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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.692-700
DOI : https://doi.org/10.7232/iems.2019.18.4.692

Effects of Long Exposures of Peppermint Aroma on Awareness in Simulated Driving

Dwita Astari Pujiartati*, Alam Faturrochman, Yassierli
Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Indonesia
Corresponding Author, E-mail: dwitaastari.p@gmail.com
May 7, 2019 September 20, 2019 October 18, 2019

ABSTRACT


Reports show that number of driving accident is still high. Among the main the factors causing the accident is sleepiness induced by fatigue and monotonous driving task. Though there is a lack of consensus, stimulant aroma has been reported as an alternative intervention to maintain awareness. However, its long exposure effect is not known yet. This research consisted of two studies. The first study was conducted to investigate people preferences against three stimulant odors: pepermint, rosemary, and basil essential oil. The second study was carried out to investigate effects of the most preferred aroma in long exposures on awareness in simulated driving



In the first study, a questionnaire was distributed to 100 participants. They were asked to rate both “liking” and “wanting” of each odor given randomly. Results of the first study indicated that the most liked and wanted odor was peppermint. However, there were no significant differences of these three aromas based on gender or ethnic group (p>0.05). Therefore, peppermint was then used in the second study.



In the second study, a total of 12 participants performed simulated driving for 2 hours under three conditions: (1) without peppermint odor exposure, (2) with peppermint odor exposure only during the simulation, and (3) with the exposure for 3 consecutive nights before the simulation. During the experiment, participants’ awareness was monitored using electroencephalograph (EEG), heart rate monitor (HRM), and Karolinska Sleepiness Scale (KSS). Results of this study shows that there was an increased awareness indicated during both, short and long exposure of peppermint odor, compared to no-odor condition. There was no significant difference (p>0.05) between the effect short and long exposure of peppermint odor. This study suggests possibility of using peppermint odor in maintaining awareness in driving.



초록


    1. INTRODUCTION

    In recent years, number of Indonesian traffic accidents has been increasing. Data shows an average increase in traffic accident of 16.59% per year from 2010- 2014 (Minister of Transportation, 2015). In 2014 there were 18,147 number of accidents involving cars. In 2016, the number of accidents increased to 105,374 cases with number of deaths up to 25,859 people. Moreover, WHO (2015) states that traffic accidents can also have an impact on the total loss of state gross income, with a figure of 2.9% -3.1%. Due to the high number of accidents and large losses, more effective and efficient solutions are needed in preventing traffic accidents.

    The causes of various traffic accidents, according to Soehodho (2009), can be divided into three main factors, namely environmental factors (including roads), humans, and vehicles. Of the three factors, human has the greatest role in the occurrence of traffic accidents, with a percentage of 66%, while 34% is due to other factors (Sugiyanto, 2017). Data from the National Transportation Safety Committee also stated that the main factors causing the accident were human factors (69.70%), facilities (21.21), and infrastructure (9.09%).

    Baron and Kalsher (1988) reported that driving behavior contributes to 90% of accidents or vehicle collisions. Among dangerous driving behaviors, fatigue or drowsiness when driving contribute to the occurrence of 4-9% of road accidents. Fatigue or drowsiness caused by sleep deprivation can have the effect of increasing errors in driving activities such as going off track, speed stability and also the potential for collisions (Baron and Kalsher, 1998).

    Researchers have proposed various interventions to inhibit fatigue development as well as drowsiness since both can lower cognitive performance (Guo et al., 2015). The interventions include consuming vitamin or mineral supplements, presenting specific scents during work, and using voice or music. Each intervention has advantages and limitations, but more evidence is needed to investigate their effectiveness. In this study, the investigation was focused on the use of specific odor. Odor exposure using essential oils appears to be more practical than other intervention methods due to its ability to get in and out of the human body with high efficiency without leaving harmful toxins (Worwood, 1991). Odor consists of thousands of very small chemical molecules. When entering the olfactory sensory system, the olfactory nerve will detect odor molecules. Different odors have different chemical molecular compositions and will have different effects on human physiological conditions, affecting mood, stress, and work capacity (Sowndhararajan and Kim, 2016). Some odors have the effect of being a relaxant that provides calm due to its ability to control the parasympathetic nerves. Some other odor groups have effects as stimulants that can stimulate sympathetic nerves (Trellakis et al., 2012).

