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dc.contributor.authorStiles, Victoria
dc.contributor.authorPearce, Matthew
dc.contributor.authorMoore, Isabel
dc.contributor.authorLangford, Joss
dc.contributor.authorRowlands, Alex
dc.date.accessioned2018-08-01T19:21:40Z
dc.date.available2018-08-01T19:21:40Z
dc.date.issued2018-08-01
dc.identifier.citationStiles, V.H., Pearce, M., Moore, I.S., Langford, J. and Rowlands, A.V. (2018) 'Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load', Medicine & Science in Sport and Exerciseen_US
dc.identifier.urihttp://hdl.handle.net/10369/9799
dc.descriptionArticle published open access (as accepted manuscript) in Medicine & Science in Sport and Exercise on 1 August 2018 available at https://doi.org/10.1249/MSS.0000000000001704en_US
dc.description.abstractPurpose 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 ‘non-running’ 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 years; height 1.72±0.08 m; mass 68.5±9.7 kg; Body Mass Index, 23.2±2.2 kg.m2; 19 [54%] women) every other week over 9-18 weeks were date-matched with self-reported training log data. Receiver-Operating-Characteristic analyses were applied to accelerometer metrics (‘Average Acceleration’, ‘Most Active-30mins’, ‘Mins≥400mg’) to discriminate between ‘running’ and ‘non-running’ days and cross-validated (leave one out cross-validation; LOOCV). Variance explained in training log criterion metrics (Miles, Duration, Training Load) by accelerometer metrics (‘Mins≥400mg’, ‘WL(workload)400-4000mg’) was examined using linear regression with LOOCV. Results ‘Most Active-30mins’ and ‘Mins≥400mg’ had >94% accuracy for correctly classifying ‘running’ and ‘non-running’ days, with validation indicating robustness. Variance explained in Miles, Duration and Training Load by ‘Mins≥400mg’ (67-76%) and ‘WL400-4000mg’ (55-69%) was high, with validation indicating robustness. Conclusion 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.en_US
dc.description.sponsorshipThis project was supported by Medical Research Council Proximity to Discover funding (Reference: MC_PC_14127) in collaboration with Activinsights Ltd, UK.en_US
dc.language.isoenen_US
dc.publisherWolters Kluweren_US
dc.relation.ispartofseriesMedicine & Science in Sport and Exercise;
dc.subjectRunnersen_US
dc.subjectwrist-wornen_US
dc.subjectaccelerometryen_US
dc.titleWrist-worn Accelerometry for Runners: Objective Quantification of Training Loaden_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1249/MSS.0000000000001704
dcterms.dateAccepted2018-06-15
rioxxterms.versionAMen_US
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en_US
rioxxterms.licenseref.startdate2018-08-01


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