Sports Medicine最新文献

Several observational and experimental studies in humans have suggested that high protein intake (PI) causes intraglomerular hypertension leading to hyperfiltration. This phenomenon results in progressive loss of renal function with long-term exposure to high-protein diets (HPDs), even in healthy people. The recommended daily allowance for PI is 0.83 g/kg per day, which meets the protein requirement for approximately 98% of the population. A HPD is defined as a protein consumption > 1.5 g/kg per day. Athletes and bodybuilders are encouraged to follow HPDs to optimize muscle protein balance, increase lean body mass, and enhance performance. A series of studies in resistance-trained athletes looking at HPD has been published concluding that there are no harmful effects of HPD on renal health. However, the aim of these studies was to evaluate body composition changes and they were not designed to assess safety or kidney outcomes. Here we review the effects of HPD on kidney health in athletes and healthy individuals with normal kidney function.

Background

Golf is played both recreationally and professionally by approximately 66.6 million people worldwide. Despite the potential for nutrition to influence golf performance, research in this area is somewhat limited.

Objective

To identify the existing literature regarding nutrition and golf and where the current research gaps lie.

Design

Scoping review. Online databases were used to retrieve data from 2003 to the present day.

Data Sources

A three-step search strategy identified relevant primary and secondary articles as well as grey literature. Published and unpublished articles in the English language, identified by searching electronic databases (ProQuest Central, Web of Science, Scopus, SPORTDiscus and PubMed) and reference searching.

Review Methods

Relevant identified studies were screened for final inclusion. Data were extracted using a standardised tool to create a descriptive analysis and a thematic summary. In summary, studies were included if they focused on nutrition, hydration, energy requirements, supplements, or body composition in relation to golf.

Results and Discussion

Our initial search found 3616 relevant articles. Eighty-two of these articles were included for the scoping review. Nutrition has the potential to impact golf performance in areas including the maintenance of energy levels, cognitive function, and body composition. Currently, there is limited research available discussing the effects of nutrition interventions related specifically to golf performance.

Conclusion

This scoping review highlights that more work is needed to provide golfers and practitioners with golf-specific nutrition research. The key areas for future golf-specific nutrition research include nutrition on cognitive performance, body composition, energy requirements, supplementation, and the potential role of nutrition for the travelling golfer. Systematic reviews could also be used to identify future priorities for nutrition and golf research.

Background

Dyslipidemia is a primary risk factor for cardiovascular disease (CVD). Exercise training (EXTr) improves some lipid markers but not others; the literature is dated and analyses may be underpowered.

Objectives

To clarify which lipid markers are altered with ExTr and establish if information size had yet reached futility.

Methods

We conducted a systematic review/meta-analysis, with meta-regression, to establish expected effect size in lipid profile with aerobic (AT), resistance (RT) and combined (CT = AT + RT) ExTr. We conducted trial sequence analysis (TSA) to control for type I and II error and establish if information size had reached futility.

Results

We included 148 relevant randomized controlled trials (RCTs) of ExTr, with 227 intervention groups, total 8673 participants; exercise 5273, sedentary control 3400. Total cholesterol (TC) MD – 5.90 mg/dL (95% confidence interval (CI) – 8.14, – 3.65), high-density lipoprotein cholesterol (HDL) 2.11 (95% CI 1.43, 2.79), low-density lipoprotein cholesterol (LDL) – 7.22 (95% CI – 9.08, – 5.35), triglycerides – 8.01 (95% CI – 10.45, – 5.58) and very low-density lipoprotein cholesterol (VLDL) – 3.85 (95% CI – 5.49, – 2.22) all showed significant but modest 3.5–11.7%, improvements following ExTr. TSA indicated all analyses exceeded minimum information size to reach futility. CT was optimal for dyslipidemia management. Meta-regression showed every extra weekly aerobic session reduced TC – 7.68 mg/dL and for every extra week of training by – 0.5 mg/dL. Each minute of session time produced an additional 2.11 mg/dL HDL increase.

Conclusion

TSA analysis revealed sufficient data exist to confirm ExTr will improve all five lipid outcomes. CT is optimal for lipid management. The modest effect observed may moderate dyslipidemia medication for primary prevention. Prediction intervals suggest TC, HDL, LDL and TGD are only improved in one-quarter of studies.

Background

Considering the finite time within a 24-h day, the distribution of time spent on movement behaviours has been found to be associated with health outcomes.

