Dataset Information

The effect of preferred music on mental workload and laparoscopic surgical performance in a simulated setting (OPTIMISE): a randomized controlled crossover study.

ABSTRACT:

Background

Worldwide, music is commonly played in the operation room. The effect of music on surgical performance reportedly has varying results, while its effect on mental workload and key surgical stressor domains has only sparingly been investigated. Therefore, the aim is to assess the effect of recorded preferred music versus operating room noise on laparoscopic task performance and mental workload in a simulated setting.

Methods

A four-sequence, four-period, two-treatment, randomized controlled crossover study design was used. Medical students, novices to laparoscopy, were eligible for inclusion. Participants were randomly allocated to one of four sequences, which decided the exposure order to music and operation room noise during the four periods. Laparoscopic task performance was assessed through motion analysis with a laparoscopic box simulator. Each period consisted of ten alternating peg transfer tasks. To account for the learning curve, a preparation phase was employed. Mental workload was assessed using the Surgery Task Load Index. This study was registered with the Netherlands Trial Register (NL7961).

Results

From October 29, 2019 until March 12, 2020, 107 participants completed the study, with 97 included for analyzation. Laparoscopic task performance increased significantly during the preparation phase. No significant beneficial effect of music versus operating room noise was observed on time to task completion, path length, speed, or motion smoothness. Music significantly decreased mental workload, reflected by a lower score of the total weighted Surgery Task Load Index in all but one of the six workload dimensions.

Conclusion

Music significantly reduced mental workload overall and of several previously identified key surgical stressor domains, and its use in the operating room is reportedly viewed favorably. Music did not significantly improve laparoscopic task performance of novice laparoscopists in a simulated setting. Although varying results have been reported previously, it seems that surgical experience and task demand are more determinative.

SUBMITTER: Fu VX

PROVIDER: S-EPMC8346395 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCompared to conventional laparoscopy, robot assisted surgery is expected to have most potential in difficult areas and demanding technical skills like minimally invasive suturing. This study was performed to identify the differences in the learning curves of laparoscopic versus robot assisted suturing.MethodNovice participants performed three suturing tasks on the EoSim laparoscopic augmented reality simulator or the RobotiX robot assisted virtual reality simulator. Each participant performed an intracorporeal suturing task, a tilted plane needle transfer task and an anastomosis needle transfer task. To complete the learning curve, all tasks were repeated up to twenty repetitions or until a time plateau was reached. Clinically relevant and comparable parameters regarding time, movements and safety were recorded. Intracorporeal suturing time and cumulative sum analysis was used to compare the learning curves and phases.ResultsSeventeen participants completed the learning curve laparoscopically and 30 robot assisted. Median first knot suturing time was 611 s (s) for laparoscopic versus 251 s for robot assisted (p < 0.001), and this was 324 s versus 165 (sixth knot, p < 0.001) and 257 s and 149 s (eleventh knot, p < 0.001) respectively on base of the found learning phases. The percentage of 'adequate surgical knots' was higher in the laparoscopic than in the robot assisted group. First knot: 71% versus 60%, sixth knot: 100% versus 83%, and eleventh knot: 100% versus 73%. When assessing the 'instrument out of view' parameter, the robot assisted group scored a median of 0% after repetition four. In the laparoscopic group, the instrument out of view increased from 3.1 to 3.9% (left) and from 3.0 to 4.1% (right) between the first and eleventh knot (p > 0.05).ConclusionThe learning curve of minimally invasive suturing shows a shorter task time curve using robotic assistance compared to the laparoscopic curve. However, laparoscopic outcomes show good end results with rapid outcome improvement.

Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.