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It is well documented that health and social benefits can be attained through participation in sport and exercise. Participation, particularly in sports, benefits from appropriate surface provisions that are safe, affordable and high quality preferably across the recreational to elite continuum. Investment, construction and research into artificial sports surfaces have increased to meet this provision. However, not all sports (e.g. golf, rugby and cricket) are suited to training and match-play on artificial turf without compromising some playing characteristics of the games. Therefore, full sport surface provision cannot be met without the use of natural turf surfaces, which also have an important role as green spaces in the built environment. Furthermore, a significant number of people participate in outdoor sport on natural turf pitches, although this is a declining trend as the number of synthetic turf surfaces increases. Despite natural turf being a common playing surface for popular sports such as soccer, rugby and cricket, few biomechanical studies have been performed using natural turf conditions. It is proposed that if natural turf surfaces are to help meet the provision of sports surfaces, advancement in the construction and sustainability of natural turf surface design is required. The design of a natural turf surface should also be informed by knowledge of surface-related overuse injury risk factors. This article reviews biomechanical, engineering, soil mechanics, turfgrass science, sports medicine and injury-related literature with a view to proposing a multidisciplinary approach to engineering a more sustainable natural turf sport surface. The present article concludes that an integrated approach incorporating an engineering and biomechanical analysis of the effects of variations in natural turf media on human movement and the effects of variations in human movement on natural turf is primarily required to address the longer-term development of sustainable natural turf playing surfaces. It also recommends that the use of 'natural turf' as a catch-all categorization in injury studies masks the spatial and temporal variation within and among such surfaces, which could be important. [ABSTRACT FROM AUTHOR]

Changes in surface hardness are likely to alter an athlete's movement strategy. Anterior cruciate ligament (ACL) injury risk assessments that are performed on a different surface to that used for training and competition may, therefore, not represent an athlete's on-field movement strategies. The aim of this study was to examine the influence of surface hardness on multidirectional field sport athletes' movement strategies in movements that are commonly used in ACL injury risk assessments (bilateral and unilateral drop jumps, and a cutting manoeuvre). Ground reaction forcesand three-dimensional lower limb kinematics were recorded from 19 healthy, male, multidirectional field sport athletes performing bilateral and unilateral drop jumps, and a 90° cutting task on Mondo track (harder surface) and artificial turf (softer surface). Continuous (statistical parametric mapping) and discrete analyses revealed alterations in vertical and horizontal braking forces and knee and hip moments between surfaces of different hardness in all three movements ( p ≤ 0.05, d  > 0.5). Injury risk assessments performed on a harder surface (e.g. Mondo track) can misrepresent an athlete's risk of ACL injury compared to the same movements performed on a softer more cushioned surface that is typically used for training and/or matches (e.g. artificial turf).

Given the high rates of both primary and secondary anterior cruciate ligament (ACL) injuries in multidirectional field sports, there is a need to develop easily accessible methods for practitioners to monitor ACL injury risk. Field-based methods to assess knee variables associated with ACL injury are of particular interest to practitioners for monitoring injury risk in applied sports settings. Knee variables or proxy measures derived from wearable inertial measurement units (IMUs) may thus provide a powerful tool for efficient injury risk management. Therefore, the aim of this study was to identify whether there were correlations between laboratory-derived knee variables (knee range of motion (RoM), change in knee moment, and knee stiffness) and metrics derived from IMUs (angular velocities and accelerations) placed on the tibia and thigh, across a range of movements performed in practitioner assessments used to monitor ACL injury risk. Ground reaction forces, three-dimensional kinematics, and triaxial IMU data were recorded from nineteen healthy male participants performing bilateral and unilateral drop jumps, and a 90° cutting task. Spearman's correlations were used to examine the correlations between knee variables and IMU-derived metrics. A significant strong positive correlation was observed between knee RoM and the area under the tibia angular velocity curve in all movements. Significant strong correlations were also observed in the unilateral drop jump between knee RoM, change in knee moment, and knee stiffness, and the area under the tibia acceleration curve (r s = 0.776, r s = -0.712, and r s = -0.765, respectively). A significant moderate correlation was observed between both knee RoM and knee stiffness, and the area under the thigh angular velocity curve (r s = 0.682 and r s = -0.641, respectively). The findings from this study suggest that it may be feasible to use IMU-derived angular velocities and acceleration measurements as proxy measures of knee variables in movements included in practitioner assessments used to monitor ACL injury risk.

