Study Methods The study will involve non-clinical validation with no patients and no
interventions. The study will investigate usability, presence, satisfaction and reliability
data for 3D telemedicine, and provide subjective feedback in the form of a semi-structured
interview.

Participants Healthy volunteers from the staff at Canniesburn Regional Plastic Surgery and
Burns Unit, Glasgow Royal Infirmary and West Glasgow ACH, Greater Glasgow and Clyde.

Inclusions and exclusion Healthy staff/students. Recruitment from all contacted, eligible
participants will be recorded. Exclusion criteria include visual problems not correctable
with glasses/dyslexia.

Consent Participants will be consented in writing for inclusion in the study. The consent
will specify that data may be recorded for research and further analysis by the research
team, and be available for technical purposes by the research team's industrial partner

Sample Size

30-40 participants for Presence and Usability Questionnaires.

Participants will examine other participants (where one acts as a "doctor" and one acts as a
"patient") to provide indicators of usability, presence and task load index. All participants
will assess other participants using the screening tasks below (estimated 10 mins maximum):

Screening Tasks: asking "patient" to follow instructions Upper Limb: Functional screening
upper limb - hands above head, brush teeth, hands behind back, pro-supination, wrist,
prehension grips (power, span, pinch, key, tripod) Lower limb: Functional assessment of lower
limb - walk, straight leg raise, take shoes off and examine sole of feet Head: examine worm's
eye view of nose, side of nose and top of scalp.

Performing a "task" Perform a form of physiotherapy or equivalent task (chosen at random)
such as describing to patient the use of an anterolateral thigh flap for tumour
reconstruction.

Outcomes assessed: validated questionnaires, time taken to complete screening tasks, NASA
task load index

Randomisation Participants will be randomized to see "patients" in 3D or 2D first. This will
be by blocked randomisation using a list and sealed envelopes.

Outcome Measures

Multiple measures of usability and presence will be used. This will be to form baseline data
on the 3D telemedicine system (e.g. System usability score >68 is "good"), but also to
determine the sensitivity of the various instruments to record overall satisfaction with the
systems. This will help to determine the most appropriate instruments for the clinical
validation.

Mental Effort Rating Scale - single instrument Likert scale (Paas 1996) University of North
Northway - 5 item - telemedicine questionnaire (Buvik 2016) System Usability Scale - 10 item
scale - industry standard for usability of technology (Brooke 1986) Presence Questionnaire -
29 item scale - assessment of presence in the system/virtual environment (PQ, Witmer 2005).

Telehealth Usability Questionnaire - 21 item scale - assessment of telemedicine NASA TLX -
task load index - 5 item modified scale - an indication of the ease of performing a task and
frustration levels.

Overall Telemedicine Satisfaction - VAS Semi Structured exit interview - will discuss with
participant their views on the telemedicine system and how we can improve.

Statistics and Analysis

The values obtained for the different rating scales will be summarised, in order to provide a
basis for future sample size calculations. With a sample size of 40, the confidence interval
around an estimated standard deviation s would be 0.819 s to 1.284 s. With a sample size of
50 it would be 0.835 s to 1.246 (Sim 2012).

The relation between the various rating scales and overall telemedicine satisfaction will be
assessed using linear mixed effects regression analysis predicting overall telemedicine
satisfaction from the rating scale and type of telemedicine, including a random effect for
participant.

The research team will examine whether higher "presence" scores (on Presence Questionnaire)
translate to less "frustration" (on NASA TLX), and whether this results in less error/time
taken to complete tasks. This will be done using linear mixed effects regression analyses.