Sports Medicine - Open最新文献

Background: Collating evidence on the relationship between training load and physical fitness adaptations in team sport players can help refine training plans.

Objectives: This systematic review and meta-analysis aimed to: (i) identify research designs and methods examining the relationship between training load and physical performance in team sport players, and (ii) synthesize key findings, advancing to meta-analysis where correlations could be established.

Methods: Eligible participants were men and women in team sports at least at the trained or developmental level. Studies included had training loads of at least two weeks with pre-post physical performance evaluations. Searches were conducted in PubMed, Scopus, SPORTDiscus, and Web of Science. Study quality was assessed using the Downs and Black scale, and evidence certainty was evaluated with the GRADE framework.

Results: Of 29,552 records screened, 40 studies with 726 players were included. Significant correlations were evident between heart rate (HR)-based training impulse (TRIMP) and changes in maximal oxygen uptake (r = 0.63, p < 0.05), velocity at a blood lactate concentration of 2 mmol·L⁻¹ (V_LT) (r = 0.47, p < 0.05), and velocity at the onset of blood lactate accumulation (r = 0.43, p < 0.01). A significant correlation between session-rating of perceived exertion (sRPE) and changes in V_LT (r = 0.29, p < 0.05) was found.

Conclusions: HR-based TRIMP methods to quantify internal training load appear to be effective indicators for potential endurance adaptations around training phases in team sport players. In contrast, sRPE and external training load measures demonstrated limited associations with performance adaptations surrounding training.

Background: The emergence of wearable technology offers enhanced real-time health management, including sleep, recovery, and exercise optimization. Despite their potential to monitor load-recovery parameters in elite athletes, the selection, combination, and interpretation or reliance of metrics in relation to perceived impact remain unclear.

Objective: This study assessed data from three wearables measuring sleep, continuous glucose, and exercise, together with the Profile of Mood State (POMS) dimensions alongside subjective ratings via the Readiness Advisor application (RA app) (Silicon Valley Exercise Analytics, svexa, Menlo Park, California, USA) to evaluate their association and value in load-recovery monitoring.

Methods: Twenty national team endurance athletes, competing at the highest international level, were monitored during one year of training, recovery, and competitions. Data collections were made with Global Positioning System (GPS) watches and heart rate monitors, Ōura rings (Ōura Health OY, Oulu, Finland), continuous glucose monitors, POMS questionnaires and subjective ratings in the RA app.

Results: Significant correlations were found between each RA question and its counterpart in a linear mixed model (r values = 0.39-0.81). However, time series analysis through autoregressive integrated moving average (ARIMA analysis) revealed individual variability.

Conclusions: These findings indicate an influence of external aspects and advocate for a multifaceted approach to the assessment of load-recovery balance, well-being and performance. Moreover, personalized analyses proved more accurate than group averages, emphasizing the need for individualized monitoring. Integrating subjective and objective data appears essential for nuanced understanding of the athlete status, advancing high-performance monitoring and athletic health management.

Background: Human Pose Estimation (HPE) has gained increasing attention in sports research due to advancements in Deep Learning (DL) movement skills, which enable precise joint localization in 2D and 3D visual data. DL-based HPE facilitates non-invasive analysis of movement patterns in real-world settings, providing actionable insights for training, performance optimisation, and injury prevention. This systematic review examines the application of DL-based HPE in sports, focusing on the availability and accessibility of training datasets, reproducibility for practitioners, and the influence of human factors. The review also offers recommendations to guide future research and applications.

Methods: A systematic search following PRISMA guidelines was conducted across four databases-Scopus, Web of Science, the Association for Computing Machinery, and SPORTDiscus, yielding 371 articles. Two independent reviewers applied inclusion and exclusion criteria to identify relevant studies, with a third reviewer resolving conflicts. Key aspects analysed included the scope of DL-based HPE applications, dataset characteristics, and algorithmic approaches. A supplementary search was conducted to include contemporary literature published since the initial search date. Data were synthesized descriptively, focusing on trends and limitations in the evidence base.

