Motivation, a complex construct that influences behavior, plays a critical role in an athlete’s success and has been extensively researched in sports sciences. However, motivation is still primarily assessed through self-report motivational questionnaires, resulting in a lack of objective, continuous measurements during athletic performance. Biosignals, increasingly used to assess psychological processes, have gained relevance due to their integration into wearable sensors, allowing non-stationary and unobtrusive data collection. Therefore, we assessed performance data, cardiovascular signals, and eye-tracking metrics to investigate the psychological and physiological processes under two different conditions designed to induce different motivational states via gamification. In this study, gamification elements, aligned with self-determination theory, were used to influence soccer players’ motivation in an immersive space, with N = 42 participants completing a passing drill in both Gamified and Non-Gamified scenarios. Features were extracted from session recordings and wearable sensors to assess whether performance or biosignals differed between conditions using a combination of machine learning and conventional statistical analysis. While self-report questionnaires and performance metrics revealed no significant differences, the machine learning classifiers were able to distinguish between scenarios based on eye-tracking-related features. The best-performing model, a k-nearest neighbor classifier, reached a macro F1-score of 82.75 %. We identified by feature importance methods that blink behavior and pupil dynamics, indicative of visual attention, were the main contributors. This study contributes to a deeper understanding of the value of integrating multimodal data and advanced evaluation methods to uncover implicit processes in applied sports contexts involving complex and heterogeneous data.
Lennartz et al. (Wed,) studied this question.