{"title":"The interplay between physical activity and aging in locomotor fractal behavior","authors":"Scott W. Ducharme , Richard E.A. van Emmerik","doi":"10.1016/j.csfx.2020.100045","DOIUrl":null,"url":null,"abstract":"<div><p>The complex organization of gait variability, or fractal dynamics, theoretically represents the adaptive capacity of the locomotor system. Prior studies suggest that fractal dynamics are sensitive to various individual constraints (e.g., age, neurological disease) and task constraints (e.g., walking speed or novel gait tasks). The purpose of this study was to determine if physical activity levels represent an additional individual constraint during walking. Fifteen young and 15 older adults walked on a treadmill at their preferred walking speed and at half of their preferred speed. Detrended fluctuation analysis was used to estimate the statistical persistence of stride time variability. Volume of physical activity was determined using a wearable monitor for 3-7 days. Habitual physical activity levels did not appear to have an effect on the fractal nature of stride-to-stride fluctuations in young adults. However, the least active older adults displayed higher scaling exponents compared to the more active older adults during slow walking. That is, less active older adults responded to the slow walking task by increasing fractal scaling, suggesting this task is more challenging and complex. These findings suggest that lower levels of habitual physical activity may represent an additional individual constraint in older but not young adults. When age and physical activity constraints are combined with a challenging slow walking task, the locomotor system of older adults may be more highly taxed, ultimately manifesting as stronger statistical persistence.</p></div>","PeriodicalId":37147,"journal":{"name":"Chaos, Solitons and Fractals: X","volume":"5 ","pages":"Article 100045"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.csfx.2020.100045","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos, Solitons and Fractals: X","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590054420300269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 1
Abstract
The complex organization of gait variability, or fractal dynamics, theoretically represents the adaptive capacity of the locomotor system. Prior studies suggest that fractal dynamics are sensitive to various individual constraints (e.g., age, neurological disease) and task constraints (e.g., walking speed or novel gait tasks). The purpose of this study was to determine if physical activity levels represent an additional individual constraint during walking. Fifteen young and 15 older adults walked on a treadmill at their preferred walking speed and at half of their preferred speed. Detrended fluctuation analysis was used to estimate the statistical persistence of stride time variability. Volume of physical activity was determined using a wearable monitor for 3-7 days. Habitual physical activity levels did not appear to have an effect on the fractal nature of stride-to-stride fluctuations in young adults. However, the least active older adults displayed higher scaling exponents compared to the more active older adults during slow walking. That is, less active older adults responded to the slow walking task by increasing fractal scaling, suggesting this task is more challenging and complex. These findings suggest that lower levels of habitual physical activity may represent an additional individual constraint in older but not young adults. When age and physical activity constraints are combined with a challenging slow walking task, the locomotor system of older adults may be more highly taxed, ultimately manifesting as stronger statistical persistence.