Results: The identified applications of DL-based HPE in sports were categorized into four domains: movement skill analysis, action recognition, augmented coaching tools, and officiating support. Most studies relied on private datasets for algorithm training and validation, limiting reproducibility and generalizability. Bespoke multi-model algorithms were the most common approach, and single person pose estimation predominated. Despite its potential, the lack of open datasets and standardized practices poses challenges for broader adoption and practical implementation. These findings were echoed in the supplementary search which added no significant findings outside what previous studies had demonstrated.

Conclusions: This review represents the first systematic evaluation of DL-based HPE from a sports science perspective, offering practical guidance for future research and applications. The findings highlight the need for open, standardized datasets and reproducible methodologies to advance the field. Future research should address these limitations while exploring innovative applications to maximize the impact of DL-based HPE in sports science.

Background and aim: The rapid aging of the global population is expected to lead to an increase in the incidence and prevalence of neurodegenerative diseases. Endurance exercise training is considered one of the most effective forms of prevention against neurodegenerative diseases. This study investigated the effects of a 5-year exercise training intervention at varying intensities on cognitive function in healthy older adults.

Methods: 1486 healthy older men (n = 735) and women (n = 751) aged 70-77 years from Trondheim, Norway, were randomly assigned to high-intensity interval training (HIIT, n = 382), moderate-intensity continuous training (MICT, n = 367), or a control group (n = 737) following the national physical activity (PA) guidelines. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) score after 3, 5 and 10 years. A linear mixed model was used to estimate the effect of the exercise intervention between the groups. Measures of physical health, such as cardiorespiratory fitness (CRF), and PA level was also measured.

Results: After 10 years, we observed no significant differences in MoCA scores between the exercise training groups and the control group, despite variations in exercise intensity and good adherence to the exercise interventions. The HIIT group consistently showed higher CRF throughout the study period.

Conclusions: While structured aerobic exercise training over 5 years improved physiological health markers in healthy older adults, its impact on mitigating cognitive decline was limited. Further long-term training studies in different populations are needed to better understand the link between exercise training and cognition in older adults. Trial Registration The Generation 100 Study is registered at ClinicalTrials.gov (Identifier: NCT01666340), registered on 21 August 2012: https://clinicaltrials.gov/study/NCT01666340.

In 2016, a literature review to examine the psychological effects of strength exercises for people who are overweight or obese was conducted, incorporating 17 included studies. Although some positive results were found, the authors concluded that there is a strong need to accumulate more evidence before drawing conclusions. Therefore, the current review aimed to update the overview of existing literature on this topic repeating the same procedures. Additional relevant literature (updated search #1 on 6 April 2022, and #2 on 19 January 2024) was identified by use of the PubMed, PsycINFO, the Psychology and Behavioural Sciences Collection, and SCOPUS databases. Following the original procedures, this review focused on studies with an independent identifiable effect of strength exercises on psychosocial determinants in people with overweight or obesity. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment Tool. Effect sizes and corresponding variance estimates were extracted or calculated for the main effects of strength exercises on psychological outcomes. In total, 27 studies (10 additional studies compared to the 2016 review) representing 12,861 participants were included. In addition to the eight broad categories of psychological outcomes (psychological disorders, inhibition, mood, outcome expectations, quality of life, self-efficacy, self-esteem, and stress), two more outcome categories (motivation and executive functions) were identified. No large differences were found between the original and updated literature review: some weak positive effects were found for inhibition, self-efficacy, self-esteem, and psychological disorders but not for the other categories. In conclusion, the literature on the effects of strength exercises on psychological outcomes is - almost 8 years after the original review - still fragmented and more evidence is needed to draw profound conclusions. Future research may consider that the psychological effects of strength exercises might be stronger among people who are overweight or obese when they are in contexts in which they can engage in favorable comparisons with normal-weight counterparts.

Background: 'FUNBALL' is a new multi-component exercise-based injury prevention programme designed specifically for youth football players. Its efficacy in reducing the overall number of injuries has been previously reported. The aim of this study was to investigate the effects of the 'FUNBALL' programme in reducing the incidence of severe injuries (absence from training/match ≥ 28 days) in young male football players.

