CrossFit, Injury Prediction, Machine Learning, Risk Factors, Shoulder Injuries, Sports Injury
Conditions
Keywords
Sports Injury, Shoulder Injuries, Risk Factors, Injury Prevention, Injury Prediction, Machine Learning, CrossFit
Brief summary
CrossFit is a modern sport, introduced to the public in 2000 and popular quickly with more than 15,000 affiliates worldwide. Due to the highly demanding nature of the workouts, it is claimed to be a sport with a high prevalence of injuries. Most preliminary retrospective studies had shown that shoulder area is injured most frequently, at about a quarter of all injuries. Therefore, the initial goal of this observational (prospective cohort) study is to learn about the incidence rates of shoulder injuries and potential risk factors in a Greek population of CrossFit participants. Based on these results, this study's ultimate purpose is to create a short warm-up program capable of reducing shoulder injuries and evaluate its effectiveness. The main questions it aims to answer are: * Are shoulder injuries as frequent as previous studies have shown to be? * Can we blame for these injuries a previous history of musculoskeletal injury or deficits of range of motion, strength, and muscular endurance? * Can a short warm up which targets revealed deficiencies be effective in reducing shoulder injuries incidence rates? Participants will be asked to: * take part in baseline measurements (personal data, previous musculoskeletal history, shoulder and core range of motion, shoulder and hip muscle strength, shoulder stabilizers endurance, functional assessment sport-specific tests) * be monitored for new shoulder injuries or aggravation of old shoulder injuries that will occur during 12 months following baseline measurements. In this case, they must refer it to their coaches to be contacted and assessed by the researcher. * be in touch with the researcher throughout the observational study and provide any required data regarding their participation
Detailed description
CrossFit is prescribed as a constantly varied, high-intensity, functional movement. It is a highly motivational way of training including a wide variety of different exercises modalities, including calisthenics, gymnastics, metabolic conditioning, and weightlifting, which includes both Olympic and powerlifting movements. This training model has been demonstrated to improve 10 physical skills: cardiovascular and respiratory endurance, stamina, strength, flexibility, power, speed, coordination, agility, balance, and accuracy. Its popularity has increased in recent years with more than 200,000 athletes competing worldwide and many more participants of a lower level. These characteristics led to concerns about CrossFit's safety, accordingly many researchers began to investigate whether the injury incidence rates are higher than other sports with retrospective studies, which were conducted via online questionnaires. The purpose of this prospective cohort study is to investigate shoulder injury rate among CrossFit participants in Greece and to design and examine the efficacy of a sport-specific injury prevention program in these kinds of injuries. One hundred and eleven CrossFit participants will be surveyed. After reading and signing informed consent form, they will be interviewed for personal data, previous injuries history and general health. Shoulders and core range of motion, shoulders and hips muscle strength, shoulder stabilizers endurance will be measured with valid and reliable instruments. Also, functional assessment will be carry out using a novel instrument which has been developed in previous stage via a pilot study. CrossFit Screening Tool had been designed as a functional assessment instrument and applied as a pilot study to 20 CrossFit athletes. Based on the results, we have selected the functional tasks which will implement the baseline measurements process. The sample will be monitored for any shoulder injury by the researcher for the following 12 months. In the term of shoulder injury is included any new or aggravated old injury which costs the athlete's absence or urges performance modifications for tissue protection. When an injury occurs, the researcher will contact the athlete, assess him using special questionnaires and clinical examination, including valid and reliable special clinical tests. She will document the kind of injury and give him or her rehabilitation and return-to-sport guidelines. Baseline data of the participants who experienced injury will be correlated with the corresponding data of those who did not, using specific statistical analysis methods to disclose the aetiologic parameters. According to the results, at the next stage, a predictive model for shoulder injury incidence will be created using machine learning. The researchers of the present study aim to generate a model which be able to prognosticate the athletes who are going to be injured in whichever shoulder presenting high accuracy and low overfitting.
