Abstract
Among adults with ADHD, one of the most common problems in daily life is procrastination. ADHD is characterized by attention as well as suboptimal decision-making deficits, indicating difficulty in making long-term reward choices. However, little is known about the relationship between suboptimal decision-making or temporal discounting (TD) and procrastination among adults with ADHD. This study aimed to investigate whether ADHD symptoms enhance the relationship between TD and procrastination. Fifty-eight university participants completed questionnaires about procrastination and an experimental task which measured TD rates in reward and punishment conditions. Only the reward condition showed that ADHD symptoms significantly strengthened the association between the TD rate and procrastination. This study revealed that even when ADHD symptoms were high, higher TD rates were associated with more procrastination, while lower TD rates were associated with less procrastination. The results suggest that procrastination interventions for adult ADHD-prone individuals need to pay attention to reward responses.
Keywords: Attention deficit hyperactivity disorder, Discounting, Procrastination, Reward processing
1. Introduction
ADHD as a neurodevelopmental disorder among adults is associated with decision-making problems (Hedge's g = 0.551) as much as attention problems (Hedge's g = 0.410) [1] compared to control groups. People with ADHD make inappropriate choices during decision-making that are not risk-seeking but seek to maximize immediate reward [2]. Temporal discounting (TD), often referred to as delayed reward decision making, is used to investigate the suboptimal decision-making deficits in ADHD. TD considers how much value is placed on future compensation, such as how much people discount future rewards relative to immediate rewards. Several tasks have been used to study this TD, including the intertemporal choice task. Participants choose between a large reward delayed, and a small reward obtained immediately. In adulthood, individuals with an ADHD diagnosis tend to discount the value of future rewards more than non-clinical participants [3]. As adults, ADHD diagnosed patients still have higher delay discount rates and persist with reward delay aversion.
However, the limitation of the intertemporal choice task includes the reward and delay features that are used. In this task, each condition was presented to the participants in the form of a questionnaire. Therefore, the participants needed to imagine delays and rewards hypothetically. The participants' response to the task is more likely to reflect their daily life behavior when actual amounts are used rather than virtual ones. To ensure the credibility of the paying experimental reward, the previous study obtained bank account numbers from participants. However, even with this instruction, participants did not experience delays immediately. In addition, few studies have examined not only rewards but also losses. Considering these limitations, the intertemporal choice task is used [4,5]. It allows participants to actually experience the delay, as in the delay aversion task [6]. Both delays and rewards occur during the experiment, and in addition, the actual amount of satisfaction increases with participants' choices. Therefore, this task can estimate individuals’ time preferences for both gains and losses with high ecological validity. Adults with ADHD did not differ significantly from the non-clinical control in the time discount rates. However, they did indicate weaker neural activity associated with decision-making—such as in the caudate and visual cortex—in reward waiting time compared to the controls [7]. Furthermore, in the loss condition, adults with ADHD have a larger discount rate than the non-clinical controls, indicating a smaller estimate of the value of large future losses [8]. Even in tasks in which real delays and the rewards and losses are reflected in the amount of the gratuity, adults with ADHD easily discount the value by time, leading to suboptimal decision-making.
1.1. Procrastination, Temporal Motivation Theory, and reward problems
Decision-making problems occur in various aspects of daily life. One of the daily decision-making problems is procrastination, which repeatedly determines whether or not to engage in the planned activity [9]. Adults with ADHD are especially likely to have problems related to procrastination in their daily life [10].
Procrastination is defined as “voluntarily delaying an intended course of action despite expecting to be worse off for the delay (p. 81)” [11]. Procrastinating is regarded as a non-functional behavior [12], as it is engaged in despite anticipating negative consequences that exceed the benefits of delayed behaviors. Procrastination has been associated with a variety of psychosocial problems. According to previous research, procrastination has negative psychological effects such as depression and anxiety symptoms and decreased well-being [13]. In addition, chronic procrastination has been associated with lower academic achievement [14], economic loss, and unemployment [15].
