E-Sports players: emotional intelligence and decision making levels

Abstract

Background

This study aimed to examine the effect of emotional intelligence levels on decision-making among individuals interested in e-sports. The population consisted of individuals aged 18 and over who are interested in e-sports.

Method

The research sample included 385 participants (178 women and 207 men; Age Average = 21.41 ± 3.24). The study utilized a 20-question information form, the Melbourne Decision Making Scale, and the Schutte Emotional Intelligence Scale to collect data.

Results

A statistically significant relationship was found between participants’ emotional intelligence scores and their decision-making scores. Higher emotional intelligence scores were associated with increased self-esteem in decision-making and improvements in various decision-making sub-dimensions; however, a negative relationship was found with careful decision-making. High emotional intelligence positively influenced the ability to use and evaluate emotions and maintain emotional control, which in turn positively affected decision-making.

Conclusion

The findings indicated that high emotional intelligence contributes positively to self-esteem and certain adaptive aspects of decision-making. However, a paradoxical pattern emerged: while emotional intelligence was positively related to maladaptive decision-making styles, it showed a negative association with careful and rational decision-making. These results suggest that emotional intelligence, although generally regarded as a protective factor, may also foster overconfidence or reliance on intuition in complex decision-making contexts. While some findings aligned with previous literature, the counterintuitive results highlight the need for further research to explore the mechanisms underlying these relationships in the specific context of e-sports.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-025-01388-9.

Introduction

Throughout human history, games have played a crucial role in both the cognitive and affective development of individuals, serving as a multidimensional tool for entertainment, socialization, and learning. Traditionally, games are defined as activities that are not bound by rigid rules, provide enjoyment to participants, incorporate elements of improvisation, and stimulate individuals both physically and mentally [24]. Games can be classified into various categories such as social, structural, motor/physical, and rule-based games, each contributing directly to individuals’ quality of life, psychological balance, and social skills [10]. However, rapid advancements in technology have profoundly transformed both the nature and the medium of gameplay. Activities that once took place in physical spaces have been transferred into virtual environments through computers and gaming consoles, paving the way for the emergence of a competitive and professional digital sport known as electronic sports (Esports) [1].

Today, esports are recognized as a form of sport that can be played individually or in teams, requiring strategic thinking, high concentration, and rapid decision-making skills, where mental performance is paramount. International organizations such as the International Esports Federation and the World Cyber Games play significant roles in setting the rules, organizing events, and establishing ethical standards in the field [11]. As of 2017, the global esports industry had reached an audience of approximately 385 million, exerting an increasing influence not only in competitive sports but also in media, marketing, and education [26].

Due to the nature of esports, players are frequently required to make critical decisions within seconds, under high cognitive load, time pressure, and rapidly changing game dynamics. In this context, the concept of emotional intelligence (EI) emerges as a determining factor in players’ ability to cope with stress, maintain focus, sustain motivation, and preserve consistent performance. Emotional intelligence is defined as the ability to perceive, understand, and manage one’s own emotions as well as those of others, and to use this emotional information effectively in guiding actions [19, 20, 25].

As a skill that can be developed, emotional intelligence is closely linked to decision-making processes in sports [2]. Decision-making is defined as the process of selecting the most appropriate option among various alternatives through the interaction of cognitive, affective, and behavioral processes [7]. In sporting contexts, this process becomes more complex due to competition pressure, time constraints, and the necessity to adapt instantly to opponents’ strategies. In esports, this complexity is even greater—an incorrect decision made within milliseconds can determine the outcome of a match. Therefore, understanding the relationship between emotional intelligence and decision-making skills is crucial for both performance enhancement strategies and psychological interventions targeting players.

Although the relationship between emotional intelligence and decision-making has been examined in traditional sports across different age and performance groups, studies addressing the interaction of these two variables in the esports context remain scarce. Most existing research focuses solely on cognitive abilities or examines isolated psychological factors such as stress management in esports athletes. However, evaluating rapid, high-stakes decision-making processes together with emotional intelligence represents an almost unexplored area within esports. This gap constitutes a significant deficiency in both sports psychology literature and esports performance analysis. Furthermore, previous research investigating relationships among various psychological variables in athletes from different contexts emphasizes the importance of conducting multidimensional analyses. This perspective underscores the need to examine the interplay between emotional intelligence and decision-making specifically within the context of e-sports, where such interactions remain largely unexplored [15, 18, 23]. In e-sports, decision-making occurs within a virtual environment where traditional physical and emotional cues are limited, and players must rely on rapid information processing and online interactions.

