Examination of the external and internal load indicators' association with overuse injuries in professional soccer players

OBJECTIVES: Research in professional soccer focusing on the relevance of external and internal load indicators for injury prevention is scarce. This study examined the relationship between load indicators and overuse injuries. DESIGN: Prospective cohort study. METHODS: Data were collected from 35 professional male soccer players over two seasons. Following load indicators were examined: total distance covered (TD), distance covered at high speed (THSR; >20kmh(-1)), number of accelerations (ACCeff; >1ms(-2)), number of decelerations (DECeff; <-1ms(-2)), and rating of perceived exertion (RPE) multiplied by duration. Cumulative 1-, 2-, 3-, 4-weekly loads and acute:chronic workload ratios (ACWR) were calculated and split into low, medium and high groups. Only overuse injuries were included in the analysis to focus on their specific relationship with the load indicators. Generalized estimating equations were applied to analyse the relationship between load indicators and overuse injuries in the subsequent week. RESULTS: In total, 64 overuse injuries were registered. For cumulative loads, results indicated an increased injury risk for higher 2- to 4-weekly loads as indicated by TD, DECeff, and RPE multiplied by duration. For ACWR, a high ratio for THSR (>1.18) resulted in a higher injury risk. In contrast, a lower injury risk was found when comparing medium ratios for ACCeff (0.87-1.12), DECeff (0.86-1.12), and RPE x duration (0.85-1.12) to low ratios. CONCLUSIONS: Findings demonstrate that mainly external load indicators are associated with increased or decreased injury risk. The monitoring of various load indicators is recommended for injury prevention in professional soccer

Are two-dimensional measured frontal plane angles related to three-dimensional measured kinematic profiles during running?

OBJECTIVES: To investigate the temporal relationship between two-dimensional measured frontal plane joint angles and three-dimensional measured kinematic profiles during the stance phase of running, and to assess the intra- and intertester reliability of the two-dimensional angles. DESIGN: Observational study. SETTING: Research laboratory. PARTICIPANTS: Fifteen injury-free elite athletes. MAIN OUTCOME MEASURES: Contralateral pelvic drop (CPD), femoral adduction (FA), hip adduction (HA) and knee valgus (KV) were measured at the deepest landing position during midstance with two-dimensional video analysis during running. CPD, HA and knee abduction were measured continuously during the entire stance phase through three-dimensional motion analysis. One-dimensional statistical parametric mapping was used to examine the temporal relationships between the two-dimensional angles and three-dimensional kinematic profiles. In addition, intra-class correlation coefficients (ICC) were calculated to assess the intra- and intertester reliability of the two-dimensional angles. RESULTS: Two-dimensional CPD, FA and HA were significantly related to the three-dimensional HA kinematic profile. Two-dimensional CPD was significantly related to the three-dimensional CPD kinematic profile. No significant relationship was found between two-dimensional KV and three-dimensional knee abduction. Excellent intra- and intertester reliability was found for the two-dimensional angles (ICC 0.90-0.99). CONCLUSIONS: These findings support implementing two-dimensional video analysis to evaluate CPD and HA during running.status: publishe

Relationships Between the External and Internal Training Load in Professional Soccer:What Can We Learn From Machine Learning?

PURPOSE: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. METHODS: Training data were collected from 38 professional soccer players over two seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using two machine learning techniques, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (LASSO), and one naive baseline method. The predictions were based on a large set of external load indicators. Using each technique, one group model involving all players and one individual model for each player was constructed. These models' performance on predicting the reported RPE values for future training sessions was compared to the naive baseline's performance. RESULTS: Both the ANN and LASSO models outperformed the baseline. Additionally, the LASSO model made more accurate predictions for the RPE than the ANN model. Furthermore, decelerations were identified as important external load indicators. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. CONCLUSIONS: Machine learning techniques may have added value in predicting the RPE for future sessions to optimize training design and evaluation. Additionally, these techniques may be used in conjunction with expert knowledge to select key external load indicators for load monitoring

Predicting Future Perceived Wellness in Professional Soccer:The Role of Preceding Load and Wellness

PURPOSE:: The influence of preceding load and perceived wellness on the future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external and internal load for different time frames in combination with pre-session wellness to predict future perceived wellness using machine learning techniques. METHODS:: Training and match data were collected from a professional soccer team. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE multiplied by duration. Predictive models were constructed using gradient boosted regression trees (GBRT) and one naive baseline method. The individual predictions of future wellness items (i.e., fatigue, sleep quality, general muscle soreness, stress levels, and mood) were based on a set of external and internal load indicators in combination with pre-session wellness. The external and internal load was computed for acute and cumulative time frames. The GBRT model's performance on predicting the reported future wellness was compared to the naive baseline's performance by means of absolute prediction error and effect size. RESULTS:: The GBRT model outperformed the baseline for the wellness items fatigue, general muscle soreness, stress levels and mood. Additionally, only the combination of external load, internal load, and pre-session perceived wellness resulted in non-trivial effects for predicting future wellness. Including the cumulative load did not improve the predictive performances. CONCLUSIONS:: The findings may indicate the importance of including both acute load and pre-session perceived wellness in a broad monitoring approach in professional soccer

Analysis of the gamma-secretase interactome and validation of its association with tetraspanin-enriched microdomains

gamma-Secretase, an aspartyl protease that belongs to the iCLiPs (intramembrane cleaving proteases) family, is a multiprotein complex that consists of presenilin (PS), nicastrin (NCT), Aph-1 and Pen-2 (ref. 1). It is responsible for generation of the beta-amyloid peptide (A beta), the primary component of senile plaques in the brains of patients with Alzheimer's disease. Although the four components are necessary and sufficient for gamma-secretase activity(2-4), additional proteins are possibly involved in its regulation. Consequently, we purified proteins associated with the active gamma-secretase complex from reconstituted PS deficient fibroblasts, using tandem affinity purification (TAP)(5) and identified a series of proteins that transiently interact with the gamma-secretase complex and are probably involved in complex maturation, membrane trafficking and, importantly, the tetraspanin web. Tetraspanins form detergent-resistant microdomains in the cell membrane and regulate cell adhesion, cell signalling and proteolysis(6,7). Association of the gamma-secretase complex with tetraspanin-enriched microdomains provides an explanation for the previously documented localization of gamma-secretase to raft-like domains(8). Thus, these studies suggest that maintenance of the integrity of tetraspanin microdomains contributes to the refinement of proteolytic activity of the gamma-secretase complex