    In this study, three stimulant odors: peppermint, rosemary and basil were investigated. All of them are from the same family of Lamiaceae and have been used for vigilance stimulants (Worwood, 1991). It is of our interest to investigate people preferences against these three odors: peppermint, rosemary, and basil. It is expected that in the future the odor as a form of intervention to prevent drowsiness during driving is generally favored by the driver, and the driver wants to get exposure to the odor. For this reason, it is necessary to determine which of the three odors gives rise to a positive perception of one's comfort towards certain odors as described by the term “liking” while the desire of someone to receive exposure to odor is described as “wanting” (Triscoli et al., 2014).

    Results of previous research seem inconclusive in terms of the effectiveness of the three odors on performance, fatigue and sleepiness (Barker et al., 2003;Garnaby, 2014;Mahachandra et al., 2015;Raudenbush et al., 2001;Raudenbush et al., 2002;Raudenbush et al., 2009). Peppermint is known to be effective in improving signal detection work (Warm et al., 1991) and increasing memory capacity (Moss et al., 2008;Zoladz and Raudenbush, 2005). Peppermint is reported to be effective in maintaining vigilance (Mahachandra et al., 2015;Zoladz and Raudenbush, 2005) and in inhibiting development of fatigue on driving (Raudenbush et al., 2009). Peppermint has been reported to be better than cherries odor (Zoladz and Raudenbush, 2005) jasmine (Zoladz and Raudenbush, 2005), and ylang-ylang (Moss et al., 2008) in affecting performance. Rosemary is known to improve cognitive performance ylang (Moss et al., 2008) and increase brain activity that reflects increased alertness in cognitive activity (Sayorwan et al., 2013). However, further evident is needed.

    Previous studies have reported that there are different strategies in giving odor exposure to human, including: short vs long exposures and continuous vs. intermittent. As the continuation of our previous studies (Garnaby, 2014;Pujiartati and Yassierli, 2017), the stimulant odor in this study was given intermittently during the driving. We hypothesized that the effect might be more effective if the odor exposure is given in longer exposure. We focused on driving task due to the higher prevalence of driving accident in Indonesia.

    2. METHODS

    2.1 Study 1: Odor Preference

    A total of 100 participants, aged between 17 and 39 years (M = 21.26, SD = 3.66), 75 men and 25 women, were requested to fill questionnare. The participants were asked the questions of “How pleasant was the smell?” and “How much do you want to smell this again?.” The first question was intended to assess the concept of “liking,” ranged from “not at all pleasant” to “very pleasant,.” The second question was aimed to quantify the concept of “wanting,” ranged from “not at all pleasant” to “very pleasant.” The participants rated their preferences in a visual analog scale (VAS) using a piece of paper on all three odors including pepermint, rosemary, and basil essential oil. The order of odor given was set random.

    2.2 Study 2: Effects of Long Exposure of Odor on Awareness

    2.2.1 Experimental Design

    Effects of long exposure of peppermint odor on maintaining awareness during driving task were evaluated using a repeated measurement design. There were three experimental conditions and each condition required simulated driving task for 2 hours. In the first condition, participants were not given any odor exposure (Condition 1, C1). In the second condition, participants were exposed to peppermint odor only during driving simulation (Condition 2, C2, short exposure). In the last condition, before doing driving simulation, participants were firstly exposed to peppermint odor for 2 consecutive nights (“long exposure”), and then the exposure was also given during driving simulation (Condition 3, C3).

    2.2.2 Participants

    All of 12 participants were male. They were recruited from the ITB university community (Loft and Remington, 2010;Shou and Ding, 2013) Participants were screened in which they should be health, not having cold, or other diseases which might interfere their smell abilities. They should also have experiences in driving. They were asked not to take any supplements, caffeine, alcohol, or cigarettes before the experiment days. Informed consent was obtained prior to the experiment, and they were free to quit from the experiment at any time.

    2.2.3 Instruments

    The experiment was carried out using a driving simulator. The simulator connects steering wheel, gas and bracket pedal of the real car with driving simulation software, AVATAR.

