Association between match-related physical activity profiles and playing positions in different tasks: A data driven approach.

Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a data analytics framework to assess the neuromuscular or metabolic characteristics of a soccer-specific exercise in relation with the expected match intensity. GPS data describing the physical tasks' external intensity during an entire season of twenty-eight semi-professional soccer players competing at the fourth Italian division were used in this study. A supervised machine-learning approach was tested in order to detect difference in playing positions in different sport-specific drills. Moreover, a non-supervised machine-learning model was used to profile the match neuromuscular and metabolic characteristics. Players' playing positions during matches and match-based exercises are characterised by specific metabolic and neuromuscular characteristics related to tactical demands, while in the small-side game these differences are not detected. Additionally, our framework permits to evaluate if the match performance request is mirrored during training drills. Practitioners could evaluate the type of stimulus performed by a player in a specific training drill in order to assess if they reflect the matches characteristics of their specific playing position.

Athletic bioimpedance-based equations underestimate fat free mass components in male elite soccer players: development and validation of new soccer-specific predictive models.

BACKGROUND: Bioelectrical impedance analysis (BIA) is a rapid and user-friendly technique for assessing body composition in sports. Currently, no sport-specific predictive equations are available, and the utilization of generalized formulas can introduce systematic bias. The objectives of this study were as follows: (i) to develop and validate new predictive models for estimating fat-free mass (FFM) components in male elite soccer players; (ii) to evaluate the accuracy of existing predictive equations. METHODS: A total of 102 male elite soccer players (mean age 24.7 ± 5.7 years), participating in the Italian first league, underwent assessments during the first half of the in-season period and were randomly divided into development and validation groups. Bioelectrical resistance (R) and reactance (Xc), representing the bioimpedance components, were measured using a foot-to-hand BIA device at a single frequency of 50 kHz. Dual-energy X-ray absorptiometry was employed to acquire reference data for FFM, lean soft tissue (LST), and appendicular lean soft tissue (ALST). The validation of the newly developed predictive equations was conducted through regression analysis, Bland-Altman tests, and the area under the curves (AUC) of regression receiver operating characteristic (RROC) curves. RESULTS: Developed models were: FFM = - 7.729 + (body mass × 0.686) + (stature2/R × 0.227) + (Xc × 0.086) + (age × 0.058), R2 = 0.97, Standard error of estimation (SEE) = 1.0 kg; LST = - 8.929 + (body mass × 0.635) + (stature2/R × 0.244) + (Xc × 0.093) + (age × 0.048), R2 = 0.96, SEE = 0.9 kg; ALST = - 24.068 + (body mass × 0.347) + (stature2/R × 0.308) + (Xc × 0.152), R2 = 0.88, SEE = 1.4 kg. Train-test validation, performed on the validation group, revealed that generalized formulas for athletes underestimated all the predicted FFM components (p < 0.01), while the new predictive models showed no mean bias (p > 0.05), with R2 values ranging from 0.83 to 0.91, and no trend (p > 0.05). The AUC scores of the RROC curves indicated an accuracy of 0.92, 0.92, and 0.74 for FFM, LST, and ALST, respectively. CONCLUSIONS: The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players. The newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.

The significance of green exercise for the health and wellbeing of Italian immigrants in Norway: a mixed-methods study.

BACKGROUND: Green exercise (physical activity in presence of nature) has beneficial effects for health and wellbeing. Green exercise is a popular form of recreation in the Nordic countries, but participation is lower among the immigrant population from non-Western countries. However, no attention has been given to immigrants from the European Economic Area regarding this topic. Given the cultural and structural differences that surround green exercise in Italy and Norway, the case of the Italian immigrants in Norway is of interest to enrich our understanding of green exercise and its significance for health and wellbeing among immigrants in the Nordic countries. METHODS: This convergent mixed methods study investigated the pathways that link green exercise to health and wellbeing among Italian immigrants in Norway. Quantitative data were collected through an online survey (n = 321), which was oversampled to better reflect the sociodemographic profile of the reference population. Logistic regression was used to model the association of green exercise with self-rated health (SRH) or satisfaction with life (SWL) before and after controlling for selected confounders (age, gender, educational level, language proficiency, social support, and childhood experiences with green exercise). Qualitative data were collected through semi-structured in-depth interviews (n = 14) and analysed thematically. Merging of the two strands was done using a simultaneous bidirectional approach. RESULTS: The logistic regression found a significant bivariate association of green exercise with both SRH and SWL, though the association remained significant only for SWL after controlling for confounders. From the thematic analysis, three themes were identified: Green exercise opportunities contributing to overall satisfaction, Closeness to nature, and Embracing a new lifestyle. The integrated findings indicate that green exercise supported the immigrants' wellbeing, especially by providing stress relief, though socioeconomic status and acculturation may have a major impact on general health. Familiarity, appreciation of nature benefits, social support, and acculturation were identified as facilitating factors. CONCLUSIONS: This study provides novel insights into how green exercise supports health, wellbeing, and inclusion among immigrants to the Nordic countries and emphasizes the importance of developing culturally adapted strategies to enhance this health-promoting activity among immigrant populations.

Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?

Surface electromyography (sEMG) is the acquisition, from the skin, of the electrical signal produced by muscle activation. Usually, sEMG is measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless electrodes have been developed to overcome this limitation. However, contactless EMG devices are still sensitive to motion artifacts and often not comfortable for long monitoring. In this study, a non-invasive contactless method to estimate parameters indicative of muscular activity and fatigue, as they are assessed by EMG, through infrared thermal imaging (IRI) and cross-validated machine learning (ML) approaches is described. Particularly, 10 healthy participants underwent five series of bodyweight squats until exhaustion interspersed by 1 min of rest. During exercising, the vastus medialis activity and its temperature were measured through sEMG and IRI, respectively. The EMG average rectified value (ARV) and the median frequency of the power spectral density (MDF) of each series were estimated through several ML approaches applied to IRI features, obtaining good estimation performances (r = 0.886, p < 0.001 for ARV, and r = 0.661, p < 0.001 for MDF). Although EMG and IRI measure physiological processes of a different nature and are not interchangeable, these results suggest a potential link between skin temperature and muscle activity and fatigue, fostering the employment of contactless methods to deliver metrics of muscular activity in a non-invasive and comfortable manner in sports and clinical applications.

Toward a New Conceptual Approach to "Intensity" in Soccer Player's Monitoring: A Narrative Review.

ABSTRACT: Pillitteri, G, Clemente, FM, Petrucci, M, Rossi, A, Bellafiore, M, Bianco, A, Palma, A, and Battaglia, G. Toward a new conceptual approach to "intensity" in soccer player's monitoring: A narrative review. J Strength Cond Res 37(9): 1896-1911, 2023-In the last decade, monitoring physiological and match-related demands in soccer has become an increasingly common practice in sports sciences. One of the great challenges during monitoring process is the identification of key indicators that permit to generalize evidence and sustain decision-making process during training prescription. Actually, one of the major debates in the scientific community and among practitioners is the identification of the "intensity" concept. Defining a given training session or exercise based on "intensity" is difficult due to the fact that a huge amount of indicators are available (related both to the performed activities and to the athletes' psychophysiological responses). These indicators can lead to specific outcomes with different interpretations. The current narrative review aims to discuss the different measures approaches used in soccer to describe the intensity for both internal and external demands. In addition, a second purpose of this review is to propose general recommendations for combining intensity indicators with the aim of defining an overall intensity score of a training session or drill.

Relationship between external and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis.

This study verified the relationship between internal load (IL) and external load (EL) and their association on injury risk (IR) prediction considering machine learning (ML) approaches. Studies were included if: (1) participants were male professional soccer players; (2) carried out for at least 2 sessions, exercises, or competitions; (3) correlated training load (TL) with non-contact injuries; (4) applied ML approaches to predict TL and non-contact injuries. TL included: IL indicators (Rating of Perceived Exertion, RPE; Session-RPE, Heart Rate, HR) and EL indicators (Global Positioning System, GPS variables); the relationship between EL and IL through index, ratio, formula; ML indicators included performance measures, predictive performance of ML methods, measure of feature importance, relevant predictors, outcome variable, predictor variable, data pre-processing, features selection, ML methods. Twenty-five studies were included. Eleven addressed the relationship between EL and IL. Five used EL/IL indexes. Five studies predicted IL indicators. Three studies investigated the association between EL and IL with IR. One study predicted IR using ML. Significant positive correlations were found between S-RPE and total distance (TD) (r = 0.73; 95% CI (0.64 to 0.82)) as well as between S-RPE and player load (PL) (r = 0.76; 95% CI (0.68 to 0.84)). Association between IL and EL and their relationship with injuries were found. RPE, S-RPE, and HR were associated with different EL indicators. A positive relationship between EL and IL indicators and IR was also observed. Moreover, new indexes or ratios (integrating EL and IL) to improve knowledge regarding TL and fitness status were also applied. ML can predict IL indicators (HR and RPE), and IR. The present systematic review was registered in PROSPERO (CRD42021245312).

Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game.

Every soccer game influences each player's performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player's energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players' responses to various game situations.

Volleyball-Specific Skills and Cognitive Functions Can Discriminate Players of Different Competitive Levels.

ABSTRACT: Formenti, D, Trecroci, A, Duca, M, Vanoni, M, Ciovati, M, Rossi, A, and Alberti, G. Volleyball-specific skills and cognitive functions can discriminate players of different competitive levels. J Strength Cond Res 36(3): 813-819, 2022-The aim of this study was to investigate whether volleyball-specific skills, physical performance, and general cognitive functions differ between players of different competitive levels. Twenty-six female volleyball players competing at 2 different levels (n = 13, regional; n = 13, provincial) were tested on volleyball-specific skills (accuracy and technique of setting, passing, spiking, and serving), change of direction speed (COD) by the modified T-test, countermovement jump (CMJ) and general cognitive functions (executive control by Flanker task and perceptual speed by visual search task). Four machine learning models were tested to detect the best one to predict players' level. Regional players presented higher passing, spiking, serving accuracy (p < 0.05) and setting, passing, spiking, and serving technique (p < 0.05) than provincial players. Regional players had also better performance in COD and CMJ than provincial players (p < 0.05). Regional players presented lower response time than provincial players in both congruent and incongruent conditions of the Flanker task, and in both 10 items and 15 items conditions of the visual search task (p < 0.05). Decision tree classifier was the machine learning model with the highest performance to discriminate regional and provincial players (93% precision and 73% recall) by considering passing technique, congruent and incongruent condition of the Flanker task, 15 items and 10 items condition of the visual search task, and spiking technique. These findings demonstrated the importance of assessing volleyball-specific skills and cognitive functions as playing a role to discriminate players of different competitive levels.

SDNN24 Estimation from Semi-Continuous HR Measures.

The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users' heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.

Importance of anthropometric features to predict physical performance in elite youth soccer: a machine learning approach.

The present study aimed to determine the contribution of soccer players' anthropometric features to predict their physical performance. Sixteen players, from a professional youth soccer academy, were recruited. Several anthropometric features such as corrected arm muscle area (AMAcorr), arm muscle circumference (AMC) and right and left suprapatellar girths (RSPG and LSPG) were employed in this study. Players' physical performance was assessed by the change of direction (COD), sprint (10-m and 20-m), and vertical jump (CMJ) tests, and Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IRT1). Using an extra tree regression (ETR) model, the anthropometric features permitted to accurately predict 10-m sprint, 20-m sprint and Yo-Yo IRTL 1 performance (p < 0.05). ETR showed that upper-body features as AMAcorr, and AMC affected 10-m and 20-m sprint performances, while lower-body features as RSPG and LSPG influenced the Yo-Yo IRTL 1 (Overall Gini importance ≥ 0.22). The model predicting COD and CMJ presented a poor level of prediction, suggesting that other factors, rather than anthropometric features, may concur to predict their changes in performance. These findings demonstrated that the upper- and lower-body anthropometric features are strictly related to sprint and aerobic fitness performance in elite youth soccer.

Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts.

Application of ultra-short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people's daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.

Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations.

Wearable physiological monitors have become increasingly popular, often worn during people's daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.

Short-Term Delayed Effects of Kinesio Taping on Sprint Cycling Performance.

Trecroci, A, Formenti, D, Rossi, A, Esposito, F, and Alberti, G. Short-term delayed effects of Kinesio taping on sprint cycling performance. J Strength Cond Res 33(5): 1232-1236, 2019-The aim of this study was to assess short-term delayed effects of Kinesio taping (KT) on maximal cycling performance. A design with repeated measures was used. Fifteen healthy trained subjects underwent 3 conditions: no KT (without tape), KT (Kinesio tape applied longitudinally on the thighs), and sham taping (Kinesio tape applied horizontally on the thighs). Each subject performed 2 sets of 3 × 6-second sprints (separated by 3 minutes) interspersed by 30 minutes of rest on a cycle ergometer. Allometric scaling of peak power (PP) and average power (AP) values were computed and analyzed for each sprint performance. Both PP and AP decreased significantly after 30 minutes (between set 1 and set 2) in all conditions, with a greater decrease in no KT condition compared with KT and sham (p < 0.01). No significant differences were observed between KT and sham conditions. The application of KT on thigh muscles attenuated the performance decrease that occurred after 30 minutes of rest between the 2 sets compared with the no KT condition. This finding suggests athletes may use KT to better manage their performance during delays in competition events.