In e-sports, decision-making occurs within a virtual environment where physical cues are largely absent, requiring players to interpret limited emotional and social signals. Moreover, the rapid pace of micro-decisions, combined with unique stressors such as online competition and potential harassment, may amplify the role of emotional intelligence in shaping decision-making styles [14, 17].

Accordingly, the present study aims to examine the relationship between emotional intelligence levels and decision-making skills among esports players. To our knowledge, this is one of the first studies to systematically explore this relationship, thereby contributing to a deeper understanding of the interplay between these psychological variables in esports performance and addressing the aforementioned gap in the literature.

Method

Research design

This study employed a descriptive research design and used a convenience sampling method to recruit participants aged 18 and over who are interested in e-sports within Türkiye. Convenience sampling was chosen as it is common and acceptable for this type of research, allowing for practical recruitment of participants from the target population [5]. The primary aim of this study was to investigate the relationship between emotional intelligence and decision-making abilities among e-sports players.

Participants

The study population comprised individuals aged 18 years and older who have an interest in e-sports within Türkiye. Participants who actively engage in e-sports activities were included, whereas those without any e-sports experience were excluded. The study employed a convenience sampling method. The sample consisted of 385 participants (mean age = 21.41 ± 3.24), including 178 females and 207 males. Participants’ active sports participation status refers to their involvement in traditional (non-e-sports) physical activities or sports, ensuring clarity and avoiding confusion with e-sports participation.

Data collection instruments

A comprehensive literature review was conducted, drawing from both domestic and international sources, to establish the theoretical framework underpinning the study. Data were collected through online surveys and face-to-face questionnaires, with participation being voluntary. Data collection took place over a period of approximately four months, from January 5 to April 25. Emotional intelligence was measured using the Revised Schutte Emotional Intelligence Scale [21]. The Turkish adaptation and psychometric validation of this scale were conducted by Tatar [22], with reported Cronbach’s alpha coefficients ranging between 0.85 and 0.90, indicating high internal consistency. Decision-making skills were assessed via the Melbourne Decision Making Questionnaire, originally developed by Mann et al. [16] and adapted to Turkish by Deniz [7]. The questionnaire has demonstrated satisfactory reliability, with Cronbach’s alpha values exceeding 0.80 in previous studies. Additionally, a Personal Information Form was administered to gather demographic data and information on participants’ e-sports preferences.

Data collection procedure

Prior to data collection, necessary approvals were obtained from institutional bodies and individuals through the Karamanoğlu Mehmetbey University Institute of Social Sciences. The questionnaires, comprising three sections, were administered to participants both online and in person, after explaining the study’s purpose and instructions for accurate completion of the forms. Participation was strictly voluntary, and informed consent was obtained from all participants.

Ethical considerations

The study was conducted following approval from the Social and Human Sciences Research Committee (Decision No: E-22618298-302.14.04-9681). Permission to use all measurement instruments was granted by the original authors via email correspondence.

Data analysis

Collected data were analyzed using IBM SPSS Statistics version 25. Descriptive statistics (means, standard deviations, frequencies) were computed to characterize the sample. Pearson correlation analysis was conducted to examine the relationship between emotional intelligence and decision-making scores. The significance level was set at p < .05 for all analyses.

Results

The results section presents descriptive statistics, group comparisons, and correlation analyses regarding emotional intelligence and decision-making among participants.

Table 1 presents the descriptive statistics for the Emotional Intelligence Scale and Decision-Making Styles. Participants’ scores on the Mood Regulation subscale were M = 41.00, SD = 7.33, and on the Evaluation of Emotions subscale were M = 33.00, SD = 6.31. The Use of Emotions subscale had a mean of M = 20.00, and the Total Emotional Intelligence score was M = 138.00, SD = 17.13. Skewness and kurtosis values for all subscales were within ± 1, indicating approximate normality. Regarding decision-making styles, the highest mean was observed in Avoidant Decision Making (M = 13.00), and the lowest in Vigilant Decision Making (M = 9.00). Skewness and kurtosis values for all decision-making subscales also fell within acceptable ranges.

