Background: Stroke, which is a common clinical cerebrovascular disease, causes approximately 83% of survivors to suffer from balance impairments. Balance and gait training (BGT) is widely used to restore balance in patients with stroke. However, its wide variety presents clinicians with a dilemma when selecting interventions. This study aimed to compare and rank BGT interventions by quantifying information based on randomized controlled trials (RCTs).Methods: We conducted a network meta-analysis (NMA) of non-gait-trained controls and head-to-head RCTs and compared the effects of 12 BGT interventions. A total of nine literature databases, including Medline, Embase, Cochrane Library, Web of Science, Scopus, SPORTDiscus, ClinicalTrials.gov, CNKI, and Chinese biomedical literature databases, were searched from their database inception to August 2023. Two authors independently selected studies and extracted data. The difference in outcomes, which were expressed as standardized mean differences and confidence intervals (CIs) of 95%, were explored in this meta-analysis.Results: A total of 66 studies with 1,933 participants were included. Effect size estimates showed that not all BGT interventions were more effective than controls, with treadmill training as the least effective for balance test batteries (SMD = −0.41, 95% CI [−1.09, 0.27]) and proactive balance (SMD = −0.50, 95% CI [−1.14, 0.14]). Body-weight-supported treadmill training with external stimulation was most effective for proactive balance and dynamic steady-state balance (SMD = 1.57, 95% CI [−0.03, 3.16]); SMD = 1.18, 95% CI [0.67, 1.68]. Virtual reality gait training (SMD = 1.37, 95% CI [0.62, 2.11]) had the best effect on improving balance test batteries, while dual-task BGT (SMD = 1.64, 95% CI [0.50, 2.78]) had the best effect on static steady-state balance. After analyses for possible impact covariates, the findings through the outcomes did not change substantially. Confidence in the evidence was generally low or very low.Conclusion: This NMA suggested that virtual reality gait training was the most effective BGT modality for improving balance test batteries. Body-weight support treadmill training with external stimulation was the most effective for improving active and dynamic balance. In addition, dual-task BGT was the best choice for improving static balance. However, balance is a multidimensional concept, and patients’ different needs should be considered when selecting BGT.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022316057, ID: CRD42022316057.

Pre-competition weight loss (WL) is a widely adopted strategy in weight-sensitive sports, particularly among combat sport. This strategy enables athletes to compete at a weight or weight classes below their habitual body mass (BM). Although research on WL for weight-sensitive sports has steadily grown, it mostly employs the term "RWL". However, there is no compelling or universally accepted definition of RWL. In this current opinion, we discussed the deficiencies and misuse of the current definition of RWL and proposed a new theoretical framework to provide a basis for better defining RWL in the future. A most commonly used definition describes RWL as losing 5% of body mass within 7 days. However, this definition is somewhat arbitrary and lacks empirical justification. This definition is based on two parameters: magnitude (5% BM) and duration (7 days), yet its cut-off remains rigid and lacks empirical support. Several studies on combat athletes have found that a relatively faster rate of WL can lead to significant negative impacts on performance and health variables. We argue that using WL rate as the core criterion is more robust, as the term "rapid" emphasizes the speed of WL. However, at what rate does WL become classified as RWL? The limited research in this area underscores the need for studies to establish a solid foundation for defining RWL. In many studies, authors use the term "RWL," especially in quantitative research. However, using the current definition, some practices reported by participants (e.g., losing 2% BM over 30 days) do not qualify as RWL. Finally, we propose that WL (not RWL) practices be classified along two dimensions: WL rate and WL magnitude. Based on rate, practices can be categorized as slow, moderate, or rapid WL. Based on magnitude, they can be classified as low, moderate, or high WL.