Brief bursts of high-intensity PA relevant to bone health can be captured by applying bone-specific thresholds of intensity to raw tri-axial accelerations averaged over 1-second epochs. Accumulating 1-2 minutes/day of high-intensity PA, equivalent to running in pre-menopausal women and slow jogging in post-menopausal women, is associated with better bone health.

The physical activity profile can be described from accelerometer data using two population-independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sexspecific percentile norms. Methods: Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): 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 (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) %fat (adolescent girls and adults); c) bone health (pre-and post-menopausal women); and d) physical function (adults with T2D). Results: Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile 'norms' showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC. Conclusion: The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age-and sexspecific norms that would be beneficial to researchers.

Objectives: Our aim is to demonstrate how a data-driven accelerometer metric, the acceleration above which a person's 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 potentially comparable across datasets. Design: Cross-sectional, secondary data analysis. Methods: 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 person's most active 60, 30 and 2 minutes are accumulated: M60ACC; M30ACC and M2ACC, respectively. Results: The proportion of participants with M60ACC (children) and M30ACC (adults) values higher than accelerations representative 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 postmenopausal women with M2ACC values meeting thresholds for bone health ranged from 6-13%. Conclusions:These metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting current physical activity guidelines. 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 potential advantages of these metrics lie.

Integrated biomechanical and engineering assessments were used to determine how humans responded to variations in turf during running and turning. Ground reaction force (AMTI, 960 Hz) and kinematic data (Vicon Peak Motus, 120 Hz) were collected from eight participants during running (3.83 m/s) and turning (10 trials per condition) on three natural turf surfaces in the laboratory. Surface hardness (Clegg hammer) and shear strength (cruciform shear vane) were measured before and after participant testing. Peak loading rate during running was significantly higher (p < .05) on the least hard surface (sandy; 101.48 BW/s ± 23.3) compared with clay (84.67 BW/s ± 22.9). There were no significant differences in running kinematics. Compared with the "medium" condition, fifth MTP impact velocities during turning were significantly (RM-ANOVA, p < .05) lower on clay (resultant: 2.30 m/s [± 0.68] compared with 2.64 m/s [± 0.70]), which was significantly (p < .05) harder "after" and had the greatest shear strength both "before" and "after" participant testing. This unique finding suggests that further study of foot impact velocities are important to increase understanding of overuse injury mechanisms.

For second metatarsal stress fracture, aspects of foot type have been identified as influencing injury risk. For third metatarsal stress fracture, a delayed forefoot loading increases injury risk. Identification of these different injury mechanisms can inform development of interventions for treatment and prevention.