Design
A randomized counterbalanced measures design was used to evaluate the effect of an NVD on helmet stability and the sweating effect on the relationship between the mounted NVD and helmet stability. The study consisted of two conditions distinguished by the existence of an NVD (370 g) mounted on the frontal part of the ballistic helmet with a mount bracket (203 g): (1) BH: Ballistic Helmet (total helmet weight = 1.5 kg), (2) BH-NVD: Ballistic Helmet with an NVD (total helmet weight = 2.1 kg). Testing sessions were separated by at least 48 h to minimize any effects of fatigue arising from the previous trial. Also, all participants visited the laboratory in advance on a separate day from the testing session to familiarize with the laboratory environment and experimental protocol and to provide anthropometric information.
Subjects
Nine participants with no history of any known diseases or neck pain were recruited (age = 24 ± 3 year; body weight = 73.0 ± 6.8 kg, height = 177.3 ± 6.8 cm, body mass index (BMI) = 23.2 ± 1.4 kg m−2, body fat = 15.4 ± 5.4%). Eight of them had completed military services and were taking part in a reserve force drill once in a year. One participant was serving his military duties in the army. Their head circumferences were previously measured and properly fitted helmets provided. Each participant was instructed to refrain from drinking any alcohol and excessive training that could induce muscle fatigue for the previous 24 h and any food intake and caffeine drinks for the previous 2 h before each trial. All participants were informed of all experimental procedures and provided informed consent prior to participation. All procedures were fully approved by the Public Institutional Review Board designated by the Ministry of Health and Welfare (P01-201,812-11-003).
Experimental protocols and procedures
Among various postures and motions that are commonly conducted during military duties, the following postures and motions in which a heavier helmet can provide a greater physical burden on the neck were chosen. At first, the participants moved boxes on a table at a controlled speed. During this task, the participants stood still in the front of the table and repeatedly moved boxes (size = 210 * 297 * 50 mm, weight of each box = 2.5 kg) from side to side. The speed of moving boxes was controlled by an electrical metronome (45 rpm). The distance between the boxes was identically set at 50 cm and the subjects were instructed to keep their eyes on the boxes which they were moving to maintain their neck flexed. Photograph analysis showed that the average neck-flexion angle of all subjects during horizontal lifting was 37 ± 5 degrees. The height of the table was individually adjusted to the level of the subject’s pelvis. The second task, shooting in a prone position, was chosen because it is a representative posture of shooting that is also included in basic military training. Instead of using an actual rifle, a sophisticated replica of an M2 rifle was used. Subjects lay prone, stared forward, and held the rifle while the magazine of the rifle was touching the ground. The target was located at a 10 m distance. The subjects were therefore instructed to just take aim at the target with the rifle. Each participant shot in their accustomed postures as all subjects had regularly participated in rifle instruction during their military service. After shooting, they walked on a treadmill for 20 min at a speed of 4 km h−1 at a slope of 16% to increase the body core temperature and to make them wet with perspiration. Thereafter, the two experimental tasks were repeated to observe the difference caused by profuse sweating. The entire protocol was began with initial stabilization for 10 min and ended with 10 min recovery. All experimental sessions were conducted in a room at 22.3 ± 0.8 °C, 22.3 ± 3.1% relative humidity (RH) and wind speed < 0.2 m·s−1. Before each test, 300 ml water was provided, after which the subjects wore a combat uniform including ballistic helmet and bulletproof vest (5.5 kg, Soft body armor for NIJ Standard Level III). Each subject adjusted a fit-band inside the helmet by themselves to make the helmet optimally fit on their head Figs. 1, 2.
