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
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.
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 aim of this study is to investigate the predictivevalue of landing stability and technique togain insight into risk factors for ankle and kneeinjuries in indoor team sport players. Seventyfivemale and female basketball, volleyball orkorfball players were screened by measuringlanding stability after a single-leg jump landingand landing technique during a repeated countermovement jump by detailed 3-dimensional kinematicsand kinetics. During the season 11 acuteankle injuries were reported along with 6 acuteand 7 overuse knee injuries by the teams’ physicaltherapist. Logistic regression analysis showedless landing stability in the forward and diagonaljump direction (OR 1.01–1.10, p ≤ 0.05) in playerswho sustained an acute ankle injury. Furthermorelanding technique with a greater ankle dorsiflexionmoment increased the risk for acuteankle injury (OR 2.16, p ≤ 0.05). A smaller kneeflexion moment and greater vertical groundreaction force increased the risk of an overuseknee injury (OR 0.29 and 1.13 respectively,p ≤ 0.05). Less one-legged landing stability andsuboptimal landing technique were shown inplayers sustaining an acute ankle and overuseknee injury compared to healthy players. Determiningboth landing stability and technique mayfurther guide injury prevention programs.
DOCUMENT
Runners often sustain lower extremity injuries (19-79%) (van Gent et al, 2007). In a theoretical model it has been described that a disturbance in perceived stress and recovery can increase the risk of sustaining an injury (Williams & Andersen, 1998). Therefore, the purpose of this study was to investigate changes in perceived stress and recovery preceding an injury of competitive runners.Methods: Twenty-four competitive runners were monitored over one full training season (46 weeks). Every week, the runners filled an on-line RESTQ-sport (Nederhof et al, 2008). Furthermore, runners and their coaches kept a log with injuries and physical complaints. A non-traumatic injury was defined as any pain, soreness or injury that was not caused by trauma and resulted from training and led to a decrease in training duration or training intensity for at least one week (Jacobsson et al, 2013). Because baseline levels of perception of stress and recovery vary largely between runners, the 19 scales of the RESTQ-Sport were normalized to Z-scores based on the runner’s individual average and standard deviation of the whole season (excl. injured periods). The normalized scores of 1, 2 and 3 weeks before the first sustained injury were compared to 0, which is the average normalized score, by repeated measures ANOVA’s.Results: Twenty-two runners sustained a non-traumatic lower extremity injury. Eight of these runners filled out the RESTQ-Sport all 3 weeks preceding the injury and their data was used for further analysis. The injuries sustained were non-traumatic injuries of the knee, Achilles tendon, ankle, foot and shin. It was shown that 1 week preceding the injury, runners scored lower than the average normalized score on “Success” (Z-score: -0.68±0.62) and 2 weeks preceding the injury runners scored higher than their average on “Fitness/Injuries” (Z-score: 1.04±1.12).Discussion: A decrease in perceived success may be a marker to predict a non-traumatic lower extremity injury. Also an increase in the perception of muscle ache, soreness, pain and vulnerability to injury (“Fitness/Injury”) preceded injuries. Thereby, monitoring changes in individual stress and recovery may help to prevent non-traumatic injuries.
DOCUMENT
Non-professional runners make extensive use of consumer-available wearable devices and smartphone apps to monitor training sessions, health, and physical performance. Despite the popularity of these products, they usually neglect subjective factors, such as psychosocial stress, unexpected daily physical (in)activity, sleep quality perception, and/or previous injuries. Consequently, the implementation of these products may lead to underperformance, reduced motivation, and running-related injuries. This paper investigates how the integration of subjective training, off-training, and contextual factors from a 24/7 perspective might lead to better individual screening and health protection methods for recreational runners. Using an online-based Ecological Momentary Assessment survey, a seven-day cohort study was conducted. Twenty participants answered daily surveys three times a day regarding subjective off-training and contextual data; e.g., health, sleep, stress, training, environment, physiology, and lifestyle factors. The results show that daily habits of people are unstructured, unlikely predictable, and influenced by factors, such as the demands of work, social life, leisure time, or sleep. By merging these factors with sensor-based data, running-related systems would be able to better assess the individual workload of recreational runners and support them to reduce their risk of suffering from running-related injuries
DOCUMENT
