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 '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. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Corresponding author contact details: Corresponding author, Dr Victoria Stiles, Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UK; Tel: +44(0)1392 722885; Fax: +44(0)1392 724726; email: v.h.stiles@exeter.ac.uk This project was supported by Medical Research Council Proximity to Discover funding (Reference: MC_PC_14127) in collaboration with Activinsights Ltd, UK. AR is with the National Institute for Health Research (NIHR) Biomedical Research Centre based at University Hospitals of Leicester and Loughborough University, the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care – East Midlands (NIHR CLAHRC – EM) and the Leicester Clinical Trials Unit. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Conflicts of Interest As a collaborative study with industry supported by MRC Proximity to Discover funding, the industry partner may potentially benefit from the outcomes from the research. However, the open-source analysis procedures employed in the current study impose no restriction for other members of the activity monitoring industry to also benefit. There are no other competing interests. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation and do not constitute endorsement by ACSM. Accepted for Publication: 15 June 2018 © 2018 American College of Sports Medicine

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