There are currently a multitude of tests used to assess readiness to return to sport (RTS) following anterior cruciate ligament reconstruction (ACLR). The aim of this study was to establish the extent to which movement strategies transfer between three common assessment tasks to help improve design of athlete testing batteries following ACLR. A cohort of 127 male patients 8-10 months post-ACLR and 45 non-injured controls took part in the study. Three movement tasks were completed (unilateral and bilateral drop jump, and 90° pre-planned cut), while ground reaction forces and three-dimensional kinematics (250 Hz) were recorded. Compared to the bilateral drop jump and cut, the unilateral drop jump had a higher proportion of work done at the ankle (d = 0.29, p < 0.001 and d = -1.87, p < 0.001, respectively), and a lower proportion of work done at the knee during the braking phase of the task (d = 0.447, p < 0.001 and d = 1.56, p < 0.001, respectively). The ACLR group had higher peak hip moments than the non-injured controls, although the proportion of work done at the ankle, knee and hip joints were similar. Movement strategies were moderately and positively related at the ankle (r s  = 0.728, p < 0.001), knee (r s  = 0.638, p < 0.001) and hip (r s  = 0.593, p < 0.001) between the unilateral and bilateral drop jump, but there was no relationship at the ankle (r s  = 0.10, p = 0.104), knee (r s  = 0.106, p = 0.166) and hip (r s  = -0.019, p = 0.808) between the unilateral drop jump and the cut. Clinicians could therefore consider omitting one of the drop jumps from assessment batteries but should include both jumping and cutting tasks., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Purpose: 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 article aims 1) to 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 data sets and 2) to illustrate the future potential of the metrics for generation of age and sex-specific percentile norms., Methods: Secondary data analyses were conducted on five diverse data sets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (n = 145), adolescent girls (n = 1669), office workers (n = 114), premenopausal (n = 1218) and postmenopausal (n = 1316) women, and adults with type 2 diabetes (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 postmenopausal women), and (d) physical function (adults with type 2 diabetes)., Results: Multiple regression analyses showed that 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 the IG accelerometer metrics facilitate the investigation of whether volume and intensity of physical activity have independent, additive, or interactive effects on health markers. In future studies, the adoption of data-driven metrics would facilitate the generation of age- and sex-specific 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 2min are accumulated: M60 ACC ; M30 ACC and M2 ACC , respectively., Results: The proportion of participants with M60 ACC (children) and M30 ACC (adults) values higher than accelerations representative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17 to 68% in children and 15 to 81% in adults, tending to decline with age. The proportion of pre-and post-menopausal women with M2 ACC values meeting thresholds for bone health ranged from 6 to 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., (Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

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 "nonrunning" 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 yr; height, 1.72 ± 0.08 m; mass, 68.5 ± 9.7 kg; body mass index, 23.2 ± 2.2 kg·m; 19 [54%] women) every other week over 9 to 18 wk were date-matched with self-reported training log data. Receiver operating characteristic analyses were applied to accelerometer metrics ("Average Acceleration," "Most Active-30mins," "Mins≥400 mg") to discriminate between "running" and "nonrunning" days and cross-validated (leave one out cross-validation). Variance explained in training log criterion metrics (miles, duration, training load) by accelerometer metrics (Mins≥400 mg, "workload (WL) 400-4000 mg") was examined using linear regression with leave one out cross-validation., Results: Most Active-30mins and Mins≥400 mg had >94% accuracy for correctly classifying "running" and "nonrunning" days, with validation indicating robustness. Variance explained in miles, duration, and training load by Mins≥400 mg (67%-76%) and WL400-4000 mg (55%-69%) was high, with validation indicating robustness., Conclusions: 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.