Patterns and correlates of physical activity in adult Norwegians: a forecasted evolution up to 2025 based on machine learning approach.

BACKGROUND: As other westerns countries, a large portion of Norwegians do not meet the minimum recommendations for weekly physical activity (PA). One of the primary targets of the WHO's Global action plan for the prevention and control of noncommunicable diseases is to reduce insufficient PA by 10% within 2025. In order to effectively increase the PA levels in the population, an in-depth understanding of PA habits within different sub-groups is therefore vital. Using a machine learning (ML) approach, the aim of this study was to investigate patterns and correlates of PA in adult Norwegians, as well as to construct a predictive model of future PA. METHODS: Data were retrieved from the Norsk Monitor survey, which consists of about 3000 items on individual characteristics and sociocultural factors. The dataset contained information about 52,477 adult Norwegians, collected between 1985 and 2013. Past patterns and changes of three PA components (Frequency, Duration, and Intensity) were initially assessed using a series of ANOVAs. A Conditional Mutual Information Maximization Method and a recursive feature elimination with cross-validation were then used to examine the factors associated with such patterns and changes. Finally, the future evolution of the three PA components up to 2025 was predicted using an autoregressive model. RESULTS: In line with previous literature, the analysis of the PA patterns showed a progressive increment of the PA Frequency (which was greater in women), while the PA Duration and Intensity (which were in general higher among men) resulted fairly stable. The PA correlates identified by the ML analysis, which include men and women of different age groups, are presented and discussed. The autoregressive model predicted a general increment of the PA Frequency and PA Intensity by 2025, while the PA Duration is predicted to reduce. Different patterns emerged among the different sub-groups, overall suggesting smaller increments of PA in men and older individuals, as compared to women and younger individuals. CONCLUSIONS: The findings of this study can inform public health efforts that aim at increasing PA levels in specific target groups. The ML approach is proposed as a useful tool in public health monitoring and assurance.

Facial asymmetry in the resting state reflects anxiety status on young males.

Facial asymmetry is considered a marker of psychological, emotional and physiological distress, while anxiety is a behavioural, psychological and physiological response to a threat to well-being. Since individuals respond to anxiety with specific patterns (e.g., muscular tension), it is reasonable to hypothesize that anxiety could contribute to facial tension and therefore facial asymmetry. Instead, since facial asymmetry is perceived as "unpleasant" from peers, its presence may be a hindrance to social adaptation contributing to generate anxiety. In this study, we investigated whether resting facial asymmetry and anxiety are associated in young population. Full frontal facial photographs of 56 Caucasian males were taken in resting state to obtain indices of asymmetry in six facial landmarks. Anxiety status was assessed using the State-Trait Anxiety Inventory (STAI-Y). Analysis of the face showed that asymmetry and laterality of specific facial landmarks were associated with anxiety. State anxiety was associated with eyebrow and lateral angle of the eye, while trait anxiety was associated with eyebrow and lateral angle of the mouth. Moreover, as compared with contralateral landmarks, the left landmarks were lifted/expanded in subjects with elevated trait/state anxiety, whereas the right landmarks were lifted/expanded in subjects with low-trait/state anxiety.

Short synthesis of polyfunctional sp3-rich threonine-derived morpholine scaffolds.

A convenient synthesis of novel complex morpholines was achieved by a two-step process involving a Petasis three-component coupling reaction of glycolaldehyde, organoboronic acid and different amines, followed by an acid- or base-mediated intramolecular cyclization. The use of threonine derivatives with glycolaldehyde in the Petasis reaction has been studied and successfully applied in the process, achieving morpholines with a higher fraction of sp3 carbon atoms compared to blockbuster drugs.

Skin grafting for the treatment of chronic leg ulcers - a systematic review in evidence-based medicine.

