Inertial sensors such as accelerometers and gyroscopes can provide a multitude of information on running gait. Running parameters such as stride time and ground contact time can all be identified within tibial accelerometry data. Within this, stride time is a popular parameter of interest, possibly due to its role in running economy. However, there are multiple methods utilised to derive stride time from tibial accelerometry data, some of which may offer complications when implemented on larger data files. Therefore, the purpose of this study was to compare previously utilised methods of stride time derivation to an original proposed method, utilising medio-lateral tibial acceleration data filtered at 2 Hz, allowing for greater efficiency in stride time output. Tibial accelerometry data from six participants training for a half marathon were utilised. One right leg run was randomly selected for each participant, in which five consecutive running stride times were calculated. Four calculation methods were employed to derive stride time. A repeated measures analysis of variance (ANOVA) identified no significant difference in stride time between stride time calculation methods (p=1.00), whilst intra-class coefficient values (all >0.95) and coefficient of variance values (all <1.5%) indicate good reliability. Results indicate that the proposed method possibly offers a simplified technique for stride time output during running gait analysis. This method may be less influenced by “double peak” error and minor fluctuations within the data, allowing for accurate and efficient automated data output in both real time and post processing.