Effects of Changes in Environmental Color Chroma on Heart Rate Variability and Stress by Gender

With increasing time spent indoors during the coronavirus disease pandemic, occupants are increasingly affected by indoor space environmental factors. Environmental color stimulates human vision and affects stress levels. This study investigated how changing environmental color chroma affected heart rate variability (HRV) and stress. The HRV of nine males and fifteen females was measured during exposure to 12 color stimuli with changes in chroma under green/blue hues and high/low-value conditions, and a stress assessment was performed. The effect of chroma on the HRV of males and females was verified, but the interaction effect between chroma and gender was not. ln(LF) and RMSSD were valid parameters. ln(LF) of males and females decreased as chroma increased under the green hue and low-value conditions; RMSSD was reduced as chroma increased in the blue hue and low-value conditions. ln(LF) decreased as chroma increased under blue hue and high-value conditions in males. Color–stress evaluation revealed that the higher chroma under high-value conditions, the more positive the stress emotion, and the lower the chroma under low-value conditions, the more negative the stress emotion. As chroma increased under low-value conditions, color is a stress factor; for men, this effect was more evident in the blue hue.


Introduction
Urbanization, industrialization, and more competitive social atmospheres caused by rapid social structural changes are creating unhappy societies and reducing self-esteem [1]. This social atmosphere increases exposure to stress, which has a negative effect on psychological and physiological human domains [2]. The ongoing coronavirus disease (COVID- 19) pandemic has made clear how much stress diseases and changes in social structures can cause among individuals and countries. National measures to curb the pandemic have caused physical and mental stress problems [3][4][5][6]. Particularly, self-isolation, a strategy to minimize exposure to COVID-19, has increased depression and resulted in productivity loss due to people changing their living spaces into studying and working spaces [7].
Korea has excellent public health facilities, with more hospital beds and medical equipment than the Organisation for Economic Co-operation and Development (OECD) average. It also has the highest number of outpatient department visits per capita among OECD countries [8]. Accordingly, Korea has a lower mortality rate from major diseases than other OECD countries and is a major medical tourism destination in Asia [9]. Korea is also recognized as an exemplary case in slowing the pace of the pandemic without enforcing regional lockdowns due to the active response and strict personal hygiene rules introduced by the government following the outbreak of COVID-19 [10]. Nevertheless, South Koreans are under more stress than people in Japan, China, the United States, the United Kingdom, and Germany [11]. Additionally, those working in the medical sector have particularly high stress levels [12,13]. The pandemic acted as an environmental factor that caused stress. Studies on the relationship between the pandemic and stress reported that during the pandemic, anxiety, depression, and stress have been higher in young people than in the elderly and higher in females than in males; furthermore, it has caused negative psychological effects [14][15][16][17]. This report suggests that the stress stimulus caused by the external environment may appear differently depending on age and gender. On the physiological stress response according to gender, Dishman et al. (2000) determined that there is no relationship between heart rate variability (HRV), which is used as a measuring indicator of stress, and gender [18]; Madden & Savard (1995) verified this lack of relationship [19]. However, Buchanan et al. (2010) found that cortisol responses to psychological stress differed by gender [20], and Traustadóttir et al. (2003) revealed a gender difference in psychological stress due to higher cortisol in older males than in older females [21]. In other words, there is an observable difference between males and females when responding to physiological and psychological stress. As the time spent in specific spaces increased during the pandemic, the factors of the spatial environment affected the occupants' stress [22], and environmental color, as an environmental factor with strong visual stimulation, affected the physiology and psychology of occupants [23]. Jonauskaite et al. (2020) clinically verified that occupants perceive colors through sight while resting and that the effects of these colors can impact stress and anxiety levels [24]. Therefore, environmental color is an environmental factor in the stress level of space users, and the stress, anxiety, and depression levels of space users vary depending on the colors in the surrounding spatial environment [24][25][26][27]. If environmental color is an environmental factor that affects the occupant's physiological and psychological aspects, the differences between physiological and psychological stress in males and females need to be understood. However, previous studies on the relationship between environmental colors and the stress of occupants commonly mention the limitation of insufficient evidence-based research [28,29]; therefore, conducting empirical research on the effect of environmental colors on the physical and psychological stress level of occupants through follow-up studies is necessary.
Although many studies have researched the effect of different colors on the autonomic nervous system, fewer studies have focused on the influence of changes in value (the lightness of the color) or chroma (the intensity of the color). Nonetheless, scholars have consistently mentioned that differences in chroma and hue affect the physiological and psychological states of occupants. In a study by Kwallek et al. (1996) [30], female workers indicated more depression, confusion, and anger in low-chroma offices, whereas male workers reported more depression, confusion, and anger in high-chroma offices. This demonstrated that the chroma of environmental colors is a major variable that influences the psychological state of occupants. Öztürk et al. (2012) [31] confirmed that work accuracy was higher in chromatic schemes than in achromatic schemes, and occupants found chromatic schemes to be more attractive and satisfactory. The chroma of environmental color affects the efficiency and evaluation of workers. Furthermore, Suk et al. (2009) [32] reported that hue, value, and chroma affect emotional responses, with chroma having a stronger effect. Zielinski (2016) [33] validated that changes in chroma affect skin conduction responses and argued that high-chroma colors are better for capturing attention and are a significant factor in color perception. Although these previous studies verified that chroma is a significant factor that influences the physiology and psychological states of occupants, research into how the chroma conditions of environmental colors affect the stress level of occupants is still in its preliminary stages. Therefore, because people are spending more time in spatial environments during the ongoing pandemic, studies should investigate environmental color chroma conditions that have a positive effect on reducing the stress level of occupants. Most previous studies performed experiments using colors close to the primary colors as stimuli, which differ from actual environmental color conditions, so they have limitations in terms of applying the results to a real-world environment. Similar to the study by Oh et al. [23], the present study used colors corresponding to the actual environment instead of primary colors as experimental stimuli to derive empirical research results applicable to the environment.
The objective of this study was to evaluate the stress levels of males and females in accordance with the various color environments by utilizing HRV analysis. HRV was chosen to measure the stress of occupants; HRV objectively evaluates stress by measuring the activity of the sympathetic and parasympathetic nerves constituting the autonomic nervous system, making it a suitable index to measure physiological stress responses. HRV measurement is a non-invasive method that does not entail any penetration of the human body and has advantages such as placing a lighter psychological burden on the subject and having a short measurement time. A stress assessment using adjectives was conducted and employed as a psychological stress response indicator to supplement HRV [34][35][36][37].
Through this, the current study aimed to provide evidence-based research findings for environmental color configurations that can decrease the stress levels of occupants.

Configuration of Experimental Colors
Although Korea provides high-quality health care through continuous quality improvement in medical services led by the government, the stress level of medical workers is high. This study configured the experimental colors using environmental color data of public health facilities, which are spaces mainly used by Korean health professionals with high stress levels, with the aim of measuring the change of the autonomic nervous system in response to a color stimulus close to the actual color of the environment, rather than primary colors. Although strong color stimulation of primary colors strongly affects the autonomic nervous system, it is difficult to apply any obtained results to real environments. Thus, results derived from color stimuli constructed by measuring color data in the real environment (public health facilities) are more applicable to real environments. We visited 22 public health facilities in Korea to collect color data by measuring the environmental colors with a spectrophotometer (Minolta CM-2500d, Konica Minolta, Osaka, Japan; Table 1) and derived the average values of the color data and used them as a basis to configure the experimental colors. Table 1. Specifications of the spectrophotometer (Minolta CM-2500d).

Model
Minolta CM-2500d environment. Similar to the study by Oh et al. [23], the present study used colors corresponding to the actual environment instead of primary colors as experimental stimuli to derive empirical research results applicable to the environment. The objective of this study was to evaluate the stress levels of males and females in accordance with the various color environments by utilizing HRV analysis. HRV was chosen to measure the stress of occupants; HRV objectively evaluates stress by measuring the activity of the sympathetic and parasympathetic nerves constituting the autonomic nervous system, making it a suitable index to measure physiological stress responses. HRV measurement is a non-invasive method that does not entail any penetration of the human body and has advantages such as placing a lighter psychological burden on the subject and having a short measurement time. A stress assessment using adjectives was conducted and employed as a psychological stress response indicator to supplement HRV [34][35][36][37]. Through this, the current study aimed to provide evidence-based research findings for environmental color configurations that can decrease the stress levels of occupants.

