Hand Versus Foot Controls for Robotic Surgery Simulation
CLUTCH WAR
Control Location Using Two Commands: Hand Versus Foot, Workload and Accuracy Response Trial: A Randomized Controlled Trial in Robotic Surgery Simulation
1 other identifier
interventional
86
1 country
1
Brief Summary
Why is the study being done? Robotic-assisted surgery offers high precision and ergonomic benefits, but it requires specialized console skills that differ significantly from traditional open or laparoscopic surgery. A fundamental part of operating a robotic surgical system is clutch control, which allows surgeons to reposition their controllers without moving the actual surgical instruments. Inefficient clutching can disrupt workflow, increase mental workload, and lead to operator fatigue. Modern platforms like the Da Vinci Xi offer two main options for clutching: hand-controlled clutching and foot-controlled clutching. Currently, there is limited research isolating how these different clutch options affect performance, especially for novice operators who have no prior robotic experience. This study aims to evaluate and compare hand clutch versus foot clutch methods to determine which approach better supports efficient skill acquisition, precision, and comfort for beginners. What happens during the study? Surgical trainees with no prior robotic surgery experience will be recruited for this study. After completing an initial baseline questionnaire and receiving a standardized orientation on the equipment, all participants will undergo a 20-minute practice session where they get to try both hand and foot clutching mechanisms. One week after the practice session, participants will return for the formal evaluation and be randomly assigned to one of two groups:
- 1.Hand Clutch Group: Participants will complete a standardized simulation task using only the hand clutch button.
- 2.Foot Clutch Group: Participants will complete the exact same simulation task using only the foot clutch pedal.
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 Jun 2026
Shorter than P25 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 25, 2026
CompletedFirst Posted
Study publicly available on registry
June 1, 2026
CompletedStudy Start
First participant enrolled
June 15, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
September 30, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
October 31, 2026
June 2, 2026
May 1, 2026
4 months
May 25, 2026
May 29, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Overall Clutch Simulation Score
The overall clutch simulation score is an automated, composite performance score generated by the Da Vinci Xi surgical simulator hardware. The score is automatically calculated by the system's tracking software as the baseline efficiency score minus specific technical penalty points. The efficiency component incorporates total task completion time and economy of instrument motion. Penalty components include discrete technical errors occurring during task performance, such as instrument collisions, instrument out of view. The final score is recorded as a continuous value ranging from 0 to 100, where a score of 100 represents perfect efficiency with zero recorded penalties, and lower scores indicate lower motion efficiency, slower task completion, or a higher frequency of technical errors.
1 week after enrollment
Secondary Outcomes (7)
Total Task Completion Time
1 week after enrollment
Economy of Motion
1 week after enrollment
Instrument Collisions
1 week after enrollment
Camera Usage
1 week after enrollment
Instrument Out-of-View Events
1 week after enrollment
- +2 more secondary outcomes
Study Arms (2)
Hand Clutch
ACTIVE COMPARATORParticipants will complete a standardized simulation task using only the hand clutch button
Foot Clutch
ACTIVE COMPARATORParticipants will complete the exact same simulation task using only the foot clutch pedal
Interventions
Participants will complete a standardized simulation task using only the hand clutch button
Participants will complete the exact same simulation task using only the foot clutch pedal
Eligibility Criteria
You may qualify if:
- Active surgical trainees (residents or fellows) currently enrolled in a recognized surgical specialty program, including general surgery, obstetrics and gynecology, urology, cardiothoracic surgery or pediatric surgery, at Prince of Songkla University
- Complete absence of prior hands-on experience performing clinical robotic surgery or undergoing formalized robotic simulator training
You may not qualify if:
- Pre-existing physical, orthopedic, or neurological conditions that impair standard motor function of the upper or lower limbs, including active movement disorders or motor control deficits capable of confounding objective simulation performance metrics.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Prince of Songkla University
Hat Yai, Changwat Songkhla, 90110, Thailand
Related Publications (19)
Perez-Salazar MJ, Caballero D, Sanchez-Margallo JA, Sanchez-Margallo FM. Comparative Study of Ergonomics in Conventional and Robotic-Assisted Laparoscopic Surgery. Sensors (Basel). 2024 Jun 14;24(12):3840. doi: 10.3390/s24123840.
