Peter Jan Beek

Appropriate induction of mental fatigue in the lab is imperative for properly investigating the effects of mental fatigue on behavior. In this study, we aimed to (1) identify a task for inducing mental fatigue that is both effective (i.e. sufficiently demanding) and feasible (i.e., sufficiently short in duration), and (2) to assess its effects on boredom, loss of motivation, and sleepiness. Therefore, we compared the following tasks: (1) an individualized TloadDback task (16 minutes), (2) an individualized TloadDback task (32 minutes), (3) an N-back task (32 minutes), and (4) a control task in the form of watching a documentary (32 minutes), using a within-participants design with 30 participants. Subjective and objective outcome measures were used to evaluate levels of fatigue, boredom, motivation and sleepliness pre- and post-task. While all investigated tasks induced mental fatigue, the individualized TloadDback struck the best balance between effectiveness and feasibility and should therefore be preferred in experimental research.

Sprint and endurance capacities seem to be mutually exclusive or at least at odds with each other. However, this relationship has not been investigated in soccer, which appeals to both well-developed sprint and endurance capacities. This study explores the potential of machine learning to identify soccer players based on their unique combinations of sprint and endurance capacities and sprint and endurance match-specific running performance. In this context, the relationships between sprint and endurance capacities and between physical capacities and match-specific running performance are examined in detail. Match-specific running data were collected from 31 young elite male soccer players over two consecutive seasons. Additionally, these participants underwent exercise testing, consisting of a 20-meter sprint test and an incremental treadmill test to measure maximal oxygen uptake (V̇O2max). Subgroups were identified using k-means clustering and subgroup discovery, based on players’ sprint and endurance capacities, sprint and endurance match-specific running performance, and playing position. Three distinct subgroups were identified using machine learning: players with high sprint capacity and sprinted meters (n = 4), players with high endurance capacity and meters ran at moderate and high intensities (n = 6), and players without high physical capacities or matching match-specific running performance (n = 14). Across all players, there was no significant relationship between 20-meter sprint speed and normalized V̇O2max (R2 = 0.085, P = 0.17), although 20-meter sprint speed was positively related to average match sprint distance (R2 = 0.168, P = 0.03) and normalized V̇O2max to average match distance at moderate and high intensities (R2 = 0.151, P = 0.04). In young elite soccer players, sprint and endurance capacities show positive, moderate, relationships with corresponding match-specific running performance, but those capacities do not appear to be mutually exclusive or opposing. Clustering allows for identification of players who may benefit from alternative strategic roles during matches, are at risk of overuse, or could benefit from individualized training. This method can assist coaches in designing tailored training programs and optimizing overall match strategy.

Soccer players are frequently categorized by playing positions, both in the scientific literature and in practice. However, the utility of this approach in evaluating physical match performance and optimizing physical training programs remains unclear. This study compares the effectiveness of categorizing soccer players by their playing position versus using unsupervised machine learning based on match-specific running performance. Match-specific running data were collected from 40 young elite male soccer players over two seasons. Thirty-one of these players completed a 20-meter sprint test and a maximal incremental treadmill test to measure maximal oxygen uptake. Players were categorized both by playing position and by subgroups derived through k-means clustering based on match-specific running performance. Differences in sprint capacity, endurance capacity, and match-specific running performance were compared between and within playing positions, as well as between and within clusters. The two categorization methods were further compared for variance within subgroups and standardized differences between subgroups for total distance (TD), low-intensity running (LIR), moderate-intensity running (MIR), high-intensity running (HIR), and sprint distance during matches. Match-specific running performance differed between playing positions, despite notable inter-individual differences in running intensities within playing positions. Clustering based on match-specific running performance revealed less variance within groups (TD: P = 0.049, LIR: P = 0.032, HIR: P = 0.033) and larger standardized differences between groups (LIR: P = 0.037, MIR: P = 0.041, HIR: P = 0.035, Sprint: P = 0.018) compared to grouping by playing position. Moreover, 20-meter sprint speed differed between the sprint and high intensity endurance clusters (25.22 vs 23.75 km/h, P = 0.012), but not between playing positions. Using unsupervised machine learning to categorize soccer players improves the identification of player groups with similar match-specific running performance, thereby supporting performance evaluation and contributing to the optimization of physical training.