Configuration of Experimental Colors
Although Korea provides high-quality health care through continuous quality improvement in medical services led by the government, the stress level of medical workers is high. This study configured the experimental colors using environmental color data of public health facilities, which are spaces mainly used by Korean health professionals with high stress levels, with the aim of measuring the change of the autonomic nervous system in response to a color stimulus close to the actual color of the environment, rather than primary colors. Although strong color stimulation of primary colors strongly affects the autonomic nervous system, it is difficult to apply any obtained results to real environments. Thus, results derived from color stimuli constructed by measuring color data in the real environment (public health facilities) are more applicable to real environments. We visited 22 public health facilities in Korea to collect color data by measuring the environmental colors with a spectrophotometer (Minolta CM-2500d, Konica Minolta, Osaka, Japan; Table 1) and derived the average values of the color data and used them as a basis to configure the experimental colors. Health Center, and Yeonje-gu Jaeban Public Health Center). In total, 554 environmental color data points were collected from these facilities. Table 2 summarizes the results of analyzing the average values of the color data according to the weights of the dominant, complementary, and accent colors applied to the walls (the spatial range where the visual perception of the space users mainly occurs). The most used Munsell color system expresses color properties by dividing them into hue, value, and chroma when representing color data. Hue indicates the properties of color, and the basic five colors (red, yellow, green, blue, and purple) are expressed as 5R, 5Y, 5G, 5B, and 5P, respectively. The intermediate colors of these colors are expressed as 5RY, 5YG, 5BG, 5PB, and 5RP. Value means the brightness of the color and varies from 0 to 10, in which black, with the darkest brightness, is displayed as 0, and the brightest white is expressed as 10. Chroma represents the intensity of the color; an achromatic color without color is expressed as 0, and the value increases according to the degree of saturation. The maximum value of chroma depends on the hue and value. The color representation of the Munsell color system is expressed in the form of "hue value/chroma" [38]. The experimental stimuli in this study consisted of green and blue hues. The values were based on the average value of the dominant color (8.6) and the average value of the accent color (6.3). Chroma was increased by three degrees on the basis of the average chroma (1.8) of the dominant color to configure low chroma (1.8), medium chroma (4.8), and high chroma (7.8) [23]. Figure 1 shows the 12 color samples configured according to these criteria and utilized to measure the HRV and assess the stress levels of males and females according to changes in chroma.  Figure 2 summarizes the five steps of this study. In step 1, we explained the outline and the precautions of the experiment through interviews with the subjects while obtaining their written consent to participate in the experiment. In step 2, we performed the Ishihara test for color blindness and color vision. In step 3, we measured HRV under 12 color environments. In step 4, we assessed stress on the color stimuli. In step 5, we conducted a multivariate analysis of variance (MANOVA) analysis on the HRV measurement and stress assessment data to examine how changes in environmental color chroma af-  Figure 2 summarizes the five steps of this study. In step 1, we explained the outline and the precautions of the experiment through interviews with the subjects while obtaining their written consent to participate in the experiment. In step 2, we performed the Ishihara test for color blindness and color vision. In step 3, we measured HRV under 12 color environments. In step 4, we assessed stress on the color stimuli. In step 5, we conducted a multivariate analysis of variance (MANOVA) analysis on the HRV measurement and stress assessment data to examine how changes in environmental color chroma affected the HRV and emotional evaluation of males and females.  Figure 2 summarizes the five steps of this study. In step 1, we explained the outline and the precautions of the experiment through interviews with the subjects while obtaining their written consent to participate in the experiment. In step 2, we performed the Ishihara test for color blindness and color vision. In step 3, we measured HRV under 12 color environments. In step 4, we assessed stress on the color stimuli. In step 5, we conducted a multivariate analysis of variance (MANOVA) analysis on the HRV measurement and stress assessment data to examine how changes in environmental color chroma affected the HRV and emotional evaluation of males and females.

Method of Measuring HRV
For HRV measurement, the participants were randomly exposed to three groups of color stimuli in the order of Group A (four colors corresponding to low chroma), Group B (four colors corresponding to medium chroma), and Group C (four colors corresponding to high chroma). Before starting the experiment, the subjects covered their eyes with an eye patch and rested for 5 min. Participants were exposed to each color stimulus for 2 min 30 s, and HRV was measured for the duration [39][40][41]. Between each stimulus, the participants covered their eyes with an eye patch and rested for 1 min [42]. Behaviors that could affect the autonomic nervous system, such as deep breathing, sneezing, coughing, and yawning, were restricted during the experiment. The HRV measurement monitor was turned on during the HRV measurement period, and only the measurer could see the monitor.

