부산대학교 도서관

Byline: VICTORIA H. STILES, Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UNITED KINGDOM; MATTHEW PEARCE, Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UNITED KINGDOM; ISABEL S. MOORE, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UNITED KINGDOM; JOSS LANGFORD, GENEActiv, Activinsights, Cambridgeshire, UNITED KINGDOM; ALEX V. ROWLANDS, Diabetes Research Centre, University of Leicester, Leicester, UNITED KINGDOM, National Institute for Health Research (NIHR), Leicester Biomedical Research Centre, Leicester, UNITED KINGDOM, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Adelaide, AUSTRALIA Abstract PURPOSE: This study aimed to apply open-source analysis code to raw habitual physical activity data from wrist-worn monitors to: 1) objectively, unobtrusively, and accurately discriminate between 'running' and 'nonrunning' days; and 2) develop and compare simple accelerometer-derived metrics of external training load with existing self-report measures. METHODS: Seven-day wrist-worn accelerometer (GENEActiv; Activinsights Ltd, Kimbolton, UK) data obtained from 35 experienced runners (age, 41.9 [+ or -] 11.4 yr; height, 1.72 [+ or -] 0.08 m; mass, 68.5 [+ or -] 9.7 kg; body mass index, 23.2 [+ or -] 2.2 kg*m; 19 [54%] women) every other week over 9 to 18 wk were date-matched with self-reported training log data. Receiver operating characteristic analyses were applied to accelerometer metrics ('Average Acceleration,' 'Most Active-30mins,' 'Mins[greater than or equal]400 mg') to discriminate between 'running' and 'nonrunning' days and cross-validated (leave one out cross-validation). Variance explained in training log criterion metrics (miles, duration, training load) by accelerometer metrics (Mins[greater than or equal]400 mg, 'workload (WL) 400-4000 mg') was examined using linear regression with leave one out cross-validation. RESULTS: Most Active-30mins and Mins[greater than or equal]400 mg had >94% accuracy for correctly classifying 'running' and 'nonrunning' days, with validation indicating robustness. Variance explained in miles, duration, and training load by Mins[greater than or equal]400 mg (67%-76%) and WL400-4000 mg (55%-69%) was high, with validation indicating robustness. CONCLUSIONS: Wrist-worn accelerometer metrics can be used to objectively, unobtrusively, and accurately identify running training days in runners, reducing the need for training logs or user input in future prospective research or commercial activity tracking. The high percentage of variance explained in existing self-reported measures of training load by simple, accelerometer-derived metrics of external training load supports the future use of accelerometry for prospective, preventative, and prescriptive monitoring purposes in runners.