    Sleepiness development during simulated driving was monitored using electroencephalography (EEG) and heart rate monitor (HRM) apparatus. EEG data were collected using Emotiv EPOC Control Panel v2.0.0.20 software. Though the EEG had 14 channels, this study focused only on 2 channels, F3 & F4 at frontal lobe. Frontal area has dominant role in attention, memory, planning, and social alertness (Abbas et al., 2014). Heart Rate signals were recorded using Polar RS800CX with 1 Hz sampling rate to compute Heart Rate Variability (HRV). Sleepiness levels were also assessed subjectively using Karolinska Sleepiness Scale (KSS) every 15 minutes. KSS level varied from 1 to 9, representing sleepiness stages from ‘extremely alert’ to ‘very sleepy, great effort to keep awake, fighting sleep. KSS is a valid tool in measuring sleepiness (Kaida et al., 2006).

    2.2.4 Procedures

    Prior to conducting the experiments, participants were given information about experimental procedures and equipment used. No hint was given about the experiment to avoid bias. Participants were given training on using the driving simulator one day before their first sessions.

    To enable experiencing sleepiness during the experiment, participants were asked to have a maximum of 4 hour- sleep on the night before the experiment day. To avoid the effect of sleep deprivation accumulation, participants were asked to get enough sleep for 3 nights before they conducted their next experiment sessions. Their sleep history was monitored using a Fitbit smartwatch.

    Before the experiment, electroencephalograph (EEG) and Heart Rate Monitor (HRM) were installed based on each apparatus manual instruction. During the experiment, peppermint essential oil was diffused every 30 second using aromatherapy diffuser.

    2.2.5 Data Processing and Analysis

    • KSS Data

      KSS slopes were calculated from KSS data obtained during 2-hour simulated driving as the indicator of sleepiness. With SSC data collected every 15 minutes, the SSC slope for two hours is produced from 8 measurement points. A positive slope in KSS indicates increment in sleepiness. KSS slopes of all three conditions were compared and tested statistically using SPSS 20. In this study, KSS data of 2 participants was excluded because of their unstable KSS rates.

    • EEG Data

      EEG data obtained from frontal regions were processed using Matlab R2009a. Data obtained from F3 and F4 channels were summed, and then the Power Spectral Density (PSD) of 5-minute sub-samples was calculated using Fast Fourier Transform (FFT) to obtain α, β, and θ wave power. Relative Power Ratio (RPR) of α and θ band, and ratio of (α+θ)/β were calculated as the indicator of sleepiness. Relative Power Ratio was calculated using the following formula:

      R e l a t i v e   P o w e r   R a t i o   ( R P R ) α = ( P S D   α P S D   α + P S D   β + P S D   θ ) × 100 % R e l a t i v e   P o w e r   R a t i o   ( R P R ) θ = ( P S D   θ P S D   α + P S D   β + P S D   θ ) × 100 %

      Slopes were then calculated for each condition. An increment or positive slope in the RPR θ or (α+θ)/β ratio demonstrates decrement in alertness, whereas increment in RPR α implies increment in alertness in driving (Kaida et al., 2006). The slopes of these EEG indicators for all three conditions were compared and tested statistically using ANOVA in SPSS 20.

    • HRV Data

      HR signals were processed using HRV Kubios software. To investigate changes in heart rate activity, square root of the mean squared differences of successive of normal to normal intervals (RMSSD), proportion of the number of normal to normal interval that differ by more than 50 ms divided by the total number of normal to normal interval (pNN50), low frequency power (LF), high frequency power (HF), and LF/HF data were calculated every 10 minutes. For each indicator, slopes were figured as the indicators of sleepiness. Those indicators were commonly used to monitor fatigue development due to mental workload (Cinaz et al., 2010). The slopes of these HRV indicators for all three conditions were compared and tested statistically using ANOVA in SPSS 20. Unfortunately, HRV data of 4 participants were excluded because their heart rate monitor could not function properly in at least one of the three conditions, resulting fluctuative HRV data.

    In this study, we also calculated the sensitivity of KSS to the treatment. For each participant, the difference between the indicator slopes in each condition is compared. For example, a comparison between the KSS C1 and C2, C1 and C3, and C2 and C3 slopes for participants 1. This comparison was carried out to see whether the indicator experienced an increase or decrease in certain participants. The number of participants who experienced an increase in indicators and the number of participants who experienced a decrease in indicators were then compared. The declineor increase, if consistently experienced by most participants, indicates the existence of the effect in driving simulation conditions. Figure 1

    3. RESULTS

    3.1 Odor Preferences

    Rates of “liking” and “wanting” for the three odor were compared based on gender and ethnic group using Chi-Square Test. As shown in Table 1, there were no significant differences between rates of “liking” and “wanting” based on gender or ethnic group (p> 0.05).