Table 1.

Descriptive statistics of emotional intelligence and Decision-Making scores (n = 385)

Variable	N	M	SD	Skewness	Kurtosis	Min	Max
Emotional Intelligence
Mood Regulation	385	41.00	7.33	-0.80	0.58	12	60
Evaluation of Emotions	385	33.00	6.31	-0.20	0.21	14	50
Use of Emotions	385	20.00	3.28	-0.10	0.33	6	30
Total Emotional Intelligence	385	138.00	17.13	0.31	0.02	41	205
Decision-Making
Self-Esteem	385	10.00	2.77	-0.29	-0.80	4	16
Vigilant Decision Making	385	9.00	2.39	0.54	-0.32	6	18
Avoidant Decision Making	385	13.00	2.75	-0.17	-0.46	6	18
Procrastinating Decision Making	385	11.00	2.54	-0.26	-0.53	5	15
Panic Decision Making	385	11.00	2.37	-0.11	-0.58	5	15

N Sample size, SD Standard deviation, M Mean

Table 2 results revealed significant differences among athlete groups (professional, regular, other) in Mood Regulation (F = 5.77, p = .003), Use of Emotions (F = 4.07, p = .018), and Total Emotional Intelligence (F = 6.26, p = .002). Post-hoc analyses showed that professional players scored significantly lower than both regular players and others in Mood Regulation and Total Emotional Intelligence, and lower than others in Use of Emotions. No significant group differences were observed for Evaluation of Emotions (p = .551). These results indicate that professional athletes may experience challenges in regulating and utilizing emotions effectively compared to their non-professional counterparts, suggesting variability in emotional intelligence components depending on athlete status.

Table 2.

One-Way ANOVA results for emotional intelligence by athlete status (n = 385)

Variable	Source	df	SS	MS	F	p	Post Hoc Differences
Mood Regulation	Between Groups	2	604.885	302.443	5.773	.003	Group 1 < Group 2 Group 1 < Group 3
	Within Groups	382	20013.676	52.392
	Total	384	20618.561
Evaluation of Emotions	Between Groups	2	47.603	23.801	0.596	.551	–
	Within Groups	382	15250.101	39.922
	Total	384	15297.704
Use of Emotions	Between Groups	2	86.370	43.185	4.074	.018	Group 1 < Group 3
	Within Groups	382	4048.965	10.599
	Total	384	4135.335
Total Emotional Intelligence	Between Groups	2	3573.688	1786.84	6.257	.002	Group 1 < Group 2 Group 1 < Group 3
	Within Groups	382	109085.361	285.564
	Total	384	112659.049

1 = Professional Player, 2 = Regular Player, 3 = Other

SS Sum of Squares, MS Mean Square, df Degrees of Freedom

p < .05

Table 3 results revealed significant differences in participants’ Emotional Intelligence Scale scores based on daily game playing time. Specifically, Evaluation of Emotions (F = 3.51, p = .015), Use of Emotions (F = 5.29, p = .001), and Total Emotional Intelligence (F = 5.89, p = .001) showed statistically significant group differences, whereas Mood Regulation approached significance (F = 2.57, p = .054). Post-hoc analyses indicated that participants who played games 1–3 h daily scored significantly higher than those playing 4–6 h in Evaluation of Emotions, Use of Emotions, and Total Emotional Intelligence. These findings suggest that moderate daily game time is associated with higher emotional intelligence levels, while increased game time beyond this range may relate to lower emotional intelligence scores.

Table 3.

