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

Rowlands, Alex V. and Dawkins, Nathan P. and Maylor, Ben and Edwardson, Charlotte L. and Fairclough, Stuart J. and Davies, Melanie J. and Harrington, Deirdre M. and Khunti, Kamlesh and Yates, Tom (2019) Enhancing the value of accelerometer-assessed physical activity : meaningful visual comparisons of data-driven translational accelerometer metrics. Sports Medicine, 5 (1). 47. ISSN 0112-1642 (https://doi.org/10.1186/s40798-019-0225-9)

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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.


Rowlands, Alex V., Dawkins, Nathan P., Maylor, Ben, Edwardson, Charlotte L., Fairclough, Stuart J., Davies, Melanie J., Harrington, Deirdre M. ORCID logoORCID: https://orcid.org/0000-0003-0278-6812, Khunti, Kamlesh and Yates, Tom;