Irisin seems to play an important role in several chronic diseases, however, the interactions between chronic training and irisin are still unclear. The purpose of this systematic review and meta-analysis was to examine the effect of chronic resistance training on circulating irisin in adults. Literature search was conducted in PubMed, Web of Science and EBSCOhost (Academic Search Complete) until December 2020. Randomized controlled trials researching irisin levels after a resistance training program for at least 8 weeks among an adult population were eligible. Other inclusion criteria comprised recruiting a control group and reporting circulating irisin through ELISA kits. Cohen’s d effect size and subgroup analyses (95% confidence level) were calculated using a random effects analysis model. Data of the seven included studies comprising 282 individuals showed an increasing and non-significant tendency after a resistance training program (d = 0.58, 95% CI: −0.25 to 1.40, p = 0.17). Subgroup analyses showed significant increases for the older adults group (p < 0.001) and when training is demanding and progressive in terms of intensity (p = 0.03). Data suggest that resistance training programs seem to increase circulating irisin, especially in older adults and in demanding and progressive training programs. However, more studies should be conducted using robust measurement methods, such as mass spectrometry, to better understand the interaction between chronic resistance exercise and irisin.

The purpose of this study was to examine the training habits of eumenorrheic active women during their menstrual cycle (MC), and its perceived influence on physical performance regarding their athletic level. A group of 1250 sportswomen filled in a questionnaire referring to demographic information, athletic performance and MC-related training habits. Of the participants, 81% reported having a stable duration of MC, with most of them (57%) lasting 26–30 days. Concerning MC-related training habits, 79% indicated that their MC affects athletic performance, although 71% did not consider their MC in their training program, with no differences or modifications in training volume or in training intensity for low-level athletes (LLA) and high-level athletes (HLA) with hormonal contraceptive (HC) use. However, LLA with a normal MC adapted their training habits more, compared with HLA, also stopping their training (47.1% vs. 16.1%, respectively). Thus, different training strategies should be designed for HLA and LLA with a normal MC, but this is not so necessary for HLA and LLA who use HC. To sum up, training adaptations should be individually designed according to the training level and use or non-use of HC, always taking into account the pain suffered during the menstrual phase in most of the athletes.

Sports injuries can affect the performance of athletes. For this reason, functional tests are used for injury assessment and prevention, analyzing physical or physiological imbalances and detecting asymmetries. The main aim of this study was to detect the asymmetries in the upper limbs (right and left arms) in athletes, using the OctoBalance Test (OB), depending on the stage of the season. Two hundred and fifty-two participants (age: 23.33 ± 8.96 years old; height: 178.63 ± 11.12 cm; body mass: 80.28 ± 17.61 kg; body mass index: 24.88 ± 4.58; sports experience: 12.52 ± 6.28 years), practicing different sports (rugby, athletics, football, swimming, handball, triathlon, basketball, hockey, badminton and volleyball), assessed with the OB in medial, superolateral, and inferolateral directions in both arms, in four moments of the season (May 2017, September 2017, February 2018 and May 2018). ANOVA test was used with repeated measures with a p ≤ 0.05, for the analysis of the different studied variances. Significant differences were found (p = 0.021) in the medial direction of the left arm, between the first (May 2017) and fourth stages (May 2018), with values of 71.02 ± 7.15 cm and 65.03 ± 7.66 cm. From the detection of asymmetries, using the OB to measure, in the medial, superolateral and inferolateral directions, mobility and balance can be assessed. In addition, it is possible to observe functional imbalances, as a risk factor for injury, in each of the stages into which the season is divided, which will help in the prevention of injuries and in the individualization of training.

The articular evaluation of range of motion (ROM) is currently used to observe imbalance or limitations as possible risk factors or predispositions to suffer future injures. The main aim of this study is to verify the concurrent validity, reliability and reproducibility of the OctoBalance Test (OB) as a valid and reliable tool to measure articular ROM of the upper limb compared to the modified-Upper Quarter Y-Balance Test (mUQYBT). The twenty-five participants were male athletes. All of them were assessed with OB and mUQYBT in medial, superolateral, and inferolateral directions in both right and left arms with a three-minute break during these attempts. The process was repeated a second time with a week gap between measurements. Pearson correlation and linear logarithmic regression were used to examine the relationship between scores obtained with OB and mUQYBT. In order to verify the reliability, the intraclass correlation coefficient (ICC) was used (3.1). Concordance and reproducibility were assessed using Bland–Altman’s graph. A perfect correlation and an almost linear logarithmic regression (R² = 0.97) were observed between both measurement systems, with values of 73.531 ± 21.226 cm in mUQYBT and 69.541 ± 16.330 cm in OB. The differences were minimal between week one and week two. The assessment with Bland’s graph showed the concordance and reproducibility of scores, showing the dispersion and the upper and lower limits. OB is shown as valid in comparison to the other test as a reliable and reproducible tool for the assessment of the articular ROM in the upper limb, and it could be used for the evaluation of injuries.

Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system’s robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance.

Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy.