Rosie Morris1, Aodhán Hickey1, Silvia Del Din1, Alan Godfrey1, Sue Lord1, Lynn Rochester2. 1. Institute of Neuroscience/Newcastle University Institute of Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality Newcastle University, Newcastle upon Tyne, United Kingdom. 2. Institute of Neuroscience/Newcastle University Institute of Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality Newcastle University, Newcastle upon Tyne, United Kingdom. Electronic address: lynn.rochester@ncl.ac.uk.
Abstract
INTRODUCTION: Gait is a marker of global health, cognition and falls risk. Gait is complex, comprised of multiple characteristics sensitive to survival, age and pathology. Due to covariance amongst characteristics, conceptual gait models have been established to reduce redundancy and aid interpretation. Previous models have been derived from laboratory gait assessments which are costly in equipment and time. Body-worn monitors (BWM) allow for free-living, low-cost and continuous gait measurement and produce similar covariant gait characteristics. A BWM gait model from both controlled and free-living measurement has not yet been established, limiting utility. METHODS: 103 control and 67 PD participants completed a controlled laboratory assessment; walking for two minutes around a circuit wearing a BWM. 89 control and 58 PD participants were assessed in free-living, completing normal activities for 7 days wearing a BWM. Fourteen gait characteristics were derived from the BWM, selected according to a previous model. Principle component analysis derived factor loadings of gait characteristics. RESULTS: Four gait domains were derived for both groups and conditions; pace, rhythm, variability and asymmetry. Domains totalled 84.84% and 88.43% of variance for controlled and 90.00% and 93.03% of variance in free-living environments for control and PD participants respectively. Gait characteristic loading was unambiguous for all characteristics apart from gait variability which demonstrated cross-loading for both groups and environments. The model was highly congruent with the original model. CONCLUSIONS: The conceptual gait models remained stable using a BWM in controlled and free-living environments. The model became more discrete supporting utility of the gait model for free-living gait.
INTRODUCTION: Gait is a marker of global health, cognition and falls risk. Gait is complex, comprised of multiple characteristics sensitive to survival, age and pathology. Due to covariance amongst characteristics, conceptual gait models have been established to reduce redundancy and aid interpretation. Previous models have been derived from laboratory gait assessments which are costly in equipment and time. Body-worn monitors (BWM) allow for free-living, low-cost and continuous gait measurement and produce similar covariant gait characteristics. A BWM gait model from both controlled and free-living measurement has not yet been established, limiting utility. METHODS: 103 control and 67 PDparticipants completed a controlled laboratory assessment; walking for two minutes around a circuit wearing a BWM. 89 control and 58 PDparticipants were assessed in free-living, completing normal activities for 7 days wearing a BWM. Fourteen gait characteristics were derived from the BWM, selected according to a previous model. Principle component analysis derived factor loadings of gait characteristics. RESULTS: Four gait domains were derived for both groups and conditions; pace, rhythm, variability and asymmetry. Domains totalled 84.84% and 88.43% of variance for controlled and 90.00% and 93.03% of variance in free-living environments for control and PDparticipants respectively. Gait characteristic loading was unambiguous for all characteristics apart from gait variability which demonstrated cross-loading for both groups and environments. The model was highly congruent with the original model. CONCLUSIONS: The conceptual gait models remained stable using a BWM in controlled and free-living environments. The model became more discrete supporting utility of the gait model for free-living gait.
Authors: Vrutangkumar V Shah; James McNames; Martina Mancini; Patricia Carlson-Kuhta; Rebecca I Spain; John G Nutt; Mahmoud El-Gohary; Carolin Curtze; Fay B Horak Journal: J Neurol Date: 2020-01-11 Impact factor: 4.849
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