Temporal Variability in Stride Kinematics during the Application of TENS: A Machine Learning Analysis.
Introduction: The purpose of our report was to use a Random Forest classification approach to predict the association between transcutaneous electrical nerve stimulation (TENS) and walking kinematics at the stride level when middle-aged and older adults performed the 6-min test of walking endurance....
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 56; no. 9; pp. 1701 - 1709 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179290731&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179290731 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Sep2024 vid: 56 iid: 9 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 179290731 179290731 179290731 10.1249/MSS.0000000000003469 179290731 ppf: 1701 ppct: 8 formats: tig: atl: Temporal Variability in Stride Kinematics during the Application of TENS: A Machine Learning Analysis. aug: au: DANESHGAR, SAJJAD HOITZ, FABIAN ENOKA, ROGER M. affil: Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO sug: subj: Transcutaneous Electric Nerve Stimulation Walking Kinematics Random Forest Classification Human Male Female Middle Age Aged Machine Learning Multimethod Studies Toes Physiology Range of Motion Descriptive Statistics Validity Comparative Studies Scales Protocols kappa Statistic Post Hoc Analysis Data Analysis Software Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Introduction: The purpose of our report was to use a Random Forest classification approach to predict the association between transcutaneous electrical nerve stimulation (TENS) and walking kinematics at the stride level when middle-aged and older adults performed the 6-min test of walking endurance. Methods: Data from 41 participants (aged 64.6 ± 9.7 yr) acquired in two previously published studies were analyzed with a Random Forest algorithm that focused on upper and lower limb, lumbar, and trunk kinematics. The four most predictive kinematic features were identified and utilized in separate models to distinguish between three walking conditions: burst TENS, continuous TENS, and control. SHAP analysis and linear mixed models were used to characterize the differences among these conditions. Results: Modulation of four key kinematic features--toe-out angle, toe-off angle, and lumbar range of motion (ROM) in coronal and sagittal planes--accurately predicted walking conditions for the burst (82%accuracy) and continuous (77% accuracy) TENS conditions compared with control. Linear mixed models detected a significant difference in lumbar sagittal ROM between the TENS conditions. SHAP analysis revealed that burst TENS was positively associated with greater lumbar coronal ROM, smaller toe-off angle, and less lumbar sagittal ROM. Conversely, continuous TENS was associated with less lumbar coronal ROM and greater lumbar sagittal ROM. Conclusions: Our approach identified four kinematic features at the stride level that could distinguish between the three walking conditions. These distinctions were not evident in average values across strides. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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