AI-Assisted Feedback for Learning Standardized Tuina Skills
AI-TUINA
Effects of Sensor- and Generative AI-Supported Personalized Feedback on the Acquisition of Standardized Tuina Skills Among Rehabilitation Trainees: A Randomized Controlled Trial
1 other identifier
interventional
81
0 countries
N/A
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for not_applicable
Started Aug 2026
Typical duration for not_applicable
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
August 12, 2026
CompletedStudy Start
First participant enrolled
August 13, 2026
CompletedFirst Posted
Study publicly available on registry
August 17, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 1, 2027
ExpectedStudy Completion
Last participant's last visit for all outcomes
August 1, 2028
August 17, 2026
August 1, 2026
9 months
August 12, 2026
August 12, 2026
Conditions
Keywords
Outcome Measures
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.
Four weeks after completion of the training intervention
Secondary Outcomes (8)
Immediate Standardized Tuina Skill Performance Measured by Total OSCE Score
Immediately after completion of the training intervention
Absolute Error From the Target Force
Baseline, immediately after the intervention, and four weeks after the intervention
Coefficient of Variation of Applied Force
Baseline, immediately after the intervention, and four weeks after the intervention
Absolute Error From the Target Operating Frequency
Baseline, immediately after the intervention, and four weeks after the intervention
Proportion of Practice Time Within the Target Performance Range
Baseline, immediately after the intervention, and four weeks after the intervention
- +3 more secondary outcomes
Study Arms (3)
AI-Supported Personalized Feedback
EXPERIMENTALParticipants will receive standardized Tuina instruction and practice using a mechanical simulation model equipped with a pressure-sensing system. After each practice attempt, participants will receive objective sensor measurements and personalized, actionable feedback generated by a generative AI system under instructor supervision.
Sensor-Based Data Feedback
ACTIVE COMPARATORParticipants will receive the same standardized Tuina instruction, practice tasks, number of practice opportunities, and training duration as the experimental group. After each practice attempt, participants will receive sensor-generated numerical measurements, performance curves, and target ranges, without AI-generated interpretation or personalized recommendations.
Traditional Instructor Feedback
ACTIVE COMPARATORParticipants will receive the same standardized Tuina instruction, practice tasks, number of practice opportunities, and training duration as the other groups. Feedback will be provided through conventional instructor observation and verbal guidance. Participants will not view sensor-generated performance data or AI-generated recommendations.
Interventions
Participants 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.
Participants 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.
Participants 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.
Eligibility Criteria
You may qualify if:
- 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.
- Able and willing to attend all scheduled training sessions and the four-week follow-up assessment.
- Willing to participate voluntarily and provide written informed consent.
You may not qualify if:
- 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.
- Direct involvement in the design, randomization, intervention delivery, outcome assessment, data management, or statistical analysis of this study.
- Concurrent participation in another training study that may substantially affect performance of the standardized Tuina skill.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Officials
- PRINCIPAL INVESTIGATOR
Xing-Chen Zhou
The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Shangcheng District, Hangzhou, Zhejiang
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- SINGLE
- Who Masked
- OUTCOMES ASSESSOR
- Masking Details
- Participants and instructors cannot be masked because of the nature of the educational interventions. OSCE performance recordings will be coded and independently evaluated by assessors who are unaware of group allocation and do not participate in intervention delivery. Group identities will be represented by coded labels during the primary statistical analysis.
- Purpose
- OTHER
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Prof.
Study Record Dates
First Submitted
August 12, 2026
First Posted
August 17, 2026
Study Start
August 13, 2026
Primary Completion (Estimated)
May 1, 2027
Study Completion (Estimated)
August 1, 2028
Last Updated
August 17, 2026
Record last verified: 2026-08
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP, ICF, ANALYTIC CODE
- Time Frame
- Beginning 12 months after publication of the primary study results and remaining available for five years.
- Access Criteria
- Data will be available to qualified researchers who submit a methodologically sound research proposal. Requests will be reviewed by the principal investigator and the responsible institutional research team. Approved requesters must sign a data use agreement, use the data only for the approved purpose, protect participant confidentiality, and agree not to attempt participant re-identification. Additional ethics approval may be required depending on the proposed use and applicable institutional policies.
De-identified individual participant data underlying the results reported in the primary publication will be considered for sharing. The shared dataset may include participant demographic and educational characteristics, randomized group assignment, assessment time points, OSCE scores, derived sensor-based performance measures, questionnaire scores, and adverse event data. Direct identifiers, the participant identification key, facial images, audio recordings, raw performance videos, identifiable free-text responses, and raw AI interaction logs will not be shared.