Simulation-based Arthroscopic Surgery Study

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data (three rotational degrees around the x, y and z axes, known as 'roll', 'pitch', and 'yaw', and three translational degrees of freedom along x, y and z axes, known as 'surge', 'sway' and 'heave') which will be analysed using validated, bespoke algorithms to calculate the number of hand movements taken whilst performing a diagnostic knee arthroscopy according to a standardised protocol.

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms to calculate the smoothness (also known as 'jerk', the first derivative of acceleration by time, or third derivative of distance by time) of hand movements taken whilst performing a diagnostic knee arthroscopy according to a standardised protocol according to a standardised protocol.

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will also collect time signatures, which can be used to work out the time taken by participants to perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data (three rotational degrees around the x, y and z axes, known as 'roll', 'pitch', and 'yaw', and three translational degrees of freedom along x, y and z axes, known as 'surge', 'sway' and 'heave') which will be analysed using validated, bespoke algorithms to calculate the number of movements (below the threshold for 'hand movements' above in outcome 1, but above the data noise threshold) taken whilst performing a diagnostic knee arthroscopy according to a standardised protocol.

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will also collect time signatures, which can be used to work out the length of time during the procedure where each hand is stationary while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will also collect time signatures, which can be used to work out the length of time during the procedure where both hands are stationary at the same time while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.

Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will be analysed for the relative activity and dominance of each hand during the procedure while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.

Validated global rating scale for assessing diagnostic knee arthroscopy performance

Previously described motion parameters of participants performing a diagnostic knee arthroscopy in theatre (see Primary outcome 1, and secondary outcomes 2-8) reported as a ratio to the 'ideal' performance as measured from the supervising clinician performing an optimal diagnostic knee arthroscopy on the same patient as the participant while wearing the wireless elbow-mounted accelerometer and gyroscopic sensors which will record 6 degree of freedom motion data to allow calculation of 'number of hand movements', 'smoothness', 'time taken', 'minor hand movements', 'stationary time', 'idle time' and dominance'

Change in participant performance on dry, bench top box trainers and anatomical simulators between baseline and 3 months using motion analysis parameters described in Primary outcome 1 and secondary outcomes 2-8 as measured by wireless elbow-mounted accelerometer and gyroscopic sensors

Use of MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) to identify resting state networks, and analyse differences in functional connectivity at baseline and three months between the intervention and control arms.

Using FSLVBM (fMRIB's Software Library Voxel Based Morphometry) to calculate voxel-wise changes in grey matter volumes at baseline and three months between the intervention and control arms. Changes in VBM imply changes in grey matter volume and represent structural brain change.

Using FDT (fMRIB's Diffusion Toolbox) to model local diffusion and changes in tractography at baseline and three months between the intervention and control arms. Changes in diffusion imply micro-structural (axonal) connectivity and represent structural brain change.

Quantitative magnetisation transfer imaging estimates liquid and semisolid (macromolecular) constituents of tissue at baseline and three months between the intervention and control arms. Changes in macromolecular content imply micro-structural (myelin) connectivity and represent structural brain change.

Qualitative survey of participants opinions of the addition of simulation to their usual clinical training programme