AbstractBackground It is crucial to balance load and recovery during short-term match congestion in basketball. Currently, it is unknown if higher total load during short-term match congestion lead to higher injury and illness rates.Objective Aim of this study was to compare injuries and illnesses and total weekly load during 1-match weeks compared to ≥2-match weeks in basketball.Design During this prospective observational study, players were monitored during a full season.Setting Two basketball teams participating in the domestic-league championship, CUP matches and Euro league were followed.Patients (or Participants) Sixteen elite male professional basketball players participated in this study. Characteristics of the players were (mean±SD): age 24.8±2.0 years, height 195.8±7.5 cm, weight 94.8±14.0 kg, body fat 11.9±5.0% and VO2max 51.9±5.3 mL·kg−1·min−1.Interventions (or Assessment of Risk Factors) In total 47 matches by basketball team A (9 players) and 41 matches by team B (7 players) were performed throughout the season. All training sessions and matches were executed as prescribed by the training and coaching staff without interference or manipulation.Main Outcome Measurements The Oslo Sports Trauma Research Center (OSTRC) Questionnaire on Health Problems was used to collect data on injuries and illnesses on a weekly base. Furthermore, players filled in s-RPE and duration for each training and match. Prevalence’s, severity scores, time-loss and total weekly load were compared for 1-match weeks and ≥2-match weeks. The data were analyzed using multi-level modeling.Results Prevalence of injuries and illnesses were 18.1% and 4.6% for 1-match weeks and 17.2% and 3.3% for ≥2-match weeks. Severity scores and time-loss were not significantly different for 1-match weeks compared to ≥2-match weeks. Total weekly load was lower during ≥2-match weeks compared to 1-match weeks.Conclusions No significant differences for injuries and illnesses were observed between 1-match weeks and ≥2-match weeks. Coaches appeared to reduce training load to compensate for multiple matches during short-term match congestion.
DOCUMENT
Background Sports injuries are highly disadvantageous for Physical Education (PE) students, because they can lead to physical discomfort, and absence from sports classes resulting in higher study career costs.Objective To investigate the magnitude of the injury problem in PE students and to explore risk factors for sustaining an injury.Design A prospective cohort study with six months follow up.Participants and setting 280 Dutch freshmen PE students.Assessment of risk factors Prior to the start of the school year, all students underwent a medical examination to assess height, weight, percentage of body fat, blood pressure, visual acuity, muscle-skeletal functioning, and cardio-respiratory endurance. During the six months follow up, an online questionnaire was conducted on a weekly basis to monitor injuries and illnesses (OSTRC Overuse Injury Questionnaire). Furthermore, every two weeks an online questionnaire (POMS and RESTQ-Sports) was administered to measure mood and perceived stress and recovery of the students.Main outcome measures Frequencies and characteristics of injuries and illnesses.Results According to the OSTRC Overuse Injury Questionnaire, 22.5% of the students had physical problems regarding injuries during the first month of the school year, and 11.2% of the students were ill. Data collection will end in February 2014. We will perform a logistic regression analysis to test whether the injured students differ significantly from non injured students based on characteristics such as age, sex, body composition, and muscle-skeletal functioning.Conclusions Preliminary results showed that the risk of sustaining an injury and becoming ill is high for freshmen PE students. Screening at the start of the school year may play an important role in identifying the students at risk.
DOCUMENT
Background: Health-enhancing physical activity (HEPA) promotion programs are implemented in sports clubs. The purpose of this study was to examine the characteristics of the insufficiently active participants that benefit from these programs. Methods: Data of three sporting programs, developed for insufficiently active adults, were used for this study. These sporting programs were implemented in different sports clubs in the Netherlands. Participants completed an online questionnaire at baseline and after six months (n = 458). Of this sample, 35.1% (n = 161) was insufficiently active (i.e. not meeting HEPA levels) at baseline. Accordingly, two groups were compared: participants who were insufficiently active at baseline, but increased their physical activity to HEPA levels after six months (activated group, n = 86) versus participants who were insufficiently active both at baseline and after six months (non-activated group, n = 75). Potential associated characteristics (demographic, social, sport history, physical activity) were included as independent variables in bivariate and multivariate logistic regression analyses.
DOCUMENT
Objectives Adherence to injury prevention programmes in football remains low, which is thought to drastically reduce the effects of injury prevention programmes. Reasons why (medical) staff and players implement injury prevention programmes, have been investigated, but player’s characteristics and perceptions about these programmes might influence their adherence. Therefore, this study investigated the relationships between player’s characteristics and adherence and between player’s perceptions and adherence following an implemented injury prevention programme. Methods Data from 98 of 221 football players from the intervention group of a cluster randomised controlled trial concerning hamstring injury prevention were analysed. Results Adherence was better among older and more experienced football players, and players considered the programme more useful, less intense, more functional and less time-consuming. Previous hamstring injuries, educational level, the programme’s difficulty and intention to continue the exercises were not significantly associated with adherence. Conclusion These player’s characteristics and perceptions should be considered when implementing injury prevention programmes.
DOCUMENT
