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Kang 2015

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An Effective Repetitive Training Schedule to Achieve Skill Proficiency Using a Novel Robotic Virtual Reality Simulator

Sung Gu Kang PhD, Byung Ju Ryu MD, Kyung Sook Yang PhD, Young Hwii Ko PhD, Seok Cho MD, Seok Ho Kang PhD, Vipul R.Patel MD,Jun Cheon PhD

Purpose

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.

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