Aim: This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature search was conducted across five databases (PubMed/MEDLINE, Web of Science, IEEE Xplore, Scopus, and SPORTDiscus), encompassing studies published between January 2015 and December 2024. After screening 3248 articles, 73 studies met the inclusion criteria (Cohen’s kappa = 0.84). Data were collected on AI techniques, biomechanical parameters, performance metrics, and implementation details. Results revealed a shift from traditional statistical models to advanced machine learning methods. Based on moderate-quality evidence from 12 studies, convolutional neural networks reached 94% agreement with international experts in technique assessment. Computer vision demonstrated accuracy within 15 mm compared to marker-based systems (6 studies, moderate quality). AI-driven training plans showed 25% accuracy improvements (4 studies, limited evidence). Random forest models predicted hamstring injuries with 85% accuracy (3 studies, moderate quality). Learning management systems enhanced knowledge transfer, raising coaches’ understanding by 45% and athlete adherence by 3.4 times. Implementing integrated AI systems resulted in a 23% reduction in reinjury rates. However, significant challenges remain, including standardizing data, improving model interpretability, validating models in real-world settings, and integrating them into coaching routines. In summary, incorporating AI into sports biomechanics marks a groundbreaking advancement, providing analytical capabilities that surpass traditional techniques. Future research should focus on creating explainable AI, applying rigorous validation methods, handling data ethically, and ensuring equitable access to promote the widespread and responsible use of AI across all levels of competitive sports.

Background/Objectives: Despite the abundant body of evidence linking high-intensity interval training (HIIT) to cardiometabolic markers, little is known about how HIIT affects liver enzymes, particularly in obese adolescents. This study aimed to investigate the effects of HIIT on metabolic dysfunction-associated steatotic liver disease (MASLD)-related biomarkers in overweight/obese adolescent girls. Methods: Thirty-three overweight/obese adolescent girls (age, 17.0 ± 1.15 yr.; body mass index, 33.3 ± 4.77 kg/m²) were randomly assigned to HIIT (n = 17) or control (n = 16) groups. The HIIT group participated in a nine-week HIIT program (three times weekly) without caloric restriction. Maximal aerobic speed, body composition indexes, blood pressure, MASLD-related biomarkers [liver enzymes (alanine aminotransferase (ALT) and aspartate aminotransferase (AST)), plasma lipids, uric acid, platelet count, and homeostasis model assessment index for insulin-resistance (HOMA-IR)] were examined at baseline and after the intervention. Results: Significant “time × group” interactions were found for body composition indexes, systolic blood pressure, maximal aerobic speed, liver enzymes ALT and AST, plasma lipids, glucose, and HOMA-IR. The HIIT program resulted in an increase in maximal aerobic speed (p = 0.035) and a decrease in body composition and plasma lipids (p < 0.01), systolic blood pressure (p = 0.011), ALT (p = 0.013), AST (p = 0.012), and HOMA-IR (p = 0.01), but no significant changes in uric acid and platelet count. None of these markers changed in the control group. Conclusions: HIIT resulted in an improvement in MASLD-related biomarkers. HIIT could be an effective exercise therapy to prevent and reverse MASLD in adolescents with obesity.

Several studies have investigated the effects of music on both submaximal and maximal exercise performance at a constant work-rate. However, there is a lack of research that has examined the effects of music on the pacing strategy during self-paced exercise. The aim of this study was to examine the effects of preferred music on performance and pacing during a 6 min run test (6-MSPRT) in young male adults. Twenty healthy male participants volunteered for this study. They performed two randomly assigned trials (with or without music) of a 6-MSPRT three days apart. Mean running speed, the adopted pacing strategy, total distance covered (TDC), peak and mean heart rate (HRpeak, HRmean), blood lactate (3 min after the test), and rate of perceived exertion (RPE) were measured. Listening to preferred music during the 6-MSPRT resulted in significant TDC improvement (Δ10%; p = 0.016; effect size (ES) = 0.80). A significantly faster mean running speed was observed when listening to music compared with no music. The improvement of TDC in the present study is explained by a significant overall increase in speed (main effect for conditions) during the music trial. Music failed to modify pacing patterns as suggested by the similar reversed “J-shaped” profile during the two conditions. Blood-lactate concentrations were significantly reduced by 9% (p = 0.006, ES = 1.09) after the 6-MSPRT with music compared to those in the control condition. No statistically significant differences were found between the test conditions for HRpeak, HRmean, and RPE. Therefore, listening to preferred music can have positive effects on exercise performance during the 6-MSPRT, such as greater TDC, faster running speeds, and reduced blood lactate levels but has no effect on the pacing strategy.