    Based on ANOVA, participants had significantly different preferences of odor in terms of “liking” and “wanting” (p = 0.000). Peppermint odor was more liked (Mean = 5.91, SD = 2.52) than rosemary (Mean = 4.30, SD = 2.50) and basil (Mean = 3.50, SD = 2.33). Peppermint odor was also more wanted (Mean = 5.14, SD = 2.7) than rosemary (Mean = 3.46, SD = 2.52) and basil (Mean = 2.85, SD = 2.53). Based on these results, the experiment in the second study was conducted using peppermint odor.

    3.2 The Effect of Long Exposure

    Sensitivity of KSS, and HRV indicators to treatment can be seen in Table 2.

    Table 2 demonstrates that there were 5 indicators that had good sensitivity. They changed over time during the experiment with high consistency (either decreasing or increasing) in more than 70% participant data across two experimental conditions. For KSS slopes, for example, 90% data from the same participant indicated that slopes of C1 (no peppermint exposure) were lower than C2 (short peppermint exposure). Similarly, 90% data from the same participant indicated that slopes of C1 (no peppermint exposure) were lower than C3 (long peppermint exposure). Finally, 70% data from the same participant indicated that slopes of C2 (short peppermint exposure) were lower than C3 (long peppermint exposure).

    Of the 3 three indicators that were calculated based on EEG frontal lobe activity data, RPR α seemed to have the highest sensitivity. Based on this indicator, 83% participants indicated consistent differences in slope between C1 and C3. However, it’s sensitivity to treatment between C1-C2 or C2-C3 were fair (50% and 67%).

    There were two HRV indicators that had relatively high sensitivity: HF and LF/HF. A total of 63% data indicated that HF and LF/HF slopes for C2 were consistently lower than C1. Similarly, 75% data for C3 were consistently lower than C1, and for C3 compared to C2.

    Figure 2 demonstrates data of a participant indicating differences in changes of KSS, RPR α, and LF in 3 experimental conditions.

    Slopes of each indicator were then tested using repeated ANOVA (Table 3). Table 3 demonstrates that there was a significant difference between KSS slopes for the three conditions (p = 0.007), meaning that the factor of treatment was significant. There was also significant difference in KSS slopes between C1 and C3 (p = 0.013). Although all three EEG indicators were not significantly different for all three conditions, ANOVA result showed that RPR α slopes in C1 were significantly different from RPR α slopes in C3. ANOVA result also showed that LF slopes were significantly different for all three conditions (p = 0.010). Further test showed that LF slopes in C1 were significantly different from LF slopes in C2 (p = 0.015) and C3 (p = 0.020).

    Table 4 and Figure 3 showed means of slope values for KSS, RPR α, and LF. The mean of KSS slopes was significantly decreased from C1 (0.027) to C2 (0.010), and to C3 (0.002). Although not statistically significant, the mean of RPR α slopes increased from C1 (-0.00012) to C2 (-0.00008). The mean of RPR α slopes increased significantly from C1 (-0.00012) to C3 (0.00002). The mean of LF slopes was fluctuative from C1 (9.107) to C2 (3.642) and to C3 (5.157).

    4. DISCUSSION

    The purpose of this study was to investigate the effectiveness of long stimulant aroma in maintaining awareness during simulated driving. This is a continuation of our previous studies. In our previous studies, we found that peppermint was able to inhibit fatigue development in doing a complex task. In this study, we hypothesized that its effectiveness would be better if the exposure was given several days prior to the simulated task. Moreover, we focused on driving situation in which sleepiness or drowsiness is more relevant than fatigue.

    Before conducting the study, we wanted to know the odor which give the best preference of people, among peppermint, basil or rosemary. Therefore, the first study was conducted. The results indicated that in terms of both “liking” and “wanting,” peppermint was the best. Interestingly, there was no effect of gender and ethnic in choosing the preference. This result implies that peppermint aroma seems to be more neutral, and can be used to in general for everybody as stimulant aroma.

