Introduction: Explosive power is considered an important factor in competitive events. Thus, strategies such as complex training (CT) and plyometric training (PLT) are effective at improving explosive power. However, it is still not clear which of the two strategies can enable greater improvements on the explosive power. Thus, the aim of this systematic review was to compare the effects of PLT and CT on the explosive power of the lower limbs. Methods: The Review Manager and GraphPad Prism programs were used to analyze the synthetic and time effects (effects over training time) on explosive power (i.e., jump ability, sprint ability) and maximum strength. Our research identified 87 studies comprising 1,355 subjects aged 10-26.4 years. Results: The results suggested the following: 1) Synthetic effects on jump ability (Hedges' g): .79 (p < .001) for unloaded PLT, 1.35 (p < .001) for loaded PLT and .85 (p < .001) for CT; 2) Synthetic effects on sprint ability: .83 (p < .001) for unloaded PLT, -2.11 (p < .001) for loaded PLT and -.78 (p < .001) for CT; 3) Synthetic effects on maximum strength: .84 (p < .001) for loaded PLT and 1.53 (p < .001) for CT; 4) The time effects of unloaded PLT and CT on explosive power were similar, but the time effects of CT on maximum strength were obviously above that of PLT. Discussion: In conclusion, unloaded PLT and CT have a similar effect on explosive performance in the short term but loaded PLT has a better effect. The improvement of the maximum strength caused by CT was greater than that induced by PLT. In addition, more than 10 weeks of training may be more beneficial for the improvement of power. Therefore, for explosive power training, we suggest adopting unloaded or light-loaded PLT during a short season and applying CT during an annual or long training cycle.
Introduction: The efficacy of low-intensity blood flow restriction (LI-BFR) training programs in bone metabolism remains unclear compared to low-intensity (LI) training and high-intensity (HI) training. The aim of this review was to quantitatively identify the effects of LI-BFR training on changes in bone formation markers (i.e., bone-specific alkaline phosphatase, BALP), bone resorption (i.e., C-terminal telopeptide of type I collagen, CTX) and bone mineral density (BMD) compared with conventional resistance training programmes. Additionally, the effectiveness of walking with and without BFR was assessed. Methods: PubMed, Scopus, SPORTDiscus, Web of Science and Google Scholar databases were searched for articles based on eligibility criteria. Review Manager Version 5.4 was used for Meta-analysis. Physiotherapy Evidence Database (PEDro) was applied to assess the methodological quality of studies. Results: 12 articles were included in the meta-analysis, with a total of 378 participants. Meta-results showed that compared with LI training, LI-BFR training induced greater increments in BALP (young adults: MD = 6.70, p < 0.001; old adults: MD = 3.94, p = 0.002), slight increments in BMD (young adults: MD = 0.05, p < 0.00001; old adults: MD = 0.01, p < 0.00001), and greater decrements in CTX (young adults: MD = -0.19, p = 0.15; old adults: MD = -0.07, p = 0.003). Compared with HI training, LI-BFR training produced smaller increments in BALP (young adults: MD = -6.87, p = 0.24; old adults: MD = -0.6, p = 0.58), similar increments in BMD (MD = -0.01, p = 0.76) and similar decrements in CTX (young adults: MD = 0, p = 0.96; old adults: MD = -0.08, p = 0.13). Although there were only two studies on walking training intervention, walking training with BFR had a better effect on bone metabolism than training without BFR. Discussion: In conclusion, LI-BFR training induces greater improvements in bone health than LI training, but is less effective than HI training. Therefore, LI-BFR training may be an effective and efficient way to improve bone health for untrained individuals, older adults, or those undergoing musculoskeletal rehabilitation. Clinical Trial Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42023411837].
This meta-analysis aimed to examine the effects of plyometric training on physical fitness attributes in handball players. A systematic literature search across PubMed, SCOPUS, SPORTDiscus, and Web of Science identified 20 studies with 563 players. Plyometric training showed significant medium-to-large effects on various attributes: countermovement jump with arms (ES = 1.84), countermovement jump (ES = 1.33), squat jump (ES = 1.17), and horizontal jump (ES = 0.83), ≤ 10-m linear sprint time (ES = -1.12), > 10-m linear sprint time (ES = -1.46), repeated sprint ability with change-of-direction time (ES = -1.53), agility (ES = -1.60), maximal strength (ES = 0.52), and force-velocity (muscle power) (ES = 1.13). No significant impact on balance was found. Subgroup analysis indicated more pronounced agility improvements in players ≤ 66.6 kg compared to > 66.6 kg (ES = -1.93 vs. -0.23, p = 0.014). Additionally, greater improvements were observed in linear sprint and repeat sprint ability when comparing training durations of > 8 weeks with those ≤ 8 weeks (ES = -2.30 to -2.89 vs. ES = -0.92 to -0.97). In conclusion, plyometric training effectively improves various physical fitness attributes, including jump performance, linear sprint ability, maximal strength, muscle power and agility.
Although physical activity (PA) has a profound impact on health, many college and university students are still physically inactive. There is some evidence to suggest that social support (SS) could impact the PA levels of students, but the internal relationship and specific effects are not very clear. The purpose of this review was to determine the strength of the relationship between SS and PA and examine whether any potential associations differed in terms of age, gender, and region among college and university students. Studies were identified using the following electronic databases: PubMed, SPORTDiscus, Web of Science, and Sociological Abstracts. Moderator analyses investigating the effects of students' age, gender, and region (nation) were performed. This review included 19 articles. The results showed total SS was significantly associated with PA (r = 0.30, 95% confidence interval [CI]: [0.22, 0.37], p < .001). With respect to different types of support, friend support was more strongly associated with PA than family support. Gender factors had a significant moderating effect on the correlation between SS and PA (QM = 17.433, p < .001). Separate analyses examining the moderating effects of gender (percentage of females) found that the association between SS and PA was stronger with the increase in female percentage. In conclusion, SS is an important factor associated with PA levels and should foster SS within intervention programs according to types of SS and gender differences to increase PA levels among college and university students.
Accurate risk assignment in childhood acute lymphoblastic leukaemia is essential to avoid under- or over-treatment. We hypothesized that time-series gene expression profiles (GEPs) of bone marrow samples during remission-induction therapy can measure the response and be used for relapse prediction. We computed the time-series changes from diagnosis to Day 8 of remission-induction, termed Effective Response Metric (ERM-D8) and tested its ability to predict relapse against contemporary risk assignment methods, including National Cancer Institutes (NCI) criteria, genetics and minimal residual disease (MRD). ERM-D8 was trained on a set of 131 patients and validated on an independent set of 79 patients. In the independent blinded test set, unfavourable ERM-D8 patients had >3-fold increased risk of relapse compared to favourable ERM-D8 (5-year cumulative incidence of relapse 38·1% vs. 10·6%; P = 2·5 × 10-3 ). ERM-D8 remained predictive of relapse [P = 0·05; Hazard ratio 4·09, 95% confidence interval (CI) 1·03-16·23] after adjusting for NCI criteria, genetics, Day 8 peripheral response and Day 33 MRD. ERM-D8 improved risk stratification in favourable genetics subgroups (P = 0·01) and Day 33 MRD positive patients (P = 1·7 × 10-3 ). We conclude that our novel metric - ERM-D8 - based on time-series GEP after 8 days of remission-induction therapy can independently predict relapse even after adjusting for NCI risk, genetics, Day 8 peripheral blood response and MRD.