Methods: 55 football teams from Kosovo, 21 in the Under-15, 22 in the Under-17, and 12 in the Under-19 age groups, were cluster-randomly assigned to the intervention or the control group. The intervention group performed the 'FUNBALL' programme after their usual warm-up at least twice per week. The control group followed their usual training routine. Teams were followed for one football season (August 2021-May 2022). The outcome for the present analysis is severe injuries.

Results: The overall incidence rate (IR) was 0.31/1000 football hours in the intervention group and 0.62/1000 football hours in the control group. Players in the age group of the Under-19s sustained the highest number of severe injuries (IR 0.77/1000 football hours). The overall number of severe injuries was significantly reduced by 49% (incidence rate ratio (IRR) 0.51; 95% CI 0.28-0.91; P = 0.02), with a 63% reduction in those that occurred during training (IRR 0.37; 95% CI 0.15-0.87; P = 0.02). When analyzed by age group, only the Under-17s showed a significant reduction of 76% (IRR 0.24; 95% CI 0.06-0.82; P = 0.02). The low number of subgroup injuries prevented statistical significance. However, a promising protective effect was observed. Severe knee injuries were reduced by 62%. By injury type, sprains or ligament injuries were reduced by 67%, and meniscus or cartilage lesions by 58%. Overuse/growth-related injuries were reduced substantially by 85%.

Conclusion: The 'FUNBALL' programme showed a large efficacy in reducing the incidence of severe injuries in young male football players. Considering that these injuries cause the longest absence from football, it is recommended to implement the programme at least twice per week to exert a preventative effect.

Trail registration number: Clinical trials NCT05137015.

Background: In baseball, batting performance can be measured using game and advanced statistics as well as hitting metrics. To date, the core set of individual characteristics or skills associated with superior batting performance remains to be identified. The aim of this scoping review was to identify and classify the individual characteristics or skills associated with baseball batting performance indicators and describe the methods used to assess these individual characteristics or skills and batting performance indicators.

Methods: A scoping review design was chosen to conduct a systematic literature search. Electronic searches of MEDLINE, SPORTDiscus, and PsycINFO databases were undertaken from inception to August 2024. Cross-sectional studies that investigated the relationship between batting performance indicators and individual characteristics or skills in male or female baseball batters were selected.

Results: Twenty-two cross-sectional studies investigating potential individual characteristics or skills of baseball batting performance met the inclusion criteria. The primary baseball batting performance indicators were grouped into three categories: game statistics, advanced statistics and hitting metrics. Anthropometric measures (height, weight), physical fitness tests (1-RM bench and squat, grip strength, jumps, medicine ball throws, sprint, trunk flexibility, etc.), visual skills (visual acuity, contrast sensitivity, etc.), perceptual skills (anticipation, visual recognition, etc.) and visuomotor skills (eye-hand coordination, reaction time, etc.) were the individual characteristics or skills associated with either game statistics, advanced statistics or hitting metrics.

Conclusions: Based on the studies included in this scoping review, the results show that several anthropometrics, physical, perceptual-cognitive, and visual skills were associated with superior game statistics, advanced statistics or hitting metrics. Greater height, weight, upper- and lower-body muscle strength, power, and speed, as well as oculomotor skills, visual system characteristics, anticipation, visual recognition, and visuomotor skills corresponded to better batting performance.

Background: Upper limb impairment is common among women receiving breast cancer treatment. Although yoga is popular and accessible in cancer support communities, its impact on upper limb function in these women is not well understood. This randomized controlled trial investigated the effects of yoga on shoulder range of motion in women recovering from breast cancer surgery. Eligible participants were Chinese women with early-stage breast cancer who had recently completed surgery and adjuvant treatment. Following the baseline assessment, they were randomly assigned (1:1:1) to a 12-week yoga intervention, a 12-week relaxation intervention (active control), or a postoperative exercise DVD (passive control). Primary outcomes measured shoulder rotation flexibility and range of motion. Secondary outcomes included health-related quality of life, fatigue, pain, sleep quality, anxiety, and depression. All outcomes were assessed at baseline, immediately post-intervention, and at 3, 6, and 12 months post-intervention.

