The association between data-driven training monitoring and athlete burnout: a chain-mediated indirect path between performance anxiety and psychological exhaustion

Data-driven training monitoring has enhanced the precision of training-load assessment, fatigue detection, and training regulation in competitive sport. However, continuous data collection, performance metric comparisons, and evaluative feedback may also constitute emerging sources of psychological stress for athletes. Drawing on the Technology Stress Generators Model, the Challenge-Hindrance Stressor Framework, and the Job Demands-Resources Model, this study examined the relationships among data-driven training monitoring stress (DTMS), evaluative anxiety, psychological exhaustion, and athlete burnout, as well as potential differences across gender and sport type.

Cross-sectional questionnaire data were collected from 588 active athletes. Confirmatory factor analysis, structural equation modeling, bootstrap tests of indirect effects, and multigroup analysis were conducted to examine the hypothesized relationships and between-group differences.

DTMS was significantly and positively associated with evaluative anxiety, psychological exhaustion, and athlete burnout. Evaluative anxiety and psychological exhaustion each constituted a significant indirect pathway between DTMS and athlete burnout, and the sequential indirect pathway through evaluative anxiety and psychological exhaustion was also statistically significant. Multigroup analysis further showed significant between-group differences in the association between DTMS and evaluative anxiety. This association was stronger among female athletes than among male athletes and stronger among athletes in physical endurance sports than among those in skill-based sports.

The findings suggest that data-driven training monitoring should extend beyond the technical objectives of data acquisition and assessment accuracy to incorporate psychologically informed practices. Particular attention should be given to the interpretation of monitoring data, the communication of anomalous indicators, and the delivery of evaluative feedback to reduce the psychological burden potentially associated with data-intensive monitoring.