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Updates in Surgery

, Volume 70, Issue 1, pp 113–119 | Cite as

Face and content validity of Xperience™ Team Trainer: bed-side assistant training simulator for robotic surgery

  • Luca Sessa
  • Cyril Perrenot
  • Song Xu
  • Jacques Hubert
  • Laurent Bresler
  • Laurent Brunaud
  • Manuela Perez
Original Article

Abstract

In robotic surgery, the coordination between the console-side surgeon and bed-side assistant is crucial, more than in standard surgery or laparoscopy where the surgical team works in close contact. Xperience™ Team Trainer (XTT) is a new optional component for the dv-Trainer® platform and simulates the patient-side working environment. We present preliminary results for face, content, and the workload imposed regarding the use of the XTT virtual reality platform for the psychomotor and communication skills training of the bed-side assistant in robot-assisted surgery. Participants were categorized into “Beginners” and “Experts”. They tested a series of exercises (Pick & Place Laparoscopic Demo, Pick & Place 2 and Team Match Board 1) and completed face validity questionnaires. “Experts” assessed content validity on another questionnaire. All the participants completed a NASA Task Load Index questionnaire to assess the workload imposed by XTT. Twenty-one consenting participants were included (12 “Beginners” and 9 “Experts”). XTT was shown to possess face and content validity, as evidenced by the rankings given on the simulator’s ease of use and realism parameters and on the simulator’s usefulness for training. Eight out of nine “Experts” judged the visualization of metrics after the exercises useful. However, face validity has shown some weaknesses regarding interactions and instruments. Reasonable workload parameters were registered. XTT demonstrated excellent face and content validity with acceptable workload parameters. XTT could become a useful tool for robotic surgery team training.

Keywords

Xperience Team Trainer First-assistant training Bed-assistant training dv-Trainer Robotic surgery simulation Surgical education 

Notes

Acknowledgements

The authors would like to thank all the participants in the study, Ecole de Chirurgie de Nancy-Lorraine and its staff.

Author contributions

Study conception and design: LS, CP, MP. Acquisition of the data: LS, CP, SX. Analysis and interpretation of data: LS, SX, CP, LB, LB. Drafting of manuscript: LS, CP, MP, JH. Critical revision of manuscript: JH, LB, LB.

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest or financial ties to disclose.

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study formal consent is not required.