One of the theoretical models that comprehensively explains procrastination is the Temporal Motivation Theory (TMT) [16], which is an extension of the decision-making model. Mathematically, it is described as a hyperbolic curve with the following equation (Utility = (Expectancy × Value)/(Sensitivity to Delay × Time Delay)). This model describes procrastination as a phenomenon in which the motivation to act increases when the deadline is imminent [11]. Utility is defined as the level of desirability of a task or choice of an individual. Motivation increases when people have confidence (Expectation) about obtaining desired rewards or outcomes (Value). Conversely, motivation decreases when the duration of obtaining the reward is long (Time Delay) or when the person tends to prefer short-term rewards (Sensitivity to delay). TMT considers people with chronic procrastination to be sensitive to delay; thus, the longer the delay in acquiring future rewards and punishments, the quicker they are to discount the value. In other words, the magnitude of this time discounting causes procrastination [16]. For example, when engaging in tasks, those with high time discount rates may increase their sensitivity to immediate rewards, thereby putting off the task to choose alternative behavior with immediate rewards.
As pointed out in the TMT, the timing of rewards and punishment has been emphasized as a contributing factor to procrastination [11,17]. This can be viewed from two perspectives: task and individual sensitivity [16]. First, at the task level, the later the reward or punishment is expected, the more the value of the delayed tasks are discounted. Next, at the individual level, the sensitivity to delay is important. Wu et al. [18] used an intertemporal choice task to test the TMT theory of procrastination. The results revealed that people who procrastinated showed a greater preference for the present reward when the alternative was longer than when it was short, and this result was not observed in the group without irrational procrastination. In addition, Steel [11] suggested that TMT can be applied to punishment rather than reward by taking the inverse of the equation. This means that people tend to prefer punishments that are distant, improbable, and small. However, the relation between punishment and procrastination is still a theoretical suggestion.
Sensitivity to delay which causes procrastination is related to distractibility, impulsivity, and lack of self-control [19], and these characteristics are consistent with the symptoms of ADHD. Furthermore, the major biological underlying factor of ADHD is inhibition impairment and preference for immediate rewards [[20], [21], [22]]. The inhibition impairment and the preference for immediate reward are reciprocal: reinforcement manipulation improves the task performance of response inhibition, and the high inhibition ability makes waiting for a large delayed reward [23]. In other words, inhibition ability is needed to maximize the earned reward. The lateral pre-frontal cortex (PFC), a brain region associated with inhibition and self-regulation, is related to intertemporal choice; in adolescents, increasing gain escalates activity in the right lateral PFC, leading to optimal decision making [24]. However, the activity of the lateral PFC is reduced more among people with ADHD than in control groups [25]. Due to the deficits in inhibition, people with ADHD may procrastinate chronically because of the difficulty in utilizing optimal decision-making strategies. Indeed, adult ADHD features chronic procrastination problems associated with thrill-seeking and avoidance functions compared to a control group [26]. In other words, people with high ADHD tendencies may reinforce the relationship that the more discounting the value of future rewards, the more prone they are to engage in procrastination. Similarly, the association between the discounting rate of punishment and procrastination may differ depending on the presence or absence of ADHD symptoms. Adults with ADHD tend to underestimate the magnitude of future punishment and more highly estimate immediate rewarding stimuli. Therefore, ADHD symptomatology may strengthen the relationship between procrastination and a large discounting rate in both reward and punishment conditions. However, the association between the discounting rate and procrastination in ADHD has not been clarified.
1.2. Purpose of the present study
The purpose of this present study is to investigate the relationship between the sensitivity of the delay in the gain and loss of rewards condition and procrastination among adults with ADHD tendencies. With the previous relationships between all variables, this study tests three hypotheses.
Hypothesis 1
Compared to the group with low ADHD symptoms, the group with high symptoms will have the larger time discount rates in both reward and loss conditions.
Hypothesis 2
In both the reward and loss conditions, participants will show positive correlations between procrastination and high time discount rates.
Hypothesis 3
ADHD symptoms will enhance the positive association of procrastination with the time discount rate for rewards and losses.
2. Methods
2.1. Participants
A convenience sample of 58 students who belonged to a private university in an urban area in Japan participated in this study. The collection of data was initiated in August 2019 and carried out until January 2021. The inclusion criteria for the study were no past or current head injury, neurologic disorder or psychosis, and no use of medication for these conditions (supplementary material 1). As a result, four students not meeting these criteria were excluded from the analysis. Fifty-four university students participated from all genders (26 males, 53.06%), ranging in age from 18 to 29 years (Mean = 21.20, SD = 2.29).
