Liam P. Pellerine, Jennifer L. Petterson, Madeline E. Shivgulam, Peter J. Johansson, P. Hettiarachchi, D. Kimmerly, Ryan J. Frayne, M. O'Brien
{"title":"步长,但不是步幅,有力地预测慢跑和跑步期间的体力活动强度","authors":"Liam P. Pellerine, Jennifer L. Petterson, Madeline E. Shivgulam, Peter J. Johansson, P. Hettiarachchi, D. Kimmerly, Ryan J. Frayne, M. O'Brien","doi":"10.1080/1091367X.2023.2188118","DOIUrl":null,"url":null,"abstract":"ABSTRACT 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.","PeriodicalId":48577,"journal":{"name":"Measurement in Physical Education and Exercise Science","volume":"27 1","pages":"352 - 361"},"PeriodicalIF":1.7000,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Step Length, But Not Stepping Cadence, Strongly Predicts Physical Activity Intensity During Jogging and Running\",\"authors\":\"Liam P. Pellerine, Jennifer L. Petterson, Madeline E. Shivgulam, Peter J. Johansson, P. Hettiarachchi, D. Kimmerly, Ryan J. Frayne, M. O'Brien\",\"doi\":\"10.1080/1091367X.2023.2188118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT 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.\",\"PeriodicalId\":48577,\"journal\":{\"name\":\"Measurement in Physical Education and Exercise Science\",\"volume\":\"27 1\",\"pages\":\"352 - 361\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement in Physical Education and Exercise Science\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/1091367X.2023.2188118\",\"RegionNum\":4,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement in Physical Education and Exercise Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/1091367X.2023.2188118","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Step Length, But Not Stepping Cadence, Strongly Predicts Physical Activity Intensity During Jogging and Running
ABSTRACT 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.
期刊介绍:
The scope of Measurement in Physical Education and Exercise Science (MPEES) covers original measurement research, special issues, and tutorials within six substantive disciplines of physical education and exercise science. Six of the seven sections of MPEES define the substantive disciplines within the purview of the original research to be published in the journal: Exercise Science, Physical Activity, Physical Education Pedagogy, Psychology, Research Methodology and Statistics, and Sport Management and Administration. The seventh section of MPEES, Tutorial and Teacher’s Toolbox, serves to provide an outlet for review and/or didactic manuscripts to be published in the journal. Special issues provide an avenue for a coherent set of manuscripts (e.g., four to five) to collectively focus in-depth on an important and timely measurement-related issue within the scope of MPEES. The primary aim of MPEES is to publish high-impact manuscripts, most of which will focus on original research, that fit within the scope of the journal.