One-Way ANOVA results for emotional intelligence by daily game playing time (n = 385)

Variable	Source	df	SS	MS	F	p	Post Hoc Differences
Mood Regulation	Between Groups	3	408.260	136.087	2.565	0.054	–
	Within Groups	381	20210.301	53.045
	Total	384	20618.561
Evaluation of Emotions	Between Groups	3	411.553	137.184	3.511	0.015	Group 1 < Group 2
	Within Groups	381	14886.151	39.071
	Total	384	15297.704
Use of Emotions	Between Groups	3	165.479	55.160	5.294	0.001	Group 1 < Group 2
	Within Groups	381	3969.856	10.420
	Total	384	4135.335
Total Emotional Intelligence	Between Groups	3	4992.502	1664.167	5.889	0.001	Group 1 < Group 2
	Within Groups	381	107666.547	282.589
	Total	384	112659.049

1 = 1–3 h, 2 = 4–6 h, 3 = 7–9 h, 4 = 10 + Hours

SS Sum of Squares, MS Mean Square, df Degrees of Freedom

p < .05

Table 4 results showed significant differences in Mood Regulation (F = 3.16, p = .008) and Total Emotional Intelligence (F = 3.75, p = .003) scores across different e-sports types. Post-hoc analyses indicated that participants engaged in “Other” e-sports types had higher Mood Regulation and Total Emotional Intelligence scores compared to those playing Fight Games and Shooting Games. No significant differences were found in Evaluation of Emotions (p = .074) or Use of Emotions (p = .820) subscales. These findings suggest that the type of e-sport may influence certain aspects of emotional intelligence, particularly mood regulation and overall emotional intelligence.

Table 4.

One-Way ANOVA results for emotional intelligence by E-Sports type (n = 385)

Variable	Source	df	SS	MS	F	p	Post Hoc Differences
Mood Regulation	Between Groups	5	825.980	165.196	3.160	0.008	Group 2 < Group 6
	Within Groups	379	19792.581	52.223
	Total	384	20618.561
Evaluation of Emotions	Between Groups	5	398.547	79.709	2.020	0.074	–
	Within Groups	379	14899.157	39.312
	Total	384	15297.704
Use of Emotions	Between Groups	5	23.939	4.788	0.440	0.820	–
	Within Groups	379	4111.396	10.848
	Total	384	4135.335
Total Emotional Intelligence	Between Groups	5	5310.179	1062.036	3.750	0.003	Group 2 < Group 6 Group 4 < Group 2
	Within Groups	379	107348.871	283.242
	Total	384	112659.049

E-Sports Types: 1 = Fight Games, 2 = Shooting Games (FPS), 3 = Real Time Strategy, 4 = Sport and Racing Games, 5 = Multiplayer (MOBA), 6 = Other

SS Sum of Squares, MS Mean Square, df Degrees of Freedom

p < .05

Table 5 presents Independent Samples t-Test results examining whether participants’ Emotional Intelligence Scale total and subscale scores differ based on participation in traditional (non-e-sports) sports. The analysis indicates no significant differences for optimism/mood regulation (p = .274), evaluation of emotions (p = .706), and total emotional intelligence (p = .164) between those who participate in participation in traditional (non-e-sports) sports and those who do not. However, significant differences were found in the use of emotions subscale (p = .015), where non-participants scored higher. Regarding decision-making dimensions, careful decision making (p = .047), avoidant decision making (p = .006), and delayed decision making (p = .014) showed significant differences, suggesting that participation in traditional (non-e-sports) sports participants exhibit lower careful decision making and higher avoidant and delayed decision tendencies compared to non-participants. No significant difference was observed in self-esteem (p = .637) or panic decision making (p = .072). These findings highlight nuanced emotional intelligence and decision-making variations linked to participation in traditional (non-e-sports) sports.

Table 5.

Independent samples t-Test results for emotional intelligence and Decision-Making by participation in traditional (non-e-sports) sports (n = 385)

Variable	Group	n	Mean	SD	Std. Error Mean	t	df	p
Emotional Intelligence
Optimism / Mood Regulation	Yes	158	40.66	7.57	0.60	-1.095	383	0.274
	No	227	41.49	7.15	0.47
Evaluation of Emotions	Yes	158	33.32	6.78	0.54	-0.706	383	0.481
	No	227	33.56	5.98	0.40
Use of Emotions	Yes	158	19.13	3.35	0.27	-2.442	383	0.015*
	No	227	19.95	3.20	0.21
Total Emotional Intelligence	Yes	158	136.24	17.48	1.39	-1.395	383	0.164
	No	227	138.71	16.85	1.12
Decision Making
Self-Esteem	Yes	158	10.34	2.87	0.23	-0.473	383	0.637
	No	227	10.47	2.71	0.18
Careful Decision Making	Yes	158	8.65	2.22	0.18	-1.993	383	0.047*
	No	227	9.14	2.48	0.16
Avoidant Decision Making	Yes	158	13.74	2.68	0.21	2.774	383	0.006*
	No	227	12.96	2.77	0.18
Delayed Decision Making	Yes	158	11.18	2.50	0.20	2.480	383	0.014*
	No	227	10.53	2.54	0.17
Panic Decision Making	Yes	158	10.84	2.42	0.19	1.807	383	0.072
	No	227	10.39	2.33	0.15