Results: Out of 760 potential participants, 444 were enrolled and randomly assigned to yoga intervention (n = 148), active control (n = 148), or passive control (n = 148). Linear Mixed Modelling indicated that, compared to the Passive control group, the Yoga group showed significant improvement in shoulder rotation flexibility scores (p = 0.004) and right hand up back scratch test scores (p = 0.013) at all time points. There was no significant difference in the back scratch test scores for the left hand up between groups. Secondary outcomes did not significantly differ among the study groups.

Conclusions: Study findings suggest that yoga intervention can improve upper-extremity mobility in women with breast cancer.

Trial registration: HKU Clinical Trials Registry, HKUCTR-1600. Registered 25 May 2013, https://www.hkuctr.com/Study/Show/36d8e3e8d051473b9c2e69bc8ee35dd1 .

Background: Knee injuries, including those requiring surgical intervention, are a leading cause of disability in U.S. Army soldiers. Previous studies investigating the risk of injury after surgery have primarily focused on civilian populations and lack specific information on the timing of new injuries.

Objective: The purpose of this study was to identify the windows of peak injury risk after knee surgery in active-duty soldiers. We hypothesized that injury risk would peak around return to duty and remain elevated beyond completion of post-operative rehabilitation.

Methods: U.S. Army administrative and medical data were extracted from the Soldier Performance, Health, and Readiness database. An accelerated failure time model adjusted for demographic-, surgical-, and military-specific variables was used to identify post-operative injury risk by procedure type.

Results: Of the 7595 soldiers who had knee surgery from 2017 to 2020, 71% (5357) sustained a new musculoskeletal injury within 4 years after surgery. Soldiers with a prior non-knee injury were estimated to sustain re-injury 11% sooner than those without a history of injury, regardless of surgical procedure. Peak hazard for injury occurred around 2.5 months after surgery when physical activity restrictions were lifted. Furthermore, soldiers were more likely to sustain an injury after reaching 14.3 months post-surgery.

Conclusions: These results indicate that both at the end of rehabilitation and 1 year post-surgery may represent critical windows for targeted injury-prevention strategies; however, future studies are needed to confirm these opportunities.

Clinical relevance: These findings suggest potential implications for guiding provider care and informing future policy in military post-operative management, with the goal of improving long-term outcomes.

Background: Individual factors related to performance in age group triathletes competing in different race distances have been explored in scientific literature. However, only a few studies have been conducted using machine learning (ML) predictive models to explore the importance of those individual factors. This study intended to build and analyze machine learning regression models that predict the performance of IRONMAN^® 70.3 age group triathletes, considering sex, age, country of origin, and event location as predictive factors. A total of 823,464 finishers´ records (625,398 men and 198,066 women) of IRONMAN^® 70.3 age group triathletes participating in 197 different events in 183 different locations between 2004 and 2020 were analyzed. The triathletes' sex, age, country of origin, event location and year, and race finish times were thus obtained and considered for the study. Four different ML regression models were built to predict the triathletes' race times from their age, sex, country of origin, and race location. The model with the best performance was then selected and further analyzed using model-agnostic interpretability tools to understand which factors would contribute most to the model predictions.

Results: The Random Forest Regressor model obtained the best predictive score. This model's partial dependence plots indicated that men under 30 years, from Switzerland or Denmark, competing in IRONMAN^®70.3 Austria/St. Polten, IRONMAN^® 70.3 Switzerland, IRONMAN^® 70.3 Sunshine Coast, and IRONMAN^® 70.3 Busselton presented the best performance.

Conclusions: Our results prove that ML models can be used to examine the complex, non-linear interactions between the factors that influence performance and gain insights that can help IRONMAN^® 70.3 age group triathletes better plan their races.