Ski injury predictive analytics from massive ski lift transportation data

Ski injury research is traditionally studied on small-scale observational studies where risk factors from univariate and multivariate statistical models are extracted. In this article, a large-scale ski injury observational study was conducted by analyzing skier transportation data from six consecutive seasons. Logistic regression and chi-square automatic interaction detection decision tree models for ski injury predictions are proposed. While logistic regression assumes a linearly weighted dependency between the predictors and the response variable, chi-square automatic interaction detection assumes a non-linear and hierarchical dependency. Logistic regression also assumes a monotonic relationship between each predictor variable and the response variable, while chi-square automatic interaction detection does not require such an assumption. In this research, the chi-square automatic interaction detection decision tree model achieved a higher odds ratio and area under the receiver operating characteristic curve in predicting ski injury. Both logistic regression and chi-square automatic interaction detection identified the daily time spent in the ski lift transportation system as the most important feature for ski injury prediction which provides solid evidence that ski injuries are early-failure events. Skiers who are at the highest risk of injury also exhibit higher lift switching behavior while performing faster runs and preferring ski slopes with higher vertical descents. The lowest injury risk is observed for skiers who spend more time in the ski lift transportation system and ski faster than the average population.

A New Ski Injury Registration System Architecture Using Mobile Applications to Enhance Skiing Safety

Mobile apps play an increasingly important role in healthcare institutions by enhancing the quality of healthcare services. Their role in sport injury prevention is also instrumental. In this article, we propose a system architecture for ski injury registration using mobile apps. Our work follows the idea of integrating and using mHealth apps to manage skiing injuries and to provide higher healthcare service quality and faster availability of data. With this work, we aim to greatly simplify the information workflow between the ski patrollers and the medical centers. Having the right information in the right place and on the right time for the injured person, the ski patroller then delivers to the medical centers that information in a format that is easy to analyze by the medical personnel and be prepared for possible interventions. To develop the mobile interfaces for the ski patrollers, nurses and doctors, we employ user-centered design. The overall system features and implementation are also explained and described in this paper. For evaluation purposes of our proposed system architecture, we have conducted a pilot test of the ski patroller system in collaboration with the ski patrollers in the ski resort of Trysil in Norway. The test had two evaluation points and based on the results of the tests, we obtained implications for enhancing the design of mobile interfaces for the proposed architecture.