Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.
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 exte...
| Published in: | Medicine & Science in Sports & Exercise Vol. 50; no. 11; pp. 2277 - 2285 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Lippincott Williams & Wilkins
Nov2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132483764&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132483764 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Nov2018 vid: 50 iid: 11 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 132483764 132483764 132483764 10.1249/MSS.0000000000001704 132483764 ppf: 2277 ppct: 8 formats: tig: atl: Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load. aug: au: STILES, VICTORIA H. PEARCE, MATTHEW MOORE, ISABEL S. LANGFORD, JOSS ROWLANDS, ALEX V. affil: Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UNITED KINGDOM sug: subj: Accelerometry Methods Wrist Physiology Workload Athletic Training Physical Activity Running Self Report Human Middle Age Adult Body Height Body Mass Index Female Male ROC Curve Linear Regression Validity Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: 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 ± 11.4 yr; height, 1.72 ± 0.08 m; mass, 68.5 ± 9.7 kg; body mass index, 23.2 ± 2.2 kg·m−2; 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≥400 m g ") 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≥400 m g , "workload (WL) 400-4000 m g ") was examined using linear regression with leave one out cross-validation. Results: Most Active-30mins and Mins≥400 m g had >94% accuracy for correctly classifying "running" and "nonrunning" days, with validation indicating robustness. Variance explained in miles, duration, and training load by Mins≥400 m g (67%–76%) and WL400–4000 m g (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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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