As Visual Simultaneous Localisation and Mapping (VSLAM) formulations are increasingly deployed on low-power mobile platforms, it is essential to manage their power consumption in the context of minimal impact on their accuracy or robustness. This is particularly challenging when the runtime changes in motion or scene complexity are unknown in advanced. Further, different VSLAM formulations have different interpretations of the scene, posing a portability challenge for any management techniques. To this effect, we propose a new light-weight runtime adaptation model that relies purely on the change in sensor motion to characterise the tracking difficulty. The model is then used to adapt two general and portable parameters, namely Dynamic Voltage and Frequency Scaling (DVFS) and redundant frame-skipping. The model is evaluated in the context of both direct and indirect keyframe-based VSLAMs (DSO and ORB-SLAM) using scenes from the ICL-NUIM and EuRoC MAV datasets. Our results show a best-case power reduction of 75% with a marginal impact on the accuracy and robustness of the VSLAM formulations over multiple runs, compared to original base real-time versions of the algorithms. Analysis of the scenes showing an impact on robustness indicates that this is caused by critical points in the trajectory, motivating the search an improved characterisation metrics.