Digital Health for Lumbar Degeneration
ConditionDegenerative Lumbar Spine Diseases
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age50-80
SponsorNational Taiwan University Hospital
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial design
Design model
Single group
Treatments tested in this trial
AI-Based Smart Assessment and Rehabilitation Training
Other interventionThrough 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.
Treatment groups
100 Participants
are divided into 1 treatment groupGroup A: AI-Based Smart Assessment and Rehabilitation TrainingExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
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).
Time frame
6 months2
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).
Time frame
6 months3
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).
Time frame
6 monthsSecondary outcomes
1
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).
Time frame
6 months2
Kinetic variables
A motion capture system is used to measure the joint kinetics. The data is used to calculate joint moments (unit: Nm)
Time frame
6 monthsSponsors and contacts
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Source ClinicalTrials.gov