2.2. Measurements
2.2.1. ADHD symptoms
The Adult ADHD Self Report Scale (ASRS) consisted of 18 items which can be divided into two parts: Part A for brief screening (six items) and Part B for assessing whole symptoms (12 items). This scale was scored using a five-point Likert scale ranging from 0 (never) to 4 (very often). Responses like “often” and “very often” in all items were regarded as positive ADHD symptomatology. In the case of items 1, 2, 3, 9, 12, 16, and 18, a response of “sometimes” was also considered positive. This study defined the ADHD-H group as people who gave four or more positive responses to the six items of Part A of the scale; Part A predicts adult ADHD diagnosis based on the DSM-Ⅳ criteria [27]. This scale had high validity with regard to clinical diagnosis of ADHD, with internal consistency (Cronbach's α = 0.63.0.72) and test-retest reliability (r = 0.58–0.77) [27].
2.2.2. Procrastination
Self-reported difficulties related to procrastination were determined using the General Procrastination Scale (GPS) [28]. Although the original English version of GPS consists of 20 items that are scored using a five-point Likert-type scale ranging from 1 (extremely uncharacteristic) to 5 (very characteristic), the Japanese version of GPS (J-GPS) [29] includes only 13 items because seven were excluded during the translation and validation process. The J-GPS includes four reverse items: statements 6, 8, 11 and 13. The reliability and validity of the J-GPS were confirmed by Hayashi [29].
2.2.3. Depressive symptoms
To measure participants’ depression, the Patient Health Questionnaire (PHQ) [30], which consists of nine items, was used. Participants responded using a 4-point scale ranging from 0 (not at all) to 3 (almost every day). Scores ranged from 0 to 27; A score of 0 to 4 indicates no depressive symptoms, 5 to 9 mild, 10 to 14 moderate, 15 to 19 moderate to severe, and 20 to 27 severe depressive symptoms. The reliability and validity of PHQ were confirmed by Kroenke et al. [30].
2.2.4. Anxiety symptoms
To measure trait anxiety, the Japanese version of the State-Trait Anxiety Inventory-Form JYZ (STAI) created by Hidano et al. [31] was used. The trait anxiety consisted of 20 items, with scores ranging from 0 to 80. The anxiety the participants usually feel is measured using a four-point scale ranging from "1: almost never" to "4: almost always". The scores are divided into five parts: Stage 1 is defined as 0 to 35 points, Stage 2 as 35 to 45 points, Stage 3 as 45 to 55 points, Stage 4 as 55 to 65 points, and Stage 5 as 65 points or more. The scores of stages 1 and 2 are defined as low anxiety, and those of stages 4 and 5 are defined as high anxiety. The reliability and validity of the Japanese version of STAI were confirmed by Hidano et al. [31].
2.2.5. Reinforcement sensitivity
The Behavioral Inhibition System (BIS)/Behavioral Activation System (BAS) Scale [32] consists of 20 items scored on a four-point Likert-type scale (1–4, which ranged from “very false for me” to “very true for me”). This study measured BIS/BAS, but for the purpose of the study, was not used in the analyses.
2.3. Experimental task
We used the repeated intertemporal choice task [5] with monetary gains and losses to estimate participants’ discount rate for reward and punishment (supplementary material 2). Participants received instructions to make decisions and choices to maximize the amount of acquisition in the GAIN condition and to minimize the amount of reduction in the LOSS condition.
2.4. Ethical consideration
Ethical approval for the study was obtained from the institutional Ethics Review Committee on Research with Human Subjects of the first author's affiliation (Japan; 2019-129). Both written and spoken communication was utilized to clarify that declining participation in the study would not have any negative impact on academic progress or credit, there would be no unfavorable treatment, and participants could withdraw their consent without facing any adverse consequences until March 2023. And written informed consent was obtained from the all participants of this study.
