This study compares biomechanical and bioelectric electromyography (EMG) normalization techniques across disparate age cohorts during walking to assess the impact of normalization methods on the functional interpretation of EMG data. The biomechanical method involved scaling EMG to a target absolute torque (EMG_TS) from a joint-specific task and the chosen bioelectric methods were peak and mean normalization taken from the EMG signal during gait, referred to as dynamic mean and dynamic peak normalization (EMG_Mean and EMG_Peak). The effects of normalization on EMG amplitude, activation pattern, and inter-subject variability were compared between disparate cohorts, including OLD (76.6 yrs N = 12) and YOUNG (26.6 yrs N = 12), in five lower-limb muscles. EMG_Peak normalization resulted in differences between YOUNG and OLD cohorts in Biceps Femoris (BF) and Medial Gastrocnemius (MG) that were not observed with EMG_Mean or EMG_TS normalization. EMG_Peak and EMG_Mean normalization also demonstrated interactions between age and the phase of gait in BF that were not seen with EMG_TS. Correlations showed that activation patterns across the gait cycle were similar between all methods for both age groups and the coefficient of variation comparisons found that EMG_TS produced the greatest inter-subject variability. We have shown that the normalization technique can influence the interpretation of findings when comparing disparate populations, highlighting the need to carefully interpret functional differences in EMG between disparate cohorts.

While the value of walking gait metrics collected using pressure-sensing walkways has shown promise for fall risk assessment, there is no consensus on the minimum number of strides required to obtain reliable metrics. This study aimed to determine the minimum stride count required for reliable single-task (ST), dual-task (DT), and difference score (DS) measurements of the spatio-temporal parameters of gait in older adult fallers and non-fallers. Forty community-dwelling older adults (74.6 ± 3.5 years) performed 10 ST and 10 DT walking passes (~100 strides total) across a GAITRite™ pressure mat. Nine truncated datasets (1–9 passes) were created from the original for each walking condition to assess agreement using two-way random effects, absolute agreement, and single-rater intraclass correlations (ICCs). ICCs demonstrated that a minimum of one pass (~10 strides) is sufficient for reliable mean gait metrics for ST and DT conditions and 10–30 strides for DS, while 10–80 strides are needed for reliable gait variability measures, depending on the metric. This study provides stride count recommendations to ensure reliable gait measurement in older adult populations, highlighting that as few as 10–30 strides are necessary for mean metrics, while variability metrics may require up to 80 strides to ensure reliability.

While accurate athlete load monitoring is crucial for preventing injury and optimizing performance, the commonly used session rating of perceived exertion training load or competition load method faces limitations due to compliance issues related to missing subjective data self-reported by the athlete and the subsequent challenges in imputing the sessional rating of perceived exertion (sRPE) component, an average value for a training or competition session. This study investigated the imputation of missing RPE scores from the mechanical work and from a Speed–Deceleration–Contact (SDC) model. A total of 1002 datasets were collected from women’s rugby sevens competitions. Using either the mechanical work or SDC, linear regression and random forest imputation models were assessed at different missingness levels and their results compared to those of a common method of daily team mean substitution (DTMS) using an ANOVA of the accuracy by the model type and missingness. The statistical equivalence was evaluated for true and imputed sRPE scores by the model and strategy. Significant interactions between the model type and missingness were found, with all the imputed scores being deemed statistically equivalent. From the ANOVA, DTMS was found to be the poorest-performing model and the random forest model was the best. However, the best-performing model was not superior to previously reported imputation approaches, which confirms the difficulty in using subjective measures of the load when missing data is a prevalent issue in team sports. Practitioners are encouraged to critically evaluate any method of imputation for an athlete’s load.

Rate of perceived exertion (RPE) is used to calculate athlete load. Incomplete load data, due to missing athlete-reported RPE, can increase injury risk. The current standard for missing RPE imputation is daily team mean substitution. However, RPE reflects an individual’s effort; group mean substitution may be suboptimal. This investigation assessed an ideal method for imputing RPE. A total of 987 datasets were collected from women’s rugby sevens competitions. Daily team mean substitution, k-nearest neighbours, random forest, support vector machine, neural network, linear, stepwise, lasso, ridge, and elastic net regression models were assessed at different missingness levels. Statistical equivalence of true and imputed scores by model were evaluated. An ANOVA of accuracy by model and missingness was completed. While all models were equivalent to the true RPE, differences by model existed. Daily team mean substitution was the poorest performing model, and random forest, the best. Accuracy was low in all models, affirming RPE as multifaceted and requiring quantification of potentially overlapping factors. While group mean substitution is discouraged, practitioners are recommended to scrutinize any imputation method relating to athlete load.

Purpose: To determine the minimum number of events (training or matches) for producing valid acceleration–speed (AS) profiles from global navigation satellite system (GNSS) data. Methods: Nine elite female soccer players participated in a 4-week training camp consisting of 19 events. AS profile metrics calculated from different combinations of athlete events were compared to force–velocity (FV) profile metrics from 2 × 40-m stand-alone sprint effort trials, using the same GNSS 10-Hz technology. Force–velocity profiles were calculated, from which AS profiles were obtained. AS profiles from training and matches were generated by plotting acceleration and speed points and performing a regression through the maximal points to obtain the AS metrics (theoretical maximal speed, x-intercept [in meters per second], theoretical maximal acceleration, y-intercept [in meters per second squared], and the slope per second). A linear mixed model was performed with the AS metrics as the outcome variables, the number of events as a fixed effect, and the participant identifier as a mixed effect. Dunnett post hoc multiple comparisons were used to compare the means of each number of event grouping (1–19 events) to those estimated from the dedicated sprint test. Results: Theoretical maximal speed and theoretical maximal acceleration means were no longer significantly different from the isolated sprint reference with 9 to 19 (small to trivial differences = −0.31 to −0.04 m·s⁻¹, P = .12–.99) and 6 to 19 (small differences = −0.4 to −0.28 m·s⁻², P = .06–.79) events, and the slopes were no longer different with 1 to 19 events (trivial differences = 0.06–0.03 s⁻¹, P = .35–.99). Conclusions: AS profiles can be estimated from a minimum of 9 days of tracking data. Future research should investigate methodology resulting in AS profiles estimated from fewer events.