Method of Measuring HRV
For HRV measurement, the participants were randomly exposed to three groups of color stimuli in the order of Group A (four colors corresponding to low chroma), Group B (four colors corresponding to medium chroma), and Group C (four colors corresponding to high chroma). Before starting the experiment, the subjects covered their eyes with an eye patch and rested for 5 min. Participants were exposed to each color stimulus for 2 min 30 s, and HRV was measured for the duration [39][40][41]. Between each stimulus, the participants covered their eyes with an eye patch and rested for 1 min [42]. Behaviors that could affect the autonomic nervous system, such as deep breathing, sneezing, coughing, and yawning, were restricted during the experiment. The HRV measurement monitor was turned on during the HRV measurement period, and only the measurer could see the monitor.
According to the recommendations of the HRV guidelines, a 5-min measurement of HRV per color is methodologically adequate. However, following this guideline would result in an experiment time of over 60 min, which the researchers judged would increase stress in the participants, making it impossible to obtain accurate data. The measurement time was reduced by considering effective parameters in ultra-short-term measurements. Recently, several mobile health-care devices have been used to estimate stress by measuring HRV; thus, there have been many discussions on HRV parameters that are effective for ultra-short-term measurements of less than 5 min. Generally, researchers agree that RMSSD is a valid parameter for ultra-short-term measurements [43]. Salahuddin [45], and Marek Malik et al. (1996) [46] verified that the low frequency (LF) and high frequency (HF) have been normalized. Therefore, this study used a log of low-frequency HRV (ln(LF)), a log of high-frequency HRV (ln(HF)), and RMSSD as analysis parameters to measure HRV in 12 color environments, as explained below.
LF and HF parameters are data values derived through frequency domain analysis. LF represents a power value between 0.04-0.15 Hz. It simultaneously reflects the activities of the sympathetic and parasympathetic nervous systems and, in long-term measurement, reflects the activity of the sympathetic nerve more [47][48][49]. A decrease in LF reflects a fatigued state, while an increase in LF is known to have a positive correlation with depression, anger, and stress [50]. HF has a power value between 0.15-0.4 Hz. It reflects the activity of the parasympathetic nervous system. The raw data of LF and HF have a distorted value because the variance is large and does not follow a normal distribution. Natural logarithmic values are taken to compensate for this, which are expressed as ln(LF) and ln(HF). RMSSD is a data value derived through time domain analysis. It is the square root value of the mean square of the RR(NN) interval difference. Additionally, it reflects shortterm variability in heart rate and the activity of the parasympathetic nervous system [48][49][50]. Under stressful conditions, parasympathetic nerves are reduced, and the HF and RMSSD parameters reflecting parasympathetic activity are decreased [18].
An HRV measuring instrument (uBio Macpa, Biosense Creative Co. Ltd., Seoul, Korea) was used to measure HRV (Table 3, left). Measurement followed the measurement and analysis method of the HRV guidelines presented by the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. It was applied with the autonomic nervous system data standard presented by the HeartMath Research Center in the United States. The instrument can measure 40 to 200 beats per minute (BPM), with an error rate of 2%. The accuracy and reliability of the instrument and analysis software have been proven in past studies [51][52][53]. An analysis software application ( Table 3, right) provided raw data by converting HRV measurement data into Excel data, and the mean pulse, complexity, total power, VLF, LF, HF, LF/HF, BPM, SDNN, RMSSD, and stress index parameter data were derived. Table 3. Measurement device specifications and software application for HRV.
LF represents a power value between 0.04-0.15 Hz. It simultaneously reflects the activities of the sympathetic and parasympathetic nervous systems and, in long-term measurement, reflects the activity of the sympathetic nerve more [47][48][49]. A decrease in LF reflects a fatigued state, while an increase in LF is known to have a positive correlation with depression, anger, and stress [50]. HF has a power value between 0.15-0.4 Hz. It reflects the activity of the parasympathetic nervous system. The raw data of LF and HF have a distorted value because the variance is large and does not follow a normal distribution. Natural logarithmic values are taken to compensate for this, which are expressed as ln(LF) and ln(HF). RMSSD is a data value derived through time domain analysis. It is the square root value of the mean square of the RR(NN) interval difference. Additionally, it reflects shortterm variability in heart rate and the activity of the parasympathetic nervous system [48][49][50]. Under stressful conditions, parasympathetic nerves are reduced, and the HF and RMSSD parameters reflecting parasympathetic activity are decreased [18].
An HRV measuring instrument (uBio Macpa, Biosense Creative Co. Ltd., Seoul, Korea) was used to measure HRV (Table 3, left). Measurement followed the measurement and analysis method of the HRV guidelines presented by the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. It was applied with the autonomic nervous system data standard presented by the HeartMath Research Center in the United States. The instrument can measure 40 to 200 beats per minute (BPM), with an error rate of 2%. The accuracy and reliability of the instrument and analysis software have been proven in past studies [51][52][53]. An analysis software application (Table 3, right) provided raw data by converting HRV measurement data into Excel data, and the mean pulse, complexity, total power, VLF, LF, HF, LF/HF, BPM, SDNN, RMSSD, and stress index parameter data were derived.

Model uBio Macpa Model uBio Macpa Software Application
Power 220 V, 60 Hz for computer system and 5V-DC through USB for measurement probe CPU Pentium IV 1 GHz above LF represents a power value between 0.04-0.15 Hz. It simultaneously reflects the activities of the sympathetic and parasympathetic nervous systems and, in long-term measurement, reflects the activity of the sympathetic nerve more [47][48][49]. A decrease in LF reflects a fatigued state, while an increase in LF is known to have a positive correlation with depression, anger, and stress [50]. HF has a power value between 0.15-0.4 Hz. It reflects the activity of the parasympathetic nervous system. The raw data of LF and HF have a distorted value because the variance is large and does not follow a normal distribution. Natural logarithmic values are taken to compensate for this, which are expressed as ln(LF) and ln(HF). RMSSD is a data value derived through time domain analysis. It is the square root value of the mean square of the RR(NN) interval difference. Additionally, it reflects shortterm variability in heart rate and the activity of the parasympathetic nervous system [48][49][50]. Under stressful conditions, parasympathetic nerves are reduced, and the HF and RMSSD parameters reflecting parasympathetic activity are decreased [18]. An HRV measuring instrument (uBio Macpa, Biosense Creative Co. Ltd., Seoul, Korea) was used to measure HRV (Table 3, left). Measurement followed the measurement and analysis method of the HRV guidelines presented by the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. It was applied with the autonomic nervous system data standard presented by the HeartMath Research Center in the United States. The instrument can measure 40 to 200 beats per minute (BPM), with an error rate of 2%. The accuracy and reliability of the instrument and analysis software have been proven in past studies [51][52][53]. An analysis software application (Table 3, right) provided raw data by converting HRV measurement data into Excel data, and the mean pulse, complexity, total power, VLF, LF, HF, LF/HF, BPM, SDNN, RMSSD, and stress index parameter data were derived.

