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Direct linkPellerine, Liam P.; Petterson, Jennifer L.; Shivgulam, Madeline E.; Johansson, Peter J.; Hettiarachchi, Pasan; Kimmerly, Derek S.; Frayne, Ryan J.; O'Brien, Myles W. – Measurement in Physical Education and Exercise Science, 2023
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…
Descriptors: Physical Activity Level, Predictor Variables, Physical Activities, Young Adults
Martin, Eric M.; True, Larissa; Pfeiffer, Karin A.; Siegel, Shannon R.; Branta, Crystal F.; Wisner, Dave; Haubenstricker, John; Seefeldt, Vern – Measurement in Physical Education and Exercise Science, 2021
Research tracking sport participation from youth to adulthood is relatively rare, as is research that tracks youth sport participation with regard to adult physical activity (PA) levels, especially in the United States. Aims of this study were: 1) To investigate the degree to which sport participation tracked across youth, adolescence, and early…
Descriptors: Athletics, Participation, Children, Adolescents
George, James D.; Paul, Samantha L.; Hyde, Annette; Bradshaw, Danielle I.; Vehrs, Pat R.; Hager, Ronald L.; Yanowitz, Frank G. – Measurement in Physical Education and Exercise Science, 2009
This study sought to develop a regression model to predict maximal oxygen uptake (VO[subscript 2max]) based on submaximal treadmill exercise (EX) and non-exercise (N-EX) data involving 116 participants, ages 18-65 years. The EX data included the participants' self-selected treadmill speed (at a level grade) when exercise heart rate first reached…
Descriptors: Metabolism, Body Composition, Physical Activities, Physical Activity Level
