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Purpose: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. Methods: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100–2000 Hz) and signal length (100–300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. Results: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: −.02 to −.08; P ≤.045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: −.05 to −.07; P <.010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤.043) but not 300 Hz (P =.069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change:.13 and.22, respectively). Conclusions: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables. [ABSTRACT FROM AUTHOR]

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = −1.44 ± 0.90; p = 0.004), recovery (SMD = −1.83 ± 0.82; p less than 0.001), performance (SMD = −0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r2 = 0.55) and recovery (r2 = 0.55) and increased stress (r2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training. [ABSTRACT FROM AUTHOR]

Management strategies for patellofemoral pain often involve modifying running distance or speed. However, the optimal modification strategy to manage patellofemoral joint (PFJ) force and stress accumulated during running warrants further investigation. This study investigated the effect of running speed on peak and cumulative PFJ force and stress in recreational runners. Twenty recreational runners ran on an instrumented treadmill at four speeds (2.5–4.2 m/s). A musculoskeletal model derived peak and cumulative (per 1 km of continuous running) PFJ force and stress for each speed. Cumulative PFJ force and stress decreased with faster speeds (9.3–33.6% reduction for 3.1–4.2 m/s vs. 2.5 m/s). Peak PFJ force and stress significantly increased with faster speeds (9.3–35.6% increase for 3.1–4.2 m/s vs. 2.5 m/s). The largest cumulative PFJ kinetics reductions occurred when speeds increased from 2.5 to 3.1 m/s (13.7–14.2%). Running at faster speeds increases the magnitude of peak PFJ kinetics but conversely results in less accumulated force over a set distance. Selecting moderate running speeds (~3.1 m/s) with reduced training duration or an interval-based approach may be more effective for managing cumulative PFJ kinetics compared to running at slow speeds. [ABSTRACT FROM AUTHOR]

Background: In Australia, the New South Wales (NSW) State Insurance Regulatory Authority has been continuously developing and implementing clinical practice guidelines to address the health and economic burden from whiplash associated disorders (WAD). Despite this, it is uncertain the extent to which the guidelines are followed. This study aimed to determine insurer and health professional compliance with recommendations of the 2014 NSW clinical practice guidelines for the management of acute WAD; and explore factors related to adherence.Methods: This was an observational study involving an audit of 288 randomly-selected claimant files from 4 insurance providers in NSW, Australia between March and October 2016. Data extracted included demographic, claim and injury details, use of health services, and insurer and health professional practices related to the guidelines. Analyses involved descriptive statistics and correlation analysis.Results: Median time for general practitioner medical consultation was 4 days post-injury and 25 days for physical treatment (e.g. physiotherapy). Rates of x-ray investigations were low (21.5%) and most patients (90%) were given active treatments in line with the guideline recommendations. The frequency of other practices recommended by the guidelines suggested lower guideline adherence in some areas such as; using the Quebec Task Force classification (19.9%); not using specialised imaging for WAD grades I and II (e.g. MRI, 45.8%); not using routine passive treatments (e.g. manual therapy, 94.0%); and assessing risk of non-recovery using relevant prognostic tools (e.g. Neck Disability Index, 12.8%). Over half of the claimants (59.0%) were referred to other professionals at 9-12 weeks post-injury, among which 31.2% were to psychologists and 68.8% to specialists (surgical specialists, 43.6%; WAD specialists, 20.5%). Legal representation and lodgment of full claim were associated with increased number of medical visits and imaging (ρ 0.23 to 0.3; p < 0.01).Conclusion: There is evidence of positive uptake of some guideline recommendations by insurers and health professionals; however, there are practices that are not compliant and might lead to poor health outcomes and greater treatment cost. Organisational, regulatory and professional implementation strategies may be considered to change practice, improve scheme performance and ultimately improve outcomes for people with WAD. [ABSTRACT FROM AUTHOR]

Measuring lower extremity impact acceleration is a common strategy to identify runners with increased injury risk. However, existing axial peak tibial acceleration (PTA) thresholds for determining high-impact runners typically rely on small samples or fixed running speeds. This study aimed to describe the distribution of axial PTA among runners at their preferred running speed, determine an appropriate adjustment for investigating impact magnitude at different speeds, and compare biomechanics between runners classified by impact magnitude. A total of 171 runners ran on an instrumented treadmill at their preferred running speed during 3D motion capture. Axial PTA was collected at the distal tibia. The relationship between axial PTA and running speed was investigated using linear regression. Runners were categorized into impact sub-groups, with high- and low-impact runners identified if their axial PTA was ±1 standard deviation of the model predicted value. Differences in demographics, training, and running biomechanics between impact sub-groups were compared. Mean axial PTA was 7.8 g across all running speeds. Axial PTA increased with running speed, with a 1.7 g increase for every 1.0 m/s increase. There were no differences in axial PTA between males and females (p = 0.214) and lower limbs (p = 0.312). High-impact runners had higher vertical loading rates (p < 0.001) and greater ankle dorsiflexion at initial contact (p < 0.001) compared to low-impact runners. No differences in age, body mass, height, or weekly running distances were observed across impact sub-groups. This study proposes a method to identify the impact classification of runners based on their axial PTA for screening, monitoring, or gait retraining., (© 2025 The Author(s). Scandinavian Journal of Medicine & Science in Sports published by John Wiley & Sons Ltd.)