Alannah K. A. McKay, T. Stellingwerff, E. Smith
et al.
Throughout the sport-science and sports-medicine literature, the term "elite" subjects might be one of the most overused and ill-defined terms. Currently, there is no common perspective or terminology to characterize the caliber and training status of an individual or cohort. This paper presents a 6-tiered Participant Classification Framework whereby all individuals across a spectrum of exercise backgrounds and athletic abilities can be classified. The Participant Classification Framework uses training volume and performance metrics to classify a participant to one of the following: Tier 0: Sedentary; Tier 1: Recreationally Active; Tier 2: Trained/Developmental; Tier 3: Highly Trained/National Level; Tier 4: Elite/International Level; or Tier 5: World Class. We suggest the Participant Classification Framework can be used to classify participants both prospectively (as part of study participant recruitment) and retrospectively (during systematic reviews and/or meta-analyses). Discussion around how the Participant Classification Framework can be tailored toward different sports, athletes, and/or events has occurred, and sport-specific examples provided. Additional nuances such as depth of sport participation, nationality differences, and gender parity within a sport are all discussed. Finally, chronological age with reference to the junior and masters athlete, as well as the Paralympic athlete, and their inclusion within the Participant Classification Framework has also been considered. It is our intention that this framework be widely implemented to systematically classify participants in research featuring exercise, sport, performance, health, and/or fitness outcomes going forward, providing the much-needed uniformity to classification practices.
K. Campbell, K. Winters-Stone, J. Wiskemann
et al.
ABSTRACT Purpose The number of cancer survivors worldwide is growing, with over 15.5 million cancer survivors in the United States alone—a figure expected to double in the coming decades. Cancer survivors face unique health challenges as a result of their cancer diagnosis and the impact of treatments on their physical and mental well-being. For example, cancer survivors often experience declines in physical functioning and quality of life while facing an increased risk of cancer recurrence and all-cause mortality compared with persons without cancer. The 2010 American College of Sports Medicine Roundtable was among the first reports to conclude that cancer survivors could safely engage in enough exercise training to improve physical fitness and restore physical functioning, enhance quality of life, and mitigate cancer-related fatigue. Methods A second Roundtable was convened in 2018 to advance exercise recommendations beyond public health guidelines and toward prescriptive programs specific to cancer type, treatments, and/or outcomes. Results Overall findings retained the conclusions that exercise training and testing were generally safe for cancer survivors and that every survivor should “avoid inactivity.” Enough evidence was available to conclude that specific doses of aerobic, combined aerobic plus resistance training, and/or resistance training could improve common cancer-related health outcomes, including anxiety, depressive symptoms, fatigue, physical functioning, and health-related quality of life. Implications for other outcomes, such as peripheral neuropathy and cognitive functioning, remain uncertain. Conclusions The proposed recommendations should serve as a guide for the fitness and health care professional working with cancer survivors. More research is needed to fill remaining gaps in knowledge to better serve cancer survivors, as well as fitness and health care professionals, to improve clinical practice.
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.
Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introduce TCM3CEval, a benchmark assessing LLMs in TCM across three dimensions: core knowledge mastery, classical text understanding, and clinical decision-making. We evaluate diverse models, including international (e.g., GPT-4o), Chinese (e.g., InternLM), and medical-specific (e.g., PLUSE). Results show a performance hierarchy: all models have limitations in specialized subdomains like Meridian & Acupoint theory and Various TCM Schools, revealing gaps between current capabilities and clinical needs. Models with Chinese linguistic and cultural priors perform better in classical text interpretation and clinical reasoning. TCM-3CEval sets a standard for AI evaluation in TCM, offering insights for optimizing LLMs in culturally grounded medical domains. The benchmark is available on Medbench's TCM track, aiming to assess LLMs' TCM capabilities in basic knowledge, classic texts, and clinical decision-making through multidimensional questions and real cases.
Manlio De Domenico, Luca Allegri, Guido Caldarelli
et al.
The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.
Guillem Capellera, Luis Ferraz, Antonio Rubio
et al.
