AI-Assisted Feedback for Learning Standardized Tuina Skills
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorZhejiang University
About this trial
The goal of this educational study is to determine whether personalized feedback generated using sensor data and generative artificial intelligence (AI) can improve the learning of standardized Tuina skills among rehabilitation trainees. Tuina is a form of manual therapy that requires learners to control the location, force, rhythm, and consistency of their hand movements.
A total of 81 rehabilitation trainees will be randomly assigned to one of three training groups: AI-supported personalized feedback, sensor-based data feedback without AI-generated recommendations, or traditional instructor feedback. All groups will receive the same standardized demonstration, training tasks, practice duration, and number of practice sessions.
The main question is whether trainees receiving AI-supported personalized feedback achieve better retention of standardized Tuina skills four weeks after training. The researchers will also compare immediate skill performance, force and rhythm control, transfer of skills to a related task, learning efficiency, self-efficacy, cognitive load, satisfaction, and the safety and acceptability of AI-generated feedback.
Skill performance will be assessed using a blinded Objective Structured Clinical Examination (OSCE) and objective sensor-based measurements. The study activities will be conducted using a mechanical simulation model and a pressure-sensing system, rather than on patients.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Aged 18 years or older.
Currently enrolled in or receiving training in rehabilitation medicine, physical therapy, rehabilitation therapy, or a related health profession.
Has not previously achieved the predefined competency standard for the standardized Tuina skill evaluated in this study.
Able to understand the study instructions and independently complete the training tasks and study questionnaires.
Disqualifiers
Current acute injury, clinically significant pain, or functional limitation involving the hand, wrist, upper limb, shoulder, neck, or lower back that may interfere with repeated manual skill practice.
Previous systematic training in the same standardized Tuina technique with performance at or above the predefined competency standard.
Any medical, physical, cognitive, or psychological condition that, in the investigator's judgment, may make participation unsafe or prevent valid completion of the study procedures.
Unable to understand the study procedures or provide informed consent.
Trial design
Design model
Parallel
Treatments tested in this trial
AI-Supported Personalized Feedback
BehavioralParticipants will complete four standardized training sessions over two weeks, with each session lasting approximately 45 to 60 minutes. A pressure-sensing system will measure force accuracy, force variability, operating frequency, rhythm stability, and time within the target range. After each practice attempt, a generative AI system will provide feedback using a structured task-gap-action format. The feedback will describe the target task, identify differences between measured performance and the predefined standard, and recommend specific actions for the next practice attempt. AI output will be restricted to educational use and overseen by instructors.
Sensor-Based Data Feedback
BehavioralParticipants will complete four standardized training sessions over two weeks, with each session lasting approximately 45 to 60 minutes. After each practice attempt, participants will view numerical sensor measurements, force-time curves, rhythm information, and predefined target ranges. No generative AI interpretation, personalized action plan, or AI-generated recommendation will be provided.
Traditional Instructor Feedback
BehavioralParticipants will complete four standardized training sessions over two weeks, with each session lasting approximately 45 to 60 minutes. Instructors will observe performance and provide conventional verbal feedback based on the standardized teaching protocol. Participants will not receive sensor-derived performance displays or AI-generated feedback.
Treatment groups
81 Participants
are divided into 3 treatment groupsGroup A: AI-Supported Personalized FeedbackExperimental treatment 1 intervention
Group B: Sensor-Based Data FeedbackActive comparator 1 intervention
Group C: Traditional Instructor FeedbackActive comparator 1 intervention
Trial outcomes
Primary outcomes
1
Standardized Tuina Skill Performance Measured by Total OSCE Score
Participants will complete a standardized Objective Structured Clinical Examination (OSCE) assessing anatomical localization, body mechanics, force control, rhythm, procedural consistency, safety, communication, and overall performance. Coded performance recordings will be independently scored by two assessors who are unaware of group allocation. The mean of the two assessors' total scores will be used for analysis. Higher scores indicate better standardized Tuina skill performance.
Time frame
Four weeks after completion of the training interventionSecondary outcomes
1
Immediate Standardized Tuina Skill Performance Measured by Total OSCE Score
Standardized Tuina skill performance will be independently evaluated by two masked assessors using the same OSCE scoring rubric as the primary outcome. The mean of the two assessors' total scores will be analyzed. Higher scores indicate better performance.
Time frame
Immediately after completion of the training intervention2
Absolute Error From the Target Force
The pressure-sensing system will calculate the absolute difference, in newtons, between the participant's mean applied force and the predefined target force. A lower absolute error indicates greater force accuracy.
Time frame
Baseline, immediately after the intervention, and four weeks after the intervention3
Coefficient of Variation of Applied Force
Force stability will be calculated as the standard deviation of applied force divided by the mean applied force and expressed as a percentage. A lower coefficient of variation indicates more stable force control.
Time frame
Baseline, immediately after the intervention, and four weeks after the intervention4
Absolute Error From the Target Operating Frequency
The absolute difference, in hertz, between the participant's measured operating frequency and the predefined target frequency will be calculated. A lower value indicates greater rhythm accuracy.
Time frame
Baseline, immediately after the intervention, and four weeks after the interventionLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Zhejiang University
Lead sponsor
First Affiliated Hospital of Zhejiang University
Sponsor institution
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: