The impact of thermal‒moisture comfort and material properties of calf compression sleeves on individuals jogging performance

2.1 Participants

The calves of 94 females (16–28 years old) were scanned using a VITUS Smart XXL body scanner (Human Solutions, Germany). From the subsequent exercise experiment, 15 females representing the most widely covered body type A in the Chinese body classification were selected as subjects. Their calf girth was 34.5 ± 2 cm, heights was 161.4 ± 3.1 cm, and weights was 53.7 ± 5.5 kg. All subjects had no history of exercise contraindications or cardiovascular diseases, and no recent lower limb joint, muscle, or skeletal injuries or abnormalities. Prior to the experiment, the subjects were informed of the experimental procedures and precautions, ensuring they understood the required actions. All participants completed a health questionnaire and signed an informed consent form before the testing.

2.2 Experimental program

The experiment focused on measuring the lower limb (calf) region of the female subjects. The main measurements (Figure 1) taken were the girth of three calf regions: upper calf, middle calf, and lower calf. For each girth, four measurement points were taken, located on the front, back, and both sides, encircling the entire calf. This resulted in a total of 12 data collection points. The RTR-503 thermal and moisture sensor (T&D, Japan) was used to collect the surface conditions of the subjects, accuracy ±0.3 °C, ±5 % RH.

Figure 1:

Test positions.

During the experiment, the subjects underwent running tests while wearing three different CCS (T1, T2, T3) and in an “Undressed (no sleeve)” control condition. The compression levels of the three CCS samples (T1–T3) were measured under standardized conditions using a pneumatic contact pressure tester (AMI3037-2) [25], following the testing methodology outlined in GB/T 24442.2-200. The mean interface pressures were as follows: T1 = 0.25 ± 0.03 kPa, T2 = 0.31 ± 0.02 kPa, and T3 = 0.26 ± 0.03 kPa. These values reflect graded compression profiles tailored for sedentary individuals engaging in light-to-moderate exercise.

The experiment consisted of three stages: S1, 5 min standing rest period; S2, 10 min exercise period, with 5 min of running at 4 km/h and 5 min at 6 km/h; and S3, 10 min recovery period. The picture of the experimenter running is shown in Figure 2. After the exercise, thermal images were captured using a FLIR ONE Pro thermal camera (USA) at a distance of 1 m from the subjects’ calves, measurement range is −20 to 400 °C, accuracy is ±0.3 °C, resolution is 0.1 °C. Additional thermal images were taken 10 min into the recovery period.

Figure 2:

Experimental picture.

During the exercise, certain safety precautions were taken to protect the safety of the subjects, and the heart rate changes and physical condition of the subjects were always paid attention to. If any discomfort occurred, the exercise was stopped immediately. In order to reduce the impact of excessive exercise on the experiment due to delayed muscle recovery, the test interval of each experimental CCS was 1 h.

Due to the varying thermal-moisture indicators of the different samples, and the potential randomness in the relationship between these indicators, the entropy method was used to analyze the weight of each indicator’s influence on the thermal-moisture properties of the materials. Subsequently, the grey relational analysis model was utilized to comprehensively evaluate the thermal-moisture comfort of the materials. The entropy method is a practical and objective weighting approach, which can be applied to assign weights to the various indicators in the thermal-moisture comfort evaluation system.

The subjective evaluation test uses the 5-level Likert scale. The design of the subjective evaluation scale is such that the higher the score (up to 5), the stronger the perceived discomfort.

2.3 Experimental materials

This study surveyed representative CCS products available on the market. By comparing the popular and functionally comprehensive products, three representative CCS were ultimately selected. These three CCS had the same size range (suitable for calf girth 33–39 cm), which matched the subjects’ measurements. Additionally, all three fabrics were constructed with a weft-knitted plain stitch structure, sharing identical material composition of 80 % nylon and 20 % spandex. The core-spun yarn consisted of nylon-covered spandex (140D nylon/40D spandex), but the thickness and material density differed. The specific parameters are shown in Table 1.

Table 1:

Material structure parameters.

No.	CCS material enlarged image	Thickness (mm)	Density (g/m2)
T1		0.551	206.6
T2		1.195	398.9
T3		0.782	265.9

The AP of the CCS was tested using the YG461E-II Material Air Permeability Tester, following the China’s GB/T 5453-1997 standard. Samples measuring 20 × 20 cm were cut from the materials and conditioned in a constant temperature and humidity chamber for 24 h prior to testing. The thermal resistance was measured using the YG(B)606N Textile Thermal Resistance Tester, thermal resistance range 0.002–2.000 (m² k)/W, resolution 0.0001 (m² k)/W, following the GB/T11048-2018 standard. The test specimens were cut to a size of 50 × 50 cm, with the side facing the human skin oriented towards the test plate, each sample was tested five times, and the average value was reported.

