Development of a Sensory Integration Test for Locomotion: A Study Protocol

Abstract

Background & Aim: As falls are common during gait in older adults, investigating the factors contributing to stable gait has gained growing interest. In this context, the contribution of visual, somatosensory, and vestibular systems (i.e., sensory integration) to gait has been studied for years, albeit primarily as individual systems. Although an earlier attempt was made to develop a test to comprehensively assess the sensory integration during gait, this effort encountered certain limitations that impacted its overall effectiveness. Thus, this study aims to develop a new test to evaluate sensory integration during gait, called the "Sensory Integration in Walking (SensIWalk) Test" and assess its validity and reliability in both young and older adults. Methods: This study is planned as an observational study. Younger (n=24, 18-35 years old) and older adults (n=24, >= 65 years old) will be invited to participate, and all measurements will be performed at the Computer Assisted Rehabilitation Environment (CAREN, Motek Medical BV, Amsterdam, The Netherlands). SensIWalk, adapted from the framework of the Clinical Test of Sensory Interaction and Balance (CTSIB) with the same six conditions, will be modified to accommodate locomotion. The conditions of SensIWalk will be as follows: 1) Walking at preferred speed on a firm surface (i.e., on the treadmill) with eyes open, 2) Walking at preferred speed on a firm surface in the dark (1.3 lux), 3) Walking at preferred speed on a firm surface with sways of the virtual reality (VR) environment (i.e., visual conflict), 4) Walking at preferred speed with foam insoles (2 cm thick) with eyes open, 5) Walking at preferred speed with foam insoles in the dark, 6) Walking at preferred speed with foam insoles with sways of the VR environment. Discussion: This study will allow delving into the underlying sensory mechanisms explaining suboptimal balance during walking by assessing the effects of sensory strategies on movement patterns. This may provide a deeper insight into the underlying mechanisms of falls in older adults, which could foster novel training or rehabilitation paradigms to decrease the risk of falls in older adults. (c) 2025 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.