Purpose Ischaemic stroke is a major cause of long-term disability and is commonly associated with gait impairments that reduce functional independence and quality of life. Conventional rehabilitation often provides limited continued gait training after discharge. Virtual reality (VR)-based functional treadmill training offers immersive, task-specific practice and can enhance recovery. Objective: To investigate the effect of VR-based functional treadmill training on spatiotemporal gait parameters in young individuals with ischaemic stroke. Methods A randomised controlled trial was conducted on 28 patients with subacute ischaemic stroke (aged 35–45 years), randomly assigned to two equal groups. The study group received VR-based treadmill training plus conventional therapy, while the control group received conventional rehabilitation including treadmill training. The intervention lasted 6 weeks. Gait velocity was the primary outcome, and other spatiotemporal gait parameters were secondary outcomes. Assessments were performed pre- and post-intervention using the Tekscan HR Walkway system. Results The study group showed greater improvement in gait velocity than the control group (1.36 ± 0.27 vs. 1.28 ± 0.20 m/s, p = 0.004, η2 = 0.26). Step time decreased significantly (484.47 ± 18.05 vs. 544.47 ± 34.97 ms, p < 0.001, η2 = 0.47), as did swing time (0.36 ± 0.01 vs. 0.37 ± 0.02 s, p = 0.013, η2 = 0.20). Stride length increased significantly (1.37 ± 0.07 vs. 1.35 ± 0.08 m, p = 0.029, η2 = 0.16). No significant difference was found in stride time (p = 0.377). Conclusions VR-based treadmill training improved several spatiotemporal gait parameters compared with conventional therapy in young stroke survivors. The findings should be interpreted cautiously due to the small sample size and the short study duration.
Purpose This exploratory study compared changes in squat strength and kendo men-strike biomechanics between velocity-based training (VBT) and percentage-based training (PBT) conditions. Methods Twelve university kendo athletes completed a 7-week back-squat intervention under VBT (n = 7) or PBT (n = 5). Allocation was based on academic year rather than randomisation. Before and after training, squat one-repetition maximum (1RM), men-strike motion time, push-off-phase rate of force development over 0–100 ms (RFD0–100 ms), and the temporal overlap of high-extension-torque phases among the hip, knee, and ankle joints were assessed. Results Absolute and relative squat 1RM increased in both conditions. Between-condition differences in change were 14.1 kg for absolute 1RM (95% CI [4.5, 23.8]) and 0.309 kg · kg–1 for relative 1RM (95% CI [0.136, 0.482]), favouring VBT. The between-condition difference for RFD0–100 ms also favoured VBT (27.62 N · kg–1 · s–1; 95% CI [–11.75, 66.98]), but the CI included zero. Differences were small and uncertain for men-strike motion time (–0.025 s; 95% CI [–0.142, 0.092]) and the overlap index (1.6 percentage points; 95% CI [–4.8, 8.1]). Conclusions Between-condition differences were clearest for squat strength, whereas evidence for kendo-specific biomechanical changes was limited. Because academic year was confounded with intervention condition, and the interventions differed in load progression and autoregulation, the findings cannot be attributed to velocity feedback alone. Evidence for transfer to faster men-strike performance remains inconclusive.
Purpose Exercise modulates lipid metabolism; however, the influence of the time of day on serum fatty acid responses remains incompletely understood. Although the effects of exercise intensity and duration are well documented, limited research has examined whether morning versus evening exercise produces different metabolic outcomes in individuals with varying training backgrounds. This study investigated the acute effects of morning compared with evening aerobic exercise on serum fatty acid profiles, including palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA), in trained and untrained men. Methods Twenty healthy men (n = 10 trained, n = 10 untrained) completed randomised morning (08:00–09:00) and evening (20:00 – 21:00) running sessions lasting approximately 45 minutes at 64–76% of their maximal heart rate (HRmax). Venous blood samples were collected before and after each session, and serum fatty acids were quantified using gas chromatography. A repeated-measures analysis of variance (ANOVA) was used to assess the effects of training status, exercise timing, and acute aerobic exercise. Results Levels of most serum fatty acids did not change significantly following exercise. However, in the untrained participants, evening exercise resulted in significant increases in C18:0 and C18:2 serum levels. Trained individuals demonstrated lower baseline C18:0 levels and lower evening SFA levels compared with the untrained participants. No significant differences were observed for C16:0, C18:1, MUFA, or PUFA levels. Conclusions The study findings suggest that exercise timing influences serum fatty acid responses, particularly in untrained individuals. Evening exercise increased levels of lipogenesis-related fatty acids, and trained individuals exhibited a more favourable basal lipid profile. Considering exercise timing may help optimise lipid regulation and reduce cardiometabolic risk.
Purpose The purpose of the study was to evaluate observer repeatability and inter-rater reproducibility of skeletal age assessment using the Tanner–Whitehouse 3 radius, ulna, and short bones (TW3-RUS) method on dual-energy X-ray absorptiometry (DXA) images, and to apply a complementary framework for evaluating observer-dependent measurement quality. Methods Seventy-one elite male youth football players from the Czech Republic were assessed at two time points separated by one year. Skeletal age was estimated from DXA hand scans by an expert and a trained novice rater using the TW3-RUS method. Both observers repeated all assessments after two weeks. Measurement quality was evaluated using the intraclass correlation coefficient ICC(2,1), standard error of measurement (SEM), Bland–Altman analysis with proportional bias testing, bootstrap confidence intervals, and a predefined ±0.5-year decision threshold. Results Observer repeatability and inter-rater reproducibility were excellent at both annual assessments (ICC = 0.965–0.999). The expert maintained consistently low measurement error (SEM < 1 month), whereas the trained novice reduced SEM from 3.7 to 2.5 months after one year of practical experience. Bland–Altman analyses demonstrated only minor systematic differences and no meaningful proportional bias in the overall sample, whereas only a minority of observations exceeded the predefined decision threshold. Conclusions DXA-derived TW3-RUS skeletal age assessment provides excellent repeatability and reproducibility under standardised conditions. Observer experience primarily improves measurement precision rather than ranking consistency. The principal contribution of this study is a complementary validation framework integrating repeatability, reproducibility, precision, agreement, and decision-oriented analyses, providing a methodological basis for evaluating observer-dependent assessment methods in sport science.
Exercise and sport sciences increasingly rely on statistical models to analyse complex data arising from experimental, observational, and longitudinal designs. However, applied research frequently shows misalignment between research questions, data structure, and analytical strategies, leading to biased inference, limited reproducibility, and reduced interpretability. Common challenges include small sample sizes, clustered and repeated measurements, suboptimal model selection, insufficient assessment of model assumptions, and the growing use of machine learning methods without transparent validation or reporting. This methodological narrative review synthesises key statistical challenges through a targeted literature search and proposes a decision-oriented framework to guide statistical model selection and reporting in exercise and sport sciences. The framework integrates study objective (explanatory vs. predictive), outcome type, data structure, and information content to support coherent and transparent analytical decisions. Classical models, penalised regression, and supervised machine learning approaches are discussed in terms of applicability, assumptions, interpretability, and validation requirements. A workflow figure and practical decision tables are provided to support model selection, test selection, validation, and robustness assessment. The overarching goal is to strengthen applied statistical reasoning and promote robust, interpretable, and reproducible quantitative research in exercise and sport sciences.
