1. Using Cadence to Predict the Walk-to-Run Transition in Children and Adolescents: A Logistic Regression Approach
- Author
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John M. Schuna, Christopher C. Moore, Elroy J. Aguiar, James Pleuss, Dusty Turner, Scott W. Ducharme, and Catrine Tudor-Locke
- Subjects
Male ,medicine.medical_specialty ,Time Factors ,Adolescent ,Physical activity ,030209 endocrinology & metabolism ,Physical Therapy, Sports Therapy and Rehabilitation ,Walking ,Logistic regression ,Article ,Body Mass Index ,Running ,Young Adult ,03 medical and health sciences ,Sex Factors ,0302 clinical medicine ,Gait (human) ,Physical medicine and rehabilitation ,Transition from walking to running ,medicine ,Humans ,Orthopedics and Sports Medicine ,Child ,Gait ,Transition (fiction) ,Body Weight ,digestive, oral, and skin physiology ,Age Factors ,030229 sport sciences ,Models, Theoretical ,Body Height ,Logistic Models ,Step frequency ,Exercise Test ,Female ,Cadence ,Psychology - Abstract
The natural transition from walking to running occurs in adults at โ 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.
- Published
- 2020
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