Using dynamic walking models to identify factors that contribute to increased risk of falling in older adults

Falls are common in older adults. The most common cause of falls is tripping while walking. Simulation studies demonstrated that older adults may be restricted by lower limb strength and movement speed to regain balance after a trip. This review examines how modeling approaches can be used to determine how different measures predict actual fall risk and what some of the causal mechanisms of fall risk are. Although increased gait variability predicts increased fall risk experimentally, it is not clear which variability measures could best be used, or what magnitude of change corresponded with increased fall risk. With a simulation study we showed that the increase in fall risk with a certain increase in gait variability was greatly influenced by the initial level of variability. Gait variability can therefore not easily be used to predict fall risk. We therefore explored other measures that may be related to fall risk and investigated the relationship between stability measures such as Floquet multipliers and local divergence exponents and actual fall risk in a dynamic walking model. We demonstrated that short-term local divergence exponents were a good early predictor for fall risk. Neuronal noise increases with age. It has however not been fully understood if increased neuronal noise would cause an increased fall risk. With our dynamic walking model we showed that increased neuronal noise caused increased fall risk. Although people who are at increased risk of falling reduce their walking speed it had been questioned whether this slower speed would actually cause a reduced fall risk. With our model we demonstrated that a reduced walking speed caused a reduction in fall risk. This may be due to the decreased kinematic variability as a result of the reduced signal-dependent noise of the smaller muscle forces that are required for slower. These insights may be used in the development of fall prevention programs in order to better identify those at increased risk of falling and to target those factors that influence fall risk most.