“Yes” = participants who engage in traditional/active sports, “No” = participants who do not

SD Standard Deviation, df Degrees of Freedom

p < .05 indicates statistical significance

According to Table 6, higher emotional intelligence (EI) is positively and significantly related to the self-esteem subscale of the decision-making scale (r = .239, p < .01), indicating that individuals with higher EI tend to have greater confidence in their decisions. In contrast, a negative correlation was found between EI and careful decision-making (r = -.325, p < .01), suggesting that emotionally intelligent individuals are less likely to be overly cautious. Interestingly, EI also showed positive and significant correlations with the avoidant, delayed, and panic decision-making subscales (r = .332; 0.318; 0.292, respectively, p < .01). While these subscales traditionally reflect maladaptive decision-making tendencies, these results highlight the complex and context-dependent nature of decision-making in esports. That is, individuals with higher EI may engage in a wider range of decision-making behaviors depending on situational demands, pressure, or uncertainty, rather than simply exhibiting maladaptive patterns. These findings should therefore be interpreted cautiously, and future research is needed to further clarify the mechanisms behind these counterintuitive associations.

Table 6.

Pearson Product-Moment correlation analysis results examining the relationship between emotional intelligence scale scores and Decision-Making scale Self-Esteem and Sub-Dimensions scores

Emotional Intelligence	N	r	P
Self Esteem	385	,239	,000**
Careful Decision Making	385	-,325	,000**
Avoidant Decision Making	385	,332	,000**
Delayed Decision Making	385	,318	,000**
Panic Decision Making	385	,292	,000**

**Correlation is significant at the 0.01 level (2-tailed)

Discussion

This study examined the influence of emotional intelligence (EI) on decision-making among e-sports enthusiasts. The overall EI score (M = 138.00) and sub-dimensions, including optimism/emotional regulation (M = 41.00), emotion evaluation (M = 33.00), and emotion utilization (M = 20.00), were above moderate levels. However, scores for “Self-Esteem” (M = 10.0) and “Careful Decision Making” (M = 9.0) were below moderate, whereas “Avoidant Decision Making” (M = 13.0), “Delayed Decision Making” (M = 11.0), and “Panic Decision Making” (M = 11.0) were at moderate levels. These results suggest that while higher EI is generally positively associated with decision-making skills and self-esteem, it does not equally enhance careful decision-making, indicating a complex relationship [3, 4, 12].

Differences in emotional intelligence (EI) levels were noted among professional and regular players, with professionals exhibiting lower optimism and EI, possibly due to competitive pressures and emotional fatigue from intensive training. Interestingly, higher EI was found to correlate positively with avoidant, delayed, and panic decision-making styles, a counterintuitive finding given that EI is typically associated with effective stress management and rational decision-making [19]. One possible explanation is that the Melbourne Decision-Making Questionnaire (Melbourne DMQ) may capture heightened risk awareness or anticipatory caution in high-pressure e-sports contexts as avoidant or panic responses, rather than reflecting true maladaptive decision-making. Additionally, e-sports athletes with higher EI might be more attuned to potential negative outcomes during gameplay, leading them to adopt more cautious or delayed decisions. This suggests that the unique cognitive and emotional demands of e-sports could modify the typical relationship between EI and decision-making, highlighting the need for context-specific assessment tools and tailored interventions [6]. Future research should investigate whether this relationship persists across different competitive e-sports environments and explore alternative measurement approaches.