Background: Physical inactivity is a highly modifiable risk factor for the development of osteoporosis but, due to a lack of research that has precisely and objectively meaured physical activity (PA) relevant to bone, the specific contribution that PA can make to bone health is poorly understood. This study examined whether a more precise measure of PA relelvant to bone was associated with meaures of bone health in pre- and post-menopausal women in UK Biobank., Methods: Time spent at intensities specific to bone health [≥750 milli-gravitational units (mg) and ≥1000 mg] were analysed from raw tri-axial acceleration data averaged over 1-second epochs from 7-day monitoring of habitual PA using accelerometry-based activity monitors (100 Hz; AX3, Axivity, UK) of 1218 pre- and 1316 post-menopausal healthy women. In a cross-sectional analysis, associations between categories of time (<1, 1-2 and ≥2 minutes) spent above the intensity thresholds and calcaneal quantitative ultrasound measures of bone health (bone mineral density T-score, BMDT-score; speed of sound, SOS; and broadband ultrasound attenuation, BUA) were examined., Results: Compared with <1 minute, spending 1-2 or ≥2 minutes/day at intensities ≥1000 mg in pre-menopausal and ≥750 mg in post-menopausal women was positively associated with BMDT-score, SOS and BUA., Conclusion: 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 Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association)

Background: Accelerometers that provide triaxial measured acceleration data are now available. However, equivalence of output between brands cannot be assumed and testing is necessary to determine whether features of the acceleration signal are interchangeable., Purpose: This study aimed to establish the equivalence of output between two brands of monitor in a laboratory and in a free-living environment., Methods: For part 1, 38 adults performed nine laboratory-based activities while wearing an ActiGraph GT3X+ and GENEActiv (Gravity Estimator of Normal Everyday Activity) at the hip. For part 2, 58 children age 10-12 yr wore a GT3X+ and GENEActiv at the hip for 7 d in a free-living setting., Results: For part 1, the magnitude of time domain features from the GENEActiv was greater than that from the GT3X+. However, frequency domain features compared well, with perfect agreement of the dominant frequency for 97%-100% of participants for most activities. For part 2, mean daily acceleration measured by the two brands was correlated (r = 0.93, P < 0.001, respectively) but the magnitude was approximately 15% lower for the GT3X+ than that for the GENEActiv at the hip., Conclusions: Frequency domain-based classification algorithms should be transferable between monitors, and it should be possible to apply time domain-based classification algorithms developed for one device to the other by applying an affine conversion on the measured acceleration values. The strong relation between accelerations measured by the two brands suggests that habitual activity level and activity patterns assessed by the GENE and GT3X+ may compare well if analyzed appropriately.

Background: Previous research has reported peak vertical acceleration and peak loading rate thresholds beneficial to bone mineral density (BMD). Such thresholds are difficult to translate into meaningful recommendations for physical activity. Cadence (steps/min) is a more readily interpretable measure of ambulatory activity., Objective: To examine relationships between cadence, peak vertical acceleration and peak loading rate during ambulation and identify the cadence associated with previously reported bone-beneficial thresholds for peak vertical acceleration (4.9 g) and peak loading rate (43 BW/s)., Methods: Ten participants completed 8 trials each of: slow walking, brisk walking, slow running, and fast running. Acceleration data were captured using a GT3×+ accelerometer worn at the hip. Peak loading rate was collected via a force plate., Results: Strong relationships were identified between cadence and peak vertical acceleration (r = .96, P < .05) and peak loading rate (r = .98, P < .05). Regression analyses indicated cadences of 157 ± 12 steps/min (2.6 ± 0.2 steps/s) and 122 ± 10 steps/min (2.0 ± 0.2 steps/s) corresponded with the 4.9 g peak vertical acceleration and 43 BW/s peak loading rate thresholds, respectively., Conclusions: Cadences ≥ 2.0 to 2.6 steps/s equate to acceleration and loading rate thresholds related to bone health. Further research is needed to investigate whether the frequency of daily occurrences of this cadence is associated with BMD.

