assessments, and individualized exercise instruction with real-time feedback will be provided through a smartphone application. The goals of this research are to: (1) develop a multimodal AI-based digital health system combining IMU sensors and smartphone cameras for real-time assessment and interactive rehabilitation training, (2) construct biomechanics- and gait-analysis models to support personalized rehabilitation for patients with lumbar degeneration, and (3) investigate the mechanisms and clinical efficacy of pelvic control exercise training combined with real-time smartphone feedback in improving function and quality of life for aging patients.
Read the detailed description
The multimodal AI-based smart assessment and rehabilitation training system developed in this study will provide patients with lumbar degeneration a convenient and precise home-based rehabilitation solution. Through the integration of wireless inertial sensors and smartphone imaging, the system can monitor pelvic and lumbar movements in real time, generate a digital twin model, and deliver instant feedback to guide patients in performing correct exercises. This design not only improves patients' self-awareness of posture and movement but also reduces the risk of improper compensatory strategies that often occur in traditional home exercise programs.
The system is particularly suitable for older adults with mobility limitations or those who have difficulties frequently visiting medical institutions. By enabling remote assessment, individualized training, and long-term monitoring, this platform ensures continuity of care and enhances patients' motivation to engage in rehabilitation. The outcomes of this project will establish a tele-rehabilitation system tailored to degenerative lumbar spine disease, support clinicians in delivering precise and effective treatment, and ultimately reduce the healthcare and economic burden on families and society.
- Conditions
- Degenerative Lumbar Spine Diseases
- Being tested
- AI-Based Smart Assessment and Rehabilitation Training
- Keywords
- degenerative lumbar spine disease, real-time detection, artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Smart Assessment and Rehabilitation TrainingOther
Through the integration of wireless inertial sensors and smartphone imaging, the system can monitor pelvic and lumbar movements in real time, generate a digital twin model, and deliver instant feedback to guide patients in performing correct exercises.
| Study group | Role | What they receive |
|---|---|---|
| AI-Based Smart Assessment and Rehabilitation Training | Experimental | The multimodal AI-based smart assessment and rehabilitation training system developed in this study will provide patients with lumbar degeneration a convenient and precise home-based rehabilitation solution. Receives: Other: AI-Based Smart Assessment and Rehabilitation Training |
What is being measured
The primary outcome is the main question the study is designed to answer.
Functional assessment: Walking speed
Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking speed (unit: m/s).
Measured: 6 months
Functional assessment: Walking distance
Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking distance (unit: m).
Measured: 6 months
Functional assessment: 5 Times Sit to Stand Test
Functional assessment is a process that allows for the identification of disability. The data from the 5 Times Sit to Stand Test is used to calculate the duration it took to complete the test (unit: s).
Measured: 6 months
Secondary outcomes (2)
Kinematic variables: Joint angles
A motion capture system is used to measure the joint kinematics. The data is used to calculate joint angles (unit: degree).
Measured: 6 months
Kinetic variables
A motion capture system is used to measure the joint kinetics. The data is used to calculate joint moments (unit: Nm)
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Aug 2025
- Study start1 Aug 2025
- Primary completion31 Jul 2028 (estimated)
- Study completion31 Jul 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Degenerative Lumbar Spine DiseasesStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07133724
- Sponsor's protocol ID
- 202502072RIND
- Status last verified
- Aug 2025
- First submitted
- 14 Aug 2025
- Last updated
- 21 Aug 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Digital Health for Aging: A Multimodal AI-Based Smart Assessment and Rehabilitation Training System for Lumbar Degeneration
- ClinicalTrials.gov ID
- NCT07133724
- Acronym / sponsor ID
- 202502072RIND
- Conditions
- Degenerative Lumbar Spine Diseases
- Keywords
- degenerative lumbar spine disease, real-time detection, artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 1 Aug 2025 (actual)
- Primary completion
- 31 Jul 2028 (estimated)
- Study completion
- 31 Jul 2028 (estimated)
- First posted
- 21 Aug 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.
This study will integrate wireless wearable sensors, smartphone imaging, and multimodal artificial intelligence (AI) to address the rehabilitation needs of patients with lumbar degeneration. Patients will undergo comprehensive functional assessments, and individualized exercise instruction with real-time feedback will be provided through a smartphone application. The goals of this research are to: (1) develop a multimodal AI-based digital health system combining IMU sensors and smartphone cameras for real-time assessment and interactive rehabilitation training, (2) construct biomechanics- and gait-analysis models to support personalized rehabilitation for patients with lumbar degeneration, and (3) investigate the mechanisms and clinical efficacy of pelvic control exercise training combined with real-time smartphone feedback in improving function and quality of life for aging patients.