The WVT was measured using the cup method, following the GB/T12704.2-2009 standard. The test specimen was fixed with the skin-facing side downward on the WVT cup, which was then placed in a sealed test chamber maintained at the preset temperature. After 1 h, the first weight measurement was taken, followed by a second measurement 1 h later. The difference in weight (Δm) was then used to calculate the WVT. The calculation equation is as follows:

where WVT is the water vapor transmission rate in g/(m² h) or g/(m²·24 h); Δm is the weight difference of the test specimen in grams (g); Δm′ is the weight difference of the blank specimen in grams (g) (if no blank test is performed, Δm′ = 0); A is the effective test area (0.00283 m² for the given apparatus); and t is the test duration in hours (h).

2.4 Data analysis methods

3.1 Thermal-moisture transfer performance results and analysis

The thermal-moisture evaluation indexes for the three CCS were AP, WVT, and TC. All three indexes are positive indicators, meaning that higher index values are better.

As can be seen from Figure 3, T1 had the best WVT, while T2 had the poorest. The increase in thickness and material density may have led to the pores in the material being blocked or reduced, causing changes to the fiber structure of the material. This could have decreased the airflow channels and increased the distance for moisture to propagate through the material, thereby reducing the WVT.

Figure 3:

CCS samples AP and WVT performances.

According to Table 2, T1 had the highest TC and the lowest thermal resistance; T2 had the lowest TC and the highest thermal resistance. These properties were linearly correlated with the changes in their thickness and material density. Specifically, the insulation performance of the materials increased as their thickness increased, while the TC decreased as the material thickness increased.

3.2 Correlation calculation and analysis

Treating the thermal-moisture indexes of the materials as a system, the reference sequence is generally composed of the optimal values of all the indexes. Here, the maximum values of the respective indexes were taken to determine the reference sequence X ₀′ = (767.51, 16.608, 0.074). The data sequences constructed from the thermal-moisture performance test values of three material samples were used as the comparative sequences, denoted as X _i  = [x _i (1), x _i (2), x _i (3)], i = 1, 2, 3, where x _i (1) represents the AP, x _i (2) represents the WVT, and x _i (3) represents the TC (see Table 3).

According to the entropy method, the factors affecting the thermal-moisture comfort of the materials are ranked from largest to smallest as: WVT > AP > TC. The weight coefficients of these three factors are 0.458, 0.289, and 0.253, respectively. The correlation between these indices and physiological comfort data is reflected in their impact on skin temperature and humidity, which determines the overall comfort performance of CCS under different sports conditions.

The relation degree results are shown in Table 4.

The rankings of the equal-weight relation degree and the weighted relation degree are the same. T1 has the highest relation degree, while T2 has the lowest. This indicates that the thickness and surface density of the CCS materials have a significant influence on their thermal-moisture comfort performance. As the thickness and surface density increase, the thermal-moisture performance of the materials becomes poorer. Conversely, as the thickness and surface density decrease, the thermal-moisture performance of the materials improves.

3.3 Temperature and humidity sensor data analysis

The weight of the influence of TC on temperature is the largest, so the temperature will differ when wearing different CCS materials.

According to the analysis results shown in Figure 4, CCS materials with different WVT, AP, and TC exhibit distinct differences in their performance during human physical activity. T1 has the highest WVT, AP, and TC, which theoretically should allow it to quickly dissipate the large amount of moisture generated by the body, resulting in rapid changes in the internal and external humidity during the exercise stage. In comparison, T2 has lower values for these performance indicators, leading to the retention of moisture within the material and a slower increase in humidity. T3 falls somewhere in between the two.

Figure 4:

Comparison of temperature and humidity change trends of CCS, (a) temperature; (b) humidity. Note: 1–5 min: S1 relaxation stage, 6–15 min: S2 slow jogging at 4 km/h to 6 km/h, 16–25 min: S3 end and rest.

3.4 LSD post hoc multiple comparison analysis

The LSD post-hoc multiple comparison analysis was conducted. A one-way ANOVA was performed on the temperature and humidity data for the four clothing conditions, and the results showed that the p-values for temperature and humidity were both less than 0.05, indicating significant differences among the four experimental groups. To further clarify the specific sources of differences, the LSD post-hoc multiple comparison analysis was employed. Its primary purpose is to identify significant differences in temperature and humidity metrics across experimental groups while controlling the overall Type I error rate, thereby avoiding the masking of nuanced differences by overarching significance – a method particularly suited for small-sample multiple comparisons, and the results are shown in Tables 5–10.