Skin grafting is one of the most common surgical procedures in the area of non-healing wounds by which skin or a skin substitute is placed over a wound to replace and regenerate the damaged skin. Chronic leg ulcers are an important problem and a major source of expense for Western countries and for which many different forms of treatment have been used. Skin grafting is a method of treatment that decreases the area of chronic leg ulcers or heals them completely, thus improving a patient's quality of life. Skin grafting is an old technique, rediscovered during the first and second world wars as the main treatment for wound closure. Nowadays, skin grafting has a pivotal role in the context of modern wound healing and tissue regeneration. The aim of this review was to track and to analyse the specific outcomes this technique achieved, especially in the last decade, in relation to venous, arterial, diabetic, rheumatoid and traumatic leg ulcers. Our main findings indicate that autologous split-thickness skin grafting still remains the gold standard in terms of safety and efficacy for chronic leg ulcers; skin grafting procedures have greater success rates in chronic venous leg ulcers compared to other types of chronic leg ulcers; skin tissue engineering, also supported by genetic manipulation, is quickly expanding and, in the near future, may provide even better outcomes in the area of treatments for long-lasting chronic wounds.

Acute effects of kinesio taping on a 6 s maximal cycling sprint performance.

Based on the hypothesis that tactile stimulation affects muscle activation levels, we theorized that taping vastus medialis and vastus lateralis muscles would improve a 6 s sprint cycling performance. Thus, the aim of this study was to determine whether the use of kinesio taping (KT) was helpful in increasing maximal-intensity cycling exercise. Sixteen active healthy subjects were enrolled in a randomized placebo, repeated measures design. All subjects were tested on a cycle ergometer under three conditions: without taping, taping along anterior thigh muscles and sham taping across the same muscle groups. Results showed a significant increase in peak power output and total work after the application compared to the condition with no tape applied (p < 0.05). No significant difference was found between the two modes of application. Our findings indicated that the tactile stimulation of KT applied longitudinally provided positive effects during a sprint cycling performance in healthy and active subjects.

Plasma MMP and TIMP evaluation in patients with deep venous thrombosis: could they have a predictive role in the development of post-thrombotic syndrome?

Post-thrombotic syndrome (PTS) is a condition that can develop in about half of the patients with deep vein thrombosis (DVT) of lower limbs. In the present study, we evaluated the expression of inflammatory biomarkers in the early phases of DVT and their correlation with the onset of PTS. Patients were enrolled after the first episode of DVT and were followed up for 1, 4, 8, 12 and 18 months. At each visit, blood sample was collected to evaluate plasma levels of matrix metalloproteinase (MMP)-1,-2,-3,-7,-8 and -9 MMP inhibitors, TIMP-1,-2, neutrophil gelatinase-associated lipocalin (NGAL) and cytokines TNF-α and IL-6. Analysis included 201 patients [86 males (42·79%) and 115 females (57·21%); average age 56 ± 7 years]. Of the 201 patients, 47 (23·38%; 21 males, 26 females) developed PTS during the follow-up period. The control group was made up of 60 individuals without DVT (22 males and 38 females). High plasma levels of MMPs, NGAL and cytokines were recorded during the acute phase after DVT. Moreover, patients with PTS showed higher levels of MMP-1 and MMP-8 with respect to patients without PTS. There is a close relationship between DVT, the individual risk of PTS and specific biomarkers such as MMPs and other related molecules, which may help guide prevention and therapy based on the patient's individual risk profile, and has to be studied in future.

Agility profile in sub-elite under-11 soccer players: is SAQ training adequate to improve sprint, change of direction speed and reactive agility performance?

The purpose of this study was to examine the effects of speed, agility and quickness (SAQ) training on acceleration (5 and 20 m), change of direction speed (CODS) and reactive agility in preadolescent soccer players. Thirty-five participants (age = 10.57 ± 0.26, body mass = 36.78 ± 5.34 kg, body height = 1.42 ± 0.05 m), randomly assigned to experimental (EG, n = 20) and control groups (CG, n = 15), completed a 12-week training intervention, 2 day/week. A significant interaction was found in 5-m sprint (P < 0.05, part η2 = 0.117) and reactive agility (P < 0.01, part η2 = 0.248) between EG and CG. In both groups, 20-m sprint time improved significantly (P < 0.05, effect size = 0.3-0.4) while performance on CODS remained unchanged after 12 weeks. These findings indicated that SAQ training could positively affect cognitive skills and initial sprint acceleration through the middle childhood, offering useful guidance to soccer coaches.