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Performance of the current simulator in the vertical position agrees with similar previous studies, which showed that flexion kinematics in the vertical position are significantly different for active versus passive flexion, and that active flexion is more repeatable than passive (Dunning et al., 2001; Johnson et al., 2000). Active flexion was

also more repeatable than passive flexion for the valgus, vertical and supinated horizontal positions. There was no difference in the varus or pronated horizontal positions. The difference in repeatability of active vs. passive flexion was also less pronounced in the

valgus position. This is likely because the kinematic pathways in the varus and valgus positions are largely limited by osseous and ligamentous constraints. In these positions, passive flexion would be expected to be guided by these constraints, thus producing a repeatable flexion pathway. By contrast, in the horizontal and vertical positions, passive flexion would not be subjected to substantial valgus or varus constraints, relying primarily on the investigator’s input.

Greater repeatability of simulated active flexion suggests that this mode of in- vitro testing should reduce the standard error of kinematic dependent variables in

biomechanics investigations, and thus increase statistical power and decrease required sample sizes. This is consistent with investigations of statistical methods in biomechanics which emphasize “a need to make every reasonable attempt to control and minimize within-subject variability.” (Bates et al., 1992; Dufek et al., 1995).

In the horizontal, varus and valgus positions, the challenge of generating active flexion is due to the relationship between the resistance moment and muscle moment arms. In the varus and valgus positions, the carrying angle changes with flexion angle (Van Roy et al., 2005). Thus, the gravity load vector in these positions causes the

resistance moment to sometimes tend toward elbow extension. Therefore, muscle load control must shift to the antagonist (Triceps) muscle throughout varus or valgus flexion. However, in these positions, the gravity load vector is perpendicular to the flexion plane,

and so the component of gravity load contributing to generate the resistance moment is relatively small compared to the muscle moments.

Generating active flexion in the horizontal position is a markedly greater challenge. At full extension, the resistance moment is at its greatest because the gravity load vector through the forearm’s center of mass is furthest from the elbow joint. Also at full extension, the agonist muscle moment arms are at their shortest (Kuxhaus et al.,

2009; Pigeon et al., 1996; An et al., 1981; Ettema et al., 1998). This creates a “worst case

scenario” in terms of mechanical advantage to initiate flexion. As flexion progresses, the resistance moment decreases in an exponential fashion to zero at 90º, where the resistance moment arm is zero. Hence, the control system must be able to quickly reduce the high agonist loads to prevent the forearm from accelerating. But if the agonist loads are decreased too rapidly, then the forearm will fall back toward extension due to the resistance moment. Flexion control is challenged further by reversal of the resistance moment past 90º, where Triceps takes over as the primary flexion controller, and the “worst case scenario” shifts toward full flexion. In contrast, in the vertical position the resistance moment is lowest at full extension and thus eases the initiation of flexion. Also the resistance moment is greatest at 90º, just where the agonist muscle moment arms are also at their maximum (Kuxhaus et al., 2009; Pigeon et al., 1996; An et al., 1981; Ettema et al., 1998).

Muscle loads are relatively lower in the vertical, varus and valgus positions, which allows the muscle/tendon complexes to be more compliant and to absorb rapid changes in actuator velocities. In contrast, horizontal flexion with a highly dynamic resistance moment, is an inverted pendulum balancing problem, and is made more difficult by the increased muscle tensions which cause the muscle/tendon complexes to become less compliant, making a more rigid system. Thus, any inadequacies of the controller (i.e. under- or over-compensating efforts) are much less tolerated because the

highly tensioned muscles efficiently transmit changes in loads, converting them into rapid accelerations which can quickly destabilize the system.

The advantage of active flexion in the varus and valgus positions was more apparent in terms of kinematic pathway and joint laxity. In these positions, simulated

muscle activation caused the arms to track at less than the laxity limits of the collateral ligaments, and thus most certainly relieved some of the stresses on these stabilizing structures. This decrease in varus-valgus joint laxity can be attributed to the simulated muscle loads overcoming the relatively large varus and valgus moments produced by gravity loading of the forearm. Thus, active flexion clearly outperforms passive flexion in these positions, suggesting that muscle tension compresses the articular surfaces of the elbow, increasing osseous stability, or alternatively guides the elbow motion pathways directly.

In the varus position with pronation, active flexion provided no significant resistance to gravity load compared to passive flexion. This may have been due to differences between the pronated and supinated flexion control protocols. In order to achieve forearm pronation, 30-40 N were applied to the Pronator Teres. The Pronator Teres has been found to cause significant elbow varus movement, relieving valgus stresses (Lin et al., 2007; Udall et al., 2009). This further validates our results, which

show that in all positions, the actively flexed arm tracked in less valgus when pronated than when supinated.

A previously reported vertical flexion simulator employed muscle loading ratios derived from EMG and muscle cross-sectional area (Amis et al., 1979; Funk et al., 1987;

Johnson et al., 2000). These ratios were the basis for the current controller. However,

there is a lack of EMG data in the literature for flexion in the varus, valgus and horizontal positions. Furthermore, and as discussed, these positions reveal and amplify any inadequacies of the control protocol or assumptions in the elbow flexion model, which are otherwise well tolerated in the vertical position. Thus the control protocols of Table 2.1 evolved from those of the original vertical simulator by addressing the various aspects of flexion in each position on an individual basis. As a result, the control protocols were developed in an iterative process. Future development should include further studies of EMG activity in all the principle flexion positions in order to refine the control protocols.

Little data exists with respect to physiologic motion pathways or in-vivo control

of muscle forces, particularly for the different anatomical positions simulated in the current investigation. Hence, this limits our ability to compare in-vitro pathways with

those which might occur in-vivo. Still, the kinematic pathways reported herein are similar

to those documented by other investigators, and display the characteristic decreasing valgus angle with increasing flexion angle (Van Roy et al., 2005). Furthermore the

passive and active mean kinematic pathways were similar in shape, again supporting the clinical relevance of this approach. The high repeatability of the simulator motion pathways suggests that it should be useful for laboratory based studies of non-surgical and surgical treatments for elbow disorders.