Total Body Water (TBW) Percentage and 3rd Space Water Were Risk Factors for Recruit Injuries in Lower Limbs: A Case-Control Study

Background: Recruit training injuries have caused serious problems for troop training and medical support. The lower limbs is the site where recruit injuries occur the most. Bio-impedance (BIA) measures body composition quickly and accurately. Our aim was to identify the risk factors for lower limbs training injuries to recruits due to body composition. Methods: A total of 282 recruits were included. Before training, use BodyStat QuadScan 4000 multifrequency BIA system to measure the body composition of recruits. After training, they were divided into two groups according to the occurrence of lower limb training injuries. The basic characteristics of the two recruits were compared by Wilcoxon rank sum test. Receiver operator characteristic (ROC) curves was performed on the indicators with statistical difference between the two groups to nd the cutoff point. Finally, multivariate logistic regression analysis was used to nd the risk factors of lower limb training injuries. Results: Compared with the lower limb uninjured group, the lean mass percentage (P = 0.003), TBW percentage (P = 0.010), extracellular water (ECW) percentage (P = 0.023), intracellular water(ICW) percentage (P = 0.027), 3rd space water (P = 0.021) and basal metabolic rate(BMR)/total weight (P = 0.014) of the lower limb injury group was higher. On the contrary, the body fat percentage (P = 0.003) and body fat mass index (BFMI) (P = 0.005) of the lower limb injury group was lower. The results of multivariate logistic regression analysis showed that TBW percentage > 65.350% (P = 0.050, OR=2.085) and 3rd space water >0.950 (P = 0.045, OR=2.342) were independent risk factors for lower limb injuries. Conclusions: TBW percentage> 65.35% and 3rd space water >0.950 were independent risk of lower limb training injuries. These recruits need to be paid more attention during training.

Background Recruit training injuries have placed a great burden on the training and medical treatment of troops [1].
The incidence of military training injuries for foreign male recruits reported in the reference is about 25% [1]. The incidence of Chinese recruit injuries was 21.04% [2]. The highest rate of training injuries is lower limb injuries [2,3].
The in uence of body composition on sports has attracted people's attention. For example, body composition affects the recovery of exercise performance [14], and differences in body composition between overtraining syndrome subjects and healthy subjects [15], and relationship between athletic performance and body composition [16].
Military training is also a special sport. Previous reference has attempted to use body composition analysis to predict the occurrence of training injuries [17]. However, the measurement of human body composition may have problems such as tedious measurement, poor accuracy, and low body composition parameters. In this study, we used the BodyStat QuadScan 4000 multifrequency BIA system for body composition measurement. And a case-control trial was designed to nd the risk factors for recruit injuries.

Study design
A case-control study design was used. Recruits undergo the test was conducted at a recruit training base in the Chinese Army. BIA measurements before participating in recruit training. After 8 weeks of recruit training, the occurrence of recruit injuries was recorded. Recruits were divided into a lower limb injury group and a lower limb uninjured group. Analyze the relationship between lower limb training injuries and body composition.
Researchers assured recruits that participation in the study was voluntary and that non-participation would not affect training results or their military career.

Inclusion / Exclusion Criteria
All recruits in the recruit training base who have passed the enlistment medical examination can be included in this study. Recruits who do not adapt to the life of the army and retire halfway will be excluded from the study. In addition, invalid measurement data will be excluded from the study.
According to the product manual, the invalid measurement data is judged, that is, data whose impedance values measured at frequencies of 5, 50, 100, and 200 kHz are not sequentially reduced or whose prediction index is greater than 1 will be excluded. Two companies were randomly selected to participate in the experiment, and no special randomization method was used. One retired recruit and 8 sets of invalid data were excluded. A total of 282 recruits were nally included in the study.

BIA Measurements
Body composition analysis was performed using the BodyStat QuadScan 4000 multifrequency BIA system. The test method is performed according to the manufacturer's instructions. Recruits are required to fast for solids and liquids for 4 to 5 hours before the test; do not engage in sports 12 hours before the test; do not drink alcohol or coffee 24 hours before the test.
This analysis instrument can measure at 5, 50, 100 and 200kHz to obtain four impedance values (IMPED5K, IMPED50K, IMPED100K, IMPED200K). TBW and fat-free mass (FFM) are predicted at a frequency of 50kHz. ECW is predicted at a frequency of 5kHz. The meanings of other parameters are as follows: Prediction marker TM =IMPED200K/IMPED5K Nutritional index=ECW/TBW 3rd space water =TBW (predicted at 50kHz)-TBW (predicted at 200kHz) TBW is the total amount of uid in the body whether in cells, outside cells, in blood etc.
Body mass index (BMI)= Body weight / (Body height)2 BFMI= Body fat / (Body height)2 Fat-free mass index (FFMI)= (Body weight-Body fat) / (Body height)2 Training injury diagnosis The diagnosis of training injuries is performed by the medics at the recruit training base according to the diagnostic criteria.

