Step Length, But Not Stepping Cadence, Strongly Predicts Physical Activity Intensity During Jogging and Running.

Device-based measures often rely on the positive relationship between walking cadence and metabolic equivalents of task (METs) to estimate physical activity. It is unknown whether this relationship remains during jogging/running. The study purpose was to investigate the relationships between METs, c...

Descripción completa

Detalles Bibliográficos
Publicado en:Measurement in Physical Education & Exercise Science Vol. 27; no. 4; pp. 352 - 362
Autores principales: Pellerine, Liam P., Petterson, Jennifer L., Shivgulam, Madeline E., Johansson, Peter J., Hettiarachchi, Pasan, Kimmerly, Derek S., Frayne, Ryan J., O'Brien, Myles W.
Formato: equations & formulas research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Oct-Dec2023
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Device-based measures often rely on the positive relationship between walking cadence and metabolic equivalents of task (METs) to estimate physical activity. It is unknown whether this relationship remains during jogging/running. The study purpose was to investigate the relationships between METs, cadence, and step length during walking and jogging/running. A treadmill protocol with 5 walking (3.2–6.4 km•hr−1) and 5 jogging/running stages (8.0–11.3 km•hr−1) was completed in 43 adults (23 ± 5 years, 19♀). Predictors of METs during walking and jogging/running were determined by generalized mixed modeling. The strongest prediction models for walking (R2 = 0.72, P <.001) and jogging/running (R2 = 0.75, P <.001) included cadence2, cadence, step length, age, and leg length (all, P <.001). Step length accounted for 49.1% and 78.3% of model variance during walking and jogging/running, respectively. METs are poorly estimated by cadence during jogging/running but step length reduces error. Strategies to measure step length in free-living settings could better predict physical activity intensity.