References

  1. 1.
    Liu M, Curet M (2015) A review of training research and virtual reality simulators for the da Vinci surgical system. Teach Learn Med 27:12–26CrossRefPubMedGoogle Scholar
  2. 2.
    Abboudi H, Khan MS, Aboumarzouk O, Guru KA, Challacombe B, Dasgupta P, Ahmed K (2013) Current status of validation for robotic surgery simulators—a systematic review. BJU Int 111:194–205CrossRefPubMedGoogle Scholar
  3. 3.
    Torkington J, Smith SG, Rees BI, Darzi A (2001) Skill transfer from virtual reality to a real laparoscopic task. Surg Endosc 15:1076–1079CrossRefPubMedGoogle Scholar
  4. 4.
    Sethi AS, Peine WJ, Mohammadi Y, Sundaram CP (2009) Validation of a novel virtual reality robotic simulator. J Endourol 23:503–508CrossRefPubMedGoogle Scholar
  5. 5.
    Perrenot C, Perez M, Tran N, Jehl JP, Felblinger J, Bresler L, Hubert J (2012) The virtual reality simulator dV-Trainer® is a valid assessment tool for robotic surgical skills. Surg Endosc 26:2587–2593CrossRefPubMedGoogle Scholar
  6. 6.
    Lendvay TS, Casale P, Sweet R, Peters C (2008) VR robotic surgery: randomized blinded study of the dV-Trainer robotic simulator. Stud Health Technol Inform 132:242–244PubMedGoogle Scholar
  7. 7.
    Kenney PA, Wszolek MF, Gould JJ, Libertino JA, Moinzadeh A (2009) Face, content, and construct validity of dV-trainer, a novel virtual reality simulator for robotic surgery. Urology 73:1288–1292CrossRefPubMedGoogle Scholar
  8. 8.
    Korets R, Graversen JA, Mues A, Gupta M, Landman J, Badani KK (2011) Face and construct validity assessment of 2nd generation robotic surgery simulator. J Urol 185 (Suppl.):e488CrossRefGoogle Scholar
  9. 9.
    Korets R, Mues AC, Graversen J, Gupta M, Landman J, Badani KK (2011) Comparison of robotic surgery skill acquisition between DV-Trainer and da Vinci surgical system: a randomized controlled study. J Urol 185 (Suppl.):e593CrossRefGoogle Scholar
  10. 10.
    Fantola G, Nguyen-Thi PL, Reibel N, Sirveaux MA, Germain A, Ayav A, Bresler L, Zarnegar R, Brunaud L (2014) Risk factors for postoperative morbidity after totally robotic gastric bypass in 302 consecutive patients. Obes Surg 25:1229–1238CrossRefGoogle Scholar
  11. 11.
    Thiel DD, Lannen A, Richie E, Dove J, Gajarawala NM, Igel TC (2013) Simulation-based training for bedside assistants can benefit experienced robotic prostatectomy teams. J Endourol 27:230–237CrossRefPubMedGoogle Scholar
  12. 12.
    Renaud M, Reibel N, Zarnegar R, Germain A, Quilliot D, Ayav A, Bresler L, Brunaud L (2013) Multifactorial analysis of the learning curve for totally robotic Roux-en-Y gastric bypass for morbid obesity. Obes Surg 23:1753–1760CrossRefPubMedGoogle Scholar
  13. 13.
    Brunaud L, Ayav A, Zarnegar R, Rouers A, Klein M, Boissel P, Bresler L (2008) Prospective evaluation of 100 robotic-assisted unilateral adrenalectomies. Surgery 144:995–1001CrossRefPubMedGoogle Scholar
  14. 14.
    Mimic® website. http://www.mimicsimulation.com/products/xperience/. Accessed June 1, 2015
  15. 15.
    Tsuda S, Scott D, Doyle J, Jones DB (2009) Surgical skills training and simulation. Curr Probl Surg 46:271–370CrossRefPubMedGoogle Scholar
  16. 16.
    McDougall EM (2007) Validation of surgical simulators. J Endourol 21:244–247CrossRefPubMedGoogle Scholar
  17. 17.
    Gallagher AG, Ritter EM, Satava RM (2003) Fundamental principles of validation, and reliability: rigorous science for the assessment of surgical education and training. Surg Endosc 17:1525–1529CrossRefPubMedGoogle Scholar
  18. 18.
    Hubert N, Gilles M, Desbrosses K, Meyer JP, Felblinger J, Hubert J (2013) Ergonomic assessment of surgeon’s physical workloads during standard and robotic assisted laparoscopic procedures. Int J Med Robot 9:142–147CrossRefPubMedGoogle Scholar
  19. 19.
    Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock PA, Meshkati N (eds) Human mental workload. North-Holland, Amsterdam, pp 139–183CrossRefGoogle Scholar
  20. 20.
    Sood A, Jeong W, Ahlawat R, Campbell L, Aggarwal S, Menon M, Bhandari M (2015) Robotic surgical skill acquisition: what one needs to know? J Minim Access Surg 11:10–15CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Italian Society of Surgery (SIC) 2017

Authors and Affiliations

  • Luca Sessa
    • 1
  • Cyril Perrenot
    • 2
  • Song Xu
    • 2
  • Jacques Hubert
    • 2
  • Laurent Bresler
    • 1
  • Laurent Brunaud
    • 1
  • Manuela Perez
    • 2
  1. 1.Department of Digestive Hepatobiliary, Endocrine, and Oncologic Surgery, INSERM U954, CHU Nancy BraboisUniversity of LorraineVandoeuvre Les NancyFrance
  2. 2.School of Surgery of Nancy, IADI, INSERM, U947, CHU Nancy BraboisLorraine UniversityNancyFrance

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