Using Cadence to Predict the Walk-to-Run Transition in Children and Adolescents: A Logistic Regression Approach. Issue 9 (3rd May 2021)

• Record Type: Journal Article
• Authors: Ducharme, Scott W.; Turner, Dusty S.; Pleuss, James D.; Moore, Christopher C.; Schuna, John M.; Tudor-Locke, Catrine; Aguiar, Elroy J.
• Is Part Of: Journal of sports sciences. Volume 39:Issue 9(2021)
• Page Start: 1039
• Page End: 1045
• Publication Date: 2021-05-03
• Subjects: Preferred transition speed -- step frequency -- locomotion -- physical activity -- gait; Sports -- Periodicals; Sports -- Physiological aspects -- Periodicals; Sports -- Psychological aspects -- Periodicals; 612.044
• DOI: 10.1080/02640414.2020.1855869
• Languages: English
• ISSNs: 0264-0414

Abstract

ABSTRACT: The natural transition from walking to running occurs in adults at ≅140 steps/min. It is unknown when this transition occurs in children and adolescents. The purpose of this study was to develop a model to predict age- and anthropometry-specific preferred transition cadences in individuals 6–20 years of age. Sixty-nine individuals performed sequentially faster 5-min treadmill walking bouts, starting at 0.22 m/s and increasing by 0.22 m/s until completion of the bout during which they freely chose to run. Steps accumulated during each bout were directly observed and converted to cadence (steps/min). A logistic regression model was developed to predict preferred transition cadences using the best subset of parameters. The resulting model, which included age, sex, height, and BMI z-score, produced preferred transition cadences that accurately classified gait behaviour (k-fold cross-validated prediction accuracy =97.02%). This transition cadence ranged from 136–161 steps/min across the developmental age range studied. The preferred transition cadence represents a simple and practical index to predict and classify gait behaviour from wearable sensors in children, adolescents, and young adults. Moreover, herein we provide an equation and an open access online R Shiny app that researchers, practitioners, or clinicians can use to predict individual-specific preferred transition cadences.