2.5. Statistical analysis
All statistical analyses were conducted using IBM SPSS statistics 26.0 software (SPSS Inc., 2017), R (version 3.6.2), and HAD17_204 [33]. The analyses were conducted in the first steps to investigate the hypotheses of examining the moderating effect of ADHD symptomatology on the discount rates and procrastination. First, a t-test was conducted to test Hypothesis 1 and compare all parameters between ADHD-H/L groups. Finally, hierarchical multiple regression analyses were conducted to examine the last purpose, which is to establish the moderating effect of ADHD symptomatology on the association between time discount rate and procrastination. When significant interactions were observed, simple slope analysis was conducted to interpret the effect of one standard deviation above or below the mean of the ASRS scores, based on the work of Hayes & Matthes [34]. Before these analyses, G*Power 3.1.9.7 [35] was used to verify that the sample size was adequate. Since this study primarily aimed to examine interactions, the power analysis of R2 increased was conducted. The results showed that the minimum sample size was 55 participants to detect interaction, based on four predicting variables (gender, age, ADHD symptoms, and time discounting rate) and one interaction (ADHD symptoms × time discounting rate) and α = 0.05, a medium effect size (f2 = 0.15), and a power of 0.80. Therefore, the current 58 participants met this requirement.
3. Results
3.1. Descriptive data
The descriptive characteristics of each ADHD high/low group and t-test for all variables are presented in Table 1, including mean, standard deviation, Cronbach's alpha, t ratio, the value of p. Only in the loss condition was the time discount rate for the low ADHD group significantly greater than that for the high ADHD group. Of the participants, 33 (61.1%) had no symptoms of PHQ, 19 (35.2%) had mild symptoms, and 2 (3.7%) had moderate or severe symptoms. Furthermore, with regard to STAI, 24 (44.4%) were less anxious, and 12 (22.2%) were highly anxious.
Table 1.
Sample characteristics and descriptive statistics for all analytic variables.
| ADHD-H (n = 22) |
ADHD-L (n = 32) |
t | p | df | 95% CI of the Difference |
||||
|---|---|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Lower | Upper | ||||
| Age | 20.45 | 1.57 | 21.69 | 2.29 | −2.35 | .02 | 52.00 | −2.29 | −.18 |
| ASRS-A | 15.00 | 1.90 | 9.53 | 3.75 | 7.04 | .00 | 48.52 | 3.91 | 7.03 |
| ASRS-B | 18.86 | 4.29 | 14.84 | 4.59 | 3.25 | .00 | 52.00 | 1.54 | 6.50 |
| Total ASRS | 33.86 | 4.98 | 24.38 | 7.71 | 5.08 | .00 | 52.00 | 5.74 | 13.24 |
| Gain | .7307 | .1266 | .7446 | .1856 | −0.28 | .78 | 44.00 | −0.11 | 0.85 |
| Unknown, No. (%) | 3 | (13.6%) | 4 | (18.2%) | |||||
| Loss | .6819 | .2024 | .7909 | .1204 | −2.01 | .05 | 24.34 | −0.22 | −0.00 |
| Unknown, No. (%) | 5 | (15.6%) | 2 | (6.25%) | |||||
| GPS | 49.77 | 8.83 | 40.50 | 12.94 | 3.00 | .00 | 52.00 | 3.07 | 15.48 |
| PHQ | 5.82 | 3.40 | 3.09 | 2.08 | 3.65 | .00 | 52.00 | 1.23 | 4.22 |
| STAI | 51.95 | 11.12 | 43.84 | 9.29 | 2.91 | .01 | 52.00 | 2.52 | 13.71 |
Note. N = 54. ASRS = Adult ADHD Self Report Scale, GPS = General Procrastination Scale, PHQ = Patient Health Questionnaire, STAI = Japanese version of the State-Trait Anxiety Inventory-Form JYZ.
3.2. Moderating effects of ADHD symptoms on time discount rates
Hierarchical multiple regression analyses using procrastination as outcome variables were conducted using all variables as predictors in the first step and adding the interaction of ADHD symptoms and time discounting rate as a predictor in the second step.
The results of GAIN and LOSS conditions are presented in Table 2, Table 3, respectively. These analyses revealed that, in the second step, the interaction effect of GAIN time discounting and ADHD symptoms was significantly associated with procrastination. Since interaction effects were observed, a post hoc simple slope analysis was conducted one standard deviation above or below the mean point of ASRS Part A (Fig. 1). ADHD symptoms significantly strengthened the association between GAIN time discounting and procrastination. Similarly, in the second step, the interaction effect of LOSS time discounting and ADHD symptoms were not significantly associated with procrastination.
Table 2.