Statistical Analysis
Statistical analysis was performed using SPSS22.0. Continuous variables are shown as mean ± standard deviation. P <0.05 was considered statistically signi cant. The Shapiro-Wilk test was used to check whether the data conforms to a normal distribution. The mean comparison between the two groups of non-normally distributed data was performed using Wilcoxon rank sum test. ROC curves were performed to provide a basis for the independent variable assignment of logistic regression analysis. The independent relationship between the occurrence of lower limb training injuries and related factors was analyzed by binary multivariate logistic regression (forward: Wald method).

General characteristics
A total of 282 recruits were included in the study. As shown in Table 1, after eight weeks of recruit training, a total of 71 recruits suffered training injuries. Among them, 66 cases of lower limb training injuries occurred, the incidence rate was 23.40%. The lower limb is the site where training injuries occur the most. Data with a normal distribution were compared using T test; data with non-normal distribution were compared with Wilcoxon rank sum test. As shown in Table 2, compared with the lower limb uninjured group, the lean mass percentage (P = 0.003), TBW percentage (P = 0.010), ECW percentage (P = 0.023), ICW percentage (P = 0.027), 3rd space water (P = 0.021) and BMR/total weight (P = 0.014) of the lower limb injury group was higher. On the contrary, the body fat percentage (P = 0.003) and BFMI (P = 0.005) of the lower limb injury group was lower.

Body Composition Roc Curve
We continued to make ROC curves for the above 8 indicators with statistical differences. The purpose is to observe the accuracy of these indicators as predictors of the occurrence of lower limb training injuries, and at the same time to provide a basis for the independent variable assignment of subsequent logistic regression analysis.
As shown in Fig. 1 and Table 3, the area under the curve (AUC) of the 8 indicators were statistically signi cant (P < 0.05). However, the AUC is not large, indicating that the prediction effect is not very good. The most sensitive indicator is BFMI; the most speci c indicator is ICW percentage.

Risk Factors Of Lower Limb Injuries
We continue to perform multivariate logistic regression analysis on the above seven indicators. Assignment is based on the cutoff point of the ROC curve, which is showed in Table 4. As shown in Table 5, the results of multivariate logistic regression analysis showed that TBW percentage > 65.350% (P = 0.050, OR = 2.085) and 3rd space water > 0.950 (P = 0.045, OR = 2.342) were independent risk factors for lower limb injuries.  Compared with the US recruits, the BMI of our recruits is lower. This may re ect the differences between China and the United States in terms of race, diet, and national conditions.
The Bodystat o cial website gives a male (30 years old) body fat percentage standard of 15-20% [18].
Considering that the 20 and 30 years olds are often grouped into the same group when the fat percentage is counted [19,20], the above standards have a certain degree of reference signi cance. The body fat percentage of recruits included in this survey is 9.344 ± 3.9142%, which is signi cantly lower than the standard value of 15-20%. And the body fat percentage of the lower limb injured group was lower than that of lower limb uninjured group (7.991 ± 2.9343%vs 9.743 ± 4.0797%, p = 0.001). Combined with the lower BMI of our army's recruits, we speculate that our army's recruits have insu cient body fat percentage. Subcutaneous fat reduces the effects of mechanical forces on muscles and bones [21]. Therefore, recruits with a low body fat percentage are more vulnerable.
Since most of the body water is contained in the Lean Body Mass, the body water percentage will increase with a loss of fat weight and a gain in lean tissue [18]. The TBW standard provided by Bodystat's o cial website is 55-65% of body weight [18]. Multivariate logistic regression analysis showed that TBW percentage > 65.35% was an independent risk factor for lower limb training injuries. The water content of various tissues and organs of the human body is different, the water content of adipose tissue is 10%, and the water content of bones is 22% [22]. The high TBW on the one hand re ects the low body fat percentage, and its impact on training injuries has been discussed previously.