The choice of e-sport genre significantly influenced emotional intelligence (EI) profiles, particularly in mood regulation, emotion evaluation, and utilization. For instance, a study by Kou and Gui [13] examined how players experience and regulate emotions in League of Legends, highlighting the impact of competitive gaming on emotional experiences and regulation strategies. This underscores the need for further exploration into the psychological impacts of specific game genres on players. Regarding sports participation, while emotional regulation skills were unaffected, differences in decision-making abilities were observed between participants and non-participants, suggesting sports may influence these skills positively. Research by Fernandes et al. [8] investigated the mediating effects of emotional intelligence and self-esteem between youth sports participation and life satisfaction, indicating that sports involvement can enhance emotional intelligence and decision-making abilities. These findings contribute to understanding the effects of sports on emotional intelligence and decision-making.

Correlation analysis revealed a positive relationship between emotional intelligence (EI) and self-esteem (r = .239, p < .01), indicating that higher EI may enhance self-esteem. This finding aligns with previous research suggesting that individuals with higher EI tend to have better self-perception and self-worth [19]. Conversely, EI negatively correlated with cautious decision-making (r = -.325, p < .01) but positively correlated with avoidant (r = .332, p < .01), delayed (r = .318, p < .01), and panicked decision-making (r = .292, p < .01). These results has shown that EI can influence decision-making styles, including rational, intuitive, dependent, spontaneous, and avoidant styles [9]. These findings emphasize the intricate interplay between EI, self-esteem, and decision-making styles, warranting further investigation into underlying mechanisms. Future research should explore how EI influences decision-making processes and how these relationships vary across different contexts and populations.

This study has certain limitations that should be considered. First, its cross-sectional design prevents conclusions about causality. Second, the reliance on self-report measures may introduce response biases. Third, the sample is limited to esports players in Türkiye, which may affect the generalizability of the findings. Therefore, caution should be exercised when extending these results to broader populations. Future research is encouraged to address these limitations by employing longitudinal designs, incorporating objective performance measures, and including more diverse participant samples.

In conclusion, while higher emotional intelligence generally benefits decision-making and self-esteem among esports players, nuances exist across different dimensions and contexts. Future research should delve deeper into these dynamics to enhance our understanding and inform effective interventions and support strategies in esports environments.

Conclusion

This study aimed to investigate the impact of emotional intelligence (EI) on decision-making abilities among e-sports players. Overall, participants demonstrated above-average EI scores, particularly in optimism/emotion regulation, evaluation of emotions, and use of emotions. Self-esteem in decision-making was lower than average, suggesting a nuanced interplay between EI and self-perception.

Significant differences in EI were observed among professional, regular, and other players, and gaming hours as well as e-sport genre were found to influence specific EI dimensions, including mood regulation, evaluation, and utilization of emotions. Participation in active sports showed mixed effects on emotional skills but correlated positively with decision-making abilities.

Correlation analyses revealed a moderate positive relationship between EI and self-esteem, a negative correlation with cautious decision-making, and positive correlations with avoidant, procrastinative, and panic decision-making styles. Notably, the positive associations between EI and avoidant, procrastinative, and panic decision-making represent unexpected and counterintuitive findings, as higher EI is typically associated with more rational and adaptive decision-making.

These findings highlight the complex and context-dependent relationship between EI and decision-making in e-sports environments, suggesting that high EI may sometimes manifest as heightened caution or sensitivity to potential negative outcomes under competitive pressure. Future research is warranted to further explore these paradoxical relationships, examine underlying mechanisms, and develop context-specific interventions to support effective decision-making and psychological well-being among e-sports players.

Furthermore, these findings suggest that training programs for e-sports athletes should not only focus on enhancing EI but also on helping players channel their emotional awareness into decisive action under pressure.

Abbreviations

EI

Emotional Intelligence

IBM

International Business Machines

SPSS

Statistical Package for the Social Sciences

N

Sample size

SD

Standard deviation

SS

Sum of Squares

MS

Mean Square

df

Degrees of Freedom

DMQ

Decision-Making Questionnaire

Authors’ contributions

Veysel Temel and Hüseyin Aydın contributed to the conception and design of the study, data collection, analysis, interpretation of results, literature review, manuscript writing and editing, statistical analysis, and critical revision of the manuscript for important intellectual content. All authors have read and approved the final version of the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The data supporting the findings of this study are available from the authors upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the ethical standards of the Karamanoğlu Mehmetbey University Ethics Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethical approval was obtained from the Social and Human Sciences Research Committee (E-22618298-302.14.04-9681).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.