The gain discounting rate, ADHD symptoms, and their interactions on procrastination.
| Procrastination |
SE | Adjusted |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| β | 95% CI of β |
B | p | ||||||
| Lower | Upper | R2 | R2 | ΔR2 | |||||
| Step 1 | .589 | .549 | .589 | ||||||
| Sex | .251 | 0.039 | 0.463 | 5.759 | .022 | 2.412 | |||
| Age | −.148 | −0.364 | 0.068 | −0.797 | .175 | 0.577 | . | ||
| Gain discounting rate | .198 | −0.021 | 0.417 | 14.153 | .075 | 7.745 | |||
| ADHD symptoms | .600 | 0.377 | 0.823 | 1.647 | .000 | 0.304 | |||
| Step 2 | .652 | .609 | .063 | ||||||
| Sex | .224 | 0.025 | 0.423 | 5.148 | .028 | 2.258 | |||
| Age | −.163 | −0.364 | 0.039 | −0.877 | .111 | 0.538 | |||
| Gain discounting rate | .408 | 0.150 | 0.666 | 29.143 | .003 | 9.112 | |||
| ADHD symptoms | .641 | 0.430 | 0.852 | 1.760 | .000 | 0.286 | |||
| Gain discounting rate × ADHD symptoms | .341 | 0.085 | 0.596 | 3.558 | .010 | 1.321 | |||
Note. N = 46; Sex (1 = Male, 2 = Female), ADHD symptoms = Adult ADHD Self-Report Scale-v1.1 Part A; Procrastination = General Procrastination Scale; β = standardized effect; B = unstandardized effect; SE = standard error.
Table 3.
The loss discounting rate, ADHD symptoms, and their interactions on procrastination.
| Procrastination |
SE | Adjusted |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| β | 95% CI of β |
B | p | ||||||
| Lower | Upper | R2 | R2 | ΔR2 | |||||
| Step 1 | .594 | .556 | .594 | ||||||
| Sex | .238 | 0.041 | 0.435 | 5.944 | .019 | 2.435 | |||
| Age | −.110 | −0.317 | 0.098 | −0.621 | .293 | 0.583 | . | ||
| Loss discounting rate | −.032 | −0.240 | 0.176 | −2.452 | .757 | 7.887 | |||
| ADHD symptoms | .667 | 0.455 | 0.880 | 1.991 | .000 | 0.315 | |||
| Step 2 | .604 | .556 | .010 | ||||||
| Sex | .215 | 0.013 | 0.417 | 5.368 | .037 | 2.497 | |||
| Age | −.089 | −0.301 | 0.122 | −0.506 | .399 | 0.594 | |||
| Loss discounting rate | −.076 | −0.301 | 0.149 | −5.832 | .498 | 8.540 | |||
| ADHD symptoms | .659 | 0.446 | 0.872 | 1.967 | .000 | 0.315 | |||
| Loss discounting rate × ADHD symptoms | .115 | −0.110 | 0.339 | 2.073 | .310 | 2.015 | |||
Note.N = 48; Sex (1 = Male, 2 = Female), ADHD symptoms = Adult ADHD Self-Report Scale-v1.1 Part A; Procrastination = General Procrastination Scale; β = standardized effect. B = unstandardized effect. SE = standard error.
Fig. 1.
The Moderating Effects of ADHD symptoms.
4. Discussion
This study aimed to examine whether ADHD symptoms emphasize the association between time discounting and procrastination.
First, compared to participants without ADHD tendencies, the ADHD symptom high group showed smaller magnitudes of time discount rates only in the LOSS condition; there were no significant group differences in the GAIN condition. Hypothesis 1 predicted there would be differences in time discount rates depending on the presence/absence of ADHD symptoms. In contrast to previous studies, this study found that participants with ADHD symptoms were less likely to discount future losses than those in the low-symptom group. Thus, this hypothesis was refuted. Tanaka et al. [8] found that adults diagnosed with ADHD discount future loss more easily than non-clinical groups. However, the difference in loss discount rates was not observed in this study. These differences in results may reflect differences in the characteristics of the sample. The participants of this study were undiagnosed with ADHD and had college-ready IQ levels. Optimal decision-making requires remembering previous gains and losses and considering current choices accordingly [36]. Therefore, low working memory capacity and difficulty in making mental efforts affect suboptimal decision-making in ADHD. Optimal choice-making becomes difficult when it is easier to abandon choices that would have a higher expected value or when the working memory capacity is not available. However, the participants of this study may have a lower degree of impairment.