Color-Stress Assessment
Color-stress assessment used adjectives expressing opposite emotions to assess the emotions felt when recognizing color samples on a 5-point Likert scale.
The words employed for the color-stress assessment were extracted from four doctoral dissertations related to color appraisal [54][55][56][57]. Table 4 below presents the seven paired adjectives to evaluate stress emotions. These words were evaluated by three experts with a Ph.D. in the field of environmental color.
After the participants took a 3-min break following the HRV measurement, the colorstress assessment was conducted by presenting 12 color samples in the order of G1-G6 and B1-B6. The participants then evaluated their perceived emotions on a 5-point scale. The color samples were the same colors as the environmental colors used in the HRV measurement, and their size was 10 × 10 cm. The assessment was conducted on a graytone background, an achromatic color series that increases color perception with a minimum effect on color interference [58].
MANOVA analysis was performed to verify the main effect of color chroma change and gender and the interaction effect between color chroma change and gender on the seven paired adjectives used in the color-stress assessment. Table 4. Stress assessment vocabulary selection process.

Participants
A total of 24 Korean people (9 male and 15 female) participated in the experiment with an average age of 21.4 years old. According to reports, people in their 20s are the most vulnerable age group to stress as the proportion of patients with mood disorders, depressive disorders, and anxiety disorders in their 20s is overwhelmingly high [59]. People in their 20s reported having high levels of depression, anxiety, and stress, not only in Korea but also in the United States, Spain, China, and Saudi Arabia [60][61][62][63][64]. Therefore, the participants of this study consisted of young people in their 20s. People in their 20s have excellent visual conditions for perceiving colors. It was also easier to accurately measure HRV due to their active physical vitality.
All the participants voluntarily expressed their intention to participate in the experiment after reviewing the contents of the experiment through online and offline notices. Participants were excluded if they were smokers, claustrophobic, mentally ill, obese, or suffered specific chronic illnesses that could affect their autonomic nervous system. Moreover, they were asked to abstain from drinking and obtain at least 6 h of sleep before the experiment. Caffeine intake was also restricted for 3 h before starting the tests.

HRV Measurement Environment
The experimental environment blocked the inflow of natural light and was composed of artificial lighting (Table 5, Osram FHF32SS EX-D, 32W, four ramps, Osram, Munich, Germany). Illuminance was measured twice, once in the morning and once in the afternoon of the experiment, with an illuminance meter ( Table 6, Luton LM-81LX). The average illuminance was 359 lux [65], and the dimensions of the experimental space were 6.0 (w) × 3.0 (d) × 2.4 m (h). Additionally, the walls and ceiling were finished with achromatic white matte paint and the floor with achromatic gray tiles. Figure 3 shows that the color space for the HRV measurement had color stimulus materials large enough (1.0 (w) × 0.8 (d) × 1.2 m (h)) to block the vision of the participants. The experimental environment was designed to be similar to an ordinary living space. The illuminance was set to the level of typical office spaces (250-500 lux) [66], and the ceiling received ceiling lighting without any color stimulus.

Model
Color Temperature CRI Light Output Germany). Illuminance was measured twice, once in the morning and once in the afternoon of the experiment, with an illuminance meter ( Table 6, Luton LM-81LX). The average illuminance was 359 lux [65], and the dimensions of the experimental space were 6.0 (w) × 3.0 (d) × 2.4 m (h). Additionally, the walls and ceiling were finished with achromatic white matte paint and the floor with achromatic gray tiles. Figure 3 shows that the color space for the HRV measurement had color stimulus materials large enough (1.0 (w) × 0.8 (d) × 1.2 m (h)) to block the vision of the participants. The experimental environment was designed to be similar to an ordinary living space. The illuminance was set to the level of typical office spaces (250-500 lux) [66], and the ceiling received ceiling lighting without any color stimulus. illuminance was 359 lux [65], and the dimensions of the experimental space were 6.0 (w) × 3.0 (d) × 2.4 m (h). Additionally, the walls and ceiling were finished with achromatic white matte paint and the floor with achromatic gray tiles. Figure 3 shows that the color space for the HRV measurement had color stimulus materials large enough (1.0 (w) × 0.8 (d) × 1.2 m (h)) to block the vision of the participants. The experimental environment was designed to be similar to an ordinary living space. The illuminance was set to the level of typical office spaces (250-500 lux) [66], and the ceiling received ceiling lighting without any color stimulus.

Statistical Analysis Method
As discussed above, for HRV measurement data analysis, the mean and standard deviation values were derived through frequency analysis centered on ln(LF), ln(HF), and RMSSD parameters to identify changes in overall parameters. MANOVA was performed to verify the main effects of chroma and gender on HRV parameters and the interaction effect between chroma and gender.
After analyzing the color-stress evaluation results, the mean values of the evaluation values of males and females for seven pairs of adjective vocabulary were expressed as a line graph. Additionally, MANOVA was performed to verify the main effects of chroma and gender and the interaction effect between chroma and gender for the seven pairs of adjective vocabulary. The applications used for statistical analysis were Predictive Ana-

Statistical Analysis Method
As discussed above, for HRV measurement data analysis, the mean and standard deviation values were derived through frequency analysis centered on ln(LF), ln(HF), and RMSSD parameters to identify changes in overall parameters. MANOVA was performed to verify the main effects of chroma and gender on HRV parameters and the interaction effect between chroma and gender.
After analyzing the color-stress evaluation results, the mean values of the evaluation values of males and females for seven pairs of adjective vocabulary were expressed as a line graph. Additionally, MANOVA was performed to verify the main effects of chroma and gender and the interaction effect between chroma and gender for the seven pairs of adjective vocabulary. The applications used for statistical analysis were Predictive Analytics SoftWare (PASW) Statistics 18 and Microsoft Excel 2019 version. Table 7 shows the mean and standard deviation of ln(LF), ln(HF), and RMSSD parameters by gender for each experimental color. The basic unit of ln(LF) and ln(HF) is ms 2 , and that of RMSSD is ms. In green hues, except for G1, the RMSSD was slightly higher in females than in males. Similarly, in blue hues, the RMSSD was higher in females than males.

HRV Results by Gender According to Changes in Environmental Color Chroma
MANOVA statistical analysis was performed on the HRV data before measurement and the three colors with chroma changes to verify whether ln(LF), ln(HF), and RMSSD parameters derived from the HRV measurement exhibit significant differences according to changes in environmental color chroma and gender and to examine the interaction effects (Table 8). To "verify the homogeneity of variances," the basic assumption for the performance of MANOVA, the M verification was conducted for BOX. As a result, "Before × G1 × G3 × G5" was p = 0.520, "Before × G2 × G4 × G6" was p = 0.638, "Before × B1 × B3 × B5" was p = 0.745, and "Before × B2 × B4 × B6" was p = 0.582, which showed no statistical difference in the covariance matrices, thereby confirming that MANOVA is possible. As seen in Table 8, in MANOVA (wilks' λ = 0.932, F(9, 209.452) = 0.783, η 2 = 0.026), as per changes in chroma under G hue-high-value conditions, there were significant differences in ln(LF) (F = 4.667, p < 0.05) according to gender. Males showed the highest ln(LF) in color G3, while ln(LF) of females tended to decrease as chroma increased, but the difference was not significant.
The interaction of chroma change with HRV did not vary according to gender, but the interaction with HRV parameters did. Generally, there was a difference in ln(LF) according to gender. There was also a difference in the RMSSD as chroma changed under blue hue-low-value conditions (B2 × B4 × B6). Table 9 shows the average values of the stress assessment to understand how the stress levels of males and females changed according to changes in environmental color chroma. Table 9. Average values of stress assessment for males and females according to changes in environmental color chroma. The interaction of chroma change with HRV did not vary according to gender, but the interaction with HRV parameters did. Generally, there was a difference in ln(LF) according to gender. There was also a difference in the RMSSD as chroma changed under blue hue-low-value conditions (B2 × B4 × B6). Table 9 shows the average values of the stress assessment to understand how the stress levels of males and females changed according to changes in environmental color chroma. Table 9. Average values of stress assessment for males and females according to changes in environmental color chroma.

B2 × B4 × B6 (Blue-low-value)
Under high-value conditions of green and blue hues (G1 × G3 × G5 and B1 × B3 × B5, respectively), stress emotions were perceived to be positive as chroma increased. On the contrary, under low-value conditions of green and blue hues (G2 × G4 × G6, B2 × B4 × B6), stress emotions were perceived to be negative as chroma decreased. Therefore, changes in chroma affect stress levels, and the effect of color is greater than that of gender. The interaction of chroma change with HRV did not vary according to gender, but the interaction with HRV parameters did. Generally, there was a difference in ln(LF) according to gender. There was also a difference in the RMSSD as chroma changed under blue hue-low-value conditions (B2 × B4 × B6). Table 9 shows the average values of the stress assessment to understand how the stress levels of males and females changed according to changes in environmental color chroma. Table 9. Average values of stress assessment for males and females according to changes in environmental color chroma.

B2 × B4 × B6 (Blue-low-value)
Under high-value conditions of green and blue hues (G1 × G3 × G5 and B1 × B3 × B5, respectively), stress emotions were perceived to be positive as chroma increased. On the contrary, under low-value conditions of green and blue hues (G2 × G4 × G6, B2 × B4 × B6), stress emotions were perceived to be negative as chroma decreased. Therefore, changes in chroma affect stress levels, and the effect of color is greater than that of gender. In MANOVA (wilks' λ = 0.953, F(9, 209.452) = 0.469, η 2 = 0.016), as per changes in chroma under B hue-low-value conditions, there were significant differences in RMSSD (F = 4.326, p < 0.05) according to gender. The RMSSD of males and females decreased as chroma increased. The interaction of chroma change with HRV did not vary according to gender, but the interaction with HRV parameters did. Generally, there was a difference in ln(LF) according to gender. There was also a difference in the RMSSD as chroma changed under blue hue-low-value conditions (B2 × B4 × B6). Table 9 shows the average values of the stress assessment to understand how the stress levels of males and females changed according to changes in environmental color chroma. Table 9. Average values of stress assessment for males and females according to changes in environmental color chroma.

