Author(s): DE JONG, A., MATTHEWS, A., HINDER, M., PITCHFORD, N., Institution: UNIVERSITY OF TASMANIA, Country: AUSTRALIA, Abstract-ID: 1835

INTRODUCTION:
Ultrarunning performance is a complex phenomenon influenced by a multitude of factors. However, existing research typically explores physiological and psychological variables in isolation, or neglects to account for heterogeneity between events (Fornasiero et al., 2018; O'Loughlin et al., 2019). Our study addresses this gap in the literature by employing an interdisciplinary approach; developing a comprehensive, integrated predictive model of ultrarunning performance.
METHODS:
Participants (n = 48; projected n = 80 by March 2026) were recruited from a diverse range of ultramarathon events (46-161km). Baseline tests included a graded exercise test to exhaustion (VO2max, anaerobic lactate threshold), and a battery of psychometric surveys. Pain perception and cognitive function were assessed via Cold Pressor, Stop-Signal, and Serial Sevens tasks. Performance was quantified as percentile finishing time relative to the gender-matched event winner to normalise differences between events. Data were analysed using backwards stepwise regression with AIC selection to identify the most parsimonious model.
RESULTS:
Preliminary findings indicated a robust significant model (p = .0007, adjusted R2 = 0.62). The strongest positive predictors included emotional intelligence (beta = 0.57) and inhibition control (beta = 0.49), indicating that affective regulation and impulse suppression are primary governors of performance, as well as pain perception (beta = 0.33) and sleep quantity (beta = 0.32). Physiological markers such as VO2max (beta = 0.35) and anaerobic lactate threshold (beta = 0.33) were also significant positive predictors. The model further suggests a 'functional arousal' profile, whereby tension (beta = 0.52) facilitated performance, whereas anger (beta = -0.46) was a significant negative predictor. Source of motivation was also key, with competitive motivation (beta = 0.37) a positive predictor, while goal-directed motivation (beta = -0.4) correlated with poorer ultrarunning performance. Notably, self-efficacy and competition load (a metric of perceived exertion) were included in the final model but did not reach significance, while transient vigour and fatigue (as recorded during taper) were omitted.
CONCLUSION:
These findings suggest that, while traditional physiological metrics remain fundamental to endurance, the integration of psychological variables offers a more comprehensive explanation of ultrarunning performance. The strong predictive power of this model ultimately demonstrates that ultrarunning success is highly predictable through an interdisciplinary lens. A completed dataset will refine this model, providing an optimised approach to endurance sports training and performance, with broader application to fields involving prolonged physical and psychological exertion (e.g., military operations, pain management, and ageing).

REFERENCES
Fornasiero et al., J. Sports Sci., 2018.
O'Loughlin et al., Int J. Environ. Res. Public Health, 2019.