Interpreting Movements
As mentioned, it has largely been the case in the medical education
literature that the number of movements an individual makes is
indicative of their overall efficiency. That is, the fewer the
movements, the more efficient the performance. While, construct
validation studies show this to be the general case when expert and
novice performances are compared,27, 28 there is less
clarity on the way that efficiency develops through the intermediate
stages of learning. In particular, one perspective on the study of human
motor control describes learning with respect to the way in which
individuals vary and explore components of the action as they search for
the optimal approach to performance.42-45 As a
consequence, different components of action can shift between phases of
stability (i.e., low movement variability) and instability (i.e., high
movement variability) as learners attempt to organize their movements.
The idea is that these shifts occur in response to constraints placed on
the performance by the task or environment, and even as a function of
changes in the learner’s ability and motivation. A good example of this
is Guerin and Kunkle’s (2004) study of individuals learning to kick a
ball over a barrier onto a target. Measurement of the participants’ kick
height and accuracy over 12 extensive practice sessions demonstrated
that they focused initially on ensuring that the ball crossed over the
barrier, with little concern for accuracy.46 However,
as they became able to clear the height of the barrier consistently,
their focus shifted to landing the ball accurately on the target. That
is, as the learning experience progressed, the height constraint
deteriorated in importance and the accuracy constraint emerged as
increasingly more pertinent.
With respect to metrics of efficiency, such as number of movements, this
type of shifting means that skill assessors need to understand the
performance constraints to which learners are currently attending. A
small study exploring the validity of an instrumented simulator for the
assessment of surgical knot tying skills provides a nice
example.8 In this study, the simulator incorporated
flexometer technology that measured the quality of the knots tied by
pre-medical undergraduate students (i.e., novices), medical clerks
(i.e., intermediates), and senior medical residents (i.e., experts).
Interestingly, the technology also permitted the experimenters to
measure the economy of action via the amount the walls of the simulated
wound moved while the participants performed. Not surprisingly, the
results showed that the completed knots were tighter and more
sustainable as the performers increased in expertise. That is, the
experts’ sutures were better than those of the intermediates, and the
intermediates’ sutures were better than those of the novices. However,
the movement economy metric revealed that the intermediates performed
far more inefficiently than both the experts and the novices. Taken
together, the two metrics reveal how the intermediate group had
sacrificed attention to efficiency in order to achieve stronger final
products. Given that learners will shift focus from outcome to
efficiency at different stages of practice, it is essential that the
assessor of clinical technical skills remembers that an objective
computerized assessment of performance efficiency– defined in terms of
number of movements or otherwise - exists independently of the outcome
of the performance. As a consequence, the relationship between
efficiency and expertise is not always direct.47 Thus,
as a construct for assessment, it may be necessary to avoid motion
efficiency as a measure of competence until after the learner’s
proficiency at reliably producing quality outcomes is well established.
In surgery, this may be particularly important, as learners will often
value accuracy at the expense of efficiency.
Furthermore, when interpreting number of movements, one should also keep
in mind the way in which contexts of performance and individual
performer differences can impact how a metric such as number of
movements is used as a competency standard. With respect to the former,
differences in task rules or equipment can have significant impacts on
movement performance. In this regard, for example, it would not be fair
to compare the number of movements associated with open surgical
performance to those associated with laparoscopic surgical
procedure.48 Indeed, even changes in the time a
performer is allotted to complete a task has shown to disrupt the
metrics of efficiency that so often characterize the most expert
performers.49 Moreover, skills that occur in closed,
static environments are fundamentally different than those that occur in
open, dynamic environments. The former afford approaches to skill
performance that are largely anticipatory in nature, which allows for
fuller action plans to be generated prior to initiation, while the
latter requires more attention to be given to online control processes
that determine the movements needed as a procedure
unfolds.31 Thus, it would also be unfair to compare
performances that permit and don’t permit prior planning, as the latter
would most certainly be associated with higher movement counts.