B2 × B4 × B6 (Blue-low-value)
Under high-value conditions of green and blue hues (G1 × G3 × G5 and B1 × B3 × B5, respectively), stress emotions were perceived to be positive as chroma increased. On the contrary, under low-value conditions of green and blue hues (G2 × G4 × G6, B2 × B4 × B6), stress emotions were perceived to be negative as chroma decreased. Therefore, changes in chroma affect stress levels, and the effect of color is greater than that of gender. The interaction of chroma change with HRV did not vary according to gender, but the interaction with HRV parameters did. Generally, there was a difference in ln(LF) according to gender. There was also a difference in the RMSSD as chroma changed under blue hue-low-value conditions (B2 × B4 × B6). Table 9 shows the average values of the stress assessment to understand how the stress levels of males and females changed according to changes in environmental color chroma. Table 9. Average values of stress assessment for males and females according to changes in environmental color chroma.

B2 × B4 × B6 (Blue-low-value)
Under high-value conditions of green and blue hues (G1 × G3 × G5 and B1 × B3 × B5, respectively), stress emotions were perceived to be positive as chroma increased. On the contrary, under low-value conditions of green and blue hues (G2 × G4 × G6, B2 × B4 × B6), stress emotions were perceived to be negative as chroma decreased. Therefore, changes in chroma affect stress levels, and the effect of color is greater than that of gender.
Under high-value conditions of green and blue hues (G1 × G3 × G5 and B1 × B3 × B5, respectively), stress emotions were perceived to be positive as chroma increased. On the contrary, under low-value conditions of green and blue hues (G2 × G4 × G6, B2 × B4 × B6), stress emotions were perceived to be negative as chroma decreased. Therefore, changes in chroma affect stress levels, and the effect of color is greater than that of gender.
MANOVA statistical analysis was conducted to verify whether there were significant differences in stress emotions according to chroma change and gender and to identify whether there was an interaction effect. Tables 10-13 show the results.
As an interval scale evaluated stress assessment, a normal distribution test was conducted and confirmed through skewness and kurtosis values. In Hopeless-hopeful, skewness was 0.064 and kurtosis was −0.933, and in Annoying-enjoyable, skewness was 0.193 and kurtosis −0.205. In Tensioned-relaxed, skewness was −0.089, and kurtosis was −0.0619. In Depressed-cheerful, skewness was 0.163, and kurtosis was −0.422. In Boring-fun, skewness was 0.168 and kurtosis −0.594, and in Overwhelming-free, skewness was −0.305 and kurtosis −0.300. In Dislike-like, skewness was −0.112, and kurtosis was −0.362. When the absolute value of the skewness index is 3.0 or less, and the absolute value of the kurtosis index is 8.0 or less, it can be seen as a normal distribution [67]. Therefore, it is confirmed that a dependent variable composed of an interval scale follows a normal distribution.
According to chroma changes, the difference in perceived stress was more intense for blue hues than for green. The main effect of chroma change was more significant than the main effect by gender, indicating a difference in perceived stress in colors according to chroma change. The interaction effect between chroma change and gender was significant for "annoying-enjoyable," "depressed-cheerful," and "dislike-like" under green and blue hue and low-value conditions; therefore, we can infer that males and females perceive stress differently because of the influence of chroma in low-value colors.

