A data-driven, meaningful, easy to interpret, population-independent accelerometer outcome variable for global surveillance

Abstract

BackgroundAccelerometer-driven physical activity guidelines are not available, likely due to the lack of consensus on meaningful and interpretable accelerometer outcomes. The aim of this paper is to demonstrate how a data-driven accelerometer metric, the acceleration above which a persons most active minutes are accumulated, can a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and comparable across datasets.\n\nMethodsSecondary data analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a persons most active 60, 30 and 2 minutes are accumulated: M60ACC; M30ACC and M2ACC, respectively.\n\nResultsThe proportion of participants with M60ACC (children) and M30ACC (adults) values higher than accelerations indicative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17-68% in children and 15%-81% in adults, tending to decline with age. The proportion of pre-and post-menopausal women with M2ACC values indicative of running and thus meeting recently presented thresholds for bone health ranged from 6-13%.\n\nConclusionThese metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting the current physical activity guidelines, across the lifespan. Translation of acceleration magnitudes into indicative activities provides a public health friendly interpretation of results. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex-specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the key advantages of these metrics lie.