Background: Cardiovascular capacity, expressed as maximal oxygen uptake (VO_2max), is a strong predictor of health and fitness and is considered a key measure of physiological function in the healthy adult population. The purpose of this study was to validate a specific step test (StepTest4all) as an adequate procedure to estimate cardiovascular capacity in young adults. Methods: The sample was composed of 56 participants, including 19 women (aged 21.05 ± 2.39 years, body mass = 57.50 ± 6.64 kg, height = 1.62 ± 0.05 m, body mass index = 22.00 ± 2.92 kg/m²) and 37 men (aged 22.05 ± 3.14 years, body mass = 72.50 ± 7.73 kg, height = 1.76 ± 0.07 m, body mass index = 23.34 ± 2.17 kg/m²). Participants were included in one of the following groups: (i) the group used to predict the VO_2max, and (ii) the group used to validate the prediction model. All participants performed the StepTest4all protocol. The step height and the intensity of the effort was determined individually. Heart rate and oxygen uptake were measured continuously during rest, effort, and recovery phases. The validation process included the following three stages: (i) mean data comparison, (ii) simple linear regression, and (iii) Bland–Altman analysis. Results: The linear regression retained, as significant predictors of the VO_2max, sex (p < 0.001) and heart rate recovery for one minute (p = 0.003). The prediction equation revealed a high relationship between measurements (R² = 63.0%, SEE = 5.58). The validation procedure revealed non-significant differences (p > 0.05) between the measured and estimated maximal oxygen uptake, high relationship (R² = 63.3%), and high agreement with Bland–Altman plots. Thus, VO_2max can be estimated with the formula: VO_2max = 22 + 0.3 · (HRR_1min) + 12 · (sex), where HRR_1min is the magnitude of the HR decrease (bpm) in one minute immediately after the step was stopped, and sex: men = 1, women = 0. Conclusions: The StepTest4all is an adequate procedure to estimate cardiovascular capacity, expressed as VO_2max, in young adults. In addition, it is possible to determine the qualitative level of cardiovascular capacity from the heart rate recovery for one minute, more specifically, poor: <20, moderate: 20 to 34, good: 35 to 49, and excellent: ≥50. This procedure has the benefit of being simple to apply and can be used by everyone, even at home, without specialist supervision.

Research question: While quality management literature has addressed products and services separately, the founders of total quality presented this management philosophy as universally oriented. The purpose of this study is to empirically test the Deming management model (DMM). Research methods: We tested the proposed research model using structural equation modeling, based on data collected from golf courses and ski resorts, in three different countries. Results and findings: The results support the application of the model to services, in general, and sports tourism, in particular. Implications: Our conclusions reinforce the recognition of the method's effectiveness and identify the cause–effect patterns, within its basic scope, highlighting the importance of leadership in the success of a quality improvement program. Lastly, a discrepancy of this study is the incapability to support the relationship between continuous improvement and customer satisfaction. Nevertheless, the results should be interpreted while considering the presented limitations.

This systematic review aimed to analyze the internal and external training load metrics used in basketball training and competition. A systematic search following PRISMA guidelines was conducted in the PubMed database from the earliest record to May 2022, and methodological quality was assessed using the Physiotherapy Evidence Database (PEDro) scale. A systematic search of the PubMed database identified 95 records, from which seven studies met the inclusion criteria. Internal load was primarily quantified using peak heart rate (HR) during 3×3 matches (%HRmax, n = 1), HR monitoring ( n = 3), training momentum ( n = 1), and rating of perceived exertion (RPE, n = 4). External load metrics included changes of direction and peripheral fatigue ( n = 1), vertical jumps ( n = 2), motion analysis, and GPS tracking ( n = 1). The most commonly applied training load measures were RPE, HR-based monitoring, and time-motion analysis. These findings provide valuable insights for optimizing workload management in basketball. Coaches can use individualized training approaches considering positional demands, integrate small-sided games to manipulate load, and apply wearable technology for real-time monitoring. Additionally, injury prevention strategies should incorporate acute-to-chronic workload ratios. However, it must be acknowledged that the restricted use of PubMed as the sole database severely limited the comprehensiveness of the review, and therefore the conclusion that only seven studies exist should be interpreted with caution. Further research is needed on female and youth players to enhance evidence-based monitoring strategies across different populations.