## 4 Discussion  
The ability to exchange and compare clinical gait analysis data between laboratories is a valuable tool to improve the monitoring of clinical treatments and rehabilitation progressions for patients with musculoskeletal disease. Gait analysis data was thus compared in three ways in the present study. First, to determine interobserver reliability; second, to determine intraobserver reliability; and finally, to determine between-laboratory intraobserver reliability of CGA data between two gait laboratories. In support of our first hypothesis, the total mean reliability was excellent for interobserver (ICCtotal = 0.79, 95% CI: 0.67-0.86) and intraobserver reliability (ICCtotal = 0.93, 95% CI: 0.87-0.96) (Table 2). For a single laboratory, we found fair-to-good or poor interobserver reliability, which suggests an observer influence dependent on subjective observer variability and the different software used for analysis (Workstation vs. Nexus) (Table 1). These results support those of previous work by [PERSON] et al. [10] (Table 3). We found excellent intraobserver reliability for one observer processing the data using the same software (excluding observer and software error). Nonetheless, ICC values for inter- and intraobserver reliability still fall under the excellent classification for a majority of the parameters observed (interobserver: 71% parameters; intraobserver: 94% parameters). The ankle displayed fair-to-good and excellent interobserver reliability, whereas the knee and hip showed excellent reliability only (Table 2). This outcome is in general agreement with the systematic review of [PERSON] et al. [3], who reported excellent intra- and interobserver reliability for the parameters of the hip and knee joints in the sagittal-plane, and a lower reliability for the ankle joint. However, the observed differences within ankle kinematics may in part be attributed to technical improvements in the applied software (Nexus, version 1.3 vs. Workstation, version 4.6 build 142). However, future research is needed to investigate this possibility. For total mean reliability, we observed fair-to-good between-laboratory intraobserver reliability (ICCtotal = 0.56, 95% CI: 0.16-0.74) and excellent inter- (ICCtotal = 0.79, 95% CI: 0.67-0.86) and intraobserver reliability (ICCtotal = 0.93, 95% CI: 0.87-0.96). Only 26% of the between-laboratory intraobserver parameters revealed excellent ICC values, whereas the remaining 74% fell within the fair-to-good and poor ranges. Significant differences (\(p\leq 0.002\)) were found in 39% of the compared parameters (Table 4). This fair-to-good and poor reliability may be caused by differences in the measurement system hardware configurations and by slight differences in marker placement between data collection sessions. Both laboratories employed electro-optical motion capturing from the same manufacturer, but use a different hardware configuration (capturing software, camera type). According to our second hypothesis, we believed that CGA data could be accurately collected between two gait laboratories, making such data interchangeable. An acceptance of this hypothesis could be affirmed for 61% of the parameters, with significant differences (\(p\leq 0.002\)) in the remaining 39% (Table 4). Between-laboratory intraobserver reliability of CGA data captured at two different gait analysis laboratories had total ICC values at the fair-to-good level, which was lower than the excellent inter- and intraobserver reliability values (Table 2). In general, differences were found at each joint (ankle, knee, hip) for angle as well as for time-dependent (% gait cycle) analysis. The Bland-Altman plots indicated a detectable amount of variation for specific parameters, such as the knee in the frontal plane (Figure 3d), when compared between laboratories. These plots also showed a small data distribution for each individual observer, when compared between laboratories and the largest (worst) bounding criteria for frontal plane knee angle (\(2.3\pm 16.1^{\circ}\)) (Figure 3d).