Next, hierarchical multiple regression analyses revealed the moderating effects of ADHD symptoms on the association between procrastination and time discount rates. The results showed that only in the reward condition, ADHD symptoms strengthened the association between procrastination and time discounting of rewards. In other words, participants with a smaller estimate of future rewards tend to procrastinate more readily, and the presence of ADHD symptoms further strengthens this association. However, there was no interaction between time discounting of loss and ADHD symptoms in the LOSS condition. From previous studies, the sign effect exists in which people do not discount future losses as much as they do rewards [5]. However, people who have problems such as obesity and smoking fail to perceive the negative consequences of their small estimates of future losses. On the other hand, procrastination, by definition, is an irrational behavior causing people to delay future losses even though they are aware of them [19]. Therefore, the no-interaction effect of this study result suggests that procrastination is an acquired behavior caused by a nonfunctional strategy that is implemented despite the ability to recognize future losses.
This study showed that in the group with high ADHD symptoms, the higher the time discount rate in GAIN condition, the more likely they were to procrastinate. In contrast, the time discount rate of the LOSS condition had no significant effect. These results suggest that the mechanism for the occurrence of procrastination in ADHD may involve reactivity to reward rather than loss. Previous research has suggested that the mechanism of procrastination is related to the timing of punishment as well as reward [37]. The results of the present study indicate that preference for immediate rewards is related to procrastination and that ADHD symptoms enhance this association.
5. Conclusions, limitations, and implications
Although the present study revealed that ADHD symptoms strengthen the association between immediate reward-seeking tendencies and procrastination, several limitations exist. The first is the amount of money used in the experimental task. There is a possibility that the amount of money in the loss condition used in this study may not have functioned as punishment for the participants. In the experimental task, the condition was the removal of a small amount of money, either 40 or 10 yen. Since the amount was small, the participants may not have perceived it as a punishment condition. This limitation may have influenced the fact that the result of Hypothesis 3 was not supported in the loss condition. Therefore, more extensive amounts should be set and time discounts for participants should also be considered. Furthermore, the number of participants in this study was determined by power analysis with a medium effect size (f2 = 0.15). Therefore, when power analysis is conducted with a small effect size (f2 = 0.02) and the required number of participants are recruited, the results can be significant even in the loss condition. The final limitation is the characteristics of the participants in this study. Participants in this study were college students who had not received an official ADHD diagnosis. Scheres et al. [38] revealed that ADHD-combined participants with all three main symptoms discounted steeper delays by greater reward and length of the experimental task compared to the inattention-dominant group and the hyperactivity/impulsivity-dominant group. Therefore, future studies should be conducted on the participants with all three symptoms of ADHD.
This study revealed that individuals with high ADHD symptoms are more likely than others to procrastinate due to immediate reward-seeking motives. This result suggests that procrastination interventions for adults with ADHD need to pay more attention to how rewards are handled. For example, motivation may need to be kept up by rewarding more frequently than with the non-clinical participants or by clarifying the value of long-term rewards in advance. Furthermore, since this study was conducted with university students, more effective procrastination interventions should be developed to ameliorate academic difficulties.
Author contribution statement
Mana Oguchi: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Toru Takahashi: Conceived and designed the experiments; Analyzed and interpreted the data.
Yusuke Nitta; Hiroaki Kumano: Conceived and designed the experiments.
Funding statement
This wrok was supported by the Ibuka Fund and the Japan Society for the Promotion of Science KAKENHI grant Number 202023103.
Data availability statement
Data will be made available on request.
Declaration of interest’s statement
The authors declare no competing interests.
Acknowledgments
The authors thank Prof. S. Tanaka from the ATR for her valuable comments. We would like to thank Editage (www.editage.com) for English language editing.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e14834.
Contributor Information
Mana Oguchi, Email: oguchima394@gmail.com.
Toru Takahashi, Email: toru1789takahashi@gmail.com.
Yusuke Nitta, Email: yymy.yusuke.1212@asagi.waseda.jp.
Hiroaki Kumano, Email: hikumano@waseda.jp.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
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Data Availability Statement
Data will be made available on request.