The comparison of the four groups revealed that there were significant differences (p < 0.05) between any two of the experimental results in terms of humidity and temperature changes, except for some instances during the S2 and S3 stages where the significance level was relatively lower.

In the S3 rest stage after exercise, the humidity of T1 finally stabilized at the lowest level, while the stabilized humidity level of T3 was higher than that of T1. This is because during exercise, the human body sweats copiously, with the sweat existing in the form of water vapor in the microclimate environment between the fabric and the skin. In the resting stage after exercise, air flow is crucial for sweat evaporation. The high WVT and AP of the T1 material create favorable conditions for air exchange inside and outside the material, facilitating the rapid transmission of water vapor generated by sweat from the interior of the fabric to the external environment and resulting in the final stabilization of humidity at the lowest level. Although the T3 material also released some moisture during the rest stage, due to its lower WVT and AP compared to T1, it might have taken a longer time to expel the sweat and moisture on the surface, leading to a relatively higher stabilized surface humidity.

3.5 Infrared thermal imaging comparison

It is necessary to conduct statistical analysis on the temperature and humidity of the three CCS materials at different stages, in order to identify the differences between the various solutions and the reasons for the aforementioned behavior of T1. Furthermore, through the use of an infrared thermal imaging camera, the static test temperatures before and after exercise can be quickly determined. The following presents a representative set of thermal imaging images, with each row showing the images from left to right for the following stages: S1, immediately after the end of S2 (exercise), and after S3 (10 min of rest).

From Figure 5, it can be observed that the surface temperatures after the exercise (S2) were significantly lower than before the exercise (S1) and after 10 min rest (S3). This is primarily due to the fact that the body effectively dissipated the generated heat through various heat dissipation mechanisms (such as sweat evaporation, blood circulation [26], etc.) during the exercise process.

Figure 5:

Comparison of different states of infrared thermal imaging.

When comparing the three materials’ thermal imaging longitudinally, it can be seen that the temperatures of the various stages for T1 are most similar to the color of the undressed, indicating that T1 has a faster heat dissipation rate and better TC. In contrast, the thermal imaging temperatures of T2 and T3 at S2 and S3 were relatively lower, which can be explained by their relatively slower heat dissipation rates.

In summary, the WVT, AP, and TC of the materials are the key factors influencing humidity changes. Theoretical analysis suggests that T1, with its excellent performance indicators, should be able to quickly expel moisture during exercise, resulting in the fastest humidity rise. However, the actual test results did not fully verify this inference, and further in-depth research is needed to elucidate the reasons.

3.6 Subjective evaluation

After analysis, the reliability coefficient of the subjective evaluation scale was 0.988, indicating that the research data has high reliability. The mean values of the thermal-moisture discomfort scores of the subjects wearing the CCS were calculated for the three stages, as shown in Figure 6.

Figure 6:

Subjective evaluation results.

As shown in Figure 6, T1 has the best thermal-moisture comfort overall (total score of 9.83), while T2 has the relatively poorest thermal-moisture comfort (total score of 11.22). This also suggests that the thickness and areal density of the CCS materials have an impact on the thermal-moisture comfort of the lower body clothing during exercise, The greater the material thickness and the higher the density, the slower the heat transfer rate to the outside world. It also restricts the permeability of sweat vapor, making it difficult for the sweat to evaporate and disperse quickly. Consequently, the thermal and moisture accumulate on the skin surface or inside the clothing, ultimately resulting in poorer thermal-moisture comfort. This subjective evaluation result is also consistent with the previous CCS material performance test results.

4.1 Effects of thickness and density on thermal-moisture transfer characteristics

The AP of the CCS materials is negatively correlated with the material thickness and density. The increase in material thickness and surface density can lead to factors such as a more compact porous structure, reduced fiber gap, and hindered air diffusion [27]. Increased thickness leads to a longer gas diffusion path, making it more difficult for air to penetrate the material, and also provides more insulation layers to further impede heat conduction and prevent the dissipation of body thermal to the external environment. Increased density, on the other hand, leads to an increase in the number of fibers per unit volume. When air diffuses inside the material, it collides with more fibers, which increases the resistance to air diffusion, slowing down the diffusion speed of the air within the material and thereby reducing the AP [28].

T1 has the best WVT (24.74 g/(m² h)), but its surface humidity is higher than that of T3. This is because during the static sitting stage (59.18 % RH), when the body sweats less and the environmental humidity exists, the relatively frequent air exchange inside and outside the material, along with the high AP of T1 (767.51 mm/s) may allow environmental humidity to more easily penetrate into the material, leading to a relatively high surface humidity and a higher humidity than T3 (54.27 % RH). However, T1 temperature is not the lowest, and the TC coefficient (0.074 W/(m K)) of T1 is the most important factors affecting temperature. The relatively fast heat transfer rate inside T1 enables it to quickly transfer the heat from the interior to the surface when the human body generates heat, resulting in a relatively higher surface temperature.