Outcome measures
The following dependent variables were surveyed at the end of each posture: helmet stability (or helmet gliding), perceived neck load and neck pain, helmet pressure and pressing pain, and perceived helmet weight. Separate Visual Analogue Scales (VASs) were used, following the procedure of Van den Oord et al. (2012). The VAS scales have 100 mm lines with verbal anchors on each side indicating extreme experiences (e.g., “no suffering from helmet gliding” vs. “extreme suffering from helmet gliding”). Ear-canal temperature was continuously measured every 1 s (MP 160, Biopac systems, US). Probes to measure ear-canal temperature were inserted in the left ear-canal and fully insulated by cotton pads (40 * 40 * 15 mm) at least 1 h before the tests started. To monitor the course of saturation inside the helmet, the microclimate temperature and humidity inside the helmet were also recorded every 5 s using a microclimate sensor (TR-72wf, T&D recorder, Japan). The sensor was attached on the inner surface of the helmet at a position 5 cm above the temple. The location was confirmed not to cause any irritation or discomfort (e.g., directly touching and pressing the scalp) in the pilot tests. The local sweat rate was measured by absorbent patches (30 * 30 mm) on the ventral side of the forearm (glabrous surface) and upper back (scapula). They were weighed inside their sealed bags before and after each protocol with an electronic microbalance (HR-200, A&D Weighing, Japan). Although the local sweat rate on the forehead is critical for analyzing the effect of sweating on helmet stability, it was not directly collected because covering the skin of the forehead with insulating film or capsules can violate the interface property between skin and helmet inner pads, leading to variation in helmet stability measurement. Therefore, instead of measuring the local sweat rate on the forehead, the sweating rate was predicted using other regional sweating rates based on the sweating sensitivity reported by Smith and Havenith (2011). The water absorbency capacity of the forehead pads of the helmet was also measured. It was separated from the helmet, completely submerged in a water tank for 5 min, taken out and vertically hung until water dripping was not observed within a 30 s interval. The increase in weight was measured and it was expressed as the mass of water gain per unit area of fabric (g cm−2) (Tang et al. 2015) by dividing the covering area of the inner pad. Body fat was calculated by using the formula of Garcia et al. (2005) derived from skinfold thickness and waist circumferences. Skinfold thickness was measured on the 3 regions (triceps, subscapular, and abdominal) with a skinfold caliper (TKK 5011a, Beta Technology Inc., USA).
Data analysis
Descriptive statistics were shown as means and standard deviations. Ear-canal temperature and microclimate data were sampled from the 8th to 9th min in each phase. LSRforehead was predicted by the following equation:
$$ LSR_{{forehead}} = \;LSR_{{upper~back}} ~ \times \left( {SS_{{forehead}} /SS_{{upper~back}} } \right) $$
(1)
where LSRupper back was measured by absorbent patches (mg cm−2). SSforehead and SSupper back indicate the overall sudomotor sensitivity of the forehead and upper back, respectively. For the calculation, the values of sudomotor sensitivity reported in Smith and Havenith (2011) were used. Because overall sudomotor sensitivity indicates a relative increase of sweating rate divided by an increase of core body temperature (unit: mg cm−2 min−1 °C−1), to calculate predicted LSRupper back and LSRforehead, ear-canal temperature measured with insulating cottons was used as an alternative of body core temperature (Taylor et al. 2014). To verify the validity and reliability of the prediction, LSRforearm was firstly calculated from LSRupper back which was measured by absorbent patches to compare the predicted and measured LSRupper arm. As a first step in the statistical analysis, Kolmogorov–Smirnov normality test was conducted to verify each sampling data distribution. Then non-parametric statistics were used with the data of which normality was not accepted. Wilcoxon signed rank test was used to compare two groups of measured data and subjective ratings on the sweating and helmet conditions. To identify the within-subjects correlation coefficient between subjective parameters, including helmet comfort, helmet stability, helmet pressure, pressing pain, and perceived helmet weight, repeated measures correlation was analyzed using the rmcorr package in the software package R (Bakdash and Marusich 2017). The outcomes were presented by a repeated measured correlation coefficient (rrm). On the other hand, between-subjects correlation was analyzed with Spearman’s rank correlation test and the correlation coefficient (ρ) was used. Among the results, the data measured while moving boxes in the BH condition at the first phase were reported. Statistical analyses identifying sweating effect were conducted based on eight subjects except a subject who could not complete the experiment due to pressing pain on the lateral part of the head, especially near the temple during the second section of moving boxes. The significance was set at P < 0.05. Statistical analyses were performed with IBM SPSS Statistics 21.0, except for the repeated measures correlation analysis which was carried out using R.