Prediction of sports injuries by psychological process monitoring

Abstract

Abstract: Objectives: Sports injuries usually have severe consequences for the concerned athletes as well as for trainers and teams. The question is if accidents can be predicted in specific cases. Can early-warning signals be detected in psychological time series? Methods: An App-based method of process-monitoring was applied for data collection of psychological parameters. Daily self-assessments using a Sports Process Questionnaire were realized by a professional soccer player during the after-care period of a psychiatric treatment. Methods for the prediction of critical events were applied (Dynamic Complexity, Recurrence Plots, dynamic inter-item correlations). Injuries may demarcate pattern transitions in the mental functioning of athletes, which could be identified by the Pattern Transition Detection Algorithm (PTDA). Results: Early-warning signals of the accident could be identified in the time series. Dynamic Complexity revealed a critical instability, Recurrence Plots a transient period, and the dynamic inter-item correlations a period of increased system coherence just before the accident. The PTDA revealed a phase transition at the occurring injury. Conclusions: Even if the analysis is based on a single case, the results are promising. Psychological self-reports allow a short-term prediction of bio-mechanical injuries and by this, can help to prevent them. Nonlinear measures can be applied to time series data collected by digital process monitoring.