Therefore, materials with a higher TC coefficient may have a greater impact on temperature changes, and the higher surface temperature of the CCS indicates better heat dissipation performance. The high TC of T1 allows heat to be transmitted more quickly, which may lead to a higher surface temperature, but also helps maintain overall thermal balance [29]. Additionally, the subjective rating results for T1 (avg. = 3.28) also support this observation. In comparison, T2 has lower WVT and AP rates. In the sedentary stage, when the environmental humidity exists, due to its lower AP, the air exchange with the outside is infrequent, and the moisture is not easy to enter the material with the air flow, which allowed T2 to better block the penetration of environmental moisture. However, in the exercise stage, when the sweating amount of the human body increases, the lower WVT and AP of T2 are not conducive to the rapid discharge and evaporation of sweat. The moisture is more easily trapped within the material, resulting in its moisture wicking efficiency being less effective than that of T1 during the exercise phase.

4.2 Comprehensive evaluation of thermal and moisture comfort performance

The four sets of experiments exhibited a trend of initial decrease followed by an increase in temperature (Figure 3). This is because, at S1 the body was in a relatively stationary state, and the metabolic activity was relatively stable, with a balance between heat generation and dissipation, resulting in a stable body temperature. In contrast, during the exercise process, the body’s metabolic activity increases, and the body dissipates the excess heat through mechanisms such as sweating from sweat glands, heat dissipation from the skin surface, and heat exhaust from the respiratory tract [30], [31]. These heat dissipation mechanisms allow the body to effectively dissipate heat during exercise, thereby reducing body temperature. Additionally, the greater range of movements during exercise increase the airflow speed between the inner and outer surfaces of the CCS, which also accelerates the heat dissipation rate. So there is a situation where the surface temperature of the human body wearing CCS is actually lower in S2 than in S1 and S3. When the exercise in S2, the rapid blood circulation continues to distribute the heat throughout the body. This means that during S3, the body’s internal heat generation persists, but without the muscle activity of running to consume this heat, the body temperature may continue to rise until the heat is gradually dissipated.

Furthermore, through the analysis of temperature and humidity sensor testing as well as infrared thermal imaging, the differences in thermal-moisture comfort performance of the different materials under static and dynamic conditions were further verified. T1 had the best moisture stability after exercise, indicating better moisture vapor discharge performance after high-intensity exercise, while T2 and T3 took longer to discharge moisture.

The subjective evaluations by the test subjects were highly consistent with the objective data. T1 received the highest ratings for thermal-moisture comfort, particularly in terms of the feeling of dryness and comfort during exercise. This further confirms the critical role that high WVT and AP play in enhancing the thermal-moisture comfort of the materials [32]. For sedentary individuals, selecting high WVT materials like T1 can effectively minimize sweat accumulation during exercise, thereby reducing skin stickiness and other discomforts, while significantly improving wearing comfort in hot and humid conditions. These findings further demonstrate that in CCS design, optimizing material parameters to achieve a balance between compression support and moisture/air permeability is crucial for enhancing the exercise experience of sedentary populations.

This study established a comprehensive evaluation model to investigate the thermal-moisture comfort of CCS under different exercise conditions. The experimental results show that there are significant differences among the four groups (p < 0.05), which provides important references for the design and evaluation of CCS.

4.3 Individual differences and their impact on experimental results

Each individual’s physical condition (such as weight, muscle mass, metabolic rate, etc.) can influence comfort and performance when wearing CCS [25]. The participants’ health status (such as the presence of sports injuries, cardiovascular diseases, etc.) can also affect the experimental results. Those in better health may exhibit better thermal adaptation under the same conditions. For female participants, different stages of the menstrual cycle may affect their perception of thermal and moisture, thereby influencing the experimental results [33].

The psychological state of the participants (such as anxiety, stress, confidence, etc.) can impact their performance and subjective evaluation of comfort. Anxiety or stress may increase sensitivity to discomfort [34]. Participants’ expectations of the experimental outcomes and personal goals (such as the expectation to improve athletic performance) may influence their performance in the experiment, thus affecting the objectivity of the data.

Therefore, it is necessary to combine quantitative and qualitative analyses, integrating quantitative physiological data (such as temperature, humidity, heart rate) with qualitative subjective evaluations (such as comfort ratings). In designing experiments, it is crucial to control for physiological and psychological factors that may affect the results, such as selecting participants with similar health conditions or conducting the experiment under consistent psychological states.