title: An effective repetitive training schedule to achieve skill proficiency using a novel robotic virtual reality simulator.
abstract: OBJECTIVE
  A robotic virtual reality simulator (Mimic dV-Trainer) can be a useful training method for the da Vinci surgical system. Herein, we investigate several repetitive training schedules and determine which is the most effective.
  
  
  METHODS
  A total of 30 medical students were enrolled and were divided into 3 groups according to the training schedule. Group 1 performed the task 1 hour daily for 4 consecutive days, group II performed the task on once per week for 1 hour for 4 consecutive weeks, and group III performed the task for 4 consecutive hours in 1 day. The effects of training were investigated by analyzing the number of repetitions and the time required to complete the "Tube 2" simulation task when the learning curve plateau was reached. The point at which participants reached a stable score was evaluated using the cumulative sum control graph.
  
  
  RESULTS
  The average time to complete the task at the learning curve plateau was 150.3 seconds in group I, 171.9 seconds in group II, and 188.5 seconds in group III. The number of task repetitions required to reach the learning curve plateau was 45 repetitions in group I, 36 repetitions in group II, and 39 repetitions in group III. Therefore, there was continuous improvement in the time required to perform the task after 40 repetitions in group I only. There was a significant correlation between improvement in each trial interval and attempt, and the correlation coefficient (0.924) in group I was higher than that in group II (0.899) and group III (0.838).
  
  
  CONCLUSIONS
  Daily 1-hour practice sessions performed for 4 consecutive days resulted in the best final score, continuous score improvement, and effective training while minimizing fatigue. This repetition schedule can be used for effectively training novices in future.
authors.affiliation: Department of Urology, Korea University School of Medicine, Seoul, Republic of Korea.
authors.forename: Sung Gu
authors.initials: SG
authors.lastname: Kang
authors.affiliation: Department of Rehabilitation Medicine, SahmYook Medical Center, Seoul, Republic of Korea.
authors.forename: Byung Ju
authors.initials: BJ
authors.lastname: Ryu
authors.affiliation: Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
authors.forename: Kyung Sook
authors.initials: KS
authors.lastname: Yang
authors.affiliation: Department of Urology, Yeungnam University College of Medicine, Daegu, Republic of Korea.
authors.forename: Young Hwii
authors.initials: YH
authors.lastname: Ko
authors.affiliation: Department of Urology, Korea University School of Medicine, Seoul, Republic of Korea.
authors.forename: Seok
authors.initials: S
authors.lastname: Cho
authors.affiliation: Department of Urology, Korea University School of Medicine, Seoul, Republic of Korea.
authors.forename: Seok Ho
authors.initials: SH
authors.lastname: Kang
authors.affiliation: Department of Urology, The Global Robotics Institute, Florida Hospital Celebration Health, Celebration, Florida; University of Central Florida School of Medicine, Orlando, Florida.
authors.forename: Vipul R
authors.initials: VR
authors.lastname: Patel
authors.affiliation: Department of Urology, Korea University School of Medicine, Seoul, Republic of Korea. Electronic address: jcheon@korea.ac.kr.
authors.forename: Jun
authors.initials: J
authors.lastname: Cheon
mesh_terms: D002983:Clinical Competence; D004504:Education, Medical, Undergraduate / Q000379:methods*; D005260:Female; D006801:Humans; D010535:Laparoscopy / Q000193:education*; D059032:Learning Curve; D008297:Male; D011446:Prospective Studies; D012371:Robotics / Q000193:education*; D000066908:Simulation Training; D013997:Time Factors; D014584:User-Computer Interface; D055815:Young Adult
keywords: Medical Knowledge; Patient Care; Practice-Based Learning and Improvement; education; learning curve; robotics; simulator