PMID: 38931624BACKGROUNDSaqib SU, Bajwa AA. The role of Da Vinci Xi robotic simulation curriculum in enhancing skills in robotic colorectal surgery. Ann Med Surg (Lond). 2023 Sep 20;85(12):6001-6007. doi: 10.1097/MS9.0000000000001342. eCollection 2023 Dec.
PMID: 38098541BACKGROUNDCannata G, Leone N, Salzano A, Rebecchi F, Morino M. Training in the use of basic functions of the daVinci Xi(R) robot: a comparative study of residents' skills. Updates Surg. 2025 Sep;77(5):1673-1682. doi: 10.1007/s13304-025-02150-z. Epub 2025 Mar 15.
PMID: 40088400BACKGROUNDMullens CL, Van Horn AL, Marsh JW, Hogg ME, Thomay AA, Schmidt CR, Boone BA. Development of a Senior Medical Student Robotic Surgery Training Elective. J Med Educ Curric Dev. 2021 Jun 29;8:23821205211024074. doi: 10.1177/23821205211024074. eCollection 2021 Jan-Dec.
PMID: 34263057BACKGROUNDHung AJ, Chen J, Gill IS. Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery. JAMA Surg. 2018 Aug 1;153(8):770-771. doi: 10.1001/jamasurg.2018.1512. No abstract available.
PMID: 29926095BACKGROUNDWile RK, Brian R, Rodriguez N, Chern H, Cruff J, O'Sullivan PS. Home practice for robotic surgery: a randomized controlled trial of a low-cost simulation model. J Robot Surg. 2023 Oct;17(5):2527-2536. doi: 10.1007/s11701-023-01688-7. Epub 2023 Aug 2.
PMID: 37531043BACKGROUNDSridhar AN, Briggs TP, Kelly JD, Nathan S. Training in Robotic Surgery-an Overview. Curr Urol Rep. 2017 Aug;18(8):58. doi: 10.1007/s11934-017-0710-y.
PMID: 28647793BACKGROUNDWong SW, Crowe P. Visualisation ergonomics and robotic surgery. J Robot Surg. 2023 Oct;17(5):1873-1878. doi: 10.1007/s11701-023-01618-7. Epub 2023 May 19.
PMID: 37204648BACKGROUNDRahimi AM, Uluc E, Hardon SF, Bonjer HJ, van der Peet DL, Daams F. Training in robotic-assisted surgery: a systematic review of training modalities and objective and subjective assessment methods. Surg Endosc. 2024 Jul;38(7):3547-3555. doi: 10.1007/s00464-024-10915-7. Epub 2024 May 30.
PMID: 38814347BACKGROUNDSoomro NA, Hashimoto DA, Porteous AJ, Ridley CJA, Marsh WJ, Ditto R, Roy S. Systematic review of learning curves in robot-assisted surgery. BJS Open. 2020 Feb;4(1):27-44. doi: 10.1002/bjs5.50235. Epub 2019 Nov 29.
PMID: 32011823BACKGROUNDCatchpole K, Cohen T, Alfred M, Lawton S, Kanji F, Shouhed D, Nemeth L, Anger J. Human Factors Integration in Robotic Surgery. Hum Factors. 2024 Mar;66(3):683-700. doi: 10.1177/00187208211068946. Epub 2022 Mar 5.
PMID: 35253508BACKGROUNDZhao B, Hollandsworth HM, Lee AM, Lam J, Lopez NE, Abbadessa B, Eisenstein S, Cosman BC, Ramamoorthy SL, Parry LA. Making the Jump: A Qualitative Analysis on the Transition From Bedside Assistant to Console Surgeon in Robotic Surgery Training. J Surg Educ. 2020 Mar-Apr;77(2):461-471. doi: 10.1016/j.jsurg.2019.09.015. Epub 2019 Sep 23.
