Different tools for the assessment of posture exist, from the simplest and cheap plumb line to complex, expensive, 3D-marker-based systems. The aim of this study is to present digital postural normative data of young adults collected through a mobile app to expand the possibilities of digital postural evaluation. A sample of 100 healthy volunteers, 50 males and 50 females, was analyzed with the mobile app Apecs-AI Posture Evaluation and Correction System^® (Apecs). The Student’s t-test evaluated differences between gender to highlight if the digital posture evaluation may differ between groups. A significant difference was present in the anterior coronal plane for axillary alignment (p = 0.04), trunk inclination (p = 0.03), and knee alignment (p = 0.01). Head inclination (p = 0.04), tibia shift (p = 0.01), and foot angle (p < 0.001) presented significant differences in the sagittal plane, while there were no significant differences in the posterior coronal plane. The intraclass correlation coefficient (ICC) was considered to evaluate reproducibility. Thirteen parameters out of twenty-two provided an ICC > 0.90, three provided an ICC > 0.60, and six variables did not meet the cut-off criteria. The results highlight that digital posture analysis of healthy individuals may present slight differences related to gender. Additionally, the mobile app showed good reproducibility according to ICC. Digital postural assessment with Apecs could represent a quick method for preventing screening in the general population. Therefore, clinicians should consider this app’s worth as an auxiliary posture evaluation tool.

Molecular composition and structural adaptation are changes in the cartilage tissue after different stimuli. Sports activities with different loads at different angles, speeds, and intensities can modify the molecular composition of the articular cartilage, hence it is crucial to understand the molecular adaptations and structural modifications generated by sports practice and this review aims to synthesize the current evidence on this topic. A systematic search until July 2022 was performed on the database Medline, Pubmed, Scopus, and Web of Science with a collection of 62,198. After the screening process, the included articles were analyzed narratively. Thirty-one studies have been included in the analysis. From the results emerged that running, swimming, ballet and handball were not correlated with detrimental structural or molecular cartilage adaptation; instead, soccer, volleyball, basketball, weightlifting, climbing, and rowing showed signs of cartilage alteration and molecular adaptation that could be early predictive degeneration's signs. From the included studies it came to light that the regions more interested in morphological cartilage changes were the knee in athletes from different disciplines. In conclusion, different sports induce different cartilage modifications both at a molecular and structural level and it is important to know the risks correlated to sports to implement preventive strategies.

Spinal cord injury (SCI) is an irreversible disease resulting in partial or total loss of sensory and motor function. The pathophysiology of SCI is characterized by an initial primary injury phase followed by a secondary phase in which reactive oxygen species (ROSs) and associated oxidative stress play hallmark roles. Physical exercise is an indispensable means of promoting psychophysical well-being and improving quality of life. It positively influences the neuromuscular, cardiovascular, respiratory, and immune systems. Moreover, exercise may provide a mechanism to regulate the variation and equilibrium between pro-oxidants and antioxidants. After a brief overview of spinal cord anatomy and the different types of spinal cord injury, the purpose of this review is to investigate the evidence regarding the effect of exercise on oxidative stress among individuals with SCI.

Body composition could influence exercise physiology. However, no one has ever studied the effect of visceral fat (VF) on heart rate (HR) trends during and after exercise by using bioimpedance analysis (BIA). This study aims to investigate BIA variables as predictors of HR trends during vigorous exercise. Ninety-six participants (age 22.5 ± 4.8 years) were included in the data analysis. After performing BIA, the HR was recorded at three time points: baseline HR (BHR), peak HR (PHR) at the end of vigorous exercise, and resting HR (RHR) 1 min after the end of the exercise. After BHR, a 30 s squat jump test was performed. Linear regression analysis showed the body fat percentage and VF as a predictor of HR recovery post-exercise (p < 0.01). However, body weight has no association with HR recovery (p > 0.05). On the other hand, BIA variables were not associated with the variation of HR from the baseline to the end of the exercise. The results show that higher VF is associated with a slower HR recovery. To schedule a training program, it would be safer to monitor visceral fat before prescribing recovery time.

Background: Adapted Physical Activity (APA) prescription is a complex decision-making process that integrates clinical guidelines and individual patient characteristics and remains strongly dependent on clinician experience. Generative artificial intelligence (AI) has recently emerged as a potential decision-support tool in exercise prescription; however, its interaction with professional expertise is still unclear. This study compared the perceived quality of APA protocols developed by expert professionals, novice professionals supported by AI, and AI operating autonomously across multiple orthopedic conditions. Methods: In this observational cross-sectional study, five real orthopedic prescriptions (scoliosis, low back pain, osteoporosis, high risk of falls, and osteoarthritis) were used to generate three APA protocols per condition: expert professional (EP), novice professional with AI support (NAI), and AI alone. All protocols were created using an identical standardized prompt and anonymized. A multidisciplinary panel of 135 professionals blindly evaluated the protocols using a structured questionnaire assessing effectiveness, safety, appropriateness, clarity, and progression. Overall quality scores were compared using Friedman tests with post hoc Wilcoxon signed-rank tests. Results: Across all conditions, EP protocols achieved the highest quality scores, followed by NAI, while AI-alone protocols consistently received the lowest ratings (all p < 0.05). NAI protocols showed intermediate performance, partially reducing the expertise gap. Post hoc analyses showed that EP protocols received significantly higher rating than AI protocols in all conditions (p < 0.01). NAI protocols received significantly higher rating than AI protocols in most conditions (p < 0.01) except osteoporosis (p = 0.362). Differences between EP and AI were most pronounced for safety (p < 0.01), appropriateness (tailoring p < 0.01), and progression (p < 0.05), whereas EP–NAI differences were smaller and condition-dependent. AI-alone protocols showed greater variability across pathologies. Conclusions: Professional expertise remains the main determinant of APA protocol quality. AI support can improve protocol structure and perceived quality when used by novice professionals but does not replace expert clinical reasoning. AI-generated protocols without human oversight are not yet suitable for autonomous APA prescription, supporting a complementary, expertise-dependent role of AI in exercise programming.