Objectives

This systematic review and meta-analysis aimed to summarise and evaluate the overflow effects of interventions targeting a single behaviour (physical activity, sedentary behaviour/screen time, or sleep) on other non-targeted behaviours among children and adolescents.

Methods

Six databases (MEDLINE [Ovid], PsycINFO [ProQuest], EMBASE [Ovid], PubMed, Web of Science and SPORTDiscus [EBSCO]) were searched for relevant studies published before 13 May, 2024. Randomised controlled trials and clustered randomised controlled trials that targeted a single behaviour and also assessed the effects on non-targeted behaviours, comprised of healthy children under the age of 18 years, were included. Movement behaviours can be measured either objectively or subjectively. The revised Cochrane risk-of-bias tool for randomised trials was adopted to evaluate the risk of bias.

Results

A total of 102 studies with 45,998 participants from 21 countries were identified, and 60 of them with 26,183 participants were incorporated into the meta-analysis. The meta-analysis demonstrated that physical activity interventions led to a reduction in the proportion of each day spent in sedentary behaviour (mean difference = − 0.95% of wear time, 95% confidence interval − 1.44, − 0.45, I² = 39%). Sedentary behaviour interventions resulted in increased standing time (mean difference = 3.87%, 95% confidence interval 1.99, 5.75, I² = 0%). Interventions targeting screen time did not yield changes in physical activity or sleep. The findings on the effectiveness of sleep interventions on non-targeted behaviours and of physical activity interventions on sleep were inconclusive.

Conclusions

Overall, the findings suggested that interventions aimed at increasing physical activity or reducing sedentary behaviour had overflow effects on non-targeted behaviours, but the effect sizes were small. Additional evidence is needed to reach definitive conclusions regarding the impact of behaviour change interventions on sleep and of the overflow effects of sleep interventions.

Background

The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Exploring individual risk factors, such as concussion history, may help to improve existing concussion risk models and algorithms.

Objectives

The primary aim of the current study was to use machine learning techniques to develop a comprehensive, prospectively coded concussion risk model in professional hockey among players exhibiting visible signs. The secondary aim was to examine whether including concussion history improves model performance.

Methods

Data from the National Hockey League (NHL) spotter program, including coded visible signs and mechanisms of injury associated with possible concussive events, were extracted from the 2018–2019 to the 2021–2022 seasons. Each unique spotter event was matched with data extracted from the medical record to determine whether the event was associated with a subsequent physician diagnosed concussion. We compared the ability of three machine learning-based approaches to identify the likelihood of physician diagnosed concussion: conditional inference tree, conditional inference random forest, and logistic regression.

Results

A total of 1563 unique events with visible signs were identified by spotters (183 leading to a concussion diagnosis). A randomly selected training sample had 1250 events (146 concussions) and the remaining set-aside test sample had 313 events (37 concussions). The obtained models performed at a high level with large effects in the training [area under the receiver operating characteristic curve (AUC) = 0.79] and set-aside test data (AUC = 0.82). Concussion history was retained in the tree and logistic regression models, with each additional prior concussion associated with a 1.32 times increased odds of concussion diagnosis.

Conclusions

We present simple tree and logistic algorithms for concussion screening and as diagnostic aids. Our results show that player concussion history can explain additional risk above and beyond that explained by visible signs and mechanisms of injury alone.

Determining whether repetitive head impacts (RHI) cause the development of chronic traumatic encephalopathy (CTE)-neuropathological change (NC) and whether pathological changes cause clinical syndromes are topics of considerable interest to the global sports medicine community. In 2022, an article was published that used the Bradford Hill criteria to evaluate the claim that RHI cause CTE. The publication garnered international media attention and has since been promoted as definitive proof that causality has been established. Our counterpoint presents an appraisal of the published article in terms of the claims made and the scientific literature used in developing those claims. We conclude that the evidence provided does not justify the causal claims. We discuss how causes are conceptualised in modern epidemiology and highlight shortcomings in the current definitions and measurement of exposures (RHI) and outcomes (CTE). We address the Bradford Hill arguments that are used as evidence in the original review and conclude that assertions of causality having been established are premature. Members of the scientific community must be cautious of making causal claims until the proposed exposures and outcomes are well defined and consistently measured, and findings from appropriately designed studies have been published. Evaluating and reflecting on the quality of research is a crucial step in providing accurate evidence-based information to the public.

Graphical abstract