Background and Objectives: Brain-derived neurotrophic factor (BDNF) supports neuronal function and plasticity, but exercise meta-analyses may reuse primary studies and combine distinct outcomes. We evaluated review-level evidence while preventing direct reuse of the same primary-study evidence within pooled syntheses in this umbrella review and meta-analysis. Methods: Thirty-nine systematic reviews with quantitative syntheses of peripheral BDNF were assessed. Review–primary-study links were normalized, methodological quality was appraised with AMSTAR 2, and estimates were classified by exercise timing and comparison design. Only reviews meeting the zero-overlap criterion were pooled using restricted maximum likelihood random-effects models with Hartung–Knapp inference. Prediction intervals were calculated; network and dose–response findings were summarized separately. Results: The corpus included 507 review–primary-study occurrences representing 289 unique normalized primary-study identities (corrected covered area [CCA], 1.99%). AMSTAR 2 overall confidence was low in eight reviews and critically low in 31. Seven reviews meeting the zero-overlap criterion contributed to the chronic-controlled synthesis, yielding a pooled SMD of 0.85 (95% CI 0.07–1.64; I2 = 93.9%; 95% prediction interval −1.20 to 2.91). Acute-controlled effects were inconclusive (four reviews; SMD 0.49, 95% CI −0.07 to 1.05; I2 = 53.8%), as were chronic pre–post effects (three reviews; SMD 0.32, 95% CI −0.04 to 0.69; I2≈0%). Quality-restricted meta-analysis was not estimable, and small-study-effect tests were not performed because no synthesis set contained at least 10 independent reviews. Conclusions: Exercise may increase peripheral BDNF in some chronic-controlled settings, but effects vary substantially. Circulating BDNF does not directly establish central BDNF engagement or enhanced neuroplasticity, and current review-level evidence does not establish a universal response, clinical benefit, superior modality, or optimal dose.
Mesut Süleymanoğulları, Cemre Didem Eyipınar, R. Muntean et al.· Journal of Clinical Medicine· 0 citations
Background Generative artificial intelligence (GenAI), particularly large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4, is rapidly being integrated into sports medicine practice. These tools are increasingly used by health professionals, coaches, and athletes for training prescription, rehabilitation, nutrition, mental health support, injury prevention, and academic writing. However, their adoption has outpaced the development of robust evidence regarding their clinical utility, accuracy, and safety, and no comprehensive synthesis of this emerging field currently exists. Aim This scoping review aimed to map the current evidence on GenAI and LLM applications in sports medicine and athlete health, evaluate their accuracy and hallucination risks across domains, and synthesise reported ethical and governance concerns. Methods The review followed PRISMA-ScR guidelines and Joanna Briggs Institute methodology. Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and CINAHL) were searched for studies published between January 2022 and March 2026. Eligibility criteria were defined using the Population–Concept–Context framework. Two independent reviewers conducted screening, full-text assessment, and data extraction, with strong inter-rater agreement (κ = 0.82). Results Of 1,847 records identified, 32 studies were included. Applications were classified into seven domains: training and exercise prescription (n = 5), nutrition (n = 4), rehabilitation (n = 3), mental health (n = 3), clinical decision support (n = 5), academic writing (n = 6), and ethics/governance (n = 6). LLM accuracy varied substantially: in a single validation study, content validity ratios for sleep recommendations ranged from 0.33 (GPT-3.5) to 0.67 (GPT-4), while only GPT-4 achieved acceptable validity for jet lag guidance (CVR = 0.68). In one bibliometric analysis, AI-generated text in sports medicine journals increased from 2.38% (early 2023) to 6.25% (late 2024). Hallucination risk was rated critical for general-purpose chatbots but substantially reduced in retrieval-augmented systems. Conclusion GenAI shows promise as a supervised decision-support tool in sports medicine, but current evidence does not support unsupervised clinical use. Key challenges include hallucination risks, a lack of sport-specific validation datasets, and insufficient ethical and governance frameworks. Addressing these gaps is essential before widespread integration into athlete health and public health contexts.
Ismail Dergaa, Mohamed Amine Dergaa, Mortadha Razzak et al.· Frontiers in Public Health· 0 citations
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