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Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics.

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posted on 23.04.2020, 11:18 by Alex V Rowlands, Nathan P Dawkins, Ben Maylor, Charlotte L Edwardson, Stuart J Fairclough, Melanie J Davies, Deirdre M Harrington, Kamlesh Khunti, Tom Yates
The lack of consensus on meaningful and interpretable physical activity outcomes from accelerometer data hampers comparison across studies. Cut-point analyses are simple to apply and easy to interpret but can lead to results that are not comparable. We propose that the optimal accelerometer metrics for data analysis are not the same as the optimal metrics for translation. Ideally, analytical metrics are precise continuous variables that cover the intensity spectrum, while translational metrics facilitate meaningful, public-health messages and can be described in terms of activities (e.g. brisk walking) or intensity (e.g. moderate-to-vigorous physical activity). Two analytical metrics that capture the volume and intensity of the 24-h activity profile are average acceleration (volume) and intensity gradient (intensity distribution). These allow investigation of independent, additive and interactive associations of volume and intensity of activity with health; however, they are not immediately interpretable. The MX metrics, the acceleration above which the most active X minutes are accumulated, are translational metrics that can be interpreted in terms of indicative activities. Using a range of MX metrics illustrates the intensity gradient and average acceleration (i.e. 24-h activity profile). The M120, M60, M30, M15 and M5 illustrate the most active accumulated minutes of the day, the M1/3DAY the most active accumulated 8 h of the day. We demonstrate how radar plots of MX metrics can be used to interpret and translate results from between- and within-group comparisons, provide information on meeting guidelines, assess individual activity profiles relative to percentiles and compare activity profiles between domains and/or time periods.


This is a secondary data analysis which received no funding. Funding for the studies described: The adolescent girls’ data are from the Girls Active evaluation, which was funded by the NIHR Public Health Research Programme (13/90/30). The adult office workers’ data are from the SMArT Work trial, which was sponsored by Loughborough University. The project was funded by the Department of Health Policy Research Programme (project No PR-R5-0213-25004). The 10-year-old children’s data are from the ‘Active Schools: Skelmersdale’ (ASSK) physical activity intervention study which was funded by West Lancashire Sport Partnership UK, West Lancashire Community Leisure UK and Edge Hill University Ormskirk UK. University of Leicester authors are supported by the NIHR Leicester Biomedical Research Centre, and the Collaboration for leadership in Applied Health Research and Care (CLAHRC) East Midlands.



Sports Medicine - Open, 2019, 5, 47

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Diabetes Research Centre


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