The gut microbiota is a complex heterogeneous microbial community modulated by endogenous and exogenous factors. Among the external causes, nutrition as well as physical activity appear to be potential drivers of microbial diversity, both at the taxonomic and functional level, likely also influencing endocrine system, and acting as endocrine organ itself. To date, clear-cut data regarding which microbial populations are modified, and by which mechanisms are lacking. Moreover, the relationship between the microbial shifts and the metabolic practical potential of the gut microbiota is still unclear. Further research by longitudinal and well-designed studies is needed to investigate whether microbiome manipulation may be an effective tool for improving human health and, also, performance in athletes, and whether these effects may be then extended to the overall health promotion of general populations. In this review, we evaluate and summarize the current knowledge regarding the interaction and cross-talks among hormonal modifications, physical performance, and microbiota content and function.

Cognitive-motor training could be used to improve open-skill sport performances, increasing cognitive demands to stimulate executive function (EF) development. Nevertheless, a distributed training proposal for the improvement of EFs is increasingly difficult to combine with seasonal sport commitments. This study aimed to investigate whether a massed basketball training program enriched with Fitlight training can improve EFs and motor performance. Forty-nine players (age = 15.0 ± 1.5 yrs) were assigned to the control and Fitlight-trained (FITL) groups, which performed 3 weeks of massed basketball practice, including 25 min per day of shooting sessions or Fitlight training, respectively. All athletes were tested in cognitive tasks (Flanker/Reverse Flanker; Digit Span) and fitness tests (Agility T-test; Yo-Yo IR1). During the intervention, exercise/session perceived effort (eRPE/sRPE) and enjoyment were collected. RM-ANOVA showed significant EFs scores increased in both groups over time, without differences between the groups. Moreover, an increased sRPE and eRPE appeared in the FITL group (p = 0.0001; p = 0.01), with no group differences in activity enjoyment and fitness tests. Three weeks of massed basketball training improved EFs and motor performance in young players. The additional Fitlight training increased the perceived cognitive effort without decreasing enjoyment, even if it seems unable to induce additional improvements in EFs.

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

The main purpose of this longitudinal study was to investigate football players’ recovery status, through hormonal response, in relation to accumulated workload at two comparable time points of the first (T1) and second half (T2) of the competitive season. Moreover, this study investigated athletes’ hormonal response to a typical weekly conditioning session (5 days before match: MD-5), at T1 and T2, to detect changes in players’ recovery capability over time. Salivary cortisol (sC) and testosterone (sT) of 24 professional players (27.8 ± 4.1 years of age) were collected before, after, and 24 hours following MD-5 in two comparable microcycles of T1 and T2. GPS training data (total and high-intensity distance) of the 7 and 28 days before sampling were used to obtain athletes’ acute and chronic workloads. Results showed a pre-training significant decrease of sT and an increase of sC (p<0.05) in T2, compared to T1. Moreover, athletes showed high sC and low sT levels before, after and 24 hours following MD-5 in T2. Workload analysis revealed significant correlations of chronic load with sC (r = 0.45, p = 0.056) and T/C ratio (r = -0.59; p = 0.007). These results suggested that, in professional football, chronic workload has a greater impact on players’ recovery time than acute workload over the sport season. Moreover, athletes’ hormonal response to the weekly conditioning session at T2 revealed an altered anabolic/catabolic balance, highlighting the key role of continuous internal and external workload monitoring during the season.

The SARS-CoV-2 outbreak led to an inevitable and drastic transition to online training systems. This study aimed to analyze the differences between live-streaming and pre-recorded training modalities in rhythmic gymnastics (RG) with coaches and gymnasts of different technical levels. A total of 238 coaches and 277 athletes affiliated with the Italian Gymnastics Federation (FGI) participated in the study. The data collection tool was a self-collected questionnaire structured in three sections: participant’s information, characteristics of live-streaming training, and characteristics of pre-recorded training. A 2 × 2 ANOVA was used for each numeric variable. A Pearson’s chi-squared test was used for each categorical variable. For the athletes, training frequency, motivation, and efficacy were significantly higher (p < 0.05) with live streaming (3.7 ± 1.5 day/week, 3.8 ± 0.9 score and 3.8 ± 0.8 score, respectively) than with a pre-recorded modality (2.2 ± 1.7 day/week, 3.1 ± 1.2 score and 3.7 ± 0.9 score, respectively), while for coaches, significant differences (p < 0.005) were found between the two modalities only for training frequency (live streaming, 3.6 ± 1.8 days/week vs. pre-recorded, 2.1 ± 1.7 days/week). The adherence (number of No:Yes) was significantly higher (p < 0.05) for the live-streaming modality than for the pre-recorded modality in gold athletes (1:74 vs. 14:61, respectively), silver athletes (12:190 vs. 28:174, respectively), and gold coaches (3:63 vs. 11:55, respectively), but it was not for silver coaches. Physical preparation was performed significantly (p < 0.005) more often (number of No:Yes) for live streaming than for the pre-recorded modality by gold athletes (9:66 vs. 34:41, respectively), silver athletes (25:177 vs. 77:125, respectively), gold coaches (8:58 vs. 37:29), and silver coaches (33:139 vs. 85:87, respectively). Free body technical preparation was performed significantly (p < 0.005) more often (number of No:Yes) for live streaming than for the pre-recorded modality by gold athletes (15:60 vs. 39:36, respectively), silver athletes (84:118 vs. 121:81, respectively), gold coaches (10:56 vs. 38:28), and silver coaches (60:112 vs. 105:67, respectively), while no differences were found for silver athletes’ and coaches’ technical preparations for apparatus training between the two modalities. In conclusion, live streaming had greater positive effects on RG training during home confinement. However, pre-recorded training could be more effective for some types of training, depending on the technical level of the athletes.

Despite the growing interest in physiological stress monitoring, an objective measure of stress is currently lacking, especially in clinical and rehabilitation contexts. With the emerging integration of artificial intelligence (AI) in data analytics, heart rate variability (HRV) has gained attention as an effective biomarker; however, the literature remains fragmented across disciplines, stress types, and methodological approaches. This scoping review aims to investigate how AI techniques are applied to HRV analysis for stress detection and prediction in adult populations. Although this review does not focus on a specific subtype of stress, its primary objective is to explore the current methodological state of the art as reported in the literature, without restrictions on stress typology. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Google Scholar for studies published between 2005 and 2025, using MeSH terms including “HRV”, “Rehabilitation”, “SCI” (for Spinal Cord Injury), “Stress”, “Sympathetic”, “Parasympathetic”, “Non-linear”, “Gamification”, “AI” and “Machine Learning”. Inclusion criteria targeted adult human populations and studies employing HRV features as input for AI and machine learning techniques for psychophysical stress assessment. Of the 566 records identified, 15 studies met the eligibility criteria. The reviewed studies exhibit substantial heterogeneity in terms of settings, populations, sensors, and algorithms with most employing supervised methods (e.g., random forest, support vector machine), alongside several applications of deep learning and explainable AI. Only one study focused specifically on physiological stress, none focused on SCI populations, and rehabilitation-related research was scarce, thereby underscoring important gaps in the current literature. Overall, HR variability analysis, especially when combined with artificial intelligence techniques, represents a promising approach for stress assessment; however, the field is methodologically fragmented and clinically underdeveloped in critical areas, underscoring the need for a multidisciplinary methodological framework.