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Statistics on study disruptions and delays and their negative impact on academic performance call for action-regulation strategies that students can use to manage their performance and well-being. In the present research, we rely on the action-regulation model of selection, optimization, and compensation (SOC), which was developed in the life span developmental literature. The aim of the present study was to establish indirect links between two specific SOC components (i.e., elective selection and optimization) and study outcomes (i.e., end-of-first-year average grade and study satisfaction) through higher self-efficacy beliefs. In 2 prospective studies conducted during 2 subsequent academic years, we tested our research model with first-year undergraduate students (n = 366 in Study 1 and n = 242 in Study 2). Results of both studies indicate that there are positive indirect relations between optimization, but not elective selection, and favorable study outcomes through self-efficacy beliefs. The present study contributes to SOC theory and the educational sciences by showing that the SOC model of action-regulation can be helpful in explaining college students' grades and study satisfaction.

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. [ABSTRACT FROM AUTHOR]

Purpose: The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the "ergodicity issue." Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field. Methods: The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level. Results: Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery. Conclusions: The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery. [ABSTRACT FROM AUTHOR]

The aim of this study was to increase our insight into older employees' achievement motivation by examining the prevalence of dominant achievement goals among a "unique" group of 172 Dutch workers who remained active after their post-statutory retirement age. Moreover, we investigated how their dominant achievement goals were linked to motivation-related outcomes (i.e., work engagement and meaning of work). Our results showed that, relative to younger workers, a significantly higher amount of older workers endorsed dominant mastery-avoidance goals. In addition, as expected, older workers with dominant mastery-approach goals scored highest, while the workers with dominant mastery-avoidance goals scored lowest in work engagement, social and personal meaning of work. Theoretical as well as practical implications of these results are discussed. (Contains 3 tables.)