Background: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into sport and exercise through wearable biosensing systems that enable continuous monitoring and data-driven training adaptation. However, their practical value for coaching depends on the validity of biosensor data, the robustness of analytical models, and the conditions under which these systems have been empirically evaluated. Methods: A structured narrative review was conducted using Scopus, PubMed, Web of Science, and Google Scholar (2010–2026), synthesising empirical and applied evidence on wearable biosensing, signal processing, and ML-based adaptive training systems. To enhance transparency, an evidence map of core empirical studies was constructed, summarising sensing modalities, cohort sizes, experimental settings (laboratory vs. field), model types, evaluation protocols, and key outcomes. Results: Evidence from field and laboratory studies indicates that wearable biosensors can reliably capture physiological (e.g., heart rate variability), biomechanical (e.g., inertial and electromyographic signals), and biochemical (e.g., sweat lactate and electrolytes) markers relevant to training load, fatigue, and recovery, provided that signal quality control and calibration procedures are applied. ML models trained on these data can support training adaptation and recovery estimation, with improved performance over traditional workload metrics in endurance, strength, and team-sport contexts when evaluated using athlete-wise or longitudinal validation schemes. Nevertheless, the evidence map also highlights recurring limitations, including sensitivity to motion artefacts, inter-session variability, distribution shift between laboratory and field settings, and overconfident predictions when contextual or psychosocial inputs are absent. Conclusions: Current empirical evidence supports the use of AI-driven biosensor systems as decision-support tools for monitoring and adaptive training, but not as autonomous coaching agents. Their effectiveness is bounded by sensor reliability, appropriate validation protocols, and human oversight. The most defensible model emerging from the evidence is human–AI collaboration, in which ML enhances precision and consistency in data interpretation, while coaches retain responsibility for contextual judgement, ethical decision-making, and athlete-centred care.

Nursing students in their training process often suffer from anxiety due to stressful situations, and emotional intelligence can help them to manage these situations. The aim of this study is to analyse the associations between the dimensions of perceived emotional intelligence and anxiety in students undergoing their training cycles in different special services in order to check if there are differences between them. It is an observational, cross-sectional and correlational study with a sample of 85 nursing students who had not received training in emotional intelligence. Two measurement instruments were used: the Trait-State Anxiety Inventory (STAI) to assess anxiety and the Trait Meta-Mood Scale (TMMS-24) to measure EI. Data were analysed using Pearson’s coefficient when the distribution was normal, and Spearman’s coefficient in the non-normal distribution. The results showed in the group—ER-Emergency and Oncology—there was a significant negative relationship between state and trait anxiety and emotional understanding and regulation. In contrast, in the Primary Care setting there was also a positive relationship between emotional perception and trait anxiety. The study concludes that nursing students who understand and manage their emotions may have a lower risk of anxiety. Furthermore, if they identify emotions appropriately, the risk of suffering from anxiety in the long term may be lower. This finding was observed when the student did the internship in Primary Care. So there is a difference depending on the clinical context.

Introduction. After stroke, muscle quality deteriorates due to neuromuscular impairments, disuse, and ageing-related changes. While prior studies have examined global muscle alterations, less is known about regional differences in muscle quality across specific limb compartments, and how these differences relate to strength and functional performance. Understanding whether anterior and posterior compartments of the upper and lower limbs are differentially affected may help target rehabilitation strategies. Conclusions. Distinct regional patterns of muscle quality loss were observed in people after stroke, with lower MT and higher EI values correlating with reduced strength. These findings highlight the importance of assessing both structural and functional parameters across limb regions to better understand muscle deterioration and guide targeted interventions in ageing and post-stroke populations. Introduction. After stroke, respiratory function and the architecture of respiratory/abdominal muscles can be compromised. We compared spirometric function with ultrasound-derived morphology and quality of the diaphragm and transversus abdominis (TrA) in people after stroke versus age- and sex-matched apparently healthy adults. Conclusions. People after stroke exhibit clinically meaningful reductions in lung volumes/flows and respiratory pressures, alongside inspiratory-phase diaphragm atrophy and quantitative–qualitative deterioration of TrA (lower TF, higher EI). These differences persist after adjustment for age, sex, BMI, and nutrition. Combined ultrasound (diaphragm and TrA) with spirometry and MIP/MEP offers a practical framework to monitor respiratory dysfunction and to guide targeted rehabilitation.