# Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
## 2 Materials and Methods  
### Study Design  
This study is a survey-based descriptive epidemiological study designed to collect data on musculoskeletal injuries in CF practitioners in Greece. The purpose of the study was to: (i) investigate the epidemiological profile among CF participants in Greece; (ii) identify the most common musculoskeletal injuries endured during CF training; (iii) determine the main risk factors for musculoskeletal injuries in Greek CF practitioners. It was approved by the Ethical Committee of the Faculty of Physiotherapy, School of Health Sciences, University of Thessaly (n.1575\(\Sigma\)E2/13-4-2020). The study was carried out using an electronic, anonymous, self-administered questionnaire. The questionnaire was based on previous relevant studies [7; 11; 15; 17] and was further modified by four experts with different backgrounds. Data were collected from April 2020 to June 2020 via an electronic survey tool using a Google-based form in Greek language. The questionnaire consisted of three parts. The first part included items concerning demographics--general information (gender, age, height, weight, training location). The second part had specific questions about CF (physical effort that work involve, CF experience, training sessions per week and duration, participation in competitions, days a week of rest, regular performance of other sports, sports activity level prior to CF, reasons of start practicing CF, supervision by a coach, professional monitoring, recovery, nutrition, etc.). The last, third part, focused on the occurrence of injuries while practicing CF, injury location and type of injury over the whole CF practice (lifetime prevalence).  
The following simple formula [37] was used for calculating the adequate sample size in our prevalence study:  
\[n\geq\frac{1.96^{2}}{\delta^{2}}p(1-p) \tag{1}\]  
where \(n\) denotes the sample size, \(p\) is the estimated prevalence and \(\delta\) is the margin of error. The minimum sample required to conduct the study was 1068 people, taking into account that there exist approximately 35,000 CF athletes all over Greece and assuming that 50% are injured, with 95% CI and 3% accuracy of injury prevalence. Since the existing number of CF participants in Greece remains unknown, the authors of this study followed the calculation method of [PERSON] et al. [11].  
The inclusion criteria were determined to include those CF practitioners who: (i) practice in Greek boxes with at least Level 1 certified trainers, (ii) aged 18 or over and (iii) had attended at least one one-week training session and were present during the data collection days. It should be noted that the study sample consists of CF practitioners of both sexes who work out in Greece regardless of their participation in competitions.  
Participants were excluded if they: (i) trained independently outside of a CF gym or outside Greece, (ii) trained in noncertified fitness centers or on their own, (iii) were performing other type of functional training, (iv) were younger than 18 years, (v) were affected by chronic osteo-articular diseases and (vi) provided incomplete questionnaires.  
The sample included 1224 CF practitioners aged 18 to 59 (443 (36%) females and 781 (64%) males) (Table 1). In this paper, the injury prediction task is considered as a two-class classification problem. Specifically, the participants of the study were divided into two groups: (1) Class 0: Healthy participants (not injured during CF) and (2) Class 1:Participants who reported having suffered an injury while practicing CF and this injury was confirmed by a medical professional. So, the main objective of the study is to build ML models that could discriminate the two aforementioned groups and therefore be able to decide whether a new testing sample (participant) will be assigned in one of the two classes.