While body experience is a key focus of treatment in psychomotor therapy (PMT), research has largely overlooked this important topic. In this study we explored three domains of body experience—body satisfaction, body attitude, and interoceptive awareness—in individuals with mental disorders in a clinical sample receiving PMT. We expected these patients to have a more negative body experience than nonclinical controls, with PTSD, mood disorders, personality disorders, and sex negatively affecting all three domains of body experience. The study involved 235 participants aged 18–59 with various mental disorders that were referred to PMT between 2008 and 2017 at a mental health center in the Netherlands. They completed questionnaires on all three of the body experience domains of interest. One sample t‐tests revealed that patients had significantly greater negative body satisfaction and body attitude than nonclinical control samples obtained from the literature, with no significant difference in interoceptive awareness. Regression analyses within the patient sample revealed that female patients and patients with mood disorders or PTSD, displayed more negative body satisfaction than patients with other mental disorders. Additionally, female patients and patients with mood disorders displayed more negative body attitude. Although the total patient group and controls had comparable interoceptive awareness, only PTSD had significantly lower interoceptive awareness. Age and personality disorders did not predict differences on any domain of body experience. This research demonstrates that body experience is disturbed in patients with mental disorders who received PMT, and that there are disorder‐specific disturbances on domains of body experience domains.

Background: Swimming is widely acknowledged for its safety and health benefits. Across the world children are receiving swimming lessons in which a variety of learning methods are employed. However, little is known about the effectiveness of those methods, and a comprehensive overview of pertinent research is lacking. Such an overview is needed for both researchers and instructors seeking to improve swimming skill acquisition in children.Objective: This scoping review aims to provide an overview of studies examining the effectiveness of motor learning methods for the acquisition of swimming skills by 5- to 12-year-old children, including an evaluation of their theoretical underpinnings, methodological quality, and core findings.Methods: This scoping review adhered to the PRISMA guidelines and followed Tricco et al.'s framework for conducting and reporting scoping reviews. Five bibliographic databases were systematically searched. Peer-reviewed studies in all languages published before 2025 were considered. Studies focusing on children with water-related fear were included. Gray literature, non-peer-reviewed studies and studies on specific groups (e.g., young, competitive swimmers or children with disabilities), or cognitive/motivational outcomes were excluded. Review selection and characterization were performed by three independent reviewers using pretested forms.Results: A total of 23 studies were included, which were classified into three main categories: traditional motor learning methods (n = 4), contemporary methods (n = 1), and atheoretical methods (n = 18). Traditional methods focused on video-based instruction and feedback (n = 4). Contemporary methods involved a single study on a non-linear swimming program (n = 1). Atheoretical methods were further classified into learn-to-swim programs (n = 12), learning environments (n = 3), and assistive devices (n = 3). Most studies (87%) reported a positive effect of the motor learning method under investigation during practice. However, significant methodological limitations were identified. Specifically, 87% of studies did not incorporate retention or transfer tests, 35% lacked control or comparison groups, and 48% did not provide detailed descriptions of the investigated intervention(s). Additionally, 83% of studies were not explicitly grounded in theoretical frameworks, except for the video-based studies and the study on a non-linear swimming program.Conclusion: The literature on this topic is scarce, generally atheoretical and of questionable methodological quality. Addressing these shortcomings in future research will improve the evidence-base for the effectiveness of theoretically inspired learning methods for the acquisition of swimming skills in children, and their long-term retention and transfer, which in turn might result in evidence-based innovations in swimming lessons.Systematic Review Registration: PRISMA (RRID:SCR_018721).