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Development and Cross-Validation of a Cadence-Based Metabolic Equation for Walking

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Abstract
The ACSM Metabolic Equation is a widely recognized equation for predicting metabolic intensity from walking speed. However, an equation that uses an observable metric (i.e., cadence [steps/min]), accounts for individual characteristics, and is validated across walking conditions may enable more accessible and accurate predictions of walking intensity. PURPOSE: To develop metabolic equations that predict metabolic intensity (oxygen consumption; mL/kg/min) from cadence using a large treadmill walking dataset (Study One) and cross-validate these equations during overground unconstrained and cadence-constrained walking conditions (Study Two). METHODS: In Study One, 193 adults (21-81 years) completed treadmill walking bouts while oxygen consumption was measured with indirect calorimetry (converted to metabolic equivalents [METs]; 1 MET=3.5 mL/kg/min=1 kcal/kg/min). Directly-observed step counts divided by bout duration produced cadence. The least squares regression of the cadence-intensity relationship produced a simple equation and a full equation was developed using best subsets regression (additional possible predictors of leg length, body mass, BMI, percent body fat, sex, and age). Predictive accuracy and bias of each cadence-based metabolic equation and the ACSM Metabolic Equation was evaluated through k-fold cross-validation. In Study Two, these three metabolic equations were applied to data collected from 20 young adults during overground walking at self-selected paces (unconstrained) and with foot-strikes entrained to music tempos (cadence-constrained). RESULTS: In Study One, the simple equation predicted walking intensity within 0.5 METs, on average, and approximately no bias (CONCLUSIONS: The simple equation performed comparably to the full equation (which accounted for individual characteristics) and appreciably better than the ACSM Metabolic Equation. The simple cadence-based metabolic equation is an improved, user-friendly tool for predicting and prescribing walking intensity with reasonable accuracy (within ~0.5 METs; 45 kcals/hr for the average American).
Type
thesis
Date
2019-05
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