Interventions
None listed
Sponsors
University of Patras
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 62 Years
Healthy volunteers
Yes
Inclusion criteria
* Healthy adults CrossFit participants of any level of experience * Novices who have already completed the trial workouts
Exclusion criteria
* Injured CrossFit participants who have not yet returned to ordinary training
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pain intensity of injured participants | Up to 12 months | VAS pain scale will be used to determine shoulder pain intensity in injured participants. |
| Shoulder injury characteristics | Up to 12 months | Injury profile will be formed using an especially designed objective and subjective assessment questionnaire, including present injury history, correlation with performed exercise, symptoms, observation abnormalities and special diagnostic tests for shoulder pain (clinical assessment). Exact kind of injury will be determined by the researcher. |
| Disability of injured participants | Up to 12 months | A self-reported questionnaire for shoulder, the SDQ (Shoulder Disability Questionnaire), will be filled by injured participants via interview method by the researcher. |
| BMI | Baseline assessment | Weight (kg) and Height (cm) data will be collected to report BMI in kg/m\^2 |
| Demographic, general history and shoulder injuries history, activity and experience level | Baseline assessment | Participant profile will be formed using an especially designed questionnaire, data will be collected regarding the age, upper limb dominance, months of experience, training volume in hours per week, level of competition, warm up and recovery routines adequacy, details of previous injuries and current situation of shoulder functionality. |
| Core rotation active range of motion | Baseline assessment | Right and left core rotation will be measured in degrees utilizing a digital goniometer (HALO digital goniometer, HALO Medical Devices) which has affixed on an aluminum stick. Participant will be holding the stick by hands at the level of scapulas spines and starting rotate his or her core to right and then left direction. |
| Shoulder active range of motion symmetries | Baseline assessment | Comparative measurements among shoulders. In an upright position, knees forward and flexed as far as it will be needed in order back and lower back to be attached on the wall, participant will move his/her shoulders toward flexion while maintaining the initial position on the wall. When movement will reach the end point of the available range of motion, the researcher will measure the distances between each wrist and the wall using a measuring tape. The distance may be symmetrical otherwise the shoulder of the upper limb with the bigger distance will be documented as deficient. Same procedure in the same body position will be followed for external and internal rotation of the shoulders but movements will be performed with 90 degrees of shoulder abduction and 90 degrees of elbow flexion. |
| Shoulder stabilizers muscle strength | Baseline assessment | Maximal strength of each shoulder external and internal rotators will be measured in kilograms of resistance utilizing a handheld digital dynamometer, K-Force Muscle Controller, via K-Force Pro application reporting (Kinvent, Montpellier, France) |
| Lateral differences in muscle strength between shoulders | Baseline assessment | The percentage of lateral differences in maximal strength of internal and external rotation between shoulders will be measured utilizing a handheld digital dynamometer, K-Force Muscle Controller, via K-Force Pro application reporting (Kinvent, Montpellier, France) |
| Hip abductors muscle strength | Baseline assessment | Maximal strength of each hip abductors muscles will be measured in kilograms of resistance with the use of a handheld digital dynamometer, K-Force Muscle Controller, via K-Force application reporting (Kinvent, Montpellier, France) |
| Lateral differences in hip abductors strength | Baseline assessment | The percentage of lateral differences in maximal strength of abduction between hips will be measured utilizing a handheld digital dynamometer, K-Force Muscle Controller, via K-Force Pro application reporting (Kinvent, Montpellier, France) |
| Muscular endurance of shoulder external rotators | Baseline assessment | Using the aforementioned dynamometer application, participants will be asked to do 3 sets of 30 seconds isometric hold at the 60% of the maximal previously produced strength by external rotators muscles (outcome 5). 5 seconds rest between sets will be used. The percentage of the time, on which participants can produce the requested amount of power, will be recorded. This measurement will reflect the endurance capability of this muscle group for each shoulder. Measurement data will be collected using a handheld digital dynamometer, K-Force Muscle Controller, via K-Force Pro application reporting (Kinvent, Montpellier, France). |
| Lateral differences in shoulder external rotators endurance | Baseline assessment | The percentage of lateral differences in external rotation isometric hold between right and left side will be exported from K-Force Pro application reporting. |
| Shoulder stability | Baseline assessment | Closed Kinetic Chain Upper Extremity Stability test (CKCUES) will be performed to evaluate shoulder stability. |
| CrossFit-specific functional parameters (flexibility, stability, power) using an innovative evaluation tool: CrossFit Functional Assessment Battery for Shoulder (CrossFit FABS) | Baseline assessment | Flexibility, stability and power will be evaluated using an innovative assessment tool which was developed especially for the purpose of the present research. CrossFit FABS (Functional Assessment Battery for Shoulder) is a CrossFit-specific, functional evaluation battery in regard to shoulder injury risks. It is composed of 6 tests which resulted from a wider tool through applied pilot research: deep air squat, shoulder mobility, upper frontal kinetic chain flexibility, overhead squat facing wall, windmill with kettlebell and sots press. Each test is scored from 0 to 3, using a 3-point scoring scale with a maximum total score of 18 points. |
| Shoulder injury incidents | Up to 12 months | Any new or aggravated pre-existing shoulder injury that will be responsible for at least 1-day training or game loss or training modification to avoid specific high-impact exercises will be recorded. Also, the whole progression of the recovery until return-to-sport will be monitored. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Predictive modeling for shoulder injury incidence in CrossFit | After study completion, 2nd year | Using machine learning it is aimed to develop a predictive model able to identify the athletes who are going to be injured in their shoulder, ensuring high accuracy and low overfitting. |
| Shoulder injury risk factors | After study completion, 2nd year | Analyzing data of the injured and non-injured participants will reveal possible risk factors for shoulder injuries. |
Countries
Greece
Outcome results
None listed