PMID: 31558428BACKGROUNDHuang Y, Lai W, Cao L, Liu J, Li X, Burdet E, Phee SJ. A Three-Limb Teleoperated Robotic System with Foot Control for Flexible Endoscopic Surgery. Ann Biomed Eng. 2021 Sep;49(9):2282-2296. doi: 10.1007/s10439-021-02766-3. Epub 2021 Apr 8.
PMID: 33834351BACKGROUNDVan't Hullenaar CDP, Mertens AC, Ruurda JP, Broeders IAMJ. Validation of ergonomic instructions in robot-assisted surgery simulator training. Surg Endosc. 2018 May;32(5):2533-2540. doi: 10.1007/s00464-017-5959-1. Epub 2017 Dec 20.
PMID: 29264759BACKGROUNDWalliczek-Dworschak U, Mandapathil M, Fortsch A, Teymoortash A, Dworschak P, Werner JA, Guldner C. Structured training on the da Vinci Skills Simulator leads to improvement in technical performance of robotic novices. Clin Otolaryngol. 2017 Feb;42(1):71-80. doi: 10.1111/coa.12666. Epub 2016 May 15.
PMID: 27133186BACKGROUNDKumar A, Smith R, Patel VR. Current status of robotic simulators in acquisition of robotic surgical skills. Curr Opin Urol. 2015 Mar;25(2):168-74. doi: 10.1097/MOU.0000000000000137.
PMID: 25574791BACKGROUNDCastaldi MT, Palmer M, Con J, Bergamaschi R. Robotic-Assisted Surgery Training (RAST): Assessment of Surgeon Console Ergonomic Skills. J Surg Educ. 2023 Nov;80(11):1723-1735. doi: 10.1016/j.jsurg.2023.08.019. Epub 2023 Sep 26.
PMID: 37770293BACKGROUNDvan der Schatte Olivier RH, Van't Hullenaar CD, Ruurda JP, Broeders IA. Ergonomics, user comfort, and performance in standard and robot-assisted laparoscopic surgery. Surg Endosc. 2009 Jun;23(6):1365-71. doi: 10.1007/s00464-008-0184-6. Epub 2008 Oct 15.
PMID: 18855053BACKGROUNDWong, S. W., & Wong, A. L. (2025). Ergonomic challenges and barriers of robotic surgery-A review. Annals of Laparoscopic and Endoscopic Surgery, 10(0). https://doi.org/10.21037/ales-25-10
BACKGROUND
Related Links
Study Officials
- STUDY CHAIR
Supakool Jearanai, MD
Department of Surgery, Faculty of Medicine, Prince of Songkla University
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- DOUBLE
- Who Masked
- INVESTIGATOR, OUTCOMES ASSESSOR
- Masking Details
- This study utilizes a investigator and outcome assessor masked design to minimize bias during the enrollment, testing administration, and data recording phases: 1. Investigator Masking: Investigators responsible for recruiting, screening, obtaining consent, and providing the initial pre-simulation orientation remain masked to group assignments. Participants are handed a sealed, opaque envelope containing their random group allocation before entering the isolated simulation room. The envelope is opened only by the participant once the doors are closed and they are alone in the room, ensuring the investigator has no knowledge of whether the participant is using the hand clutch or foot clutch modality. 2. Outcome Assessor Masking: The simulation task is performed entirely in isolation. Once the participant completes the simulation and exits the room, the outcome assessor enters the room to record the objective performance metrics directly from the simulator software report screen. Becaus
- Purpose
- OTHER
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Assistant Professor
Study Record Dates
First Submitted
May 25, 2026
First Posted
June 1, 2026
Study Start
June 15, 2026
Primary Completion (Estimated)
September 30, 2026
Study Completion (Estimated)
October 31, 2026
Last Updated
June 2, 2026
Record last verified: 2026-05
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, ANALYTIC CODE
- Time Frame
- The identified IPD will be shared after completion of the study in GitHub. There is no end date.
- Access Criteria
- Anyone.
De-identified individual participant data will be made available via a dedicated GitHub repository upon publication.