Purpose: The aims of this study were to quantify the relation between ground reaction force (GRF) and peak acceleration from hip- and wrist-worn accelerometers and determine peak acceleration cut-points associated with a loading rate previously demonstrated as beneficial to bone (43 body weights (BW)·s⁻¹) in premenopausal women., Methods: Forty-seven premenopausal women (age, 39.2 ± 5.6 yr; mass, 65.9 ± 11.0 kg; height, 1.67 ± 0.06 m) performed walking (slow, fast, and with bag), floor sweeping, running (slow and fast), jumping (low, <5 cm; high, >5 cm), and box drop (20 cm) trials. Peak accelerations were sampled at 100 Hz by GENEActiv and ActiGraph GT3X+ accelerometers (ActiGraph LLC, Pensacola, FL) worn at the hip (vertical and resultant) and the wrist (resultant). A force plate (960 Hz, AMTI) was used to assess peak vertical GRF and peak loading rate for eight steps per activity. Receiver operating characteristic curves were used to determine the optimal peak acceleration cut-points associated with a loading rate of 43 BW·s⁻¹ in 37 participants, and these cut-points were cross-validated in the remaining 10 participants., Results: For all activities combined, peak accelerations were positively and significantly (P < 0.001) correlated with peak vertical GRF (hip r > 0.8, wrist r > 0.7) and peak loading rate (hip r > 0.7, wrist r > 0.57). Irrespective of monitor type and wear site, peak acceleration discriminated between loading rates above and below 43 BW·s⁻¹ with high levels of accuracy (area under the curve >0.92, P < 0.001). Overall classification agreement was >85% for both monitors worn at either the wrist or hip in the cross-validation sample., Conclusion: GENEActiv and ActiGraph GT3X+ accelerometers worn at the wrist or hip can be used as an unobtrusive tool to identify the occurrence of loading rates likely beneficial to bone in premenopausal women during their daily activity.

The purpose of this study was to assess the relationship of accelerometer output, in counts (ActiGraph GT1M) and as raw accelerations (ActiGraph GT3X+ and GENEA), with ground reaction force (GRF) in adults. Ten participants (age: 29.4 ± 8.2 yr, mass: 74.3 ± 9.8 kg, height: 1.76 ± 0.09 m) performed eight trials each of: slow walking, brisk walking, slow running, faster running and box drops. GRF data were collected for one step per trial (walking and running) using a force plate. Low jumps and higher jumps (one per second) were performed for 20 s each on the force plate. For box drops, participants dropped from a 35 cm box onto the force plate. Throughout, three accelerometers were worn at the hip: GT1M, GT3X+ and GENEA. A further GT3X+ and GENEA were worn on the left and right wrist, respectively. GT1M counts correlated with peak impact force (r = 0.85, p < 0.05), average resultant force (r = 0.73, p < 0.05) and peak loading rate (r = 0.76, p < 0.05). Accelerations from the GT3X+ and GENEA correlated with average resultant force and peak loading rate irrespective of whether monitors were worn at the hip or wrist (r > 0.82, p < 0.05, r > 0.63 p < 0.05, respectively). In conclusion, accelerometer count and raw acceleration output correlate positively with GRF and thus may be appropriate for the quantification of activity beneficial to bone. Wrist-worn monitors show a similar relationship with GRF as hip-worn monitors, suggesting that wrist-worn monitors may be a viable option for future studies looking at bone health., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

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.

Research suggests that heightened impacts, altered joint movement patterns, and changes in friction coefficient from the use of artificial surfaces in sport increase the prevalence of overuse injuries. The purposes of this study were to (a) develop procedures to assess a tennis-specific movement, (b) characterize the ground reaction force (GRF) impact phases of the movement, and (c) assess human response during impact with changes in common playing surfaces. In relation to the third purpose it was hypothesized that surfaces with greatest mechanical cushioning would yield lower impact forces (PkFz) and rates of loading. Six shod volunteers performed 8 running forehand trials on each surface condition: baseline, carpet, acrylic, and artificial turf. Force plate (960 Hz) and kinematic data (120 Hz) were collected simultaneously for each trial. Running forehand foot plants are typically characterized by 3 peaks in vertical GRF prior to a foot-off peak. Group mean PkFz was significantly lower and peak braking force was significantly higher on the baseline surface compared with the other three test surfaces (p<0.05). No significant changes in initial kinematics were found to explain unexpected PkFz results. The baseline surface yielded a significantly higher coefficient of friction compared with the other three test surfaces (p<0.05). While the hypothesis is rejected, biomechanical analysis has revealed changes in surface type with regard to GRF variables.