Digital Health for Lumbar Degeneration

NCT ID: NCT07133724

Last Updated: 2025-08-21

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 100 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-08-01
• Study Completion Date: 2028-07-31

Brief 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. 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.

Conditions

Degenerative Lumbar Spine Diseases

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

AI-Based Smart Assessment and Rehabilitation Training

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.

Group Type: EXPERIMENTAL

AI-Based Smart Assessment and Rehabilitation Training

Intervention Type: OTHER

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.

Interventions

AI-Based Smart Assessment and Rehabilitation Training

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Age between 50-80 years to capture the typical characteristics of lumbar degeneration in this age group.

2. No history of low back pain lasting more than one week or severe enough to interrupt work within the past year.

3. Normal lumbar functional mobility.

4. Ability to walk independently for more than 10 meters.

Exclusion Criteria

1. Presence of systemic joint diseases such as ankylosing spondylitis, rheumatoid arthritis, or multiple sclerosis, which may significantly affect lumbar mobility and gait patterns.

2. Central nervous system disorders (e.g., spinal cord injury, stroke, or Parkinson's disease) that may influence gait and motor control.

3. Vestibular system disorders, to avoid balance abnormalities interfering with gait testing.

4. History of spinal or lower limb surgery, as postoperative changes may affect the accuracy of gait data.

5. Inability to communicate or follow instructions.

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Taiwan University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

National Taiwan University Hospital

Taipei, , Taiwan

Site Status: RECRUITING

Countries

Taiwan

Central Contacts

Wei-Li Hsu, Ph.D.

Role: CONTACT

wlhsu@ntu.edu.tw

886-2-3366-8127

Facility Contacts

Wei-Li Hsu, Ph.D.

Role: primary

wlhsu@ntu.edu.tw

886-2-3366-8127

Other Identifiers

202502072RIND

Identifier Type: -

Identifier Source: org_study_id