Background:Medial Tibial Stress Syndrome (MTSS) is common among military recruits and to our knowledge; the factors that might put the military recruits at higher risk of incidence of MTSS are not well known.Objectives:This study was done to investigate the association between some anthropometric and anatomical factors and the prevalence of MTSS among military recruits.Patients and Methods:One hundred and eighty one randomly selected military recruits were included in this cross sectional study. Using history taking and physical examinations they were tested for MTSS. Accordingly the subjects were assigned to the case (those with MTSS) and control groups (normal healthy subjects). Using standard guidelines, the anthropometric and anatomical criteria of the subjects were measured. The correlation between the measurements and the prevalence of MTSS was tested using statistical analysis.Results:Data of all the 181 subjects with the mean age of 30.7 ± 4.68 years were Included in the final analysis. The prevalence of MTSS was found to be 16.6% (30 people). Internal and external rotation range of motion, iliospinale height, the score of navicular drop test, and the trochanteric tibial lateral length were significantly different between healthy subjects and patients with MTSS (P < 0.05).Discussion:The prevalence of MTSS was relatively lower in this study comparing to other studies on military personnel. It was not probably due to type of military shoes or exercise area surface (none of them were standardized); it could be due to low intensity trainings and the long intervals between training sessions.

Background:The body composition varies according to different life styles (i.e. intake calories and caloric expenditure). Therefore, it is wise to record military personnel’s body composition periodically and encourage those who abide to the regulations. Different methods have been introduced for body composition assessment: invasive and non-invasive. Amongst them, the Jackson and Pollock equation is most popular.Objectives:The recommended anthropometric prediction equations for assessing men’s body composition were compared with dual-energy X-ray absorptiometry (DEXA) gold standard to develop a modified equation to assess body composition and obesity quantitatively among Iranian military men.Patients and Methods:A total of 101 military men aged 23 - 52 years old with a mean age of 35.5 years were recruited and evaluated in the present study (average height, 173.9 cm and weight, 81.5 kg). The body-fat percentages of subjects were assessed both with anthropometric assessment and DEXA scan. The data obtained from these two methods were then compared using multiple regression analysis.Results:The mean and standard deviation of body fat percentage of the DEXA assessment was 21.2 ± 4.3 and body fat percentage obtained from three Jackson and Pollock 3-, 4- and 7-site equations were 21.1 ± 5.8, 22.2 ± 6.0 and 20.9 ± 5.7, respectively. There was a strong correlation between these three equations and DEXA (R² = 0.98).Conclusions:The mean percentage of body fat obtained from the three equations of Jackson and Pollock was very close to that of body fat obtained from DEXA; however, we suggest using a modified Jackson-Pollock 3-site equation for volunteer military men because the 3-site equation analysis method is simpler and faster than other methods.

The purpose was to investigate the effects of a 7-day creatine (Cr) loading protocol at the end of four weeks of β-alanine supplementation (BA) on physical performance, blood lactate, cognitive performance, and resting hormonal concentrations compared to BA alone. Twenty male military personnel (age: 21.5 ± 1.5 yrs; height: 1.78 ± 0.05 m; body mass: 78.5 ± 7.0 kg; BMI: 23.7 ± 1.64 kg/m2) were recruited and randomized into two groups: BA + Cr or BA + placebo (PL). Participants in each group (n = 10 per group) were supplemented with 6.4 g/day of BA for 28 days. After the third week, the BA + Cr group participants were also supplemented with Cr (0.3 g/kg/day), while the BA + PL group ingested an isocaloric placebo for 7 days. Before and after supplementation, each participant performed a battery of physical and cognitive tests and provided a venous blood sample to determine resting testosterone, cortisol, and IGF-1. Furthermore, immediately after the last physical test, blood lactate was assessed. There was a significant improvement in physical performance and mathematical processing in the BA + Cr group over time (p < 0.05), while there was no change in the BA + PL group. Vertical jump performance and testosterone were significantly higher in the BA + Cr group compared to BA + PL. These results indicate that Cr loading during the final week of BA supplementation (28 days) enhanced muscular power and appears to be superior for muscular strength and cognitive performance compared to BA supplementation alone.

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Purpose This study investigates the applicability of optimized machine learning (ML) approach for the prediction of Medial tibial stress syndrome (MTSS) using anatomic and anthropometric predictors. Method To this end, 180 recruits were enrolled in a cross-sectional study of 30 MTSS (30.36 ± 4.80 years) and 150 normal (29.70 ± 3.81 years). Twenty-five predictors/features, including demographic, anatomic, and anthropometric variables, were selected as risk factors. Bayesian optimization method was used to evaluate the most applicable machine learning algorithm with tuned hyperparameters on the training data. Three experiments were performed to handle the imbalances in the data set. The validation criteria were accuracy, sensitivity, and specificity. Results The highest performance (even 100%) was observed for the Ensemble and SVM classification models while using at least 6 and 10 most important predictors in undersampling and oversampling experiments, respectively. In the no-resampling experiment, the best performance (accuracy = 88.89%, sensitivity = 66.67%, specificity = 95.24%, and AUC = 0.8571) was achieved for the Naive Bayes classifier with the 12 most important features. Conclusion The Naive Bayes, Ensemble, and SVM methods could be the primary choices to apply the machine learning approach in MTSS risk prediction. These predictive methods, alongside the eight common proposed predictors, might help to more accurately calculate the individual risk of developing MTSS at the point of care.