Discussion
With developments in technology, an increasing number of mobile devices and applications have been used to measure HRV and examine stress levels. Accordingly, parameters for valid HRV measurements taken in under less than 5 min have been developed, such as normalized LF, HF, and RMSSD (Salahuddin et [46]) parameters that were used in this study. According to previous studies, LF reflects sympathetic and parasympathetic nerve activity in short-term measurements, and HF reflects parasympathetic nervous system activity. The main effects of HRV parameters according to changes in chroma and gender were verified. However, the interaction effects between changes in chroma and HRV parameters for males and females were not. This was due to the use of colors in the range of environment colors, which provided a relatively weak stimulus compared to primary colors as the experimental stimulus [23]. Differences between data must be distinct to verify the interaction effect between variables; however, there was no significant change when using colors with weak visual stimulus. Therefore, future research should supplement this limitation. In addition, no interaction effect was observed in short-term HRV measurements; therefore, whether there is an interaction effect between each variable must be investigated through long-term HRV measurements in future studies.
The parameters with statistically significant results were as follows: ln(LF) showed significant results for gender in all changes in environmental color chroma, except for under blue hue-low-value conditions. During chroma changes (G1 × G3 × G5) under green huehigh-value conditions, ln(LF) of males was the highest for G3, but there was no significant change for females. Considering that ln(LF) reflects both sympathetic and parasympathetic activity in short-term measurements and positively correlates with depression, anger, and stress state [47][48][49], it can be determined that stress was stimulated in the case of males for G3. Under green hue-low-value conditions, ln(LF) decreased in both males and females as chroma increased, reflecting a fatigued state. This indicates that although the low-value high-chroma color of the green hue does not stimulate stress, it may give the body a sense of fatigue and reduce physical vitality.
Under blue hue-high-value conditions, ln(LF) of males decreased as chroma increased, whereas ln(LF) of females did not show any change. Blue hue, high-value, and high chroma color were noted to give males a sense of fatigue and slightly reduce their physical vitality. RMSSD of males and females decreased as chroma increased under blue hue-low-value conditions, and the difference was statistically significant. It can be inferred that blue hue, low-value, and high chroma color stimulates stress in males and females.
This study aimed to investigate the difference between physiological stress response through HRV measurement and psychological stress response through color-stress evaluation. The color-stress evaluation results showed statistically significant differences, with the stress emotion perceived through color varying with changes in chroma. The higher the chroma, the more positive the stress emotion was perceived under high-value conditions. The lower the chroma, the more negative the stress emotion was perceived under low-value conditions. These results support the findings of Öztürk et al. [31], Suk et al. [32], and Zielinski [33].
From the color-stress evaluation results, the main effect by gender was observed under G hue-low-value and B hue-low-value conditions. This indicates that under low-value conditions, the stress emotion of males and females according to changes in chroma can be perceived differently. In the HRV measurement results, it was previously noted that both males and females became fatigued under G hue-low-value conditions and were in a stressed state under B hue conditions. Thus, there is a slight difference between the physiological and psychological stress responses of males and females due to changes in chroma under low-value conditions. This demonstrates that when applying low-value colors to a spatial environment, colors should be applied carefully, considering the differences in the stress responses depending on gender.
This research measured HRV due to color-stress in the space where lighting is installed, and depending on the lighting environment, the results may vary. According to Davis et al. [68], while CRI is the only international standardized method that evaluates the coloring ability of a light source when applied to an LED, it has been indicated that the appropriate evaluation regarding coloring rendering could be difficult. Moreover, according to Durmus et al. [69], CCT can lack accuracy in delivering color information. Royer et al. [70] examined an experiment case to investigate the influence of color expression on the perception of an architectural environment. As a result, standardized color reproduction of ANSI/IES TM-30-20 and/or CIE 224:2017 and CIE S026: 2018 should be reported, and the range of color rendering characteristics should be sampled. In addition, it was stated that a minimum population of 30 for the experiment was recommended, but that power analysis was necessary to decide an appropriate sample size. Therefore, the condition of lighting in the color experimental condition is critical, and sufficient consideration is required. Furthermore, it is necessary for statistical analysis to proceed in determining the population needs.

Conclusions
This study investigated how changes in environmental color chroma affected the HRV and stress emotions of males and females to derive evidence-based research results for recommending colors that have positive effects on the stress levels of occupants and for limiting the use of colors with negative effect on environmental color scheme planning. The main findings were as follows.
First, the interaction effect between changes in environmental color chroma and the HRV of males and females was not verified. This study did not use colors with high visual stimulus as the experimental stimulus; therefore, the differences in the data for each variable and the differences obtained through short-term HRV measurement were insignificant. Future studies should address these matters to validate the interaction effect.
Second, the main effect of HRV parameters for males and females while changing the chroma of environmental colors was verified, and the valid parameters were ln(LF) and RMSSD. Physical vitality decreased for both males and females as they became fatigued due to decreasing ln(LF), as chroma increased under green hue-low-value conditions. Additionally, the RMSSD of males and females decreased as chroma increased under blue hue-low-value conditions. Considering that RMSSD is an indicator that reflects the activity of the parasympathetic nervous system, it can be viewed that the high chroma under blue hue-low-value conditions stimulated stress in males and females. These results show that the use of colors under low-value-high-chroma conditions is highly likely to affect physiological stress; hence, colors with these conditions should be used with care in environmental color schemes.
Third, according to the stress assessment results of environmental colors, the main effect according to chroma change was verified; the higher the chroma in the value range, the more positive the emotions, and the lower the chroma in the low-value range, the more negative the emotions. These results support the findings of previous studies [31][32][33]. However, the stress emotions according to changes in chroma under low-value conditions were perceived differently according to gender. This implies that there may be slight differences between the physiological stress responses by HRV and the psychological stress responses through color evaluation by gender. This finding suggests that low-value colors should be applied with caution in color schemes, as there may be differences in the stress response depending on gender.
This study used and verified HRV measurement research methodology by applying it to the field of environmental color and has significance in proposing a convergent research methodology. However, this methodology is still in the basic research stage, and it is necessary to continuously validate whether HRV parameters respond effectively to color stimuli through further experiments in the future. This scientific research method has the potential to be established as an evidence-based design methodology in the field of environmental color.

Limitations
In this study, we aimed to assess the stress levels of males and females according to chroma changes in diverse color environments using HRV measurement. While this study has practical significance in that its derived evidence-based research outcomes applicable to the environmental color field, there were some limitations. First, the experimental environment of this study was nearly identical to that of general office spaces. However, as this is a color-related study, a more specialized contemplation of color temperatures and color rendering, which are factors associated with the lighting of the experimental environment, is required. Second, the measuring time of the HRV measurement experiment was minimized considering the psychological stress of the subjects. During this process, HRV was measured for 2 min and 30 s, and the subject performed the experiment in one day. However, it has been suggested that the ideal HRV measuring time is 5 min and that HRV data should be measured repetitively across a designated period to secure more accurate experimental data. In addition, it will be necessary to secure more than 30 people as a population to improve the reliability of research data.