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.
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.
Background Running-related injuries (RRIs) can be considered the primary enemy of runners. Most literature on injury prediction and prevention overlooks the mental aspects of overtraining and under-recovery, despite their potential role in injury prediction and prevention. Consequently, knowledge on the role of mental aspects in RRIs is lacking. Objective To investigate mental aspects of overtraining and under-recovery by means of an online injury prevention programme. Methods and analysis The ‘Take a Mental Break!’ study is a randomised controlled trial with a 12 month follow-up. After completing a web-based baseline survey, half and full marathon runners were randomly assigned to the intervention group or the control group. Participants of the intervention group obtained access to an online injury prevention programme, consisting of a running-related smartphone application. This app provided the participants of the intervention group with information on how to prevent overtraining and RRIs with special attention to mental aspects. The primary outcome measure is any self-reported RRI over the past 12 months. Secondary outcome measures include vigour, fatigue, sleep and perceived running performance. Regression analysis will be conducted to investigate whether the injury prevention programme has led to a lower prevalence of RRIs, better health and improved perceived running performance. Ethics and dissemination The Medical Ethics Committee of the University Medical Center Utrecht, the Netherlands, has exempted the current study from ethical approval (reference number: NL64342.041.17). Results of the study will be communicated through scientific articles in peer-reviewed journals, scientific reports and presentations on scientific conferences.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Alcohol use disorder (AUD) is a major problem. In the USA alone there are 15 million people with an AUD and more than 950,000 Dutch people drink excessively. Worldwide, 3-8% of all deaths and 5% of all illnesses and injuries are attributable to AUD. Care faces challenges. For example, more than half of AUD patients relapse within a year of treatment. A solution for this is the use of Cue-Exposure-Therapy (CET). Clients are exposed to triggers through objects, people and environments that arouse craving. Virtual Reality (VRET) is used to experience these triggers in a realistic, safe, and personalized way. In this way, coping skills are trained to counteract alcohol cravings. The effectiveness of VRET has been (clinically) proven. However, the advent of AR technologies raises the question of exploring possibilities of Augmented-Reality-Exposure-Therapy (ARET). ARET enjoys the same benefits as VRET (such as a realistic safe experience). But because AR integrates virtual components into the real environment, with the body visible, it presumably evokes a different type of experience. This may increase the ecological validity of CET in treatment. In addition, ARET is cheaper to develop (fewer virtual elements) and clients/clinics have easier access to AR (via smartphone/tablet). In addition, new AR glasses are being developed, which solve disadvantages such as a smartphone screen that is too small. Despite the demand from practitioners, ARET has never been developed and researched around addiction. In this project, the first ARET prototype is developed around AUD in the treatment of alcohol addiction. The prototype is being developed based on Volumetric-Captured-Digital-Humans and made accessible for AR glasses, tablets and smartphones. The prototype will be based on RECOVRY, a VRET around AUD developed by the consortium. A prototype test among (ex)AUD clients will provide insight into needs and points for improvement from patient and care provider and into the effect of ARET compared to VRET.
Ballet en moderne dans zijn een vorm van topsport. De druk op dansers is enorm. Lange en intensieve werkdagen, veel reizen en verschillende werkplekken maken het lastig om lichaam en geest goed te verzorgen. Hierdoor liggen blessures en mentale klachten op de loer. Nederlandse dansgezelschappen willen meer aandacht gaan besteden aan preventieve maatregelen om fysieke en mentale problemen bij hun dansers te voorkomen. Het ontbreekt hen echter aan kennis en kunde om dit innovatieve vraagstuk op te kunnen pakken. Het Nationale Ballet en het Scapino Ballet hebben het lectoraat Performing Arts Medicine van Codarts (Hogeschool voor de Kunsten Rotterdam) benaderd om antwoord te krijgen op de vraag hoe dansers op de hoogste podia, op gezonde wijze, hun beste performance kunnen laten zien. Gezamenlijk is deze praktijkvraag omgevormd naar drie onderzoeksdoelstellingen: 1. Opstellen van meetinstrumenten om de fysieke en mentale gezondheid van dansers te screenen en te monitoren; 2. Ontwerpen van een web-based systeem dat automatisch en real-time informatie uit de ontwikkelde meetinstrumenten kan inlezen, analyseren en interpreteren; 3. Ontwikkelen van een Fit to Perform protocol dat aanbevelingen geeft ten aanzien van het verbeteren van de fysieke en mentale gesteldheid van de danser. Het consortium bestaat uit de volgende organisaties: - Praktijkgerichte onderzoeksinstellingen: Codarts Rotterdam en Hogeschool van Amsterdam; - Universiteiten: ErasmusMC, Technische Universiteit Eindhoven en Vrije Universiteit Amsterdam; - Praktijkinstellingen: Het Nationale Ballet en het Scapino Ballet; - Overige instellingen: het Nederlands Paramedisch Instituut (NPi) en het Nationale Centrum Performing Arts (NCPA). Bij de samenstelling van het consortium is gekozen voor een goede mix tussen praktijkorganisaties, onderzoeksinstituten en onderwijsinstellingen. Daarnaast is er sprake van cross-sectorale samenwerking doordat kennis vanuit de podiumkunsten, sport, gezondheidszorg, onderwijs en technologie met elkaar verbonden wordt.