Detailed description
The multimodal AI-based smart assessment and rehabilitation training system developed in this study will provide patients with lumbar degeneration a convenient and precise home-based rehabilitation solution. Through the integration of wireless inertial sensors and smartphone imaging, the system can monitor pelvic and lumbar movements in real time, generate a digital twin model, and deliver instant feedback to guide patients in performing correct exercises. This design not only improves patients' self-awareness of posture and movement but also reduces the risk of improper compensatory strategies that often occur in traditional home exercise programs.
The system is particularly suitable for older adults with mobility limitations or those who have difficulties frequently visiting medical institutions. By enabling remote assessment, individualized training, and long-term monitoring, this platform ensures continuity of care and enhances patients' motivation to engage in rehabilitation. The outcomes of this project will establish a tele-rehabilitation system tailored to degenerative lumbar spine disease, support clinicians in delivering precise and effective treatment, and ultimately reduce the healthcare and economic burden on families and society.
Study design
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-Based Smart Assessment and Rehabilitation Training | Experimental | Other: AI-Based Smart Assessment and Rehabilitation TrainingThe multimodal AI-based smart assessment and rehabilitation training system developed in this study will provide patients with lumbar degeneration a convenient and precise home-based rehabilitation solution. |
Who may qualify
- Ages
- 50 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Age between 50-80 years to capture the typical characteristics of lumbar degeneration in this age group.
- No history of low back pain lasting more than one week or severe enough to interrupt work within the past year.
- Normal lumbar functional mobility.
- Ability to walk independently for more than 10 meters.
Key exclusion criteria (5 listed)
- Presence of systemic joint diseases such as ankylosing spondylitis, rheumatoid arthritis, or multiple sclerosis, which may significantly affect lumbar mobility and gait patterns.
- Central nervous system disorders (e.g., spinal cord injury, stroke, or Parkinson's disease) that may influence gait and motor control.
- Vestibular system disorders, to avoid balance abnormalities interfering with gait testing.
- History of spinal or lower limb surgery, as postoperative changes may affect the accuracy of gait data.
- Inability to communicate or follow instructions.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Smart Assessment and Rehabilitation TrainingThrough the integration of wireless inertial sensors and smartphone imaging, the system can monitor pelvic and lumbar movements in real time, generate a digital twin model, and deliver instant feedback to guide patients in performing correct exercises. | Other | AI-Based Smart Assessment and Rehabilitation Training |
Primary outcome
Functional assessment: Walking speed
Time frame: 6 months
Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking speed (unit: m/s).
Functional assessment: Walking distance
Time frame: 6 months
Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking distance (unit: m).
Functional assessment: 5 Times Sit to Stand Test
Time frame: 6 months
Functional assessment is a process that allows for the identification of disability. The data from the 5 Times Sit to Stand Test is used to calculate the duration it took to complete the test (unit: s).
2 secondary outcomes
Kinematic variables: Joint angles
Time frame: 6 months
Kinetic variables
Time frame: 6 months
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 21 Aug 2025.
Related trials for Degenerative Lumbar Spine Diseases
Recruiting
Post-Market Clinical Study of Functional Motion Outcomes in Subjects Treated With the Minuteman Device
Degenerative Conditions of the Lumbar Spine, Lumbar Spinal Stenosis
Active, not recruiting
Post Marketing Study of MagnetOs Easypack Putty Standalone Compared to Demineralized Bone Matrix or Fibers Mixed With Autograft in Patients Undergoing Posterolateral Lumbar Fusion
Degenerative Disc Disease, Spine Fusion
Recruiting
The NOTICE Study: Neurosurgery and OrThopedIcs Communication Evaluation Study Following Lumbar Fusions
Lumbar Spine Degeneration, Lumbar Spine Instability
Not yet recruitingObservational study
Relationship Between Disability, Flexibility, Back Muscles, and Thoracolumbar Fascia in Patients With Lumbar Disc Degeneration
Intervertebral Disc Degeneration
RecruitingObservational study
Thoracic-Lumbar Arthrodesis- Implanet Jazz
Spondylolisthesis, Spinal Stenosis
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Based Smart Assessment and Rehabilitation Training in people with Degenerative Lumbar Spine Diseases, run by National Taiwan 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 4 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: looking for participants now, as of the sponsor's last update on 21 Aug 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?
Contact Wei-Li Hsu, Ph.D. (886-2-3366-8127, wlhsu@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Functional assessment: Walking speed (time frame: 6 months). The study lists 3 primary outcomes in all. 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 21 Aug 2025.