Background: Rapid change in the commercial market can threaten consistency of activity data comparisons as devices are superseded. Purpose: To determine the level of agreement between two generations of Fitbit™ device for step count and activity level in a free-living environment. Methods: Thirty-seven healthy participants (17 women, 20 men; M ± SD: age 34 ± 8 y; body mass index 25.4 ± 3.9 kg/m2) wore a Fitbit Flex™ and Flex 2™ on their non-dominant wrist over two weeks in a free-living environment. A waist-mounted ActiGraph GT3X+ was also worn to provide a comparison of step count data obtained against a commercial device. Results: Comparison of step count between two generations of Fitbit™ device (Mean Absolute Percentage Error, 12%; Standard Error of Mean, 102.58 steps/d (p = .039); ICC = 0.955) showed closer inter-device agreement than comparison of step count data between commercial (Fitbit™) and research (ActiGraph GT3X+) grades of device (Mean Absolute Percentage Error, 31%; Standard Error of Mean, 124.6 steps/d (p < .001); ICC = 0.915). Statistically significant differences were identified for the Standard Error of Mean between generations of Fitbit™ device (p = .039) and grades of device (p < .001). A comparison of ‘fairly’ and ‘very’ active minutes showed no statistically significant difference between generations of Fitbit™ (p = .980); Mean Absolute Percentage Error, 38%; ICC = 0.908. The number of days of data captured for step count was comparable between to the two grades of device. Conclusion: Users should be aware of potential variations in data estimates from different generations of Fitbit™ device, with step count data providing a more consistent comparison metric.
Denise M. Jones, Harvi F. Hart, Kay M. Crossley, Ilana N. Ackerman and Joanne L. Kemp
David L. Carey, Justin Crow, Kok-Leong Ong, Peter Blanch, Meg E. Morris, Ben J. Dascombe and Kay M. Crossley
Purpose: To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. Methods: A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. Results: The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of “safe” training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Conclusions: Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics.
Lachlan E. Garrick, Bryce C. Alexander, Anthony G. Schache, Marcus G. Pandy, Kay M. Crossley and Natalie J. Collins
Context: It is important to validate single-leg squat visual rating criteria used in clinical practice and research. Foot orthoses may improve single-leg squat performance in those who demonstrate biomechanics associated with increased risk of lower limb injury. Objective: Validate visual rating criteria proposed by Crossley et al, by determining whether athletes rated as poor single-leg squat performers display different single-leg squat biomechanics than good performers; and evaluate immediate effects of foot orthoses on single-leg squat biomechanics in poor performers. Design: Comparative cross-sectional study. Setting: University laboratory. Participants: 79 asymptomatic athletes underwent video classification of single-leg squat performance based on established visual rating criteria (overall impression, trunk posture, pelvis “in space,” hip movement, and knee movement), and were rated as good (n = 23), fair (n = 41), or poor (n = 15) performers. Intervention: A subset of good (n = 16) and poor (n = 12) performers underwent biomechanical assessment, completing 5 continuous single-leg squats on their dominant limb while 3-dimensional motion analysis and ground reaction force data were recorded. Poor performers repeated the task standing on prefabricated foot orthoses. Main Outcome Measures: Peak external knee adduction moment (KAM) and peak angles for the trunk, hip, knee, and ankle. Results: Compared with good performers, poor performers had a significantly lower peak KAM (mean difference = 0.11 Nm/kg, 95% confidence interval = 0.02 to 0.2 Nm/kg), higher peak hip adduction angle (−4.3°, −7.6° to −0.9°), and higher peak trunk axial rotation toward their stance limb (3.8°, 0.4° to 7.2°). Foot orthoses significantly increased the peak KAM in poor performers (−0.06 Nm/kg, −0.1 to −0.01 Nm/kg), with values approximating those observed in good performers. Conclusions: Findings validate Crossley et al’s visual rating criteria for single-leg squat performance in asymptomatic athletes, and suggest that “off-the-shelf” foot orthoses may be a simple intervention for poor performers to normalize the magnitude of the external KAM during single-leg squat.