Background/aim We aimed to investigate the magnitude and characteristics of injuries and illnesses in Dutch physical education teacher education (PETE) students.Methods During the first 21 weeks of the academic year, 245 first-year students registered their health problems online using the Oslo Sports Trauma Research Centre (OSTRC) Questionnaire on Health Problems.Results A total of 276 injuries, 140 illnesses and 69 unclassified health problems were reported. We found an injury incidence rate of 11.7 injuries per 1000 hours (95% CI 10.4 to 13.2). Injury characteristics were: 42% overuse injuries, 62% causing absence from sports (median injury time loss=2 days) and 64% reinjuries. Most injuries were located at the knee, lower leg (anterior) and ankle. The duration of the illnesses was short (<1 week).Summary and conclusions We implemented a new registration method in the PETE academic programme. The results show that the risk for health problems is high for PETE students. Prevention is necessary, and to decrease injuries prevention programmes should focus on the lower extremities.
DOCUMENT
Objectives To report (1) the injury incidence in recreational runners in preparation for a 8-km or 16-km running event and (2) which factors were associated withan increased injury risk. Methods Prospective cohort study in Amsterdam, the Netherlands. Participants (n=5327) received a baseline survey to determine event distance (8 km or 16 km), main sport, running experience, previous injuries, recent overuse injuries and personal characteristics. Three days after the race, they received a follow-up survey to determine duration of training period, running distance per week, training hours, injuries during preparation and use oftechnology. Univariate and multivariate regression models were applied to examine potential risk factors for injuries. Results 1304 (24.5%) participants completed both surveys. After excluding participants with current health problems, no signed informed consent, missing or incorrect data, we included 706 (13.3%) participants. In total, 142 participants (20.1%) reported an injury during preparation for the event. Univariate analyses (OR: 1.7, 95% CI 1.1 to 2.4) and multivariate analyses (OR: 1.7, 95% CI 1.1 to 2.5) showed that injury history was a significant risk factor for running injuries (Nagelkerke R-square=0.06). Conclusion An injury incidence for recreational runners in preparation for a running event was 20%. A previous injury was the only significant risk factor for runningrelated injuries.
DOCUMENT
The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to sustain an injury. As a first step Self-reported data on training parameters and injuries from high-level runners (duration=37 weeks, n=23, male=16, female=7) were used to identify the most predictive variables for injuries, and train a machine learning tree algorithm to predict an injury. The model was validated by splitting the data in training and a test set. The 10 most important variables were identified from 85 possible variables using the Random Forest algorithm. To predict at an earliest stage, so the runner or the coach is able to intervene, the variables were classified by time to build tree algorithms up to 7 weeks before the occurrence of an injury. By building machine learning algorithms using existing self-reported training data can enable prospective identification of high-level runners who are likely to develop an injury. Only the established prediction model needs to be verified as correct.
DOCUMENT