Listening to self-selected motivational music (SSMM) during warm-ups and caffeine (CAF) intake prior to exercise can independently enhance athletic performance among female athletes. Likewise, the potential synergistic effects of these interventions have not yet been thoroughly examined. Objective: The purpose of the study was to assess the independent and combined effects of SSMM during warm-up and pre-exercise CAF intake on maximal short-duration performance in female athletes. Methods: Seventeen female handball players (aged 16.7 ± 0.4 years) participated in a randomized, double-blind, crossover study. Each athlete completed four conditions: (i) placebo (PLA) with no interventions, (ii) music and placebo (MUS), (iii) caffeine intake only (CAF), and (iv) a combination of music and caffeine (MUS + CAF). Performance assessments included the countermovement jump (CMJ), modified agility t-test (MAT), repeated-sprint ability (RSA) test (mean and peak sprint performance), and rating of perceived exertion (RPE). Results: The MUS (p > 0.05; p < 0.01; p < 0.01; p < 0.001, respectively), CAF (all p < 0.001), and MUS + CAF (all p < 0.01) conditions significantly outperformed the PLA condition in CMJ, MAT, RSA mean, and RSA peak measures. No significant differences were observed between the CAF and MUS + CAF conditions; however, the best performances were recorded during MUS + CAF. RPE scores remained consistent across conditions. Conclusions: Warm-up routines incorporating either SSMM or a moderate dose of CAF (6 mg·kg⁻¹) enhance anaerobic performance in female athletes. While both interventions are effective independently, CAF intake elicits a stronger effect. Although no significant difference was demonstrated for this combination, the concurrent use of SSMM and CAF appears to produce a potential effect, emerging as the most effective strategy for optimizing anaerobic performance.

Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice.

Purpose: During Ramadan month, Muslims often continue training to maintain their muscle performance. However, trainers should stress the importance of the time of day chosen for practicing resistance training (RT) during Ramadan fasting to avoid health and performance alteration. Thus, this study aimed to investigate the effects of the timing of RT during Ramadan intermittent fasting (RIF) on muscle strength and hypertrophy in healthy male adults. Methods: Forty men were randomly allocated to 2 matched groups: 20 practicing whole-body RT in the late afternoon in a fasted state and 20 training in the late evening in a fed state (FED). Both groups performed 4 days per week of whole-body RT (∼75%–85% 1-repetition maximum, 12 repetitions/3–4 sets) during RIF. Anthropometric measurements and 1-repetition-maximum test for the squats, deadlift, and bench press were measured. A cross-sectional area of the quadriceps and biceps brachii was examined using ultrasound scans. All measurements were taken at 4 time points: 24 hours before the start of Ramadan, on the 15th day of Ramadan, on the 29th day of Ramadan, and 21 days after Ramadan. Results: Post hoc tests indicated significant preimprovement to postimprovement in FED at the 29th day of Ramadan for the 1-repetition-maximum test for the squats (P = .02; effect size = 0.21) and deadlift (P = .03; effect size = 0.24) when compared to 24 hours before the start of Ramadan. No significant changes were observed in the fasted-state group or for cross-sectional area for both groups. Conclusions: Practicing RT during RIF appears not to have adverse effects on muscle hypertrophy and strength no matter whether it is practiced in FED or fasted state. However, RT had greater effects on muscle strength when applied in FED. Our findings suggest that training sessions should be scheduled after breaking the fast during Ramadan.