Resilience in sports: a multidisciplinary, dynamic, and personalized perspective.

Athletes are exposed to various psychological and physiological stressors, such as losing matches and high training loads. Understanding and improving the resilience of athletes is therefore crucial to prevent performance decrements and psychological or physical problems. In this review, resilience is conceptualized as a dynamic process of bouncing back to normal functioning following stressors. This process has been of wide interest in psychology, but also in the physiology and sports science literature (e.g. load and recovery). To improve our understanding of the process of resilience, we argue for a collaborative synthesis of knowledge from the domains of psychology, physiology, sports science, and data science. Accordingly, we propose a multidisciplinary, dynamic, and personalized research agenda on resilience. We explain how new technologies and data science applications are important future trends (1) to detect warning signals for resilience losses in (combinations of) psychological and physiological changes, and (2) to provide athletes and their coaches with personalized feedback about athletes' resilience.

Articles with impact: insights into 10 years of research with machine learning.

Worldwide scientific output is growing faster and faster. Academics should not only publish much and fast, but also publish research with impact. The aim of this study is to use machine learning to investigate characteristics of articles that were published in the Journal of Applied Physiology between 2009 and 2018, and characterize high-impact articles. Article impact was assessed for 4,531 publications by three common impact metrics: the Altmetric Attention Scores, downloads, and citations. Additionally, a broad collection of (more than 200) characteristics was collected from the article's title, abstract, authors, keywords, publication, and article engagement. We constructed random forest (RF) regression models to predict article impact and articles with the highest impact (top-25% and top-10% for each impact metric), which were compared with a naive baseline method. RF models outperformed the baseline models when predicting the impact of unseen articles (P < 0.001 for each impact metric). Also, RF models predicted top-25% and top-10% high-impact articles with a high accuracy. Moreover, RF models revealed important article characteristics. Higher impact was observed for articles about exercise, training, performance and V̇o2max, reviews, human studies, articles from large collaborations, longer articles with many references and high engagement by scientists, practitioners and public or via news outlets and videos. Lower impact was shown for articles about respiratory physiology or sleep apnea, editorials, animal studies, and titles with a question mark or a reference to places or individuals. In summary, research impact can be predicted and better understood using a combination of article characteristics and machine learning.NEW & NOTEWORTHY Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.

Influence of gait mode and body orientation on following a walking avatar.

Regulating distance with a moving object or person is a key component of human movement and of skillful interpersonal coordination. The current set of experiments aimed to assess the role of gait mode and body orientation on distance regulation using a cyclical locomotor tracking task in which participants followed a virtual leader. In the first experiment, participants moved in the backward-forward direction while the body orientation of the virtual leader was manipulated (i.e., facing towards, or away from the follower), hence imposing an incongruence in gait mode between leader and follower. Distance regulation was spatially less accurate when followers walked backwards. Additionally, a clear trade-off was found between spatial leader-follower accuracy and temporal synchrony. Any perceptual effects were overshadowed by the effect of one's gait mode. In the second experiment we examined lateral following. The results suggested that lateral following was also constrained strongly by perceptual information presented by the leader. Together, these findings demonstrated how locomotor tracking depends on gait mode, but also on the body orientation of whoever is being followed.

Asymmetric interpersonal coupling in a cyclic sports-related movement task.

In interactive sports, teammates and/or opponents mutually tune their behavior. Expert performance thus implies certain interactive abilities, which critically depend on perceptual coupling. To illustrate this assertion, we examined the coordination dynamics with asymmetric interaction of dyads performing a sports-related cyclical movement task. In pairs, basketball players performed lateral defensive slides in in-phase, until a cue prompted them to switch to antiphase coordination. We assessed how these switches were mediated by phase adaptations of each agent under bidirectional (i.e., agents facing one another) and unidirectional (i.e., one agent facing the back of the other) visual interaction conditions. This imposed asymmetry in visual coupling exemplified an imbalance in the interaction (or 'interact-ability') between two agents. The results concurred the asymmetric coupling: during the switch the agent facing the other adapted his phasing more than the other agent. Furthermore, also in the bidirectional condition the coupling revealed dyad-intrinsic asymmetries (e.g., related to implicit follower-leader strategies). Together, this illustrates that interpersonal coordination is characterized by asymmetric coupling between the agents, and highlights how mutual perception of pertinent information mediates interpersonal coordination. This study offered a first step towards analyzing interpersonal coordination dynamics in relation to 'interact-ability'.