Understanding trajectories in multi-agent scenarios requires addressing various tasks, including predicting future movements, imputing missing observations, inferring the status of unseen agents, and classifying different global states. Traditional data-driven approaches often handle these tasks separately with specialized models. We introduce TranSPORTmer, a unified transformer-based framework capable of addressing all these tasks, showcasing its application to the intricate dynamics of multi-agent sports scenarios like soccer and basketball. Using Set Attention Blocks, TranSPORTmer effectively captures temporal dynamics and social interactions in an equivariant manner. The model's tasks are guided by an input mask that conceals missing or yet-to-be-predicted observations. Additionally, we introduce a CLS extra agent to classify states along soccer trajectories, including passes, possessions, uncontrolled states, and out-of-play intervals, contributing to an enhancement in modeling trajectories. Evaluations on soccer and basketball datasets show that TranSPORTmer outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation. https://youtu.be/8VtSRm8oGoE
IntroductionExercise is a crucial method for preventing geriatric depression. In this cross-sectional study, we investigated the associations between exercise habits in adolescence and old age and geriatric depressive symptoms.MethodsThis study used baseline data from the Bunkyo Health Study, a prospective observational cohort study investigating the preventive effects of physical activity on causative diseases requiring long-term care. This analysis included 1,629 older adults (687 men and 942 women) aged 65–84 years who participated in the Bunkyo Health Study. Participants were divided into four groups according to their exercise habits in adolescence and old age: never exercised (none-none; NN), exercised only in old age (none-active; NA), exercised only in adolescence (active-none; AN), and exercised in adolescence and old age (active-active; AA). Geriatric depressive symptoms were defined as the short version of the Geriatric Depression Scale score ≥ 5, including depression tendency. Multivariate-adjusted logistic regression models were used to estimate the odds ratios (ORs) and associated 95% confidence intervals in each group for the prevalence of geriatric depressive symptoms compared with the NN group.ResultsThe ORs for geriatric depressive symptoms were notably lower in the AN, NA, and AA groups than in the NN group in both older men and older women.ConclusionThese results indicate that older adults with exercise habits in adolescence and/or in old age exhibit a lower prevalence of geriatric depressive symptoms.
Objective: Current treatment for knee Osteoarthritis (OA) includes exercise and intra-articular injections with corticosteroid (CS), hyaluronic acid (HA), etc., which address OA-related pain and functional limitation. While these interventions can be given together, little is known about the efficacy of a multi-modal approach. The purpose of this scoping review is to examine studies that compare combining exercise and intra-articular knee injections to exercise alone for the management of knee OA. Methods: A search was performed using PubMed, CINAHL, and Clinicaltrials.gov with MeSH terms “knee osteoarthritis” AND “exercise” AND “injections”. Abstracts were screened to meet inclusion criteria of both intervention groups including exercise and one group receiving an injection for treatment of knee OA. Full text articles were screened to meet inclusion criteria and rated using the Pedro Scale. Results: 11 studies that met inclusion criteria. The included studies utilized CS, hyaluronic acid (HA), and Bone Marrow Concentrate (BMC), botulinum toxin A, or a combination of dextrose and lidocaine injections. Most studies included supervised exercise interventions with all studies including strengthening of the quadriceps. CS and exercise compared to exercise alone showed similar improvements in pain. The HA injection studies yielded mixed results with two studies finding HA and exercise was not superior than exercise alone while two other studies found that HA and exercise were superior. Conclusion: There was a paucity of literature investigating multimodal approaches. Most of the included studies did not find superior effects of adding a knee injection to exercise compared to exercise alone for knee OA.
Arturo Ladriñán-Maestro, Arturo Ladriñán-Maestro, Arturo Ladriñán-Maestro
et al.
IntroductionInjury prevention and performance enhancement are paramount goals in sports. Myofascial Pain Syndrome, primarily caused by myofascial trigger points, can result in referred pain, stiffness, muscle shortening, and weakness. This study aimed to assess the impact of dry needling (DN) on latent myofascial trigger points on vertical jump performance in female volleyball players.MethodsA single-blind, randomized controlled clinical trial was conducted with twenty-six healthy female volleyball players who had no lower limb injuries in the last six months, exhibited latent trigger points in the triceps surae muscles, and were familiar with the countermovement jump test. Participants were randomly assigned to either a control group or an experimental group (which received a single DN session). Vertical jump performance variables, including jump height as the primary outcome, were assessed using a force platform at five time points: before the intervention, immediately post-treatment, 24 h post-treatment, 72 h post-treatment, and one-week post-intervention.ResultsThe experimental group showed significantly lower values for vertical jump height, flight time, velocity, strength, and power immediately after the needling intervention (p < 0.05). However, these values were significantly higher one-week post-intervention across all variables (p < 0.01). These findings indicate that DN initially decreases jumping performance, but improvements are observed one week after the intervention. In the comparison between groups, the experimental group exhibited higher values at the one-week follow-up for vertical jump height, flight time, speed, and power compared to the control group (p < 0.05).ConclusionsDN appears to be an effective technique for improving vertical jump performance in female volleyball players one week after its application.
Clinical Trial RegistrationClinicalTrials.gov, identifier (NCT06184672).