    Based on the average level of “liking” and “wanting” of the three scents, peppermint, basil and rosemary, only peppermint scents had a relatively positive average value. Peppermint had a value of “liking” and “wanting” on average above 5, while basil and rosemary are below 5 (scale 0-10). “Liking” was used to assess a person's perception of the pleasantness of a particular odor, while “wanting” was used to assess the desire to receive further exposure (Triscoli et al., 2014). With a low average value of “liking” which was equal to and 4.3 and 3.5, rosemary and basil gave unpleasant perceptions to the majority of participants. A low average value was also found in the “wanting” question which was equal to 3.46 and 2.85. These results suggested that most respondents did not want to receive further exposure to the scent of rosemary or basil. With an average value of “liking” of 5.91 and “wanting” of 5.14, the aroma of peppermint can still be used as a stimulus because it still creates a perception that tends to be positive.

    Based on KSS data, we found that long exposure of peppermint (C3) was able to produce lower development of sleepiness level. C3 resulted in lower slopes of KSS, compared with no odor condition (C1). Result of this study showed that after getting used to peppermint aroma, participants still felt that the level of drowsiness was reduced. As explained before, KSS is a valid tool in measuring sleepiness (Kaida et al., 2006). High level of sleepiness, indicated by KSS, can be assosiated with driving performance disturbance (Åkerstedt et al., 2014), and will affect driving safety. Based on KSS data, exposure of peppermint odor seems to be prospective as an alternative intervention to maintain driving performance. The peppermint ability to affect performance had been studied in the previous studies (Barker et al., 2003;Garnaby, 2014;Mahachandra et al., 2015;Raudenbush et al., 2001;Raudenbush et al., 2002, Raudenbush et al., 2009), we argue that its effectiveness will be better if the exposure is given longer.

    Compared to KSS, lower sensitivity was found for indicators of HRV and EEG. Our previous study found that effect of peppermint odor could be detected by the EEG indicator of (α+θ)/β (Mahachandra et al., 2015). In this study, most of participants demonstrated significant increasing (α+θ)/β in the condition of long peppermint exposure (C3). However, non-consistent trend was found for C1 and C2. However, C3 was generally associated with higher slopes, compared to C1 and C2.

    In this study, we found that the indicator of RPR α had a better sensitivity than (α+θ)/β. RPR α was known to be a robust indicator for detecting fatigue (Puspasari et al., 2017). Interestingly, we found higher slopes of RPR α during C3, indicating the ability of long peppermint exposure to inhibit fatigue development and reduce sleepiness. Moreover, both short (C2) and long peppermint exposure (C3) were able to produce lower LF/HF slopes, compared to C1, although it was not statistically significant. This result is inline with the result of a previous study (Mahachandra et al., 2015).

    Conflicting results have been reported on the sensitivity of HRV indicators in detecting sleepiness or awareness. Our previous study found that HRV indicators did not performed well in detecting sleepiness (Mahachandra et al., 2012). On the contrary, Kwak et al. (2015) found that peppermint odor exposure during driving resulted significant changes in HRV indicators. Cinaz et al. (2010) stated that both frequency-domain and time-domain indicators can be used to monitor fatigue development due to mental workload. In our study, LF indicator showed significant changes over time if peppermint odor exposure is given. We speculate that the inconsistency may due to different exposure strategies of their studies.

    Several limitations of this study are worthnoting. First, we assumed that a 2-days exposure effect was sufficient, and we could not control the participants in using the aromatheraphy sprayer during these 2 nights. To minimize effect of bias, we made a written agreement signed by the participants and we controled the peppermint exposure by giving a reminder to the participants when they had to start and stop the exposure. The second limitation of this study is regarding with the effect of sleep deprivation to the sleepiness development during the experiment. In this study, participants were made more drowsy by limiting sleep hours before the experiment. Whereas, many other conditions that influence sleepiness development during driving such as night driving and monotonous road were asummed to be random factors. In the next study, these factors could be incorporated in investigating the effectiveness of peppermint in affecting sleepinees.

    Figure

    IEMS-18-4-692_F1.gif

    Driving simulator.

    IEMS-18-4-692_F2.gif

    KSS, RPR α, and LF sample from participant 9.

    IEMS-18-4-692_F3.gif

    Estimated means for KSS, RPR α, and LF slopes for each treatment (condition).

    Table

    Differences in “liking” and “wanting” rates based on gender and ethnic groups

    Sensitivity of the indicators to treatment

    Results of ANOVA test

    Descriptive statistics for KSS, RPR α, and LF slopes in all condition

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